Gene sequence variations with utility in determining the treatment of disease, in genes relating to drug processing

Abstract

Methods for identifying and utilizing variances in genes relating to efficacy and safety of medical therapy and other aspects of medical therapy are described, including methods for selecting an effective treatment.

Description

BACKGROUND OF THE INVENTION

[0002] This application concerns the field of mammalian therapeutics and the selection of therapeutic regimens utilizing host genetic information, including gene sequence variances within the human genome in human populations.

[0003] The information provided below is not admitted to be prior art to the present invention, but is provided solely to assist the understanding of the reader.

[0004] Many drugs or other treatments are known to have highly variable safety and efficacy in different individuals. A consequence of such variability is that a given drug or other treatment may be effective in one individual, and ineffective or not well-tolerated in another individual. Thus, administration of such a drug to an individual in whom the drug would be ineffective would result in wasted cost and time during which the patient's condition may significantly worsen. Also, administration of a drug to an individual in whom the drug would not be tolerated could result in a direct worsening of the patient's condition and could even result in the patient's death.

[0005] For some drugs, over 90% of the measurable intersubject variation in selected pharmacokinetic parameters has been shown to be heritable. For a limited number of drugs, DNA sequence variances have been identified in specific genes that are involved in drug action or metabolism, and these variances have been shown to account for the variable efficacy or safety of the drugs in different individuals. As the sequence of the human genome is completed, and as additional human gene sequence variances are identified, the power of genetic methods for predicting drug response will further increase. This application concerns methods for identifying and exploiting gene sequence variances that account for interpatient variation in drug response, particularly interpatient variation attributable to pharmacokinetic factors and interpatient variation in drug tolerability or toxicity.

[0006] The efficacy of a drug is a function of both pharmacodynamic effects and pharmacokinetic effects, or bioavailability. In the present invention, interpatient variability in drug safety, tolerability and efficacy are discussed in terms of the genetic determinants of interpatient variation in absorption, distribution, metabolism, and excretion, i.e. pharmacokinetic parameters.

[0007] Adverse drug reactions are a principal cause of the low success rate of drug development programs (less than one in four compounds that enters human clinical testing is ultimately approved for use by the U.S. Food and Drug Administration (FDA)). Adverse drug reactions can be categorized as 1) mechanism based reactions and 2) idiosyncratic, “unpredictable” effects apparently unrelated to the primary pharmacologic action of the compound. Although some side effects appear shortly after administration, in some instances side effects appear only after a latent period. Adverse drug reactions can also be categorized into reversible and irreversible effects. The methods of this invention are useful for identifying the genetic basis of both mechanism based and ‘idiosyncratic’ toxic effects, whether reversible or not. Methods for identifying the genetic sources of interpatient variation in efficacy and mechanism based toxicity may be initially directed to analysis of genes affecting pharmacokinetic parameters, while the genetic causes of idiosyncratic adverse drug reactions are more likely to be attributable to genes affecting variation in pharmacodynamic responses or immunological responsiveness.

[0008] Absorption is the first pharmacokinetic parameter to consider when determining the causes of intersubject variation in drug response. The relevant genes depend on the route of administration of the compound being evaluated. For orally administered drugs the major steps in absorption may occur during exposure to salivary enzymes in the mouth, exposure to the acidic environment of the stomach, exposure to pancreatic digestive enzymes and bile in the small intestine, exposure to enteric bacteria and exposure to cell surface proteins throughout the gastrointestinal tract. For example, uptake of a drug that is absorbed across the gastrointestinal tract by facilitated transport may vary on account of allelic variation in the gene encoding the transporter protein. Many drugs are lipophilic (a property which promotes passive movement across biological membranes). Variation in levels of such drugs may depend, for example, on the enterohepatic circulation of the drug, which may be affected by genetic variation in liver canalicular transporters, or intestinal transporters; alternatively renal reabsorbtion mechanisms may vary among patients as a consequence of gene sequence variances. If a compound is delivered parenterally then absorption is not an issue, however transcutaneous administration of a compound may be subject to genetically determined variation in skin absorptive properties.

[0009] Once a drug or candidate therapeutic intervention is absorbed, injected or otherwise enters the bloodstream it is distributed to various biological compartments via the blood. The drug may exist free in the blood, or, more commonly, may be bound with varying degrees of affinity to plasma proteins. One classic source of interpatient variation in drug response is attributable to amino acid polymorphisms in serum albumin, which affect the binding affinity of drugs such as warfarin. Consequent interpatient variation in levels of free warfarin have a significant effect on the degree of anticoagulation. From the blood a compound diffuses into and is retained in interstitial and cellular fluids of different organs to different degrees. Interpatient variation in the levels of a drug in different anatomical compartments may be attributable to variation in the genetically encoded chemical environment of those tissues (cell surface proteins, matrix proteins, cytoplasmic proteins and other factors)

[0010] Once absorbed by the gastrointestinal tract, compounds encounter detoxifying and metabolizing enzymes in the tissues of the gastrointestinal system. Many of these enzymes are known to be polymorphic in man and account for well studied variation in pharmacokinetic parameters of many drugs. Subsequently compounds enter the hepatic portal circulation in a process commonly known as first pass. The compounds then encounter a vast array of xenobiotic detoxifying mechanisms in the liver, including enzymes that are expressed solely or at high levels only in liver. These enzymes include the cytochrome P450s, glucuronlytransferases, sulfotransferases, acetyltransferases, methyltransferases, the glutathione conjugating system, flavine monooxygenases, and other enzymes known in the art. Polymorphisms have been detected in all of these metabolizing systems, however the genetic factors responsible for intersubject variation have only been partially identified, and in some cases not yet identified at all. Biotransformation reactions in the liver often have the effect of converting lipophilic compounds into hydrophilic molecules that are then more readily excreted. Variation in these conjugation reactions may affect half-life and other pharmacokinetic parameters. It is important to note that metabolic transformation of a compound not infrequently gives rise to a second or additional compounds that have biological activity greater than, less than, or different from that of the parent compound. Metabolic transformation may also be responsible for producing toxic metabolites.

[0011] Biotransformation reactions can be divided into two phases. Phase I are oxidation-reduction reactions and phase II are conjugation reactions. The enzymes involved in both of these phases are located predominantly in the liver, however biotransformation can also occur in the kidney, gastrointestinal tract, skin, lung, and other organs. Phase I reactions occur predominantly in the endoplasmic reticulum, while phase II reactions occur predominantly in the cytosol. Both types of reactions can occur in the mitochondria, nuclear envelope, or plasma membrane. One skilled in the art can, for some compounds, make reasonable predictions concerning likely metabolic systems given the structure of the compound. Experimental means of assessing relevant biotransformation systems are also described.

[0012] Drug-induced disease or toxicity presents a unique series of challenges to drug developers, as these reactions are often not predictable from preclinical studies and may not be detected in early clinical trials involving small numbers of subjects. When such effects are detected in later stages of clinical development they often result in termination of a drug development program because, until now, there have been no effective tools to seek the determinants of such reactions. When a drug is approved despite some toxicity, its clinical use is frequently severely constrained by the possible occurrence of adverse reactions in even a small group of patients. The likelihood of such a compound becoming first line therapy is small (unless there are no competing products). Thus, clinical trials that lead to detection of genetic causes of adverse events and subsequently to the creation of genetic tests to identify and screen out patients susceptible to such events have the potential to (i) enable approval of compounds for genetically circumscribed populations or (ii) enable repositioning of approved compounds for broader clinical use.

[0013] Similarly, many compounds are not approved due to unimpressive efficacy. The identification of genetic determinants of pharmacokinetic variation may lead to identification of a genetically defined population in whom a significant response is occurring. Approval of a compound for this population, defined by a genetic diagnostic test, may be the only means of getting regulatory approval for a drug. As healthcare becomes increasingly costly, the ability to allocate healthcare resources effectively becomes increasingly urgent. The use of genetic tests to develop and rationally administer medicines represents a powerful tool for accomplishing more cost effective medical care.

SUMMARY OF THE INVENTION

[0014] The present invention is concerned generally with the field of pharmacology, specifically pharmacokinetics and toxicology, and more specifically with identifying and predicting inter-patient differences in response to drugs in order to achieve superior efficacy and safety in selected patient populations. It is further concerned with the genetic basis of inter-patient variation in response to therapy, including drug therapy, and with methods for determining and exploiting such differences to improve medical outcomes. Specifically, this invention describes the identification of genes and gene sequence variances useful in the field of therapeutics for optimizing efficacy and safety of drug therapy by allowing prediction of pharmacokinetic and/or toxicologic behavior of specific drugs in specific patients. Relevant pharmacokinetic processes include absorption, distribution, metabolism and excretion. Relevant toxicological processes include both dose related and idiosyncratic adverse reactions to drugs, including, for example, hepatotoxicity, blood dyscrasias and immunological reactions. The invention also describes methods for establishing diagnostic tests useful in (i) the development of, (ii) obtaining regulatory approval for and (iii) safe and efficacious clinical use of pharmaceutical products. These variances may be useful either during the drug development process or in guiding the optimal use of already approved compounds. DNA sequence variances in candidate genes (i.e. genes that may plausibly affect the action of a drug) are tested in clinical trials, leading to the establishment of diagnostic tests useful for improving the development of new pharmaceutical products and/or the more effective use of existing pharmaceutical products. Methods for identifying genetic variances and determining their utility in the selection of optimal therapy for specific patients are also described. In general, the invention relates to methods for identifying and dealing effectively with the genetic sources of interpatient variation in drug response, including both variable efficacy as determined by pharmacokinetic variability and variable toxicity as determined by pharmacokinetic factors or by other genetic factors (e.g. factors responsible for idiosyncratic drug response).

[0015] The inventors have determined that the identification of gene sequence variances in genes that may be involved in drug action are useful for determining whether genetic variances account for variable drug efficacy and safety and for determining whether a given drug or other therapy may be safe and effective in an individual patient. Provided in this invention are identifications of genes and sequence variances which can be useful in connection with predicting differences in response to treatment and selection of appropriate treatment of a disease or condition. A target gene and variances have utility in pharmacogenetic association studies and diagnostic tests to improve the use of certain drugs or other therapies including, but not limited to, the drug classes and specific drugs identified in the 1999 Physicians' Desk Reference (53rd edition), Medical Economics Data, 1998, or the 1995 United States Pharmacopeia XXIII National Formulary XVIII, Interpharm Press, 1994, or other sources as described below.

[0016] The terms “disease” or “condition” are commonly recognized in the art and designate the presence of signs and/or symptoms in an individual or patient that are generally recognized as abnormal. Diseases or conditions may be diagnosed and categorized based on pathological changes. Signs may include any objective evidence of a disease such as changes that are evident by physical examination of a patient or the results of diagnostic tests which may include, among others, laboratory tests to determine the presence of DNA sequence variances or variant forms of certain genes in a patient. Symptoms are subjective evidence of disease or a patients condition, i.e. the patients perception of an abnormal condition that differs from normal function, sensation, or appearance, which may include, without limitations, physical disabilities, morbidity, pain, and other changes from the normal condition experienced by an individual. Various diseases or conditions include, but are not limited to; those categorized in standard textbooks of medicine including, without limitation, textbooks of nutrition, allopathic, homeopathic, and osteopathic medicine. In certain aspects of this invention, the disease or condition is selected from the group consisting of the types of diseases listed in standard texts such as Harrison's Principles of Internal Medicine (14th Ed) by Anthony S. Fauci, Eugene Braunwald, Kurt J. Isselbacher, et al. (Editors), McGraw Hill, 1997, or Robbins Pathologic Basis of Disease (6th edition) by Ramzi S. Cotran, Vinay Kumar, Tucker Collins & Stanley L. Robbins, W B Saunders Co., 1998, or the Diagnostic and Statistical Manual of Mental Disorders: DSM-IV (4th edition), American Psychiatric Press, 1994, or other texts described below.

[0017] In connection with the methods of this invention, unless otherwise indicated, the term “suffering from a disease or condition” means that a person is either presently subject to the signs and symptoms, or is more likely to develop such signs and symptoms than a normal person in the population. Thus, for example, a person suffering from a condition can include a developing fetus, a person subject to a treatment or environmental condition which enhances the likelihood of developing the signs or symptoms of a condition, or a person who is being given or will be given a treatment which increase the likelihood of the person developing a particular condition. For example, tardive dyskinesia is associated with long-term use of anti-psychotics; dyskinesias, paranoid ideation, psychotic episodes and depression have been associated with use of L-dopa in Parkinson's disease; and dizziness, diplopia, ataxia, sedation, impaired mentation, weight gain, and other undesired effects have been described for various anticonvulsant therapies, alopecia and bone marrow suppression are associated with cancer chemotherapeutic regimens, and immunosuppression is associated with agents to limit graft rejection following transplantation. Thus, methods of the present invention which relate to treatments of patients (e.g., methods for selecting a treatment, selecting a patient for a treatment, and methods of treating a disease or condition in a patient) can include primary treatments directed to a presently active disease or condition, secondary treatments which are intended to cause a biological effect relevant to a primary treatment, and prophylactic treatments intended to delay, reduce, or prevent the development of a disease or condition, as well as treatments intended to cause the development of a condition different from that which would have been likely to develop in the absence of the treatment.

[0018] The term “therapy” refers to a process that is intended to produce a beneficial change in the condition of a mammal, e.g., a human, often referred to as a patient. A beneficial change can, for example, include one or more of: restoration of function, reduction of symptoms, limitation or retardation of progression of a disease, disorder, or condition or prevention, limitation or retardation of deterioration of a patient's condition, disease or disorder. Such therapy can involve, for example, nutritional modifications, administration of radiation, administration of a drug, behavioral modifications, and combinations of these, among others.

[0019] The term “drug” as used herein refers to a chemical entity or biological product, or combination of chemical entities or biological products, administered to a person to treat or prevent or control a disease or condition. The chemical entity or biological product is preferably, but not necessarily a low molecular weight compound, but may also be a larger compound, for example, an oligomer of nucleic acids, amino acids, or carbohydrates including without limitation proteins, oligonucleotides, ribozymes, DNAzymes, glycoproteins, lipoproteins, and modifications and combinations thereof. A biological product is preferably a monoclonal or polyclonal antibody or fragment thereof such as a variable chain fragment; cells; or an agent or product arising from recombinant technology, such as, without limitation, a recombinant protein, recombinant vaccine, or DNA construct developed for therapeutic, e.g., human therapeutic, use. The term “drug” may include, without limitation, compounds that are approved for sale as pharmaceutical products by government regulatory agencies (e.g., U.S. Food and Drug Administration (USFDA or FDA), European Medicines Evaluation Agency (EMEA), and a world regulatory body governing the International Conference of Harmonization (ICH) rules and guidelines), compounds that do not require approval by government regulatory agencies, food additives or supplements including compounds commonly characterized as vitamins, natural products, and completely or incompletely characterized mixtures of chemical entities including natural compounds or purified or partially purified natural products. The term “drug” as used herein is synonymous with the terms “medicine”, “pharmaceutical product”, or “product”. Most preferably the drug is approved by a government agency for treatment of a specific disease or condition.

[0020] The term “candidate therapeutic intervention” refers to a drug, agent or compound that is under investigation, either in laboratory or human clinical testing for a specific disease, disorder, or condition.

[0021] A “low molecular weight compound” has a molecular weight <5,000 Da, more preferably <2500 Da, still more preferably <1000 Da, and most preferably <700 Da.

[0022] Those familiar with drug use in medical practice will recognize that regulatory approval for drug use is commonly limited to approved indications, such as to those patients afflicted with a disease or condition for which the drug has been shown to be likely to produce a beneficial effect in a controlled clinical trial. Unfortunately, it has generally not been possible with current knowledge to predict which patients will have a beneficial response, with the exception of certain diseases such as bacterial infections where suitable laboratory methods have been developed. Likewise, it has generally not been possible to determine in advance whether a drug will be safe in a given patient. Regulatory approval for the use of most drugs is limited to the treatment of selected diseases and conditions. The descriptions of approved drug usage, including the suggested diagnostic studies or monitoring studies, and the allowable parameters of such studies, are commonly described in the “label” or “insert” which is distributed with the drug. Such labels or inserts are preferably required by government agencies as a condition for marketing the drug and are listed in common references such as the Physicians Desk Reference (PDR). These and other limitations or considerations on the use of a drug are also found in medical journals, publications such as pharmacology, pharmacy or medical textbooks including, without limitation, textbooks of nutrition, allopathic, homeopathic, and osteopathic medicine.

[0023] Many widely used drugs are effective in a minority of patients receiving the drug, particularly when one controls for the placebo effect. For example, the PDR shows that about 45% of patients receiving Cognex (tacrine hydrochloride) for Alzheimer's disease show no change or minimal worsening of their disease, as do about 68% of controls (including about 5% of controls who were much worse). About 58% of Alzheimer's patients receiving Cognex were minimally improved, compared to about 33% of controls, while about 2% of patients receiving Cognex were much improved compared to about 1% of controls. Thus a tiny fraction of patients had a significant benefit. Response to many cancer chemotherapy drugs is even worse. For example, 5-fluorouracil is standard therapy for advanced colorectal cancer, but only about 20-40% of patients have an objective response to the drug, and, of these, only 1-5% of patients have a complete response (complete tumor disappearance; the remaining patients have only partial tumor shrinkage). Conversely, up to 20-30% of patients receiving 5-FU suffer serious gastrointestinal or hematopoietic toxicity, depending on the regimen.

[0024] Thus, in a first aspect, the invention provides a method for selecting a treatment for a patient suffering from a disease or condition by determining whether or not a gene or genes in cells of the patient (in some cases including both normal and disease cells, such as cancer cells) contain at least one sequence variance which is indicative of the effectiveness of the treatment of the disease or condition. The gene or genes are preferably specified herein, in Table 1, 3, or 4. Preferably the at least one variance includes a plurality of variances which may provide a haplotype or haplotypes. Preferably the joint presence of the plurality of variances is indicative of the potential effectiveness or safety of the treatment in a patient having such plurality of variances. The plurality of variances may each be indicative of the potential effectiveness of the treatment, and the effects of the individual variances may be independent or additive, or the plurality of variances may be indicative of the potential effectiveness if at least 2, 3, 4, or more appear jointly. The plurality of variances may also be combinations of these relationships. The plurality of variances may include variances from one, two, three or more gene loci.

[0025] In preferred embodiments of aspects of the invention involving genes relating to pharmacokinetic parameters that affect efficacy and safety, e.g. drug-induced disease or drug-induced, disorder, or dysfunction or other drug-induced pathophysiologic disease, or protection or sensitivity to toxic compounds, the gene product is involved in a function as described in the Background of the Invention or otherwise described herein.

[0026] In some cases, the selection of a method of treatment, i.e., a therapeutic regimen, may incorporate selection of one or more from a plurality of medical therapies. Thus, the selection may be the selection of a method or methods which is/are more effective or less effective than certain other therapeutic regimens (with either having varying safety parameters). Likewise or in combination with the preceding selection, the selection may be the selection of a method or methods, which is safer than certain other methods of treatment in the patient.

[0027] The selection may involve either positive selection or negative selection or both, meaning that the selection can involve a choice that a particular method would be an appropriate method to use and/or a choice that a particular method would be an inappropriate method to use. Thus, in certain embodiments, the presence of the at least one variance is indicative that the treatment will be effective or otherwise beneficial (or more likely to be beneficial) in the patient. Stating that the treatment will be effective means that the probability of beneficial therapeutic effect is greater than in a person not having the appropriate presence or absence of particular variances. In other embodiments, the presence of the at least one variance is indicative that the treatment will be ineffective or contra-indicated for the patient. For example, a treatment may be contra-indicated if the treatment results, or is more likely to result, in undesirable side effects, or an excessive level of undesirable side effects. A determination of what constitutes excessive side-effects will vary, for example, depending on the disease or condition being treated, the availability of alternatives, the expected or experienced efficacy of the treatment, and the tolerance of the patient. As for an effective treatment, this means that it is more likely that desired effect will result from the treatment administration in a patient with a particular variance or variances than in a patient who has a different variance or variances. Also in preferred embodiments, the presence of the at least one variance is indicative that the treatment is both effective and unlikely to result in undesirable effects or outcomes, or vice versa (is likely to have undesirable side effects but unlikely to produce desired therapeutic effects).

[0028] In reference to response to a treatment, the term “tolerance” refers to the ability of a patient to accept a treatment, based, e.g., on deleterious effects and/or effects on lifestyle. Frequently, the term principally concerns the patients perceived magnitude of deleterious effects such as nausea, weakness, dizziness, and diarrhea, among others. Such experienced effects can, for example, be due to general or cell-specific toxicity, activity on non-target cells, cross-reactivity on non-target cellular constituents (non-mechanism based), and/or side effects of activity on the target cellular substituents (mechanism based), or the cause of toxicity may not be understood. In any of these circumstances one may identify an association between the undesirable effects and variances in specific genes.

[0029] Adverse responses to drugs constitute a major medical problem, as shown in two recent meta-analyses (Lazarou, J. et al, Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies, JAMA 279:1200-1205, 1998; Bonn, Adverse drug reactions remain a major cause of death, Lancet 351:1183, 1998). An estimated 2.2 million hospitalized patients in the United Stated had serious adverse drug reactions in 1994, with an estimated 106,000 deaths (Lazarou et al.). To the extent that some of these adverse events are due to genetically encoded biochemical diversity among patients in pathways that effect drug action, the identification of variances that are predictive of such effects will allow for more effective and safer drug use.

[0030] In embodiments of this invention, the variance or variant form or forms of a gene is/are associated with a specific response to a drug. The frequency of a specific variance or variant form of the gene may correspond to the frequency of an efficacious response to administration of a drug. Alternatively, the frequency of a specific variance or variant form of the gene may correspond to the frequency of an adverse event resulting from administration of a drug. Alternatively the frequency of a specific variance or variant form of a gene may not correspond closely with the frequency of a beneficial or adverse response, yet the variance may still be useful for identifying a patient subset with high response or toxicity incidence because the variance may account for only a fraction of the patients with high response or toxicity. In such a case the preferred course of action is identification of a second or third or additional variances that permit identification of the patient groups not usefully identified by the first variance. Preferably, the drug will be effective in more than 20% of individuals with one or more specific variances or variant forms of the gene, more preferably in 40% and most preferably in >60%. In other embodiments, the drug will be toxic or create clinically unacceptable side effects in more than 10% of individuals with one or more variances or variant forms of the gene, more preferably in >30%, more preferably in >50%, and most preferably in >70% or in more than 90%.

[0031] Also in other embodiments, the method of selecting a treatment includes eliminating a treatment, where the presence or absence of the at least one variance is indicative that the treatment will be ineffective or contra-indicated, e.g., would result in excessive weight gain. In other preferred embodiments, in cases in which undesirable side-effects may occur or are expected to occur from a particular therapeutic treatment, the selection of a method of treatment can include identifying both a first and second treatment, where the first treatment is effective to treat the disease or condition, and the second treatment reduces a deleterious effect of the first treatment.

[0032] The phrase “eliminating a treatment” refers to removing a possible treatment from consideration, e.g., for use with a particular patient based on the presence or absence of a particular variance(s) in one or more genes in cells of that patient, or to stopping the administration of a treatment which was in the course of administration.

[0033] Usually, the treatment will involve the administration of a compound preferentially active or safe in patients with a form or forms of a gene, where the gene is one identified herein. The administration may involve a combination of compounds. Thus, in preferred embodiments, the method involves identifying such an active compound or combination of compounds, where the compound is less active or is less safe or both when administered to a patient having a different form of the gene.

[0034] Also in preferred embodiments, the method of selecting a treatment involves selecting a method of administration of a compound, combination of compounds, or pharmaceutical composition, for example, selecting a suitable dosage level and/or frequency of administration, and/or mode of administration of a compound. The method of administration can be selected to provide better, preferably maximum therapeutic benefit. In this context, “maximum” refers to an approximate local maximum based on the parameters being considered, not an absolute maximum.

[0035] Also in this context, a “suitable dosage level” refers to a dosage level which provides a therapeutically reasonable balance between pharmacological effectiveness and deleterious effects. Often this dosage level is related to the peak or average serum levels resulting from administration of a drug at the particular dosage level.

[0036] Similarly, a “frequency of administration” refers to how often in a specified time period a treatment is administered, e.g., once, twice, or three times per day, every other day, once per week, etc. For a drug or drugs, the frequency of administration is generally selected to achieve a pharmacologically effective average or peak serum level without excessive deleterious effects (and preferably while still being able to have reasonable patient compliance for self-administered drugs). Thus, it is desirable to maintain the serum level of the drug within a therapeutic window of concentrations for the greatest percentage of time possible without such deleterious effects as would cause a prudent physician to reduce the frequency of administration for a particular dosage level.

[0037] A particular gene or genes can be relevant to the treatment of more than one disease or condition, for example, the gene or genes can have a role in the initiation, development, course, treatment, treatment outcomes, or health-related quality of life outcomes of a number of different diseases, disorders, or conditions. Thus, in preferred embodiments, the disease or condition or treatment of the disease or condition is any which involves a gene from the gene list described herein as Tables 1, 3, and 4.

[0038] Determining the presence of a particular variance or plurality of variances in a particular gene in a patient can be performed in a variety of ways. In preferred embodiments, the detection of the presence or absence of at least one variance involves amplifying a segment of nucleic acid including at least one of the at least one variances. Preferably a segment of nucleic acid to be amplified is 500 nucleotides or less in length, more preferably 100 nucleotides or less, and most preferably 45 nucleotides or less. Also, preferably the amplified segment or segments includes a plurality of variances, or a plurality of segments of a gene or of a plurality of genes.

[0039] In another aspect determining the presence of a set of variances in a specific gene related to treatment of pharmacokinetic parameters associated efficacy or safety, e.g. drug-induced disease, disorder, dysfunction, or other toxicity-related gene or genes listed in Tables 1, 3 and 4 may entail a haplotyping test that requires allele specific amplification of a large DNA segment of no greater than 25,000 nucleotides, preferably no greater than 10,000 nucleotides and most preferably no greater than 5,000 nucleotides. Alternatively one allele may be enriched by methods other than amplification prior to determining genotypes at specific variant positions on the enriched allele as a way of determining haplotypes. Preferably the determination of the presence or absence of a haplotype involves determining the sequence of the variant site or sites by methods such as chain terminating DNA sequencing or minisequencing, or by oligonucleotide hybridization or by mass spectrometry.

[0040] The term “genotype” in the context of this invention refers to the alleles present in DNA from a subject or patient, where an allele can be defined by the particular nucleotide(s) present in a nucleic acid sequence at a particular site(s). Often a genotype is the nucleotide(s) present at a single polymorphic site known to vary in the human population.

[0041] In preferred embodiments, the detection of the presence or absence of the at least one variance involves contacting a nucleic acid sequence corresponding to one of the genes identified above or a product of such a gene with a probe. The probe is able to distinguish a particular form of the gene or gene product or the presence or a particular variance or variances, e.g., by differential binding or hybridization. Thus, exemplary probes include nucleic acid hybridization probes, peptide nucleic acid probes, nucleotide-containing probes which also contain at least one nucleotide analog, and antibodies, e.g., monoclonal antibodies, and other probes as discussed herein. Those skilled in the art are familiar with the preparation of probes with particular specificities. Those skilled in the art will recognize that a variety of variables can be adjusted to optimize the discrimination between two variant forms of a gene, including changes in salt concentration, temperature, pH and addition of various compounds that affect the differential affinity of GC vs. AT base pairs, such as tetramethyl ammonium chloride. (See Current Protocols in Molecular Biology by F. M. Ausubel, R. Brent, R. E. Kngston, D. D. Moore, J. D. Seidman, K. Struhl, and V. B. Chanda (editors, John Wiley & Sons.)

[0042] In other preferred embodiments, determining the presence or absence of the at least one variance involves sequencing at least one nucleic acid sample. The sequencing involves sequencing of a portion or portions of a gene and/or portions of a plurality of genes which includes at least one variance site, and may include a plurality of such sites. Preferably, the portion is 500 nucleotides or less in length, more preferably 200 or 100 nucleotides or less, and most preferably 45 nucleotides or less in length. Such sequencing can be carried out by various methods recognized by those skilled in the art, including use of dideoxy termination methods (e.g., using dye-labeled dideoxy nucleotides) and the use of mass spectrometric methods. In addition, mass spectrometric methods may be used to determine the nucleotide present at a variance site. In preferred embodiments in which a plurality of variances is determined, the plurality of variances can constitute a haplotype or collection of haplotypes. Preferably the methods for determining genotypes or haplotypes are designed to be sensitive to all the common genotypes or haplotypes present in the population being studied (for example, a clinical trial population).

[0043] The terms “variant form of a gene”, “form of a gene”, or “allele” refer to one specific form of a gene in a population, the specific form differing from other forms of the same gene in the sequence of at least one, and frequently more than one, variant sites within the sequence of the gene. The sequences at these variant sites that differ between different alleles of the gene are termed “gene sequence variances” or “variances” or “variants”. The term “alternative form” refers to an allele that can be distinguished from other alleles by having distinct variances at least one, and frequently more than one, variant sites within the gene sequence. Other terms known in the art to be equivalent include mutation and polymorphism, although mutation is often used to refer to an allele associated with a deleterious phenotype. In preferred aspects of this invention, the variances are selected from the group consisting of the variances listed in the variance tables herein or in a patent or patent application referenced and incorporated by reference in this disclosure. In the methods utilizing variance presence or absence, reference to the presence of a variance or variances means particular variances, i.e., particular nucleotides at particular polymorphic sites, rather than just the presence of any variance in the gene.

[0044] Variances occur in the human genome at approximately one in every 500-1,000 bases within the human genome when two alleles are compared. When multiple alleles from unrelated individuals are compared the density of variant sites increases as different individuals, when compared to a reference sequence, will often have sequence variances at different sites. At most variant sites there are only two alternative nucleotides involving the substitution of one base for another or the insertion/deletion of one or more nucleotides. Within a gene there may be several variant sites. Variant forms of the gene or alternative alleles can be distinguished by the presence of alternative variances at a single variant site, or a combination of several different variances at different sites (haplotypes).

[0045] It is estimated that there are 3,300,000,000 bases in the sequence of a single haploid human genome. All human cells except germ cells are normally diploid. Each gene in the genome may span 100-10,000,000 bases of DNA sequence or 100-20,000 bases of mRNA. It is estimated that there are between 60,000 and 120,000 genes in the human genome. The “identification” of genetic variances or variant forms of a gene involves the discovery of variances that are present in a population. The identification of variances is required for development of a diagnostic test to determine whether a patient has a variant form of a gene that is known to be associated with a disease, condition, or predisposition or with the efficacy or safety of the drug. Identification of previously undiscovered genetic variances is distinct from the process of “determining” the status of known variances by a diagnostic test (often referred to as genotyping). The present invention provides exemplary variances in genes listed in the gene tables, as well as methods for discovering additional variances in those genes and a comprehensive written description of such additional possible variances. Also described are methods for DNA diagnostic tests to determine the DNA sequence at a particular variant site or sites.

[0046] The process of “identifying” or discovering new variances involves comparing the sequence of at least two alleles of a gene, more preferably at least 10 alleles and most preferably at least 50 alleles (keeping in mind that each somatic cell has two alleles. The analysis of large numbers of individuals to discover variances in the gene sequence between individuals in a population will result in detection of a greater fraction of all the variances in the population. Preferably the process of identifying reveals whether there is a variance within the gene; more preferably identifying reveals the location of the variance within the gene; more preferably identifying provides knowledge of the sequence of the nucleic acid sequence of the variance, and most preferably identifying provides knowledge of the combination of different variances that comprise specific variant forms of the gene (referred to as alleles). In identifying new variances it is often useful to screen different population groups based on racial, ethnic, gender, and/or geographic origin because particular variances may differ in frequency between such groups. It may also be useful to screen DNA from individuals with a particular disease or condition of interest because they may have a higher frequency of certain variances than the general population.

[0047] The process of genotyping involves using diagnostic tests for specific variances that have already been identified. It will be apparent that such diagnostic tests can only be performed after variances and variant forms of the gene have been identified. Identification of new variances can be accomplished by a variety of methods, alone or in combination, including, for example, DNA sequencing, SSCP, heteroduplex analysis, denaturing gradient gel electrophoresis (DGGE), heteroduplex cleavage (either enzymatic as with T4 Endonuclease 7, or chemical as with osmium tetroxide and hydroxylamine), computational methods (described in “VARIANCE SCANNING METHOD FOR IDENTIFYING GENE SEQUENCE VARIANCES” filed Oct. 14, 1999, Ser. No. 09/419,705, and other methods described herein as well as others known to those skilled in the art. (See, for example: Cotton, R. G. H., Slowly but surely towards better scanning for mutations, Trends in Genetics 13(2): 43-6, 1997 or Current Protocols in Human Genetics by N. C. Dracoli, J. L. Haines, B. R. Korf, D. T. Moir, C. C. Morton, C. E. Seidman, D. R. Smith, and A. Boyle (editors), John Wiley & Sons.)

[0048] In the context of this invention, the term “analyzing a sequence” refers to determining at least some sequence information about the sequence,, e.g., determining the nucleotides present at a particular site or sites in the sequence, particularly sites that are known to vary in a population, or determining the base sequence of all of a portion of the particular sequence.

[0049] In the context of this invention, the term “haplotype” refers to a cis arrangement of two or more polymorphic nucleotides, i.e., variances, on a particular chromosome, e.g., in a particular gene. The haplotype preserves information about the phase of the polymorphic nucleotides—that is, which set of variances were inherited from one parent, and which from the other. A genotyping test does not provide information about phase. For example, an individual heterozygous at nucleotide 25 of a gene (both A and C are present) and also at nucleotide 100 (both G and T are present) could have haplotypes 25A-100G and 25C-100T, or alternatively 25A-100T and 25C-100G. Only a haplotyping test can discriminate these two cases definitively.

[0050] The terms “variances”, “variants” and “polymorphisms”, as used herein, may also refer to a set of variances, haplotypes or a mixture of the two. Further, the term variance, variant or polymorphism (singular), as used herein, also encompasses a haplotype. This usage is intended to minimize the need for cumbersome phrases such as: “. . . measure correlation between drug response and a variance, variances, haplotype, haplotypes or a combination of variances and haplotypes . . . ”, throughout the application. Instead, the italicized text in the foregoing sentence can be represented by the word “variance”, “variant” or “polymorphism”. Similarly, the term genotype, as used herein, means a procedure for determining the status of one or more variances in a gene, including a set of variances comprising a haplotype. Thus phrases such as “. . . genotype a patient . . . ” refer to determining the status of one or more variances, including a set of variances for which phase is known (i.e. a haplotype).

[0051] In preferred embodiments of this invention, the frequency of the variance or variant form of the gene in a population is known. Measures of frequency known in the art include “allele frequency”, namely the fraction of genes in a population that have one specific variance or set of variances. The allele frequencies for any gene should sum to 1. Another measure of frequency known in the art is the “heterozygote frequency” namely, the fraction of individuals in a population who carry two alleles, or two forms of a particular variance or variant form of a gene, one inherited from each parent. Alternatively, the number of individuals who are homozygous for a particular form of a gene may be a useful measure. The relationship between allele frequency, heterozygote frequency, and homozygote frequency is described for many genes by the Hardy-Weinberg equation, which provides the relationship between allele frequency, heterozygote frequency and homozygote frequency in a freely breeding population at equilibrium. Most human variances are substantially in Hardy-Weinberg equilibrium. In a preferred aspect of this invention, the allele frequency, heterozygote frequency, and homozygote frequencies are determined experimentally. Preferably a variance has an allele frequency of at least 0.01, more preferably at least 0.05, still more preferably at least 0.10. However, the allele may have a frequency as low as 0.001 if the associated phenotype is, for example, a rare form of toxic reaction to a treatment or drug. Beneficial responses may also be rare.

[0052] In this regard, “population” refers to a defined group of individuals or a group of individuals with a particular disease or condition or individuals that may be treated with a specific drug identified by, but not limited to geographic, ethnic, race, gender, and/or cultural indices. In most cases a population will preferably encompass at least ten thousand, one hundred thousand, one million, ten million, or more individuals, with the larger numbers being more preferable. In a preferred aspect of this invention, the population refers to individuals with a specific disease or condition that may be treated with a specific drug. In an aspect of this invention, the allele frequency, heterozygote frequency, or homozygote frequency of a specific variance or variant form of a gene is known. In preferred embodiments of this invention, the frequency of one or more variances that may predict response to a treatment is determined in one or more populations using a diagnostic test.

[0053] It should be emphasized that it is currently not generally practical to study an entire population to establish the association between a specific disease or condition or response to a treatment and a specific variance or variant form of a gene. Such studies are preferably performed in controlled clinical trials using a limited number of patients that are considered to be representative of the population with the disease. Since drug development programs are generally targeted at the largest possible population, the study population will generally consist of men and women, as well as members of various racial and ethnic groups, depending on where the clinical trial is being performed. This is important to establish the efficacy of the treatment in all segments of the population.

[0054] In the context of this invention, the term “probe” refers to a molecule which detectably distinguishes between target molecules differing in structure. Detection can be accomplished in a variety of different ways depending on the type of probe used and the type of target molecule. Thus, for example, detection may be based on discrimination of activity levels of the target molecule, but preferably is based on detection of specific binding. Examples of such specific binding include antibody binding and nucleic acid probe hybridization. Thus, for example, probes can include enzyme substrates, antibodies and antibody fragments, and nucleic acid hybridization probes. Thus, in preferred embodiments, the detection of the presence or absence of the at least one variance involves contacting a nucleic acid sequence which includes a variance site with a probe, preferably a nucleic acid probe, where the probe preferentially hybridizes with a form of the nucleic acid sequence containing a complementary base at the variance site as compared to hybridization to a form of the nucleic acid sequence having a non-complementary base at the variance site, where the hybridization is carried out under selective hybridization conditions. Such a nucleic acid hybridization probe may span two or more variance sites. Unless otherwise specified, a nucleic acid probe can include one or more nucleic acid analogs, labels or other substituents or moieties so long as the base-pairing function is retained.

[0055] As is generally understood, administration of a particular treatment, e.g., administration of a therapeutic compound or combination of compounds, is chosen depending on the disease or condition which is to be treated. Thus, in certain preferred embodiments, the disease or condition is one for which administration of a treatment is expected to provide a therapeutic benefit.

[0056] As used herein, the terms “effective” and “effectiveness” includes both pharmacological effectiveness and physiological safety. Pharmacological effectiveness refers to the ability of the treatment to result in a desired biological effect in the patient. Physiological safety refers to the level of toxicity, or other adverse physiological effects at the cellular, organ and/or organism level (often referred to as side-effects) resulting from administration of the treatment. On the other hand, the term “ineffective” indicates that a treatment does not provide sufficient pharmacological effect to be therapeutically useful, even in the absence of deleterious effects, at least in the unstratified population. (Such a treatment may be ineffective in a subgroup that can be identified by the presence of one or more sequence variances or alleles.) “Less effective” means that the treatment results in a therapeutically significant lower level of pharmacological effectiveness and/or a therapeutically greater level of adverse physiological effects, e.g., greater liver toxicity.

[0057] Thus, in connection with the administration of a drug, a drug which is “effective against” a disease or condition indicates that administration in a clinically appropriate manner results in a beneficial effect for at least a statistically significant fraction of patients, such as a improvement of symptoms, a cure, a reduction in disease load, reduction in tumor mass or cell numbers, extension of life, improvement in quality of life, or other effect generally recognized as positive by medical doctors familiar with treating the particular type of disease or condition.

[0058] Effectiveness is measured in a particular population. In conventional drug development the population is generally every subject who meets the enrollment criteria (i.e. has the particular form of the disease or condition being treated). It is an aspect of the present invention that segmentation of a study population by genetic criteria can provide the basis for identifying a subpopulation in which a drug is effective against the disease or condition being treated.

[0059] The term “deleterious effects” refers to physical effects in a patient caused by administration of a treatment which are regarded as medically undesirable. Thus, for example, deleterious effects can include a wide spectrum of toxic effects injurious to health such as death of normally functioning cells when only death of diseased cells is desired, nausea, fever, inability to retain food, dehydration, damage to critical organs such as arrythmias, renal tubular necrosis, fatty liver, or pulmonary fibrosis leading to coronary, renal, hepatic, or pulmonary insufficiency among many others. In this regard, the term “adverse reactions” refers to those manifestations of clinical symptomology of pathological disorder or dysfunction is induced by administration or a drug, agent, or candidate therapeutic intervention. In this regard, the term “contraindicated” means that a treatment results in deleterious effects such that a prudent medical doctor treating such a patient would regard the treatment as unsuitable for administration. Major factors in such a determination can include, for example, availability and relative advantages of alternative treatments, consequences of non-treatment, and permanency of deleterious effects of the treatment.

[0060] It is recognized that many treatment methods, e.g., administration of certain compounds or combinations of compounds, may produce side-effects or other deleterious effects in patients. Such effects can limit or even preclude use of the treatment method in particular patients, or may even result in irreversible injury, disorder, dysfunction, or death of the patient. Thus, in certain embodiments, the variance information is used to select both a first method of treatment and a second method of treatment. Usually the first treatment is a primary treatment which provides a physiological effect directed against the disease or condition or its symptoms. The second method is directed to reducing or eliminating one or more deleterious effects of the first treatment, e.g., to reduce a general toxicity or to reduce a side effect of the primary treatment. Thus, for example, the second method can be used to allow use of a greater dose or duration of the first treatment, or to allow use of the first treatment in patients for whom the first treatment would not be tolerated or would be contra-indicated in the absence of a second method to reduce deleterious effects or to potentiate the effectiveness of the first treatment.

[0061] In a related aspect, the invention provides a method for selecting a method of treatment for a patient suffering from a disease or condition by comparing at least one variance in at least one gene in the patient, with a list of variances in the gene from Tables 1, 3 and 4, or other gene related to pharmacokinetic parameters, or organ and tissue damage, or inordinate immune response, which are indicative of the effectiveness or safety of at least one method of treatment. Preferably the comparison involves a plurality of variances or a haplotype indicative of the effectiveness of at least one method of treatment. Also, preferably the list of variances includes a plurality of variances.

[0062] Similar to the above aspect, in preferred embodiments the at least one method of treatment involves the administration of a compound effective in at least some patients with a disease or condition; the presence or absence of the at least one variance is indicative that the treatment will be effective in the patient; and/or the presence or absence of the at least one variance is indicative that the treatment will be ineffective or contra-indicated in the patient; and/or the treatment is a first treatment and the presence or absence of the at least one variance is indicative that a second treatment will be beneficial to reduce a deleterious effect or potentiate the effectiveness of the first treatment; and/or the at least one treatment is a plurality of methods of treatment. For a plurality of treatments, preferably the selecting involves determining whether any of the methods of treatment will be more effective than at least one other of the plurality of methods of treatment. Yet other embodiments are provided as described for the preceding aspect in connection with methods of treatment using administration of a compound; treatment of various diseases, and variances in particular genes.

[0063] In the context of variance information in the methods of this invention, the term “list” refers to one or more variances which have been identified for a gene of potential importance in accounting for inter-individual variation in treatment response. Preferably there is a plurality of variances for the gene, preferably a plurality of variances for the particular gene. Preferably, the list is recorded in written or electronic form. For example, identified variances of identified genes are recorded for some of the genes in Tables 3 and 4, additional variances for genes in Table 1 are provided in Table 1 of Stanton & Adams, application Ser. No. 09/300,747, supra, and additional gene variance identification tables are provided in a form which allows comparison with other variance information. The possible additional variances in the identified genes are provided in Table 3 in Stanton & Adams, application Ser. No. 09/300,747, supra.

[0064] In addition to the basic method of treatment, often the mode of administration of a given compound as a treatment for a disease or condition in a patient is significant in determining the course and/or outcome of the treatment for the patient. Thus, the invention also provides a method for selecting a method of administration of a compound to a patient suffering from a disease or condition, by determining the presence or absence of at least one variance in cells of the patient in at least one identified gene from Tables 1, 3, and 4, where such presence or absence is indicative of an appropriate method of administration of the compound. Preferably, the selection of a method of treatment (a treatment regimen) involves selecting a dosage level or frequency of administration or route of administration of the compound or combinations of those parameters. In preferred embodiments, two or more compounds are to be administered, and the selecting involves selecting a method of administration for one, two, or more than two of the compounds, jointly, concurrently, or separately. As understood by those skilled in the art, such plurality of compounds may be used in combination therapy, and thus may be formulated in a single drug, or may be separate drugs administered concurrently, serially, or separately. Other embodiments are as indicated above for selection of second treatment methods, methods of identifying variances, and methods of treatment as described for aspects above.

[0065] In another aspect, the invention provides a method for selecting a patient for administration of a method of treatment for a disease or condition, or of selecting a patient for a method of administration of a treatment, by comparing the presence or absence of at least one variance in a gene as identified above in cells of a patient, with a list of variances in the gene, where the presence or absence of the at least one variance is indicative that the treatment or method of administration will be effective in the patient. If the at least one variance is present in the patient's cells, then the patient is selected for administration of the treatment.

[0066] In preferred embodiments, the disease or the method of treatment is as described in aspects above, specifically including, for example, those described for selecting a method of treatment.

[0067] In another aspect, the invention provides a method for identifying a subset of patients with enhanced or diminished response or tolerance to a treatment method or a method of administration of a treatment where the treatment is for a disease or condition in the patient. The method involves correlating one or more variances in one or more genes as identified in aspects above in a plurality of patients with response to a treatment or a method of administration of a treatment. The correlation may be performed by determining the one or more variances in the one or more genes in the plurality of patients and correlating the presence or absence of each of the variances (alone or in various combinations) with the patient's response to treatment. The variances may be previously known to exist or may also be determined in the present method or combinations of prior information and newly determined information may be used. The enhanced or diminished response should be statistically significant, preferably such that p=0.10 or less, more preferably 0.05 or less, and most preferably 0.02 or less. A positive correlation between the presence of one or more variances and an enhanced response to treatment is indicative that the treatment is particularly effective in the group of patients having those variances. A positive correlation of the presence of the one or more variances with a diminished response to the treatment is indicative that the treatment will be less effective in the group of patients having those variances. Such information is useful, for example, for selecting or de-selecting patients for a particular treatment or method of administration of a treatment, or for demonstrating that a group of patients exists for which the treatment or method of treatment would be particularly beneficial or contra-indicated. Such demonstration can be beneficial, for example, for obtaining government regulatory approval for a new drug or a new use of a drug

[0068] In preferred embodiments, the variances are in at least one of the identified genes listed in Tables 1, 3, and 4, or are particular variances described herein. Also, preferred embodiments include drugs, treatments, variance identification or determination, determination of effectiveness, and/or diseases as described for aspects above or otherwise described herein.

[0069] In preferred embodiments, the correlation of patient responses to therapy according to patient genotype is carried out in a clinical trial, e.g., as described herein according to any of the variations described. Detailed description of methods for associating variances with clinical outcomes using clinical trials are provided below. Further, in preferred embodiments the correlation of pharmacological effect (positive or negative) to treatment response according to genotype or haplotype in such a clinical trial is part of a regulatory submission to a government agency leading to approval of the drug. Most preferably the compound or compounds would not be approvable in the absence of the genetic information allowing identification of an optimal responder population.

[0070] As indicated above, in aspects of this invention involving selection of a patient for a treatment, selection of a method or mode of administration of a treatment, and selection of a patient for a treatment or a method of treatment, the selection may be positive selection or negative selection. Thus, the methods can include eliminating a treatment for a patient, eliminating a method or mode of administration of a treatment to a patient, or elimination of a patient for a treatment or method of treatment.

[0071] Also, in methods involving identification and/or comparison of variances present in a gene of a patient, the methods can involve such identification or comparison for a plurality of genes. Preferably, the genes are functionally related to the same disease or condition, or to the aspect of disease pathophysiology that is being subjected to pharmacological manipulation by the treatment (e.g., a drug), or to the activation or inactivation or elimination of the drug, and more preferably the genes are involved in the same biochemical process or pathway.

[0072] In another aspect, the invention provides a method for identifying the forms of a gene in an individual, where the gene is one specified as for aspects above, by determining the presence or absence of at least one variance in the gene. In preferred embodiments, the at least one variance includes at least one variance selected from the group of variances identified in variance tables herein. Preferably, the presence or absence of the at least one variance is indicative of the effectiveness of a therapeutic treatment in a patient suffering from a disease or condition and having cells containing the at least one variance.

[0073] The presence or absence of the variances can be determined in any of a variety of ways as recognized by those skilled in the art. For example, the nucleotide sequence of at least one nucleic acid sequence which includes at least one variance site (or a complementary sequence) can be determined, such as by chain termination methods, hybridization methods or by mass spectrometric methods. Likewise, in preferred embodiments, the determining involves contacting a nucleic acid sequence or a gene product of one of one of the genes with a probe which specifically identifies the presence or absence of a form of the gene. For example, a probe, e.g., a nucleic acid probe, can be used which specifically binds, e.g., hybridizes, to a nucleic acid sequence corresponding to a portion of the gene and which includes at least one variance site under selective binding conditions. As described for other aspects, determining the presence or absence of at least two variances and their relationship on the two gene copies present in a patient can constitute determining a haplotype or haplotypes.

[0074] Other preferred embodiments involve variances related to types of treatment, drug responses, diseases, nucleic acid sequences, and other items related to variances and variance determination as described for aspects above.

[0075] In yet another aspect, the invention provides a pharmaceutical composition which includes a compound which has a differential effect in patients having at least one copy, or alternatively, two copies of a form of a gene as identified for aspects above and a pharmaceutically acceptable carrier, excipient, or diluent. The composition is adapted to be preferentially effective to treat a patient with cells containing the one, two, or more copies of the form of the gene.

[0076] In preferred embodiments of aspects involving pharmaceutical compositions, active compounds, or drugs, the material is subject to a regulatory limitation, restriction, or recommendation on approved uses or indications, e.g., by the U.S. Food and Drug Administration (FDA), limiting or recommending limiting approved use of the composition to patients having at least one copy of the particular form of the gene which contains at least one variance. Alternatively, the composition is subject to a regulatory limitation, restriction, or recommendation on approved uses indicating or recommending that the composition is not approved for use or should not be used in patients having at least one copy of a form of the gene including at least one variance. Also in preferred embodiments, the composition is packaged, and the packaging includes a label or insert indicating or suggesting beneficial therapeutic approved use of the composition in patients having one or two copies of a form of the gene including at least one variance. Alternatively, the label or insert limits or recommends limiting approved use of the composition to patients having zero or one or two copies of a form of the gene including at least one variance. The latter embodiment would be likely where the presence of the at least one variance in one or two copies in cells of a patient means that the composition would be ineffective or deleterious to the patient. Also in preferred embodiments, the composition is indicated for use in treatment of a disease or condition that is one of those identified for aspects above. Also in preferred embodiments, the at least one variance includes at least one variance from those identified herein.

[0077] The term “packaged” means that the drug, compound, or composition is prepared in a manner suitable for distribution or shipping with a box, vial, pouch, bubble pack, or other protective container, which may also be used in combination. The packaging may have printing on it and/or printed material may be included in the packaging.

[0078] In preferred embodiments, the drug is selected from the drug classes or specific exemplary drugs identified in an example, in a table herein, and is subject to a regulatory limitation or suggestion or warning as described above that limits or suggests limiting approved use to patients having specific variances or variant forms of a gene identified in Examples or in the gene list provided below in order to achieve maximal benefit and avoid toxicity or other deleterious effect.

[0079] A pharmaceutical composition can be adapted to be preferentially effective in a variety of ways. In some cases, an active compound is selected which was not previously known to be differentially active, or which was not previously recognized as a therapeutic compound. Alternatively the compound was previously known as a therapeutic compound, but the composition is formulated in a manner appropriate for administration for treatment of a disease or condition for which a gene of this invention is involved in treatment response, and the active compound had not been formulated appropriately for such use before. For example, a compound may previously have been formulated for topical treatment of a skin condition, but is found to be effective in IV or other internal treatment of a disease identified for this invention. For compounds that are differentially effective on the gene, such alternative formulations are adapted to be preferentially effective. In some cases, the concentration of an active compound which has differential activity can be adjusted such that the composition is appropriate for administration to a patient with the specified variances. For example, the presence of a specified variance may allow or require the administration of a much larger dose, which would not be practical with a previously utilized composition. Conversely, a patient may require a much lower dose, such that administration of such a dose with a prior composition would be impractical or inaccurate. Thus, the composition may be prepared in a higher or lower unit dose form, or prepared in a higher or lower concentration of the active compound or compounds. In yet other cases, the composition can include additional compounds useful to enable administration of a particular active compound in a patient with the specified variances, which was not in previous compositions, e.g., because the majority of patients did not require or benefit from the added component.

[0080] The term “differential” or “differentially” generally refers to a statistically significant different level in the specified property or effect. Preferably, the difference is also functionally significant. Thus, “differential binding or hybridization” is sufficient difference in binding or hybridization to allow discrimination using an appropriate detection technique. Likewise, “differential effect” or “differentially active” in connection with a therapeutic treatment or drug refers to a difference in the level of the effect or activity which is distinguishable using relevant parameters and techniques for measuring the effect or activity being considered. Preferably the difference in effect or activity is also sufficient to be clinically significant, such that a corresponding difference in the course of treatment or treatment outcome would be expected, at least on a statistical basis.

[0081] Also usefully provided in the present invention are probes which specifically recognize a nucleic acid sequence corresponding to a variance or variances in a gene as identified in aspects above or a product expressed from the gene, and are able to distinguish a variant form of the sequence or gene or gene product from one or more other variant forms of that sequence, gene, or gene product under selective conditions. Those skilled in the art recognize and understand the identification or determination of selective conditions for particular probes or types of probes. An exemplary type of probe is a nucleic acid hybridization probe, which will selectively bind under selective binding conditions to a nucleic acid sequence or a gene product corresponding to one of the genes identified for aspects above. Another type of probe is a peptide or protein, e.g., an antibody or antibody fragment which specifically or preferentially binds to a polypeptide expressed from a particular form of a gene as characterized by the presence or absence of at least one variance. Thus, in another aspect, the invention concerns such probes. In the context of this invention, a “probe” is a molecule, commonly a nucleic acid, though also potentially a protein, carbohydrate, polymer, or small molecule, that is capable of binding to one variance or variant form of the gene to a greater extent than to a form of the gene having a different base at one or more variance sites, such that the presence of the variance or variant form of the gene can be determined. Preferably the probe distinguishes at least one variance identified in Examples, tables or lists below or in Tables 1 or 3 of Stanton & Adams, application Ser. No. 09/300,747, supra.

[0082] In preferred embodiments, the probe is a nucleic acid probe 6, 7, 8, 9, 10, 11, 12, 13, 14 or preferably at least 17 nucleotides in length, more preferably at least 20 or 22 or 25, preferably 500 or fewer nucleotides in length, more preferably 200 or 100 or fewer, still more preferably 50 or fewer, and most preferably 30 or fewer. In preferred embodiments, the probe has a length in a range from any one of the above lengths to any other of the above lengths (including endpoints). The probe specifically hybridizes under selective hybridization conditions to a nucleic acid sequence corresponding to a portion of one of the genes identified in connection with above aspects. The nucleic acid sequence includes at least one and preferably two or more variance sites. Also in preferred embodiments, the probe has a detectable label, preferably a fluorescent label. A variety of other detectable labels are known to those skilled in the art. Such a nucleic acid probe can also include one or more nucleic acid analogs.

[0083] In preferred embodiments, the probe is an antibody or antibody fragment which specifically binds to a gene product expressed from a form of one of the above genes, where the form of the gene has at least one specific variance with a particular base at the variance site, and preferably a plurality of such variances.

[0084] In connection with nucleic acid probe hybridization, the term “specifically hybridizes” indicates that the probe hybridizes to a sufficiently greater degree to the target sequence than to a sequence having a mismatched base at least one variance site to allow distinguishing such hybridization. The term “specifically hybridizes” thus means that the probe hybridizes to the target sequence, and not to non-target sequences, at a level which allows ready identification of probe/target sequence hybridization under selective hybridization conditions. Thus, “selective hybridization conditions” refer to conditions which allow such differential binding. Similarly, the terms “specifically binds” and “selective binding conditions” refer to such differential binding of any type of probe, e.g., antibody probes, and to the conditions which allow such differential binding. Typically hybridization reactions to determine the status of variant sites in patient samples are carried out with two different probes, one specific for each of the (usually two) possible variant nucleotides. The complementary information derived from the two separate hybridization reactions is useful in corroborating the results.

[0085] Likewise, the invention provides an isolated, purified or enriched nucleic acid sequence of 15 to 500 nucleotides in length, preferably 15 to 100 nucleotides in length, more preferably 15 to 50 nucleotides in length, and most preferably 15 to 30 nucleotides in length, which has a sequence which corresponds to a portion of one of the genes identified for aspects above. Preferably the lower limit for the preceding ranges is 17, 20, 22, or 25 nucleotides in length. In other embodiments, the nucleic acid sequence is 30 to 300 nucleotides in length, or 45 to 200 nucleotides in length, or 45 to 100 nucleotides in length. The nucleic acid sequence includes at least one variance site. Such sequences can, for example, be amplification products of a sequence which spans or includes a variance site in a gene identified herein. Likewise, such a sequence can be a primer, or amplification oligonucleotide which is able to bind to or extend through a variance site in such a gene. Yet another example is a nucleic acid hybridization probe comprised of such a sequence. In such probes, primers, and amplification products, the nucleotide sequence can contain a sequence or site corresponding to a variance site or sites, for example, a variance site identified herein. Preferably the presence or absence of a particular variant form in the heterozygous or homozygous state is indicative of the effectiveness of a method of treatment in a patient.

[0086] Likewise, the invention provides a set of primers or amplification oligonucleutides (e.g., 2, 3, 4, 6, 8, 10 or even more) adapted for binding to or extending through at least one gene identified herein. In preferred embodiments the set includes primers or amplification oligonucleotides adapted to bind to or extend through a plurality of sequence variances in a gene(s) identified herein. The plurality of variances preferably provides a haplotype. Those skilled in the art are familiar with the use of amplification oligonucleotides (e.g., PCR primers) and the appropriate location, testing and use of such oligonucleotides. In certain embodiments, the oligonucleotides are designed and selected to provide variance-specific amplification.

[0087] In reference to nucleic acid sequences which “correspond” to a gene, the term “correspond” refers to a nucleotide sequence relationship, such that the nucleotide sequence has a nucleotide sequence which is the same as the reference gene or an indicated portion thereof, or has a nucleotide sequence which is exactly complementary in normal Watson-Crick base pairing, or is an RNA equivalent of such a sequence, e.g., an mRNA, or is a cDNA derived from an mRNA of the gene.

[0088] In another aspect, the invention provides a kit containing at least one probe or at least one primer (or other amplification oligonucleotide) or both (e.g., as described above) corresponding to a gene or genes listed in Tables 1, 3, and 4 or other gene related to a drug-induced disease or condition, or other gene involved in absorption, distribution, metabolism, excretion, or in toxicity-related modification of a drug. The kit is preferably adapted and configured to be suitable for identification of the presence or absence of a particular variance or variances, which can include or consist of a nucleic acid sequence corresponding to a portion of a gene. A plurality of variances may comprise a haplotype of haplotypes. The kit may also contain a plurality of either or both of such probes and/or primers, e.g., 2, 3, 4, 5, 6, or more of such probes and/or primers. Preferably the plurality of probes and/or primers are adapted to provide detection of a plurality of different sequence variances in a gene or plurality of genes, e.g., in 2, 3, 4, 5, or more genes or to amplify and/or sequence a nucleic acid sequence including at least one variance site in a gene or genes. Preferably one or more of the variance or variances to be detected are correlated with variability in a treatment response or tolerance, and are preferably indicative of an effective response to a treatment. In preferred embodiments, the kit contains components (e.g., probes and/or primers) adapted or useful for detection of a plurality of variances (which may be in one or more genes) indicative of the effectiveness of at least one treatment, preferably of a plurality of different treatments for a particular disease or condition. It may also be desirable to provide a kit containing components adapted or useful to allow detection of a plurality of variances indicative of the effectiveness of a treatment or treatment against a plurality of diseases. The kit may also optionally contain other components, preferably other components adapted for identifying the presence of a particular variance or variances. Such additional components can, for example, independently include a buffer or buffers, e.g., amplification buffers and hybridization buffers, which may be in liquid or dry form, a DNA polymerase, e.g., a polymerase suitable for carrying out PCR (e.g., a thermostable DNA polymerase), and deoxy nucleotide triphosphates (dNTPs). Preferably a probe includes a detectable label, e.g., a fluorescent label, enzyme label, light scattering label, or other label. Preferably the kit includes a nucleic acid or polypeptide array on a solid phase substrate. The array may, for example, include a plurality of different antibodies, and/or a plurality of different nucleic acid sequences. Sites in the array can allow capture and/or detection of nucleic acid sequences or gene products corresponding to different variances in one or more different genes. Preferably the array is arranged to provide variance detection for a plurality of variances in one or more genes which correlate with the effectiveness of one or more treatments of one or more diseases, which is preferably a variance as described herein.

[0089] The kit may also optionally contain instructions for use, which can include a listing of the variances correlating with a particular treatment or treatments for a disease or diseases and/or a statement or listing of the diseases for which a particular variance or variances correlates with a treatment efficacy and/or safety.

[0090] Preferably the kit components are selected to allow detection of a variance described herein, and/or detection of a variance indicative of a treatment, e.g., administration of a drug, pointed out herein.

[0091] Additional configurations for kits of this invention will be apparent to those skilled in the art.

[0092] The invention also includes the use of such a kit to determine the genotype(s) of one or more individuals with respect to one or more variance sites in one or more genes identified herein. Such use can include providing a result or report indicating the presence and/or absence of one or more variant forms or a gene or genes which are indicative of the effectiveness of a treatment or treatments.

[0093] In another aspect, the invention provides a method for determining a genotype of an individual in relation to one or more variances in one or more of the genes identified in above aspects by using mass spectrometric determination of a nucleic acid sequence which is a portion of a gene identified for other aspects of this invention or a complementary sequence. Such mass spectrometric methods are known to those skilled in the art. In preferred embodiments, the method involves determining the presence or absence of a variance in a gene; determining the nucleotide sequence of the nucleic acid sequence; the nucleotide sequence is 100 nucleotides or less in length, preferably 50 or less, more preferably 30 or less, and still more preferably 20 nucleotides or less. In general, such a nucleotide sequence includes at least one variance site, preferably a variance site which is informative with respect to the expected response of a patient to a treatment as described for above aspects.

[0094] As indicated above, many therapeutic compounds or combinations of compounds or pharmaceutical compositions show variable efficacy and/or safety in various patients in whom the compound or compounds is administered. Thus, it is beneficial to identify variances in relevant genes, e.g., genes related to the action or toxicity of the compound or compounds. Thus, in a further aspect, the invention provides a method for determining whether a compound has a differential effect due to the presence or absence of at least one variance in a gene or a variant form of a gene, where the gene is a gene identified for aspects above.

[0095] The method involves identifying a first patient or set of patients suffering from a disease or condition whose response to a treatment differs from the response (to the same treatment) of a second patient or set of patients suffering from the same disease or condition, and then determining whether the occurrence or frequency of occurrence of at least one variance in at least one gene differs between the first patient or set of patients and the second patient or set of patients. A correlation between the presence or absence of the variance or variances and the response of the patient or patients to the treatment indicates that the variance provides information about variable patient response. In general, the method will involve identifying at least one variance in at least one gene. An alternative approach is to identify a first patient or set of patients suffering from a disease or condition and having a particular genotype, haplotype or combination of genotypes or haplotypes, and a second patient or set of patients suffering from the same disease or condition that have a genotype or haplotype or sets of genotypes or haplotypes that differ in a specific way from those of the first set of patients. Subsequently the extent and magnitude of clinical response can be compared between the first patient or set of patients and the second patient or set of patients. A correlation between the presence or absence of a variance or variances or haplotypes and the response of the patient or patients to the treatment indicates that the variance provides information about variable patient response and is useful for the present invention.

[0096] The method can utilize a variety of different informative comparisons to identify correlations. For example a plurality of pairwise comparisons of treatment response and the presence or absence of at least one variance can be performed for a plurality of patients. Likewise, the method can involve comparing the response of at least one patient homozygous for at least one variance with at least one patient homozygous for the alternative form of that variance or variances. The method can also involve comparing the response of at least one patient heterozygous for at least one variance with the response of at least one patient homozygous for the at least one variance. Preferably the heterozygous patient response is compared to both alternative homozygous forms, or the response of heterozygous patients is grouped with the response of one class of homozygous patients and said group is compared to the response of the alternative homozygous group.

[0097] Such methods can utilize either retrospective or prospective information concerning treatment response variability. Thus, in a preferred embodiment, it is previously known that patient response to the method of treatment is variable.

[0098] Also in preferred embodiments, the disease or condition is as for other aspects of this invention; for example, the treatment involves administration of a compound or pharmaceutical composition.

[0099] In preferred embodiments, the method involves a clinical trial, e.g., as described herein. Such a trial can be arranged, for example, in any of the ways described herein, e.g., in the Detailed Description.

[0100] The present invention also provides methods of treatment of a disease or condition, preferably a disease or condition related to pharmacokinetic parameters, e.g. absorption, distribution, metabolism, or excretion, that affect a drug or candidate therapeutic intervention regarding efficacy and or safety, i.e. drug-induced disease, disorder or dysfunction or other toxicity effects or clinical symptomatology. Such methods combine identification of the presence or absence of particular variances, preferably in a gene or genes from Tables 1, 3, and 4, with the administration of a compound; identification of the presence of particular variances with selection of a method of treatment and administration of the treatment; and identification of the presence or absence of particular variances with elimination of a method of treatment based on the variance information indicating that the treatment is likely to be ineffective or contra-indicated, and thus selecting and administering an alternative treatment effective against the disease or condition. Thus, preferred embodiments of these methods incorporate preferred embodiments of such methods as described for such sub-aspects.

[0101] As used herein, a “gene” is a sequence of DNA present in a cell that directs the expression of a “biologically active” molecule or “gene product”, most commonly by transcription to produce RNA and translation to produce protein. The “gene product’ is most commonly a RNA molecule or protein or a RNA or protein that is subsequently modified by reacting with, or combining with, other constituents of the cell. Such modifications may include, without limitation, modification of proteins to form glycoproteins, lipoproteins, and phosphoproteins, or other modifications known in the art. RNA may be modified without limitation by polyadenylation, splicing, capping or export from the nucleus or by covalent or noncovalent interactions with proteins. The term “gene product” refers to any product directly resulting from transcription of a gene. In particular this includes partial, precursor, and mature transcription products (i.e., pre-mRNA and mRNA), and translation products with or without further processing including, without limitation, lipidation, phosphorylation, glycosylation, or combinations of such processing

[0102] The term “gene involved in the origin or pathogenesis of a disease or condition” refers to a gene that harbors mutations or polymorphisms that contribute to the cause of disease, or variances that affect the progression of the disease or expression of specific characteristics of the disease. The term also applies to genes involved in the synthesis, accumulation, or elimination of products that are involved in the origin or pathogenesis of a disease or condition including, without limitation, proteins, lipids, carbohydrates, hormones, or small molecules.

[0103] The term “gene involved in the action of a drug” refers to any gene whose gene product affects the efficacy or safety of the drug or affects the disease process being treated by the drug, and includes, without limitation, genes that encode gene products that are targets for drug action, gene products that are involved in the metabolism, activation or degradation of the drug, gene products that are involved in the bioavailability or elimination of the drug to the target, gene products that affect biological pathways that, in turn, affect the action of the drug such as the synthesis or degradation of competitive substrates or allosteric effectors or rate-limiting reaction, or, alternatively, gene products that affect the pathophysiology of the disease process via pathways related or unrelated to those altered by the presence of the drug compound. (Particular variances in the latter category of genes may be associated with patient groups in whom disease etiology is more or less susceptible to amelioration by the drug. For example, there are several pathophysiological mechanisms in hypertension, and depending on the dominant mechanism in a given patient, that patient may be more or less likely than the average hypertensive patient to respond to a drug that primarily targets one pathophysiological mechanism. The relative importance of different pathophysiological mechanisms in individual patients is likely to be affected by variances in genes associated with the disease pathophysiology.) The “action” of a drug refers to its effect on biological products within the body. The action of a drug also refers to its effects on the signs or symptoms of a disease or condition, or effects of the drug that are unrelated to the disease or condition leading to unanticipated effects on other processes. Such unanticipated processes often lead to adverse events or toxic effects. The terms “adverse event” or “toxic” event” are known in the art and include, without limitation, those listed in the FDA reference system for adverse events.

[0104] In accordance with the aspects above and the Detailed Description below, there is also described for this invention an approach for developing drugs that are explicitly indicated for, and/or for which approved use is restricted to individuals in the population with specific variances or combinations of variances, as determined by diagnostic tests for variances or variant forms of certain genes involved in the disease or condition or involved in the action or metabolism or transport of the drug. Such drugs may provide more effective treatment for a disease or condition in a population identified or characterized with the use of a diagnostic test for a specific variance or variant form of the gene if the gene is involved in the action of the drug or in determining a characteristic of the disease or condition. Such drugs may be developed using the diagnostic tests for specific variances or variant forms of a gene to determine the inclusion of patients in a clinical trial.

[0105] Thus, the invention also provides a method for producing a pharmaceutical composition by identifying a compound which has differential activity or effectiveness against a disease or condition in patients having at least one variance in a gene, preferably in a gene from Tables 1, 3 and 4, compounding the pharmaceutical composition by combining the compound with a pharmaceutically acceptable carrier, excipient, or diluent such that the composition is preferentially effective in patients who have at least one copy of the variance or variances. In some cases, the patient has two copies of the variance or variances. In preferred embodiments, the disease or condition, gene or genes, variances, methods of administration, or method of determining the presence or absence of variances is as described for other aspects of this invention.

[0106] Similarly, the invention provides a method for producing a pharmaceutical agent by identifying a compound which has differential activity against a disease or condition in patients having at least one copy of a form of a gene, preferably a gene listed in Table 1, having at least one variance and synthesizing the compound in an amount sufficient to provide a pharmaceutical effect in a patient suffering from the disease or condition. The compound can be identified by conventional screening methods and its activity confirmed. For example, compound libraries can be screened to identify compounds which differentially bind to products of variant forms of a particular gene product, or which differentially affect expression of variant forms of the particular gene, or which differentially affect the activity of a product expressed from such gene. Alternatively, the design of a compound can exploit knowledge of the variances provided herein to avoid significant allele specific effects, in order to reduce the likelihood of significant pharmacogenetic effects during the clinical development process. Preferred embodiments are as for the preceding aspect.

[0107] In another aspect, the invention provides a method of treating a disease or condition in a patient by selecting a patient whose cells have an allele of an identified gene, preferably a gene selected from the genes listed in Table 1, and determining whether that alteration provides a differential effect (with respect to reducing or alleviating a disease or condition, or with respect to variation in toxicity or tolerance to a treatment) in patients with at least one copy of at least one allele of the gene as compared to patients with at least one copy of one alternative allele. The presence of such a differential effect indicates that altering the level or activity of the gene provides at least part of an effective treatment for the disease or condition.

[0108] Preferably the allele contains a variance as shown in Tables 3 and 4 or other variance table herein, or in Table 1 or 3 of Stanton & Adams, application Ser. No. 09/300,747, supra. Also preferably, the altering involves administering to the patient a compound preferentially active on at least one but less than all alleles of the gene.

[0109] Preferred embodiments include those as described above for other aspects of treating a disease or condition.

[0110] As recognized by those skilled in the art, all the methods of treating described herein include administration of the treatment to a patient.

[0111] In a further aspect, the invention provides a method for determining a method of treatment effective to treat a disease or condition by altering the level of activity of a product of an allele of a gene selected from the genes listed in Tables 1, 3 or 4, and determining whether that alteration provides a differential effect related to reducing or alleviating a disease or condition as compared to at least one alternative allele or an alteration in toxicity or tolerance of the treatment by a patient or patients. The presence of such a differential effect indicates that altering that level of activity provides at least part of an effective treatment for the disease or condition.

[0112] Preferably the method for determining a method of treatment is carried out in a clinical trial, e.g., as described above and/or in the Detailed Description below.

[0113] In still another aspect, the invention provides a method for evaluating differential efficacy of or tolerance to a treatment in a subset of patients who have a particular variance or variances in at least one gene, preferably a gene in Tables 1, 3, or 4, by utilizing a clinical trial. In preferred embodiments, the clinical trial is a Phase I, II, III, or IV trial. Preferred embodiments include the stratifications and/or statistical analyses as described below in the Detailed Description.

[0114] In yet another aspect, the invention provides experimental methods for finding additional variances in a gene provided in Tables 3 and 4. A number of experimental methods can also beneficially be used to identify variances. Thus, the invention provides methods for producing cDNA (Example 12) and detecting additional variances in the genes provided in Tables 1 and 2 using the single strand conformation polymorphism (SSCP) method (Example 13), the T4 Endonuclease VII method (Example 14) or DNA sequencing (Example 15) or other methods pointed out below. The application of these methods to the identified genes will provide identification of additional variances that can affect inter-individual variation in drug or other treatment response. One skilled in the art will recognize that many methods for experimental variance detection have been described (in addition to the exemplary methods of examples 13, 14, and 15) which can be utilized. These additional methods include chemical cleavage of mismatches (see, e.g., Ellis T P, et al., Chemical cleavage of mismatch: a new look at an established method. Human Mutation 11(5):345-53, 1998), denaturing gradient gel electrophoresis (see, e.g., Van Orsouw N J, et al., Design and application of 2-D DGGE-based gene mutational scanning tests. Genet Anal. 14(5-6):205-13, 1999) and heteroduplex analysis (see, e.g., Ganguly A, et al., Conformation-sensitive gel electrophoresis for rapid detection of single-base differences in double-stranded PCR products and DNA fragments: evidence for solvent-induced bends in DNA heteroduplexes. Proc Natl Acad Sci U S A. 90 (21):10325-9, 1993). Table 3 of Stanton & Adams, application Ser. No. 09/300,747, supra, provides a description of the additional possible variances that could be detected by one skilled in the art by testing an identified gene in Tables 1 and 2 using the variance detection methods described or other methods which are known or are developed.

[0115] The present invention provides a method for treating a patient at risk for drug responsiveness, i.e., efficacy differences associated with pharmacokinetic parameters, and safety concerns, i.e. drug-induced disease, disorder, or dysfunction or diagnosed with organ failure or a disease associated with drug-induced organ failure. The methods include identifying such a patient and determining the patient's genotype or haplotype for an identified gene or genes. The patient identification can, for example, be based on clinical evaluation using conventional clinical metrics and/or evaluation of a genetic variance or variances in one or more genes, preferably a gene or genes from Tables 1, 3 and 4. The invention provides a method for using the patient's genotype status to determine a treatment protocol which includes a prediction of the efficacy and safety of a therapy for concurrent treatment in light of drug-induced disease or an drug-induced or drug associated pathological condition. In a related aspect, the invention features a treatment protocol that provides a prediction of patient outcome. Such predictions are based on a demonstrated correlation between a particular type of treatment and outcome, efficacy, safety, likelihood of development of drug-induced disease, disorder, or dysfunction, or other such parameter relevant to clinical treatment decisions as evaluated by a normal prudent physician.

[0116] In an another related aspect, the invention provides a method for identifying a patient for participation in a clinical trial of a therapy for the treatment of drug-induced disease, disorder, or dysfunction, or an associated drug-induced toxicity. The method involves determining the genotype or haplotype of a patient with (or at risk for) a drug-induced disease, disorder, or dysfunction. Preferably the genotype is for a variance in a gene from Table 1. Patients with eligible genotypes are then assigned to a treatment or placebo group, preferably by a blinded randomization procedure. In preferred embodiments, the selected patients have no copies, one copy or two copies of a specific allele of a gene or genes identified in Table 1. Alternatively, patients selected for the clinical trial may have zero, one or two copies of an allele belonging to a set of alleles, where the set of alleles comprise a group of related alleles. One procedure for rigorously defining a set of alleles is by applying phylogenetic methods to the analysis of haplotypes. (See, for example: Templeton A. R., Crandall K. A. and C. F. Sing A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation. Genetics 1992 Oct;132(2):619-33.) Regardless of the specific tools used to group alleles, the trial would then test the hypothesis that a statistically significant difference in response to a treatment can be demonstrated between two groups of patients each defined by the presence of zero, one or two alleles (or allele groups) at a gene or genes. Said response may be a desired or an undesired response. In a preferred embodiment, the treatment protocol involves a comparison of placebo vs. treatment response rates in two or more genotype-defined groups. For example a group with no copies of an allele may be compared to a group with two copies, or a group with no copies may be compared to a group consisting of those with one or two copies. In this manner different genetic models (dominant, co-dominant, recessive) for the transmission of a treatment response trait can be tested. Alternatively, statistical methods that do not posit a specific genetic model, such as contingency tables, can be used to measure the effects of an allele on treatment response.

[0117] In another preferred embodiment, patients in a clinical trial can be grouped (at the end of the trial) according to treatment response, and statistical methods can be used to compare allele (or genotype or haplotype) frequencies in two groups. For example responders can be compared to nonresponders, or patients suffering adverse events can be compared to those not experiencing such effects. Alternatively response data can be treated as a continuous variable and the ability of genotype to predict response can be measured. In a preferred embodiments patients who exhibit extreme phenotypes are compared with all other patients or with a group of patients who exhibit a divergent extreme phenotype. For example if there is a continuous or semi-continuous measure of treatment response (for example the Alzheimer's Disease Assessment Scale, the Mini-Mental State Examination or the Hamilton Depression Rating Scale) then the 10% of patients with the most favorable responses could be compared to the 10% with the least favorable, or the patients one standard deviation above the mean score could be compared to the remainder, or to those one standard deviation below the mean score. One useful way to select the threshold for defining a response is to examine the distribution of responses in a placebo group. If the upper end of the range of placebo responses is used as a lower threshold for an ‘outlier response’ then the outlier response group should be almost free of placebo responders. This is a useful threshold because the inclusion of placebo responders in a ‘true’ response group decreases the ability of statistical methods to detect a genetic difference between responders and nonresponders.

[0118] In a related aspect, the invention provides a method for developing a disease management protocol that entails diagnosing a patient with a disease or a disease susceptibility, determining the genotype of the patient at a gene or genes correlated with treatment response and then selecting an optimal treatment based on the disease and the genotype (or genotypes or haplotypes). The disease management protocol may be useful in an education program for physicians, other caregivers or pharmacists; may constitute part of a drug label; or may be useful in a marketing campaign.

[0119] By “disease management protocol” or “treatment protocol” is meant a means for devising a therapeutic plan for a patient using laboratory, clinical and genetic data, including the patient's diagnosis and genotype. The protocol clarifies therapeutic options and provides information about probable prognoses with different treatments. The treatment protocol may provide an estimate of the likelihood that a patient will respond positively or negatively to a therapeutic intervention. The treatment protocol may also provide guidance regarding optimal drug dose and administration and likely timing of recovery or rehabilitation. A “disease management protocol” or “treatment protocol” may also be formulated for asymptomatic and healthy subjects in order to forecast future disease risks based on laboratory, clinical and genetic variables. In this setting the protocol specifies optimal preventive or prophylactic interventions, including use of compounds, changes in diet or behavior, or other measures. The treatment protocol may include the use of a computer program.

[0120] In preferred embodiments, the method further involves determining the patient's allele status and selecting those patients having at least one wild type allele, preferably having two wild type alleles for an identified gene, as candidates likely to develop drug-induced pathological conditions or drug-associated pathological disease or conditions. In a preferred embodiment, the treatment protocol involves a comparison of the allele status of a patient with a control population and a responder population. This comparison allows for a statistical calculation of a patient's likelihood of responding to a therapy, e.g., a calculation of the correlation between a particular allele status and treatment response. In the context of this aspect, the term “wild-type allele” refers to an allele of a gene which produces a product having a level of activity which is most common in the general population. Two different alleles may both be wild-type alleles for this purpose if both have essentially the same level of activity (e.g., specific activity and numbers of active molecules).

[0121] In preferred embodiments of above aspects involving prediction of drug efficacy, the prediction of drug efficacy involves candidate therapeutic interventions that are known or have been identified to be affected by pharmacokinetic parameters, i.e. absorption, distribution, metabolism, or excretion. These parameters may be associated with hepatic or extra-hepatic biological mechanisms. Preferably the candidate therapeutic intervention will be effective in patients with the genotype of a least one allele, and preferably two alleles from Tables 1, 3 and 4, but have a risk of drug ineffectiveness, i.e. nonresponsive to a drug or candidate therapeutic intervention.

[0122] In particular applications of the invention, all of the above aspects involving a gene variance evaluation or treatment selection or patient selection or method of treatment, the method includes a determination of the genotypic allele status of the patient, where a determination of the patient's allele status as being heterozygous or homozygous, is predictive of the patient having a poor response to a candidate therapeutic intervention and development of drug-induced disease, disorder, or dysfunction.

[0123] In preferred embodiments, the above methods are used for or include identification of a safety or toxicity concern involving a drug-induced disease, disorder, or dysfunction and/or the likelihood of occurrence and/or severity of said disease, disorder, or dysfunction.

[0124] In preferred embodiments, the invention is suitable for identifying a patient with non-drug-induced disease, disorder, or dysfunction but with dysfunction related to aberrant enzymatic metabolism or excretion of endogenous biologically relevant molecules or compounds. The method preferably involves determination of the allele status or variance presence or absence determination for at least one gene from Tables 1, 3, 4.

[0125] In another aspect, the invention provides a method for treating a patient at risk for a drug-induced disease, disorder or dysfunction by a) identifying a patient with such a risk, b) determining the genotypic allele status of the patient, and c) converting the data obtained in step b) into a treatment protocol that includes a comparison of the genotypic allele status determination with the allele frequency of a control population. This comparison allows for a statistical calculation of the patient's risk for having drug-induced disease, disorder, or dysfunction, e.g., based on correlation of the allele frequencies for a population with response or disease occurrence and/or severity. In preferred embodiments, the method provides a treatment protocol that predicts a patient being heterozygous or homozygous for an identified allele to exhibit signs and or symptoms of drug-induced disease, disorder, or dysfunction and a patient who is wild-type homozygous for the said allele, as responding favorably to these therapies.

[0126] In a related aspect, the invention provides a method for treating a patient at risk for or diagnosed with drug-induced disease or pathological condition or dysfunction using the methods of the above aspect and conducting a step c) which involves determining the gene allele load status of the patient. This method further involves converting the data obtained in steps b) and c) into a treatment protocol that includes a comparison of the allele status determinations of these steps with the allele frequency of a control population. This affords a statistical calculation of the patient's risk for having drug-induced disease, disorder or dysfunction. In a preferred embodiment, the method is useful for identifying drug-induced disease, disorder or dysfunction. In addition, in related embodiments, the methods provide a treatment protocol that predicts a patient to be at high risk for drug-induced disease, disorder or dysfunction responding by exhibiting signs and symptoms of drug-induced toxicity, disorders, dysfunction if the patient is determined as having a genotype or allelic difference in the identified gene or genes. Such patients are preferably given alternative therapies.

[0127] The invention also provides a method for improving the safety of candidate therapies for the identification of a drug-induced disease, disorder, or dysfunction. The method includes the step of comparing the relative safety of the candidate therapeutic intervention in patients having different alleles in one or more than one of the genes listed in Tables 1, 3, and 4. Preferably, administration of the drug is preferentially provided to those patients with an allele type associated with increased efficacy. In a preferred embodiment, the alleles of identified gene or genes used are wild-type and those associated with altered biological activity.

[0128] As used herein, by “therapy associated with drug-induced disease” is meant any therapy resulting in pathophysiologic dysfunction or signs and symptoms of failure or dysfunction, or those associated with the pathophysiological manifestations of a disorder. A suitable therapy can be a pharmacological agent, drug, or therapy that alters a pathways identified to affect the molecular structure or function of the parent candidate therapeutic intervention thereby affecting drug-induced disease or disorder progression of any of the described organ system dysfunctions.

[0129] By “drug-induced disease” or “drug-induced syndrome” is meant any physiologic condition that may be correlated with medical therapy by a drug, agent, or candidate therapeutic intervention.

[0130] By “drug-induced dysfunction” is meant a physiologic disorder or syndrome that may be correlated with medical therapy by a drug, agent, or candidate therapeutic intervention in which symptomology is similar to drug-induced disease. Specifically included are: a) hemostasis dysfunction; b) cutaneous disorders; c) cardiovascular dysfunction; d) renal dysfunction; e) pulmonary dysfunction; f) hepatic dysfunction; g) systemic reactions; and h) central nervous system dysfunction.

[0131] By “drug associated disorder” is meant a physiologic dysfunction that may be correlated with medical therapy by a drug, agent, or candidate therapeutic intervention. The drug associated disorder may include disease, disorder, or dysfunction.

[0132] By “pathway” or “gene pathway” is meant the group of biologically relevant genes involved in a pharmacodynamic or pharmacokinetic mechanism of drug, agent, or candidate therapeutic intervention. These mechanisms may further include any physiologic effect the drug or candidate therapeutic intervention renders.

[0133] As used herein, a “clinical trial” is the testing of a therapeutic intervention in a volunteer human population for the purpose of determining whether a therapeutic intervention is safe and/or efficacious in the human volunteer or patient population for a given disease, disorder, or condition. The analysis of safety and efficacy in genetically defined subgroups differing by at least one variance is of particular interest.

[0134] As used herein “clinical study” is that part of a clinical trial that involves determination of the effect a candidate therapeutic intervention on human subjects. It includes clinical evaluations of physiologic responses including pharmacokinetic (absorption, distribution, bioavailability, and excretion) as well as pharmacodynamic (physiologic response and efficacy) parameters. A pharmacogenetic clinical study is a clinical study that involves testing of one or more specific hypotheses regarding the effect of a genetic variance or variances (or set of variances, i.e. haplotype or haplotypes) in enrolled subjects or patients on response to a therapeutic intervention. These hypotheses are articulated before the study in the form of primary or secondary endpoints. For example the endpoint may be that in a particular genetic subgroup the rate of objectively defined responses exceeds some predefined threshold.

[0135] As used herein, “supplemental applications” are those in which a candidate therapeutic intervention is tested in a human clinical trial in order for the product to have an expanded label to include additional indications for therapeutic use. In these cases, the previous clinical studies of the therapeutic intervention, i.e. those involving the preclinical safety and Phase I human safety studies can be used to support the testing of the particular candidate therapeutic intervention in a patient population for a different disease, disorder, or condition than that previously approved in the U.S. In these cases, a limited Phase II study is performed in the proposed patient population. With adequate signs of efficacy, a Phase III study is designed. All other parameters of clinical development for this category of candidate therapeutic interventions proceeds as described above for interventions first tested in human candidates.

[0136] As used herein, “outcomes” or “therapeutic outcomes” are used to describe the results and value of healthcare intervention. Outcomes can be multi-dimensional, e.g., including one or more of the following: improvement of symptoms; regression of the disease, disorder, or condition; economic outcomes of healthcare decisions.

[0137] As used herein, “pharmacoeconomics” is the analysis of a therapeutic intervention in a population of patients diagnosed with a disease, disorder, or condition that includes at least one of the following studies: cost of illness study (COI); cost benefit analysis (CBA), cost minimization analysis (CMA), or cost utility analysis (CUA), or an analysis comparing the relative costs of a therapeutic intervention with one or a group of other therapeutic interventions. In each of these studies, the cost of the treatment of a disease, disorder, or condition is compared among treatment groups. As used herein, costs are those economic variables associated with a disease, disorder, or condition fall into two broad categories: direct and indirect. Direct costs are associated with the medical and non-medical resources used as therapeutic interventions, including medical, surgical, diagnostic, pharmacologic, devices, rehabilitation, home care, nursing home care, institutional care, and prosthesis. Indirect costs are associated with loss of productivity due to the disease, disorder, or condition suffered by the patient or relatives. A third category, the tangible and intangible losses due to pain and suffering of a patient or relatives often is included in indirect cost studies.

[0138] As used herein, “health-related quality of life” is a measure of the impact of the disease, disorder, or condition on an individual's or group of patient's activities of daily living. Preferably, included in pharmacoeconomic studies is an analysis of the health-related quality of life. Standardized surveys or questionnaires for general health-related quality of life or disease, disorder, or condition specific determine the impact the disease, disorder, or condition has on an individuals day to day life activities or specific activities that are affected by a particular disease, disorder, or condition.

[0139] As used herein, the term “stratification” refers to the creation of a distinction between patients on the basis of a characteristic or characteristics of the patient. Generally, in the context of clinical trials, the distinction is used to distinguish responses or effects in different sets of patients distinguished according to the stratification parameters. For the present invention, stratification preferably includes distinction of patient groups based on the presence or absence of particular variance or variances in one or more genes. The stratification may be performed only in the course of analysis or may be used in creation of distinct groups or in other ways.

[0140] By “drug efficacy” is meant the determination of an appropriate drug, drug dosage, administration schedule, and prediction of therapeutic utility.

[0141] By “allele load” is meant the relative ratio of identified gene alleles in the patient's chromosomal DNA.

[0142] By “identified allele” is meant a particular gene isoform that can be distinguished from other identified gene isoforms using the methods of the invention.

[0143] By “PCR, PT-PCR, or ligase chain reaction amplification” is meant subjecting a DNA sample to a Polymerase Chain Reaction step or ligase-mediated chain reaction step, or RNA to a RT-PCR step, such that, in the presence of appropriately designed primers, a nucleic acid fragment is synthesized or fails to be synthesized and thereby reveals the allele status of a patient. The nucleic acid may be further analyzed by DNA sequencing using techniques known in the art.

[0144] By “gene allele status” is meant a determination of the relative ratio of wild type identified alleles compared to an allelic variant that may encode a gene product of reduced catalytic activity. This may be accomplished by nucleic acid sequencing, RT-PCR, PCR, examination of the identified gene translated protein, a determination of the identified protein activity, or by other methods available to those skilled in the art.

[0145] By “treatment protocol” is meant a therapy plan for a patient using genetic and diagnostic data, including the patient's diagnosis and genotype. The protocol enhances therapeutic options and clarifies prognoses. The treatment protocol may include an indication of whether or not the patient is likely to respond positively to a candidate therapeutic intervention that is known to affect physiologic function. The treatment protocol may also include an indication of appropriate drug dose, recovery time, age of disease onset, rehabilitation time, symptomology of attacks, and risk for future disease. A treatment protocol, including any of the above aspects, may also be formulated for asymptomatic and healthy subjects in order to forecast future disease risks an determine what preventive therapies should be considered or invoked in order to lessen these disease risks. The treatment protocol may include the use of a computer software program to analyze patient data.

[0146] By “patient at risk for a disease” or “patient with high risk for a disease” is meant a patient identified or diagnosed as having drug-induced disease, disorder, dysfunction or having a genetic predisposition or risk for acquiring drug-induced disease, disorder or dysfunction, where the predisposition or risk is higher than average for the general population or is sufficiently higher than for other individuals as to be clinically relevant. Such risk can be evaluated, for example, using the methods of the invention and techniques available to those skilled in the art.

[0147] By “converting” is meant compiling genotype determinations to predict either prognosis, drug efficacy, or suitability of the patient for participating in clinical trials of a candidate therapeutic intervention with known propensity of drug-induced disease, disorder or dysfunction. For example, the genotype may be compiled with other patient parameters such as age, sex, disease diagnosis, and known allelic frequency of a representative control population. The converting step may provide a determination of the statistical probability of the patient having a particular disease risk, drug response, or patient outcome.

[0148] By “prediction of patient outcome” is meant a forecast of the patient's likely health status. This may include a prediction of the patient's response to therapy, rehabilitation time, recovery time, cure rate, rate of disease progression, predisposition for future disease, or risk of having relapse.

[0149] By “therapy for the treatment of a disease” is meant any pharmacological agent or drug with the property of healing, curing, or ameliorating any symptom or disease mechanism associated with drug-induced disease, disorder or dysfunction.

[0150] By “responder population” is meant a patient or patients that respond favorably to a given therapy.

[0151] In another aspect, the invention provides a method for determining whether there is a genetic component to intersubject variation in a surrogate treatment response. The method involves administering the treatment to a group of related (preferably normal) subjects and a group of unrelated (preferably normal) subjects, measuring a surrogate pharmacodynamic or pharmacokinetic drug response variable in the subjects, performing a statistical test measuring the variation in response in the group of related subjects and, separately in the group of unrelated subjects, comparing the magnitude or pattern of variation in response or both between the groups to determine if the responses of the groups are different, using a predetermined statistical measure of difference. A difference in response between the groups is indicative that there is a genetic component to intersubject variation in the surrogate treatment response.

[0152] In preferred embodiments, the size of the related and unrelated groups is set in order to achieve a predetermined degree of statistical power.

[0153] In another aspect, the invention provides a method for evaluating the combined contribution of two or more variances to a surrogate drug response phenotype in subjects (preferably normal subjects) by a. genotyping a set of unrelated subjects participating in a clinical trial or study, e.g., a Phase I trial, of a compound. The genotyping is for two or more variances (which can be a haplotype), thereby identifying subjects with specific genotypes, where the two or more specific genotypes define two or more genotype-defined groups. A drug is administered to subjects with two or more of said specific genotypes, and a surrogate pharmacodynamic or pharmacokinetic drug response variable is measured in the subjects. A statistical test or tests is performed to measure response in the groups separately, where the statistical tests provide a measurement of variation in response with each group. The magnitude or pattern of variation in response or both is compared between the groups to determine if the groups are different using a predetermined statistical measure of difference.

[0154] In preferred embodiments, the specific genotypes are homozygous genotypes for two variances. In preferred embodiments, the comparison is between groups of subjects differing in three or more variances, e.g., 3, 4, 5, 6, or even more variances.

[0155] In another aspect, the invention provides a method for providing contract research services to clients (preferably in the pharmaceutical and biotechnology industries), by enrolling subjects (e.g., normal and/or patient subjects) in a clinical drug trial or study unit (preferably a Phase I drug trial or study unit) for the purpose of genotyping the subjects in order to assess the contribution of genetic variation to variation in drug response, genotyping the subjects to determine the status of one or more variances in the subjects, administering a compound to the subjects and measuring a surrogate drug response variable, comparing responses between two or more genotype-defined groups of subjects to determine whether there is a genetic component to the interperson variability in response to said compound; and reporting the results of the Phase I drug trial to a contracting entity. Clearly, intermediate results, e.g., response data and/or statistical analysis of response or variation in response can also be reported.

[0156] In preferred embodiments, at least some of the subjects have disclosed that they are related to each other and the genetic analysis includes comparison of groups of related individuals. To encourage participation of sufficient numbers of related individuals, it can be advantageous to offer or provide compensation to one or more of the related individuals based on the number of subjects related to them who participate in the clinical trial, or on whether at least a minimum number of related subjects participate, e.g., at least 3, 5, 10, 20, or more.

[0157] In a related aspect, the invention provides a method for recruiting a clinical trial population for studies of the influence of genetic variation on drug response, by soliciting subjects to participate in the clinical trial, obtaining consent of each of a set of subjects for participation in the clinical trial, obtaining additional related subjects for participation in the clinical trial by compensating one or more of the related subjects for participation of their related subjects at a level based on the number of related subjects participating or based on participation of at least a minimum specified number of related subjects, e.g., at minimum levels as specified in the preceding aspect.

[0158] In addition to application of the present invention to drug-induced diseases and conditions, the present invention also provides for the use of variances in genes and gene pathways involved in drug absorption, distribution, metabolism, or excretion (e.g., as specified in any of Tables 1, 3, and 4 herein) of a drug. Thus, the above aspects can be utilized in connection with virtually any type of drug. For example, the pharmacogenetic effect, and the determination of such effect, of variances in genes in pathways involved in drug absorption, distribution, metabolism, or excretion can be utilized, for example, for in connection with drugs and drug classes as described Stanton, International Application No. PCT/US00/01392, filed Jan. 20, 2000, entitled GENE SEQUENCE VARIATIONS WITH UTILITY IN DETERMINING THE TREATMENT OF DISEASE. Further, the particular drug and/or pharmacogenetic determination can also be applied in the context of any disease, disorder, or dysfunction for which a drug treatment is considered or tested, e.g., any of the diseases, disorders, or conditions pointed out in Stanton (Id.). Still further, such analysis and use of pharmacogenetic information for genes involved in drug adsorption, distribution, metabolism, and excretion can also be combined with any of the different aspects described for genes involved in treatment response for other diseases, conditions, and dysfunctions as described in Stanton (Id.).

[0159] The use of variance information for genes involved in drug adsorption, distribution, metabolism, and excretion for any drug is advantageous, as those processes can affect the efficacy of any drug. Therefore, variances in such genes that alter one or more of those parameters can be significant in determining interpatient variation in treatment response. Additional aspects and embodiments as described in Stanton, International Application No. PCT/US00/01392, filed Jan. 20, 2000, entitled GENE SEQUENCE VARIATIONS WITH UTILITY IN DETERMINING THE TREATMENT OF DISEASE, are also included in the scope of this invention.

[0160] By “pathway” or “gene pathway” is meant the group of biologically relevant genes involved in a pharmacodynamic or pharmacokinetic mechanism of drug, agent, or candidate therapeutic intervention. These mechanisms may further include any physiologic effect the drug or candidate therapeutic intervention renders. Included in this are “biochemical pathways” which is used in its usual sense to refer to a series of related biochemical processes (and the corresponding genes and gene products) involved in carrying out a reaction or series of reactions. Generally in a cell, a pathway performs a significant process in the cell.

[0161] By “pharmacological activity” used herein is meant a biochemical or physiological effect of drugs, compounds, agents, or candidate therapeutic interventions upon administration and the mechanism of action of that effect.

[0162] The pharmacological activity is then determined by interactions of drugs, compounds, agents, or candidate therapeutic interventions, or their mechanism of action, on their target proteins or macromolecular components. By “agonist” or “mimetic” or “activators” is meant a drug, agent, or compound that activate physiologic components and mimic the effects of endogenous regulatory compounds. By “antagonists”, “blockers” or “inhibitors” is meant drugs, agents, or compounds that bind to physiologic components and do not mimic endogenous regulatory compounds, or interfere with the action of endogenous regulatory compounds at physiologic components. These inhibitory compounds do not have intrinsic regulatory activity, but prevent the action of agonists. By “partial agonist” or “partial antagonist” is meant an agonist or antagonist, respectively, with limited or partial activity. By “negative agonist” or “inverse antagonists” is meant that a drug, compound, or agent that can interact with a physiologic target protein or macromolecular component and stabilizes the protein or component such that agonist-dependent conformational changes of the component do not occur and agonist mediated mechanism of physiological action is prevented. By “modulators” or “factors” is meant a drug, agent, or compound that interacts with a target protein or macromolecular component and modifies the physiological effect of an agonist.

[0163] As used herein the term “chemical class” refers to a group of compounds that share a common chemical scaffold but which differ in respect to the substituent groups linked to the scaffold. Examples of chemical classes of drugs include, for example, phenothiazines, piperidines, benzodiazepines and aminoglycosides. Members of the phenothiazine class include, for example, compounds such as chlorpromazine hydrochloride, mesoridazine besylate, thioridazine hydrochloride, acetophenazine maleate trifluoperazine hydrochloride and others, all of which share a phenothiazine backbone. Members of the piperidine class include, for example, compounds such as meperidine, diphenoxylate and loperamide, as well as phenylpiperidines such as fentanyl, sufentanil and alfentanil, all of which share the piperidine backbone. Chemical classes and their members are recognized by those skilled in the art of medicinal chemistry.

[0164] As used herein the term “surrogate marker” refers to a biological or clinical parameter that is measured in place of the biologically definitive or clinically most meaningful parameter. In comparison to definitive markers, surrogate markers are generally either more convenient, less expensive, provide earlier information or provide pharmacological or physiological information not directly obtainable with definitive markers. Examples of surrogate biological parameters: (i) testing erythrocyte membrane acetylcholinesterase levels in subjects treated with an acetylcholinesterase inhibitor intended for use in Alzheimer's disease patients (where inhibition of brain acetylcholinesterase would be the definitive biological parameter); (ii) measuring levels of CD4 positive lymphocytes as a surrogate marker for response to a treatment for acquired immune deficiency syndrome (AIDS). Examples of surrogate clinical parameters: (i) performing a psychometric test on normal subjects treated for a short period of time with a candidate Alzheimer's compound in order to determine if there is a measurable effect on cognitive function. The definitive clinical test would entail measuring cognitive function in a clinical trial in Alzheimer's disease patients. (ii) Measuring blood pressure as a surrogate marker for myocardial infarction. The measurement of a surrogate marker or parameter may be an endpoint in a clinical study or clinical trial, hence “surrogate endpoint”.

[0165] As used herein the term “related” when used with respect to human subjects indicates that the subjects are known to share a common line of descent; that is, the subjects have a known ancestor in common. Examples of preferred related subjects include sibs (brothers and sisters), parents, grandparents, children, grandchildren, aunts, uncles, cousins, second cousins and third cousins. Subjects less closely related than third cousins are not sufficiently related to be useful as “related” subjects for the methods of this invention, even if they share a known ancestor, unless some related individuals that lie between the distantly related subjects are also included. Thus, for a group of related individuals, each subject shares a known ancestor within three generations or less with at least one other subject in the group, and preferably with all other subjects in the group or has at least that degree of consanguinity due to multiple known common ancestors. More preferably, subjects share a common ancestor within two generations or less, or otherwise have equivalent level of consanguinity. Conversely, as used herein the term “unrelated”, when used in respect to human subjects, refers to subjects who do not share a known ancestor within 3 generations or less, or otherwise have known relatedness at that degree.

[0166] As used herein the term “pedigree” refers to a group of related individuals, usually comprising at least two generations, such as parents and their children, but often comprising three generations (that is, including grandparents or grandchildren as well). The relation between all the subjects in the pedigree is known and can be represented in a genealogical chart.

[0167] As used herein the term “hybridization”, when used with respect to DNA fragments or polynucleotides encompasses methods including both natural polynucleotides, non-natural polynucleotides or a combination of both. Natural polynucleotides are those that are polymers of the four natural deoxynucleotides (deoxyadenosine triphosphate [dA], deoxycytosine triphosphate [dC], deoxyguanine triphosphate [dG] or deoxythymidine triphosphate [dT], usually designated simply thymidine triphosphate [T]) or polymers of the four natural ribonucleotides (adenosine triphosphate [A], cytosine triphosphate [C], guanine triphosphate [G] or uridine triphosphate [U]). Non-natural polynucleotides are made up in part or entirely of nucleotides that are not natural nucleotides; that is, they have one or more modifications. Also included among non-natural polynucleotides are molecules related to nucleic acids, such as peptide nucleic acid [PNA]). Non-natural polynucleotides may be polymers of non-natural nucleotides, polymers of natural and non-natural nucleotides (in which there is at least one non-natural nucleotide), or otherwise modified polynucleotides. Non-natural polynucleotides may be useful because their hybridization properties differ from those of natural polynucleotides. As used herein the term “complementary”, when used in respect to DNA fragments, refers to the base pairing rules established by Watson and Crick: A pairs with T or U; G pairs with C. Complementary DNA fragments have sequences that, when aligned in antiparallel orientation, conform to the Watson-Crick base pairing rules at all positions or at all positions except one. As used herein, complementary DNA fragments may be natural polynucleotides, non-natural polynucleotides, or a mixture of natural and non-natural polynucleotides.

[0168] As used herein “amplify” when used with respect to DNA refers to a family of methods for increasing the number of copies of a starting DNA fragment. Amplification of DNA is often performed to simplify subsequent determination of DNA sequence, including genotyping or haplotyping. Amplification methods include the polymerase chain reaction (PCR), the ligase chain reaction (LCR) and methods using Q beta replicase, as well as transcription-based amplification systems such as the isothermal amplification procedure known as self-sustained sequence replication (3 SR, developed by T. R. Gingeras and colleagues), strand displacement amplification (SDA, developed by G. T. Walker and colleagues) and the rolling circle amplification method (developed by P. Lizardi and D. Ward).

[0169] As used herein “contract research services for a client” refers to a business arrangement wherein a client entity pays for services consisting in part or in whole of work performed using the methods described herein. The client entity may include a commercial or non-profit organization whose primary business is in the pharmaceutical, biotechnology, diagnostics, medical device or contract research organization (CRO) sector, or any combination of those sectors. Services provided to such a client may include any of the methods described herein, particularly including clinical trial services, and especially the services described in the Detailed Description relating to a Pharmacogenetic Phase I Unit. Such services are intended to allow the earliest possible assessment of the contribution of a variance or variances or haplotypes, from one or more genes, to variation in a surrogate marker in humans. The surrogate marker is generally selected to provide information on a biological or clinical response, as defined above.

[0170] As used herein, “comparing the magnitude or pattern of variation in response” between two or more groups refers to the use of a statistical procedure or procedures to measure the difference between two different distributions. For example, consider two genotype-defined groups, AA and aa, each homozygous for a different variance or haplotype in a gene believed likely to affect response to a drug. The subjects in each group are subjected to treatment with the drug and a treatment response is measured in each subject (for example a surrogate treatment response). One can then construct two distributions: the distribution of responses in the AA group and the distribution of responses in the aa group. These distributions may be compared in many ways, and the significance of any difference qualified as to its significance (often expressed as a p value), using methods known to those skilled in the art. For example, one can compare the means, medians or modes of the two distributions, or one can compare the variance or standard deviations of the two distributions. Or, if the form of the distributions is not known, one can use nonparametric statistical tests to test whether the distributions are different, and whether the difference is significant at a specified level (for example, the p<0.05 level, meaning that, by chance, the distributions would differ to the degree measured less than one in 20 similar experiments). The types of comparisons described are similar to the analysis of heritability in quantitative genetics, and would draw on standard methods from quantitative genetics to measure heritability by comparing data from related subjects.

[0171] Another type of comparison that can be usefully made is between related and unrelated groups of subjects. That is, the comparison of two or more distributions is of particular interest when one distribution is drawn from a population of related subjects and the other distribution is drawn from a group of unrelated subjects, both subjected to the same treatment. (The related subjects may consist of small groups of related subjects, each compared only to their relatives.) A comparison of the distribution of a drug response variable (e.g. a surrogate marker) between two such groups may provide information on whether the drug response variable is under genetic control. For example, a narrow distribution in the group(s) of related subjects (compared to the unrelated subjects) would tend to indicate that the measured variable is under genetic control (i.e. the related subjects, on account of their genetic homogeneity, are more similar than the unrelated individuals). The degree to which the distribution was narrower in the related individuals (compared to the unrelated individuals) would be proportionate to the degree of genetic control. The narrowness of the distribution could be quantified by, for example, computing variance or standard deviation. In other cases the shape of the distribution may not be known and nonparametric tests may be preferable. Nonparametric tests include methods for comparing medians such as the sign test, the slippage test, or the rank correlation coefficient (the nonparametric equivalent of the ordinary correlation coefficient). Pearson's Chi square test for comparing an observed set of frequencies with an expected set of frequencies can also be useful.

[0172] The present invention provides a number of advantages. For example, the methods described herein allow for use of a determination of a patient's genotype for the timely administration of the most suitable therapy for that particular patient. The methods of this invention provide a basis for successfully developing and obtaining regulatory approval for a compound even though efficacy or safety of the compound in an unstratified population is not adequate to justify approval. From the point of view of a pharmaceutical or biotechnology company, the information obtained in pharmacogenetic studies of the type described herein could be the basis of a marketing campaign for a drug. For example, a marketing campaign that emphasized the superior efficacy or safety of a compound in a genotype or haplotype restricted patient population, compared to a similar or competing compound used in an undifferentiated population of all patients with the disease. In this respect a marketing campaign could promote the use of a compound in a genetically defined subpopulation, even though the compound was not intrinsically superior to competing compounds when used in the undifferentiated population with the target disease. In fact even a compound with an inferior profile of action in the undifferentiated disease population could become superior when coupled with the appropriate pharmacogenetic test.

[0173] By “comprising” is meant including, but not limited to, whatever follows the word “comprising”. Thus, use of the term “comprising” indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present. By “consisting of” is meant including, and limited to, whatever follows the phrase “consisting of”. Thus, the phrase “consisting of” indicates that the listed elements are required or mandatory, and that no other elements may be present. By “consisting essentially of” is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. Thus, the phrase “consisting essentially of” indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present depending upon whether or not they affect the activity or action of the listed elements.

[0174] Other features and advantages of the invention will be apparent from the following description of the preferred embodiments thereof, and from the claims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0175] First, the content of tables provided in this description is briefly described.

[0176] Table 1, the ADME/Toxicology Gene Table, lists genes that may be involved in pharmacological responses involving adsorption, distribution, metabolism, excretion affecting efficacy or safety of drug response. The table has seven columns. Column 1, headed “Class” provides broad groupings of genes relevant to the pharmacology of absorption, distribution, metabolism, or excretion of drugs. The categories are: adsorption and distribution, Phase I drug metabolism, Phase II drug metabolism, excretion, oxidative stress, and immune response. Column 2, headed “Pathway”, provides a more detailed categorization of the different classes of genes by indicating the overall purpose of large groups of genes. These pathways contain genes implicated in the etiology or treatment response of the various patient outcomes detailed in Table 2. Column 3, headed “Function”, further categorizes the pathways listed in column 2.

[0177] Column 4, headed “Name”, lists the genes belonging to the class, pathway and function shown to the left (in columns 1-3). The gene names given are generally those used in the OMIM database or in GenBank, however one skilled in the art will recognize that many genes have more than one name, and that it is a straightforward task to identify synonymous names. For example, many alternate gene names are provided in the OMIM record for a gene.

[0178] In column 5, headed “OMIM”, the Online Mendelian Inheritance in Man (OMIM) record number is listed for each gene in column 4. This record number can be entered next to the words: “Enter one or more search keywords:” at the OMIM world wide web site. The url is: http://www3.ncbi.nlm.nih.gov/Omim/searchomim.html. An OMIM record exists for most characterized human genes. The record often has useful information on the chromosome location, function, alleles, and human diseases or disorders associated with each gene.

[0179] Column 6, headed “GID”, provides the GenBank identification number (hence GID) of a genomic, cDNA, or partial sequence of the gene named in column 4. Usually the GID provides the record of a cDNA sequence. Many genes have multiple Genbank accession numbers, representing different versions of a sequence 5 obtained by different research groups, or corrected or updated versions of a sequence. As with the gene name, one skilled in the art will recognize that alternative GenBank records related to the named record can be obtained easily. All other GenBank records listing sequences that are alternate versions of the sequences named in the table are equally suitable for the inventions described in this application. (One straightforward way to obtain additional GenBank records for a gene is on the internet. General instructions can be found at the NCBI web site at: http://www3.ncbi.nlm.nih.gov. More specifically, the GenBank record number in column 6 can be entered at the url: http://www3.ncbi.nlm.nih.gov/Entrez/nucleotide.html. Once the GenBank record has been retrieved one can click on the “nucleotide neighbors” link and additional GenBank records from the same gene will be listed.

[0180] Column 7, headed “locus”, provides the chromosome location of the gene listed on the same row. The chromosome location helps confirm the identity of the named gene if there is any ambiguity.

[0181] Table 2 is a matrix showing the intersection of genes and patient outcomes—that is, which categories of genes are most likely to account for interpatient variation in response to treatments. Column 1 is similar to the ‘Class’ column in Table 1, while column 2 is a combination of the ‘Pathway’ and ‘Function’ columns in Table 1. It is intended that the summary terms listed in columns 1 and 2 be read as referring to all the genes in the corresponding sections of Table 1. The remaining columns in Table 2 lists potential effects on efficacy or on eight patient outcomes. The information in the Table lies in the shaded boxes at the intersection of various ‘Pathways” (the rows) and the patient outcomes (the nine columns) An intersection box is shaded when a row corresponding to a particular pathway (and by extension all the genes listed in that pathway in Table 1) intersects a column for a specific effect on patient outcome in response to a candidate therapeutic intervention such that the pathway and genes are of possible use in explaining interpatient differences in response (patient outcomes) to candidate therapeutic interventions. Thus the Table enables one skilled in the art to identify therapeutically relevant genes in patients with one of the nine patient outcomes for the purposes of stratification of these patients based upon genotype and subsequent correlation of genotype with drug response. The shaded intersections indicate preferred sets of genes for understanding the basis of interpatient variation in response to therapy of the indicated disease indication, and in that respect are exemplary. Any of the genes in the table may account for interpatient variation in response to treatments for any of the diseases listed. Thus, the shaded boxes indicate the gene pathways that one skilled in the art would first investigate in trying to understand interpatient variation in response to a candidate therapeutic indications with the listed patient outcomes.

[0182] Table 3 lists DNA sequence variances in genes relevant to the methods described in the present invention. These variances were identified by the inventors in studies of selected genes listed in Table 1, and are provided here as useful for the methods of the present invention. The variances in Table 3 were discovered by one or more of the methods described below in the Detailed Description or Examples. Table 3 has eight columns. Column 1, the “Name” column, contains the Human Genome Organization (HUGO) identifier for the gene. Column 2, the “GID” column provides the GenBank accession number of a genomic, cDNA, or partial sequence of a particular gene. Column 3, the “OMIM_ID” column contains the record number corresponding to the Online Mendelian Inheritance in Man database for the gene provided in columns 1 and 2. This record number can be entered at the world wide web site http://www3.ncbi.nlm.nih.gov/Omim/searchomim.html to search the OMIM record on the gene. Column 4, the VGX_Symbol column, provides an internal identifier for the gene. Column 5, the “Description” column provides a descriptive name for the gene, when available. Column 6, the “Variance_Start” column provides the nucleotide location of a variance with respect to the first listed nucleotide in the GenBank accession number provided in column 2. That is, the first nucleotide of the GenBank accession is counted as nucleotide 1 and the variant nucleotide is numbered accordingly. Column 7, the “variance” column provides the nucleotide location of a variance with respect to an ATG codon believed to be the authentic ATG start codon of the gene, where the A of ATG is numbered as one (1) and the immediately preceding nucleotide is numbered as minus one (−1). This reading frame is important because it allows the potential consequence of the variant nucleotide to be interpreted in the context of the gene anatomy (5′ untranslated region, protein coding sequence, 3′ untranslated region). Column 7 also provides the identity of the two variant nucleotides at the indicated position. For example, in the first entry in Table 3, DG90040, the variance is 191G>A, indicating the presence of a G or an A at nucleotide 232 of GenBank sequence DG90040. Column 8, the “CDS_Context” column indicates whether the variance is in a coding region but silent (S); in a coding region and results in an amino acid change (e.g., R347C, where the letters are one letter amino acid abbreviations and the number is the amino acid residue in the encoded amino acid sequence which is changed); in a sequence 5′ to the coding region (5); or in a sequence 3′ to the coding region (3). As indicated above, interpreting the location of the variance in the gene depends on the correct assignment of the initial ATG of the encoded protein (the translation start site). It should be recognized that assignment of the correct ATG may occasionally be incorrect in GenBank, but that one skilled in the art will know how to carry out experiments to definitively identify the correct translation initiation codon (which is not always an ATG). In the event of any potential question concerning the proper identification of a gene or part of a gene, due for example, to an error in recording an identifier or the absence of one or more of the identifiers, the priority for use to resolve the ambiguity is GenBank accession number, OMIM identification number, HUGO identifier, common name identifier.

[0183] If a haplotype for any of the genes listed in this table has been identified, a series of nucleotides (A, C, G, T) are listed separated by commas and to the left of each listing is the associated nucleotide location also separated by commas in brackets. For example, if the haplotype listing is T,G,C,A [12, 245, 385, 612] there is a T at position 12, a G at position 246, a C at position 385, and an A at position 612. Below this list will occur the identified variance start, variance, and CDS context for the identified single nucleotide polymorphisms as described above.

[0184] Table 4 lists additional DNA sequence variances (in addition to those in Table 3) in genes relevant to the methods of the present invention (i.e. selected genes from Table 1). These variances were identified by various research groups and published in the scientific literature over the past 20 years. The inventors realized that these variances may be useful for understanding interpatient variation in response to treatment of the diseases listed in Table 2, and more generally useful for the methods of the present invention. The columns of Table 4 are similar to those of Table 3, and therefore the descriptions of the rows and columns in Table 3 (above) pertain to Table 4, as do the other remarks.

[0185] I. Pharmacokinetic Parameters and Effects on Efficacy

[0186] The pharmacokinetic parameters with potential effects on efficacy are absorption, distribution, metabolism, and excretion. These parameters affect efficacy broadly by modulating the availability of a compound at the site(s) of action. Interpatient variation in the availability of a compound drug, agent, or candidate therapeutic intervention can result in a reduction of the available compound or more compound at the site of action with a corresponding altered clinical effect. Differences in these parameters, therefore, can be a potential foundation of interpatient variability to drug response.

[0187] A. Pharmacokinetic Parameters that Result in a Reduction of Available Drug

[0188] 1. Absorption—Depending on the solubility of the drug, and its ability to passively cross membranes is fundamental to the ability of the drug, agent, or candidate therapeutic intervention to effectively enter the circulation and gain access to the principle site of action. For enteral delivery or administration, absorption is a critical first step in the pharmacologic process. Within the gastrointestinal tract, absorption of a drug, agent, or candidate therapeutic intervention can be affected by the pH of the contents, speed of gastric emptying, and presence of chelating or binding molecules to the drug, agent or candidate therapeutic intervention. Each of these parameters can effectively reduce the rate of passive absorption of the drug across the gastrointestinal mucosal membrane.

[0189] 2. Distribution—Once absorbed, the drug, agent or candidate therapeutic intervention must be delivered or distributed to the primary site of pharmacologic action. Although distribution is dependent on regional blood flow and cardiac output; distribution may be further affected by the rate and extent of sequestration of the drug into biological spaces that render the product unavailable to the principle or primary site of pharmacologic site of action. For example, many drugs are actively transported into biological compartments. These processes, if over- or under active may affect the availability and hence reduce the efficacy of the product. Further, only unbound drug may be effective to a cell, tissue, or physiological process, and bound product may be transported to a space that is physiologically unrelated to the pharmacologic mechanism of action or may be of deleterious adverse or toxic consequence.

[0190] 3. Metabolism—Induction of metabolic enzymes to covalently modify the parent drug, agent or candidate therapeutic intervention may reduce the ability of the parent drug to elicit a pharmacologic action. Metabolism may affect the target active site binding, rate and extent of distribution and excretion, and overall availability of the active molecule.

[0191] 4. Excretion—If the excretion of the drug or drug metabolite is rapid, less drug is available to elicit a pharmacologic effect.

[0192] B. Pharmacokinetic Parameters that Result in More Available Drug

[0193] 1. Absorption—Enhanced absorption of drugs, agents or candidate therapeutic interventions may result in increased drug availability. For example, in some cases of decreased gastric emptying, there is an enhanced degree of absorption by prolonging contact with gastrointestinal mucosal membranes. In others, a change in the solubility of the drug may enhance the passive transport across the gastrointestinal mucosal membrane.

[0194] 2. Distribution—Since free drug is the form that renders pharmacologic action and is metabolized and excreted, drug binding may serve to protect the drug from mechanisms of inactivation. The rate and extent of drug binding affects the free drug concentration relative to the total concentration.

[0195] 3. Metabolism—If drug metabolism induction is occurring and the inducer is rapidly removed without adjustment in the dose of the drug, drug metabolism may be decreased and adverse effects or toxicities may occur.

[0196] 4. Excretion—If inhibition of active transport of the parent drug or metabolite across the bile cannicula or the renal tubule, there is a net result of enhanced drug availability.

[0197] II. Impaired Drug Tolerability and Drug-Induced Disease, Disorder, Dysfunction or Toxicity

[0198] In response to chemical substances, drugs, or xenobiotics, drug-induced disease, disorder, dysfunction, or toxicity manifests as cellular damage or organ physiologic dysfunction, with one potentially leading to the other.

[0199] Adverse drug reactions can be categorized as 1) mechanism based reactions which are exaggerations of pharmacologic effects and 2) idiosyncratic, unpredictable effects unrelated to the primary pharmacologic action. Although some side effects appear shortly after administration of a drug, some side effects appear long after drug administration or after cessation of the drug. Furthermore, these reactions can be categorized by reversible or irreversible manifestations of the drug-induced toxicity referring to whether the clinical symptomology subsides or persists upon withdrawal of the offending agent.

[0200] In the first category, excessive drug effects may result from alterations of pharmacokinetic parameters by either drug-drug interactions, pathophysiologic disease mediated alterations in the organs or processes involved in absorption, distribution, metabolism, or excretion, or genetic predisposition to heightened pharmacodynamic effect of the drug. The excessive or heightened response may be receptor or drug target or non-receptor or non-drug target mediated.

[0201] There are a large number of adverse events that are suspected and or known to occur as a result of administration of a drug, agent, or candidate therapeutic intervention. For example, many antineoplastic agents act by prevention of cell division in dividing cells or promoting cytotoxicity via disruption of DNA synthesis, transcription, and formation of mitotic spindles. These agents, unfortunately, do not distinguish between normal and cancerous cells, e.g. normally dividing cells and cancer cells are equally killed. Therefore, adverse events of antineoplastic agents include bone marrow suppression leading to anemia, leukopenia, and thrombocytopenia; immunosuppression rendering the patient susceptible and vulnerable to infectious agents; and initiation of mutagenesis and the formation of alternate forms of cancer, in many cases, acute myeloid leukemia.

[0202] In another example of predictable adverse events related to drug therapy is immunosuppression as a result of therapy to reduce or ablate immune response. This therapy includes but is not exclusive to prevention of graft vs. host or autoimmune disease. These agents, e.g. corticosteroids, cyclosporine, and azathioprine, all suppress humoral or cell-mediated immunity. Patients taking these agents are rendered susceptible to microbial infections, particular opportunistic infections such as cytomegalovirus, pneumocystis carnii, Candida, and sperigillus. Furthermore, long-term immunosuppressive therapy is associated with increased risk of developing lymphoma. Individual drugs are associated with renal injury (cyclosporine) and interstitial pneumonitis (azathioprine).

[0203] In the second category of adverse events, idiosyncratic reactions arise often by unpredictable, unknown mechanisms or reactions that evoke immunologic reactions or unanticipated cytotoxicity.

[0204] Adverse reactions in this category are often found together, because often it is difficult to ascertain the etiology of the offending reaction. These toxic events can be specific for a target organ, e.g. ototoxicity, nephrotoxicity, hepatotoxicity, neurotoxicity, etc. or are caused by reactive metabolic intermediates and are toxic or create local damage usually near the site of metabolism.

[0205] Immunologic reactions to drugs are thought or result from the combination of the drug or agent with a protein to form an antigenic protein-drug complex that stimulates the immune system response. Without the formation of a complex, most small molecular drugs are unable, alone, to elicit an immunological response. First exposure to the offending drug produces a latent reaction, subsequent exposures usually results in heightened and rapid immunological response. These allergic reactions, characterized by immunohypersensitivity, are most dramatic in anaphylaxis. There are other immune responses that result in adverse reactions or toxicities. They include but are not limited to: 1) immune response mediated cytotoxicity which occurs when the drug-protein complex binds to the surface of a cell and this cell-complex is then recognized by circulating antibodies; 2) serum sickness which occurs when immune complexes of drug and antibody are found in the circulation; and 3) lupus syndromes in which the drug or reactive intermediate interact with nuclear material to stimulate the formation of antinuclear antibodies.

[0206] In addition to the immune phenomena described above, there are other drug reactions that are syndromes involving allergic reactions. These reactions include, but are not limited to, skin e rashes, drug induced fever, pulmonary reactions, hepatocellular or cholestatic reactions, interstitial nephritis, and lymphadenopathy. Further, there are some drug reactions that mimic allergic reactions but are not immune related. For example, such reactions are due to direct release of mediators by drugs and are called anaphylactoid reactions. An example of this type of adverse event is reaction to radiocontrast dye.

[0207] These are common adverse drug reactions that may prevent a candidate therapeutic intervention from use, continued development, and marketing rights. Some of these reactions are reversible, others are not.

[0208] Adverse drug reactions include, but are not limited to, the following organs systems: a) hemostasis which encompass blood dyscrasias (feature of over half of all drug-related deaths) which are bone marrow aplasia, granulocytopenia, aplastic anemia, leukopenia, pancytopenia, lymphoid hyperplasia, hemolytic anemia, and thrombocytopenia; b) cutaneous which encompass urticaria, macules, papules, angioedema, morbilliform-maculopapular rash, toxic epidermal necrolysis, erythema multiforme, erythema nodosum, contact dermititis, vesicles, petechiae, exfolliative dermititis, fixed drug eruptions, and severe skin rash (Stevens-Johnson syndrome); c) cardiovascular which includes arrythmias, QT prolongation, cardiomyopathy, hypotension, or hypertension; d) renal which includes glomerulonephritis and tubular necrosis; e) pulmonary which includes asthma, acute pneumonitis, eosinophilic pneumonitis, fibrotic and pleural reactions, and interstitial fibrosis; f) hepatic which includes steatosis, hepatocellular damage and cholestasis; g) systemic which includes anaphylaxis, vasiculitis, fever, lupus erythematosus syndrome; and h) the central nervous system which includes tinnitus and dizziness, acute dystonic reactions, parkinsonian syndrome, coma, convulsions, depression and psychosis, and respiratory depression.

[0209] In the cases whereby severe, fatal reactions occur after drug administration, there may be a warning label in the product insert.

[0210] For example, tricyclic antidepressants can cause central nervous system depression, seizures, respiratory arrest, cardiac arrythmias and arrest. The mechanism for the injury is a result of the increased synaptic concentrations of biogenic amines and inhibition of postsynaptic receptors.

[0211] Acetominophen can cause hepatic necrosis as a result of prolonged high dose usage or overdose. In the hepatocyte, acetominophen is converted to a toxic metabolite that binds to glutathione. As the concentration of acetominophen increases the levels of glutathione are depleted and the toxic acetominophen metabolite then binds liver macromolecules. Aggregation of polymorphonuclear neutrophils in hepatic microcirculation may cause ischemia and foster necrotic events.

[0212] Halothane can cause hepatic necrosis as well as prodrome fever and jaundice. Interestingly, the liver effects of halothane are usually after a first time exposure. The hepatic reaction is thought to occur via a genetic predisposition to deranged metabolism with the formation of toxic metabolites.

[0213] III. Pharmacokinetic Parameters as Potential Mechanisms of Drug-Induced Adverse Reactions Leading to Disease, Disorder, Dysfunction or Toxicities

[0214] A. Absorption

[0215] Absorption is the pharmacokinetic parameter that describes the rate and extent of the drug, agent, or candidate therapeutic intervention leaves the site of administration. Although absorption is critical for the drug, agent, or candidate therapeutic intervention to ultimately reach the site of physiologic action, the term bioavailability is the parameter that is clinically relevant. Bioavailability is the term used to define the extent to which the active component of the drug, agent, or candidate therapeutic intervention reaches the its site of physiologic action or a biological fluid to which has access to the site of biological action. Although bioavailability is related to all pharmacokinetic parameters, e.g. absorption, distribution, metabolism, and excretion, bioavailability is primarily dependent on the first ability of the drug, agent, or candidate therapeutic intervention to be absorbed from the site of delivery, i.e. cross cellular membranes.

[0216] There are many factors that influence absorption of a drug, agent, or candidate therapeutic intervention. For example, compound solubility, conditions of absorption, and route of administration. In the present invention, we concern ourselves with genes that are involved in the active or passive process of drug, agent, or candidate therapeutic intervention absorption through a biological membrane.

[0217] The absorption surface is dependent on the route of administration. For example, absorption of drugs can occur via 1) oral (enteral); 2) sublingual; 3) injections (parenteral, i.e., intravenous, intramuscular, intraarterial, intrathecal, intraperiotoneal, or subcutaneous); 4) rectal; 5) inhalation (pulmonary); 6) topical application (skin and eye). In each of these routes of administration, the adsorption rate and extent is dependent on the concentration of the drug at the site, the patency of the epithelial cells, local biological conditions, and function of the active or passive transport.

[0218] Absorption can affect both the efficacy and safety of a drug, agent, or candidate therapeutic intervention. For example, for a compound to achieve full pharmacologic potential, it must be available at the target site, be active, and be unbound. In regards to safety, absorption affects safety in one or more of the following: site of delivery pain, necrosis, or irritation; rate of administration; and erratic available concentrations.

[0219] B. Distribution

[0220] The distribution of the drug, agent, or candidate therapeutic intervention is dependent on the rate and extent the compound enters the bloodstream. Once in the bloodstream, the compound may be distributed to the interstitial and cellular fluids. The distribution of drugs to target tissues can be categorized into two phases. The first distribution phase, is dependent on cardiac output and regional blood flow, both of which are dependent on the health and status of the cardiovascular system. In a second distribution phase, diffusion into tissues is dependent on the level and extent that the drug, agent, or candidate therapeutic intervention is bound. Drug binding by proteins found in the blood can serve to protect the compound from modifications by enzymes, proteins, or compounds in the circulation and or limit the bioavailability of the compound to enter target tissues or individual cells.

[0221] Drug entry into tissues requires free drug, and drug binding proteins may limit this active or passive transport. Once distributed into tissues, the drug may be sequestered within that tissue, to render full pharmacologic activity or to prevent that drug from reaching the appropriate target tissue.

[0222] Distribution can affect both the efficacy and safety of a drug, agent, or candidate therapeutic intervention. For example, for a compound to achieve full pharmacologic potential, it must be available at the target site, be active, and be unbound. In regards to safety, distribution affects safety in one or more of the following: distribution to a tissue that is more or less affected by the pharmacologic action of the compound, erratic available concentrations, and tissue specific distribution characteristics.

[0223] C. Metabolism

[0224] Drugs or xenobiotics, are usually found in the circulation bound to plasma proteins, generally but not exclusive to serum albumin. It is the bound form of the drug that is taken up by the hepatocyte. Bile salts in the circulation are taken up via organic anion transporters. Once inside the hepatocyte, the drug or bile salt is a substrate for a series of reactions that are either oxidative or reductive or reactions that are conjugative steps in the metabolism of the substrate. Generally these chemical modifications are a refined process to render the substrate more hydrophilic, or polar, to be more likely excreted in the bile (via the intestinal tract) or urine (via the kidneys). However, there are exceptions whereby the redox reactions produce reactive intermediates or products that retard elimination. Except for their role in detoxification, there is little in common among the enzymes involved in the redox detoxification reactions. For certain enzymes there are specific groups that will act as substrates, for others there are general classes of chemical compounds that will be suitable substrates for a given enzyme or enzymes.

[0225] In the mammalian liver these mechanisms to detoxify and/or enhance the excretion of metabolic by-products, endogenous substrates, and exogenous molecules. The ability to determine whether hepatic function if inadequate is based upon clinical observation, e.g., the presence of jaundice, right upper quadrant abdominal discomfort or pain, pruritis, or by clinical laboratory analyses, e.g., aspartate transaminase (AST or SGOT) or alanine transferase (ALT or SGPT). The hepatic metabolic and excretory mechanisms are critical for short- and long-term survival and are inheritable characteristics. These hepatic biotransformations mechanisms have broad substrate specificity that have been evolutionarily inherited for the host protection from environmental, biological, and chemical substances.

[0226] There are two categories of drug, agent, or candidate therapeutic intervention biotransformation (metabolism). In the first, phase I, functionalization reactions occur. Phase I reactions introduce or expose a functional group to the parent compound. In general, phase I reactions render the parent compound pharmacologically inactive, however there are examples of phase I reaction activation or retention of activity. In phase II reactions, biosynthetic reactions occur. Phase II conjugation reactions leads to a covalent linkage between a functional group on the parent compound with glucuronic acid, sulfate, glutathione, amino acids, or acetate. The metabolic conversion of drugs is the liver, however, all tissues have enzymatic activity.

[0227] Factors affecting drug biotransformation are 1) induction of metabolizing enzymes, 2) inhibition of enzymatic reactions, and 3) genetic polymorphisms. It is the interplay of these factors and the health and well being of the patient or subject that determines the fate of parent drug molecules in the body.

[0228] The first factor affecting drug biotransformation is induction of metabolizing enzyme activity. The metabolic processes that modify drugs or chemicals (oxidation, reduction, or conjugation) can be induced to significant enzymatic activity. Under physiological conditions, the induction process is in place to coordinately metabolize excess substrates. The induction process can be both at the level of enzymatic activity and increased protein levels of the pertinent enzyme or enzymes. Induction may include one or several of the enzymatic pathways or processes in response to the presence of drugs, xenobiotics, endogenous substrates, or metabolic by-products. There may or may not be increased toxicity as a result of increased concentrations of metabolites. Further, induction of phase I reactive processes (oxidation or reduction reactions) may or may not induce the phase II reactive processes (conjugation reactions).

[0229] The second factor affecting drug biotransformation is the inhibition of metabolic enzymes. Enzymatic inhibition can occur via 1) competition of two or more substrates for the enzymatic active site, 2) suicide inhibitors, or 3) depletion of required cofactors for the enzymatic pathways or processes in phase I or phase II reactions.

[0230] In competitive inhibition, two or more drugs, xenobiotics, or substrates present can interact with the active site of the enzyme. If one drug binds specifically to the enzymatic active site or to an other intracellular regulatory protein molecule, other compounds are blocked from binding and remain unbound. In this case, unmetabolized parent drug or xenobiotic remains in the circulation, potentially for extended periods of time. Competitive inhibition is dependent on the relative specificity of the substrates for the enzymatic active site and the concentration of the drugs or substrates. An example of competitive drug biotransformation inhibition are cimetidine and ketoconazole which inhibit oxidative drug metabolism by forming a tight complex with the heme iron complex of cytochrome P450, and macrolide antibiotics such as erythromycin and troleandomycin are metabolized to products bind to heme groups on the cytochrome P450 molecules.

[0231] In the second case, the inhibition of enzymes involved in the drug biotransformation process may also occur by suicide inactivation. In these cases, the drug or xenobiotic may interact and covalently modify or render inactive the enzyme involved in the metabolic pathway. In this way, the parent drug compound or molecule is not metabolized, nor is it free to interact with another molecule. Examples of suicide inactivators are secobarbital and synthetic steroids (norethindrone or ethinyl estradiol) which bind to cytochrome P450 and destroy the heme portion of the enzyme unit.

[0232] In the third case, inhibition of the enzymes involved in the drug biotransformation pathway can also occur by agents or compounds or physiological status that deplete NADPH or other cofactors required for the enzymatic reactions to occur. In the cases of phase I oxidation or reduction, lack of oxygen or NADPH, may reduce the efficiency and activity of a particular enzyme. In phase II reactions, cofactors provide specific groups for the enzymatic covalent modification of the drug or xenobiotic. These phase II cofactors are required for conjugation biotransformation reactions to occur and depletion of these cofactors would be rate limiting.

[0233] The third factor that can affect drug biotransformation is genetic polymorphism. Differences among individuals to metabolize drugs have long been known. Observed phenotypic differences, as determined by amount of drug excreted, through polymorphically controlled pathway/s has lead to a generalized classification of slow (poor) metabolizers and fast (rapid or extensive) metabolizers. In general, poor metabolizers are those with impaired metabolism of a drug via a polymorphic pathway have been associated with an increased incidence of adverse effects. In addition, to date all major deficiencies in drug metabolizing activity are inherited as autosomal recessive traits. Fast or rapid metabolizers are those individuals with processes that extensively metabolize a drug via a polymorphic pathway. The fast or rapid metabolizers have been associated with an increased incidence of ineffective treatment. In these individuals active drug is rapidly metabolized to less active or inactive metabolites such that a reassessment of the pharmacokinetic parameters and dosing regimen may require analysis or readjustment, respectively, for appropriate therapy to occur.

[0234] The first observed and catalogued genetic polymorphism associated with drug metabolism was described for isoniazid. Isoniazid is a primary drug prescribed for the chemotherapy of tuberculosis. Marked interindividual variation in the elimination of this drug was observed and genetic studies of families revealed that this variation was genetically controlled. Isoniazid is predominantly metabolized via N-acetylation. In the analysis of the phenotypically distinct individuals, it was shown that slow acetylators were homozygous for a recessive gene and fast acetylators were homozygous or heterozygous for the wild type gene. It has been determined that the incidence of the slow acetylator phenotype is approximately 50% for U.S. Caucasians and blacks, 60-70% of Northern Europeans, and 5-10% in Asians. Other drugs have been shown to be polymorphically acetylated, e.g. sulfonamides (sulfadiazine, sulfamethazine, sulfapyridine, sulfameridine, and sulfadoxine), aminoglutethimide, amonafide, amrinone, dapsone, dipyrone, endralazine, hydralazine, prizidilol, and procainamide. Other drugs that first undergo metabolism and then polymorphically acetylated are clonazepam and caffeine.

[0235] Another common genetic polymorphism associated with oxidative metabolism is exemplified by the drug debrisoquine (a sympatholytic antihypertensive). It was discovered that variable inter-patient hypotensive response was due to differing metabolic rates of debrisoquine 4-hydroxylase. Further analysis of family studies revealed that oxidative metabolic reactions are under monogeneic control. A cytochrome P450 enzyme, CYP2D6, was determined to be the target gene for debrisoquine 4-hydroxylase activity. Poor metabolizers of desbrisoquine are homozygous for a recessive CYP2D6 allele and rapid or fast metabolizers are homozygous or heterozygous for the wild type CYP2D6 allele. Urinary metabolic ratio can be determined after administration of a probe drug and phenotypic assignments (poor or extensive metabolizer) can be identified. The extent of debrisoquine metabolic analysis achieved clinical importance as it was determined that other drugs were poorly metabolized in individuals that poorly metabolized debrisoquine. For example, anti-arryhthmics such as flecainide, propafenone, and mexiletine; antidepressants such as amitryptiline, clomipramine, desipramine, fluoetine, imipramine, maprotiline, mianserin, paroxetine, and nortriptyline; neuroleptics such as haloperidol, perphenazine, and thioridazine; antianginals such as perhexilene; opioids such as dextromethorphan and codeine; and amphetamines such as methylenedioxymethamphetamine. Further, many β-adrenergic antagonists are metabolized and are subject to polymorphic influence in elimination patterns.

[0236] Another example of a genetic polymorphism affecting oxidative metabolism was described for mephenytoin, a drug prescribed for epilepsy. It was shown that a deficiency in the 4′-hydroxylation of S-mephenytoin is inherited as an autosomal recessive trait. The other main metabolic pathway, N-methylation of R-mephenytoin to 5-phenyl-5-ethylhydantoin remains unaffected. Individuals with poor metabolic rate of mephenytoin are subject to adverse central effects, i.e. sedation. Other drugs can be grouped into the poor mephenytoin metabolizers are mephobarbital, hexobarbital, side-chain oxidization of propanolol, the demethylation of imipramine, and the metabolism of diazepam and desmethyldiazepam. Further analysis of other drugs such as the metabolism of antidepressant drugs (citalopram), the proton pump inhibitor omeprazol, the antimalarial drugs pantoprazole and lansoprazole cosegregate with mephenytoin metabolites.

[0237] Because the majority of metabolic enzymes for the conduct of drug biotransformation occurs in the liver, impairment of liver function as a result of hepatic pathological conditions or other disease states can lead to alterations of hepatic or other organ metabolic drug biotransformation. Liver disease pathologies such as hepatitis, alcoholic liver disease, fatty liver disease, biliary cirrhosis, and hepatocarcinomas can impair function of normal physiological metabolic pathways. Further, decreases in hepatic circulation as a result of cardiac insufficiency, hypertension, vascular obstruction, or vascular insult can affect the rate and extent of drug biotransformation. For example drugs with a high hepatocyte extraction ratio would have different metabolism rates affected by alterations of hepatic circulation. Changes in liver blood flow can affect the rate and extent of the metabolism and the clearance of the parent drug. In all cases of hepatic pathological conditions, the affect on drug biotransformation and clearance of parent drugs or metabolized products will be dependent on the severity and extent of the liver organ and hepatocellular damage.

[0238] Although hepatic damage may affect the metabolism and clearance of a parent drug or metabolic by-product, residual concentrations of parent drug or metabolic by-products may be deleterious to the liver and its metabolic functions. Following nonparenteral (enteral) administration of a drug, a significant portion of the drug will be metabolized by intestinal or hepatic enzymes before it reaches the general circulation. This first pass effect may generate active drug (administered drug was a prodrug), inactive drug, or toxic drug. Prior to circulation of the metabolized product, circulation to the kidney, the major organ for excretion of the hydrophilic moiety, and excretion via the urine will occur. Therefore, a metabolic product of hepatic metabolic pathways can affect the liver, kidney, and other organs of the body prior to excretion.

[0239] 1. Phase I Drug Biotransformation: Oxidation and Reduction Reactions

[0240] Enzymatic Oxidation of Drugs

[0241] In oxidative metabolism, oxidases catalyze the transfer of electrons from substrate to oxygen, generating either hydrogen peroxide or superoxide anions. There are two oxidases present in hepatocytes; they are aldehyde oxidases and monoamine oxidases. Both of these enzymes have broad substrate specificity and contribute broadly to the metabolism of drugs. A third oxidase, xanthine oxidase, may contribute to the oxidation of drugs, due its ability to catalyze the oxidation of heterocyclic aromatic amines, for example methotrexate and 6-mercaptopurine. Xanthine oxidase in intact tissues is present as a NAD-dependent dehydrogenase, and is converted to an oxidase when there is disruption of the tissue, for example during hepatic cellular damage.

[0242] Aldehyde oxidase catalyzes the oxidation of fatty aldehydes to carboxylic acids and the hydroxylation of substituted pyridines, pyrimidines, purines, and pteridines. Generally, xenobiotic aromatic nitrogen heterocycles are metabolized by this enzyme.

[0243] Monoamine oxidase is present in two forms, A and B. They are dimeric proteins consisting of identical subunits and FAD is covalently linked to the protein through a cysteinyl residue. Catalytic cycles of monoamine oxidases A or B occur in discrete steps that take an amine and convert it to an aldehyde, while in the process creating hydrogen peroxide and ammonia. These oxidases have a broad specificity; they protect mitochondrial proteins from xenobiotic amines and hydrazines. Further neurotransmitters are metabolized through this route, e.g. serotonin, dopamine, and catecholamines. Primary alkylamines containing unsubstituted methylene group or groups adjacent to the nitrogen exhibits activity. Activity increases as the length of a side chain, with optimal side length being C6. These enzymes also catalyze the oxidation of secondary and tertiary amines and acyclic amines. Hydrazines can be oxidized by these oxidases. Substrates for monoamine oxidases include but are not exclusive to the following amines: benzylamine, dopamine, tyramine, epinephrine, N-methylbenzylamine, and N,N-dimethlybenzylamine; and the following hydrazines: procarbazine 1,2-dimethylhydrazine.

[0244] Mono-oxygenases are present in liver cell homogenates and contain two distinct types of xenobiotic mono-oxygenases. They are the cytochrome P450 and the flavin-dependent mono-oxygenases.

[0245] The liver microsomal P-450 system consists of a flavoprotein, and a family of related, but distinct, hemoproteins. The flavoprotein catalyzes the transfer of the electrons from NADPH to the hemoprotein, and is the mono-oxygenase. The reaction also requires phosphatidylcholine. The reductase is a monomeric flavoprotein that contains both FAD and FMN. The reductase is specific for NADPH as a reductant, but other oxidants can be substituted. In addition to cytochrome P-450, the flavoprotein catalyzes reduction of quinones, nitro, and azo compounds.

[0246] There are many P450 gene families. Subsequent cloning and sequence determination has afforded the ability to divide this gene family into three main groups, CYP1, CYP2, and CYP3, that are responsible for the majority of drug biotransformation. There are further subdivisions in each of these families, examples being CYP2D6, CYP3A4, CYP2E1, as well as others.

[0247] Examples of enzymatic inductive processes that affect biotransformation reactions involve the P450 gene family. Specifically, glucocorticoids and anticonvulsants induce CYP3A4; isoniazid, acetone, and chronic ethanol consumption for CYP2E1. Many inducers of the cytochrome P450 enzymes also induce conjugation metabolic enzymes, e.g. glucuronosyltransferases.

[0248] In contrast to the monooxygenases, multiple forms of the terminal oxidase (P-450) are present in the hepatocyte. There are many distinct isoforms characterized in different species including humans. It should be noted that mitochondrial P-450 exhibit little or no activity in the metabolism of drugs, xenobiotics, biological compounds, or chemicals. Representative functional groups oxidated by the microsomal P-450 system are as follows: alkanes (hexane, decane, hexadecane); alkenes (vinyl chloride, aflatoxin-B1, dieldrin); aromatic hydrocarbons (naphthylene, bromobenzene, benzo(a)pyrene, biphenyl); alipathic amines (aminopyrine, benzphetamine, ethylmorphine); heterocyclic amines (3-acetylpyridine, 4,4′-bipyridine, quinoline); amides (N-acetlyaminofluorene, urethane); ethers (indemethacin, pheancetin, p-nitroanisole); and sulfides (chloropromazine, thioanisole).

[0249] There are many P450s that have been identified in human liver. Substrate specificities vary among these P-450 dependent mono-oxygenases. For example, P4501A1 prefers polycyclic aromatic hydrocarbons; P-4501A2 prefers arylamines, arylamides; P-450A26 prefers coumarin, 7-ethoxycoumarin; P-450 2C8, 2C9, 2C10 prefers tolbutamide, hexobarbital; P-450 2C18 prefers mephenytoin; P-450 mp-1, mp-2 prefers debrisoquine and related amines; P450 2E1 prefers ethanol, N-nitrosoalkylamines, vinyl monomers; P-450 3A3, 3A4, 3A5, 3A7 prefers dihydropyridines, cyclosporin, lovastatin, aflatoxins.

[0250] The effect of genetic polymorphism of the P450s has been known for some time. For example, debrisoquine and related drugs; alfentanil, tolbutamide; (S)mephenytoin. Because the P450s can be induced by xenobiotics, an enhanced metabolic rate or efficiency can lead to one drug affecting the potency, efficacy, dosing of another. For example, women taking rifampicin or barbiturates can lead to metabolic inactivation of synthetic oral contraceptives.

[0251] The flavin-containing mono-oxygenases are the principle enzymes catalyzing the N-oxidation of tertiary amine drugs to N-oxides. The N-oxides are found in abundance in serum. Although isoforms have been identified and the catalytic cycle is similar to the cytochrome P450 system, flavin-containing mono-oxygenases substrate specificity differs. Unlike the other flavin-bearing mono-oxygenases, these flavin-containing mono-oxygenases are present in the cell as very reactive oxygen-activated form. It is believed that particular protein structure stabilizes the nucleophilic molecule. Since the molecule is so highly reactive, precise substrate-to-enzyme fit is unnecessary. The following lists substrate types and examples oxidized by the flavin-containing mono-oxygenases: tertiary amines (trifluroperazine, bromopheniramine, morphine, nicotine, pargyline); secondary amines (desipramine, methamphetamine, propanolol); hydrazines (1,1-demethlyhydrazine, N-aminopiperidine, 1-methyl-1-phenylhydrazine); thiols and disulfides (dithiothreitol, β-mercaptomethanol, thiophenol); thiocarbamides (thiourea, methimazole, propylthiouracil); sulfides (dimethylsulfide, sulindac sulfide).

[0252] Examples of drugs that undergo oxidative reactions are: N-dealkylation (imipramine, diazepam, codeine, erythromycin, morphine, tamoxifen, theophylline); O-dealkylation (codeine, indomethacin, dextromethorphan); alipathic hydroxylation (tolbutamide, ibuprofen, pentobarbital, meprobamate, cyclosporin, midazolam); aromatic hydroxylation (phenytoin, phenobarbital, propanolol, phenylbutazone, ethinyl estradiol); N-oxidation (chlorpheniramine, dapsone); S-oxidation (cimetidine, chlorpromazine, thioridazine); deamination (diazepam, amphetamine).

[0253] Enzymatic Reduction of Drugs

[0254] The reductases are a class of enzymes that are involved in the metabolic reduction of xenobiotics. This class of enzymes includes the aldehyde and ketone reductases, the quinone reductases, the nitro and nitroso reductases, the azoreductases, the N-oxide reductases, and the sulfoxide reductases. These classes of enzymes are involved in sequential one-electron reduction of some functional groups and produce radicals that can produce damage cellular components directly or indirectly.

[0255] The dehydrogenases consist of alcohol dehydrogenases, aldehyde dehydrogenases, or dihydrodiol dehydrogenases. This class of enzymes is involved in the catalysis of hydrogen transfer to a hydrogen acceptor, usually a pyridine nucleotide.

[0256] Hydrolysis of Drugs

[0257] Alternative reactions of detoxification and metabolism of drugs and xenobiotics are initial steps of hydrolysis. Esters, amides, imides, or other functional groups that are generated as a result of a hydrolysis reaction can alter the hydophilicity of a molecule and enhance urinary excretion. Hydrolysis occurs both enzymatically and nonenzymatically. Hydrolysis of proteins before they are degraded has been suggested as a step in the process of the aging of intracellular proteins. Antibodies with an affinity for certain esters and certain proteases e.g. 3-phosphoglyceraldehyde dehydrogenase and carbonic anhydrase, have been shown to have esterase activity.

[0258] Enzymatic hydrolysis of drugs and xenobiotics include the following enzymes: esterases, amidases, imidases, and epoxide hydratases. Examples of drugs undergoing hydrolysis reactions are: procaine, aspirin, clofibrate, lidocaine, procainamide, indomethacin.

[0259] Other hydrolytic processes include reactions owing to both enzymes in tissues, circulation, and those elaborated by microorganisms in the lower bowel; for example, sulfatases, glucoronidases, and phosphatases.

[0260] 2. Phase II Drug Biotransformation: Conjugation Reactions

[0261] In addition, to the redox reactions of the hepatocyte to detoxify or metabolize xenobiotics, there are a series of conjugation reactions. The substrates for these reactions are generally the products from the redox reactions described above. These conjugation reactions involve donation of a suitable hydrophilic molecular group to an accepting xenobiotic or its metabolite. The major function of these covalent modifications is to render the parent compound pharmacologically inactive. The covalent addition of such a group to a parent drug or compound not only inactivates the substrate but also renders the recipient molecule more polar and is more readily excreted via the bile ducts into the intestinal tract or via the urine.

[0262] Lipophilic compounds that have one of the functional groups that can serve as an acceptor undergo enzymatic catalysis with a second, donor substrate. The conjugation reactions include the following broad categories: glucuronidation, sulfation, methylation, N-acetylation, and conjugation with amino acids. The enzymes involved in these reactions are as follows: UDP-glucuronyltransferase, alcohol sulfotransferase, amine N-sulfotransferase, phenol sulfotransferase, glutathione transferase, catechol O-methyltransferase, amine N-methyltransferase, histamine N-methyltransferase, thiol S-methyltransferase, benzoyl-CoA glycine acyltransferase, acetyltransacetylase, cysteine S-conjugate N-acetyltransferase, cysteine S-conjugate N-acetyltransferase, cysteine conjugate β-lyase, thioltransferase, and rhodanese. Each of these enzymes has donor and acceptor specificities. The importance of these reactions in the detoxification and metabolism of drugs and xenobiotics are discussed in the examples

[0263] Examples of drugs that are known to be conjugated are: glucuronidation (acetominophen, morphine, diazepam); sulfation (acetominophen, steroids, methyldopa); acetylation (sulfonamides, isoniazid, dapsone, clonazepan).

[0264] D. Excretion

[0265] Excretion of parent drugs and metabolites can occur in the excretory organs, namely the kidneys, liver, lungs, skin, and breasts (milk). The kidneys are the most important organs for the excretion of drugs and metabolites. Renal excretion involves glomerular filtration, active tubular absorption, and passive tubule reabsorption. The more hydrophilic the compound is the more readily excreted via urine. In addition, many drugs and metabolites are excreted via the bile into the intestinal tract. These metabolites may be excreted in the feces, or may be reabsorbed by the gastrointestinal epithelial cell lining. Organic anions and cations, steroids, fatty acids, and other drugs may be specifically transported into the bile canniculus.

[0266] In all of the metabolism and excretion routes, the physiologic goal is to detoxify and rid the body of drugs, xenobiotics, endogenous or exogenous chemicals, or compounds that may or may not be deleterious to the major organs of the body. In principle the detoxification mechanisms function to attain this goal, however there are many cases of major organ toxicity upon exposure to drugs or metabolites of drugs. Although drugs and drug metabolites predominantly affect the liver and kidneys due to the circulatory and physiological processes, other organs can be affected. In the present invention, we address specific genes that may have polymorphic sites affecting metabolic rates to ultimately affect these major organ functions.

[0267] 1. Excretion of Drugs and Drug Metabolites via the Bile

[0268] After parent drugs or xenobiotics are metabolized by redox and or conjugation reactions, the modified products can then be actively transported into the bile cannicula. The transport occurs in an energy dependent fashion requiring ATP. It has been shown that the transporters involved in the active transport from the basolateral (sinusoidal) to the apical (canalicular) surfaces of hepatocytes are members of the ATP binding cassette (ABC) family. The transmembrane electrical potential required to maintain the chemical and electrical potentials required for this active transport is provided by the Na+/K+ ATPases located on the basolateral membrane. Other ion transporters are the potassium channel, sodium-bicarbonate symporter, chloride-bicarbonate anion exchanger, and the chloride channel. In the cholangiocyte there are other ion transporters, for example chloride-bicarbonate anion exchanger, isoform 2, and other organic-solute transporters. Bile acids, phosphatidyl chorine, organic anions, organic cations, and cholesterol are actively transported. Approximately 5% of the transporters is multi-drug resistance protein 1 (MDR1) and the remaining are the phospholipid transporter multi-drug resistance protein 3 (MDR3), alicular multispecific organic-anion transporter (multi-drug resistance associated protein (MRP2 or cMOAT), canalicular bile-salt-export pump (BSEP or SPGP(sister of p-glycoprotein)), sodium-taurocholate cotransporter, organic anion-transporting polypeptide, glutathione transporter, and a chloride-bicarbonate anion exchanger are also involved in the transport.

[0269] These transporters have been identified to move specific molecules or compounds across biological membranes. For example, the MDR1 protein mediates the canicular excretion of bulky lipophilic cations, e.g. anticancer drugs, calcium channel blockers, cyclosporine A, and various other drugs. In contrast, the MDR3 protein transports phosphatidyl choline from the inner leaflet to the outer leaflet of the canicular membrane. Phosphatidyl choline then can be selectively extracted by intracanicular bile salts and secreted into bile as vesicles or mixed micelles. MRP2 is involved in the transport of amphipathic anionic substrates e.g. leukotriene C4, glutathione-S conjugates, glucuronides (bilirubin diglucuronide and estradiol-17b-glucuronide), sulfate conjugates, and is responsible for the generation of bile flow independent of bile salts within the bile cannicula. SPGP is the canicular bile salt export pump in the mammalian liver.

[0270] The hepatocyte has the ability to recruit the ATP-requiring transporters when faced with excessive metabolites. After synthesis, these transporters are stored in compartments that, in response to cAMP, can be actively moved through the cell to the membrane and fused to the cannicula. The active movement from the intracellular compartment to the membrane requires microtubules, cytoplasmic kinesin, cytoplasmic dynesin, and calcium. It has been shown that peptides activate phophosinositide 3 kinase, and increased turnover of phosphoinostides drives the formation of 3′phophoinositol, which can activate the transporter in the membrane and ultimately increases movement to the cannicular membrane. Signaling pathways via the activation of rab5 stimulate the active movement of the transporters to the internal compartment.

[0271] 2. Excretion of Drugs and Drug Metabolites via the Kidney

[0272] Excretion of drugs or drug metabolites via the kidney and into the urine involves three processes: 1) glomerular filtration, 2) active tubular secretion, and 3) passive tubular reabsorption. The amount of drug or metabolites entering the tubular lumen is dependent on its fractional plasma protein binding and glomerular filtration rate. In the proximal renal tubule anions and cations are actively transported by carrier mediated tubular secretion and bases are transported by a separate system that secretes choline, histamine, and other endogenous bases. In the proximal and distal tubules there is passive reabsorption of these molecules. The concentration gradient for back-diffusion is created by sodium and other inorganic ions and water.

[0273] IV. Identification of Interpatient Variation in Response; Identification of Genes and Variances Relevant to Drug Action; Development of Diagnostic Tests; and Use of Variance Status to Determine Treatment

[0274] Development of therapeutics in man follows a course from compound discovery and analysis in a laboratory (preclinical development) to testing the candidate therapeutic intervention in human subjects (clinical development). The preclinical development of candidate therapeutic interventions for use in the treatment of human diseases, disorders, or conditions begins at the discovery stage whereby a candidate therapy is tested in vitro to achieve a desired biochemical alteration of a biochemical or physiological event. If successful, the candidate is generally tested in animals to determine toxicity, adsorption, distribution, metabolism and excretion in a living species. Occasionally, there are available animal models that mimic human diseases, disorders, and conditions in which testing the candidate therapeutic intervention can provide supportive data to warrant proceeding to test the compound in humans. It is widely recognized that preclinical data is imperfect in predicting response to a compound in man. Both safety and efficacy have to ultimately be demonstrated in humans. Therefore, given economic constraints, and considering the complexities of human clinical trials, any technical advance that increases the likelihood of successfully developing and registering a compound, or getting new indications for a compound, or marketing a compound successfully against competing compounds or treatment regimens, will find immediate use. Indeed, there has been much written about the potential of pharmacogenetics to change the practice of medicine. In this application we provide descriptions of the methods one skilled in the art would use to advance compounds through clinical trials using genetic stratification as a tool to circumvent some of the difficulties typically encountered in clinical development, such as poor efficacy or toxicity. We also provide specific genes, variation in which may account for interpatient variation in treatment response, and further we provide specific exemplary variances in those genes that may account for variation in treatment response.

[0275] The study of sequence variation in genes that mediate and modulate the action of drugs may provide advances at virtually all stages of drug development. For example, identification of amino acid variances in a drug target during preclinical development would allow development of non-allele selective agents. During early clinical development, knowledge of variation in a gene related to drug action could be used to design a clinical trial in which the variances are taken account of by, for example, including secondary endpoints that incorporate an analysis of response rates in genetic subgroups. In later stages of clinical development the goal might be to first establish retrospectively whether a particular problem, such as liver toxicity, can be understood in terms of genetic subgroups, and thereby controlled using a genetic test to screen patients. Thus genetic analysis of drug response can aid successful development of therapeutic products at any stage of clinical development. Even after a compound has achieved regulatory approval its commercialization can be aided by the methods of this invention, for example by allowing identification of genetically defined responder subgroups in new indications (for which approval in the entire disease population could not be achieved) or by providing the basis for a marketing campaign that highlights the superior efficacy and/or safety of a compound coupled with a genetic test to identify preferential responders. Thus the methods of this invention will provide medical, economic and marketing advantages for products, and over the longer term increase therapeutic alternatives for patients.

[0276] As indicated in the Summary above, certain aspects of the present invention typically involve the following process, which need not occur separately or in the order stated. Not all of these described processes must be present in a particular method, or need be performed by a single entity or organization or person. Additionally, if certain of the information is available from other sources, that information can be utilized in the present invention. The processes are as follows: a) variability between patients in the response to a particular treatment is observed; b) at least a portion of the variable response is correlated with the presence or absence of at least one variance in at least one gene; c) an analytical or diagnostic test is provided to determine the presence or absence of the at least one variance in individual patients; d) the presence or absence of the variance or variances is used to select a patient for a treatment or to select a treatment for a patient, or the variance information is used in other methods described herein.

[0277] A. Identification of Interpatient Variability in Response to a Treatment

[0278] Interpatient variability is the rule, not the exception, in clinical therapeutics. One of the best sources of information on interpatient variability is the nurses and physicians supervising the clinical trial who accumulate a body of first hand observations of physiological responses to the drug in different normal subjects or patients. Evidence of interpatient variation in response can also be measured statistically, and may be best assessed by descriptive statistical measures that examine variation in response (beneficial or adverse) across a large number of subjects, including in different patient subgroups (men vs. women; whites vs. blacks; Northern Europeans vs. Southern Europeans, etc.).

[0279] In accord with the other portions of this description, the present invention concerns DNA sequence variances that can affect one or more of:

[0280] i. The susceptibility of individuals to a disease;

[0281] ii. The course or natural history of a disease;

[0282] iii. The response of a patient with a disease to a medical intervention, such as, for example, a drug, a biologic substance, physical energy such as radiation therapy, or a specific dietary regimen. (The terms ‘drug’, ‘compound’ or ‘treatment’ as used herein may refer to any of the foregoing medical interventions.) The ability to predict either beneficial or detrimental responses is medically useful.

[0283] Thus variation in any of these three parameters may constitute the basis for initiating a pharmacogenetic study directed to the identification of the genetic sources of interpatient variation. The effect of a DNA sequence variance or variances on disease susceptibility or natural history (i and ii, above) are of particular interest as the variances can be used to define patient subsets which behave differently in response to medical interventions such as those described in (iii). The methods of this invention are also useful in a clinical development program where there is not yet evidence of interpatient variation (perhaps because the compound is just entering clinical trials) but such variation in response can be reliably anticipated. It is more economical to design pharmacogenetic studies from the beginning of a clinical development program than to start at a later stage when the costs of any delay are likely to be high given the resources typically committed to such a program.

[0284] In other words, a variance can be useful for customizing medical therapy at least for either of two reasons. First, the variance may be associated with a specific disease subset that behaves differently with respect to one or more therapeutic interventions (i and ii above); second, the variance may affect response to a specific therapeutic intervention (iii above). Consider for exemplary purposes pharmacological therapeutic interventions. In the first case, there may be no effect of a particular gene sequence variance on the observable pharmacological action of a drug, yet the disease subsets defined by the variance or variances differ in their response to the drug because, for example, the drug acts on a pathway that is more relevant to disease pathophysiology in one variance-defined patient subset than in another variance-defined patient subset. The second type of useful gene sequence variance affects the pharmacological action of a drug or other treatment. Effects on pharmacological responses fall generally into two categories; pharmacokinetic and pharmacodynamic effects. These effects have been defined as follows in Goodman and Gilman's Pharmacologic Basis of Therapeutics (ninth edition, McGraw Hill, New York, 1986): “Pharmacokinetics” deals with the absorption, distribution, biotransformations and excretion of drugs. The study of the biochemical and physiological effects of drugs and their mechanisms of action is termed “pharmacodynamics.”

[0285] Useful gene sequence variances for this invention can be described as variances which partition patients into two or more groups that respond differently to a therapy or that correlate with differences in disease susceptibility or progression, regardless of the reason for the difference, and regardless of whether the reason for the difference is known. The latter is true because it is possible, with genetic methods, to establish reliable associations even in the absence of a pathophysiological hypothesis linking a gene to a phenotype, such as a pharmacological response, disease susceptibility or disease prognosis.

[0286] B. Identification of Specific Genes and Correlation of Variances in Those Genes with Response to Treatment of Diseases or Conditions

[0287] It is useful to identify particular genes which do or are likely to mediate the efficacy or safety of a treatment method for a disease or condition, particularly in view of the large number of genes which have been identified and which continue to be identified in humans. As is further discussed in section C below, this correlation can proceed by different paths. One exemplary method utilizes prior information on the pharmacology or pharmacokinetics or pharmacodynamics of a treatment method, e.g., the action of a drug, which indicates that a particular gene is, or is likely to be, involved in the action of the treatment method, and further suggests that variances in the gene may contribute to variable response to the treatment method. For example if a compound is known to be glucuronidated then a glucuronyltransferase is likely involved. If the compound is a phenol, the likely glucuronyltransferase is UGT1 (either the UGT1*1 or UGT1*6 transcripts, both of which catalyze the conjugation of planar phenols with glucuronic acid). Similar inferences can be made for many other biotransformation reactions.

[0288] Alternatively, if such information is not known, variances in a gene can be correlated empirically with treatment response. In this method, variances in a gene which exist in a population can be identified. The presence of the different variances or haplotypes in individuals of a study group, which is preferably representative of a population or populations of known geographic, ethnic and/or racial background, is determined. This variance information is then correlated with treatment response of the various individuals as an indication that genetic variability in the gene is at least partially responsible for differential treatment response. It may be useful to independently analyze variances in the different geographic, ethnic and/or racial groups as the presence of different genetic variances in these groups (i.e. different genetic background) may influence the effect of a specific variance. That is, there may be a gene×gene interaction involving one unstudied gene, however the indicated demographic variables may act as a surrogate for the unstudied allele. Statistical measures known to those skilled in the art are preferably used to measure the fraction of interpatient variation attributable to any one variance, or to measure the response rates in different subgroups defined genetically or defined by some combination of genetic, demographic and clinical criteria.

[0289] Useful methods for identifying genes relevant to the pharmacological action of a drug or other treatment are known to those skilled in the art, and include review of the scientific literature combined with inferential or deductive reasoning that one skilled in the art of molecular pharmacology and molecular biology would be capable of; large scale analysis of gene expression in cells treated with the drug compared to control cells; large scale analysis of the protein expression pattern in treated vs. untreated cells, or the use of techniques for identification of interacting proteins or ligand-protein interactions, such as yeast two-hybrid systems.

[0290] C. Development of a Diagnostic Test to Determine Variance Status

[0291] In accordance with the description in the Summary above, the present invention generally concerns the identification of variances in genes which are indicative of the effectiveness of a treatment in a patient. The identification of specific variances, in effect, can be used as a diagnostic or prognostic test. Correlation of treatment efficacy and/or toxicity with particular genes and gene families or pathways is provided in Stanton et al., U.S. Provisional Application Ser. No. 60/093,484, filed Jul. 20, 1998, entitled GENE SEQUENCE VARIANCES WITH UTILITY IN DETERMINING THE TREATMENT OF DISEASE (concerns the safety and efficacy of compounds active on folate or pyrimidine metabolism or action) and Stanton, U.S. Provisional Application Ser. No. 60/121,047, filed Feb. 22, 1999, entitled GENE SEQUENCE VARIANCES WITH UTILITY IN DETERMINING THE TREATMENT OF DISEASE (concerning Alzheimer's disease and other dementias and cognitive disorders), which are hereby incorporated by reference in their entireties including drawings.

[0292] Genes identified in the examples below and in the Tables and Figures can be used in the methods of the present invention. A variety of genes which the inventors realize may account for interpatient variation in patient outcome response to candidate therapeutic interventions are listed in Tables 1, 3, and 4. Gene sequence variances in said genes are particularly useful for aspects of the present invention.

[0293] Methods for diagnostic tests are well known in the art. Generally in this invention, the diagnostic test involves determining whether an individual has a variance or variant form of a gene that is involved in the disease or condition or the action of the drug or other treatment or effects of such treatment. Such a variance or variant form of the gene is preferably one of several different variances or forms of the gene that have been identified within the population and are known to be present at a certain frequency. In an exemplary method, the diagnostic test involves determining the sequence of at least one variance in at least one gene after amplifying a segment of said gene using a DNA amplification method such as the polymerase chain reaction (PCR). In this method DNA for analysis is obtained by amplifying a segment of DNA or RNA (generally after converting the RNA to cDNA) spanning one or more variances in the gene sequence. Preferably, the amplified segment is <500 bases in length, in an alternative embodiment the amplified segment is <100 bases in length, most preferably <45 bases in length.

[0294] In some cases it will be desirable to determine a haplotype instead of a genotype. In such a case the diagnostic test is performed by amplifying a segment of DNA or RNA (cDNA) spanning more than one variance in the gene sequence and preferably maintaining the phase of the variances on each allele. The term “phase” refers to the relationship of variances on a single chromosomal copy of the gene, such as the copy transmitted from the mother (maternal copy or maternal allele) or the father (paternal copy or paternal allele). The haplotyping test may take part in two phases, where first genotyping tests at two or more variant sites reveal which sites are heterozygous in each patient or normal subject. Subsequently the phase of the two or more variant sites can be determined. In performing a haplotyping test preferably the amplified segment is >500 bases in length, more preferably it is >1,000 bases in length, and most preferably it is >2,500 bases in length. One way of preserving phase is to amplify one strand in the PCR reaction. This can be done using one or a pair of oligonucleotide primers that terminate (i.e. have a 3′ end that stops) opposite the variant site, such that one primer is perfectly complementary to one variant form and the other primer is perfectly complementary to the other variant form. Other than the difference in the 3′ most nucleotide the two primers are identical (forming an allelic primer pair). Only one of the allelic primers is used in any PCR reaction, depending on which strand is being amplified. The primer for the opposite strand may also be an allelic primer, or it may prime from a non-polymorphic region of the template. This method exploits the requirement of most polymerases for perfect complementarity at the 3′ terminus of the primer in a primer-template complex. See, for example: Lo Y M, Patel P, Newton C R, Markham A F, Fleming K A and J S Wainscoat. (1991) Direct haplotype determination by double ARMS: specificity, sensitivity and genetic applications. Nucleic Acids Res July 11;19(13):3561-7.

[0295] It is apparent that such diagnostic tests are performed after initial identification of variances within the gene, which allows selection of appropriate allele specific primers.

[0296] Diagnostic genetic tests useful for practicing this invention belong to two types: genotyping tests and haplotyping tests. A genotyping test simply provides the status of a variance or variances in a subject or patient. For example suppose nucleotide 150 of hypothetical gene X on an autosomal chromosome is an adenine (A) or a guanine (G) base. The possible genotypes in any individual are AA, AG or GG at nucleotide 150 of gene X.

[0297] In a haplotyping test there is at least one additional variance in gene X, say at nucleotide 810, which varies in the population as cytosine (C) or thymine (T). Thus a particular copy of gene X may have any of the following combinations of nucleotides at positions 150 and 810: 150A-810C, 150A-810T, 150G-810C or 150G-810T. Each of the four possibilities is a unique haplotype. If the two nucleotides interact in either RNA or protein, then knowing the haplotype can be important. The point of a haplotyping test is to determine the haplotypes present in a DNA or cDNA sample (e.g. from a patient). In the example provided there are only four possible haplotypes, but, depending on the number of variances in the gene and their distribution in human populations there may be three, four, five, six or more haplotypes at a given gene. The most useful haplotypes for this invention are those which occur commonly in the population being treated for a disease or condition. Preferably such haplotypes occur in at least 5% of the population, more preferably in at least 10%, still more preferably in at least 20% of the population and most preferably in at least 30% or more of the population. Conversely, when the goal of a pharmacogenetic program is to identify a relatively rare population that has an adverse reaction to a treatment, the most useful haplotypes may be rare haplotypes, which may occur in less than 5%, less than 2%, or even in less than 1% of the population. One skilled in the art will recognize that the frequency of the adverse reaction provides a useful guide to the likely frequency of salient causative haplotypes.

[0298] Based on the identification of variances or variant forms of a gene, a diagnostic test utilizing methods known in the art can be used to determine whether a particular form of the gene, containing specific variances or haplotypes, or combinations of variances and haplotypes, is present in at least one copy, one copy, or more than one copy in an individual. Such tests are commonly performed using DNA or RNA collected from blood, cells, tissue scrapings or other cellular materials, and can be performed by a variety of methods including, but not limited to, PCR based methods, hybridization with allele-specific probes, enzymatic mutation detection, chemical cleavage of mismatches, mass spectrometry or DNA sequencing, including minisequencing. Methods for haplotyping are described above. In particular embodiments, hybridization with allele specific probes can be conducted in two formats: (1) allele specific oligonucleotides bound to a solid phase (glass, silicon, nylon membranes) and the labeled sample in solution, as in many DNA chip applications, or (2) bound sample (often cloned DNA or PCR amplified DNA) and labeled oligonucleotides in solution (either allele specific or short—e.g. 7mers or 8mers—so as to allow sequencing by hybridization). Preferred methods for diagnostic testing of variances are described in four patent applications Stanton et al, entitled A METHOD FOR ANALYZING POLYNUCLEOTIDES, Ser. Nos. 09/394,467; 09/394,457; 09/394,774; and 09/394,387; all filed Sep. 10, 1999. The application of such diagnostic tests is possible after identification of variances that occur in the population. Diagnostic tests may involve a panel of variances from one or more genes, often on a solid support, which enables the simultaneous determination of more than one variance in one or more genes.

[0299] D. Use of Variance Status to Determine Treatment

[0300] The present disclosure describes exemplary gene sequence variances in genes identified in a gene table herein (e.g., Tables 3 and 4), and variant forms of these gene that may be determined using diagnostic tests. As indicated in the Summary, such a variance-based diagnostic test can be used to determine whether or not to administer a specific drug or other treatment to a patient for treatment of a disease or condition. Preferably such diagnostic tests are incorporated in texts such as are described in Clinical Diagnosis and Management by Laboratory Methods (19th Ed) by John B. Henry (Editor) W B Saunders Company, 1996; Clinical Laboratory Medicine : Clinical Application of Laboratory Data, (6th edition) by R. Ravel, Mosby-Year Book, 1995, or other medical textbooks including, without limitation, textbooks of medicine, laboratory medicine, therapeutics, pharmacy, pharmacology, nutrition, allopathic, homeopathic, and osteopathic medicine; preferably such a test is developed as a ‘home brew’ method by a certified diagnostic laboratory; most preferably such a diagnostic test is approved by regulatory authorities, e.g., by the U.S. Food and Drug Administration, and is incorporated in the label or insert for a therapeutic compound, as well as in the Physicians Desk Reference.

[0301] In such cases, the procedure for using the drug is restricted or limited on the basis of a diagnostic test for determining the presence of a variance or variant form of a gene. Alternatively the use of a genetic test may be advised as best medical practice, but not absolutely required, or it may be required in a subset of patients, e.g. those using one or more other drugs, or those with impaired liver or kidney function. The procedure that is dictated or recommended based on genotype may include the route of administration of the drug, the dosage form, dosage, schedule of administration or use with other drugs; any or all of these may require selecting or determination consistent with the results of the diagnostic test or a plurality of such tests. Preferably the use of such diagnostic tests to determine the procedure for administration of a drug is incorporated in a text such as those listed above, or medical textbooks, for example, textbooks of medicine, laboratory medicine, therapeutics, pharmacy, pharmacology, nutrition, allopathic, homeopathic, and osteopathic medicine. As previously stated, preferably such a diagnostic test or tests are required by regulatory authorities and are incorporated in the label or insert as well as the Physicians Desk Reference.

[0302] Variances and variant forms of genes useful in conjunction with treatment methods may be associated with the origin or the pathogenesis of a disease or condition. In many useful cases, the variant form of the gene is associated with a specific characteristic of the disease or condition that is the target of a treatment, most preferably response to specific drugs or other treatments. Examples of diseases or conditions ameliorable by the methods of this invention are identified in the Examples and tables below; in general treatment of disease with current methods, particularly drug treatment, always involves some unknown element (involving efficacy or toxicity or both) that can be reduced by appropriate diagnostic methods.

[0303] Alternatively, the gene is involved in drug action, and the variant forms of the gene are associated with variability in the action of the drug. For example, in some cases, one variant form of the gene is associated with the action of the drug such that the drug will be effective in an individual who inherits one or two copies of that form of the gene. Alternatively, a variant form of the gene is associated with the action of the drug such that the drug will be toxic or otherwise contra-indicated in an individual who inherits one or two copies of that form of the gene.

[0304] In accord with this invention, diagnostic tests for variances and variant forms of genes as described above can be used in clinical trials to demonstrate the safety and efficacy of a drug in a specific population. As a result, in the case of drugs which show variability in patient response correlated with the presence or absence of a variance or variances, it is preferable that such drug is approved for sale or use by regulatory agencies with the recommendation or requirement that a diagnostic test be performed for a specific variance or variant form of a gene which identifies specific populations in which the drug will be safe and/or effective. For example, the drug may be approved for sale or use by regulatory agencies with the specification that a diagnostic test be performed for a specific variance or variant form of a gene which identifies specific populations in which the drug will be toxic. Thus, approved use of the drug, or the procedure for use of the drug, can be limited by a diagnostic test for such variances or variant forms of a gene; or such a diagnostic test may be considered good medical practice, but not absolutely required for use of the drug.

[0305] As indicated, diagnostic tests for variances as described in this invention may be used in clinical trials to establish the safety and efficacy of a drug. Methods for such clinical trials are described below and/or are known in the art and are described in standard textbooks. For example, diagnostic tests for a specific variance or variant form of a gene may be incorporated in the clinical trial protocol as inclusion or exclusion criteria for enrollment in the trial, to allocate certain patients to treatment or control groups within the clinical trial or to assign patients to different treatment cohorts. Alternatively, diagnostic tests for specific variances may be performed on all patients within a clinical trial, and statistical analysis performed comparing and contrasting the efficacy or safety of a drug between individuals with different variances or variant forms of the gene or genes. Preferred embodiments involving clinical trials include the genetic stratification strategies, phases, statistical analyses, sizes, and other parameters as described herein.

[0306] Similarly, diagnostic tests for variances can be performed on groups of patients known to have efficacious responses to the drug to identify differences in the frequency of variances between responders and non-responders. Likewise, in other cases, diagnostic tests for variance are performed on groups of patients known to have toxic responses to the drug to identify differences in the frequency of the variance between those having adverse events and those not having adverse events. Such outlier analyses may be particularly useful if a limited number of patient samples are available for analysis. It is apparent that such clinical trials can be or are performed after identifying specific variances or variant forms of the gene in the population. In defining outliers it is useful to examine the distribution of responses in the placebo group; outliers should preferably have responses that exceed in magnitude the extreme responses in the placebo group.

[0307] The identification and confirmation of genetic variances is described in certain patents and patent applications. The description therein is useful in the identification of variances in the present invention. For example, a strategy for the development of anticancer agents having a high therapeutic index is described in Housman, International Application PCT/US/94 08473 and Housman, INHIBITORS OF ALTERNATIVE ALLELES OF GENES ENCODING PROTEINS VITAL FOR CELL VIABILITY OR CELL GROWTH AS A BASIS FOR CANCER THERAPEUTIC AGENTS, U.S. Pat. No. 5,702,890, issued Dec. 30, 1997, which are hereby incorporated by reference in their entireties. Also, a number of gene targets and associated variances are identified in Housman et al., U.S. patent application Ser. No. 09/045,053, entitled TARGET ALLELES FOR ALLELE-SPECIFIC DRUGS, filed Mar. 19, 1998, which is hereby incorporated by reference in its entirety, including drawings.

[0308] The described approach and techniques are applicable to a variety of other diseases, conditions, and/or treatments and to genes associated with the etiology and pathogenesis of such other diseases and conditions and the efficacy and safety of such other treatments.

[0309] Useful variances for this invention can be described generally as variances which partition patients into two or more groups that respond differently to a therapy (a therapeutic intervention), regardless of the reason for the difference, and regardless of whether the reason for the difference is known.

[0310] III. From Variance List to Clinical Trial: Identifying Genes and Gene Variances that Account for Variable Responses to Treatment

[0311] There are a variety of useful methods for identifying a subset of genes from a large set of candidate genes that should be prioritized for further investigation with respect to their influence on inter-individual variation in disease predisposition or response to a particular drug. These methods include for example, (1) searching the biomedical literature to identify genes relevant to a disease or the action of a drug, (2) screening the genes identified in step 1 for variances. A large set of exemplary variances are provided in Tables 3 and 4. Other methods include (3) using computational tools to predict the functional effects of variances in specific genes, (4) using in vitro or in vivo experiments to identify genes which may participate in the response to a drug or treatment, and to determine the variances which affect gene, RNA or protein function, and may therefore be important genetic variables affecting disease manifestations or drug response, and (5) retrospective or prospective clinical trials. Computational tools are described in U.S. patent application, Stanton et al., Ser. No. 09/300,747, filed Apr. 26, 1999, entitled GENE SEQUENCE VARIANCES WITH UTILITY IN DETERMINING THE TREATMENT OF DISEASE, and in Stanton et al., Ser. No. 09/419,705, filed Oct. 14, 1999, entitled VARIANCE SCANNING METHOD FOR IDENTIFYING GENE SEQUENCE VARIANCES, which are hereby incorporated by reference in their entireties, including drawings. Other methods are considered below in some detail.

[0312] (1) To begin, one preferably identifies, for a given treatment, a set of candidate genes that are likely to affect disease phenotype or drug response. This can be accomplished most efficiently by first assembling the relevant medical, pharmacological and biological data from available sources (e.g., public databases and publications). One skilled in the art can review the literature (textbooks, monographs, journal articles) and online sources (databases) to identify genes most relevant to the action of a specific drug or other treatment, particularly with respect to its utility for treating a specific disease, as this beneficially allows the set of genes to be analyzed ultimately in clinical trials to be reduced from an initial large set. Specific strategies for conducting such searches are described below. In some instances the literature may provide adequate information to select genes to be studied in a clinical trial, but in other cases additional experimental investigations of the sort described below will be preferable to maximize the likelihood that the salient genes and variances are moved forward into clinical studies. Specific genes relevant to understanding interpatient variation in patient outcome response to candidate therapeutic interventions are listed in Table 1. In Table 2 preferred sets of genes for analysis of variable therapeutic response in specific diseases are highlighted. These genes are exemplary; they do not constitute a complete set of genes that may account for variation in clinical response. Experimental data are also useful in establishing a list of candidate genes, as described below.

[0313] (2) Having assembled a list of candidate genes generally the second step is to screen for variances in each candidate gene. Experimental and computational methods for variance detection are described in this invention, and tables of exemplary variances are provided (Tables 3, and 4) as well as methods for identifying additional variances and a written description of such possible additional variances in the cDNAs of genes that may affect drug action (see Stanton & Adams, application Ser. No. 09/300,747, filed Apr. 26, 1999, entitled GENE SEQUENCE VARIANCES WITH UTILITY IN DETERMINING THE TREATMENT OF DISEASE, incorporated in its entirety.

[0314] (3) Having identified variances in candidate genes the next step is to assess their likely contribution to clinical variation in patient response to therapy, preferably by using informatics-based approaches such as DNA and protein sequence analysis and protein modeling. The literature and informatics-based approaches provide the basis for prioritization of candidate genes, however it may in some cases be desirable to further narrow the list of candidate genes, or to measure experimentally the phenotype associated with specific variances or sets of variances (e.g. haplotypes).

[0315] (4) Thus, as a third step in candidate gene analysis, one skilled in the art may elect to perform in vitro or in vivo experiments to assess the functional importance of gene variances, using either biochemical or genetic tests. (Certain kinds of experiments—for example gene expression profiling and proteome analysis—may not only allow refinement of a candidate gene list but may also lead to identification of additional candidate genes.) Combination of two or all of the three above methods will provide sufficient information to narrow and prioritize the set of candidate genes and variances to a number that can be studied in a clinical trial with adequate statistical power.

[0316] (5) The fourth step is to design retrospective or prospective human clinical trials to test whether the identified allelic variance, variances, or haplotypes or combination thereof influence the efficacy or toxicity profiles for a given drug or other therapeutic intervention. It should be recognized that this fourth step is the crucial step in producing the type of data that would justify introducing a diagnostic test for at least one variance into clinical use. Thus while each of the above four steps are useful in particular instances of the invention, this final step is indispensable. Further guidance and examples of how to perform these five steps are provided below.

[0317] (6) A fifth (optional) step entails methods for using a genotyping test in the promotion and marketing of a treatment method. It is widely appreciated that there is a tendency in the pharmaceutical industry to develop many compounds for well established therapeutic targets. Examples include beta adrenergic blockers, hydroxymethylglutaryl (HMG) CoA reductase inhibitors (statins), dopamine D2 receptor antagonists and serotonin transporter inhibitors. Frequently the pharmacology of these compounds is quite similar in terms of efficacy and side effects. Therefore the marketing of one compound vs. other members of the class is a challenging problem for drug companies, and is reflected in the lesser success that late products typically achieve compared to the first and second approved products. It occurred to the inventors that genetic stratification can provide the basis for identifying a patient population with a superior response rate or improved safety to one member of a class of drugs, and that this information can be the basis for commercialization of that compound. Such a commercialization campaign can be directed at caregivers, particularly physicians, or at patients and their families, or both.

[0318] 1. Identification of Candidate Genes Relevant to the Action of a Drug

[0319] Practice of this invention will often begin with identification of a specific pharmaceutical product, for example a drug, that would benefit from improved efficacy or reduced toxicity or both, and the recognition that pharmacogenetic investigations as described herein provide a basis for achieving such improved characteristics. The question then becomes which genes and variances, such as those provided in this application in Tables 1, 3, and 4, would be most relevant to interpatient variation in response to the drug. As discussed above, the set of relevant genes includes both genes involved in the disease process and genes involved in the interaction of the patient and the treatment—for example genes involved in pharmacokinetic and pharmacodynamic action of a drug. The biological and biomedical literature and online databases provide useful guidance in selecting such genes. Specific guidance in the use of these resources is provided below.

[0320] Review the Literature and Online Sources

[0321] One way to find genes that affect response to a drug in a particular disease setting is to review the published literature and available online databases regarding the pathophysiology of the disease and the pharmacology of the drug. Literature or online sources can provide specific genes involved in the disease process or drug response, or describe biochemical pathways involving multiple genes, each of which may affect the disease process or drug response.

[0322] Alternatively, biochemical or pathological changes characteristic of the disease may be described; such information can be used by one skilled in the art to infer a set of genes that can account for the biochemical or pathologic changes. For example, to understand variation in response to a drug that modulates serotonin levels in a central nervous system (CNS) disorder associated with altered levels of serotonin one would preferably study, at a minimum, variances in genes responsible for serotonin biosynthesis, release from the cell, receptor binding, presynaptic reuptake, and degradation or metabolism. Genes responsible for each of these functions should be examined for variation that may account for interpatient differences in drug response or disease manifestations. As recognized by those skilled in the art, a comprehensive list of such genes can be obtained from textbooks, monographs and the literature.

[0323] There are several types of scientific information, described in some detail below, that are valuable for identifying a set of candidate genes to be investigated with respect to a specific disease and therapeutic intervention. First there is the medical literature, which provides basic information on disease pathophysiology and therapeutic interventions. A subset of this literature is devoted to specific description of pathologic conditions. Second there is the pharmacology literature, which will provide additional information on the mechanism of action of a drug (pharmacodynamics) as well as its principal routes of metabolic transformation (pharmacokinetics) and the responsible proteins. Third there is the biomedical literature (principally genetics, physiology, biochemistry and molecular biology), which provides more detailed information on metabolic pathways, protein structure and function and gene structure. Fourth, there are a variety of online databases that provide additional information on metabolic pathways, gene families, protein function and other subjects relevant to selecting a set of genes that are likely to affect the response to a treatment.

[0324] Medical Literature

[0325] A good starting place for information on molecular pathophysiology of a specific disease is a general medical textbook such as Harrison's Principles of Internal Medicine, 14th edition, (2 Vol Set) by A. S. Fauci, E. Braunwald, K. J. Isselbacher, et al. (editors), McGraw Hill, 1997, or Cecil Textbook of Medicine (20th Ed) by R. L. Cecil, F. Plum and J. C. Bennett (Editors) W B Saunders Co., 1996. For pediatric diseases texts such as Nelson Textbook of Pediatrics (15th edition) by R. E. Behrman, R. M. Kliegman, A. M. Arvin and W. E. Nelson (Editors), W B Saunders Co., 1995 or Oski's Principles and Practice of Pediatrics (3rd Edition) by J. A. Mamillan & F. A. Oski Lippincott-Raven, 1999 are useful introductions. For obstetrical and gynecological disorders texts such as Williams Obstetrics (20th Ed) by F. G. Cunningham, N. F. Gant, P. C. McDonald et al. (Editors), Appleton & Lange, 1997 provide general information on disease pathophysiology. For psychiatric disorders texts such as the Comprehensive Textbook of Psychiatry, VI (2 Vols) by H. I. Kaplan and B. J. Sadock (Editors), Lippincott, Williams & Wilkins, 1995, or The American Psychiatric Press Textbook of Psychiatry (3rd edition) by R. E. Hales, S. C. Yudofsky and J. A. Talbott (Editors) Amer Psychiatric Press, 1999 provide an overview of disease nosology, pathophysiological mechanisms and treatment regimens.

[0326] In addition to these general texts, there are a variety of more specialized medical texts that provide greater detail about specific disorders which can be utilized in developing a list of candidate genes and variances relevant to interpatient variation in response to a treatment. For example, within the field of medicine there are standard textbooks for each of the subspecialties. Some specific examples include:

[0327] Heart Disease: A Textbook of Cardiovascular Medicine (2 Volume set) by E. Braunwald (Editor), W B Saunders Co., 1996.

[0328] Hurst's the Heart, Arteries and Veins (9th Ed) (2 Vol Set) by R. W. Alexander, R. C. Schlant, V. Fuster, W. Alexander and E. H. Sonnenblick (Editors) McGraw Hill, 1998.

[0329] Principles of Neurology (6th edition) by R. D. Adams, M. Victor (editors), and A. H. Ropper (Contributor), McGraw Hill, 1996.

[0330] Sleisenger & Fordtran's Gastrointestinal and Liver Disease: Pathophysiology Diagnosis, Management (6th edition) by M. Feldman, B. F. Scharschmidt and M. Sleisenger (Editors), W B Saunders Co., 1997.

[0331] Textbook of Rheumatology (5th edition) by W. N. Kelley, S. Ruddy, E. D. Harris Jr. and C. B. Sledge (Editors) (2 volume set) W B Saunders Co., 1997.

[0332] Williams Textbook of Endocrinology (9th edition) by J. D. Wilson, D. W. Foster, H. M. Kronenberg and Larsen (Editors), W B Saunders Co., 1998.

[0333] Wintrobe's Clinical Hematology (10th Ed) by G. R. Lee, J. Foerster (Editor) and J. Lukens (Editors) (2 Volumes) Lippincott, Williams & Wilkins, 1998.

[0334] Cancer: Principles & Practice of Oncology (5th edition) by V. T. Devita, S. A. Rosenberg and S. Hellman (editors), Lippincott-Raven Publishers, 1997.

[0335] Principles of Pulmonary Medicine (3rd edition) by S. E. Weinberger & J Fletcher (Editors), W B Saunders Co., 1998.

[0336] Diagnosis and Management of Renal Disease and Hypertension (2nd edition) by A. K. Mandal & J. C. Jennette (Editors), Carolina Academic Press, 1994.Massry & Glassock's Textbook of Nephrology (3rd edition) by S. G. Massry & R. J. Glassock (editors) Williams & Wilkins, 1995.

[0337] The Management of Pain by J. J. Bonica, Lea and Febiger, 1992

[0338] Ophthalmology by M. Yanoff & J. S. Duker, Mosby Year Book, 1998

[0339] Clinical Ophthalmology: A Systemic Approach by J. J. Kanski, Butterworth-Heineman, 1994.Essential Otolaryngoloy by J. K. Lee Appleton and Lange 1998.

[0340] In addition to these subspecialty texts there are many textbooks and monographs that concern more restricted disease areas, or specific diseases. Such books provide more extensive coverage of pathophysiologic mechanisms and therapeutic options. The number of such books is too great to provide examples for all but a few diseases, however one skilled in the art will be able to readily identify relevant texts. One simple way to search for relevant titles is to use the search engine of an online bookseller such as http://www.amazon.com or http://www.barnesandnoble.com using the disease or drug (or the group of diseases or drugs to which they belong) as search terms. For example a search for asthma would turn up titles such as Asthma: Basic Mechanisms and Clinical Management (3rd edition) by P. J. Barnes, I. W. Rodger and N. C. Thomson (Editors), Academic Press, 1998 and Airways and Vascular Remodelling in Asthma and Cardiovascular Disease: Implications for Therapeutic Intervention, by C. Page & J. Black (Editors), Academic Press, 1994.

[0341] Pathology Literature

[0342] In addition to medical texts there are texts that specifically address disease etiology and pathologic changes associated with disease. A good general pathology text is Robbins Pathologic Basis of Disease (6th edition) by R. S. Cotran, V. Kumar, T. Collins and S. L. Robbins, W B Saunders Co., 1998. Specialized pathology texts exist for each organ system and for specific diseases, similar to medical texts. These texts are useful sources of information for one skilled in the art for developing lists of genes that may account for some of the known pathologic changes in disease tissue. Exemplary texts are as follows:

[0343] Bone Marrow Pathology 2nd edition, by B. J. Bain, I. Lampert. & D. Clark, Blackwell Science, 1996

[0344] Atlas of Renal Pathology by F. G. Silva, W. B. Saunders, 1999.

[0345] Fundamentals of Toxicologic Pathology by W. M. Haschek and C. G. Rousseaux, Academic Press, 1997.

[0346] Gastrointestinal Pathology by P. Chandrasoma, Appleton and Lange, 1998.

[0347] Ophthalmic Pathology with Clinical Correlations by J. Sassani, Lippincott-Raven, 1997.

[0348] Pathology of Bone and Joint Disorders by F. McCarthy, F. J. Frassica and A. Ross, W. B. Saunders, 1998.

[0349] Pulmonary Pathology by M. A. Grippi, Lippicott-Raven, 1995.

[0350] Neuropathology by D. Ellison, L. Chimelli, B. Harding, S. Love & J. Lowe, Mosby Year Book, 1997.

[0351] Greenfield's Neuropatholgy 6th edition by J. G. Greenfield, P. L. Lantos & D. I. Graham, Edward Arnold, 1997.

[0352] Pharmacology, Pharmacogenetics and Pharmacy Literature

[0353] There are also both general and specialized texts and monographs on pharmacology that provide data on pharmacokinetics and pharmacodynamics of drugs. The discussion of pharmacodynamics (mechanism of action of the drug) in such texts is often supported by a review of the biochemical pathway or pathways that are affected by the drug. Also, proteins related to the target protein are often listed; it is important to account for variation in such proteins as the related proteins may be involved in drug pharmacology. For example, there are 14 known serotonin receptors. Various pharmacological serotonin agonists or antagonists have different affinities for these different receptors. Variation in a specific receptor may affect the pharmacology not only of drugs targeted to that receptor, but also drugs that are principally agonists or antagonists of different receptors. Such compounds may produce different effects on two allelic forms of a non-targeted receptor; for example on variant form may bind the compound with higher affinity than the other, or a compound that is principally an antagonist for one allele may be a partial agonist for another allele. Thus genes encoding proteins structurally related to the target protein should be screened for variance in order to successfully realize the methods of the present invention. A good general pharmacology text is Goodman & Gilman's the Pharmacological Basis of Therapeutics (9th Ed) by J. G. Hardman, L. E. Limbird, P. B. Molinoff, R. W. Ruddon and A. G. Gilman (Editors) McGraw Hill, 1996. There are also texts that focus on the pharmacology of drugs for specific disease areas, or specific classes of drugs (e.g. natural products) or adverse drug interactions, among other subjects. Specific examples include:

[0354] The American Psychiatric Press Textbook of Psychopharmacology (2nd edition) by A. F. Schatzberg & C. B. Nemeroff (Editors), American Psychiatric Press, 1998.

[0355] Essential Psychopharmacology: Neuroscientific Basis and Practical Applications by N. Muntner and S. M. Stahl, Cambridge Univ Press, 1996.

[0356] There are also texts on pharmacogenetics which are particularly useful for identifying genes which may contribute to variable pharmacokinetic response. In addition there are texts on some of the major xenobiotic metabolizing proteins, such as the cytochrome P450 genes.

[0357] Pharmacogenetics of Drug Metabolism (International Encyclopedia of Pharmacology and Therapeutics) by Werner Kalow (Editor) Pergamon Press, 1992.

[0358] Genetic Factors in Drug Therapy: Clinical and Molecular Pharmacogenetics by D. A Price Evans, Cambridge Univ Press, 1993.

[0359] Pharmacogenetics (Oxford Monographs on Medical Genetics, 32) by W. W. Weber, Oxford Univ Press, 1997.

[0360] Cytochrome P 450: Structure, Mechanism, and Biochemistry by P. R. Ortiz de Montellano (Editor), Plenum Publishing Corp, 1995.

[0361] Appleton & Lange's Review of Pharmacy, 6th edition, (Appleton & Lange's Review Series) by G. D. Hall & B. S. Reiss, Appleton & Lange, 1997.

[0362] Genetics, Biochemistry and Molecular Biology Literature

[0363] In addition to the medical, pathology, and pharmacology texts listed above there are several information sources that one skilled in the art will turn to for information on the genetic, physiologic, biochemical, and molecular biological aspects of the disease, disorder or condition or the effect of the therapeutic intervention on specific physiologic processes. The biomedical literature may include information on nonhuman organisms that is relevant to understanding the likely disease or pharmacological pathways in man.

[0364] Also provided below are illustrative texts which will aid in the identification of a pathway or pathways, and a gene or genes that may be relevant to interindividual variation in response to a therapy. Textbooks of biochemistry, genetics and physiology are often useful sources for such pathway information. In order to ascertain the appropriate methods to analyze the effects of an allelic variance, variances, or haplotypes in vitro, one skilled in the art will review existing information on molecular biology, cell biology, genetics, biochemistry; and physiology. Such texts are useful sources for general and specific information on the genetic and biochemical processes involved in disease and in drug action, as well as experimental procedures that may be useful in performing in vitro research on an allelic variance, variances, or haplotype.

[0365] Texts on gene structure and function and RNA biochemistry will be useful in evaluating the consequences of variances that do not change the coding sequence (silent variances). Such variances may alter the interaction of RNA with proteins or other regulatory molecules affecting RNA processing, polyadenylation, or export.

[0366] Molecular and Cellular Biology

[0367] Molecular Cell Biology by H. Lodish, D. Baltimore, A. Berk, L. Zipurksy & J. Damell, W H Freeman & Co., 1995.

[0368] Essentials of Molecular Biology, D. Freifelder and MalacinskiJones and Bartlett, 1993.

[0369] Genes and Genomes: A Changing Perspective, M. Singer and P. Berg, 1991. University Science Books

[0370] Gene Structure and Expression, J. D. Hawkins, 1996. Cambridge University Press

[0371] Molecular Biology of the Cell, 2nd edition, B. Alberts et al., Garland Publishing, 1994.

[0372] Molecular Genetics

[0373] The Metabolic and Molecular Bases of Inherited Disease by C. R. Scriver, A. L. Beaudet, W. S. Sly (Editors), 7th edition, McGraw Hill, 1995

[0374] Genetics and Molecular Biology, R. Schleif, 1994. 2nd edition, Johns Hopkins University Press

[0375] Genetics, P. J. Russell, 1996. 4th edition, Harper Collins

[0376] An Introduction to Genetic Analysis, Griffiths et al. 1993. 5th edition, W. H. Freeman and Company

[0377] Understanding Genetics: A molecular approach, Rothwell, 1993. Wiley-Liss

[0378] General Biochemistry

[0379] Biochemistry, L. Stryer, 1995. W. H. Freeman and Company

[0380] Biochemistry, D. Voet and J. G. Voet, 1995. John Wiley and Sons

[0381] Principles of Biochemistry, A. L. Lehninger, D. L. Nelson, and M. M. Cox, 1993. Worth Publishers

[0382] Biochemistry, G. Zubay, 1998. Wm. C. Brown Communications

[0383] Biochemistry, C. K. Mathews and K. E. van Holde, 1990. Benjamin/Cummings

[0384] Transcription

[0385] Eukaryotic Transcriptiuon Factors, D. S. Latchman, 1995. Academic Press

[0386] Eukaryotic Gene Transcription, S. Goodboum (ed.), 1996. Oxford University Press.

[0387] Transcription Factors and DNA Replication, D. S. Pederson and N. H. Heintz, 1994. CRC Press/R. G. Landes Company

[0388] Transcriptional Regulation, S. L. McKnight and K. Yamamoto (eds.), 1992. 2 volumes, Cold Spring Harbor Laboratory Press

[0389] RNA

[0390] Control of Messenger RNA Stability, J. Belasco and G. Brawerman (eds.), 1993. Academic Press

[0391] RNA -Protein Interactions, Nagai and Mattaj (eds.), 1994. Oxford University Press

[0392] mRNA Metabolism and Post-transcriptional Gene Regulation, Harford and Morris (eds.), 1997. Wiley-Liss

[0393] Translation

[0394] Translational Control, J. W. B. Hershey, M. B. Mathews, and N. Sonenberg (eds.), 1995. Cold Spring Harbor Laboratory Press

[0395] General Physiology

[0396] Textbook of Medical Physiology 9th Edtion by A. C. Guyton and J. E. Hall W. B. Saunders, 1997

[0397] Review of Medical Physiology, 18th Edition by W. F. Ganong, Appleton and Lange, 1997

[0398] Online Databases

[0399] Those skilled in the art are familiar with how to search the biomedical literature, such as, e.g., libraries, online PubMed, abstract listings, and online mutation databases. One particularly useful resource is maintained at the web site of the National Center for Biotechnology Information (ncbi): http:/www.ncbi.nlm.nih.gov/. From the ncbi site one can access Online Mendelian Inheritance in Man (OMIM),. OMIM can be found at: http://www3.ncbi.nlm.nih.gov/Omim/searchomim.html. OMIM is a medically oriented database of genetic information with entries for thousands of genes. The OMIM record number is provided for many of the genes in Tables 1, 3, and 4 (see column 3), and constitutes an excellent entry point for identification of references that point to the broader literature. Another useful site at NCBI is the Entrez browser, located at http://www3.ncbi.nlm.nih.gov/Entrez/. One can search genomes, polynucleotides, proteins, 3D structures, taxonomy or the biomedical literature (PubMed) via the Entrez site. More generally links to a number of useful sites with biomedical or genetic data are maintained at sites such as Med Web at the Emory University Health Sciences Center Library: http://WWW.MedWeb.Emory.Edu/MedWeb/: Riken, a Japanese web site at: http:/www.rtc.riken.go.jp/othersite.html with links to DNA sequence, structural, molecular biology, bioinformatics, and other databases; at the Oak Ridge National Laboratory web site: http://www.ornl.gov/hgmis/links.html; or at the Yahoo website of Diseases and Conditions: http://dir.yahoo.com/health/diseases and conditions/index.html. Each of the indicated web sites has additional useful links to other sites.

[0400] Another type of database with utility in selecting the genes on a biochemical pathway that may affect the response to a drug are databases that provide information on biochemical pathways. Examples of such databases include the Kyoto Encyclopedia of Genes and Genomes (KEGG), which can be found at: http://www.genome.ad.jp/kegg/kegg.html. This site has pictures of many biochemical pathways, as well as links to other metabolic databases such as the well known Boehringer Mannheim biochemical pathways charts: http://www.expasy.ch/cgi-bin/search-biochem-index. The metabolic charts at the latter site are comprehensive, and excellent starting points for working out the salient enzymes on any given pathway.

[0401] Each of the web sites mentioned above has links to other useful web sites, which in turn can lead to additional sites with useful information. Research Libraries

[0402] Those skilled in the art will often require information found only at large libraries. The National Library of Medicine (http://www.nlm.nih.gov/) is the largest medical library in the world and its catalogs can be searched online. Other libraries, such as university or medical school libraries are also useful to conduct searches. Biomedical books such as those referred to above can often be obtained from online bookstores as described above.

[0403] Biomedical Literature

[0404] To obtain up to date information on drugs and their mechanism of action and biotransformation; disease pathophysiology; biochemical pathways relevant to drug action and disease pathophysiology; and genes that encode proteins relevant to drug action and disease one skilled in the art will consult the biomedical literature. A widely used, publicly accessible web site for searching published journal articles is PubMed (http://www.ncbi.nlm.nih.gov/PubMed/). At this site, one can search for the most recent articles (within the last 1-2 months) or older literature (back to 1966). Many Journals also have their own sites on the world wide web and can be searched online. For example see the IDEAL web site at: http://www.apnet.com/www/ap/aboutid.html. This site is an online library, featuring full text journals from Academic Press and selected journals from W. B. Saunders and Churchill Livingstone. The site provides access (for a fee) to nearly 2000 scientific, technical, and medical journals.

[0405] Experimental Methods for Identification of Genes Involved in the Action of a Drug

[0406] There are a number of experimental methods for identifying genes and gene products that mediate or modulate the effects of a drug or other treatment. They encompass analyses of RNA and protein expression as well as methods for detecting protein—protein interactions and protein—ligand interactions. Two preferred experimental methods for identification of genes that may be involved in the action of a drug are (1) methods for measuring the expression levels of many mRNA transcripts in cells or organisms treated with the drug (2) methods for measuring the expression levels of many proteins in cells or organisms treated with the drug.

[0407] RNA transcripts or proteins that are substantially increased or decreased in drug treated cells or tissues relative to control cells or tissues are candidates for mediating the action of the drug. Preferably the level of an mRNA is at least 30% higher or lower in drug treated cells, more preferably at least 50% higher or lower, and most preferably two fold higher or lower than levels in non-drug treated control cells. The analysis of RNA levels can be performed on total RNA or on polyadenylated RNA selected by oligodT affinity. Further, RNA from different cell compartments can be analyzed independently—for example nuclear vs. cytoplasmic RNA. In addition to RNA levels, RNA kinetics can be examined, or the pool of RNAs currently being translated can be analyzed by isolation of RNA from polysomes. Other useful experimental methods include protein interaction methods such as the yeast two hybrid system and variants thereof which facilitate the detection of protein—protein interactions. Preferably one of the interacting proteins is the drug target or another protein strongly implicated in the action of the compound being assessed.

[0408] The pool of RNAs expressed in a cell is sometimes referred to as the transcriptome. Methods for measuring the transcriptome, or some part of it, are known in the art. A recent collection of articles summarizing some current methods appeared as a supplement to the journal Nature Genetics. (The Chipping Forecast. Nature Genetics supplement, volume 21, January 1999.) A preferred method for measuring expression levels of mRNAs is to spot PCR products corresponding to a large number of specific genes on a nylon membrane such as Hybond N Plus (Amersham-Pharmacia). Total cellular mRNA is then isolated, labeled by random oligonucleotide priming in the presence of a detectable label (e.g. alpha 33P labeled radionucleotides or dye labeled nucleotides), and hybridized with the filter containing the PCR products. The resulting signals can be analyzed by commercially available software, such as can be obtained from Clontech/Molecular Dynamics or Research Genetics, Inc.

[0409] Experiments have been described in model systems that demonstrate the utility of measuring changes in the transcriptome before and after changing the growth conditions of cells, for example by changing the nutrient environment. The changes in gene expression help reveal the network of genes that mediate physiological responses to the altered growth condition. Similarly, the addition of a drug to the cellular or in vivo environment, followed by monitoring the changes in gene expression can aid in identification of gene networks that mediate pharmacological responses.

[0410] The pool of proteins expressed in a cell is sometimes referred to as the proteome. Studies of the proteome may include not only protein abundance but also protein subcellular localization and protein-protein interaction. Methods for measuring the proteome, or some part of it, are known in the art. One widely used method is to extract total cellular protein and separate it in two dimensions, for example first by size and then by isoelectric point. The resulting protein spots can be stained and quantitated, and individual spots can be excised and analyzed by mass spectrometry to provide definitive identification. The results can be compared from two or more cell lines or tissues, at least one of which has been treated with a drug. The differential up or down modulation of specific proteins in response to drug treatment may indicate their role in mediating the pharmacologic actions of the drug. Another way to identify the network of proteins that mediate the actions of a drug is to exploit methods for identifying interacting proteins. By starting with a protein known to be involved in the action of a drug—for example the drug target—one can use systems such as the yeast two hybrid system and variants thereof (known to those skilled in the art; see Ausubel et al., Current Protocols in Molecular Biology, op. cit.) to identify additional proteins in the network of proteins that mediate drug action. The genes encoding such proteins would be useful for screening for DNA sequence variances, which in turn may be useful for analysis of interpatient variation in response to treatments. For example, the protein 5-lipoxygenase (5LO) is an enzyme which is at the beginning of the leukotriene biosynthetic pathway and is a target for anti-inflammatory drugs used to treat asthma and other diseases. In order to detect proteins that interact with 5-lipoxygenase the two-hybrid system was recently used to isolate three different proteins, none previously known to interact with 5LO. (Provost et al., Interaction of 5-lipoxygenase with cellular proteins. Proc. Natl. Acad. Sci. U.S.A. 96: 1881-1885, 1999.) A recent collection of articles summarizing some current methods in proteomics appeared in the August 1998 issue of the journal Electrophoresis (volume 19, number 11). Other useful articles include: Blackstock W P), et al. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 17 (3): p. 121-7, 1999, and Patton W. F., Proteome analysis II. Protein subcellular redistribution: linking physiology to genomics via the proteome and separation technologies involved. J Chromatogr B Biomed Sci App. 722(1-2):203-23. 1999.

[0411] Since many of these methods can also be used to assess whether specific polymorphisms are likely to have biological effects, they are also relevant in section 3, below, concerning methods for assessing the likely contribution of variances in candidate genes to clinical variation in patient responses to therapy.

[0412] 2. Screen for Variances in Genes that may be Related to Therapeutic Response

[0413] Having identified a set of genes that may affect response to a drug the next step is to screen the genes for variances that may account for interindividual variation in response to the drug. There are a variety of levels at which a gene can be screened for variances, and a variety of methods for variance screening. The two main levels of variance screening are genomic DNA screening and cDNA screening. Genomic variance detection may include screening the entire genomic segment spanning the gene from 2 kb to 10 kb upstream of the transcription start site to the polyadenylation site, or 2 to 10 kb beyond the polyadenylation site. Alternatively genomic variance detection may (for intron containing genes) include the exons and some region around them containing the splicing signals, for example, but not all of the intronic sequences. In addition to screening introns and exons for variances it is generally desirable to screen regulatory DNA sequences for variances. Promoter, enhancer, silencer and other regulatory elements have been described in human genes. The promoter is generally proximal to the transcription start site, although there may be several promoters and several transcription start sites. Enhancer, silencer and other regulatory elements may be intragenic or may lie outside the introns and exons, possibly at a considerable distance, such as 100 kb away. Variances in such sequences may affect basal gene expression or regulation of gene expression. In either case such variation may affect the response of an individual patient to a therapeutic intervention, for example a drug, as described in the examples. Thus in practicing the present invention it is useful to screen regulatory sequences as well as transcribed sequences, in order to identify variances that may affect gene transcription. Frequently the genomic sequence of a gene can be found in the sources above, particularly by searching GenBank or Medline (PubMed). The name of the gene can be entered at a site such as Entrez: http://www.ncbi.nlm.nih.gov/Entrez/nucleotide.html. Using the genomic sequence and information from the biomedical literature one skilled in the art can perform a variance detection procedure such as those described in examples 15, 16 and 17.

[0414] Variance detection is often first performed on the cDNA of a gene for several reasons. First, available data on functional sequence variances suggests that variances in the transcribed portion of a gene may be most likely to have functional consequences as they can affect the interaction of the transcript with a wide variety of cellular factors during the complex processes of RNA transcription, processing and translation, with consequent effects on RNA splicing, stability, translational efficiency or other processes. Second, as a practical matter the cDNA sequence of a gene is often available before the genomic structure is known, although the reverse will be true in the future as the sequence of the human genome is determined. Third, the cDNA is often compact compared to the genomic locus, and can be screened for variances with much less effort. If the genomic structure is not known then only the cDNA sequence can be scanned for variances. Methods for preparing cDNA are described in Example 14. Methods for variance detection on cDNA are described below and in the examples.

[0415] In general it is preferable to catalog genetic variation at the genomic DNA level because there are an increasing number of well documented instances of functionally important variances that lie outside of transcribed sequence. Also, to properly use optimal genetic methods to assess the contribution of a candidate gene to variation in a phenotype of interest it is desirable to understand the character of sequence variation in the candidate gene: what is the nature of linkage disequilibrium between different variances in the gene; are there sites of recombination within the gene; what is the extent of homoplasy in the gene (i.e. occurrence of two variant sites that are identical by state but not identical by descent because the same variance arose at least twice in human evolutionary history on two different haplotypes); what are the different haplotypes and how can they be grouped to increase the power of genetic analysis?

[0416] Methods for variance screening have been described, including DNA sequencing. See for example: U.S. Pat. No. 5,698,400: Detection of mutation by resolvase cleavage; U.S. Pat. No. 5,217,863: Detection of mutations in nucleic acids; and U.S. Pat. No. 5,750,335: Screening for genetic variation, as well as the examples and references cited therein for examples of useful variance detection procedures. Detailed variance detection procedures are also described in examples 15, 16 and 17. One skilled in the art will recognize that depending on the specific aims of a variance detection project (number of genes being screened, number of individuals being screened, total length of DNA being screened) one of the above cited methods may be preferable to the others, or yet another procedure may be optimal. A preferred method of variance detection is chain terminating DNA sequencing using dye labeled primers, cycle sequencing and software for assessing the quality of the DNA sequence as well as specialized software for calling heterozygotes. The use of such procedures has been described by Nickerson and colleagues. See for example: Rieder M. J., et al. Automating the identification of DNA variations using quality-based fluorescence re-sequencing: analysis of the human mitochondrial genome. Nucleic Acids Res. 26 (4):967-73, 1998, and: Nickerson D. A., et al. PolyPhred: automating the detection and genotyping of single nucleotide substitutions using fluorescence-based resequencing. Nucleic Acids Res. 25 (14):2745-51, 1997.Although the variances provided in Tables 3, and 4 consist principally of cDNA variances, it is an aspect of this invention that detection of genomic variances is also a useful method for identification of variances that may account for interpatient variation in response to a therapy.

[0417] Another important aspect of variance detection is the use of DNA from a panel of human subjects that represents a known population. For example, if the subjects are being screened for variances relevant to a specific drug development program it is desirable to include both subjects with the target disease and healthy subjects in the panel, because certain variances may occur at different frequencies in the healthy and disease populations and can only be reliably detected by screening both populations. Also, for example, if the drug development program is taking place in Japan, it is important to include Japanese individuals in the screening population. In general, it is always desirable to include subjects of known geographic, racial or ethnic identity in a variance screening experiment so the results can be interpreted appropriately for different patient populations, if necessary. Also, in order to select optimal sets of variances for genetic analysis of a gene locus it is desirable to know which variances have occurred recently—perhaps on multiple different chromosomes—and which are ancient. Inclusion of one or more apes or monkeys in the variance screening panel is one way of gaining insight into the evolutionary history of variances. Chimpanzees are preferred subjects for inclusion in a variance screening panel.

[0418] 3. Assess the Likely Contribution of Variances in Candidate Genes to Clinical Variation in Patient Responses to Therapy

[0419] Once a set of genes likely to affect disease pathophysiology or drug action has been identified, and those genes have been screened for variances, said variances (e.g., provided in Tables 3, and 4) can be assessed for their contribution to variation in the pharmacological or toxicological phenotypes of interest. Such studies are useful for reducing a large number of candidate variances to a smaller number of variances to be tested in clinical trials. There are several methods which can be used in the present invention for assessing the medical and pharmaceutical implications of a DNA sequence variance. They range from computational methods to in vitro and/or in vivo experimental methods, to prospective human clinical trials, and also include a variety of other laboratory and clinical measures that can provide evidence of the medical consequences of a variance. In general, human clinical trials constitute the highest standard of proof that a variance or set of variances is useful for selecting a method of treatment, however, computational and in vitro data, or retrospective analysis of human clinical data may provide strong evidence that a particular variance will affect response to a given therapy, often at lower cost and in less time than a prospective clinical trial. Moreover, at an early stage in the analysis when there are many possible hypotheses to explain interpatient variation in treatment response, the use of informatics-based approaches to evaluate the likely functional effects of specific variances is an efficient way to proceed.

[0420] Informatics-based approaches to the prediction of the likely functional effects of variances include DNA and protein sequence analysis (phylogenetic approaches and motif searching) and protein modeling (based on coordinates in the protein database, or pdb; see http://www.rcsb.org/pdb/). See, for example: Kawabata et al. The Protein Mutant Database. Nucleic Acids Research 27: 355-357, 1999; also available at: http://pmd.ddbj.nig.ac.ip. Such analyses can be performed quickly and inexpensively, and the results may allow selection of certain genes for more extensive in vitro or in vivo studies or for more variance detection or both.

[0421] The three dimensional structure of many medically and pharmaceutically important proteins, or homologs of such proteins in other species, or examples of domains present in such proteins, is known as a result of x-ray crystallography studies and, increasingly, nuclear magnetic resonance studies. Further, there are increasingly powerful tools for modeling the structure of proteins with unsolved structure, particularly if there is a related (homologous) protein with known structure. (For reviews see: Rost et al., Protein fold recognition by prediction-based threading, J. Mol. Biol. 270:471-480, 1997; Firestine et al., Threading your way to protein function, Chem. Biol. 3:779-783, 1996) There are also powerful methods for identifying conserved domains and vital amino acid residues of proteins of unknown structure by analysis of phylogenetic relationships. (Deleage et al., Protein structure prediction: Implications for the biologist, Biochimie 79:681-686, 1997; Taylor et al., Multiple protein structure alignment, Protein Sci. 3:1858-1870, 1994) These methods can permit the prediction of functionally important variances, either on the basis of structure or evolutionary conservation. For example, a crystal structure can reveal which amino acids comprise a small molecule binding site. The identification of a polymorphic amino acid variance in the topological neighborhood of such a site, and, in particular, the demonstration that at least one variant form of the protein has a variant amino acid which impinges on (or which may otherwise affect the chemical environment around) the small molecule binding pocket differently from another variant form, provides strong evidence that the variance may affect the function of the protein. From this it follows that the interaction of the protein with a treatment method, such an administered compound, will likely be variable between different patients. One skilled in the art will recognize that the application of computational tools to the identification of functionally consequential variances involves applying the knowledge and tools of medicinal chemistry and physiology to the analysis.

[0422] Phylogenetic approaches to understanding sequence variation are also useful. Thus if a sequence variance occurs at a nucleotide or encoded amino acid residue where there is usually little or no variation in homologs of the protein of interest from non-human species, particularly evolutionarily remote species, then the variance is more likely to affect function of the RNA or protein. Computational methods for phylogenetic analysis are known in the art, (see below for citations of some methods).

[0423] Computational methods are also useful for analyzing DNA polymorphisms in transcriptional regulatory sequences, including promoters and enhancers. One useful approach is to compare variances in potential or proven transcriptional regulatory sequences to a catalog of all known transcriptional regulatory sequences, including consensus binding domains for all transcription factor binding domains. See, for example, the databases cited in: Burks, C. Molecular Biology Database List. Nucleic Acids Research 27: 1-9, 1999, and links to useful databases on the internet at: http://www.oup.co.uk/nar/Volume—27/issue—01/summary/gkc105_gml.html. In particular see the Transcription Factor Database (Heinemeyer, T., et al. (1999) Expanding the TRANSFAC database towards an expert system of regulatory molecular mechanisms. Nucleic Acids Res. 27: 318-322, or on the internet at: http://193.175.244.40/TRANSFAC/index.html). Any sequence variances in transcriptional regulatory sequences can be assessed for their effects on mRNA levels using standard methods, either by making plasmid constructs with the different allelic forms of the sequence, transfecting them into cells and measuring the output of a reporter transcript, or by assays of cells with different endogenous alleles of variances. One example of a polymorphism in a transcriptional regulatory element that has a pharmacogenetic effect is described by Drazen et al. (1999) Pharmacogenetic association between ALOX5 promoter genotype and the response to anti-asthma treatment. Nature Genetics 22: 168-170. Drazen and co-workers found that a polymorphism in an Sp1-transcription factor binding domain, which varied among subjects from 3-6 tandem copies, accounted for varied expression levels of the 5-lipoxygenase gene when assayed in vitro in reporter construct assays. This effect would have been flagged by an informatics analysis that surveyed the 5-lipoxygenase candidate promoter region for transcriptional regulatory sequences (resulting in discovery of polymorphism in the Sp1 motif).

[0424] 4. Perform in vitro or in vivo Experiments to Assess the Functional Importance of Gene Variances

[0425] There are two broad types of studies useful for assessing the likely importance of variances: analysis of RNA or protein abundance (as described above in the context of methods for identifying candidate genes for explaining interpatient variation in treatment response) or analysis of functional differences in different variant forms of a gene, mRNA or protein. Studies of functional differences may involve direct measurements of biochemical activity of different variant forms of an mRNA or protein, or may involve assaying the influence of a variance or variances on various cell properties, including both tissue culture and in vivo studies.

[0426] The selection of an appropriate experimental program for testing the medical consequences of a variance may differ depending on the nature of the variance, the gene, and the disease. For example if there is already evidence that a protein is involved in the pharmacologic action of a drug, then the in vitro or in vivo demonstration that an amino acid variance in the protein affects its biochemical activity is strong evidence that the variance will have an effect on the pharmacology of the drug in patients, and therefore that patients with different variant forms of the gene may have different responses to the same dose of drug. If the variance is silent with respect to protein coding information, or if it lies in a noncoding portion of the gene (e.g., a promoter, an intron, or a 5′- or 3′-untranslated region) then the appropriate biochemical assay may be to assess mRNA abundance, half life, or translational efficiency. If, on the other hand, there is no substantial evidence that the protein encoded by a particular gene is relevant to drug pharmacology, but instead is a candidate gene on account of its involvement in disease pathophysiology, then the optimal test may be a clinical study addressing whether two patient groups distinguished on the basis of the variance respond differently to a therapeutic intervention. This approach reflects the current reality that biologists do not sufficiently understand gene regulation, gene expression and gene function to consistently make accurate inferences about the consequences of DNA sequence variances for pharmacological responses.

[0427] In summary, if there is a plausible hypothesis regarding the effect of a protein on the action of a drug, then in vitro and in vivo approaches, including those described below, will be useful to predict whether a given variance is therapeutically consequential. If, on the other hand, there is no evidence of such an effect, then the preferred test is an empirical clinical measure of the impact to the variance on efficacy or toxicity in vivo (which requires no evidence or assumptions regarding the mechanism by which the variance may exert an effect on a therapeutic response). However, given the expense and statistical constraints of clinical trials, it is preferable to limit clinical testing to variances for which there is at least some experimental or computational evidence of a functional effect.

[0428] In another aspect of the invention a powerful, high throughput approach to the genetics of drug response is to study variation in drug response phenotypes among cell lines derived from related individuals. Consider a cellular drug response phenotype that is readily measured, and that varies among cell lines. The demonstration of Mendelian transmission of the drug response phenotype in cell lines from related individuals would constitute evidence of a genetic component to the drug response phenotype. The expected pattern of segregation depends on making an assumption about the genetic model: recessive, dominant or co-dominant alleles will produce different proportions in the progeny of a cross. The value of studying cell lines as surrogates for people is that experiments can be performed for a small fraction of the cost. The value of studying cell lines from related individuals is that genetic effects on drug response are likely to be much easier to identify when genetic background among the subjects is substantially similar. In particular, in cell lines from a pedigree it is known that only four parental alleles are segregating in the children, and that any two children are on average 50% genetically identical. In a more heterogeneous genetic background (i.e. cell lines from unrelated subjects) the effect of allelic variation at multiple genes that modulate the measured drug response phenotypes is more likely to create a nearly continuous distribution of responses (except in cases where the product of one gene accounts for most of the measured drug response phenotype).

[0429] Many cell lines have been derived from groups of related individuals, or pedigrees. A commercial source of such cell lines is the Human Genetic Cell Respository, supported by the National Institute of General Medical Sciences (NIGMS) and housed at the Coriell Cell Repository, Camden, N.J. A directory of these cell lines is available on the world wide web: http://locus.umdnj.edu/nigms/. One preferred set of cell lines for pharmacogenetic studies, available from the Coriell Cell Repository, is the set of cell lines used by the Centre d'Etudes du Polymorphisme Humain (CEPH) consortium (Paris, France) to establish a detailed genetic map of man. See, for example: Gyapay, G., Morissette, J., Vignal, A., et al. (1994) The 1993-94 Genethon human genetic linkage map. Nature Genetics 7(2 Spec No):246-339. More current data on the CEPH genetic linkage map can be found on the world wide web at: http://landru.cephb.fr/cephdb/. Lymphoblastoid cell lines from 57 CEPH families are available from the Coriell Repository. In most cases the families consist of four grandparents, two parents and between four and twelve children.

[0430] The principal attraction of the CEPH cell lines for pharmacogenetic studies is that a detailed genetic map of nearly 12,000 polymorphic markers has been established via an international effort, and the map data are freely available on the world wide web. In other words the genotypes of thousands of polymorphic markers are known in most of the CEPH cell lines (not all markers were studied in all cell lines). As a result, one skilled in the art can determine the chromosomal location of any locus that controls a Mendelian trait in these cell lines, using software for linkage analysis such as the programs LINKAGE, CRIMAP and MAPMAKER. (See, for example: Lander, E. S., Green, P., Abrahamson, J., et al. (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1(2): 174-81. See also: Terwilliger, J. and J. Ott (1994), Handbook of Human Linkage Analysis. John Hopkins University Press, Baltimore for a more exhaustive description of linkage analysis methods.)

[0431] One set of interesting Mendelian traits to study using the CEPH cell lines (or similar cell lines from pedigrees) and the genetic approach just described are drug response phenotypes. Consider, for example, a G protein coupled receptor that exists in two allelic forms that behave differently in the presence of a compound being developed for human clinical use (e.g. one receptor binds the compound with higher affinity than the other). Methods for assaying G protein mediated signal transduction are well known in the art. By adding the compound (either at a fixed concentration or at a series of different concentrations) to a family-derived set of lymphoblastoid cell lines (which of course must express the G protein coupled receptor) and measuring the signal produced it should be possible to detect the segregation of the grandparental alleles in the parents and the segregation of parental alleles in the children. For example, consider two alleles of the receptor: if allele A produces a greater signal than allele B at a given concentration of the compound, and if one parent is an AB heterozygote while the other parent is a BB heterozygote then the levels of signal in the children should be medium (in AB heterozygotes) or low (in BB homozygotes). The detection of such a pattern in cell lines of the family would constitute evidence that the G protein coupled receptor polymorphism was responsible for intersubject differences in response to the compound. (More generally, the detection of any discrete partitioning of responses in the data—high and low, or high medium and low—is suggestive of genetic control, with the genetic model to be inferred from the pattern of inheritance, and support for the hypothesis to come from the analysis of multiple families.) It is not necessary to know the identify of the variant gene in advance (as in the G protein coupled receptor example just provided). The pattern of segregation of the drug response phenotype in the cell lines of the various members of the CEPH families can be compared to the pattern of segregation of the thousands of polymorphic markers already typed in the same cell lines.

[0432] Those polymorphic markers that co-segregate with the drug response phenotype are candidates for marking the location of the locus responsible for the drug response phenotype. By performing the same experiment in cell lines from multiple (e.g. from two up to 57 CEPH) families the list of candidate polymorphic markers generally narrows to a few, all of which (or nearly all of which) are from the same chromosomal region—viz. the region harboring the gene responsible for the drug response phenotype. Knowing (i) the chromosomal location of the gene (or genes) implicated by the linkage analysis, together with (ii) information about the location and function of genes in that chromosomal region (available from online databases, for example, those at the U.S. National Center for Biotechnology Information; see http://www.ncbi.nlm.nih.gov/LocusLink/), and further (iii) knowing something of the pharmacology of the compound and consequently the metabolic and regulatory pathways likely to influence its action, should constrain the list of candidate genes likely to be responsible for the observed variation to a small number of genes. These genes (if there is more than one) can be systematically evaluated for pharmacogenetic impact by identifying polymorphisms and testing whether they cosegregate with drug response phenotypes in the pedigrees, in new pedigrees, in cells from unrelated individuals, or in vivo in a population of nonrelated individuals, for example in a clinical trial.

[0433] Some drug response phenotypes may not behave as Mendelian traits, but may rather be continuous (quantitative) traits under the control of several genes. Variation at any of the relevant gene loci could affect drug response, often to different extents. Robust methods for mapping quantitative trait loci (QTL) are known in the art. For example, see: Shugart, Y. Y. and Goldgar, D. E. (1999) Multipoint genomic scanning for quantitative loci: effects of map density, sibship size and computational approach. Eur J Hum Genet 7(2):103-9. It is worth emphasizing that in the approach described (using the CEPH cell lines) there is no need for genotyping in order to map the drug response traits in the cell lines; the effort already expended to produce a human linkage map in the CEPH cell lines can be exploited.

[0434] Cell responses that could be usefully characterized by the above methods include for example the level of signaling in a pathway that mediates the response to a compound (as in the G protein coupled receptor assays where levels of a second messenger are measured), compound uptake, compound metabolism, levels of metabolites affected by a compound, levels of proteins (including enzymes in biochemical pathways related to the action of the compound), levels of an inhibitory complex formed by a compound, and other assays known to those skilled in the art of pharmacology and assay development. For example, a study of the genetic basis of variation in response to the antineoplastic drug 5-fluorouracil might include measurement of cell uptake of 5-FU, conversion of 5-FU to inactive metabolites such as 5,6-dihydrofluorouridine or fluoro-beta alanine, conversion of 5-FU to active metabolites such as 5-fluorodeoxyuridine, levels of thymidylate synthetase (an enzyme inhibited by 5-FU), levels of 5, 10 methylenetetrahydrofolate (a folate co-factor essential for 5-FU mediated inhibition of thymidylate synthetase) and the enzymes that produce it, or levels of nucleotide pools or the enzymes that produce them. All of the relevant transporters and enzymes are expressed in lymphoblastoid cells, even though 5-FU is not routinely used in the therapy of lymphoid malignancies.

[0435] However, a limitation of lymphoblastoid cell lines for the methods described above is that they are not suitable for all of the different types of assays one might wish to perform. One alternative is to use fibroblast cell lines, which have already been derived from multiple different families. Fibroblasts are not available from the CEPH pedigrees, however a set of fibroblasts from known pedigrees could be genotyped at a set of highly polymorphic markers to produce a genetic map. Another approach is to treat lymphoblastoid cells with a procedure or agent that induces differentiation to a different cell type, such as an adipocye or a myocyte. For example, there are genes which effectively control differentiation programs (e.g. peroxisome proliferator activated receptor [PPAR] gamma mediates adipocyte differentiation, myoD mediates myocyte differentiation); introduction of such a gene into a cell line of one type can alter its differentiated state to another cell type. Alternatively, stimulation of the gene product of such a regulatory gene (e.g. treatment of cells with the PPAR gamma agonist troglitazone) can be used to induce differentiation to a different cell type. Such procedures are known in the art, and may be effectively applied to human lymphoblasts.

[0436] In preferred embodiments of the above methods the cells used are from the CEPH pedigrees. Preferably at least one pedigree is studied, more preferably two pedigrees, still more preferably five pedigrees and most preferably eight pedigrees or more. It is useful to perform a statistical calculation to determine how many pedigrees and cell lines should be studied to achieve a given power to detect an effect, making assumptions about the magnitude of the effect.

[0437] In another aspect, described below, the methods described above can be used to identify mRNAs that vary in levels between cell lines as a result of genetically controlled regulatory factors, such as, for example, polymorphisms in promoters that affect the binding or action of transcriptional regulatory factors. Such variation in mRNA levels may be responsible for intersubject variation in drug response.

[0438] Experimental Methods: Genomic DNA Analysis

[0439] Variances in DNA may affect the basal transcription or regulated transcription of a gene locus. Such variances may be located in any part of the gene but are most likely to be located in the promoter region, the first intron, or in 5′ or 3′ flanking DNA, where enhancer or silencer elements may be located. Methods for analyzing transcription are well known to those skilled in the art and exemplary methods are briefly described above and in some of the texts cited elsewhere in this application. Transcriptional run off assay is one useful method. Detailed protocols can be found in texts such as: Current Protocols in Molecular Biology edited by: F. M. Ausubel, et al. John Wiley & Sons, Inc, 1999, or: Molecular Cloning: A Laboratory Manual by J. Sambrook, E. F. Fritsch and T Maniatis. 1989. 3 vols, 2nd edition, Cold Spring Harbor Laboratory Press

[0440] Experimental Methods: RNA Analysis

[0441] RNA variances may affect a wide range of processes including RNA splicing, polyadenylation, capping, export from the nucleus, interaction with translation initiation, elongation or termination factors, or the ribosome, or interaction with cellular factors including regulatory proteins, or factors that may affect mRNA half life. However, the effect of most RNA sequence variances on RNA function, if any, should ultimately be measurable as an effect on RNA or protein levels—either basal levels or regulated levels or levels in some abnormal cell state, such as cells from patients with a disease. Therefore, one preferred method for assessing the effect of RNA variances on RNA function is to measure the levels of RNA produced by different alleles in one or more conditions of cell or tissue growth. Said measuring can be done by conventional methods such as Northern blots or RNAase protection assays (kits available from Ambion, Inc.), or by methods such as the Taqman assay (developed by the Applied Biosystems Division of the Perkin Elmer Corporation), or by using arrays of oligonucleotides or arrays of cDNAs attached to solid surfaces. Systems for arraying cDNAs are available commercially from companies such as Nanogen and General Scanning. Complete systems for gene expression analysis are available from companies such as Molecular Dynamics. For recent reviews of systems for high throughput RNA expression analysis see the supplement to volume 21 of Nature Genetics entitled “The Chipping Forecast”, especially articles beginning on pages 9, 15, 20 and 25.

[0442] Additional methods for analyzing the effect of variances on RNA include secondary structure probing, and direct measurement of half life or turnover. Secondary structure can be determined by techniques such as enzymatic probing (using enzymes such as T1, T2 and S1 nuclease), chemical probing or RNAase H probing using oligonucleotides. Most RNA structural assays are performed in vitro, however some techniques can be performed on cell extracts or even in living cells, using fluorescence resonance energy transfer to monitor the state of RNA probe molecules.

[0443] In another aspect the methods described above (relating to the use of cell lines from pedigrees to genetically map phenotypes that can be studied in tissue culture cells) can be used to identify mRNAs that vary in levels between individuals as a result of genetically controlled factors. Genetic factors include both cis-acting polymorphisms, such as might be present in promoters (e.g. polymorphisms that affect the binding or action of transcription factors) as well as trans-acting factors such as might be present in transcription factors (e.g. an amino acid polymorphism that affects the interaction of a transcription factor with a promoter element, or that might affect levels of the transcription factor itself). Variation in mRNA levels may contribute to intersubject variation in drug response, disease susceptibility or disease manifestations. (See above for example of promoter polymorphism in 5-lipoxygenase and its effect on response to anti-asthma medications.)

[0444] The methods for identifying mRNAs which vary in abundance as a consequence of genetic mechanisms are similar to those described above for drug response phenotypes. First, by examining whether levels of an mRNA segregate in one or more pedigrees it is possible to infer whether there is a genetic component to the variation. Second, by inspecting the CEPH genotype data it is possible to identify genetic markers that cosegregate with the mRNA expression levels (either increased or decreased) and thereby map the chromosomal location of the locus or loci that control mRNA levels. Third, by inspection of the genes at the chromosomal locus controlling mRNA levels it should be possible to identify one or a few genes that are likely responsible for the effect. These genes can then be definitively evaluated by discovering variances and testing if they predict mRNA levels (or other phenotypes) in the pedigree cell lines, in cell lines from unrelated individuals, or in vivo. Fourth, the above analysis can be performed on cell lines subjected to various pharmacological or nutritional manipulations. For example cell lines from one or more pedigrees can be treated with a drug, or deprived of an amino acid and mRNA levels measured at various times after treatment. Any variable differences in mRNA levels in response to the treatment, if they segregate in pedigrees, can be subjected to steps 1-3. Fifth, this analysis can be performed at very large scale using arrays of gridded cDNAs, PCR products or oligonucleotides corresponding to an unlimited number of genes. In each experiment the RNA from the pedigree cell lines (treated or not) is isolated, labeled using standard methods and hybridized to the grids containing the nucleic acids corresponding to the genes being investigated. Current commercial methods permit up to 400,000 oligonucleotides (more than the total number of human genes) to be queried in one experiment, although lower density formats are also well suited to the methods described. Thus, in a comparatively modest number of experiments the entire transcript population of lymphoblasts (probably <25,000 unique transcripts) can be queried for genetically controlled variation in mRNA abundance. Other types of cell lines can be subjected to similar analysis.

[0445] The variation in mRNA levels due to gene polymorphisms is likely to be of small magnitude (generally two-fold differences or less are expected). Therefore a key aspect of experimental systems used to measure mRNA levels is their accuracy. Preferably a system capable of resolving mRNAs that differ in abundance (measured in molecules per cell, or relative to a standard such as total mRNA or one or more specific RNAs such as actin or clathrin or glucose-6-phosphare dehydrogenase) is sufficiently sensitive to detect differences as small as 50%, more preferably as small as 30%, and most preferably as small as 20%.

[0446] There are 757 individuals in the 57 CEPH cell lines. Thus all the CEPH cell lines could fit in eight 96 well microtiter plates. Microtiter plates provide a convenient format for growing cells and for performing cell manipulations, such as those described above, using multichannel pipettes or automated pipetting robots. By growing cells in large volume flasks, counting them (by hemocytometer or Coulter counter or other means) and then aliquoting them robotically to 96 well plates it is possible to assure that each well has nearly the same number of cells. A large number of plates can be prepared in this way and then stored frozen in appropriate medium until needed for experiments.

[0447] Experimental Methods: Protein Analysis

[0448] There are a variety of experimental methods for investigating the effect of an amino acid variance on response of a patient to a treatment. The preferred method will depend on the availability of cells expressing a particular protein, and the feasibility of a cell-based assay vs. assays on cell extracts, on proteins produced in a foreign host, or on proteins prepared by in vitro translation.

[0449] For example, the methods and systems listed below can be utilized to demonstrate differential expression, stability and/or activity of different variant forms of a protein, or in phenotype/genotype correlations in a model system.

[0450] For the determination of protein levels or protein activity a variety of techniques are available. The in vitro protein activity can be determined by transcription or translation in bacteria, yeast, baculovirus, COS cells (transient), Chinese Hamster Ovary (CHO) cells, or studied directly in human cells, or other cell systems can be used. Further, one can perform pulse chase experiments to determine if there are changes in protein stability (half-life).

[0451] One skilled in the art can construct cell based assays of protein function, and then perform the assays in cells with different genotypes or haplotypes. For example, identification of cells with different genotypes, e.g., cell lines established from families and subsequent determination of relevant protein phenotypes (e.g., expression levels, post translational modifications, activity assays) may be performed using standard methods.

[0452] Assays of protein levels or function can also be performed on cell lines (or extracts from cell lines) derived from pedigrees in order to determine whether there is a genetic component to variation in protein levels or function. The experimental analysis is as above for RNAs, except the assays are different. Experiments can be performed on naive cells or on cells subjected to various treatments, including pharmacological treatments.

[0453] In another approach to the study of amino acid variances one can express genes corresponding to different alleles in experimental organisms and examine effects on disease phenotype (if relevant in the animal model), or on response to the presence of a compound. Such experiments may be performed in animals that have disrupted copies of the homologous gene (e.g. gene knockout animals engineered to be deficient in a target gene), or variant forms of the human gene may be introduced into germ cells by transgenic methods, or a combination of approaches may be used. To create animal strains with targeted gene disruptions a DNA construct is created (using DNA sequence information from the host animal) that will undergo homologous recombination when inserted into the nucleus of an embryonic stem cell. The targeted gene is effectively inactivated due to the insertion of non-natural sequence—for example a translation stop codon or a marker gene sequence that interrupts the reading frame. Well known PCR based methods are then used to screen for those cells in which the desired homologous recombination event has occurred. Gene knockouts can be accomplished in worms, drosophila, mice or other organisms. Once the knockout cells are created (in whatever species) the candidate therapeutic intervention can be administered to the animal and pharmacological or biological responses measured, including gene expression levels. If variant forms of the gene are useful in explaining interpatient variation in response to the compound in man, then complete absence of the gene in an experimental organism should have a major effect on drug response. As a next step various human forms of the gene can be introduced into the knockout organism (a technique sometimes referred to as a knock-in). Again, pharmacological studies can be performed to assess the impact of different human variances on drug response. Methods relevant to the experimental approaches described above can be found in the following exemplary texts:

[0454] General Molecular Biology Methods

[0455] Molecular Biology: A project approach, S. J. Karcher, Fall 1995. Academic Press

[0456] DNA Cloning: A Practical Approach, D. M. Glover and B. D. Hayes (eds). 1995. IRL/Oxford University Press. Vol. 1—Core Techniques; Vol 2—Expression Systems; Vol. 3—Complex Genomes; Vol. 4—Mammalian Systems.

[0457] Short Protocols in Molecular Biology, Ausubel et al. October 1995. 3rd edition, John Wiley and Sons

[0458] Current Protocols in Molecular Biology Edited by: F. M. Ausubel, R. Brent, R. E. Kingston, D. D. Moore, J. G. Seidman, K. Struhl, (Series Editor: V. B. Chanda), 1988

[0459] Molecular Cloning: A laboratory manual, J. Sambrook, E. F. Fritsch. 1989. 3 vols, 2nd edition, Cold Spring Harbor Laboratory Press

[0460] Polymerase Chain Reaction (PCR)

[0461] PCR Primer: A laboratory manual, C. W. Diffenbach and G. S. Dveksler (eds.). 1995. Cold Spring Harbor Laboratory Press.

[0462] The Polymerase Chain Reaction, K. B. Mullis et al. (eds.), 1994. Birkhauser

[0463] PCR Strategies, M. A. Innis, D. H. Gelf, and J. J. Sninsky (eds.), 1995. Academic Press

[0464] General Procedures for Discipline Specific Studies

[0465] Current Protocols in Neuroscience Edited by: J. Crawley, C. Gerfen, R. McKay, M. Rogawski, D. Sibley, P. Skolnick, (Series Editor: G. Taylor), 1997.

[0466] Current Protocols in Pharmacology Edited by: S. J. Enna/M. Williams, J. W. Ferkany, T. Kenakin, R. E. Porsolt, J. P. Sullivan, (Series Editor: G. Taylor),1998.

[0467] Current Protocols in Protein Science Edited by: J. E. Coligan, B. M. Dunn, H. L. Ploegh, D. W. Speicher, P. T. Wingfield, (Series Editor: Virginia Benson Chanda), 1995.

[0468] Current Protocols in Cell Biology Edited by: J. S. Bonifacino, M. Dasso, J. Lippincott-Schwartz, J. B. Harford, K. M. Yamada, (Series Editor: K. Morgan) 1999.

[0469] Current Protocols in Cytometry Managing Editor: J. P. Robinson, Z. Darzynkiewicz (ed)/P. Dean (ed), A. Orfao (ed), P. Rabinovitch (ed), C. Stewart (ed), H. Tanke (ed), L. Wheeless (ed), (Series Editor: J. Paul Robinson), 1997.

[0470] Current Protocols in Human Genetics Edited by: N. C. Dracopoli, J. L. Haines, B. R. Korf, et al., (Series Editor: A. Boyle), 1994.

[0471] Current Protocols in Immunology Edited by: J. E. Coligan, A. M. Kruisbeek, D. H. Margulies, E. M. Shevach, W. Strober, (Series Editor: R. Coico), 1991.

[0472] IV. Clinical Trials

[0473] A clinical trial is the definitive test of the utility of a variance or variances for the selection of optimal therapy. A clinical trial in which an interaction of gene variances and clinical outcomes (desired or undesired) is explored will be referred to herein as a “pharmacogenetic clinical trial”. Pharmacogenetic clinical trials require no knowledge of the biological function of the gene containing the variance or variances to be assessed, nor any knowledge of how the therapeutic intervention to be assessed works at a biochemical level. The pharmacogenetics effects of a variance can be addressed at a purely statistical level: either a particular variance or set of variances is consistently associated with a significant difference in a salient drug response parameter (e.g. response rate, effective dose, side effect rate, etc.) or not. On the other hand, if there is information about either the biochemical basis of a therapeutic intervention or the biochemical effects of a variance, then a pharmacogenetic clinical trial can be designed to test a specific hypothesis. In preferred embodiments of the methods of this application the mechanism of action of the compound to be genetically analyzed is at least partially understood.

[0474] Methods for performing clinical trials are well known in the art. (see e.g. Guide to Clinical Trials by Bert Spilker, Raven Press, 1991; The Randomized Clinical Trial and Therapeutic Decisions by Niels Tygstrup (Editor), Marcel Dekker; Recent Advances in Clinical Trial Design and Analysis (Cancer Treatment and Research, Ctar 75) by Peter F. Thall (Editor) Kluwer Academic Pub, 1995. Clinical Trials: A Methodologic Perspective by Steven Piantadosi, Wiley Series in Probability and Statistics, 1997). However, performing a clinical trial to test the genetic contribution to interpatient variation in drug response entails additional design considerations, including (i) defining the genetic hypothesis or hypotheses, (ii) devising an analytical strategy for testing the hypothesis, including determination of how many patients will need to be enrolled to have adequate statistical power to measure an effect of a specified magnitude (power analysis), (iii) definition of any primary or secondary genetic endpoints, and (iv) definition of methods of statistical genetic analysis, as well as other aspects. In the outline below some of the major types of genetic hypothesis testing, power analysis and statistical testing and their application in different stages of the drug development process are reviewed. One skilled in the art will recognize that certain of the methods will be best suited to specific clinical situations, and that additional methods are known and can be used in particular instances.

[0475] A. Performing a Clinical Trial: Overview

[0476] As used herein, a “clinical trial” is the testing of a therapeutic intervention in a volunteer human population for the purpose of determining whether it is safe and/or efficacious in the treatment of a disease, disorder, or condition. The present invention describes methods for achieving superior efficacy and/or safety in a genetically defined subgroup defined by the presence or absence of at least one gene sequence variance, compared to the effect that could be obtained in a conventional trial (without genetic stratification).

[0477] A “clinical study” is that part of a clinical trial that involves determination of the effect of a candidate therapeutic intervention on human subjects. It includes clinical evaluation of physiologic responses including pharmacokinetic (bioavailability as affected by drug absorption, distribution, metabolism and excretion) and pharmacodynamic (physiologic response and efficacy) parameters. A pharmacogenetic clinical study (or clinical trial) is a clinical study that involves testing of one or more specific hypotheses regarding the interaction of a genetic variance or variances (or set of variances, i.e. haplotype or haplotypes) on response to a therapeutic intervention. Pharmacogenetic hypotheses are formulated before the study, and may be articulated in the study protocol in the form of primary or secondary endpoints. For example an endpoint may be that in a particular genetic subgroup the rate of objectively defined responses exceeds the response rate in a control group (either the entire control group or the subgroup of controls with the same genetic signature as the treatment subgroup they are being compared to) or exceeds that in the whole treatment group (i.e. without genetic stratification) by some predefined relative or absolute amount.

[0478] For a clinical study to commence enrollment and proceed to treat subjects at an institution that receives any federal support (most medical institutions in the U.S.), an application that describes in detail the scientific premise for the therapeutic intervention and the procedures involved in the study, including the endpoints and analytical methods to be used in evaluating the data, must be reviewed and accepted by a review panel, often termed an Institutional Review Board (IRB). Similarly any clinical study that will ultimately be evaluated by the FDA as part of a new drug or product application (or other application as described below), must be reviewed and approved by an IRB. The IRB is responsible for determining that the trial protocol is safe, conforms to established ethical principles and guidelines, has risks proportional to any expected benefits, assures equitable selection of patients, provides sufficient information to patients (via a consent form) to insure that they can make an informed decision about participation, and insures the privacy of participants and the confidentiality of any data collected. (See the report of the National Commission for Protection of Human Subjects of Biomedical and Behavioral Research (1978). The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research. Washington, D.C.: DHEW Publication Number (OS) 78-0012. For a recent review see: Coughlin, S. S. (ed.) (1995) Ethics in Epidemiology and Clinical Research. Epidemiology Resources, Newton, Mass.) The European counterpart of the U.S. FDA is the European Medicines Evaluation Agency (EMEA). Similar agencies exist in other countries and are responsible for insuring, via the regulatory process, that clinical trials conform to similar standards as are required in the U.S. The documents reviewed by an IRB include a clinical protocol containing the information described above, and a consent form.

[0479] It is also customary, but not required, to prepare an investigator's brochure which describes the scientific hypothesis for the proposed therapeutic intervention, the preclinical data, and the clinical protocol. The brochure is made available to any physician participating in the proposed or ongoing trial.

[0480] The supporting preclinical data is a report of all the in vitro, in vivo animal or previous human trial or other data that supports the safety and/or efficacy of a given therapeutic intervention. In a pharmacogenetic clinical trial the preclinical data may also include a description of the effect of a specific genetic variance or variances on biochemical or physiologic experimental variables in vitro or in vivo, or on treatment outcomes, as determined by in vivo studies in animals or humans (for example in an earlier trial), or by retrospective genetic analysis of clinical trial or other medical data (see below) used to formulate or strengthen a pharmacogenetic hypothesis. For example, case reports of unusual pharmacological responses in individuals with rare alleles (e.g. mutant alleles), or the observation of clustering of pharmacological responses in family members may provide the rationale for a pharmacogenetic clinical trial.

[0481] The clinical protocol provides the relevant scientific and therapeutic introductory information, describes the inclusion and exclusion criteria for human subject enrollment, including genetic criteria if relevant (e.g. if genotype is to be among the enrollment criteria), describes in detail the exact procedure or procedures for treatment using the candidate therapeutic intervention, describes laboratory analyses to be performed during the study period, and further describes the risks (both known and possible) involving the use of the experimental candidate therapeutic intervention. In a clinical protocol for a pharmacogenetic clinical trial, the clinical protocol will further describe the genetic variance and/or variances hypothesized to account for differential responses in the normal human subjects or patients and supporting preclinical data, if any, a description of the methods for genotyping, genetic data collection and data handling as well as a description of the genetic statistical analysis to be performed to measure the interaction of the variance or variances with treatment response. Further, the clinical protocol for a pharmacogenetic clinical trial will include a description of the genetic study design. For example patients may be stratified by genotype and the response rates in the different groups compared, or patients may be segregated by response and the genotype frequencies in the different responder or nonresponder groups measured. One or more gene sequence variances or a combination of variances and/or haplotypes may be studied.

[0482] The informed consent document is a description of the therapeutic intervention and the clinical protocol in simple language (e.g. third grade level) for the patient to read, understand, and, if willing, agree to participate in the study by signing the document. In a pharmacogenetic clinical study the informed consent document will describe, in simple language, the use of a genetic test or a limited set of genetic tests to determine the subject or patient's genotype at a particular gene variance or variances, and to further ascertain whether, in the study population, particular variances are associated with particular clinical or physiological responses. The consent form should also describe procedures for assuring privacy and confidentiality of genetic information.

[0483] The U.S. FDA reviews proposed clinical trials through the process of an Investigational New Drug Application (IND). The IND is composed of the investigator's brochure, the supporting in vitro and in vivo animal or previous human data, the clinical protocol, and the informed consent forms. In each of the sections of the IND, a specific description of a single allelic variance or a number of variances to be tested in the clinical study will be included. For example, in the investigator's brochure a description of the gene or genes hypothesized to account, at least in part, for differential responses will be included as well as a description of a genetic variance or variances in one or more candidate genes. Further, the preclinical data may include a description of in vivo, in vitro or in silico studies of the biochemical or physiologic effects of a variance or variances (e.g., haplotype) in a candidate gene or genes, as well as the predicted effects of the variance or variances on efficacy or toxicology of the candidate therapeutic intervention. The results of retrospective genetic analysis of response data in patients treated with the candidate therapy may be the basis for formulating the genetic hypotheses to be tested in the prospective trial. The U.S. FDA reviews applications with particular attention to safety and toxicological data to ascertain whether candidate compounds should be tested in humans.

[0484] The established phases of clinical development are Phase I, II, III, and IV. The fundamental objectives for each phase become increasingly complex as the stages of clinical development progress. In Phase I, safety in humans is the primary focus. In these studies, dose-ranging designs establish whether the candidate therapeutic intervention is safe in the suspected therapeutic concentration range. However, it is common practice to obtain information about surrogate markers of efficacy even in phase I clinical trials. In a pharmacogenetic clinical trial there may be an analysis of the effect of a variance or variances on Phase I safety or surrogate efficacy parameters. At the same time, evaluation of pharmacokinetic parameters (e.g., adsorption, distribution, metabolism, and excretion) may be a secondary objective; again, in a pharmacogenetic clinical study there may be an analysis of the effect of sequence variation in genes that affect absorption, distribution, metabolism and excretion of the candidate compound on pharmacokinetic parameters such as peak blood levels, half life or tissue distribution of the compound. As clinical development stages progress, trial objectives focus on the appropriate dose and method of administration required to elicit a clinically relevant therapeutic response. In a pharmacogenetic clinical trial, there may be a comparison of the effectiveness of several doses of a compound in patients with different genotypes, in order to identify interactions between genotype and optimal dose. For this purpose the doses selected for late stage clinical testing may be greater, equal or less than those chosen based upon preclinical safety and efficacy determinations. Data on the function of different alleles of genes affecting pharmacokinetic parameters could provide the basis for selecting an optimal dose or range or doses of a compound or biological. Genes involved in drug metabolism may be particularly useful to study in relation to understanding interpatient variation in optimal dose. Genes involved in drug metabolism include the cytochrome P450s, especially 2D6, 3A4, 2C9, 2E1, 2A6 and 1A1; the glucuronyltransferases; the acetyltransferases; the methyltransferases; the sulfotransferases; the glutathione system; the flavine monooxygenases and other enzymes known in the art.

[0485] An additional objective in the latter stages of clinical development is demonstration of the effect of the therapeutic intervention on a broad population. In phase III trials, the number of individuals enrolled is dictated by a power analysis. The number of patients required for a given pharmacogenetic clinical trial will be determined by prior knowledge of variance or haplotype frequency in the study population, likely response rate in the treated population, expected magnitude of pharmacogenetic effect (for example, the ratio of response rates between a genetic subgroup and the unfractionated population, or between two different genetic subgroups); nature of the genetic effect, if known (e.g. dominant effect, codominant effect, recessive effect); and number of genetic hypotheses to be evaluated (including number of genes and/or variances to be studied, number of gene or variance interactions to be studied). Other considerations will likely arise in the design of specific trials.

[0486] Clinical trials should be designed to blind both human subjects and study coordinators from biasing that may otherwise occur during the testing of a candidate therapeutic invention. Often the candidate therapeutic intervention is compared to best medical treatment, or a placebo (a compound, agent, device, or procedure that appears identical to the candidate therapeutic intervention but is therapeutically inert). The combination of a placebo group and blind controls for potentially confounding factors such as prejudice on the part of study participants or investigators, insures that real, rather than perceived or expected, effects of the candidate therapeutic intervention are measured in the trials. Ideally blinding extends not only to trial subjects and investigators but also to data review committees, ancillary personnel, statisticians, and clinical trial monitors.

[0487] In pharmacogenetic clinical studies, a placebo arm or best medical control group may be required in order to ascertain the effect of the allelic variance or variances on the efficacy or toxicology of the candidate therapeutic intervention as well as placebo or best medical therapy. It will be important to assure that the composition of the control and test populations are matched, to the degree possible, with respect to genetic background and allele frequencies. This is particularly true if the variances being investigated may have an effect on disease manifestations (in addition to a hypothesized effect on response to treatment). It is likely that standard clinical trial procedures such as insuring that treatment and control groups are balanced for race, sex and age composition and other non-genetic factors relevant to disease will be sufficient to assure that genetic background is controlled, however a preferred practice is to explicitly test for genetic stratification between test and control groups. Methods for minimizing the possibility of spurious results attributable to genetic stratification between two comparison groups include the use of surrogate markers of geographic, racial and/or ethnic background, such as have been described by Rannala and coworkers. (See, for example: Rannala B, and J L Mountain. 1997 Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci USA Aug 19;94(17):9197-201.) One procedure would be to assure that surrogate markers of genetic background (such as those described by Rannala and Mountain) occur at comparable frequency in two comparison groups.

[0488] Open label trials are unblinded; in single blind trials patients are kept unaware of treatment assignments; in double blind trials both patients and investigators are unaware of the treatment groups; a combination of these procedures may be instituted during the trial period. Pharmacogenetic clinical trial design may include one or a combination of open label, single blind, or double blind clinical trial designs. Reduction of biases attributable to the knowledge of either the type of treatment or the genotype of the normal subjects or patients is an important aspect of study design. So, for example, even in a study that is single blind with respect to treatment, it should be possible to keep both patients and caregivers blinded to genotype during the study.

[0489] In designing a clinical trial it is important to include termination endpoints such as adverse clinical events, inadequate study participation either in the form of lack of adherence to the clinical protocol or loss to follow up, (e.g. such that adequate power is no longer assured), lack of adherence on the part of trial investigators to the trial protocol, or lack of efficacy or positive response within the test group. In a pharmacogenetic clinical trial these considerations obtain not only in the entire treatment group, but also in the genetically defined subgroups. That is, if a dangerous toxic effect manifests itself predominantly or exclusively in a genetically defined subpopulation of the total treatment population it may be deemed inappropriate to continue treating that genetically defined subgroup. Such decisions are typically made by a data safety monitoring committee, a group of experts not including the investigators, and generally not blinded to the analysis, who review the data from an ongoing trial on a regular basis.

[0490] It is important to note that medicine is a conservative field, and clinicians are unlikely to change their behavior on the basis of a single clinical trial. Thus it is likely that, in most instances, two or more clinical trials will be required to convince physicians that they should change their prescribing habits in view of genetic information. Large scale trials represent one approach to providing increased data supporting the utility of a genetic stratification. In such trials the stringent clinical and laboratory data collection characteristic of traditional trials is often relaxed in exchange for a larger patient population. Important goals in large scale pharmacogenetic trials will include establishing whether a pharmacogenetic effect is detectable in all segments of a population. For example, in the North American population one might seek to demonstrate a pharmacogenetic effect in people of African, Asian, European and Hispanic (i.e. Mexican and Puerto Rican) origin, as well as in native American people. (It generally will not be practical to segment patients by geographical origin in a standard clinical trial, due to loss of power.) Another goal of a large scale clinical trial may be to measure more precisely, and with greater confidence, the magnitude of a pharmacogenetic effect first identified in a smaller trial. Yet another undertaking in a large scale clinical trial may be to examine the interaction of an established pharmacogenetic variable (e.g. a variance in gene A, shown to affect treatment response in a smaller trial) with other genetic variances (either in gene A or in other candidate genes). A large scale trial provides the statistical power necessary to test such interactions.

[0491] In designing all of the above stages of clinical testing investigators must be attentive to the statistical problems raised by testing multiple different hypotheses, including multiple genetic hypotheses, in subsets of patients. Bonferroni's correction or other suitable statistical methods for taking account of multiple hypothesis testing will need to be judiciously applied. However, in the early stages of clinical testing, when the main goal is to reduce the large number of potential hypotheses that could be tested to a few that will be tested, based on limited data, it may be impractical to rigidly apply the multiple testing correction.

[0492] B. Phase I Clinical Trials

[0493] 1. Introduction

[0494] Phase I clinical trials are generally designed primarily to establish a safe dose and schedule of administration for a new compound. At the same time, Phase I is the first opportunity to study the clinical pharmacology of a new compound in man. Relevant studies may include aspects of pharmacokinetic behavior, side effects and toxicity. In addition to these well established purposes, Phase I trials are increasingly being used to gather information relevant to early assessment of efficacy. Such information can be useful in making an early yes/no decision about the further development of a compound, or a family of related compounds, all being tested simultaneously in Phase I trials. Since Phase I trials are typically conducted in normal volunteers (compounds for cancer and some other terminal diseases are an exception), surrogate markers of drug effect are measured, rather than disease response. The development of sophisticated surrogate markers of pharmacodynamic effects has allowed more information on efficacy to be gathered in Phase I, and this trend will almost certainly continue as basic understanding of disease pathophysiology increases, and as more products are developed for disease prophylaxis.

[0495] Phase I studies are typically performed on a small number (<60) of healthy volunteers. Consequently, Phase I studies as currently designed are not amenable to genetic analysis: the number of subjects is simply too small to detect, with adequate statistical certainty, any genetic effects on drug response that are short of all or none in magnitude. In fact, no genetic analyses of Phase I studies have been published or described in public meetings.

[0496] As described in detail elsewhere in this application, it is highly desirable to gather the information necessary to make informed decisions about clinical development as early as possible in the development process, particularly once human testing has begun and costs therefore mount quickly. Timely information may allow a drug to be killed early, or may result in an accelerated program of clinical trials. In addition to information about efficacy and safety, it is useful to have information about the existence and magnitude of genetic effects on efficacy and toxicity at the earliest possible stage. If properly managed, genetically determined heterogeneity in drug response may not be an obstacle to development. On the contrary, it may provide the basis for identification of a patient population in whom both high efficacy and safety can be achieved. Clear delineation of such a population may facilitate smaller, more targeted trials and more rapid clinical development. Consequently, the early identification of genetic determinants of drug response will, in the future, increasingly become a priority of clinical development.

[0497] Phase I trials are not necessarily confined to the initial stages of human clinical development. It is not unusual for Phase I trials to be initiated at a later stage of clinical development in order to, for example, clarify basic questions about clinical pharmacology that have arisen as a result of Phase II study data. It may be that the most efficient way to advance the genetic understanding of pharmacological responses to a compound in Phase II is to perform a Phase I trial using a specific genetic design, as described below.

[0498] 2. Phase I Trials Designed for Genetic Analysis

[0499] In this invention we describe two exemplary novel methods for organization of Phase I trials that will facilitate identification and measurement of the genetic component of variation in treatment response using modest numbers of subjects. We describe how these methods can be practiced by selectively enrolling subjects who share genetic characteristics, either as a result of a familial relationship or as a result of genetic homogeneity at candidate loci believed to affect response to the candidate treatment. We show how the analysis of such individuals substantially increases the power of genetic analysis compared to analysis of unrelated individuals. We also describe methods for operating a Phase I unit capable of carrying out the novel genetic analyses

[0500] The two types of Pharmacogenetic Phase I Units described in this application will be referred to as the Pharmacogenetic Phase I Relatives Unit and the Pharmacogenetic Phase I Outliers Unit, or the Relatives Unit and the Outliers Unit for short. The term Pharmacogenetic Phase I Unit will be used to refer to both types of Phase I Unit. The Relatives Unit requires a population comprised of groups of related individuals. The related individuals may be parents and offspring, groups of sibs, or of cousins, or any mixture of these or other groups of related individuals. The Outliers Unit requires the initial enrollment of a large number of unrelated volunteers (at least several hundreds of subjects, preferably at least one thousand, more preferably at least five thousand, and most preferably ten thousand or more individuals) willing to provide DNA for genotyping on an as-needed basis (many of these volunteers will never participate in a trial). Subsequently, small numbers of individuals are drawn from this large population for specific clinical trials, based on their genetic homogeneity at candidate loci believed likely to account for intersubject variation in response to the candidate compound.

[0501] The concept underlying these two types of Pharmacogenetic Phase I Units is similar: the idea is to recruit multiple small groups of subjects who are genetically more homogeneous than would be possible with standard nongenetic recruitment criteria. If there is a genetic component to treatment response then there should be more intragroup homogeneity and more intergroup heterogeneity in drug response measures (e.g. surrogate measures of drug response) than would be expected by chance, and there should be statistically significant differences in drug response measures between the different groups. The magnitude of such differences can provide an estimate of the magnitude of the genetic component of intersubject variation in drug response.

[0502] 3. Pharmacogenetic Phase I Relatives Unit

[0503] In the Pharmacogenetic Phase I Relatives Unit, one is comparing groups of related individuals to each other and to other groups of related individuals. The underlying assumption is that one can assess the magnitude of the genetic component of variation in drug response (if any) by comparing drug response traits in related individuals with those of unrelated individuals. Two types of effect would suggest the presence of a genetic component to variation in drug response measures. First, the distribution of drug responses in related individuals may be different from that observed in the entire group, or in a group comprised of unrelated individuals. For example, a statistically significant narrowing of the distribution (e.g. smaller standard deviation in groups of related individuals compared to unrelated individuals) would indicate that individuals who share alleles are more similar to each other than individuals who do not share (as many) alleles, implying that the drug response trait is partially affected by a heritable factor or factors. Second, the mean value of the drug response measure (whether blood pressure or a cognitive test) may vary between groups of related individuals, indicating that different alleles at loci relevant to drug response are present in the different families. (Note that the relevant trait is not blood pressure or cognition, but the response of blood pressure or cognition to a pharmacological intervention.)

[0504] Individuals can be related in any of several ways, most preferably as parent and child or as siblings. Parent-child pairs, in particular, enable one to use simple statistical techniques (e.g., regression) in order to assess the degree to which response to surrogate markers is influenced by genetic differences among individuals. However, parent-child pairs may be less suitable for some surrogate markers, especially those related to candidate drugs used to treat age-related disorders. In such a context, one can readily use clusters of siblings and/or cousins, uncle/nephew pairs or other groups of related individuals to assess the degree of genetic determination of response to a surrogate marker.

[0505] An attractive aspect of the Pharmacogenetic Phase I Relatives Unit (unlike the Outliers Unit) is that it does not require any laboratory tests to implement. One infers the degree of gene sharing between individuals from their relationship to each other. A parent is 50% genetically identical to each of his or her children; sibs are 50% genetically identical to each other on average; uncles/aunts are 25% identical to nieces/nephews on average, and so forth. Thus the degree to which two related individuals are expected to be similar as a result of genetic factors is known. Therefore no tests to determine genetic status are required (i.e. no genotyping); in fact, no knowledge of the relevant candidate loci is required at all (albeit knowledge of the relevant genes is required to develop a useful genetic diagnostic test at a later stage). Thus, the Relatives Unit provides a clear picture of the importance of heredity factors in determining drug response, regardless of our understanding of the mechanism of action of the drug, or any other aspect of drug pharmacology.

[0506] The rationale is as follows: if a surrogate drug response trait (i.e., a surrogate marker of pharmacodynamic effect that can be measured in normal subjects) is under genetic control, then related individuals, such as sibs (who share 50% of their alleles at autosomal loci on average), should have more similar responses than unrelated individuals, who share a much smaller fraction of alleles. In other words, individuals who share more alleles at the loci that affect drug response should be more similar to each other than individuals who, on average, share fewer alleles. By using statistical methods known in the art the distribution of traits of related individuals can be compared to the degree of variation in a set of unrelated individuals. The potential for insight from this kind of analysis is reflected in the fact that twin studies (in which traits of identical twins are compared to those of fraternal twins) indicate that differences among individuals in pharmacokinetic variables (e.g. compound half life, peak concentration) can be strongly genetically determined. (For a summary of such pharmacokinetic studies, see Propping, P. [1978] Pharmacogenetics. Rev. Physiol. Biochem. Pharmacol. 83: 123-173.) Such studies are important because they clearly reveal genetic determination of pharmacogenetic traits (although they may overestimate its degree; see Falconer, D. S. and Mackay, T. [1996] Introduction to Quantitative Genetics, Addison Wesley Longman Ltd.).

[0507] The type of study proposed here, whether it involves comparison of parents and offspring, groups of sibs, or other groups of relatives, will also reveal the extent of genetic determination, and without requiring twins. This is a two-fold advantage; pairs of twins are more difficult to obtain than parent-child or sib-sib pairs, and one avoids the uncertainty about the genetic inferences gained from twin analysis.

[0508] Drug responses among related and unrelated individuals may be continuously or discretely distributed. In the former case, it is likely that many loci have some effect on the trait, while in the latter case, variation could be attributable to Mendelian segregation of alleles in a family (or families) with, for example, AA homozygotes giving one phenotype and Aa heterozygotes and aa homozygotes giving a second phenotype, all in the context of a relatively homogeneous genetic background.

[0509] There is a wealth of analytical techniques known in the art that can be used to assess the mode of inheritance for a particular trait and to determine the degree to which differences among individuals are genetically determined. These techniques include cluster analysis and discriminant analysis used to define traits with variable expression and the fitting of a variety of genetic models to the data, including generalized single-locus models, mixed models in which a trait is determined by a major locus and by many minor loci, and a so-called polygenic model in which many loci contribute variation to the trait, the result being a continuously-distributed phenotype (For further details, see Eaves, L. J. [1977] Inferring the causes of human variation, Journal of the Royal Statistical Society A 140: 324-355 and Cloninger, C. R. [1988] Complex Human Traits. Pp. 312-317 in: Proceedings of the Second International Conference on Quantitative Genetics, eds., B. S. Weir, E. J. Eisen, M. M. Goodman, and G. Namkoong, Sinauer Associates, Inc). Specific statistical techniques involved in the fitting and analysis of these genetic models are also well known in the art; they include parametric and nonparametric correlation, regression, and one-way and two-way analysis of variance (For further details, see Mather, K. and Jinks, J. L. [1977] Introduction to Biometrical Genetics, Cornell University Press and Falconer, D. S. and Mackay, T. [1996] Introduction to Quantitative Genetics, Addison Wesley Longman Ltd.) Many, perhaps most, traits of pharmacogenetic interest will be continuously-distributed. In this context, the central statistical comparison is one between the differences among average traits of different families (say, groups of sibs), or among all the members of several such families, as compared to the differences among traits within families (among sibs). If such differences in so-called mean squares are large enough (as compared to the differences expected under the null hypothesis of no family differences), one can infer that there is a genetic component to differences among families.

[0510] Standard theory known in the art indicates that there is an inverse relationship between study size and the ability to detect a given genetic effect. So, for example, assume that the 50% of the variation among individuals is due to genetic differences. A Phase 1 trial composed of sixty individuals consisting of thirty parent-child pairs may or may not allow one to detect such a genetic effect, given the standard criterion for statistical significance (P<0.05), depending on assumptions one makes about the number of loci that have major effects. However, a trial composed of 120 individuals consisting of sixty parent-child pairs would likely be sufficient to provide statistically significant evidence for a 50% heritable drug response effect. Once one parent-child pair is recruited, it is generally advantageous statistically to add additional parent-child combinations as opposed to adding additional children for a given parent.

[0511] If 75% or more of the variation in drug response among individuals is due to genetic differences, a Phase 1 trial composed of sixty individuals consisting of thirty parent-child pairs would allow one to detect such a genetic effect, given the standard criterion for statistical significance (P<0.05).

[0512] Similar calculations can be made if one analyzes siblings in a Phase I trial, instead of using parent-child pairs. These calculations indicate that the more powerful approach for a Relatives Unit is generally to focus on parent-child pairs as opposed to the use of groups of siblings, especially if minimizing the number of subjects is an objective of the study. However, the use of groups of siblings may be necessary or preferable, especially if the trait in question is manifested only at a specific age. In such a case, one can readily use standard theory to compare alternative designs for the study. The overall point is that the statistical framework associated with the Relatives Unit will allow one to choose the approach that is best-suited for a given trait.

[0513] In general, techniques for measuring whether pharmacodynamic traits are under genetic control using surrogate markers of drug efficacy will be useful in obtaining an early assessment of the extent of genetically determined variation in drug response for a given therapeutic compound. Such information provides an informed basis for either stopping development at the earliest possible stage or, preferably, continuing development, but with a plan to identify and control for genetic variation so as to allow rapid progression through the regulatory approval process.

[0514] For example, it is well known that clinical trials to assess the efficacy of candidate drugs for Alzheimer's disease are long and expensive, and most such drugs are only effective in a fraction of patients. Using surrogate measures of response in normals drawn from a population of related individuals might help to assess the contribution of genetic variation to variation in treatment response. For an acetylcholinesterase inhibitor, relevant surrogate pharmacodynamic measures might include testing erythrocyte membrane acetylcholinesterase levels in drug treated normal subjects, or testing performance on a psychometric test of short term memory, or other measures that are affected by treatment (and ideally that correlate with clinical efficacy).

[0515] Similarly, antidepressant drugs can produce a variety of effects on mood in normal subjects. Careful measurement and statistical analysis of such responses in related and unrelated normal subjects could provide an early indication of whether there is a genetic component to drug response (and hence clinical efficacy). The observation of significant variation among families would provide evidence of a pharmacogenetic effect and justify the substantial expenditure necessary for a full pharmacogenetic drug development program. Conversely, the absence of any significant familial influence on drug response in a Pharmacogenetics Relatives Unit could provide an early termination point for pharmacogenetic studies.

[0516] Again, the proposed studies do not require any knowledge of candidate loci, nor is DNA collection or genotyping required. One needs only a reliable surrogate pharmacodynamic assay and groups of related normal individuals. Standard statistical methods should permit the magnitude of the pharmacogenetic effect to be estimated. It should be a criteria for deciding whether to proceed with more intensive, gene-focused pharmacogenetic analysis during later stages of development.

[0517] 4. Pharmacogenetic Phase I Outliers Unit

[0518] The prerequisites for a Pharmacogenetic Phase I Outliers Unit, as well as the type of information that can be obtained, differ in several respects from a Pharmacogenetic Phase I Relatives Unit. First, the Outliers Unit requires some knowledge of the molecular pharmacology of the candidate compound—enough knowledge to select at least one candidate gene. Second, the Outliers Unit provides information on the effect, if any, of known genetic variation in the candidate gene or genes on variation in the drug response measures. This is advantageous in that it sets the stage for pharmacogenetic analysis in later stages of clinical development. Third, the Outliers Unit does not require recruitment of relatives. Instead, one initially recruits a large population of individuals from which small subsets are drawn as necessary for specific trials based on their genotypes. All of the individuals in the large population are initially asked to provide DNA samples (from blood or other readily available tissue such as buccal mucosa) which can subsequently be genotyped at candidate loci of potential relevance to a particular candidate drug of interest. Over time a database of genotypes can be assembled, potentially reducing the need for genotyping later. From this large collection of subjects one then selects a group of individuals with genotypes expected to homogeneous for the drug response trait of interest (assuming that the candidate gene(s) play a significant role in drug response). The individuals with identical (and preferably homozygous) genotypes at the candidate gene(s) might comprise a collection of the common genotypes or haplotypes, or they may include some rare genotypes/haplotypes as well. The main point is that one can recruit groups consisting of any mixture of genotypes or haplotypes in order to assess the role that variation in the candidate gene(s) may play in trait determination. In this method, then, one recruits a population for clinical genetic investigation utilizing methods in statistical genetics to optimize the size and genetic composition of the population.

[0519] The mechanics of an Outlier Unit are as follows. Several thousand subjects are enrolled in the Outlier Unit with the understanding that they provide a blood sample from which DNA is extracted and stored. Each time a new outlier study is performed their sample may be genotyped. (It will not be necessary to genotype all subjects for all trials—just enough to identify subjects with the desired genotypes or haplotypes. Subjects may be paid a fee for each genotyping analysis done on their sample, regardless of whether the sample is used.) Only rarely will a particular subject have a genotype that meets the criteria for a specific outlier study (see below). When a match occurs, that subject will be invited to participate in that study. The genotyping done to identify subjects for a study will be determined by the candidate genes deemed relevant to pharmacology of the candidate drug, and by the polymorphisms or haplotypes in those candidate genes. Ideally DNA samples from several thousand subjects will be arrayed in 96 or 384 well plates so that the genotyping or haplotyping of large numbers of subjects can be performed using automated methods. Any highly accurate and inexpensive genotyping procedure will suffice, such as the methods described elsewhere in this application. Clearly it is desirable to have a stable population for genotyping, given the investment required to recruit subjects, isolate and array DNA, and accumulate a database of genotype data. Since most subjects will only rarely be invited to participate in clinical trials, the ongoing participation of subjects in the Outliers Unit must be assured by other means—for example, by a modest annual payment for remaining in the Outliers Unit, plus a fee for each occasion on which their sample is genotyped.

[0520] The power of the Outliers Unit lies in the ability to rapidly enroll individuals with virtually any desired genotype in a Phase I clinical trial. Suppose, for example, that one wants to determine the drug response phenotype of individuals homozygous for rare alleles at candidate loci. Consider a compound for which there are two loci believed likely to influence response to treatment. The first locus has alleles A and a, while the second has alleles B and b. If these loci do in fact contribute significantly to treatment response then homozygotes would be expected to exhibit the most extreme responses (assuming a dominant or codominant model). One could also measure epistatic (gene X gene) interactions on the presumption that drug response measures might be extreme in individuals homozygous for specific alleles of the two candidate genes. So, for example, one would perform a Phase I study consisting of measuring a surrogate drug response in individuals with genotypes AA/BB, aa/BB, AA/bb and aa/bb and then statistically comparing the distribution of a trait in each of these groups with the distribution of the same trait in the other groups and/or in the unfractionated (total) population. The statistical techniques for such comparisons are known in the art and include parametric and nonparametric analyses to detect differences in population averages, such as the t-test and the Mann-Whitney U test. If individuals of a given rare genotype do have significantly different surrogate drug responses when compared to each other, or when compared to the rest of the population, one can infer that the locus likely affects the trait.

[0521] The size requirements of the source population of individuals will depend on the range of allele frequencies to be analyzed. For example, if the allele frequencies for A and a are, say, 0.15 and 0.85, and for B and b are 0.2 and 0.8 then the frequency of AA homozygotes is expected to be 2.25% and BB homozygotes 4%. In the absence of any linkage between the loci, the frequency of AA/BB double homozygotes is expected to be 0.0225×0.04=0.0009 or about one subject in 1000. At least five subjects of each genotype should be recruited for the Outlier Unit, and preferably at least ten subjects. Thus, for studies of two loci in which the minor allele frequency for both loci is in the 0.15-0.20 range, the recruitment of individuals that are potential outliers for the trait under investigation (i.e., homozygotes at the candidate loci) will require at least 1,000 individuals and preferably 5,000 or more.

[0522] One of the most useful aspects of the Outlier Unit is that individuals with rare genotypes can be pharmacologically assessed in a small study. This addresses a serious limitation of conventional clinical trials with respect to the investigation of polygenic traits or the effect of rare alleles. Even conventional Phase III studies, which typically have the largest number of patients, are usually of insufficient size to address simple one-locus hypotheses about efficacy or toxicity with adequate statistical power (e.g. 80% or 90% power). The problem is that for each new allele that must be considered (e.g. five common haplotypes at a candidate locus) the comparison groups are reduced and statistical power is diminished. It is therefore an especially challenging problem to test the effect of multiple alleles at a single locus, let alone interaction of alleles at several loci in determining drug response. The Outlier Unit provides a way to efficiently test for the effects of multiple alleles at a candidate locus (e.g. haplotypes), or to test for interactions between two or more candidate loci by allowing ready identification of groups of individuals who, on account of being homozygous at one or several loci of interest, should be outliers for the drug response traits of interest.

[0523] The information that can be gained from an Outliers Unit is of great value in designing subsequent efficacy trials, as it provides a basis for constraining the number of hypotheses to be tested. In lieu of such information, one is compelled to statistically test a variety of genetic models for a number of candidate loci. The correction for multiple testing necessitated by such uncertainty about the genetic model is frequently large enough to put statistically significant results beyond reach. On the other hand, if the phenotypic effect of each allele at a locus (or the effect of at least some alleles) is known from the Outliers Unit study, one is then able to design a Phase II or Phase III study that tests a relatively small number of genetic hypotheses, thereby considerably improving the statistical power of the genetic analysis in efficacy trials.

[0524] Consider a locus with two alleles, one with frequency 0.95 and the other 0.05, as revealed by genotyping the individuals in the large source population for the Outliers Unit. The two alleles combine to make three genotypes which are observed to differ in their response to a candidate compound of interest. There are several statistical comparisons that one can undertake in order to determine whether different alleles at this locus are associated with differences in response. One is to compare the average response of, say, individuals who are homozygous for the rare allele with the average response of individuals chosen at random from the source population. In this instance, the Outlier Unit is composed of a group of individuals with the rare genotype and an equal-sized group composed of random genotypes (including the rare genotype). (In general, equal group sizes are statistically more efficient; they are not necessary, however, which is fortunate since some alleles of interest might be so rare that finding, say, even ten individuals who are homozygous would be difficult.) A second kind of statistical comparison would be to compare equal-sized groups of the three genotypes (AA, Aa, aa), in order to determine whether the presence or absence of a particular allele has a significant effect on the drug response trait. In this instance, the Outlier Unit is preferably composed of equal-sized groups of the three genotypes.

[0525] Assume that being a homozygote for the rare allele of the locus described in the preceding paragraph causes a 15% average difference in a pharmacokinetic parameter (e.g., the area under curve of drug concentration in blood) as compared to random individuals. Assume further that the Outliers Unit has a total of sixty individuals, including thirty individuals of the rare genotype and thirty individuals chosen at random. Finally, assume that the variance of individual responses is identical within the two groups and that it is equal to 0.1. Standard statistical theory indicates that thirty individuals per group is not adequate to statistically prove that there is a significant difference in average uptake rate between the groups (P<0.05). Instead, with an increase to 108 individuals in each group, one would be able to provide statistical evidence for this effect. However, if we assume that homozygosity for an allele at the candidate locus causes a 30% difference in area under curve then the number of individuals required to provide statistical evidence for a difference between the two groups (for P<0.05 and holding all other assumptions constant) is only twenty-seven. The number of individuals required to detect a 60% difference in area under curve (all other assumptions constant) is only seven. This calculation assumes that the loci in question affect only the average trait in each of the two groups and that the shapes of the trait distribution are identical in the two groups. While conclusions based upon such an assumption are biologically meaningful and statistically robust, in some circumstances there may be differences in the shape of the trait distributions associated with different genotypes. In particular, one or more classes of homozygous genotypes may have a narrower trait distribution (smaller variance) than another, or than the population as a whole. Such a difference can be accounted for in the analysis; in fact, it would be expected to reduce the number of subjects needed for the Outliers Unit trial (since the smaller variance of one distribution reduces the overlap between it and the other trait distributions to which it is being compared). In fact, the assumption of identical variances in the homozygote and total groups is not necessarily the biologically most likely case: it is reasonable to expect that the variance of the trait in the genetically more homogeneous group may be less (if the locus in question in fact contributes to variation in the drug response trait). This effect would result in a smaller population being adequate to show a genetically determined component to the difference in treatment effect between the two groups.

[0526] Serious adverse effects occuring at low frequency are often detected in the later stages of drug development. In some cases such effects have a significant genetic component. To address this issue preemptively, an Outlier Unit can perform trials in which subjects are selected to represent only the rare alleles at one or more loci that are candidates for influencing the response to treatment. For example, variances occurring at 5% allele frequency are expected to occur in homozygous form in 0.25% of the population (0.05×0.05), and therefore may rarely, if ever, be encountered in early clinical development. Yet such subjects could readily be identified by genotyping the hundreds to thousands of patients enrolled in a Phase I Outliers Unit.

[0527] Alternatively, by insuring that all common genotypes are represented in an Outlier Unit study the contribution of a major candidate locus can be tested with a powerful statistical design. Consider a locus with five haplotypes, A, B, C, D and E, with frequencies 0.3, 0.25, 0.2, 0.15, and 0.05 (plus several additional alleles with frequency lower than 0.05). A comparison of groups of homozygous for each of the haplotypes—that is AA, BB, CC, DD and EE homozygotes—each group of equal size, provides a powerful design to measure the contribution of variation at the candidate locus to variation in drug response In this case, determination of sample sizes rests upon assumptions about the differences in average trait values for each haplotype. All other things being equal, detecting a difference is easiest when a subset of the haplotypes appears to be appreciably distinct from the rest. Such a situation allows one to make a reasonably principled decision to lump haplotypes so that one compares, say, one haplotype with all of the others. In such a circumstance, sample size calculations for testing a difference in average responses would be roughly similar to those described above. More generally, one can assess the overall heterogeneity of the traits associated with each haplotype (say, with a parametric or nonparametric analysis of variance) and one can also make individual comparisons between haplotypes (by using a multiple comparison procedure if the initial analysis of variance reveals significant heterogeneity) The identification of genetically determined phenotypic variation at such a locus the can reduce the likelihood of discrepant results due to genetic stratification in later trials.

[0528] In another embodiment of the invention, it would be useful to prospectively determine the status of polymorphisms at genes that are involved in the pharmacokinetic or pharmacodynamic action of many drugs. This would save genotyping the large Outliers Unit population each time a new project is initiated. Demand for genotyped groups of patients can be anticipated from pharmaceutical and biotechnology companies and contract research organizations (CROs). Genotyping might initially focus on common pharmacological targets such as estrogen receptors or other nuclear receptors, or on adrenergic receptors, serotonin receptors, dopamine receptors and other G protein coupled receptors. The pre-genotyped Outlier Unit population could be part of a package of services (along with genotyping assay development capability, high-throughput genotyping capacity and software and expertise in statistical genetics) designed to accelerate pharmacogenetic Phase I studies. Eventually, as the databank of genotypes is expanded, individuals with virtually any genotype or combination of genotypes can be called in for precisely designed physiological or toxicological studies designed to test for pharmacogenetic effects.

[0529] As noted earlier, the Pharmacogenetic Phase I Relatives Unit and the Pharmacogenetic Phase I Outlier Unit can provide useful information at almost any stage of clinical development. It is not unusual, for example, for a product in Phase II or even Phase III testing to be remanded to Phase I in order to clarify some aspect of toxicology or physiology. In this context, either or both of the Pharmacogenetic Phase I Units would be extremely useful to a drug development company, as studies in groups of related individuals (Relatives Unit) or in defined genetic subgroups drawn from a large genotyped population (Outliers Unit) would be an economical and efficient way to clarify the nature and extent of pharmacogenetic effects, if any, thereby paving the way for future rational development of the compound.

[0530] 5. Surrogate Endpoints

[0531] As explained above, some of the most attractive applications of Pharmacogenetic Phase I Units depend on the availability of surrogate markers for pharmacodynamic drug action. The most useful surrogate markers are those which can be used in normal subjects in Phase I; which can be measured easily, inexpensively and accurately, and for which there is compelling data linking the surrogate marker with some clinically important aspect of disease biology, such as disease manifestations in various organ systems, disease progression, disease morbidity or mortality, or disparate other clinical indices known in the art. The utility of surrogate markers increases in proportion to the difficulty and cost of clincal development. Thus for a disease like Alzheimer's, where long trials involving many patients are standard, the use of surrogate measures of, for example, cognitive ability, are highly desirable.

[0532] The standard endpoints of Phase I trials are also useful measures for analysis in a Pharmacogenetic Phase I Unit. For example, studies of compound adsorption, distribution, metabolism, excretion and bioavailability may be analyzed for their genetic component. Similarly, toxic responses and dose-related side effects may be analyzed by the pharmacogenetic methods of this invention.

[0533] 6. Establishing and Operating a Phase I Pharmacogenetic Relatives Unit

[0534] First, it should be noted that the information that can be gained from a Pharmacogenetic Phase I Unit provides for substantial cost savings in later stages of clinical development. Therefore it is to be expected that even if the cost of operating a Pharmacogenetic Phase I Unit exceeds the cost of operating a conventional Phase I Unit, the overall costs of clinical development are likely to be lower, thereby justifying the costs of the Pharmacogenetic Phase I Unit. Nonetheless, it is clearly desirable to operate a Pharmacogenetic Phase I Unit as efficiently as possible. In order to make a Phase I unit an efficient business operation it is useful to (i) use statistical genetic methods to design studies that require the minimal number of subjects to achieve adequate statistical power (e.g. power of 80% to detect an effect at the P<0.05 level), in order to keep subject costs at a minimum, (ii) take measures to reduce the turnover of participating subjects, in view of the long term investment made in patient recruitment and (in the case of the Outliers Unit) genotyping. This may be accomplished by offering subjects financial or other incentives to encourage sustained participation in the Pharmacogenetic Phase I Unit. The types of incentives that would be useful differ between the two types of Phase I Units (see below). (iii) Secure rights to reuse genotype data and, ideally, phenotypic data collected during each Pharmacogenetic Phase I Unit trial, in order to create a database that over time will save costs by eliminating the need to repetitively genotype the same loci, and may eventually produce information of broad utility in clinical pharmacology research: namely a database on the heritability of phenotypic responses to various broad classes of compounds (benzodiazepines, statins, taxanes, etc.) and the major classes of genes involved. Such a database could become a product.

[0535] In order to efficiently set up a Phase I Pharmacogenetic Relatives Unit family participation can be encouraged by appropriate incentive compensation. For example, subjects with no participating family members might be paid $200 for participation in a study; two sibs participating in the same study might each be paid $300; if they could encourage another sib (or cousin) to participate the three related individuals might each be paid $350 for each study; parent-sib pairs might be paid $400 for each study, and so forth. This type of compensation would encourage subjects to recruit their relatives to participate in Phase I studies. To the extent that certain types of blood relationship are more useful for efficient genetical analysis, those types of related individuals could be compensated most highly. This type of compensation would increase the cost of studies, however the increased speed of setting up the Relatives Unit, and the increased retention of subjects, would compensate over time. The optimal location to establish a Pharmacogenetic Relatives Unit is in a city with a stable population, many large families, and a open attitudes toward modern technology. The size of a Relatives Unit need be little more than 150 subjects, though 250 would allow greater flexibility in drawing related subjects from different racial or ethnic groups (see below), and allow for more trials to be performed simultaneously. 400-500 subjects would be most preferable. Greater than 500 subjects would provide little benefit while increasing costs substantially.

[0536] Ideally subjects in the pharmacogenetic Phase I unit are of known ethnic/racial/geographic background and willing to participate in Phase I studies, for pay, over a period of years. For specific studies in a Relatives Unit subjects from one or more racial, ethnic or geographically defined group may be analyzed in order to (i) mirror the population in which Phase II or Phase III trials are to be conducted; (ii) determine if there are measurable differences in pharmacogenetic effects in different racial, ethnic or geographically defined groups; (iii) study the most homogeneous group possible in order to increase the chances of detecting a particular type of genetic effect.

[0537] Ideally consent for genotyping should be obtained at the same time that subjects are enrolled. Appropriate consent forms will be drafted and approved by an independent review board. It would be most efficient if blanket consent for genotyping any polymorphic site or sites deemed relevant to the pharmacology of any candidate drug could be obtained. However, if this somewhat broad type of consent is deemed inappropriate by the review board then consent could be somewhat narrowed by adding the qualification that any loci that are genotyped be relevant to a customer project. A third, more onerous arrangement would be obtain consent to genotype polymorphic sites in loci relevant to specific families of compounds, or to obtain consent for genotyping a specific list of genes. Another, still less desirable solution would be to obtain consent for genotyping on a project-by-project basis (for example by mailing out reply cards to all subjects for each study), after the specific polymorphic sites to be genotyped have been selected.

[0538] Another essential element of operating a Relatives Unit is having adequate quality control measures. One crucial aspect of quality control is an independent testing method to confirm the relatedness of the recruited subjects This can be accomplished by genotyping multiple (10-50) highly polymorphic loci, such as short tandem repeat sequences, in individuals believed to be related. By comparing the degree of genetic identity observed with that expected from the purported relation (e.g. 50% in the case of sibs) it is possible to ensure with considerable certainty that all related individuals are in fact related as they believe themselves to be. (Inconsistency between genotyping and reported relationship would be dealt with simply by not enrolling the unrelated individuals in any trials.)

[0539] As indicated above, methods for retention of subjects in a Phase I Outliers Unit preferably consist of making modest payments for continuing participation (i.e. continued permission to genotype under the limits of the consent); additional payments for genotyping analysis, whether or not it results in a request to participate in a clinical study; and, of course, generous compensation for participation in each Outliers Unit clinical study.

[0540] Phase I of clinical development is generally focused on safety, although drug companies are increasingly obtaining information on pharmacokinetics and surrogate pharmacodynamic markers in early trials. Phase I studies are typically performed with a small number (<60) of normal, healthy volunteers usually at single institutions. The primary endpoints in these studies usually relate to pharmacokinetic parameters (i.e. adsorption, distribution, metabolism and bioavailability), and dose-related side effects. In a Phase I pharmacogenetic clinical trial, stratification based upon allelic variance or variances of a candidate gene or genes related to pharmacokinetic parameters may allow early assessment of potential genetic interactions with treatment.

[0541] Phase I studies of some diseases (e.g. cancer or other medically intractable diseases for which no effective medical alternative exists) may include patients who satisfy specified inclusion criteria. These safety/limited-efficacy studies can be conducted at multiple institutions to ensure rapid enrollment of patients. In a pharmacogenetic Phase I study that includes patients, or a mixture of patients and normals, the status of a variance or variances suspected to affect the efficacy of the candidate therapeutic intervention may be used as part of the inclusion criteria. Alternatively, analysis of variances or haplotypes in patients with different treatment responses may be among the endpoints. It is not unusual for such a Phase I study design to include a double-blind, balanced, random-order, crossover sequence (separated by washout periods), with multiple doses on separate occasions and both pharmacokinetic and pharmacodynamic endpoints.

[0542] 2. Phase I Trials with Subjects Drawn from Large Populations and/or from Related Volunteer Subjects: The Pharmacogenetic Phase I Unit Concept

[0543] In general it is useful to be able to assess the contribution of genetic variation to treatment response at the earliest possible stage of clinical development. Such an assessment, if accurate, will allow efficient prioritization of candidate compounds for subsequent detailed pharmacogenetic studies; only those treatments where there is early evidence of a significant interaction of genetic variation with treatment response would be advanced to pharmacogenetic studies in later stages of development. In this invention we describe methods for achieving early insight—in Phase I—into the contribution of genetic variation to variation in surrogate treatment response variables. It occurred to the inventors that this can be accomplished by bringing the power of genetic linkage analysis and outlier analysis to Phase I testing via the recruitment of a very large Phase I population including a large number of individuals who have consented in advance to genetic studies (occasionally referred to hereinafter as a Pharmacogenetic Phase I Unit). In one embodiment of a Pharmacogenetic Phase I Unit many of the subjects are related to each other by blood. (Currently Phase I trials are performed in unrelated individuals, and there is no consideration of genetic recruitment criteria, or of genetic analysis of surrogate markers.) There are several novel ways in which a large population, or a population comprised at least in part of related individuals, could be useful in early clinical trials. Some of the most attractive applications depend on the availability of surrogate markers for pharmacodynamic drug action which can be used early in clinical development, preferably in normal subjects in Phase I. Such surrogate markers are increasingly used in Phase I, as drug development companies seek to make early yes/no decisions about compounds.

[0544] Recruitment of a population optimized for clinical genetic investigation may entail utilization of methods in statistical genetics to select the size and composition of the population. For example powerful methods for detecting and mapping quantitative trait loci in sibpairs have been developed. These methods can provide some estimate of the statistical power derived from a given number of groups of closely related individuals. Ideally subjects in the pharmacogenetic Phase I unit are of known ethnic/racial/geographic background and willing to participate in Phase I studies, for pay, over a period of years. The population is preferably selected to achieve a specified degree of statistical power for genetic association studies, or is selected in order to be able to reliably identify a certain number of individuals with rare genotypes, as discussed below. Family participation could be encouraged by appropriate incentive compensation. For example, individual subjects might be paid $200 for participation in a study; two sibs participating in the same study might each be paid $300; if they could encourage another sib (or cousin) to participate the three related individuals might each be paid $350, and so forth. This type of compensation would encourage subjects to recruit their relatives to participate in Phase I studies. (It would also increase the cost of studies, however the type of data that can be obtained can not be duplicated with conventional approaches.) The optimal location to establish such a Phase I unit is a city with a stable population, many large families, and a positive attitude about gene technology. The Pharmacogenetic Phase I Unit population can then be used to test for the existence of genetic variation in response to any drug as a first step in deciding whether to proceed with extensive pharmacogenetic studies in later stages of clinical development. Specific uses of a large Phase I unit in which some or all subjects are related include:

[0545] a. It should be possible, for virtually any compound, to assess the magnitude of the genetic contribution to variation in drug response (if any) by comparing variation in drug response traits among related vs. non-related individuals. The rationale is as follows: if a surrogate drug response trait (i.e., a surrogate marker of pharmacodynamic effect that can be measured in normal subjects) is under strong genetic control then related individuals, who share 25% (cousins) or 50% (sibs) of their alleles, should have less divergent responses (less intragroup variance) than unrelated individuals, who share a much smaller fraction of alleles. That is, individuals who share alleles at the genes that affect drug response should be more similar to each other (i.e. have a narrower distribution of responses, whether measured by variance, standard deviation or other means) than individuals who, on average, share very few alleles. By using statistical methods known in the art the degree of variation in a set of data from related individuals (each individual would only be compared with his/her relatives, but such comparisons would be performed within each group of relatives and a summary statistic developed) could be compared to the degree of variation in a set of unrelated individuals (the same subjects could be used, but the second comparison would be across related groups). Account would be taken of the degree of similarity expected between related individuals, based on the fraction of the genome they shared by descent. Thus the extent of variation in the surrogate response marker between identical twins should be less than between sibs, which should be less than between first cousins, which should be less than that between second cousins, and so forth, if there is a genetic component to the variation. It is well known from twin studies (in which, for example, variation between identical twins is compared to variation between fraternal twins) that pharmacokinetic variables (e.g. compound half life, peak concentration) are frequently over 90% heritable; the type of study proposed here (comparison of variation within groups of sibs and cousins to variation between unrelated subjects) would also show this genetic effect, without requiring the recruitment of monozygotic twins. For a summary of pharmacokinetic studies in twins see: Propping, Paul (1978) Pharmacogenetics. Rev. Physiol. Biochem. Pharmacol. 83: 123-173.

[0546] It may be that the pattern of drug responses that distinguishes related individuals from non-related individuals is more complex than, for example, variance or standard deviation. For example, there may be two discrete phenotypes characteristic of intrafamilial variation (a bimodal distribution) that are not a feature of variation between unrelated individuals (where, for example, variation might be more nearly continuous). Such a pattern could be attributable to Mendelian inheritance operating on a restricted set of alleles in a family (or families) with, for example, AA homozygotes giving one phenotype and AB heterozygotes and BB homozygotes giving a second phenotype, all in the context of a relatively homogeneous genetic background. In contrast, variation among non-related subjects would be less discrete due to a greater degree of variation in genetic background and the presence of additional alleles C, D and E at the candidate locus. Statistical measures of the significance of such differences in distribution, including nonparametric methods such as chi square and contingency tables, are known in the art.

[0547] The methods described herein for measuring whether pharmacodynamic traits are under genetic control, using surrogate markers of drug efficacy in phase I studies which include groups of related individuals, will be useful in obtaining an early assessment of the extent of genetically determined variation in drug response for a given therapeutic compound. Such information provides an informed basis for either stopping development at the earliest possible stage or, preferably, continuing with development but with a plan for identifying and controlling for genetic variation so as to allow rapid progression through the regulatory approval process.

[0548] For example, it is well known that Alzheimer's trials are long and expensive, and most drugs are only effective in a fraction of patients. Using surrogate measures of response in normals drawn from a population of related individuals would help to assess the contribution of genetic variation to variation in treatment response. For an acetylcholinesterase inhibitor, relevant surrogate pharmacodynamic measures could include testing erythrocyte membrane acetylcholinesterase levels in drug treated normal subjects, or performing psychometric tests that are affected by treatment (and ideally that correlate with clinical efficacy) and measuring the effect of treatment. As another example, antidepressant drugs can produce a variety of effects on mood in normal subjects—or no effect at all. Careful monitoring and measurement of such responses in related vs. unrelated normal subjects, and statistical comparison of the degree of variation in each group, could provide an early readout on whether there is a genetic component to drug response (and hence clinical efficacy). The observation of similar effects in family members, and comparatively dissimilar effects in unrelated subjects would provide compelling evidence of a pharmacogenetic effect and justify the substantial expenditure necessary for a full pharmacogenetic drug development program. Conversely, the absence of any significant family influence on drug response would provide an early termination point for pharmacogenetic studies. Note that the proposed studies do not require any knowledge of candidate genes, nor is DNA collection or genotyping required—simply a reliable surrogate pharmacodynamic assay and small groups of related normal individuals. Refined statistical methods should permit the magnitude of the pharmacogenetic effect to be measured, which could be a further criteria for deciding whether to proceed with pharmacogenetic analysis. The greater the differential in magnitude or pattern of variance between the related and the unrelated subjects, the greater the extent of genetic control of the trait.

[0549] Not all drug response traits are under the predominant control of one locus. Many such traits are under the control of multiple genes, and may be referred to as quantitative trait loci. It is then desirable to identify the major loci contributing to variation in the drug response trait. This can be done for example, to map quantitative trait loci in a population of drug treated related normals. Either a candidate gene approach or a genome wide scanning approach can be used. (For review of some relevant methods see: Hsu L, Aragaki C, Quiaoit F. (1999) A genome-wide scan for a simulated data set using two newly developed methods. Genet Epidemiol 17 Suppl 1:S621-6; Zhao L P , Aragaki C, Hsu L, Quiaoit F. (1998) Mapping of complex traits by single-nucleotide polymorphisms. Am J Hum Genet 63(l):225-40; Stoesz M R, Cohen J C, Mooser V, et al. (1997) Extension of the Haseman-Elston method to multiple alleles and multiple loci: theory and practice for candidate genes. Ann Hum Genet 61 (Pt 3):263-74.)) However, this method would require at least 100 patients (preferably 200, and still more preferably >300) to have adequate statistical power, and each patient would have to be genotyped at a few polymorphic loci (candidate gene approach) or hundreds of polymorphic loci (genome scanning approach).

[0550] b. With a large Phase I population of normal subjects that need not be related (a second type of Pharmacogenetic Phase I Unit) it is possible to efficiently identify and recruit for any Phase I trial a set of individuals comprising virtually any combination of genotypes present in a population (for example, all common genotypes, or a group of genotypes expected to represent outliers for a drug response trait of interest). This method preferably entails obtaining blood or other tissue (e.g. buccal smear) in advance from a large number of the subjects in the Phase I unit. Ideally consent for genotyping would be obtained at the same time. It would be most efficient if blanket consent for genotyping any polymorphic site or sites could be obtained. Second best would be consent for testing any site relevant to any customer project (not specific at the time of initial consent). Third best would be consent to genotype polymorphic sites relevant to specific disease areas. Another, less desirable, solution would be to obtain consent for genotyping on a project by project basis (for example by mailing out reply cards), after the specific polymorphic sites to be genotyped are known.

[0551] One useful way to screen for pharmacogenetic effects in Phase I is to recruit homozygotes for a variance or variances of interest in one or more candidate genes. For example, consider a compound for which there are two genes that are strong candidates for influencing response to treatment. Gene X has alleles A and A′, while gene Y has alleles B and B′. If these genes do in fact contribute significantly to response then one would expect that, regardless of the mode of inheritance (recessive, codominant, dominant, polygenic) homozygotes would exhibit the most extreme responses. One would also expect epistatic interactions, if any, to be most extreme in double homozygotes. Thus one would ideally perform a surrogate drug response test in Phase I volunteers doubly homozygous at both X and Y. That is, test AA/BB, A′A′/BB, AA/B′B′ and A′A′/′B′ subjects. If the allele frequencies for A and A′ are 0.15 and 0.85, and for B and B′ 0.2 and 0.8 then the frequency of AA homozygotes is expected to be 2.25% and BB homozygotes 4%. In the absence of any linkage between the genes, the frequency of AA/BB double homozygotes is expected to be 0.0225×0.04=0.0009 or 0.09%, or about 1 subject in 1000. Ideally at least 5 subjects of each genotype are recruited for the Phase I study, and preferably at least 10 subject. Thus, even for variances of moderately low allele frequency (15%, 20%), the identification of potential outliers (i.e. homozygotes) for the candidate genes of interest will require a large population. Preferably the Phase I unit has enrolled at least 1,000 normal individuals, more preferably 2,000, still more preferably 5,000 and most preferably 10,000 or more. In another application of the large, genotyped Phase I population it may be useful to identify individuals with rare variances in candidates genes (either homozygous or heterozygous), in order to determine whether those variances are predisposing to extreme pharmacological responses to the compound. For example, variances occurring at 5% allele frequency are expected to occur in homozygous form in 0.25% of the population (0.05×0.05), and therefore may rarely, if ever, be encountered in early clinical development. Yet it may be serious adverse effects occurring in just such a small group that create problems in later stages of drug development. In yet another application of the large genotyped Phase I population, subjects may be selected to represent the known common variances in one or more genes that are candidates for influencing the response to treatment. By insuring that all common genotypes are represented in a Phase I trial the likelihood of misleading results due to genetic stratification (resulting in discrepancy with results of later, larger trials can be reduced.

[0552] It would be useful to prospectively genotype the large Phase I population for variances that are commonly the source of interpatient variation in drug response, since demand for genotyped groups of such patients can be anticipated from pharmaceutical companies and contract research organizations (CROs). For example, genotyping might initially focus on common pharmacological targets such as estrogen receptors, adrenergic receptors, or serotonin receptors. The pre-genotyped Phase I population could be part of a package of services (along with genotyping assay development capability, high throughput genotyping capacity and software and expertise in statistical genetics) designed to accelerate pharmacogenetic Phase I studies. Eventually, as the databank of genotypes built up, individuals with virtually any genotype or combination of genotypes could be called in for precisely designed physiological or toxicological studies designed to test for pharmacogenetic effects.

[0553] One of the most useful aspects of the Pharmacogenetic Phase I Unit is that subjects with rare genotypes can be pharmacologically assessed in a small study. This addresses a serious limitation of conventional clinical trials with respect to the investigation of polygenic traits or the effect of rare alleles. Unfortunately even Phase III studies, as currently performed, are often barely powered to address simple one variance hypotheses about efficacy or toxicity. The problem, of course, is that each time a new genetic variable is introduced the comparison groups are cut in halves or thirds (or even smaller groups if there are multiple haplotypes at each gene). It is therefore a challenging problem to test the interaction of several genes in determining drug response. Yet the character of drug response data in populations—there is often a continuous distribution of responses among different individuals—suggests that drug responses may often be mediated by several genes. (On the other hand, there are an increasing number of well documented single gene, or even single variance, pharmacogenetic effects in the literature, showing that it is possible to detect the effect of a single variance.) One approach to identifying pharmacogenetic effects is to focus on finding the single gene variances that have the largest effects. This approach can be undertaken within the scale of current clinical trials. However, in order to develop a test which predicts a large fraction of the quantitative variation in a drug response trait it may be desirable to test the effect of multiple genes, including the interaction of variances at different genes, which may be non-additive (referred to as epistasis). The Pharmacogenetic Phase I Unit provides a way to efficiently test for gene interactions or multigene effects by, for example, allowing easy identification of individuals who, on account of being homozygous at several loci of interest, should be outliers for the drug response phenotypes of interest if there is a gene×gene interaction. Testing drug response in a small number of such individuals will provide a quick read on gene interaction. Obtaining genetic data on the pharmacodynamic action of a compound in Phase I should also provide a crude measure of allele affects—which variances or haplotypes increase pharmacological responses and which decrease them. This information is of great value in designing subsequent trials, as it constrains the number of hypotheses to be tested, thereby enabling powerful statistical designs. This is because when the effect of variances on drug response measures is unknown one is forced to statistically test all the possible effects of each allele (e.g. two tailed tests). As the number of genetically defined groups increases (e.g. as a result of multiple variances or haplotypes) there is a loss of statistical power due to multiple testing correction. On the other hand, if the relative phenotypic effect of each allele at a locus is known (or can be hypothesized) from Phase I data then each individual in a subsequent clinical trial contributes useful information—there is a specific prediction of response based on that individuals combination of genotypes or haplotypes, and testing the fit of the actual data to those predictions provides for powerful statistical designs. (It is also possible to measure allele effects biochemically, of course, to establish which alleles have positive and which negative effects, but at considerable cost.)

[0554] It is important to note that Phase I trials can provide useful information at almost any stage of clinical development. It is not unusual, for example, for a product in Phase II or even Phase III testing to be remanded to Phase I in order to clarify some aspect of toxicology or physiology. In this context a Pharmacogenetic Phase I Unit would be extremely useful to a drug development company. Phase I studies in defined genetic subgroups drawn from a large genotyped population, or in groups of related individuals, would be the most economical and efficient way to clarify the existence of pharmacogenetic effects, if any, paving the way for future rational development of the product.

[0555] C. Phase II Clinical Trials

[0556] Phase II studies generally include a limited number of patients (<100) who satisfy a set of predefined inclusion criteria and do not satisfy any predefined exclusion criteria of the trial protocol. Phase II studies can be conducted at single or multiple institutions. Inclusion/exclusion criteria may include historical, clinical and laboratory parameters for a disease, disorder, or condition; age; gender; reproductive status (i.e. pre- or postmenopausal); coexisting medical conditions; psychological, emotional or cognitive state, or other objective measures known to those skilled in the art. In a pharmacogenetic Phase II trial the inclusion/exclusion criteria may include one or more genotypes or haplotypes. Alternatively, genetic analysis may be performed at the end of the trial. The primary goals in Phase II testing may include (i) identification of the optimal medical indication for the compound, (ii) definition of an optimal dose or range or doses, balancing safety and efficacy considerations (dose-finding studies), (iii) extended safety studies (complementing Phase I safety studies), (iv) evaluation of efficacy in patients with the targeted disease or condition, either in comparison to placebo or to current best therapy. To some extent these goals may be achieved by performing multiple trials with different goals. Likewise, Phase II trials may be designed specifically to evaluate pharmacogenetic aspects of the drug candidate. Primary efficacy endpoints typically focus on clinical benefit, while surrogate endpoints may measure treatment response variables such as clinical or laboratory parameters that track the progress or extent of disease, often at lesser time, cost or difficulty than the definitive endpoints. A good surrogate marker must be convincingly associated with the definitive outcome. Examples of surrogate endpoints include tumor size as a surrogate for survival in cancer trials, and cholesterol levels as a surrogate for heart disease (e.g. myocardial infarction) in trials of lipid lowering cardiovascular drugs. Secondary endpoints supplement the primary endpoint and may be selected to help guide further clinical studies.

[0557] In a pharmacogenetic Phase II clinical trial, retrospective or prospective design will include the stratification of patients based upon a variance or variances in a gene or genes suspected of affecting treatment response. The gene or genes may be involved in mediating pharmacodynamic or pharmacokinetic response to the candidate therapeutic intervention. The parameters evaluated in the genetically stratified trial population may include primary, secondary or surrogate endpoints. Pharmacokinetic parameters—for example, dosage, absorption, toxicity, metabolism, or excretion—may also be evaluated in genetically stratified groups.. Other parameters that may be assessed in parallel with genetic stratification include gender, race, ethnic or geographic origin (population history) or other demographic factors.

[0558] While it is optimal to initiate pharmacogenetic studies in phase I, as described above, it may be the case that pharmacogenetic studies are not considered until phase II, when problems relating either to efficacy or toxicity are first encountered. It is highly desirable to initiate pharmacogenetic studies no later than Phase II of a clinical development plan because (1) phase III studies tend to be large and expensive—not an optimal setting in which to explore untested pharmacogenetic hypotheses; (2) phase III studies are typically designed to test one fairly narrow hypothesis regarding efficacy of one or a few dose levels in a specific disease or condition. Phase II studies are often numerous, and are intended to provide a broad picture of the pharmacology of the candidate compound. This is a good setting for initial pharmacogenetic studies. Several pharmacogenetic hypotheses may be tested in phase II, with the goal of eliminating all but one or two.

[0559] D. Phase III Clinical Trials

[0560] Phase III studies are generally designed to measure efficacy of a new treatment in comparison to placebo or to an established treatment method. Phase II studies are often performed at multiple sites. The design of this type of trial includes power analysis to ensure the sufficient data will be gathered to demonstrate the anticipated effect, making assumptions about response rate based on earlier trials. As a result Phase III trials frequently include large numbers of patients (up to 5,000). Primary endpoints in Phase III studies may include reduction or arrest of disease progression, improvement of symptoms, increased longevity or increased disease-free longevity, or other clinical measures known in the art. In a pharmacogenetic Phase III clinical study, the endpoints may include determination of efficacy or toxicity in genetically defined subgroups. Preferably the genetic analysis of outcomes will be confined to an assessment of the impact of a small number of variances or haplotypes at a small number of genes, said variances having already been statistically associated with outcomes in earlier trials. Most preferably variances at only one or two genes will be assessed.

[0561] After successful completion of one or more Phase III studies, the data and information from all trials conducted to test a new treatment method are compiled into a New Drug Application (NDA) and submitted for review by the U.S. FDA, which has authority to grant marketing approval in the U.S. and its territories. The NDA includes the raw (unanalyzed) clinical data, i.e. the patient by patient measurements of primary and secondary endpoints, a statistical analysis of all of the included data, a document describing in detail any observed side effects, tabulation of all patients who dropped-out of trials and detailed reasons for their termination, and any other available data pertaining to ongoing in vitro or in vivo studies since the submission of the investigational new drug (IND) application. If pharmacoeconomic objectives are a part of the clinical trial design then data supporting cost or economic analyses are included in the NDA. In a pharmacogenetic clinical study, the pharmacoeconomic analyses may include genetically stratified assessment of the candidate therapeutic intervention in a cost benefit analysis, cost of illness study, cost minimization study, or cost utility analysis. The analysis may also be simultaneously stratified by standard criteria such as race/ethnicity/geographic origin, sex, age or other criteria. Data from a genetically stratified analysis may be used to support an application for approval for marketing of the candidate therapeutic intervention.

[0562] E. Phase IV Clinical Trials

[0563] Phase IV studies occur after a therapeutic intervention has been approved for marketing, and are typically conducted for surveillance of safety, particularly occurrence of rare side effects. The other principal reason for Phase IV studies is to produce information and relationships useful for marketing a drug. In this regard pharmacogenetic analysis may be very useful in Phase IV trials. Consider, for example, a drug that is the fourth or fifth member of a drug class (say statins, or thiazidinediones or fluoropyrimidines) to obtain marketing approval, and which does not differ significantly in clinical effects—efficacy or safety—from other members of the drug class. The first, second and third drugs in the class will likely have a dominant market position (based on their earlier introduction into the marketplace) that is difficult to overcome, particularly in the absence of differentiating clinical effects. However, it is possible that the new drug produces a superior clinical effect—for example, higher response rate, greater magnitude of response or fewer side effects—in a genetically defined subgroup. The genetic subgroup with superior response may constitute a larger fraction of the total patient population than the new drug would likely achieve otherwise. In this instance, there is a clear rationale for performing a Phase IV pharmacogenetic trial to identify a variance or variances that mark a patient population with superior clinical response. Subsequently a marketing campaign can be designed to alert patients, physicians, pharmacy managers, managed care organizations and other parties that, with the use of a rapid and inexpensive genetic test to identify eligible patients, the new drug is superior to other members of the class (including the market leading first, second and third drugs introduced). The high responder subgroup defined by a variance or variances may also exhibit a superior response to other drugs in the class (a class pharmacogenetic effect), or the superior efficacy in the genetic subgroup may be specific to the drug tested (a compound-specific pharmacogenetic effect).

[0564] In a Phase IV pharmacogenetic clinical trial, both retrospective and prospective analysis can be performed. In both cases, the key element is genetic stratification based on a variance or variances or haplotype. Phase IV trials will often have adequate sample size to test more than one pharmacogenetic hypothesis in a statistically sound way.

[0565] F. Unconventional Clinical Development

[0566] Although the above listed phases of clinical development are well-established, there are cases where strict Phase I, II, III development does not occur, for example, in the clinical development of candidate therapeutic interventions for debilitating or life threatening diseases, or for diseases where there is presently no available treatment. Some of the mechanisms established by the FDA for such studies include Treatment INDs, Fast-Track or Accelerated reviews, and Orphan Drug Status. In a clinical development program for a candidate therapeutic of this type there is a useful role for pharmacogenetic analysis, in that the candidate therapeutic may not produce a sufficient benefit in all patients to justify FDA approval, however analysis of outcome in genetic subgroups may lead to identification of a variance or variances that predict a response rate sufficient for FDA approval.

[0567] As used herein, “supplemental applications” are those in which a candidate therapeutic intervention is tested in a human clinical trial in order to gain an expanded label indication, expanding recommended use to new medical indications. In these applications, previous clinical studies of the therapeutic intervention, i.e. preclinical safety and Phase I human safety studies can be used to support the testing of the therapeutic intervention in a new indication. Pharmacogenetic analysis is also useful in the context of clinical trials to support supplemental applications. Since these are, by definition, focused on diseases not selected for initial development the overall efficacy may not be as great as for the leading indication(s). The identification of genetic subgroups with high response rates may enable the rapid approval of supplemental applications for expanded label indications. In such instances part of the label indication may be a description of the variance or variances that define the group with superior response.

[0568] As used herein, “outcomes” or “therapeutic outcomes” describe the results and value of healthcare intervention. Outcomes can be multi-dimensional, and may include improvement of symptoms; regression of a disease, disorder, or condition; prevention of a disease or symptom; cost savings or other measures.

[0569] Pharmacoeconomics is the analysis of a therapeutic intervention in a population of patients diagnosed with a disease, disorder, or condition that includes at least one of the following studies: cost of illness study (COI); cost benefit analysis (CBA), cost minimization analysis (CMA), or cost utility analysis (CUA), or an analysis comparing the relative costs of a therapeutic intervention with one or a group of other therapeutic interventions. In each of these studies, the cost of the treatment of a disease, disorder, or condition is compared among treatment groups. Costs have both direct (therapeutic interventions, hospitalization) and indirect (loss of productivity) components. Pharmacoeconomic factors may provide the motivation for pharmacogenetic analysis, particularly for expensive therapies that benefit only a fraction of patients. For example, interferon alpha is the only treatment that can cure hepatitis C virus infection, however viral infection is completely and permanently eliminated in less than a quarter of patients. Nearly half of patients receive virtually no benefit from alfa interferon, but may suffer significant side effects. Treatment costs are ˜$10,000 per course. A pharmacogenetic test that could predict responders would save much of the cost of treating patients not able to benefit from interferon alpha therapy, and could provide the rationale for treating a population in a cost efficient manner, where treatment would otherwise be unaffordable.

[0570] As used herein, “health-related quality of life” is a measure of the impact of a disease, disorder, or condition on a patient's activities of daily living. An analysis of the health-related quality of life is often included in pharmacoeconomic studies.

[0571] As used herein, the term “stratification” refers to the partitioning of patients into groups on the basis of clinical or laboratory characteristics of the patient. “Genetic stratification” refers to the partitioning of patients or normal subjects into groups based on the presence or absence of a variance or variances in one or more genes. The stratification may be performed at the end of the trial, as part of the data analysis, or may come at the beginning of a trial, resulting in creation of distinct groups for statistical or other purposes.

[0572] G. Power Analysis in Pharmacogenetic Clinical Trials

[0573] The basic goal of power calculations in clinical trial design is to insure that trials have adequate patients and controls to fairly assess, with statistical significance, whether the candidate therapeutic intervention produces a clinically significant benefit.

[0574] Power calculations in clinical trials are related to the degree of variability of the drug response phenotypes measured and the treatment difference expected between comparison groups (e.g. between a treatment group and a control group). The smaller the variance within each group being compared, and the greater the difference in response between the two groups, the fewer patients are required to produce convincing evidence of an effect of treatment. These two factors (variance and treatment difference) determine the degree of precision required to answer a specific clinical question.

[0575] The degree of precision may be expressed in terms of the maximal acceptable standard error of a measurement, the magnitude of variation in which the 95% confidence interval must be confined or the minimal magnitude of difference in a clinical or laboratory value that must be detectable (at a statistically significant level, and with a specified power for detection) in a comparison to be performed at the end of the trial (hypothesis test). The minimal magnitude is generally set at the level that represents the minimal difference that would be considered of clinical importance.

[0576] In pharmacogenetic clinical trials there are two countervailing effects with respect to power. First, the comparison groups are reduced in size (compared to a conventional trial) due to genetic partitioning of both the treatment and control groups into two or more subgroups. However, it is reasonable to expect that variability for a trait is smaller within groups that are genetically homogeneous with respect to gene variances affecting the trait. If this is the case then power is increased as a function of the reduction in variability within (genetically defined) groups.

[0577] In general it is preferable to power a pharmacogenetic clinical trial to see an effect in the largest genetically defined subgroups. For example, for a variance with allele frequencies of 0.7 and 0.3 the common homozygote group will comprise 49% of all patients (0.7×0.7×100). It is most desirable to power the trial to observe an effect (either positive or a negative) in this group. If it is desirable to measure an effect of therapy in a small genetic group (for example, the 9% of patients homozygous for the rare allele) then genotyping should be considered as an enrollment criterion to insure a sufficient number of patients are enrolled to perform an adequately powered study.

[0578] Statistical methods for powering clinical trials are known in the art. See, for example: Shuster, J. J. (1990) Handbook of Sample Size Guidelines for Clinical Trials. CRC Press, Boca Raton, Fla.; Machin, D. and M. J. Campbell (1987) Statistical Tables for the Design of Clinical Trials. Blackwell, Oxford, UK; Donner, A. (1984) Approaches to Sample Size Estimation in the Design of Clinical Trials—A Review. Statistics in Medicine 3: 199-214.

[0579] H. Statistical Analysis of Clinical Trial Data

[0580] There are a variety of statistical methods for measuring the difference between two or more groups in a clinical trial. One skilled in the art will recognize that different methods are suited to different data sets. In general, there is a family of methods customarily used in clinical trials, and another family of methods customarily used in genetic epidemiological studies. Methods in quantitative and population genetics designed to measure the association between genotypes and phenotypes, and to map and measure the effect of quantitative trait loci are also relevant to the task of measuring the impact of a variance on response to a treatment. Methods from any of these disciplines may be suitable for performing statistical analysis of pharmacogenetic clinical trial data, as is known to those skilled in the art.

[0581] Conventional clinical trial statistics include hypothesis testing and descriptive methods, as elaborated below. Guidance in the selection of appropriate statistical tests for a particular data set is provided in texts such as: Biostatistics: A Foundation for Analysis in the Health Sciences, 7th edition (Wiley Series in Probability and Mathematical Statistics, Applied Probability and statistics) by Wayne W. Daniel, John Wiley & Sons, 1998; Bayesian Methods and Ethics in a Clinical Trial Design (Wiley Series in Probability and Mathematical Statistics. Applied Probability Section) by J. B. Kadane (Editor), John Wiley & Sons, 1996. Examples of specific hypothesis testing and descriptive statistical procedures that may be useful in analyzing clinical trial data are listed below.

[0582] A. Hypothesis Testing Statistical Procedures

[0583] (1) One-sample procedures (binomial confidence interval, Wilcoxon signed rank test, permutation test with general scores, generation of exact permutational distributions)

[0584] (2) Two-sample procedures (t-test, Wilcoxon-Mann-Whitney test, Normal score test, Median test, Van der Waerden test, Savage test, Logrank test for censored survival data, Wilcoxon-Gehan test for censored survival data, Cochran-Armitage trend test, permutation test with general scores, generation of exact permutational distributions)

[0585] (3) R×C contingency tables (Fisher's exact test, Pearson's chi-squared test, Likelihood ratio test, Kruskal-Wallis test, Jonckheere-Terpstra test, Linear-by linear association test, McNemar's test, marginal homogeneity test for matched pairs)

[0586] (4) Stratified 2×2 contingency tables (test of homogeneity for odds ratio, test of unity for the common odds ratio, confidence interval for the common odds ratio)

[0587] (5) Stratified 2×C contingency tables (all two-sample procedures listed above with stratification, confidence intervals for the odds ratios and trend, generation of exact permutational distributions)

[0588] (6) General linear models (simple regression, multiple regression, analysis of variance —ANOVA—, analysis of covariance, response-surface models, weighted regression, polynomial regression, partial correlation, multiple analysis of variance —MANOVA—, repeated measures analysis of variance).

[0589] (7) Analysis of variance and covariance with a nested (hierarchical) structure.

[0590] (8) Designs and randomized plans for nested and crossed experiments (completely randomized design for two treatment, split-splot design, hierarchical design, incomplete block design, latin square design)

[0591] (9) Nonlinear regression models

[0592] (10) Logistic regression for unstratified or stratified data, for binary or ordinal response data, using the logit link function, the normit function or the complementary log-log function.

[0593] (11) Probit, logit, ordinal logistic and gompit regression models.

[0594] (12) Fitting parametric models to failure time data that may be right-, left-, or interval-censored. Tested distributions can include extreme value, normal and logistic distributions, and, by using a log transformation, exponential, Weibull, lognormal, loglogistic and gamma distributions.

[0595] (13) Compute non-parametric estimates of survival distribution with right-censored data and compute rank tests for association of the response variable with other variables.

[0596] B. Descriptive Statistical Methods

[0597] Factor analysis with rotations

[0598] Canonical correlation

[0599] Principal component analysis for quantitative variables.

[0600] Principal component analysis for qualitative data.

[0601] Hierarchical and dynamic clustering methods to create tree structure, dendrogram or phenogram.

[0602] Simple and multiple correspondence analysis using a contingency table as input or raw categorical data.

[0603] Specific instructions and computer programs for performing the above calculations can be obtained from companies such as: SAS/STAT Software, SAS Institute Inc., Cary, N.C., U.S.A; BMDP Statistical Software, BMDP Statistical Software Inc., Los Angeles, Calif., USA; SYSTAT software, SPSS Inc., Chicago, Ill., USA; StatXact & LogXact, CYTEL Software Corporation, Cambridge, Mass., USA.

[0604] C. Statistical Genetic Methods Useful for Analysis of Pharmacogenetic Data

[0605] A wide spectrum of mathematical and statistical tools may be useful in the analysis of data produced in pharmacogenetic clinical trials, including methods employed in molecular, population, and quantitative genetics, as well as genetic epidemiology. Methods developed for plant and animal breeding may be useful as well, particularly methods relating to the genetic analysis of quantitative traits.

[0606] Analytical methods useful in the analysis of genetic variation among individuals, populations and species of various organisms are described in the following texts: Molecular Evolution, by W- H. Li, Sinauer Associates, Inc., 1997; Principles of Population Genetics, by D. L. Hartl and A. G. Clark, 1996; Genetics and Analysis of Quantitative Traits, By M. Lynch and B. Walsh, Sinauer Associates, Inc., Principles of Quantitative Genetics, by D. S. Falconer and T. F. C. Mackay, Longman, 1996; Genetic Variation and Human Disease, by K. M. Weiss, Cambridge University Press, 1993; Fundamentals of Genetic Epidemiology, by M. J. Khoury, T. H. Beaty, and B. H. Cohen, Oxford University Press, 1993; Handbook of Genetic Linkage, by J. Terwilliger J. Ott, Johns Hopkins University Press, 1994.

[0607] The types of statistical analysis performed in different branches of genetics are outlined below as a guide to the relevant literature and publicly available software, some of which is cited.

[0608] Molecular Evolutionary Genetics

[0609] Patterns of nucleotide variation among individuals, families/populations and across species and genera,

[0610] Alignment of sequences and description of variation/polymorphisms among the aligned sequences, amounts of similarities and dissimilarities,

[0611] Measurement of molecular variation among various regions of a gene, testing of neutrality models,

[0612] Rates of nucleotide changes among coding and the non-coding regions within and among populations,

[0613] Construction of phylogenetic trees using methods such as neighborhood joining and maximum parsimony; estimation of ages of variances using coalescent models,

[0614] Population Genetics

[0615] Patterns of distribution of genes among genotypes and populations. Hardy-Weinberg equilibrium, departures form the equilibrium

[0616] Genotype and haplotype frequencies, levels of heterozygosities, polymorphism information contents of genes, estimation of haplotypes from genotypes; the E-M algorithm, and parsimony methods

[0617] Estimation of linkage disequilibrium and recombination

[0618] Hierarchical structure of populations, the F-statistics, estimation of inbreeding, selection and drift

[0619] Genetic admixture/migration and mutation frequencies

[0620] Spatial distribution of genotypes using spatial autocorrelation methods

[0621] Kin-structured maintenance of variation and migration

[0622] Quantitative Genetics

[0623] Phenotype as the product of the interaction between genotype and environment

[0624] Additive, dominance and epistatic variance on the phenotype

[0625] Effects of homozygosity, heterozygosity and developmental homeostasis

[0626] Estimation of heritability: broad sense and narrow sense

[0627] Determination of number of genes governing a character

[0628] Determination of quantitative trait loci (QTLs) using family information or population information, and using linkage and/or association studies

[0629] Determination of quantitative trait nucleotide (QTN) using a combination linkage disequilibrium methods and cladistic approaches

[0630] Determination of individual causal nucleotide in the diploid or haploid state on the phenotype using the method of measured genotype approaches, and combined effects or synergistic interaction of the causal mutations on the phenotype

[0631] Determination of relative importance of each of the mutations on a given phenotype using multivariate methods, such as discriminant function, principal component and step-wise regression methods

[0632] Determination of direct and indirect effect of polymorphisms on a complex phenotype using path analysis (partial regression ) methods

[0633] Determination of the effects of specific environment on a given genotype—genotype×environment interactions using joint regression and additive and multiplicative parameter methods.

[0634] Genetic Epidemiology

[0635] Determination of sample size based on the disease and the marker frequency in the “case” and in the “control” populations

[0636] Stratification of study population based on gender, ethnic, socio-economic variation

[0637] Establishing a “causal relationship” between genotype and disease, using, using various association and linkage approaches—viz., case-control designs, family studies (if available), transmission disequilibrium tests etc.,

[0638] Linkage analysis between markers and a candidate locus using two-point and multipoint approaches. Computer programs used for genetic analysis are: Dna SP version 3.0, by Juilo Rozas, University of Barcelona, Spain. Http://www.bio.ub.es/-Julio; Arlequin 1.1 by S. Schnieder, J -M Kueffer, D. Roessli and L. Excoffier. University of Geneva, Switzerland, http://anthropologie.unige.ch/arlequin. PAUP*4, by D. L. Swofford, Sinauer Associates, Inc., 1999. SYSTAT software, SPSS Inc., Chicago, Ill., 1998; . Linkage User's Guide, by J. Ott, Rockefeller University, Http://Linkage.rockefeller.edu/soft/linkage

[0639] Guidance in the selection of appropriate genetic statistical tests for analysis of data can be obtained from texts such as: Fundamentals of Genetic Epidemiology (Monographs in Epidemiology and Biostatistics, Vol 22) by M. J. Khoury, B. H. Cohen & T. H. Beaty, Oxford Univ Press, 1993; Methods in Genetic Epidemiology by Newton E. Morton, S. Karger Publishing, 1983; Methods in Observational Epidemiology, 2nd edition (Monographs in Epidemiology and Biostatistics, V. 26) by J. L. Kelsey (Editor), A. S. Whittemore & A. S. Evans, 1996; Clinical Trials: Design, Conduct, and Analysis (Monographs in Epidemiology and Biostatistics, Vol 8) by C. L. Meinert & S. Tonascia, 1986)

[0640] I. Retrospective Clinical Trials

[0641] In general the goal of retrospective clinical trials is to test and refine hypotheses regarding genetic factors that are associated with drug responses. The best supported hypotheses can subsequently be tested in prospective clinical trials, and data from the prospective trials will likely comprise the main basis for an application to register the drug and predictive genetic test with the appropriate regulatory body. In some cases, however, it may become acceptable to use data from retrospective trials to support regulatory filings. Exemplary strategies and criteria for stratifying patients in a retrospective clinical trial are provided below.

[0642] Clinical Trials to Study the Effect of One Gene Locus on Drug Response

[0643] A. Stratify Patients by Genotype at One Candidate Variance in the Candidate Gene Locus

[0644] 1. Genetic stratification of patients can be accomplished in several ways, including the following (where ‘A’ is the more frequent form of the variance being assessed and ‘a’ is the less frequent form):

[0645] (a) AA vs. aa

[0646] (b) AA vs. Aa vs. aa

[0647] (c) AA vs. (Aa+aa)

[0648] (d) (AA+Aa) vs. aa.

[0649] 2. The effect of genotype on drug response phenotype may be affected by a variety of nongenetic factors. Therefore it may be beneficial to measure the effect of genetic stratification in a subgroup of the overall clinical trial population. Subgroups can be defined in a number of ways including, for example, biological, clinical, pathological or environmental criteria. For example, the predictive value of genetic stratification can be assessed in a subgroup or subgroups defined by:

[0650] a. Biological Criteria

[0651] i. gender (males vs. females)

[0652] ii. age (for example above 60 years of age). Two, three or more age groups may be useful for defining subgroups for the genetic analysis.

[0653] iii. hormonal status and reproductive history, including pre- vs. post-menopausal status of women, or multiparous vs. nulliparous women

[0654] iv. ethnic, racial or geographic origin, or surrogate markers of ethnic, racial or geographic origin. (For a description of genetic markers that serve as surrogates of racial/ethnic origin see, for example: Rannala, B. and J. L. Mountain, Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci U S A , 94 (17): 9197-9201, 1997. Other surrogate markers could be used, including biochemical markers.)

[0655] b. Clinical Criteria

[0656] i. Disease status. There are clinical grading scales for many diseases. For example, the status of Alzheimer's Disease patients is often measured by cognitive assessment scales such as the mini-mental status exam (MMSE) or the Alzheimer's Disease Assessment Scale (ADAS), which includes a cognitive component (ADAS-COG). There are also clinical assessment scales for many other diseases, including cancer.

[0657] ii. Disease manifestations (clinical presentation).

[0658] iii. Radiological staging criteria.

[0659] c. Pathological criteria:

[0660] i. Histopathologic features of disease tissue, or pathological diagnosis. (For example there are many varieties of lung cancer: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, bronchoalveolar carcinoma, etc., each of which may—which, in combination with genetic variation, may correlate with

[0661] ii. Pathological stage. A variety of diseases, particularly cancer, have pathological staging schemes

[0662] iii. Loss of heterozygosity (LOH)

[0663] iv. Pathology studies such as measuring levels of a marker protein

[0664] v. Laboratory studies such as hormone levels, protein levels, small molecule levels

[0665] 3. Measure frequency of responders in each genetic subgroup. Subgroups may be defined in several ways.

[0666] i. more than two age groups

[0667] ii. reproductive status such as pre or post-menopausal

[0668] 4. Stratify by haplotype at one candidate locus where the haplotype is made up of two variances, three variances or greater than three variances.

[0669] Data from already completed clinical trials can be retrospectively reanalyzed. Since the questions are new, the data can be treated as if it were a prospective trial, with identified variances or haplotypes as stratification criteria or endpoints in clinically stratified data (e.g. what is the frequency of a particular variance in a response group compared to nonresponsders). Care should be taken to in studying a population in which there may be a link between drug-related genes and disease-related genes.

[0670] Retrospective pharmacogenetic trials can be conducted at each of the phases of clinical development, if sufficient data is available to correlate the physiologic effect of the candidate therapeutic intervention and the allelic variance or variances within the treatment population. In the case of a retrospective trial, the data collected from the trial can be re-analyzed by imposing the additional stratification on groups of patients by specific allelic variances that may exist in the treatment groups. Retrospective trials can be useful to ascertain whether a hypothesis that a specific variance has a significant effect on the efficacy or toxicity profile for a candidate therapeutic intervention.

[0671] A prospective clinical trial has the advantage that the trial can be designed to ensure the trial objectives can be met with statistical certainty. In these cases, power analysis, which includes the parameters of allelic variance frequency, number of treatment groups, and ability to detect positive outcomes can ensure that the trial objectives are met.

[0672] In designing a pharmacogenetic trial, retrospective analysis of Phase II or Phase III clinical data can indicate trial variables for which further analysis is beneficial. For example, surrogate endpoints, pharmacokinetic parameters, dosage, efficacy endpoints, ethnic and gender differences, and toxicological parameters may result in data that would require further analysis and re-examination through the design of an additional trial. In these cases, analysis involving statistics, genetics, clinical outcomes, and economic parameters may be considered prior to proceeding to the stage of designing any additional trials. Factors involved in the consideration of statistical significance may include Bonferroni analysis, permutation testing, with multiple testing correction resulting in a difference among the treatment groups that has occurred as a result of a chance of no greater than 20%, i.e. p<0.20. Factors included in determining clinical outcomes to be relevant for additional testing may include, for example, consideration of the target indication, the trial endpoints, progression of the disease, disorder, or condition during the trial study period, biochemical or pathophysiologic relevance of the candidate therapeutic intervention, and other variables that were not included or anticipated in the initial study design or clinical protocol. Factors to be included in the economic significance in determining additional testing parameters include sample size, accrual rate, number of clinical sites or institutions required, additional or other available medical or therapeutic interventions approved for human use, and additional or other available medical or therapeutic interventions concurrently or anticipated to enter human clinical testing. Further, there may be patients within the treatment categories that present data that fall outside of the average or mean values, or there may be an indication of multiple allelic loci that are involved in the responses to the candidate therapeutic intervention. In these cases, one could propose a prospective clinical trial having an objective to determine the significance of the variable or parameter and its effect on the outcome of the parent Phase II trial. In the case of a pharmacogenetic difference, i.e. a single or multiple allelic difference, a population could be selected based upon the distribution of genotypes. The candidate therapeutic intervention could then be tested in this group of volunteers to test for efficacy or toxicity. The repeat prospective study could be a Phase I limited study in which the subjects would be healthy human volunteers, or a Phase II limited efficacy study in which patients which satisfy the inclusion criteria could be enrolled. In either case, the second, confirmatory trial could then be used to systematically ensure an adequate number of patients with appropriate phenotype is enrolled in a Phase III trial.

[0673] A placebo controlled pharmacogenetics clinical trial design will be one in which target allelic variance or variances will be identified and a diagnostic test will be performed to stratify the patients based upon presence, absence, or combination thereof of these variances. In the Phase II or Phase III stage of clinical development, determination of a specific sample size of a prospective trial will be described to include factors such as expected differences between a placebo and treatment on the primary or secondary endpoints and a consideration of the allelic frequencies.

[0674] The design of a pharmacogenetics clinical trial will include a description of the allelic variance impact on the observed efficacy between the treatment groups. Using this type of design, the type of genetic and phenotypic relationship display of the efficacy response to a candidate therapeutic intervention will be analyzed. For example, a genotypically dominant allelic variance or variances will be those in which both heterozygotes and homozygotes will demonstrate a specific phenotypic efficacy response different from the homozygous recessive genotypic group. A pharmacogenetic approach is useful for clinicians and public health professionals to include or eliminate small groups of responders or non-responders from treatment in order to avoid unjustified side-effects. Further, adjustment of dosages when clear clinical difference between heterozygous and homozygous individuals may be beneficial for therapy with the candidate therapeutic intervention

[0675] In another example, a recessive allelic variance or variances will be those in which only the homozygote recessive for that or those variances will demonstrate a specific phenotypic efficacy response different from the heterozygotes or homozygous dominants. An extension of these examples may include allelic variance or variances organized by haplotypes from additional gene or genes.

[0676] V. Variance Identification and Use

[0677] A. Initial Identification of Variances in Genes

[0678] Selection of Population Size and Composition

[0679] Prior to testing to identify the presence of sequence variances in a particular gene or genes, it is useful to understand how many individuals should be screened to provide confidence that most or nearly all pharmacogenetically relevant variances will be found. The answer depends on the frequencies of the phenotypes of interest and what assumptions we make about heterogeneity and magnitude of genetic effects. Prior to testing to identify the presence of sequence variances in a particular gene or genes, it is useful to understand how many individuals should be screened to provide confidence that most or nearly all pharmacogenetically relevant variances will be found. The answer depends on the frequencies of the phenotypes of interest and what assumptions we make about heterogeneity and magnitude of genetic effects. At the beginning we only know phenotype frequencies (e.g. responders vs. nonresponders, frequency of various side effects, etc.).

[0680] The most conservative assumption (resulting in the lowest estimate of allele frequency, and consequently the largest suggested screening population) is (i) that the phenotype (e.g. toxicity or efficacy) is multifactorial (i.e. can be caused by two or more variances or combinations of variances), (ii) that the variance of interest has a high degree of penetrance (i.e. is consistently associated with the phenotype), and (iii) that the mode of transmission is Mendelian dominant. Consider a pharmacogenetic study designed to identify predictors of efficacy for a compound that produces a 15% response rate in a nonstratified population. If half the response is substantially attributable to a given variance, and the variance is consistently associated with a positive response (in 80% of cases) and the variance need only be present in one copy to produce a positive result then ˜10% of the subjects are likely heterozygotes for the variance that produces the response. The Hardy-Weinberg equation can be used to infer an allele frequency in the range of 5% from these assumptions (given allele frequencies of 5%/95% then: 2×0.05×0.95=0.095, or 9.5% heterozygotes are expected, and 0.05×0.05=0.0025, or 0.25% homozygotes are expected. They sum to 9.5%+0.25%=9.75% likely responders, 80% of whom, or 7.6%, are likely real responders due to presence of the positive response allele. Thus about half of the 15% responders are accounted for.). From the Table it can be seen that, in order to have a 99% chance of detecting an allele present at a frequency of 5% nearly 50 subjects should be screened for variances, assuming that the variances occur in the screening population at the same frequency as they occur in the patient population. Similar analyses can be performed for other assumptions regarding likely magnitude of effect, penetrance and mode of genetic transmission.

[0681] At the beginning we only know phenotype frequencies (e.g. responders vs. nonresponders, frequency of various side effects, etc.). As an example, the occurrence of serious 5-FU/FA toxicity—e.g. toxicity requiring hospitalization is often >10%. The occurrence of life threatening toxicity is in the 1-3% range (Buroker et al. 1994). The occurrence of complete remissions is on the order of 2-8%. The lowest frequency phenotypes are thus on the order of ˜2%. If we assume that (i) homogeneous genetic effects are responsible for half the phenotypes of interest and (ii) for the most part the extreme phenotypes represent recessive genotypes, then we need to detect alleles that will be present at ˜10% frequency (0.1×0.1=0.01, or 1% frequency of homozygotes) if the population is at Hardy-Weinberg equilibrium. To have a ˜99% chance of identifying such alleles would require searching a population of 22 individuals (see Table below). If the major phenotypes are associated with heterozygous genotypes then we need to detect alleles present at ˜0.5% frequency (2×0.005×0.995=0.00995, or ˜1% frequency of heterozygotes). A 99% chance of detecting such alleles would require ˜40 individuals (Table below). Given the heterogeneity of the North American population we cannot assume that all genotypes are present in Hardy-Weinberg proportions, therefore a substantial oversampling may be done to increase the chances of detecting relevant variances: For our initial screening, usually, 62 individuals of known race/ethnicity are screened for variance. Variance detection studies can be extended to outliers for the phenotypes of interest to cover the possibility that important variances were missed in the normal population screening.

Allele
frequencies	n = 5	n = 10	n = 15	n = 20	n = 25	n = 30	n = 35	n = 50
p = .99,	9.56	18.21	26.03	33.10	39.50	45.28	50.52	63.40
p = .97,	26.26	45.62	59.90	70.43	78.19	83.92	88.14	95.24
p = .95,	40.13	64.15	78.53	87.15	92.30	95.39	97.24	99.65
p = .93,	51.60	76.58	88.66	94.51	97.34	98.71	99.38	99.93
p = .9, q =	65.13	87.84	95.76	98.52	99.48	99.82	99.94	>99.9
p = .8, q =	89.26	98.84	99.88	99.99	>99.9	>99.9	>99.9	>99.9
p = .7, q =	97.17	99.92	99.99	>99.9	>99.9	>99.9	>99.9	>99.9

[0682] Likelihood of Detecting Polymorphism in a Population as a Function of Allele Frequency & Number of Individuals Genotyped

[0683] The table above shows the probability (expressed as percent) of detecting both alleles (i.e. detecting heterozygotes) at a biallelic locus as a function of (i) the allele frequencies and (ii) the number of individuals genotyped. The chances of detecting heterozygotes increases as the frequencies of the two alleles approach 0.5 (down a column), and as the number of individuals genotyped increases (to the right along a row). The numbers in the table are given by the formula: 1−(p)2n−(q)2n. Allele frequencies are designated p and q and the number of individuals tested is designated n. (Since humans are diploid, the number of alleles tested is twice the number of individuals, or 2n.)

[0684] While it is preferable that numbers of individuals, or independent sequence samples, are screened to identify variances in a gene, it is also very beneficial to identify variances using smaller numbers of individuals or sequence samples. For example, even a comparison between the sequences of two samples or individuals can reveal sequence variances between them. Preferably, 5, 10, or more samples or individuals are screened.

[0685] Source of Nucleic Acid Samples

[0686] Nucleic acid samples, for example for use in variance identification, can be obtained from a variety of sources as known to those skilled in the art, or can be obtained from genomic or cDNA sources by known methods. For example, the Coriell Cell Repository (Camden, N.J.) maintains over 6,000 human cell cultures, mostly fibroblast and lymphoblast cell lines comprising the NIGMS Human Genetic Mutant Cell Repository. A catalog (http://locus.umdnj.edu/nigms) provides racial or ethnic identifiers for many of the cell lines. It is preferable to perform polymorphism discovery on a population that mimics the population to be evaluated in a clinical trial, both in terms of racial/ethnic/geographic background and in terms of disease status. Otherwise, it is generally preferable to include a broad population sample including, for example, (for trials in the United States): Caucasians of Northern, Central and Southern European origin, Africans or African-Americans, Hispanics or Mexicans, Chinese, Japanese, American Indian, East Indian, Arabs and Koreans.

[0687] Source of Human DNA, RNA and cDNA Samples

[0688] PCR based screening for DNA polymorphism can be carried out using either genomic DNA or cDNA produced from mRNA. For many genes, only cDNA sequences have been published, therefore the analysis of those genes is, at least initially, at the cDNA level since the determination of intron-exon boundaries and the isolation of flanking sequences is a laborious process. However, screening genomic DNA has the advantage that variances can be identified in promoter, intron and flanking regions. Such variances may be biologically relevant. Therefore preferably, when variance analysis of patients with outlier responses is performed, analysis of selected loci at the genomic level is also performed. Such analysis would be contingent on the availability of a genomic sequence or intron-exon boundary sequences, and would also depend on the anticipated biological importance of the gene in connection with the particular response.

[0689] When cDNA is to be analyzed it is very beneficial to establish a tissue source in which the genes of interest are expressed at sufficient levels that cDNA can be readily produced by RT-PCR. Preliminary PCR optimization efforts for 19 of the 29 genes in Table 2 reveal that all 19 can be amplified from lymphoblastoid cell mRNA. The 7 untested genes belong on the same pathways and are expected to also be PCR amplifiable.

[0690] PCR Optimization

[0691] Primers for amplifying a particular sequence can be designed by methods known to those skilled in the art, including by the use of computer programs such as the PRIMER software available from Whitehead Institute/MIT Genome Center. In some cases it is preferable to optimize the amplification process according to parameters and methods known to those skilled in the art; optimization of PCR reactions based on a limited array of temperature, buffer and primer concentration conditions is utilized. New primers are obtained if optimization fails with a particular primer set.

[0692] Variance Detection Using T4 Endonuclease VII Mismatch Cleavage Method

[0693] Any of a variety of different methods for detecting variances in a particular gene can be utilized, such as those described in the patents and applications cited in section A above. An exemplary method is a T4 EndoVII method. The enzyme T4 endonuclease VII (T4E7) is derived from the bacteriophage T4. T4E7 specifically cleaves heteroduplex DNA containing single base mismatches, deletions or insertions. The site of cleavage is 1 to 6 nucleotides 3′ of the mismatch. This activity has been exploited to develop a general method for detecting DNA sequence variances (Youil et al. 1995; Mashal and Sklar, 1995). A quality controlled T4E7 variance detection procedure based on the T4E7 patent of R. G. H. Cotton and co-workers. (Del Tito et al., in press) is preferably utilized. T4E7 has the advantages of being rapid, inexpensive, sensitive and selective. Further, since the enzyme pinpoints the site of sequence variation, sequencing effort can be confined to a 25-30 nucleotide segment.

[0694] The major steps in identifying sequence variations in candidate genes using T4E7 are: (1) PCR amplify 400-600 bp segments from a panel of DNA samples; (2) mix a fluorescently-labeled probe DNA with the sample DNA; (3) heat and cool the samples to allow the formation of heteroduplexes; (4) add T4E7 enzyme to the samples and incubate for 30 minutes at 37° C., during which cleavage occurs at sequence variance mismatches; (5) run the samples on an ABI 377 sequencing apparatus to identify cleavage bands, which indicate the presence and location of variances in the sequence; (6) a subset of PCR fragments showing cleavage are sequenced to identify the exact location and identity of each variance.

[0695] The T4E7 Variance Imaging procedure has been used to screen particular genes. The efficiency of the T4E7 enzyme to recognize and cleave at all mismatches has been tested and reported in the literature. One group reported detection of 81 of 81 known mutations (Youil et al. 1995) while another group reported detection of 16 of 17 known mutations (Mashal and Sklar, 1995). Thus, the T4E7 method provides highly efficient variance detection.

[0696] DNA Sequencing

[0697] A subset of the samples containing each unique T4E7 cleavage site is selected for sequencing. DNA sequencing can, for example, be performed on ABI 377 automated DNA sequencers using BigDye chemistry and cycle sequencing. Analysis of the sequencing runs will be limited to the 30-40 bases pinpointed by the T4E7 procedure as containing the variance. This provides the rapid identification of the altered base or bases.

[0698] In some cases, the presence of variances can be inferred from published articles which describe Restriction Fragment Length Polymorphisms (RFLP). The sequence variances or polymorphisms creating those RFLPs can be readily determined using convention techniques, for example in the following manner. If the RFLP was initially discovered by the hybridization of a cDNA, then the molecular sequence of the RFLP can be determined by restricting the cDNA probe into fragments and separately hybridizing to a Southern blot consisting of the restriction digestion with the enzyme which reveals the polymorphic site, identifying the sub-fragment which hybridizes to the polymorphic restriction fragment, obtaining a genomic clone of the gene (e.g., from commercial services such as Genome Systems (Saint Louis, Mo.) or Research Genetics (Ala. ) which will provide appropriate genomic clones on receipt of appropriate primer pairs). Using the genomic clone, restrict the genomic clone with the restriction enzyme which revealed the polymorphism and isolate the fragment which contains the polymorphism, e.g., identifying by hybridization to the cDNA which detected the polymorphism. The fragment is then sequenced across the polymorphic site. A copy of the other allele can be obtained by PCT from addition samples.

[0699] Variance Detection Using Sequence Scanning

[0700] In addition to the physical methods, e.g., those described above and others known to those skilled in the art (see, e.g., Housman, U.S. Pat. No. 5,702,890;

[0701] Housman et al., U.S. patent application Ser. No. 09/045,053), variances can be detected using computational methods, involving computer comparison of sequences from two or more different biological sources, which can be obtained in various ways, for example from public sequence databases. The term “variance scanning” refers to a process of identifying sequence variances using computer-based comparison and analysis of multiple representations of at least a portion of one or more genes. Computational variance detection involves a process to distinguish true variances from sequencing errors or other artifacts, and thus does not require perfectly accurate sequences. Such scanning can be performed in a variety of ways, preferably, for example, as described in Stanton et al., filed Oct. 14, 1999, Ser. No. 09/419,705.

[0702] While the utilization of complete cDNA sequences is highly preferred, it is also possible to utilize genomic sequences. Such analysis may be desired where the detection of variances in or near splice sites is sought. Such sequences may represent full or partial genomic DNA sequences for a gene or genes. Also, as previously indicated, partial cDNA sequences can also be utilized although this is less preferred. As described below, the variance scanning analysis can simply utilize sequence overlap regions, even from partial sequences. Also, while the present description is provided by reference to DNA, e.g., cDNA, some sequences may be provided as RNA sequences, e.g., mRNA sequences. Such RNA sequences may be converted to the corresponding DNA sequences, or the analysis may use the RNA sequences directly.

[0703] B. Determination of Presence or Absence of Known Variances

[0704] The identification of the presence of previously identified variances in cells of an individual, usually a particular patient, can be performed by a number of different techniques as indicated in the Summary above. Such methods include methods utilizing a probe which specifically recognizes the presence of a particular nucleic acid or amino acid sequence in a sample. Common types of probes include nucleic acid hybridization probes and antibodies, for example, monoclonal antibodies, which can differentially bind to nucleic acid sequences differing in one or more variance sites or to polypeptides which differ in one or more amino acid residues as a result of the nucleic acid sequence variance or variances. Generation and use of such probes is well-known in the art and so is not described in detail herein.

[0705] Preferably, however, the presence or absence of a variance is determined using nucleotide sequencing of a short sequence spanning a previously identified variance site. This will utilize validated genotyping assays for the polymorphisms previously identified. Since both normal and tumor cell genotypes can be measured, and since tumor material will frequently only be available as paraffin embedded sections (from which RNA cannot be isolated), it will be necessary to utilize genotyping assays that will work on genomic DNA. Thus PCR reactions will be designed, optimized, and validated to accommodate the intron-exon structure of each of the genes. If the gene structure has been published (as it has for some of the listed genes), PCR primers can be designed directly. However, if the gene structure is unknown, the PCR primers may need to be moved around in order to both span the variance and avoid exon-intron boundaries. In some cases one-sided PCR methods such as bubble PCR (Ausubel et al. 1997) may be useful to obtain flanking intronic DNA for sequence analysis.

[0706] Using such amplification procedures, the standard method used to genotype normal and tumor tissues will be DNA sequencing. PCR fragments encompassing the variances will be cycle sequenced on ABI 377 automated sequencers using Big Dye chemistry

[0707] C. Correlation of the Presence or Absence of Specific Variances with Differential Treatment Response

[0708] Prior to establishment of a diagnostic test for use in the selection of a treatment method or elimination of a treatment method, the presence or absence of one or more specific variances in a gene or in multiple genes is correlated with a differential treatment response. (As discussed above, usually the existence of a variable response and the correlation of such a response to a particular gene is performed first.) Such a differential response can be determined using prospective and/or retrospective data. Thus, in some cases, published reports will indicate that the course of treatment will vary depending on the presence or absence of particular variances. That information can be utilized to create a diagnostic test and/or incorporated in a treatment method as an efficacy or safety determination step.

[0709] Usually, however, the effect of one or more variances is separately determined. The determination can be performed by analyzing the presence or absence of particular variances in patients who have previously been treated with a particular treatment method, and correlating the variance presence or absence with the observed course, outcome, and/or development of adverse events in those patients. This approach is useful in cases in which observation of treatment effects was clearly recorded and cell samples are available or can be obtained. Alternatively, the analysis can be performed prospectively, where the presence or absence of the variance or variances in an individual is determined and the course, outcome, and/or development of adverse events in those patients is subsequently or concurrently observed and then correlated with the variance determination.

[0710] Analysis of Haplotypes Increases Power of Genetic Analysis

[0711] In some cases, variation in activity due to a single gene or a single genetic variance in a single gene may not be sufficient to account for a clinically significant fraction of the observed variation in patient response to a treatment, e.g., a drug, there may be other factors that account for some of the variation in patient response. Drug response phenotypes may vary continuously, and such (quantitative) traits may be influenced by a number of genes (Falconer and Mackay, Quantitative Genetics, 1997). Although it is impossible to determine a priori the number of genes influencing a quantitative trait, potentially only one or a few loci have large effects, where a large effect is 5-20% of total variation in the phenotype (Mackay, 1995).

[0712] Having identified genetic variation in enzymes that may affect action of a specific drug, it is useful to efficiently address its relation to phenotypic variation. The sequential testing for correlation between phenotypes of interest and single nucleotide polymorphisms may be adequate to detect associations if there are major effects associated with single nucleotide changes; certainly it is useful to this type of analysis. However there is no way to know in advance whether there are major phenotypic effects associated with single nucleotide changes and, even if there are, there is no way to be sure that the salient variance has been identified by screening cDNAs. A more powerful way to address the question of genotype-phenotype correlation is to assort genotypes into haplotypes. (A haplotype is the cis arrangement of polymorphic nucleotides on a particular chromosome.) Haplotype analysis has several advantages compared to the serial analysis of individual polymorphisms at a locus with multiple polymorphic sites.

[0713] (1) Of all the possible haplotypes at a locus (2n haplotypes are theoretically possible at a locus with n binary polymorphic sites) only a small fraction will generally occur at a significant frequency in human populations. Thus, association studies of haplotypes and phenotypes will involve testing fewer hypotheses. As a result there is a smaller probability of Type I errors, that is, false inferences that a particular variant is associated with a given phenotype.

[0714] (2) The biological effect of each variance at a locus may be different both in magnitude and direction. For example, a polymorphism in the 5′ UTR may affect translational efficiency, a coding sequence polymorphism may affect protein activity, a polymorphism in the 3′ UTR may affect mRNA folding and half life, and so on. Further, there may be interactions between variances: two neighboring polymorphic amino acids in the same domain—say cys/arg at residue 29 and met/val at residue 166—may, when combined in one sequence, for example, 29 cys-166 val, have a deleterious effect, whereas 29 cys-166 met, 29 arg-166 met and 29 arg-166 val proteins may be nearly equal in activity. Haplotype analysis is the best method for assessing the interaction of variances at a locus.

[0715] (3) Templeton and colleagues have developed powerful methods for assorting haplotypes and analyzing haplotype/phenotype associations (Templeton et al., 1987). Alleles which share common ancestry are arranged into a tree structure (cladogram) according to their (inferred) time of origin in a population (that is, according to the principle of parsimony). Haplotypes that are evolutionarily ancient will be at the center of the branching structure and new ones (reflecting recent mutations) will be represented at the periphery, with the links representing intermediate steps in evolution. The cladogram defines which haplotype-phenotype association tests should be performed to most efficiently exploit the available degrees of freedom, focusing attention on those comparisons most likely to define functionally different haplotypes (Haviland et al., 1995). This type of analysis has been used to define interactions between heart disease and the apolipoprotein gene cluster (Haviland et al 1995) and Alzheimer's Disease and the Apo-E locus (Templeton 1995) among other studies, using populations as small as 50 to 100 individuals. The methods of Templeton have also been applied to measure the genetic determinants of variation in the angiotensin-I converting enzyme gene. (Keavney, B., McKenzie, C. A., Connoll, J. M. C., et al. Measured haplotype analysis of the angiotensin-I converting enzyme gene. Human Molecular Genetics 7: 1745-1751.)

[0716] Methods for Determining Haplotypes

[0717] The goal of haplotyping is to identify the common haplotypes at selected loci that have multiple sites of variance. Haplotypes are usually determined at the cDNA level. Several general approaches to identification of haplotyes can be employed. Haplotypes may also be estimated using computational methods or determined definitively using experimental approaches. Computational approaches generally include an expectation maximization (E-M) algorithm (see, for example: Excoffier and Slatkin, Mol. Biol. Evol. 1995) or a combination of Parsimony (see below) and E-M methods.

[0718] Haplotypes can be determined experimentally without requirement of a haplotyping method by genotyping samples from a set of pedigrees and observing the segregation of haplotypes. For example families collected by the Centre d'Etude du Polymorphisme Humaine (CEPH) can be used. Cell lines from these families are available from the Coriell Repository. This approach will be useful for cataloging common haplotypes and for validating methods on samples with known haplotypes. The set of haplotypes determined by pedigree analysis can be useful in computational methods, including those utilizing the E-M algorithm.

[0719] Haplotypes can also be determined directly from cDNA using the T4E7 procedure. T4E7 cleaves mismatched heteroduplex DNA at the site of the mismatch. If a heteroduplex contains only one mismatch, cleavage will result in the generation of two fragments. However, if a single heteroduplex (allele) contains two mismatches, cleavage will occur at two different sites resulting in the generation of three fragments. The appearance of a fragment whose size corresponds to the distance between the two cleavage sites is diagnostic of the two mismatches being present on the same strand (allele). Thus, T4E7 can be used to determine haplotypes in diploid cells.

[0720] An alternative method, allele specific PCR, may be used for haplotyping. The utility of allele specific PCR for haplotyping has already been established (Michalatos-Beloin et al., 1996; Chang et al. 1997). Opposing PCR primers are designed to cover two sites of variance (either adjacent sites or sites spanning one or more internal variances). Two versions of each primer are synthesized, identical to each other except for the 3′ terminal nucleotide. The 3′ terminal nucleotide is designed so that it will hybridize to one but not the other variant base. PCR amplification is then attempted with all four possible primer combinations in separate wells. Because Taq polymerase is very inefficient at extending 3′ mismatches, the only samples which will be amplified will be the ones in which the two primers are perfectly matched for sequences on the same strand (allele). The presence or absence of PCR product allows haplotyping of diploid cell lines. At most two of four possible reactions should yield products. This procedure has been successfully applied, for example, to haplotype the DPD amino acid polymorphisms.

[0721] Parsimony methods are also useful for classifying DNA sequences, haplotypes or phenotypic characters. Parsimony principle maintains that the best explanation for the observed differences among sequences, phenotypes (individuals, species) etc., is provided by the smallest number of evolutionary changes. Alternatively, simpler hypotheses are preferable to explain a set of data or patterns, than more complicated ones, and ad hoc hypotheses should be avoided whenever possible (Molecular Systematics, Hillis et al., 1996). Parsimony methods thus operate by minimizing the number of evolutionary steps or mutations (changes from one sequence/character) required to account for a given set of data.

[0722] For example, supposing we want to obtain relationships among a set of sequences and construct a structure (tree/topology), we first count the minimum number of mutations that are required for explaining the observed evolutionary changes among a set of sequences. A structure (topology) is constructed based on this number. When once this number is obtained, another structure is tried. This process is continued for all reasonable number of structures. Finally, the structure that required the smallest number of mutational steps is chosen as the likely structure/evolutionary tree for the sequences studied.

[0723] For haplotypes identified herein, haplotypes were identified by examining genotypes from each cell line. This list of genotypes was optimized to remove variance sites/individuals with incomplete information, and the genotype from each remaining cell line was examined in turn. The number of heterozygotes in the genotype were counted, and those genotypes containing more than one heterozygote were discarded, and the rest were gathered in a list for storage and display. For haplotypes identified herein, haplotypes were identified by examining genotypes from each cell line. This list of genotypes was optimized to remove variance sites/individuals with incomplete information, and the genotype from each remaining cell line was examined in turn. The number of heterozygotes in the genotype were counted, and those genotypes containing more than one heterozygote were discarded, and the rest were gathered in a list for storage and display.

[0724] D. Selection of Treatment Method Using Variance Information

[0725] 1. General

[0726] Once the presence or absence of a variance or variances in a gene or genes is shown to correlate with the efficacy or safety of a treatment method, that information can be used to select an appropriate treatment method for a particular patient. In the case of a treatment which is more likely to be effective when administered to a patient who has at least one copy of a gene with a particular variance or variances (in some cases the correlation with effective treatment is for patients who are homozygous for a variance or set of variances in a gene) than in patients with a different variance or set of variances, a method of treatment is selected (and/or a method of administration) which correlates positively with the particular variance presence or absence which provides the indication of effectiveness. As indicated in the Summary, such selection can involve a variety of different choices, and the correlation can involve a variety of different types of treatments, or choices of methods of treatment. In some cases, the selection may include choices between treatments or methods of administration where more than one method is likely to be effective, or where there is a range of expected effectiveness or different expected levels of contra-indication or deleterious effects. In such cases the selection is preferably performed to select a treatment which will be as effective or more effective than other methods, while having a comparatively low level of deleterious effects. Similarly, where the selection is between method with differing levels of deleterious effects, preferably a method is selected which has low such effects but which is expected to be effective in the patient.

[0727] Alternatively, in cases where the presence or absence of the particular variance or variances is indicative that a treatment or method of administration is more likely to be ineffective or contra-indicated in a patient with that variance or variances, then such treatment or method of administration is generally eliminated for use in that patient.

[0728] 2. Diagnostic Methods

[0729] Once a correlation between the presence and absence of at least one variance in a gene or genes and an indication of the effectiveness of a treatment, the determination of the presence or absence of that at least one variance provides diagnostic methods, which can be used as indicated in the Summary above to select methods of treatment, methods of administration of a treatment, methods of selecting a patient or patients for a treatment and others aspects in which the determination of the presence or absence of those variances provides useful information for selecting or designing or preparing methods or materials for medical use in the aspects of this invention. As previously stated, such variance determination or diagnostic methods can be performed in various ways as understood by those skilled in the art.

[0730] In certain variance determination methods, it is necessary or advantageous to amplify one or more nucleotide sequences in one or more of the genes identified herein. Such amplification can be performed by conventional methods, e.g., using polymerase chain reaction (PCR) amplification. Such amplification methods are well-known to those skilled in the art and will not be specifically described herein. For most applications relevant to the present invention, a sequence to be amplified includes at least one variance site, which is preferably a site or sites which provide variance information indicative of the effectiveness of a method of treatment or method of administration of a treatment, or effectiveness of a second method of treatment which reduces a deleterious effect of a first treatment method, or which enhances the effectiveness of a first method of treatment. Thus, for PCR, such amplification generally utilizes primer oligonucleotides which bind to or extent through at least one such variance site under amplification conditions.

[0731] For convenient use of the amplified sequence, e.g., for sequencing, it is beneficial that the amplified sequence be of limited length, but still long enough to allow convenient and specific amplification. Thus, preferably the amplified sequence has a length as described in the Summary.

[0732] Also, in certain variance determination, it is useful to sequence one or more portions of a gene or genes, in particular, portions of the genes identified in this disclosure. As understood by persons familiar with nucleic acid sequencing, there are a variety of effective methods. In particular, sequencing can utilize dye termination methods and mass spectrometric methods. The sequencing generally involves a nucleic acid sequence which includes a variance site as indicated above in connection with amplification. Such sequencing can directly provide determination of the presence or absence of a particular variance or set of variances, e.g., a haplotype, by inspection of the sequence (visually or by computer). Such sequencing is generally conducted on PCR amplified sequences in order to provide sufficient signal for practical or reliable sequence determination.

[0733] Likewise, in certain variance determinations, it is useful to utilize a probe or probes. As previously described, such probes can be of a variety of different types.

[0734] VI. Pharmaceutical Compositions, Including Pharmaceutical Compositions Adapted to be Preferentially Effective in Patients Having Particular Genetic Characteristics

[0735] A. General

[0736] The methods of the present invention, in many cases will utilize conventional pharmaceutical compositions, but will allow more advantageous and beneficial use of those compositions due to the ability to identify patients who are likely to benefit from a particular treatment or to identify patients for whom a particular treatment is less likely to be effective or for whom a particular treatment is likely to produce undesirable or intolerable effects. However, in some cases, it is advantageous to utilize compositions which are adapted to be preferentially effective in patients who possess particular genetic characteristics, i.e., in whom a particular variance or variances in one or more genes is present or absent (depending on whether the presence or the absence of the variance or variances in a patient is correlated with an increased expectation of beneficial response). Thus, for example, the presence of a particular variance or variances may indicate that a patient can beneficially receive a significantly higher dosage of a drug than a patient having a different

[0737] B. Regulatory Indications and Restrictions

[0738] The sale and use of drugs and the use of other treatment methods usually are subject to certain restrictions by a government regulatory agency charged with ensuring the safety and efficacy of drugs and treatment methods for medical use, and approval is based on particular indications. In the present invention it is found that variability in patient response or patient tolerance of a drug or other treatment often correlates with the presence or absence of particular variances in particular genes. Thus, it is expected that such a regulatory agency may indicate that the approved indications for use of a drug with a variance-related variable response or toleration include use only in patients in whom the drug will be effective, and/or for whom the administration of the drug will not have intolerable deleterious effects, such as excessive toxicity or unacceptable side-effects. Conversely, the drug may be given for an indication that it may be used in the treatment of a particular disease or condition where the patient has at least one copy of a particular variance, variances, or variant form of a gene. Even if the approved indications are not narrowed to such groups, the regulatory agency may suggest use limited to particular groups or excluding particular groups or may state advantages of use or exclusion of such groups or may state a warning on the use of the drug in certain groups. Consistent with such suggestions and indications, such an agency may suggest or recommend the use of a diagnostic test to identify the presence or absence of the relevant variances in the prospective patient. Such diagnostic methods are described in this description. Generally, such regulatory suggestion or indication is provided in a product insert or label, and is generally reproduced in references such as the Physician's Desk Reference (PDR). Thus, this invention also includes drugs or pharmaceutical compositions which carry such a suggestion or statement of indication or warning or suggestion for a diagnostic test, and which may also be packaged with an insert or label stating the suggestion or indication or warning or suggestion for a diagnostic test.

[0739] In accord with the possible variable treatment responses, an indication or suggestion can specify that a patient be heterozygous, or alternatively, homozygous for a particular variance or variances or variant form of a gene. Alternatively, an indication or suggestion may specify that a patient have no more than one copy, or zero copies, of a particular variance, variances, or variant form of a gene.

[0740] A regulatory indication or suggestion may concern the variances or variant forms of a gene in normal cells of a patient and/or in cells involved in the disease or condition. For example, in the case of a cancer treatment, the response of the cancer cells can depend on the form of a gene remaining in cancer cells following loss of heterozygosity affecting that gene. Thus, even though normal cells of the patient may contain a form of the gene which correlates with effective treatment response, the absence of that form in cancer cells will mean that the treatment would be less likely to be effective in that patient than in another patient who retained in cancer cells the form of the gene which correlated with effective treatment response. Those skilled in the art will understand whether the variances or gene forms in normal or disease cells are most indicative of the expected treatment response, and will generally utilize a diagnostic test with respect to the appropriate cells. Such a cell type indication or suggestion may also be contained in a regulatory statement, e.g., on a label or in a product insert.

[0741] C. Preparation and Administration of Drugs and Pharmaceutical Compositions Including Pharmaceutical Compositions Adapted to be Preferentially Effective in Patients Having Particular Genetic Characteristics

[0742] A particular compound useful in this invention can be administered to a patient either by itself, or in pharmaceutical compositions where it is mixed with suitable carriers or excipient(s). In treating a patient exhibiting a disorder of interest, a therapeutically effective amount of a agent or agents such as these is administered. A therapeutically effective dose refers to that amount of the compound that results in amelioration of one or more symptoms or a prolongation of survival in a patient.

[0743] Toxicity and therapeutic efficacy of such compounds can be determined by standard pharmaceutical procedures in cell cultures or experimental animals, e.g., for determining the LD50 (the dose lethal to 50% of the population) and the ED50 (the dose therapeutically effective in 50% of the population). The dose ratio between toxic and therapeutic effects is the therapeutic index and it can be expressed as the ratio LD50/ED50. Compounds which exhibit large therapeutic indices are preferred. The data obtained from these cell culture assays and animal studies can be used in formulating a range of dosage for use in human. The dosage of such compounds lies preferably within a range of circulating concentrations that include the ED50 with little or no toxicity. The dosage may vary within this range depending upon the dosage form employed and the route of administration utilized.

[0744] For any compound used in the method of the invention, the therapeutically effective dose can be estimated initially from cell culture assays. For example, a dose can be formulated in animal models to achieve a circulating plasma concentration range that includes the IC50 as determined in cell culture. Such information can be used to more accurately determine useful doses in humans. Levels in plasma may be measured, for example, by HPLC.

[0745] The exact formulation, route of administration and dosage can be chosen by the individual physician in view of the patient's condition. (See e.g. Fingl et. al., in The Pharmacological Basis of Therapeutics, 1975, Ch. 1 p.1). It should be noted that the attending physician would know how to and when to terminate, interrupt, or adjust administration due to toxicity, or to organ dysfunctions. Conversely, the attending physician would also know to adjust treatment to higher levels if the clinical response were not adequate (precluding toxicity). The magnitude of an administrated dose in the management of disorder of interest will vary with the severity of the condition to be treated and the route of administration. The severity of the condition may, for example, be evaluated, in part, by standard prognostic evaluation methods. Further, the dose and perhaps dose frequency, will also vary according to the age, body weight, and response of the individual patient. A program comparable to that discussed above may be used in veterinary medicine.

[0746] Depending on the specific conditions being treated, such agents may be formulated and administered systemically or locally. Techniques for formulation and administration may be found in Remington's Pharmaceutical Sciences, 18th ed., Mack Publishing Co., Easton, Pa. (1990). Suitable routes may include oral, rectal, transdermal, vaginal, transmucosal, or intestinal administration; parenteral delivery, including intramuscular, subcutaneous, intramedullary injections, as well as intrathecal, direct intraventricular, intravenous, intraperitoneal, intranasal, or intraocular injections, just to name a few.

[0747] For injection, the agents of the invention may be formulated in aqueous solutions, preferably in physiologically compatible buffers such as Hanks's solution, Ringer's solution, or physiological saline buffer. For such transmucosal administration, penetrants appropriate to the barrier to be permeated are used in the formulation. Such penetrants are generally known in the art.

[0748] Use of pharmaceutically acceptable carriers to formulate the compounds herein disclosed for the practice of the invention into dosages suitable for systemic administration is within the scope of the invention. With proper choice of carrier and suitable manufacturing practice, the compositions of the present invention, in particular, those formulated as solutions, may be administered parenterally, such as by intravenous injection. The compounds can be formulated readily using pharmaceutically acceptable carriers well known in the art into dosages suitable for oral administration. Such carriers enable the compounds of the invention to be formulated as tablets, pills, capsules, liquids, gels, syrups, slurries, suspensions and the like, for oral ingestion by a patient to be treated.

[0749] Agents intended to be administered intracellularly may be administered using techniques well known to those of ordinary skill in the art. For example, such agents may be encapsulated into liposomes, then administered as described above. Liposomes are spherical lipid bilayers with aqueous interiors. All molecules present in an aqueous solution at the time of liposome formation are incorporated into the aqueous interior. The liposomal contents are both protected from the external microenvironment and, because liposomes fuse with cell membranes, are efficiently delivered into the cell cytoplasm. Additionally, due to their hydrophobicity, small organic molecules may be directly administered intracellularly.

[0750] Pharmaceutical compositions suitable for use in the present invention include compositions wherein the active ingredients are contained in an effective amount to achieve its intended purpose. Determination of the effective amounts is well within the capability of those skilled in the art, especially in light of the detailed disclosure provided herein. In addition to the active ingredients, these pharmaceutical compositions may contain suitable pharmaceutically acceptable carriers comprising excipients and auxiliaries which facilitate processing of the active compounds into preparations which can be used pharmaceutically. The preparations formulated for oral administration may be in the form of tablets, dragees, capsules, or solutions. The pharmaceutical compositions of the present invention may be manufactured in a manner that is itself known, e.g., by means of conventional mixing, dissolving, granulating, dragee-making, levitating, emulsifying, encapsulating, entrapping or lyophilizing processes.

[0751] Pharmaceutical formulations for parenteral administration include aqueous solutions of the active compounds in water-soluble form. Additionally, suspensions of the active compounds may be prepared as appropriate oily injection suspensions. Suitable lipophilic solvents or vehicles include fatty oils such as sesame oil, or synthetic fatty acid esters, such as ethyl oleate or triglycerides, or liposomes. Aqueous injection suspensions may contain substances which increase the viscosity of the suspension, such as sodium carboxymethyl cellulose, sorbitol, or dextran. Optionally, the suspension may also contain suitable stabilizers or agents which increase the solubility of the compounds to allow for the preparation of highly concentrated solutions.

[0752] Pharmaceutical preparations for oral use can be obtained by combining the active compounds with solid excipient, optionally grinding a resulting mixture, and processing the mixture of granules, after adding suitable auxiliaries, if desired, to obtain tablets or dragee cores. Suitable excipients are, in particular, fillers such as sugars, including lactose, sucrose, mannitol, or sorbitol; cellulose preparations such as, for example, maize starch, wheat starch, rice starch, potato starch, gelatin, gum tragacanth, methyl cellulose, hydroxypropylmethyl-cellulose, sodium carboxymethylcellulose, and/or polyvinylpyrrolidone (PVP). If desired, disintegrating agents may be added, such as the cross-linked polyvinyl pyrrolidone, agar, or alginic acid or a salt thereof such as sodium alginate. Dragee cores are provided with suitable coatings. For this purpose, concentrated sugar solutions may be used, which may optionally contain gum arabic, talc, polyvinyl pyrrolidone, carbopol gel, polyethylene glycol, and/or titanium dioxide, lacquer solutions, and suitable organic solvents or solvent mixtures. Dyestuffs or pigments may be added to the tablets or dragee coatings for identification or to characterize different combinations of active compound doses.

[0753] Pharmaceutical preparations which can be used orally include push-fit capsules made of gelatin, as well as soft, sealed capsules made of gelatin and a plasticizer, such as glycerol or sorbitol. The push-fit capsules can contain the active ingredients in admixture with filler such as lactose, binders such as starches, and/or lubricants such as talc or magnesium stearate and, optionally, stabilizers. In soft capsules, the active compounds may be dissolved or suspended in suitable liquids, such as fatty oils, liquid paraffin, or liquid polyethylene glycols. In addition, stabilizers may be added.

[0754] The invention described herein features methods for determining the appropriate identification of a patient diagnosed with a neurological disease or neurological dysfunction based on an analysis of the patient's allele status for a gene listed in Tables 1, 3, and 4. Specifically, the presence of at least one allele indicates that a patient will respond to a candidate therapeutic intervention aimed at treating a neurological clinical symptoms. In a preferred approach, the patient's allele status is rapidly diagnosed using a sensitive PCR assay and a treatment protocol is rendered. The invention also provides a method for forecasting patient outcome and the suitability of the patient for entering a clinical drug trial for the testing of a candidate therapeutic intervention for a neurological disease, condition, or dysfunction.

[0755] The findings described herein indicate the predictive value of the target allele in identifying patients at risk for neurologic disease or neurologic dysfunction. In addition, because the underlying mechanism influenced by the allele status is not disease-specific, the allele status is suitable for making patient predictions for diseases not affected by the pathway as well.

[0756] The following examples, which describe exemplary techniques and experimental results, are provided for the purpose of illustrating the invention, and should not be construed as limiting.

EXAMPLE 1

Effect of Pharmacokinetic parameters on Efficacy of Drugs and Candidate Therapeutic Interventions

[0757] The efficacy of a compound is determined by a combination of pharmacodynamic and pharmacokinetic effects. Both types of effect are under genetic control. In the present invention, the genetic determinants of efficacy are discussed in terms of variation in the genes that encode proteins responsible for absorption, distribution, metabolism, and excretion of compounds, i.e. pharmacokinetic parameters.

[0758] The pharmacokinetic parameters with potential effects on efficacy include absorption, distribution, metabolism, and excretion. These parameters affect efficacy broadly by controlling the availability of a compound at the site(s) of action. Interpatient variability in the availability of a compound can result in undertreatment or overtreatment, or in adverse reactions due to levels of a compound or its metabolite(s). Differences in the genes responsible for pharmacokinetic variation, therefore, can be a potential source of interpatient variability in drug response.

[0759] Impact of Stratification Based Upon Genotype in Drug Development for Drugs, Compounds, or Candidate Therapeutic Interventions that may Efficacy

[0760] Clozapine induced agranulocytosis has been associated in some reports with specific HLA haplotypes or with HSP70 variants. These reports suggest that a gene within the HLA region is associated with agranulocytosis in response to clozapine therapy. In a recent study, two ethnic groups were analyzed for genetic markers for agranulocytosis. Tumor necrosis factor microsatellites d3 and b4 were found in higher frequencies in patients that experience clozapine-induced agranulocytosis. These data, while they need to be confirmed by additional studies, are suggestive that tumor necrosis factor polymorphisms may also be associated with clozapine-induced agranulocytosis.

[0761] In this invention we provide additional genes and gene sequence variances that may account for variability in toxic responses. The Detailed Description above demonstrates how identification of a candidate gene or genes (e.g. gene pathways), genetic stratification, clinical trial design, and diagnostic genotyping can lead to improved medical management of a disease and/or approval of a drug, or broader use of an already approved drug. Gene pathways including, but not limited to, those that are outlined in the gene pathway, Table 1, are useful in identifying the sources of interpatient variation in efficacy as well as in the adverse events summarized in the column headings of Table 2, Discussed in detail below are exemplary candidate genes for the analysis of pharmacokinetic variability in clinical development, using the methods described above.

[0762] Advantages of Inclusion of Pharmacogenetic Stratification in Clinical Development of Agents: Impact on Efficacy

[0763] The advantages of a clinical research and drug development program that includes the use of polymorphic genotyping for the stratification of patients for the appropriate selection of candidate therapeutic intervention includes 1) identification of patients that may respond earlier and show signs and symptoms of efficacious therapy, 2) identification of the primary gene and relevant polymorphic variance that directly affects efficacy endpoints, 3) identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes, and 4) identification of allelic variances or haplotypes in genes that indirectly affects efficacy, safety or both.

[0764] By identifying subsets of patients, based upon genotype, that experience efficacious therapeutic benefit in response to the administration of a drug, agent or candidate therapeutic intervention, optimal selection may reduce level and extent of the appearance or manifestation of a side effect or toxicity. Appropriate genotyping and correlation to dosing regimen, or selection of optimal therapy would be beneficial to the patient, caregivers, medical personnel, and the patient's loved ones.

[0765] As an example of identification of the primary gene and relevant polymorphic variance that directly affects efficacy, safety, or both one could select an gene pathway as described in the Detailed Description, and determine the effect of genetic polymorphism and therapy efficacy, safety, or both within that given pathway. For example, referring to Table 2, genes involved in absorption and distribution, phase I and phase II metabolism, and excretion the optimization of therapy of by an agent known to have an efficacious effect by determining whether the patient has a predisposing genotype in which the selected agents are more effective and or are more safe. In considering an optimization protocol, one could potentially predetermine the genotypic profile of these genes involved in the manifestation of the adverse effect, or those genes preeminently responsible for drug response. By embarking on the previously described gene pathway approach, it is technical feasibility to determine the relevant genes within such a targeted drug development program.

[0766] Identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes may speed drug development for therapeutic alternatives. There is a need for therapies that are targeted to a disease and symptom management with limited or no undesirable side effects. Identification of a specific variance or variances within genes involved in the manifestation of clinical efficacious endpoints or therapeutic benefit and specific genetic polymorphisms of these critical genes may assist the development of novel agents and the identification of those patients that may best benefit from therapy of these candidate therapeutic alternatives.

[0767] By identifying allelic variances or haplotypes in genes that indirectly affects efficacy, safety of any class of drugs, one could target specific secondary drug or agent therapeutic actions that affect the overall therapeutic action of these agents.

[0768] Pharmacogenomics studies for these drugs, or other agent, compound, drug, or candidate therapeutic intervention, could be performed by identifying genes that are involved in the function of a drug including, but not limited to absorption, distribution, metabolism, or elimination, the interaction of the drug with its target as well as potential alternative targets, the response of the cell to the binding of a drug to a target, the metabolism (including synthesis, biodistribution or elimination) of natural compounds which may alter the activity of the drug by complementary, competitive or allosteric mechanisms that potentiate or limit the effect of the drug, and genes involved in the etiology of the disease that alter its response to a particular class of therapeutic agents. It will be recognized to those skilled in the art that this broadly includes proteins involved in pharmacokinetics as well as genes involved in pharmacodynamics. This also includes genes that encode proteins homologous to the proteins believed to carry out the above functions are also worth evaluation as they may carry out similar functions. Together the foregoing proteins constitute the candidate genes for affecting response of a patient to the therapeutic intervention. Using the methods described above, variances in these genes can be identified, and research and clinical studies can be performed to establish an association between a drug response or toxicity and specific variances.

EXAMPLE 2

Drug-Induced Toxicity: Blood Dyscrasias

[0769] I. Description of Blood Dyscrasias

[0770] Blood dyscrasias are a feature of over half of all drug-related deaths and include, but are not limited to, bone marrow aplasia, granulocytopenia, aplastic anemia, leukopenia, lymphoid hyperplasia, hemolytic anemia, and thrombocytopenia. All of these syndromes include pancytopenia to some degree.

[0771] Bone marrow aplasia—is defined as a profound loss of bone marrow resulting in pancytopenia. Drugs known to cause bone marrow aplasia include, but are not limited to, chloramphenicol, gold salts, mephenytoin, penicillamine, phenylbutazone, and trimethadione. In general these drugs are not first line therapy due to the rare occurrence of marrow aplasia. Specific forms of aplasia include:

[0772] Granulocytopenia—is defined as a loss of polymorphonuclear neutrophils to a count lower than 500. Granulocytopenia primarily predisposes the patient to bacterial and fungal infections. Drugs known to cause granulocytopenia include, but are not limited to, captopril, cephalosporins, choral hydrate, chlorpropamide, penicillins, phenothiazines, phenylbutazone, phenytoin, procainamide, propranolol, and tolbutamide.

[0773] Aplastic anemia—is a disorder involving an inability of the hematologic cells to regenerate and thus there is a dramatic depletion of one or more of the following cell types: neutrophils, platelets, or reticulocytes. Drugs associated with producing aplastic anemia are: 1) agents or compounds that produce bone marrow depression, for example cytotoxic drugs used in cancer chemotherapy; 2) agents or compounds that frequently, but inevitably, produce marrow aplasia, for example benzene; 3) agents or compounds that are associated with aplastic anemia, for example chloramphenicol, antiprotozoals, and sulfonamides.

[0774] Aplastic anemia is almost always a result of damage to the hematopoietic stem cells. There are two possible routes for the destruction of these cells: 1) direct damage to the stem cell DNA, and 2) cell cycle dependant depletion of later stage progenitor cells. In the first case, drugs or agents bind to and randomly damage the genetic material. This type of aplasia is associated with both early aplasia (immediate or direct cytotoxicity) or later myelodysplasia and leukemia. In the latter case, mitotically and metabolically active progenitor cells are preferentially affected and progenitor cell depletion may lead to unregulated proliferation of spared stem cells.

[0775] Leukopenia—is defined when the circulating peripheral white cell count falls below 5-10×109 cells per liter. Circulating leukocytes consist of neutrophils, monocytes, basophils, eosinophils, and lymphocytes.

[0776] Neutropenia is defined when the peripheral neutrophil count falls below 2×109 cells per liter. There are a number of drugs families that can cause neutropenia including, but not exclusive to, antiarrythmics (procainamide, propanolol, quinidine), antibiotics (chloramphenicol, penicillins, sulfonamides, trimethorpimmethoxazole, para-aminosalicyclic acid, rifampin, vancomycin, isoniazid, nitrofurantoin), antimalarials (dapsone, qunine, pyrimethamine), anticonvulsants (phenytoin, mephenytoin, trimethadione, ethosuximide, carbamazepine), hypoglycemic agents (tolbutamide, chlorpropamide), antihistamines (cimetadine, brompheniramine, tripelennamine), antihypertensives (methydopa, captopril), antiinflammatory agents (aminopyrine, phenylbutazone, gold salts, ibuprofen, indomethacin), diuretics (acetazolamide, hydrochlorothiazide, chlorthalidone), phenothiazines (chlorpormazine, promazine, prochlorperazine), antimetabolite immunosuppresive agents, cytotoxic agents (alkylating agents, antimetabolites, anthracyclines, vinca alkyloids, cis-platinum, hydroxyarea, actinomycin D), and other agents (alpha and gamma interferon, allopurinol, ethanol, levamisole, penicillamine).

[0777] Lymphoid hyperplasia—is characterized by reactive changes within the T-cell regions of the lymph node that encroach on, and at times appear to efface, the germinal follicles. In these regions, the T-cells undergo progressive transformation to immunoblasts. These reactions are encountered particularly in response to drug-induced immunoreactivity. Drugs known to cause lymphoid hyperplasia are phenytoin, and mephenytoin.

[0778] Hemolytic anemia—is characterized by the premature destruction of red cells, accumulation of hemoglobin metabolic by-products, and a marked increase in erythroporesis within the bone marrow. Drugs know to cause hemolytic anemia include, but are not excluded to, methyldopa, penicillin, sulfonamides, and vitamin E deficiency.

[0779] Thrombocytopenia—is characterized by a marked reduction in the number of circulating platelets to a level below 100,000/mm3. Drug-induced thrombocytopenia may result from decreased production of platelets or decreased platelet survival or both. Drugs known to cause thrombocytopenia include, but are not excluded to, ethanol, acetominophen, acetazolamide, acetylsalicyclic acid, 5-aminosalicylic acid, carbamazepine, chlorpheniramine, cimetadine, digitoxin, diltiazem, ethychlorynol, gold salts, heparin, hydantoins, isoniazid, levodopa, meprobamate, methyldopa, penicillamine, phenylbutazone, procainamide, quinidine, quinine, ranitidine, Rauwolfa alkaloids, rifampin, sulfonamides, sulfonylureas, cytotoxic drugs, and thiazide diuretics.

[0780] II. Impact of Stratification Based Upon Genotype in Drug Development for Drugs, Compounds, or Candidate Therapeutic Interventions that may Induce Blood Dyscrasias

[0781] Clozapine induced agranulocytosis is associated with differing HLA types and HSP70 variants in patients for whom responded to clozapine therapy but developed agranulocytosis. This is suggestive that a gene within the MHC region is associated with the manifestation of agranulocytosis in response to clozapine therapy. In a recent study, two ethnic groups were analyzed for genetic markers for the agranulocytosis. Tumor necrosis factor microsatellites d3 and b4 were found in higher frequencies in patients that experience clozapine-induced agranulocytosis. These data are suggestive that there is an involvement of tumor necrosis factor constellation polymorphism and clozapine-induced agranulocytosis.

[0782] There is evidence to suggest that there are safety response differences to drug therapy in reference to development of blood dyscrasias which may be attributable to genotypic differences between individuals. There is provided in this invention examples of gene pathways that are implicated in the disease process or its therapy and those that potentially cause this variability. The Detailed Description above demonstrates how identification of a candidate gene or genes and gene pathways, stratification, clinical trial design, and implementation of genotyping for appropriate medical management of a given disease can be used to identify the genetic cause of variations in clinical response to therapy, new diagnostic tests, new therapeutic approaches for treating this disorder, and new pharmaceutical products or formulations for therapy. Gene pathways including, but not limited to, those that are outlined in the gene pathway Table 1, and pathway matrix Table 2 and discussed below are candidates for the genetic analysis and product development using the methods described above.

[0783] Advantages of Inclusion of Pharmacogenetic Stratification in Clinical Development of Agents that May Cause Blood Dyscrasias

[0784] The advantages of a clinical research and drug development program that includes the use of polymorphic genotyping for the stratification of patients for the appropriate selection of candidate therapeutic intervention includes 1) identification of patients that may respond earlier and show signs and symptoms of blood dyscrasias, 2) identification of the primary gene and relevant polymorphic variance that directly affects manifestation of a blood disorder, 3) identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes, and 4) identification of allelic variances or haplotypes in genes that indirectly affects efficacy, safety or both.

[0785] By identifying subsets of patients, based upon genotype, that experience blood dyscrasias in response to the administration of a drug, agent or candidate therapeutic intervention, optimal selection may reduce level and extent of the hemostatic damage. Appropriate genotyping and correlation to dosing regimen, or selection of optimal therapy would be beneficial to the patient, caregivers, medical personnel, and the patient's loved ones.

[0786] As an example of identification of the primary gene and relevant polymorphic variance that directly affects efficacy, safety, or both one could select an gene pathway as described in the Detailed Description, and determine the effect of genetic polymorphism and therapy efficacy, safety, or both within that given pathway. For example, referring to Table 2, genes involved in drug transport, phase I and phase II metabolism, protection from reactive intermediate damage, and immune responsiveness the optimization of therapy of by an agent known to have a blood dyscrasia side effect by determining whether the patient has a predisposing genotype in which the selected agents are more effective and or are more safe. In considering an optimization protocol, one could potentially predetermine the genotypic profile of these genes involved in the manifestation of the adverse effect, or those genes preeminently responsible for drug response. By embarking on the previously described gene pathway approach, it is technical feasibility to determine the relevant genes within such a targeted drug development program.

[0787] Identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes may speed drug development for therapeutic alternatives. There is a need for therapies that are targeted to a disease and symptom management with limited or no undesirable side effects. Identification of a specific variance or variances within genes involved in the pathophysiologic manifestation of blood dyscrasisas and specific genetic polymorphisms of these critical genes may assist the development of novel agents and the identification of those patients that may best benefit from therapy of these candidate therapeutic alternatives.

[0788] By identifying allelic variances or haplotypes in genes that indirectly affects efficacy, safety of any class of drugs that has an effect on the prevention, progression, or symptoms of blood dyscrasias, one could target specific secondary drug or agent therapeutic actions that affect the overall therapeutic action of hemoprotective agents.

[0789] Pharmacogenomics studies for these drugs, or other agent, compound, drug, or candidate therapeutic intervention, could be performed by identifying genes that are involved in the function of a drug including, but not limited to absorption, distribution, metabolism, or elimination, the interaction of the drug with its target as well as potential alternative targets, the response of the cell to the binding of a drug to a target, the metabolism (including synthesis, biodistribution or elimination) of natural compounds which may alter the activity of the drug by complementary, competitive or allosteric mechanisms that potentiate or limit the effect of the drug, and genes involved in the etiology of the disease that alter its response to a particular class of therapeutic agents. It will be recognized to those skilled in the art that this broadly includes proteins involved in pharmacokinetics as well as genes involved in pharmacodynamics. This also includes genes that encode proteins homologous to the proteins believed to carry out the above functions are also worth evaluation as they may carry out similar functions. Together the foregoing proteins constitute the candidate genes for affecting response of a patient to the therapeutic intervention. Using the methods described above, variances in these genes can be identified, and research and clinical studies can be performed to establish an association between a drug response or toxicity and specific variances.

EXAMPLE 3

Drug-Induced Toxicity: Cutaneous Toxicity

[0790] Drug-induced cutaneous toxicity includes, but is not excluded to, eczematous: photodermititis (phototoxic and photoallergic), exfoliative dermititis; maculopapular eruption; papulosquamous reactions: psoriaform, lichus planus, or pityriasis rosea-like; vesiculobullous reactions; toxic epidermal necrolysis; pustular-acneform reactions; urticaria and erythemas: urticaria, erythema multiforme; nodular lesions: erythema nodosum, vasiculitis reaction; telangiectatic and LE reactions; pigmentary reaction; other cutaneous reactions: fixed drug reactions, alopecia, hypertrichosis, macules, papules, angioedema, morbilliform-maculopapular rash, toxic epidermal necrolysis, erythema multiforme, erythema nodosum, contact dermititis, vesicles, petechiae, exfolliative dermititis, fixed drug eruptions, and severe skin rash (Stevens-Johnson syndrome).

[0791] Drugs known to be associated with cutaneous toxicities include, but are not exclusive of, antineoplastic agents, sulfonamides, hydantoins and others listed for each type of toxicity.

[0792] Uticaria and angioedema—is defined as the transient appearance of elevated, erythematous pruitic wheals (hives) or serpiginous exanthem. The appearance of uticaria is perceived as ongoing immediate hypersensitivity reaction. Angioedema is defined as uticaria, but involving deeper dermal and subdermal sites. Uticaria and angioedema appear to result from dilation of local postcapillary venules. Degranulation of cutaneous mast cells may be involved.

[0793] Drugs associated with uticaria and angioedema include, but are not excluded to, antimicrobials include, but not exclusive of, 5-aminosalicylic acid, aminoglycosides, cephalosporins, ethambutol, isoniazid, metronidazole, miconazole, nalidixic acid, penicillins, quinine, rifampin, spectinomycin, sulfonamides, and other drugs: asparaginase, aspirin and other non-steroidal antiinflammatory agenets, calcitonin, chloral hydrate, chlorambucil, cimetidine, cyclophosphamide, daunorubicin, ergotamine, ethchlorvynol, doxorubicin, ethosuximide, ethylenediamine, glucocorticoids, melphalan, penicillamine, phenothiazines, procainamide, procarbazine, quinidine, tartazine, thiazide diuretics, thiotepa.

[0794] Morbilliform-maculopapular rash—are rashes that result in eruptions or are morbilliform in nature.

[0795] Drugs associated with rashes include, but are not limited to, 5-aminosalicyclic acid, cephalosporins, erythromycin, gentamicin, penicillins, streptomycin, sulfonamides, allopurinol, barbiturates, captopril, coumarin, gold salts, hydantoins, thiazide diuretics.

[0796] Toxic epidermal necrolysis and erythroderma and exfoliative dermititis

[0797] Cutaneous erythroderma, edema, scaling, and fissuring may occur in response to certain drugs. Drugs associated with these types of cutaneous reactions include, but are limited to, allopurinol, amikacin, captopril, carbamazepine, chloral hydrate, chlorambucil, chloroquine, chlorpromazine, cyclosporine, diltiazem, ethambutol, ethylenediamine, glutethimide, gold salts, griseofulvin, hydantoins, hydroxychloroquine, minoxidil, nifedipine, nonsteroid antiinflammatory agents, penicillin, phenobarbital, rifampin, spironolactone, sulfonamides, trimethadione, trimethoprim, tocainamide, tocainide, vancomycin, verpamil.

[0798] Erythema mutliforme—is characterized by a hypersensitivity reaction in blood vessels of the dermis. The hypersensitivity is the result of immune complexes formed by small molecules interacting with proteinaceous components of the blood vessels. In cases whereby the mucosal membranes of the mouth and eye are involved, is referred to as Stevens-Johnson syndrome. Typically the cutaneous lesions, blisters and painful erosions occur in the mout and eye.

[0799] Drugs associated with erythema mulitforme include, but are not limited to, allopurinol, acetominophen, amikacin, barbiturates, carbamazepine, chloroquine, chlorporamide, clindamycin, ethambutol, ethosuximide, gold salts, glucocorticoids, hydantoins, hydralazine, hydroxyurea, mechlorethamine, meclofenamate, penicillins, phenothiazides, phenophthalein, phenylbutazone, rifampin, streptomycin, sulfonamides, sulfonylureas, sulindac, vaccines.

[0800] Fixed drug eruptions

[0801] Drug associated with fixed drug eruptions include, but are not excluded to, acetominophen, 5-aminosalicyclic acid, aspirin, barbiturates, benzodiazepines, barbiturates, chloroquine, dapsone, dimethylhydrinate, gold salts, hydralazine, hyoscine, ibuprofen, iodides, meprobamate, methanamine, metronidazole, penicillins, phenobarbital, phenolphthalein, phenothiazides, phenylbutazone, procarbazine, pseudoephedrine, quinine, saccharin, streptomycin, sulfonamides, and tetracyclines.

[0802] Erythema nodosum—is an innflammatory reaction in subcutaneous fat which represents a hypersentivity reaction to a number of antigenic stimuli. Multiple red, painful nodules do not ulcerate but involute and leave a yellow-purple bruises. Small molecules intreracting with proteinaceous components forma asensitizing antigen.

[0803] Drugs associated with producing erythema nododum include, but are not excluded to, bromides, oral contraceptives, penicillins, and sulfonamides.

[0804] Contact dermititis—is characterized by eruptions on histological analysis to epidermal intercellular edema (spongiosis). Contact dermititis can be caused by allergic or irritant mechanisms. Allergic contact dermititis is a delayed hypersensitivity reaction that can occur in response to a variety of small molecules that when bound to proteinaceous components of the skin form a sensitizing antigen. The antigen is processed by Langerhans' cells in the epidermis, presenting the antigen to the circulating T lymphocytes. Irritant dermititis is produced by substances that irritate or have a direct toxic effect on the skin.

[0805] Drugs associated with contact dermititis side effects include, but are not limited to, ambroxol, amikacin, antihistamines, bacitracin, benzalkonium chloride, benzocaine, benzyl chloride, cetl alcohol, chloramphenicol, chlorpormazine, clioquinol, colophony, ethylenediamine, fluorouracil, formaldehyde, gentamycin, glucocorticoids, glutaraldehyde, heparin, hexachlorophene, iodochlorhydroxyquin, lanolin, local anesthestics, minoxidil, naftin, neimycin, nitrofurazone, opiates, para-aminobenzoic acid, parabens, penicillins, phenothiazines, prolflavine, propylene glycol, streptomycin, sulfonamides, thimerosal, timolol.

[0806] Impact of Stratification Based Upon Genotype in Drug Development for Drugs, Compounds, or Candidate Therapeutic Interventions that May Induce Cutaneous Reactions

[0807] Recently, it has been described that there is a deletion polymorphism in the B2 bradykinin receptor gene (B2BKR). It was revealed that there is a 9 base pair deletion in exon 1 of the B2BKR gene and upon inspection of patients experienceing angioedema, patients with immunochemical evidence of angioedema were homozygous for no deletion at that site. These results were suggestive of B2BKR genotype influence on the clinical status and manifestation angioedema.

[0808] There is evidence to suggest that there are safety response differences to drug therapy in reference to development of cutaneous reactions which may be attributable to genotypic differences between individuals. There is provided in this invention examples of gene pathways that are implicated in the disease process or its therapy and those that potentially cause this variability. The Detailed Description above demonstrates how identification of a candidate gene or genes and gene pathways, stratification, clinical trial design, and implementation of genotyping for appropriate medical management of a given disease can be used to identify the genetic cause of variations in clinical response to therapy, new diagnostic tests, new therapeutic approaches for treating this disorder, and new pharmacuetical products or formulations for therapy. Gene pathways including, but not limited to, those that are outlined in the gene pathway Table 1, and pathway matrix Table 2 and discussed below are candidates for the genetic analysis and product development using the methods described above.

[0809] Advantages of Inclusion of Pharmacogenetic Stratification in Clinical Development of Agents that May Cause Cutaneous Reactions

[0810] The advantages of a clinical research and drug development program that includes the use of polymorphic genotyping for the stratification of patients for the appropriate selection of candidate therapeutic intervention includes 1) identification of patients that may respond earlier and show signs and symptoms of cutaneous reactions, 2) identification of the primary gene and relevant polymorphic variance that directly affects manifestation of a cutaneous disorder, 3) identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes, and 4) identification of allelic variances or haplotypes in genes that indirectly affects efficacy, safety or both.

[0811] By identifying subsets of patients, based upon genotype, that experience cutaneous reactions in response to the administration of a drug, agent or candidate therapeutic intervention, optimal selection may reduce level and extent of the skin damage. Appropriate genotyping and correlation to dosing regimen, or selection of optimal therapy would be beneficial to the patient, caregivers, medical personnel, and the patient's loved ones.

[0812] As an example of identification of the primary gene and relevant polymorphic variance that directly affects efficacy, safety, or both one could select an gene pathway as described in the Detailed Description, and determine the effect of genetic polymorphism and therapy efficacy, safety, or both within that given pathway. For example, referring to Table 2, genes involved in drug transport, phase I and phase II metabolism, protection from reactive intermediate damage, and immune responsiveness, the optimization of therapy of by an agent known to have a cutaneous side effect by determining whether the patient has a predisposing genotype in which the selected agents are more effective and or are more safe. In considering an optimization protocol, one could potentially predetermine the genotypic profile of these genes involved in the manifestation of the adverse effect, or those genes preeminently responsible for drug response. By embarking on the previously described gene pathway approach, it is technical feasibility to determine the relevant genes within such a targeted drug development program.

[0813] Identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes may speed drug development for therapeutic alternatives. There is a need for therapies that are targeted to a disease and symptom management with limited or no undesirable side effects. Identification of a specific variance or variances within genes involved in the pathophysiologic manifestation of cutaneous reactions and specific genetic polymorphisms of these critical genes may assist the development of novel agents and the identification of those patients that may best benefit from therapy of these candidate therapeutic alternatives.

[0814] By identifying allelic variances or haplotypes in genes that indirectly affects efficacy, safety of any class of drugs that has an effect on the prevention, progression, or symptoms of cutaneous reactions, one could target specific secondary drug or agent therapeutic actions that affect the overall therapeutic action.

[0815] Pharmacogenomics studies for these drugs, or other agents, compounds, or candidate therapeutic interventions, could be performed by identifying genes that are involved in the the function of a drug including, but not limited to absorption, distribution, metabolism, or elimination , the interaction of the drug with its target as well as potential alternative targets, the response of the cell to the binding of a drug to a target, the metabolism (including synthesis, biodistribution or elimination) of natural compounds which may alter the activity of the drug by complementary, competitive or allosteric mechanisms that potentiate or limit the effect of the drug, and genes involved in the etiology of the disease that alter its response to a particular class of therapeutic agents. It will be recognized to those skilled in the art that this broadly includes proteins involved in pharmacokinetics as well as genes involved in pharmacodynamics. This also includes genes that encode proteins homologous to the proteins believed to carry out the above functions are also worth evaluation as they may carry out similar functions. Together, the foregoing proteins constitute the candidate genes for affecting response of a patient to the therapeutic intervention. Using the methods described above, variances in these genes can be identified, and research and clinical studies can be performed to establish an association between a drug response or toxicity and specific variances.

EXAMPLE 5

Drug-Induced CNS Toxicity

[0816] Drug-induced central nervous system toxicity includes CNS stimulation or CNS depression. Characteristics of CNS toxicity include, but are not limited to, tinnitus and dizziness, acute dystonic reactions, parkinsonian syndrome, coma, convulsions, depression and psychosis, sweating, mydriasis, hyperpyrexia, centrally mediated cardiovascular involvement (hypertension, tachycardia, extrasystoles, arrythmias, circulatory collapse) and respiratory depression or tachypnea. Drugs known to be associated with CNS toxicity include, but are not exclusive of, salicylates, antipsychotics, sedatives, cholinergics,

[0817] Impact of Stratification Based Upon Genotype in Drug Development for Drugs, Compounds, or Candidate Therapeutic Interventions that May Induce CNS Toxicity

[0818] There is evidence to suggest that there are safety response differences to drug therapy in reference to development of CNS toxicities which may be attributable to genotypic differences between individuals. There is provided in this invention examples of gene pathways that are implicated in the disease process or its therapy and those that potentially cause this variability. The Detailed Description above demonstrates how identification of a candidate gene or genes and gene pathways, stratification, clinical trial design, and implementation of genotyping for appropriate medical management of a given disease can be used to identify the genetic cause of variations in clinical response to therapy, new diagnostic tests, new therapeutic approaches for treating this undesirable adverse effect, and new pharmacuetical products or formulations for therapy. Gene pathways including, but not limited to, those that are outlined in the gene pathway Table 1, and pathway matrix Table 2 and discussed below are candidates for the genetic analysis and product development using the methods described above.

[0819] Advantages of Inclusion of Pharmacogenetic Stratification in Clinical Development of Agents that May Cause CNS Toxicities

[0820] The advantages of a clinical research and drug development program that includes the use of polymorphic genotyping for the stratification of patients for the appropriate selection of candidate therapeutic intervention includes 1) identification of patients that may respond earlier and show signs and symptoms of CNS toxicities, 2) identification of the primary gene and relevant polymorphic variance that directly affects manifestation of a CNS toxicity, 3) identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes, and 4) identification of allelic variances or haplotypes in genes that indirectly affects efficacy, safety or both.

[0821] By identifying subsets of patients, based upon genotype, that experience CNS toxicity in response to the administration of a drug, agent or candidate therapeutic intervention, optimal selection may reduce level and extent of the neurologic damage. Appropriate genotyping and correlation to dosing regimen, or selection of optimal therapy would be beneficial to the patient, caregivers, medical personnel, and the patient's loved ones.

[0822] As an example of identification of the primary gene and relevant polymorphic variance that directly affects efficacy, safety, or both one could select an gene pathway as described in the Detailed Description, and determine the effect of genetic polymorphism and therapy efficacy, safety, or both within that given pathway. For example, referring to Table 2, genes involved in drug transport, phase I and phase II metabolism, protection from reactive intermediate damage, the optimization of therapy of by an agent known to inpart CNS toxic or undesirable side effect or effects by determining whether the patient has a predisposing genotype in which the selected agents are more effective and or are more safe. In considering an optimization protocol, one could potentially predetermine the genotypic profile of these genes involved in the manifestation of the adverse effect, or those genes preeminently responsible for drug response. By embarking on the previously described gene pathway approach, it is technical feasibility to determine the relevant genes within such a targeted drug development program.

[0823] Identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes may speed drug development for therapeutic alternatives. There is a need for therapies that are targeted to a disease and symptom management with limited or no undesirable side effects. Identification of a specific variance or variances within genes involved in the pathophysiologic manifestation of CNS toxicities and specific genetic polymorphisms of these critical genes may assist the development of novel agents and the identification of those patients that may best benefit from therapy of these candidate therapeutic alternatives.

[0824] By identifying allelic variances or haplotypes in genes that indirectly affects efficacy, safety of any class of drugs that has an effect on the prevention, progression, or symptoms of CNS toxicities, one could target specific secondary drug or agent therapeutic actions that affect the overall therapeutic action of neuroprotective agents.

[0825] Pharmacogenomics studies for these drugs, or other agent, compound, drug, or candidate therapeutic intervention, could be performed by identifying genes that are involved in the the function of a drug including, but not limited to absorption, distribution, metabolism, or elimination, the interaction of the drug with its target as well as potential alternative targets, the response of the cell to the binding of a drug to a target, the metabolism (including synthesis, biodistribution or elimination) of natural compounds which may alter the activity of the drug by complementary, competitive or allosteric mechanisms that potentiate or limit the effect of the drug, and genes involved in the etiology of the disease that alter its response to a particular class of therapeutic agents. It will be recognized to those skilled in the art that this broadly includes proteins involved in pharmacokinetics as well as genes involved in pharmacodynamics. This also includes genes that encode proteins homologous to the proteins believed to carry out the above functions are also worth evaluation as they may carry out similar functions. Together the foregoing proteins constitute the candidate genes for affecting response of a patient to the therapeutic intervention. Using the methods described above, variances in these genes can be identified, and research and clinical studies can be performed to establish an association between a drug response or toxicity and specific variances.

EXAMPLE 6

Drug-Induced Liver Toxicity

[0826] Drug-induced liver disease or drug-induced liver toxicity can manifest as zonal necrosis, nonspecific focal hepatitis, viral hepatitis-like reactions, inflammatory or noninflammatory cholestasis, small or large droplet fatty liver, granulomas, chronic hepatitis, fibrosis, tumors, or vascular lesions.

[0827] In the majority of the cases of known drug-induced liver toxicity, the drug is metabolized to a form that is deleterious to hepatic, or extrahepatic function. There are many endogenous or exogenous compounds that may be considered to attenuate or ablate toxic hepatocyte-produced metabolite mechanisms or effects of hepatic or extrahepatic damage.

[0828] In hepatocellular damage, free oxygen radicals may be generated in the hepatic metabolic processes that are deleterious to intracellular organelles, DNA, or metabolic pathways. There are endogenous cytoprotective agents that may prevent free radical-mediated damage such as retinoids, flavins, reduced glutathione, vitamin E,S-adenylylmethionine, and the enzyme superoxide dismutase (SOD). In animal models in which SOD activity is diminished or absent, the liver function was normal, but the sensitivity to toxin challenge was heightened.

[0829] In cholestatic damage, the bile salt uptake, metabolism, secretion, or transport is compromised and the residual increased bile salt concentrations are deleterious to hepatocyte function. The increase in bile salts is the main metabolic disturbance that initially leads to jaundice and pruritis and can progress to pancreatitis, hyperbilirubinemia, biliary cirrhosis, and hepatic encephalopathy.

[0830] In both cases of drug-induced liver toxicity, the drug must first be absorbed and enter in the hepatic circulation. Further, clinically it is often difficult to determine whether cholestatic damage leads to hepatocellular damage or whether hepatocellular damage leads to cholestatic damage. In many cases, until the patient is symptomatic, the underlying damage mechanisms may be clinically overlooked. By the time the drug-induced liver disease is symptomatic, the damage, be it hepatocellular or cholestatic or both, may be irreversible.

[0831] Identification of Genes involved in Drug-Induced Liver Toxicity

[0832] Thus, in the process of identifying drug- or xenobiotic-induced liver toxicity, one skilled in the art would identify key metabolic enzymes or bile cannicula transport processes that would be linked with either hepatocellular damage or cholestasis or combination of hepatocellular damage or cholestasis.

[0833] Hepatocellular damage may be the result of direct chemical mediated effects, may be severe, and usually is associated with damage within organelles, DNA and membranes. Clinically there is a marked elevation of SGOT and SGPT as well as other enzymes. In cases of cholestasis there is jaundice, pruritis, a marked elevation of bile salts and alkaline phosphatase activity, but not an elevation of SGOT or SGPT. In cases of toxic liver disease there is difficulty, at least initially to determine the underlying etiology. Clinically, symptoms may not appear as clear as described above. Further, depending on the rate and extent of the damage, hepatocellular damage may be masked or asymptomatic until liver impairment has induced cholestasis.

[0834] Potentially hepatotoxic agents can be divided broadly into two groups: intrinsic hepatotoxins and idiosyncratic hepatotoxins. Intrinsic hepatotoxins produce acute liver damage in a predictable, dose-dependent fashion shortly after ingestion or exposure. Generally, all subjects exposed will uniformly exhibit signs and symptoms. In this category, the effects seen in humans can be mimicked in animal models. Examples of intrinsic hepatotoxins are carbon tetrachloride, 2-nitropropane, trichloroethane, the octapeptide toxins of the Amanita mushroom species, and the antipyretic, acetominophen. In some of these cases, toxic metabolites result in covalent modification of hepatocyte macromolecules or reactive oxygen intermediates leads to peroxidation of cell membrane lipids or other intracellular molecules.

[0835] In contrast, idiosyncratic hepatotoxins produce liver damage in an unpredictable, dose-independent manner after a latent period of ingestion or exposure. Animal models or experimental data is generally incapable of predicting the effect in humans. Further, idiosyncratic hepatotoxins do not uniformly affect a population; a subset of the group exposed may or may not exhibit signs or symptoms. Range of symptoms are from mild to severe and is thought to coincide with differences in the pathways of drug or xenobiotic biotransformation or immune-mediated drug sensitivity (drug allergy). In idiosyncratic drug-induced liver disease, fever, arthralgias, rash, eosinophilia, are often prominent and indicate a hypersensitivity reaction.

[0836] Impact of Stratification Based Upon Genotype in Drug Development for Drugs, Compounds, or Candidate Therapeutic Interventions that may Induce Hepatotoxicity

[0837] Genes encoding proteins with catalytic function that are involved in the metabolism of drugs or xenobiotics are listed in Tables 1 and 2 below. Further listed are those proteins that are involved in the uptake, transport, or secretion into the bile cannicula. Below are further specific example of drug-specific effects on the liver.

[0838] Acetaminophen-Induced Liver Disease

[0839] Acetominophen is a readily available, easy to administer analgesic that is an example of a intrinsic hepatotoxin. This hepatotoxin causes zonal necrosis and acute liver failure and is associated with renal failure. Although a high dose (10-15 grams) is required for significant liver injury to occur, the onset of initial symptoms does not occur until hours after ingestion. The progression of symptoms occurs including progressive liver failure with hepatic encephalopathy, prolongation of prothrombin time, hypoglycemia, and lactic acidosis. The liver injury is caused by a toxic metabolite of acetominophen via the P450 metabolizing system. This toxic intermediate at low concentrations is conjugated with glutathione. However, in toxic doses, the conjugating enzymes stores are exhausted and the reactive intermediate reacts with intracellular proteins and results in cellular dysfunction and ultimately death. The rate of metabolism is dependent on the concentrations of both P450 and glutathione. Speeding this toxic pathway may include increasing the available P450 or reducing the availablility of glutathione, e.g. using known inducers of P450 such as ethanol and and phenobarbital; and known inhibitors of glutathione concentrations, e.g., ethanol and fasting. Acetominophen toxicity is completely reversed if the drug is removed. Chronic ingestion may produce subclinical liver injury, centrilobular necrosis, or chronic hepatitis; however all reversible if the drug is removed.

[0840] Amiodarone-Induced Liver Disease

[0841] Amiodarone is used in treatment of refractory arrythmias. In some patients amiodarone produces mild to moderate increases of serum transaminases which are generally accompanied by engorgement of lysosomes with phospholipid. In a fraction of the patients, a more severe liver injury develops which histologically resembles alcoholic hepatitis: fat infiltration of hepatocytes, focal necrosis, fibrosis, polymorphonuclear leukocyte infiltrates, and Mallory bodies. The lesion may progress to micronodular cirrhosis, with portal hypertension and liver failure. Hepatomegaly is seen, but jaundice is rare.

[0842] Amiodarone accumulates in lysosomes and inhibits lysosomal phopholipases, however the connection between this mechanism and alcoholic hepatitis histopathology is unknown. Unfortunately, rapid discontinuation of amiodarone increases the risk of cardiac arrythmias.

[0843] Chlopromazine-Induced Liver Disease

[0844] Chlorpromazine is an anti-psychotic agent which, in a small portion of the patient population can produce a cholestatic reaction. Symptoms include fever, anorexia, arthalgias, pruritis, jaundice, and eosinophilia is common. This idiosyncratic type of liver toxicity suggests a hypersensitivity type reaction. The symptoms subside over a period of weeks following discontinuation, Rarely, residual cholestatic disease occurs, treatment for pruritis and fat-soluble vitamin supplementation may be required, but eventual recovery almost always occurs.

[0845] Erythromycin-Induced Liver Disease

[0846] Erythromycin, a broad spectrum antibiotic, can be accompanied by a cholestatic reaction. Inflammatory cell infiltration and liver cell necrosis may occur. The hepatoptoxicity presents as right upper quadrant pain, fever, and variable cholestatic symptoms. The prognosis is uniform and will occur after readminstration of the drug, The mechanism of action is unknown.

[0847] Halothane-Induced Liver Disease

[0848] Halothane is a gaseous anthesthetic and can, in rare instances, cause a viral-like hepatitis syndrome. In severe cases, this hepatotoxicity, may cause fatal massive heaptic necrosis. Severe reactions seem to appear after previous or multiple exposure to halothane. It is known that the P450 metabolites of this xenobiotic are responsible for the mechanism of hepatic injury.

[0849] Isoniazid (INH)-Induced Liver Disease

[0850] Isoniazid is used as a single drug in the prophylaxis of tuberculosis. In 10-20% of of the persons taking INH, subclinical liver injury occurs. The conversion of INH to acetylhydrazine is via acetylation. In slow acetylators, INH is more hepatotoxic. The conversion of INH to acetylhydrazine to diacetylhydrazine is impaired. In slow acetylators, the acetylhydrazine is not well metabolized and is further oxidized by one of the P450 enzymes to a toxic, reactive molecule that is responsible for the liver disease. Discontinuation of the drug returns the enzymatic levels to normal and the liver is able to restore activity.

[0851] Sodium Valproate-Induced Liver Disease

[0852] Sodium valproate is an anti-epileptic agent that is routinely prescribed for petit mal epilepsy and in some cases produces severe hepatotoxicity. Similar to INH, sodium valproate is accompanied by a high incidence of transient, slight and asymptomatic increases in serum transaminases. Usually the increased enzyme activity appears after weeks of treatment. In rare cases of severe liver toxicity, the nonspecific systemic and digestive symptoms are followed by jaundice, evidence of liver failure, as well as encephalopathy and coagulopathy. The mechanism of hepatotoxicity is unknown, however there are theories that there is impairment of mitochondiral oxidation of long-chain fatty acids by a metabolite of the parent drug. Symptoms subside with little to no residual liver dysfunction after discontinuing the drug.

[0853] Oral Contraceptive Induced Liver Disease

[0854] Estrogen, progesterone, and combination oral contraceptives can produce several adverse effects on the heptobiliary system. They are 1) hepatocellular cholestasis, 2) liver cell neoplasias, 3) increased predisposition to cholesterol and gall stone formation, 4) hepatic vein thrombosis. These cholestatic hepatotoxic effects are attributed to estrogen's direct effect on bile formation. The mechanism of action is unknown.

[0855] There is evidence to suggest that there are safety response differences to drug therapy in reference to development of drug-induced liver toxicity which may be attributable to genotypic differences between individuals. There is provided in this invention examples of gene pathways that are implicated in the disease process or its therapy and those that potentially cause this variability. The Detailed Description above demonstrates how identification of a candidate gene or genes and gene pathways, stratification, clinical trial design, and implementation of genotyping for appropriate medical management of a given disease can be used to identify the genetic cause of variations in clinical response to therapy, new diagnostic tests, new therapeutic approaches for treating this disorder, and new pharmacuetical products or formulations for therapy. Gene pathways including, but not limited to, those that are outlined in the gene pathway Table 1, and pathway matrix Table 2 and discussed below are candidates for the genetic analysis and product development using the methods described above.

[0856] Advantages of Inclusion of Pharmacogenetic Stratification in Clinical Development of Agents that May Cause Liver Toxicity

[0857] The advantages of a clinical research and drug development program that includes the use of polymorphic genotyping for the stratification of patients for the appropriate selection of candidate therapeutic intervention includes 1) identification of patients that may respond earlier and show signs and symptoms of liver toxicity, 2) identification of the primary gene and relevant polymorphic variance that directly affects manifestation of a liver disorder, 3) identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes, and 4) identification of allelic variances or haplotypes in genes that indirectly affects efficacy, safety or both.

[0858] By identifying subsets of patients, based upon genotype, that experience drug-induced liver toxicity in response to the administration of a drug, agent or candidate therapeutic intervention, optimal selection may reduce level and extent of the hepatic damage. Appropriate genotyping and correlation to dosing regimen, or selection of optimal therapy would be beneficial to the patient, caregivers, medical personnel, and the patient's loved ones.

[0859] As an example of identification of the primary gene and relevant polymorphic variance that directly affects efficacy, safety, or both one could select an gene pathway as described in the Detailed Description, and determine the effect of genetic polymorphism and therapy efficacy, safety, or both within that given pathway. For example, referring to Table 2, genes involved in drug transport, phase I and phase II metabolism, excretion, hepatic cannicular uptake and concentration, and protection from reactive intermediate damage the optimization of therapy by an agent known to have a hepatic side effect by determining whether the patient has a predisposing genotype in which the selected agents are more effective and or are more safe. In considering an optimization protocol, one could potentially predetermine the genotypic profile of these genes involved in the manifestation of the adverse effect, or those genes preeminently responsible for drug response. By embarking on the previously described gene pathway approach, it is technical feasibility to determine the relevant genes within such a targeted drug development program.

[0860] Identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes may speed drug development for therapeutic alternatives. There is a need for therapies that are targeted to a disease and symptom management with limited or no undesirable side effects. Identification of a specific variance or variances within genes involved in the pathophysiologic manifestation of drug-induced liver toxicity and specific genetic polymorphisms of these critical genes may assist the development of novel agents and the identification of those patients that may best benefit from therapy of these candidate therapeutic alternatives.

[0861] By identifying allelic variances or haplotypes in genes that indirectly affects efficacy, safety of any class of drugs that has an effect on the prevention, progression, or symptoms of drug induced liver toxicity, one could target specific secondary drug or agent therapeutic actions that affect the overall therapeutic action of hepatoprotective agents.

[0862] Pharmacogenomics studies for these drugs, or other agent, compound, drug, or candidate therapeutic intervention, could be performed by identifying genes that are involved in the the function of a drug including, but not limited to absorption, distribution, metabolism, or elimination, the interaction of the drug with its target as well as potential alternative targets, the response of the cell to the binding of a drug to a target, the metabolism (including synthesis, biodistribution or elimination) of natural compounds which may alter the activity of the drug by complementary, competitive or allosteric mechanisms that potentiate or limit the effect of the drug, and genes involved in the etiology of the disease that alter its response to a particular class of therapeutic agents. It will be recognized to those skilled in the art that this broadly includes proteins involved in pharmacokinetics as well as genes involved in pharmacodynamics. This also includes genes that encode proteins homologous to the proteins believed to carry out the above functions are also worth evaluation as they may carry out similar functions. Together the foregoing proteins constitute the candidate genes for affecting response of a patient to the therapeutic intervention. Using the methods described above, variances in these genes can be identified, and research and clinical studies can be performed to establish an association between a drug response or toxicity and specific variances.

EXAMPLE 7

Drug-Induced Cardiovascular Toxicity

[0863] Drug induced cardiovascular toxicities include but are not excluded to arrythmias, tachycardia, extrasystoles, circulatory collapse, QT prolongation, cardiomyopathy, hypotension, or hypertension. Drugs known to elicit these type of responses include but are not excluded to theophylline, hydantoins, doxorubicin, daunorubicin.

[0864] Arrythmias—If the normal sequence of electrical impulse and propagation through myocardial tissue is perturbed, an arrythmia occurs. Broadly, arrythmias fall into one of three categories: bradyarrythmias (slowing or failure of the initiating impulse), heart block (an impaired propagation through node tissue or atrial or ventricular muscle), and tachyarrythmias (abnormal rapid heart rhythms). Subcategories include: sinus bradycardia, atrioventricular block (AV block), sinus tachycardia, ventricular tachycardia, atrial flutter, multifocal atrial tachycardia, polymorphic ventricular tachycardia with or without QT prolongation, frequent or difficult to terminate ventricular tachycardia, atrial tachycardia with or without AV block, ventricular bigeminy, and ventricular fibrillation. Drugs known to induce these types of arrythmias include, but are not excluded to, digitalis, verapamil, diltiazem, b-adrenergic blockers, clonidine, methyldopa, quinidine, flecainide, propafenone, theophylline, sotalol, procainamide, disopyramide, certain non-cardioactive drugs ( ), and amiodarone.

[0865] Heart Rate, Tachycardia-Heart rate is under both sympathetic and parasympathic control. The influence of heart rate on cardiac output is paramount. Drugs affecting heart rate include, but are not limited to, sympathomimetics, parasympathomimetics, and agents or compounds affecting these two central inputs.

[0866] Extasystoles—is defined as premature myocardial excitation. Extrasystoles can include atrial, nodal, or ventricular. Other asynchronous pathologies may result from these systoles. Drugs known to be associated with extra systoles include, but are not excluded to, agents that prolong the depolarization time, agents that leave a residual available intracellular calcium, or agents that alter the function of the K+ or Na+ channel activity.

[0867] QT Prolongation—is the interval on an electrocardiogram that indicates ventricular action potential duration. QT prolongation can lead to uncoordinated atrial and ventricular action potentials. In these circumstances of delayed or prolonged polymorphic ventricular after depolarizations, resultant abnormal triggering of secondary, uncoordinated depolarizations can occur. Two of these conditions are explained as follows and may be associated with underlying rapid or slow heart rate: 1) under conditions of residual excess intracellular calcium (myocardial ischemia, adrenergic stress, digitalis intoxication), and 2) under conditions of marked prolongation of cardiac action potential (agents (antiarrythmics or others) that prolong action potential duration).

[0868] Cardiomyopathy—There are broadly three categories of cardiomyopathies: dilated, hypertrophic, and restrictive. These cardiac muscular diseases can be of mechanical or acquired origin.

[0869] Dilated cardiomyopathies are generally caused by myocardial injury that results in depressed systolic function and progressive ventricular dilatation. Drug induced dilated cardiomyopathy can occur in the presence of, but are not excluded to, ethanol, chenotherapeutic agents, elemental compounds, and catecholamimetics.

[0870] Hypertrophic cardiomyopathy is the presentation of grossly assymetric (eccentric) or symmetric (concentric) hyoertrophy of the left ventricle in the absence of another cardiac or systemic disease capable of producing the disproportionate increase in ventricle mass. In drug induced hypertrophic cardiomyopathy, there may be compensatory hypertrophy of the left ventricle in response to inordinate and or sustained hypertension or prolonged reduced or insufficient cardiac output as a result of myocardial injury or noncardiac mediated physiological events.

[0871] Restrictive cardiomyopathies are the result of a primary abnormality of diastolic function (impaired filling). Impaired diastolic function can occur as a result of morphologically detectable myocardial or endomyocardial disease, interstitial deposition of deposition of abnormal substances (infiltrative), intracellular accumulation of abnormal substances (strage diseases), or as a result of endomyocardial disease. In the last category, anthracyclines have been associated with both dilated and restrictive cardiomyopathies.

[0872] Blood Pressure—Blood pressure is regulated in a complex interplay of neural and endocrine mechanisms. These mechanisms are aimed at the physiologic contorl of cardiac output, delivery of blood components to the tissues, and removal of metabolic by-products from the tissues.

[0873] Hypertension is defined as the elevated arterial blood pressure either an increase of systolic or diastolic pressure or both. Secondary hypertension can be associated with drugs and chemicals including, but not limited to, cyclosporine, oral contraceptives, glucocorticoids, mineralocorticoids, sympathomimetics, tyramine, and MAO inhibitors.

[0874] Hypotension is defined as the reduction in blood pressure that is associated with orthostatic hypotension, syncope, head injury, hepatic failure, antidiuresis, myocardial infarction and cardiogenic shock. Drug-induced hypotension is associated drugs including, but not exclusive of, parasympathomimetics, diuretics, and direct acting cardiac agents.

[0875] Impact of Stratification Based Upon Genotype in Drug Development for Drugs, Compounds, or Candidate Therapeutic Interventions that may Induce Cardiovascular Toxicity

[0876] There is evidence to suggest that there are safety response differences to drug therapy in reference to development of cardiovascular toxicity which may be attributable to genotypic differences between individuals. There is provided in this invention examples of gene pathways that are implicated in the disease process or its therapy and those that potentially cause this variability. The Detailed Description above demonstrates how identification of a candidate gene or genes and gene pathways, stratification, clinical trial design, and implementation of genotyping for appropriate medical management of a given disease can be used to identify the genetic cause of variations in clinical response to therapy, new diagnostic tests, new therapeutic approaches for treating this disorder, and new pharmacuetical products or formulations for therapy. Gene pathways including, but not limited to, those that are outlined in the gene pathway Table 1, and pathway matrix Table 2 and discussed below are candidates for the genetic analysis and product development using the methods described above.

[0877] Advantages of Inclusion of Pharmacogenetic Stratification in Clinical Development of Agents that May Cause Cardiovascular Toxicity

[0878] The advantages of a clinical research and drug development program that includes the use of polymorphic genotyping for the stratification of patients for the appropriate selection of candidate therapeutic intervention includes 1) identification of patients that may respond earlier and show signs and symptoms of cardiovascular toxicity, 2) identification of the primary gene and relevant polymorphic variance that directly affects manifestation of a cardiovascular disorder, 3) identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes, and 4) identification of allelic variances or haplotypes in genes that indirectly affects efficacy, safety or both.

[0879] By identifying subsets of patients, based upon genotype, that experience cardiovascular toxicities in response to the administration of a drug, agent or candidate therapeutic intervention, optimal selection may reduce level and extent of the cardiovascular damage. Appropriate genotyping and correlation to dosing regimen, or selection of optimal therapy would be beneficial to the patient, caregivers, medical personnel, and the patient's loved ones.

[0880] As an example of identification of the primary gene and relevant polymorphic variance that directly affects efficacy, safety, or both one could select an gene pathway as described in the Detailed Description, and determine the effect of genetic polymorphism and therapy efficacy, safety, or both within that given pathway. For example, referring to Table 2, genes involved in drug transport, phase I and phase II metabolism, and protection from reactive intermediate damage the optimization of therapy of by an agent known to have a cardiovascular side effect by determining whether the patient has a predisposing genotype in which the selected agents are more effective and or are more safe. In considering an optimization protocol, one could potentially predetermine the genotypic profile of these genes involved in the manifestation of the adverse effect, or those genes preeminently responsible for drug response. By embarking on the previously described gene pathway approach, it is technical feasibility to determine the relevant genes within such a targeted drug development program.

[0881] Identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes may speed drug development for therapeutic alternatives. There is a need for therapies that are targeted to a disease and symptom management with limited or no undesirable side effects. Identification of a specific variance or variances within genes involved in the pathophysiologic manifestation of cardiovascular toxicities and specific genetic polymorphisms of these critical genes may assist the development of novel agents and the identification of those patients that may best benefit from therapy of these candidate therapeutic alternatives.

[0882] By identifying allelic variances or haplotypes in genes that indirectly affects efficacy, safety of any class of drugs that has an effect on the prevention, progression, or symptoms of cardiovascular toxicities, one could target specific secondary drug or agent therapeutic actions that affect the overall therapeutic action of cardiovascular protective agents.

[0883] Pharmacogenomics studies for these drugs, or other agent, compound, drug, or candidate therapeutic intervention, could be performed by identifying genes that are involved in the the function of a drug including, but not limited to absorption, distribution, metabolism, or elimination, the interaction of the drug with its target as well as potential alternative targets, the response of the cell to the binding of a drug to a target, the metabolism (including synthesis, biodistribution or elimination) of natural compounds which may alter the activity of the drug by complementary, competitive or allosteric mechanisms that potentiate or limit the effect of the drug, and genes involved in the etiology of the disease that alter its response to a particular class of therapeutic agents. It will be recognized to those skilled in the art that this broadly includes proteins involved in pharmacokinetics as well as genes involved in pharmacodynamics. This also includes genes that encode proteins homologous to the proteins believed to carry out the above functions are also worth evaluation as they may carry out similar functions. Together the foregoing proteins constitute the candidate genes for affecting response of a patient to the therapeutic intervention. Using the methods described above, variances in these genes can be identified, and research and clinical studies can be performed to establish an association between a drug response or toxicity and specific variances.

EXAMPLE 8

Drug-Induced Pulmonary Toxicity

[0884] Drug induced pulmonary toxicity includes, but is not excluded to, asthma, acute pneumonitis, eosinophilic pneumonitis, fibrotic and pleural reactions, and interstitial fibrosis. Drug know to elicit pulmonary toxicity include, but are not excluded to, salicylates, nitrofuratoin, busulfan, nitrofurantoin, and bleomycin.

[0885] Impact of Stratification Based Upon Genotype in Drug Development for Drugs, Compounds, or Candidate Therapeutic Interventions that may Induce Pulmonary Toxicities

[0886] There is evidence to suggest that there are safety response differences to drug therapy in reference to development of pulmonary toxicities which may be attributable to genotypic differences between individuals. There is provided in this invention examples of gene pathways that are implicated in the disease process or its therapy and those that potentially cause this variability. The Detailed Description above demonstrates how identification of a candidate gene or genes and gene pathways, stratification, clinical trial design, and implementation of genotyping for appropriate medical management of a given disease can be used to identify the genetic cause of variations in clinical response to therapy, new diagnostic tests, new therapeutic approaches for treating this disorder, and new pharmacuetical products or formulations for therapy. Gene pathways including, but not limited to, those that are outlined in the gene pathway Table 1, and pathway matrix Table 2 and discussed below are candidates for the genetic analysis and product development using the methods described above.

[0887] Advantages of Inclusion of Pharmacogenetic Stratification in Clinical Development of Agents that May Cause Pulmonary Toxicities

[0888] The advantages of a clinical research and drug development program that includes the use of polymorphic genotyping for the stratification of patients for the appropriate selection of candidate therapeutic intervention includes 1) identification of patients that may respond earlier and show signs and symptoms of pulmonary toxicity, 2) identification of the primary gene and relevant polymorphic variance that directly affects manifestation of a pulmonary disorder, 3) identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes, and 4) identification of allelic variances or haplotypes in genes that indirectly affects efficacy, safety or both.

[0889] By identifying subsets of patients, based upon genotype, that experience pulmonary toxicities in response to the administration of a drug, agent or candidate therapeutic intervention, optimal selection may reduce level and extent of the pulmonary damage. Appropriate genotyping and correlation to dosing regimen, or selection of optimal therapy would be beneficial to the patient, caregivers, medical personnel, and the patient's loved ones.

[0890] As an example of identification of the primary gene and relevant polymorphic variance that directly affects efficacy, safety, or both one could select an gene pathway as described in the Detailed Description, and determine the effect of genetic polymorphism and therapy efficacy, safety, or both within that given pathway. For example, referring to Table 2, genes involved in drug transport, phase I and phase II metabolism, excretion, protection from reactive intermediate damage, and immune responsiveness, the optimization of therapy of by an agent known to have a pulmonary side effect by determining whether the patient has a predisposing genotype in which the selected agents are more effective and or are more safe. In considering an optimization protocol, one could potentially predetermine the genotypic profile of these genes involved in the manifestation of the adverse effect, or those genes preeminently responsible for drug response. By embarking on the previously described gene pathway approach, it is technical feasibility to determine the relevant genes within such a targeted drug development program.

[0891] Identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes may speed drug development for therapeutic alternatives. There is a need for therapies that are targeted to a disease and symptom management with limited or no undesirable side effects. Identification of a specific variance or variances within genes involved in the pathophysiologic manifestation of pulmonary toxicity and specific genetic polymorphisms of these critical genes may assist the development of novel agents and the identification of those patients that may best benefit from therapy of these candidate therapeutic alternatives.

[0892] By identifying allelic variances or haplotypes in genes that indirectly affects efficacy, safety of any class of drugs that has an effect on the prevention, progression, or symptoms of pulmonary toxicity, one could target specific secondary drug or agent therapeutic actions that affect the overall therapeutic action of pulmonary protective agents.

[0893] Pharmacogenomics studies for these drugs, or other agent, compound, drug, or candidate therapeutic intervention, could be performed by identifying genes that are involved in the the function of a drug including, but not limited to absorption, distribution, metabolism, or elimination, the interaction of the drug with its target as well as potential alternative targets, the response of the cell to the binding of a drug to a target, the metabolism (including synthesis, biodistribution or elimination) of natural compounds which may alter the activity of the drug by complementary, competitive or allosteric mechanisms that potentiate or limit the effect of the drug, and genes involved in the etiology of the disease that alter its response to a particular class of therapeutic agents. It will be recognized to those skilled in the art that this broadly includes proteins involved in pharmacokinetics as well as genes involved in pharmacodynamics. This also includes genes that encode proteins homologous to the proteins believed to carry out the above functions are also worth evaluation as they may carry out similar functions. Together the foregoing proteins constitute the candidate genes for affecting response of a patient to the therapeutic intervention. Using the methods described above, variances in these genes can be identified, and research and clinical studies can be performed to establish an association between a drug response or toxicity and specific variances.

EXAMPLE 9

Drug-Induced Renal Toxicity

[0894] Drug-induced renal toxicity includes, but is not exclusived to, glomerulonephritis and tubular necrosis. Drugs associated with eliciting renal toxicity include, but are not excluded to, penicillamine, aminoglycoside antibiotics, cyclosporine, amphotericin B, phenacetin, and salicylates.

[0895] Impact of Stratification Based Upon Genotype in Drug Development for Drugs, Compounds, or Candidate Therapeutic Interventions that may InduceRenal Toxicity

[0896] There is evidence to suggest that there are safety response differences to drug therapy in reference to development of renal toxicity which may be attributable to genotypic differences between individuals. There is provided in this invention examples of gene pathways that are implicated in the disease process or its therapy and those that potentially cause this variability. The Detailed Description above demonstrates how identification of a candidate gene or genes and gene pathways, stratification, clinical trial design, and implementation of genotyping for appropriate medical management of a given disease can be used to identify the genetic cause of variations in clinical response to therapy, new diagnostic tests, new therapeutic approaches for treating this disorder, and new pharmacuetical products or formulations for therapy. Gene pathways including, but not limited to, those that are outlined in the gene pathway Table 1, and pathway matrix Table 2 and discussed below are candidates for the genetic analysis and product development using the methods described above.

[0897] Advantages of Inclusion of Pharmacogenetic Stratification in Clinical Development of Agents that May Cause or are Associated with Renal Toxicity

[0898] The advantages of a clinical research and drug development program that includes the use of polymorphic genotyping for the stratification of patients for the appropriate selection of candidate therapeutic intervention includes 1) identification of patients that may respond earlier and show signs and symptoms of renal toxicity, 2) identification of the primary gene and relevant polymorphic variance that directly affects manifestation of a renal disorder, 3) identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes, and 4) identification of allelic variances or haplotypes in genes that indirectly affects efficacy, safety or both.

[0899] By identifying subsets of patients, based upon genotype, that experience renal toxicities in response to the administration of a drug, agent or candidate therapeutic intervention, optimal selection may reduce level and extent of the renal damage. Appropriate genotyping and correlation to dosing regimen, or selection of optimal therapy would be beneficial to the patient, caregivers, medical personnel, and the patient's loved ones.

[0900] As an example of identification of the primary gene and relevant polymorphic variance that directly affects efficacy, safety, or both one could select an gene pathway as described in the Detailed Description, and determine the effect of genetic polymorphism and therapy efficacy, safety, or both within that given pathway. For example, referring to Table 2, genes involved in drug transport, phase I and phase II metabolism, and renal tubular uptake and concentration the optimization of therapy of by an agent known to have a renal side effect by determining whether the patient has a predisposing genotype in which the selected agents are more effective and or are more safe. In considering an optimization protocol, one could potentially predetermine the genotypic profile of these genes involved in the manifestation of the adverse effect, or those genes preeminently responsible for drug response. By embarking on the previously described gene pathway approach, it is technical feasibility to determine the relevant genes within such a targeted drug development program.

[0901] Identification of pathophysiologic relevant variance or variances and potential therapies affecting those allelic genotypes or haplotypes may speed drug development for therapeutic alternatives. There is a need for therapies that are targeted to a disease and symptom management with limited or no undesirable side effects. Identification of a specific variance or variances within genes involved in the pathophysiologic manifestation of renal toxicity and specific genetic polymorphisms of these critical genes may assist the development of novel agents and the identification of those patients that may best benefit from therapy of these candidate therapeutic alternatives.

[0902] By identifying allelic variances or haplotypes in genes that indirectly affects efficacy, safety of any class of drugs that has an effect on the prevention, progression, or symptoms of renal toxicity, one could target specific secondary drug or agent therapeutic actions that affect the overall therapeutic action of renal protective agents.

[0903] Pharmacogenomics studies for these drugs, or other agent, compound, drug, or candidate therapeutic intervention, could be performed by identifying genes that are involved in the the function of a drug including, but not limited to absorption, distribution, metabolism, or elimination , the interaction of the drug with its target as well as potential alternative targets, the response of the cell to the binding of a drug to a target, the metabolism (including synthesis, biodistribution or elimination) of natural compounds which may alter the activity of the drug by complementary, competitive or allosteric mechanisms that potentiate or limit the effect of the drug, and genes involved in the etiology of the disease that alter its response to a particular class of therapeutic agents. It will be recognized to those skilled in the art that this broadly includes proteins involved in pharmacokinetics as well as genes involved in pharmacodynamics. This also includes genes that encode proteins homologous to the proteins believed to carry out the above functions are also worth evaluation as they may carry out similar functions. Together the foregoing proteins constitute the candidate genes for affecting response of a patient to the therapeutic intervention. Using the methods described above, variances in these genes can be identified, and research and clinical studies can be performed to establish an association between a drug response or toxicity and specific variances.

EXAMPLE 10

Hardy-Weinberg Equilibrium

[0904] Evolution is the process of change and diversification of organisms through time, and evolutionary change affects morphology, physiology and reproduction of organisms, including humans. These evolutionary changes are the result of changes in the underlying genetic or hereditary material. Evolutionary changes in a group of interbreeding individuals or Mendelian population, or simply populations, are described in terms of changes in the frequency of genotypes and their constituent alleles. Genotype frequencies for any given generation is the result of the mating among members (genotypes) of their previous generation. Thus, the expected proportion of genotypes from a random union of individuals in a given population is essential for describing the total genetic variation for a population of any species. For example, the expected number of genotypes that could form from the random union of two alleles, A and a, of a gene are AA, Aa and aa. The expected frequency of genotypes in a large, random mating population was discovered to remain constant from generation to generation; or achieve Hardy-Weinberg equilibrium, named after its discoverers. The expected genotypic frequencies of alleles A and a (AA, 2Aa, aa) are conventionally described in terms of p2+2pq+q2 in which p and q are the allele frequencies of A and a. In this equation (p2+2pq+q2=1), p is defined as the frequency of one allele and q as the frequency of another allele for a trait controlled by a pair of alleles (A and a). In other words, p equals all of the alleles in individuals who are homozygous dominant (AA) and half of the alleles in individuals who are heterozygous (Aa) for this trait. In mathematical terms, this is

p=AA+ ½Aa

[0905] Likewise, q equals the other half of the alleles for the trait in the population, or

q=aa+ ½Aa

[0906] Because there are only two alleles in this case, the frequency of one plus the frequency of the other must equal 100%, which is to say

p+q= 1

[0907] Alternatively,

p= 1−q OR q=1−p

[0908] All possible combinations of two alleles can be expressed as:

(p+q)2=1

[0909] or more simply,

p 2 +2pq+q2=1

[0910] In this equation, if p is assumed to be dominant, then p2 is the frequency of homozygous dominant (AA) individuals in a population, 2pq is the frequency of heterozygous (Aa) individuals, and q2 is the frequency of homozygous recessive (aa) individuals.

[0911] From observations of phenotypes, it is usually only possible to know the frequency of homozygous dominant or recessive individuals, because both dominant and recessives will express the distinguishable traits. However, the Hardy-Weinberg equation allows us to determine the expected frequencies of all the genotypes, if only p or q is known. Knowing p and q, it is a simple matter to plug these values into the Hardy-Weinberg equation (p2+2pq+q2=1). This then provides the frequencies of all three genotypes for the selected trait within the population. This illustration shows Hardy-Weinberg frequency distributions for the genotypes AA, Aa, and aa at all values for frequencies of the alleles, p and q. It should be noted that the proportion of heterozygotes increases as the values of p and q approach 0.5.

[0912] Linkage Disequilibirum

[0913] Linkage is the tendency of genes or DNA sequences (e.g. SNPs) to be inherited together as a consequence of their physical proximity on a single chromosome. The closer together the markers are, the lower the probability that they will be separated during DNA crossing over, and hence the greater the probability that they will be inherited together. Suppose a mutational event introduces a “new” allele in the close proximity of a gene or an allele. The new allele will tend to be inherited together with the alleles present on the “ancestral,” chromosome or haplotype. However, the resulting association, called linkage disequilibrium, will decline over time due to recombination. Linkage disequilibrium has been used to map disease genes. In general, both allele and haplotype frequencies differ among populations. Linkage disequilibrium is varied among the populations, being absent in some and highly significant in others.

[0914] Quantification of the Relative Risk of Observable Outcomes of a Pharmacogenetics Trial

[0915] Let PlaR be the placebo response rate (0% ( PlaR ( 100%) and TntR be the treatment response rate (0% ( TntR ( 100%) of a classical clinical trial. ObsRR is defined as the relative risk between TntR and PlaR:

ObsRR=TntR/PlaR.

[0916] Suppose that in the treatment group there is a polymorphism in relation to drug metabolism such as the treatment response rate is different for each genotypic subgroup of patients. Let q be the allele a frequency of a recessive biallelic locus (e.g. SNP) and p=1−q the allele A frequency. Following Hardy-Weinberg equilibrium, the relative frequency of homozygous and heterozygous patients are as follow:

AA: p2

Aa: 2pq

aa: q2

[0917] with

(p2+2pq+q2)=1.

[0918] Let's define AAR, AaR, aaR as respectively the response rates of the AA, Aa and aa patients. We have the following relationship:

TntR=AAR*p 2+AaR*2pq+aaR*q2.

[0919] Suppose that the aa genotypic group of patients has the lowest response rate, i.e. a response rate equal to the placebo response rate (which means that the polymorphism has no impact on natural disease evolution but only on drug action) and let's define ExpRR as the relative risk between AAR and aaR, as

ExpRR=AAR/aaR.

[0920] From the previous equations, we have the following relationships:

ObsRR (ExpRR(1/PlaR

TntR/PlaR= (AAR*p2+AaR*2pq+aaR*q2)/PlaR

[0921] The maximum of the expected relative risk, max(ExpRR), corresponding to the case of heterozygous patients having the same response rate as the placebo rate, is such that:

[0922] 1 $$ ObsRR = ExpRR * ⁢ p2 + 2 ⁢ pq + q2 ⇔ ExpRR = ( ObsRR - 2 ⁢ pq - q2 ) / p2

[0923] The minimum of the expected relative risk, min(ExpRR), corresponding to the case of heterozygous patients having the same response rate as the homozygous non-affected patients, is such that: 2$\mathrm{ObsRR}={\mathrm{ExpRR}}^{*}\left(\mathrm{p2}+2\mathrm{pq}\right)+\mathrm{q2}\iff \mathrm{ExpRR}=\left(\mathrm{ObsRR}-\mathrm{q2}\right)/\left(\mathrm{p2}+2\mathrm{pq}\right)$

[0924] For example, if q=0.4, PlaR=40% and ObsRR=1.5 (i.e. TntR=60%), then 1.6 (ExpRR (2.4. This means that the best treatment response rate we can expect in a genotypic subgroup of patients in these conditions would be 95.6% instead of 60%.

[0925] This can also be expressed in terms of maximum potential gain between the observed difference in response rates (TntR−PlaR) without any pharmacogenetic hypothesis and the maximum expected difference in response rates (max(ExpRR)*PlaR−TntR) with a strong pharmacogenetic hypothesis: 3$\left(\max \left(\mathrm{ExpRR}\right)*\mathrm{PlaR}-\mathrm{TntR}\right)=\left[\left(\mathrm{ObsRR}-2\mathrm{pq}-\mathrm{q2}\right)/\mathrm{p2}\right]*\mathrm{PlaR}-\&AutoLeftMatch;\mathrm{TntR}\iff \left(\max \left(\mathrm{ExpRR}\right)*\mathrm{PlaR}-\mathrm{TntR}\right)=\&AutoLeftMatch;\left[\mathrm{TntR}-\mathrm{PlaR}*\left(2\mathrm{pq}+\mathrm{q2}\right)-\mathrm{Tntr}*\mathrm{p2}\right]/\mathrm{p2}\iff \left(\max \left(\mathrm{ExpRR}\right)*\mathrm{PlaR}-\mathrm{TntR}\right)=\left[\mathrm{TntR}*\left(1-\mathrm{p2}\right)-\mathrm{PlaR}*\left(2\mathrm{pq}+\mathrm{q2}\right)\right]/\mathrm{p2}\iff \left(\max \left(\mathrm{ExpRR}\right)*\mathrm{PlaR}-\mathrm{TntR}\right)=\&AutoLeftMatch;\left[\left(1-\mathrm{p2}\right)/\mathrm{p2}\right]*\left(\mathrm{TntR}-\mathrm{PlaR}\right)$

[0926] that is for the previous example,

(95.6%−60%)=[(1−0.62)/0.62]*(60% −40%)=35.6%

[0927] Suppose that, instead of one SNP, we have p loci of SNPs for one gene. This means that we have 2p possible haplotypes for this gene and (2p)(2p−1)/2 possible genotypes. And with 2 genes with p1 and p2 SNP loci, we have [(2p1)(2p1−1)/2]*[(2p2)(2p2−1)/2] possibilities; and so on. Examining haplotypes instead of combinations of SNPs is especially useful when there is linkage disequilibrium enough to reduce the number of combinations to test, but not complete since in this latest case one SNP would be sufficient. Yet the problem of frequency above still remains with haplotypes instead of SNPs since the frequency of a haplotype cannot be higher than the highest SNP frequency involved. Hence cladograms.

[0928] Statistical Methods to be used in Objective Analyses

[0929] The statistical significance of the differences between variance frequencies can be assessed by a Pearson chi-squared test of homogeneity of proportions with n−1 degrees of freedom. Then, in order to determine which variance(s) is(are) responsible for an eventual significance, we can consider each variance individually against the rest, up to n comparisons, each based on a 2×2 table. This should result in chi-squared tests that are individually valid, but taking the most significant of these tests is a form of multiple testing. A Bonferroni's adjustment for multiple testing will thus be made to the P-values, such as p*=1−(1−p)n. Chi square on 3 genotypes, on haplotypes.

[0930] The statistical significance of the difference between genotype frequencies associated to every variance can be assessed by a Pearson chi-squared test of homogeneity of proportions with 2 degrees of freedom, using the same Bonferroni's adjustment as above.

[0931] Testing for unequal haplotype frequencies between cases and controls can be considered in the same framework as testing for unequal variance frequencies since a single variance can be considered as a haplotype of a single locus. The relevant likelihood ratio test compares a model where two separate sets of haplotype frequencies apply to the cases and controls, to one where the entire sample is characterized by a single common set of haplotype frequencies. This can be performed by repeated use of a computer program (Terwilliger and Ott, 1994, Handbook of Human Linkage Analysis, Baltimore, John Hopkins University Press) to successively obtain the log-likelihood corresponding to the set of haplotpe frequency estimates on the cases (1nLcase), on the controls (1nLcontrol), and on the overall (1nLcombined). The test statistic 2((1nLcase)+(1nLcontrol)−(1nLcombined)) is then chi-squared with r−1 degrees of freedom (where r is the number of haplotypes).

[0932] To test for potentially confounding effects or effect-modifiers, such as sex, age, etc., logistic regression can be used with case-control status as the outcome variable, and genotypes and covariates (plus possible interactions) as predictor variables.

EXAMPLE 11

Exemplary Pharmacogenetic Analysis Steps

[0933] In accordance with the discussion of distribution frequencies for variances, alleles, and haplotypes, variance detection, and correlation of variances or haplotypes with treatment response variability, the points below list major items which will typically be performed in an analysis of the pharmacogenetic determination of the effects of variances in the treatment of a disease and the selection/optimization of treatment.

[0934] 1) List candidate gene/genes for a known genetic disease, and assign them to the respective metabolic pathways.

[0935] 2) Determine their alleles, observed and expected frequencies, and their relative distributions among various ethnic groups, gender, both in the control and in the study (case) groups.

[0936] 3) Measure the relevant clinical/phenotypic (biochemical/physiological) variables of the disease.

[0937] 4) If the causal variance/allele in the candidate gene is unknown, then determine linkage disequilibria among variances of the candidate gene(s).

[0938] 5) Divide the regions of the candidate genes into regions of high linkage disequilibrium and low disequilibrium.

[0939] 6) Develop haplotypes among variances that show strong linkage disequilibrium using the computation methods.

[0940] 7) Determine the presence of rare haplotypes experimentally. Confirm if the computationally determined rare haplotypes agree with the experimentally determined haplotypes.

[0941] 8) If there is a disagreement between the experimentally determined haplotypes and the computationally derived haplotypes, drop the computationally derived rare haplotypes, construct cladograms from these haplotypes using the Templeton (1987) algorithm.

[0942] 9) Note regions of high recombination. Divide regions of high recombination further to see patterns of linkage disequilibria.

[0943] 10) Establish association between cladograms and clinical variables using the nested analysis of variance as presented by Templeton (1995), and assign causal variance to a specific haplotype.

[0944] 11) For variances in the regions of high recombination, use permutation tests for establishing associations between variances and the phenotypic variables.

[0945] 12) If two or more genes are found to affect a clinical variable determine the relative contribution of each of the genes or variances in relation to the clinical variable, using step-wise regression or discriminant function or principal component analysis.

[0946] 13) Determine the relative magnitudes of the effects of any of the two variances on the clinical variable due to their genetic (additive, dominant or epistasis) interaction.

[0947] 14) Using the frequency of an allele or haplotypes, as well as biochemical/clinical variables determined in the in vitro or in vivo studies, determine the effect of that gene or allele on the expression of the clinical variable, according to the measured genotype approach of Boerwinkle et al (Ann. Hum. Genet 1986).

[0948] 15) Stratify ethnic/clinical populations based on the presence or absence of a given allele or a haplotype.

[0949] 16) Optimize drug dosages based on the frequency of alleles and haplotypes as well as their effects using the measured genotype approach as a guide.

EXAMPLE 12

Method for Producing cDNA

[0950] In order to identify sequence variances in a gene by laboratory methods it is in some instances useful to produce cDNA(s) from multiple human subjects. (hi other instances it may be preferable to study genomic DNA.). Methods for producing cDNA are known to those skilled in the art, as are methods for amplifying and sequencing the cDNA or portions thereof. An example of a useful cDNA production protocol is provided below. As recognized by those skilled in the art, other specific protocols can also be used.

[0951] cDNA Production

[0952] Make sure that all tubes and pipette tips are RNase-free. (Bake them overnight at 100° C. in a vaccum oven to make them RNase-free.)

[0953] 1. Add the following to a RNase-free 0.2 ml micro-amp tube and mix gently:

[0954] 24 ul water (DEPC treated)

[0955] 12 ul RNA (lug/ul)

[0956] 12 ul random hexamers (50 ng/ul)

[0957] 2. Heat the mixture to 70° C. for ten minutes.

[0958] 3. Incubate on ice for 1 minute.

[0959] 4. Add the following:

[0960] 16 ul 5×Synthesis Buffer

[0961] 8ul 0.1M DTT

[0962] 4 ul 10 mM dNTP mix (10 mM each dNTP)

[0963] 4 ul SuperScript RT II enzyme

[0964] Pipette gently to mix.

[0965] 5. Incubate at 42° C. for 50 minutes.

[0966] 6. Heat to 70° C. for ten minutes to kill the enzyme, then place it on ice.

[0967] 7. Add 160 ul of water to the reaction so that the final volume is 240 ul.

[0968] 8. Use PCR to check the quality of the cDNA. Use primer pairs that will give a ˜800 base pair long piece. See “PCR Optimization” for the PCR protocol.

[0969] The following chart shows the reagent amounts for a 20 ul reaction, a 80 ul reaction, and a batch of 39 (which makes enough mix for 36) reactions:

	20 ul X 1 tube	80 ul X 1 tube	80 ul X 39 tubes
water	6 ul	24 ul	936
RNA	3 ul	12 ul
random hexamers	3 ul	12 ul	468
synthesis buffer	4 ul	16 ul	624
0.1 M DTT	2 ul	8 ul	312
10 mM dNTP	1 ul	4 ul	156
SSRT	1 ul	4 ul	156

EXAMPLE 13

Method for Detecting Variances by Single Strand Conformation Polymorphism (SSCP) Analysis

[0970] This example describes the SSCP technique for identification of sequence variances of genes. SSCP is usually paired with a DNA sequencing method, since the SSCP method does not provide the nucleotide identity of variances. One useful sequencing method, for example, is DNA cycle sequencing of 32P labeled PCR products using the Femtomole DNA cycle sequencing kit from Promega (WI) and the instructions provided with the kit. Fragments are selected for DNA sequencing based on their behavior in the SSCP assay.

[0971] Single strand conformation polymorphism screening is a widely used technique for identifying an discriminating DNA fragments which differ from each other by as little as a single nucleotide. As originally developed by Orita et al. (Detection of polymorphisms of human DNA by gel electrophoresis as single-strand conformation polymorphisms. Proc Natl Acad Sci USA. 86(8):2766-70, 1989), the technique was used on genomic DNA, however the same group showed that the technique works very well on PCR amplified DNA as well. In the last 10 years the technique has been used in hundreds of published papers, and modifications of the technique have been described in dozens of papers. The enduring popularity of the technique is due to (1) a high degree of sensitivity to single base differences (>90%) (2) a high degree of selectivity, measured as a low frequency of false positives, and (3) technical ease. SSCP is almost always used together with DNA sequencing because SSCP does not directly provide the sequence basis of differential fragment mobility. The basic steps of the SSCP procedure are described below.

[0972] When the intent of SSCP screening is to identify a large number of gene variances it is useful to screen a relatively large number of individuals of different racial, ethnic and/or geographic origins. For example, 32 or 48 or 96 individuals is a convenient number to screen because gel electrophoresis apparatus are available with 96 wells (Applied Biosystems Division of Perkin Elmer Corporation), allowing 3×32, 2×48 or 96 samples to be loaded per gel.

[0973] The 32 (or more) individuals screened should be representative of most of the worlds major populations. For example, an equal distribution of Africans, Europeans and Asians constitutes a reasonable screening set. One useful source of cell lines from different populations is the Coriell Cell Repository (Camden, N.J.), which sells EBV immortalized lyphoblastoid cells obtained from several thousand subjects, and includes the racial/ethnic/geographic background of cell line donors in its catalog. Alternatively, a panel of cDNAs can be isolated from any specific target population.

[0974] SSCP can be used to analyze cDNAs or genomic DNAs. For many genes cDNA analysis is preferable because for many genes the full genomic sequence of the target gene is not available, however, this circumstance will change over the next few years. To produce cDNA requires RNA. Therefore each cell lines is grown to mass culture and RNA is isolated using an acid/phenol protocol, sold in kit form as Trizol by Life Technologies (Gaithersberg, Md.). The unfractionated RNA is used to produce cDNA by the action of a modified Maloney Murine Leukemia Virus Reverse Transcriptase, purchased in kit form from Life Technologies (Superscript II kit). The reverse transcriptase is primed with random hexamer primers to initiate cDNA synthesis along the whole length of the RNAs. This proved useful later in obtaining good PCR products from the 5′ ends of some genes. Alternatively, oligodT can be used to prime cDNA synthesis.

[0975] Material for SSCP analysis can be prepared by PCR amplification of the cDNA in the presence of one α 32p labeled dNTP (usually α 32p dCTP). Usually the concentration of nonradioactive dCTP is dropped from 200 uM (the standard concentration for each of the four dNTPs) to about 100 uM, and 32p dCTP is added to a concentration of about 0.1-0.3 uM. This involves adding a 0.3-1 ul (3-10 uCi) of 32P cCTP to a 10 ul PCR reaction. Radioactive nucleotides can be purchased from DuPont/New England Nuclear.

[0976] The customary practice is to amplify about 200 base pair PCR products for SSCP, however, an alternative approach is to amplify about 0.8-1.4 kb fragments and then use several cocktails of restriction endonucleases to digest those into smaller fragments of about 0.1-0.4 kb, aiming to have as many fragments as possible between 0.15 and 0.3 kb. The digestion strategy has the advantage that less PCR is required, reducing both time and costs. Also, several different restriction enzyme digests can be performed on each set of samples (for example 96 cDNAs), and then each of the digests can be run separately on SSCP gels. This redundant method (where each nucleotide is surveyed in three different fragments) reduces both the false negative and false positive rates. For example: a site of variance might lie within 2 bases of the end of a fragment in one digest, and as a result not affect the conformation of that strand; the same variance, in a second or third digest, would likely lie in a location more prone to affect strand folding, and therefore be detected by SSCP.

[0977] After digestion, the radiolabelled PCR products are diluted 1:5 by adding formamide load buffer (80% formamide, 1×SSCP gel buffer) and then denatured by heating to 90% C for 10 minutes, and then allowed to renature by quickly chilling on ice. This procedure (both the dilution and the quick chilling) promotes intra- (rather than inter-) strand association and secondary structure formation. The secondary structure of the single strands influences their mobility on nondenaturing gels, presumably by influencing the number of collisions between the molecule and the gel matrix (i.e., gel sieving). Even single base differences consistently produce changes in intrastrand folding sufficient to register as mobility differences on SSCP.

[0978] The single strands were then resolved on two gels, one a 5.5% acrylamide, 0.5×TBE gel, the other an 8% acrylamide, 10% glycerol, 1×TTE gel. (Other gel recipes are known to those skilled in the art.) The use of two gels provides a greater opportunity to recognize mobility differences. Both glycerol and acrylamide concentration have been shown to influence SSCP performance. By routinely analyzing three different digests under two gel conditions (effectively 6 conditions), and by looking at both strands under all 6 conditions, one can achieve a 12-fold sampling of each base pair of cDNA. However, if the goal is to rapidly survey many genes or cDNAs then a less redundant procedure would be optimal.

EXAMPLE 14

Method for Detecting Variances by T4 Endonuclease VII (T4E7) Mismatch Cleavage Method

[0979] The enzyme T4 endonuclease VII is derived from the bacteriophage T4. T4 endonuclease VII is used by the bacteriophage to cleave branched DNA intermediates which form during replication so the DNA can be processed and packaged. T4 endonuclease can also recognize and cleave heteroduplex DNA containing single base mismatches as well as deletions and insertions. This activity of the T4 endonuclease VII enzyme can be exploited to detect sequence variances present in the general population.

[0980] The following are the major steps involved in identifying sequence variations in a candidate gene by T4 endonuclease VII mismatch cleavage:

[0981] 1. Amplification by the polymerase chain reaction (PCR) of 400-600 bp regions of the candidate gene from a panel of DNA samples The DNA samples can either be cDNA or genomic DNA and will represent some cross section of the world population.

[0982] 2. Mixing of a fluorescently labeled probe DNA with the sample DNA.

[0983] Heating and cooling the mixtures causing heteroduplex formation between the probe DNA and the sample DNA.

[0984] 3. Addition of T4 endonuclease VII to the heteroduplex DNA samples. T4 endonuclease will recognize and cleave at sequence variance mismatches formed in the heteroduplex DNA.

[0985] 4. Electrophoresis of the cleaved fragments on an ABI sequencer to determine the site of cleavage.

[0986] 5. Sequencing of a subset of PCR fragments identified by T4 endonuclease VI to contain variances to establish the specific base variation at that location.

[0987] A more detailed description of the procedure is as follows:

[0988] A candidate gene sequence is downloaded from an appropriate database. Primers for PCR amplification are designed which will result in the target sequence being divided into amplification products of between 400 and 600 bp. There will be a minimum of a 50 bp of overlap not including the primer sequences between the 5′ and 3′ ends of adjacent fragments to ensure the detection of variances which are located close to one of the primers.

[0989] Optimal PCR conditions for each of the primer pairs is determined experimentally. Parameters including but not limited to annealing temperature, pH, MgCl2 concentration, and KCl concentration will be varied until conditions for optimal PCR amplification are established. The PCR conditions derived for each primer pair is then used to amplify a panel of DNA samples (cDNA or genomic DNA) which is chosen to best represent the various ethnic backgrounds of the world population or some designated subset of that population.

[0990] One of the DNA samples is chosen to be used as a probe. The same PCR conditions used to amplify the panel are used to amplify the probe DNA. However, a flourescently labeled nucleotide is included in the deoxy-nucleotide mix so that a percentage of the incorporated nucleotides will be fluorescently labeled.

[0991] The labeled probe is mixed with the corresponding PCR products from each of the DNA samples and then heated and cooled rapidly. This allows the formation of heteroduplexes between the probe and the PCR fragments from each of the DNA samples. T4 endonuclease VII is added directly to these reactions and allowed to incubate for 30 min. at 37 C. 10 ul of the Formamide loading buffer is added directly to each of the samples and then denatured by heating and cooling. A portion of each of these samples is electrophoresed on an ABI 377 sequencer. If there is a sequence variance between the probe DNA and the sample DNA a mismatch will be present in the heteroduplex fragment formed. The enzyme T4 endonuclease VII will recognize the mismatch and cleave at the site of the mismatch. This will result in the appearance of two peaks corresponding to the two cleavage products when run on the ABI 377 sequencer.

[0992] Fragments identified as containing sequencing variances are subsequently sequenced using conventional methods to establish the exact location and sequence variance.

EXAMPLE 15

Method for Detecting Variances by DNA Sequencing

[0993] Sequencing by the Sanger dideoxy method or the Maxim Gilbert chemical cleavage method is widely used to determine the nucleotide sequence of genes.

[0994] Presently, a worldwide effort is being put forward to sequence the entire human genome. The Human Genome Project as it is called has already resulted in the identification and sequencing of many new human genes. Sequencing can not only be used to identify new genes, but can also be used to identify variations between individuals in the sequence of those genes.

[0995] The following are the major steps involved in identifying sequence variations in a candidate gene by sequencing:

[0996] 1. Amplification by the polymerase chain reaction (PCR) of 400-700 bp regions of the candidate gene from a panel of DNA samples The DNA samples can either be cDNA or genomic DNA and will represent some cross section of the world population.

[0997] 2. Sequencing of the resulting PCR fragments using the Sanger dideoxy method. Sequencing reactions are performed using flourescently labeled dideoxy terminators and fragments are separated by electrophoresis on an ABI 377 sequencer or its equivalent.

[0998] 3. Analysis of the resulting data from the ABI 377 sequencer using software programs designed to identify sequence variations between the different samples analyzed.

[0999] A more detailed description of the procedure is as follows:

[1000] A candidate gene sequence is downloaded from an appropriate database.

[1001] Primers for PCR amplification are designed which will result in the target sequence being divided into amplification products of between 400 and 700 bp. There will be a minimum of a 50 bp of overlap not including the primer sequences between the 5′ and 3′ ends of adjacent fragments to ensure the detection of variances which are located close to one of the primers.

[1002] Optimal PCR conditions for each of the primer pairs is determined experimentally. Parameters including but not limited to annealing temperature, pH, MgCl2 concentration, and KCl concentration will be varied until conditions for optimal PCR amplification are established. The PCR conditions derived for each primer pair is then used to amplify a panel of DNA samples (cDNA or genomic DNA) which is chosen to best represent the various ethnic backgrounds of the world population or some designated subset of that population.

[1003] PCR reactions are purified using the QIAquick 8 PCR purification kit (Qiagen cat# 28142) to remove nucleotides, proteins and buffers. The PCR reactions are mixed with 5 volumes of Buffer PB and applied to the wells of the QlAquick strips. The liquid is pulled through the strips by applying a vacuum. The wells are then washed two times with 1 ml of buffer PE and allowed to dry for 5 minutes under vacuum. The PCR products are eluted from the strips using 60 ul of elution buffer.

[1004] The purified PCR fragments are sequenced in both directions using the Perkin Elmer ABI Prism™ Big Dye™ terminator Cycle Sequencing Ready Reaction Kit (Cat# 4303150). The following sequencing reaction is set up: 8.0 ul Terminator Ready Reaction Mix, 6.0 ul of purified PCR fragment, 20 picomoles of primer, deionized water to 20 ul. The reactions are run through the following cycles 25 times: 96° C. for 10 second, annealing temperature for that particular PCR product for 5 seconds, 60° C. for 4 minutes.

[1005] The above sequencing reactions are ethanol precipitated directly in the PCR plate, washed with 70% ethanol, and brought up in a volume of 6 ul of formamide dye. The reactions are heated to 90° C. for 2 minutes and then quickly cooled to 4° C. 1 ul of each sequencing reaction is then loaded and run on an ABI 377 sequencer.

[1006] The output for the ABI sequencer appears as a series of peaks where each of the different nucleotides, A, C, G, and T appear as a different color. The nucleotide at each position in the sequence is determined by the most prominent peak at each location. Comparison of each of the sequencing outputs for each sample can be examined using software programs to determine the presence of a variance in the sequence. One example of heterozygote detection using sequencing with dye labeled terminators is described by Kwok et. al (Kwok, P. -Y.; Carlson, C.; Yager, T. D., Ankener, W., and D. A. Nickerson, Genomics 23, 138-144, 1994). The software compares each of the normalized peaks between all the samples base by base and looks for a 40% decrease in peak height and the concomitant appearance of a new peak underneath. Possible variances flagged by the software are further analyzed visually to confirm their validity.

EXAMPLE 16

Exemplary Pharmacogenetic Analysis Steps—Biological Function Analysis

[1007] In many cases when a gene which may affect drug action is found to exhibit variances in the gene, RNA, or protein sequence, it is preferable to perform biological experiments to determine the biological impact of the variances on the structure and function of the gene or its expressed product and on drug action. Such experiments may be performed in vitro or in vivo using methods known in the art.

[1008] The points below list major items which may typically be performed in an analysis of the effects of variances in the treatment of a disease and the selection/optimization of treatment using biological studies to determine the structure and function of variant forms of a gene or its expressed product.

[1009] 1) List candidate gene/genes for a known genetic disease, and assign them to the respective metabolic pathways.

[1010] 2) Identify variances in the gene sequence, the expressed mRNA sequence or expressed protein sequence.

[1011] 3) Match the position of variances to regions of the gene, mRNA, or protein with known biological functions. For example, specific sequences in the promotor of a gene are known to be responsible for determining the level of expression of the gene; specific sequences in the mRNA are known to be involved in the processing of nuclear mRNA into cytoplasmic mRNA including splicing and polyadenylation; and certain sequences in proteins are known to direct the trafficking of proteins to specific locations within a cell and to constitute active sites of biological functions including the binding of proteins to other biological consituents or catalytic functions. Variances in sites such as these, and others known in the art, are candidates for biological effects on drug action.

[1012] 4) Model the effect of the variance on mRNA or protein structure. Computational methods for predicting the structure of mRNA are known and can be used to assess whether a specific variance is likely to cause a substantial change in the structure of mRNA. Computational methods can also be used to predict the structure of peptide sequences enabling predictions to be made concerning the potential impact of the variance on protein function. Most useful are structures of proteins determined by X-ray diffraction, NMR or other methods known in the art which provide the atomic structure of the protein. Computational methods can be used to consider the effect of changing an amino acid within such a structure to determine whether such a change would disrupt the structure and/or function of the protein. Those skilled in the art will recognize that this analysis can be performed on crystal structures of the protein known to have a variance as well as homologous proteins expressed from different loci in the human genome, or homologous proteins from other species, or non-homologous but analogous proteins with similar functions from humans or other species.

[1013] 5) Produce the gene, mRNA or protein in amounts sufficient to experimentally characterize the structure and function of the gene, mRNA or protein. It will be apparent to those skilled in the art that by comparing the activity of two genes or their products which differ by a single variance, the effect of the variance can be determined. Methods for producing genes or gene products which differ by one or more bases for the purpose of experimental analysis are known in the art.

[1014] 6) Experimental methods known in the art can be used to determine whether a specific variance alters the transcription of a gene and translation into a gene product. This involves producing amounts of the gene by molecular cloning sufficient for in vitro or in vivo studies. Methods for producing genes and gene products are known in the art and include cloning of segments of genetic material in prokaryotes or eukarotic hosts, run off transcription and cell-free translation assays that can be performed in cell free extracts, transfection of DNA into cultured cells, introduction of genes into live animals or embryos by direct injection or using vehicles for gene delivery including transfection mixtures or viral vectors.

[1015] 7) Experimental methods known in the art can be used to determine whether a specific variance alters the ability of a gene to be transcribed into RNA. For example, run off transcription assays can be performed in vitro or expression can be characterized in transfected cells or transgenic animals.

[1016] 8) Experimental methods known in the art can be used to determine whether a specific variance alters the processing, stability, or translation of RNA into protein. For example, reticulocyte lysate assays can be used to study the production of protein in cell free systems, transfection assays can be designed to study the production of protein in cultured cells, and the production of gene products can be measured in transgenic animals.

[1017] 9) Experimental methods known in the art can be used to determine whether a specific variant alters the activity of an expressed protein product. For example, protein can be producted by reticulocyte lystae systems or by introducing the gene into prokaryotic organisms such as bacteria or lowre eukaryotic organisms such as yeast or fungus), or by introducing the gene into cultured cells or transgenic animals. Protein produced in such systems can be extracted or purified and subjected to bioassays known to those in the art as measures of the nction of that particular protein. Bioassays may involve, but are not limited to, binding, inhibiton, or catalytic functions.

[1018] 10) Those skilled in the art will recognize that it is sometimes preferred to perform the above experiments in the presence of a specific drug to determine whether the drug has differential effects on the activity being measured. Alternatively, studies may be performed in the presence of an analogue or metabolite of the drug.

[1019] 11) Using methods described above, specific variances which alter the biological function of a gene or its gene product that could have an impact on drug action can be identified. Such variances are then studied in clinical trial populations to determine whether the presence or absence of a specific variance correlates with observed clinical outcomes such as efficacy or toxicity.

[1020] 12) It will be further recognized that there may be more than one variance within a gene that is capable of altering the biological function of the gene or gene product. These variances may exhibit similar, synergistic effects, or may have opposite effects on gene function. In such cases, it is necessary to consider the haplotype of the gene, namely the combination of variances that are present within a single allele, to assess the composite function of the gene or gene product.

[1021] 13) Perform clinical trials with stratification of patients based on presence or absence of a given variance, allele or haplotype of a gene. Establish associations between observed drug responses such as toxicity, efficacy, drug response, or dose toleration and the presence or absense of a specific variance, allele, or haplotype.

[1022] 14) Optimize drug dosage or drug usage based on the presence of the variant.

EXAMPLE 17

Stratification of Patients by Genotype in Prospective Clinical Trials

[1023] In a prospective clinical trial, patients will be stratified by genotype to determine whether the observed outcomes are different in patients having different genotypes. A critical issue is the design of such trials to assure that a sufficient number of patients are studied to observe genetic effects.

[1024] The number of patients required to achieve statistical significance in a conventional clinical trial is calculated from:

N= 2(zα+z2β)2/(δ/σ)2 (two tailed test)  1.1

[1025] From this equation it may be inferred that the size of a genetically defined subgroup Ni required to achieve statistical significance for an observed outcome associated with variance or haplotype “i” can be calculated as:

N i =2(zα+z2β)2/(δi/σi)2  1.2

[1026] If Pi is the prevalence of the genotype “i” in the population, the total number of patients that need to be incorporated in a clinical trial Ng to identify a population with haplotype “i” of size Ni is given by:

N g =N i /P i   1.3

[1027] It should be noted that Ng describes the total number of patients that need to be genotyped in order to identify a subset of Ni patients with genotype “i”.

[1028] If genotyping is used as means for statistical stratification of patients, Ng represents the number of patients that would need to be enrolled in a trial to achieve statistical significance for subgroup “i”. If genotyping is used as a means for inclusion, it represents the number of patients that need to screened to identify a population of Ni individuals for an appropriately powered clinical trial. Thus, Ng is a critical determinant of the scope of the clinical trial as well as Ni.

[1029] A clinical trial can also be designed to test associations for multiple genetic subgroups “j” defined by a single allele in which case:

N g =max(Ng1) for i=1 . . . j  1.4

[1030] If more than one subgroup is tested, but there is no overlap in the patients contained within the subgroups, these can be considered to be independent hypotheses and no multiple testing correction should be required. If consideration of more than one subgroup constitutes multiple testing, or if individual patients are included in multiple subgroups, then statistical corrections may required in the values of zα or z2β which would increase the number of patients required.

[1031] It should be emphasized that a clinical trial of this nature may not provide statistically significant data concerning associations with any genotype other than “i”. The total number of patients that would be required in a clinical trial to test more than one genetically defined subgroup would be determined by the maximum value of Ng for any single subgroup.

[1032] The power of pharmacogenomics to improve the efficiency of clinical trials arises from the fact it is possible to have Ng<N. The goal of pharmacogenomic analysis is to identify a genetically define subgroup in which the magnitude of the clinical response is greater and the variability in response is reduced. These observations correspond to an increase in the magnitude of the (mean) observed response δ or a decrease the degree of variability σ. Since the value of Ni calculated in equation 1.2 decreases non-linearly as the square of these changes, the total number of patients Ng can also decrease non-linearly, resulting in a clinical trial that requires fewer patients to achieve statistical significance. If δ1 and σ1 are not different than δ and σ, then Ng is greater than N as given by Ng=N1/P1. Values of δ1 and σ1 that give Ng<N can be calculated:

N g <N if: Pi>[(δ/σ)2]/[(δi/σ1)2]  1.5

[1033] It is apparent from this analysis that Ng is not uniformly less than N, even with modest improvements in the values for δi and σi.

[1034] As with a conventional clinical trial, the incorporation of an appropriate control group in the study design is critical for achieving success. In the case of a prospective clinical trial, the control group commonly is selected on the basis of the same inclusion criteria as the treatment group, but is treated with placebo or a standard therapeutic regimen rather than the investigational drug. In the case of a study with subgroups that are defined by haplotype, the ideal control group for a treatment subgroup with hapotype “i” is a placebo-treated subgroup with haplotype “i”. This is often a critical control, since haplotypes which may be associated with the response to treatment may also affect the natural course of the disease.

[1035] A critical issue in considering control groups is that σ for the control group placebo treated population with haplotype “i” may not be equivalent to that of the control population. If so, 1.5 may overestimate the benefits of any reduction in σi in the treatment response group if there is not also a reduction in σi in the control group.

[1036] If σ of the treatment and control groups are not equivalent, δ would be still calculated as the difference in the response of the two groups, but σ would be different in the two groups with values of σ0 or σ1 respectively. In this case, the number of patients in the genetically defined subgroup N1 would be defined by:

N i =(σZα+σiZβ)2/δ2  2.1

[1037] The total number of patients that would need to be enrolled in such a trial would be the maximium of

N or N/Pi  2.2

[1038] It will be apparent that such an analysis remains sensitive to increases in δ, but is less sensitive to changes in σ which are not also reflected in the control group.

[1039] Certain analysis may be performed by comparing individuals with one haplotype against the entire normal population. Such an analysis may be used to establish the selectivity of the response associated with a specific haplotype. For example, it may be desirable to establish that the response or toxicity observed in a specific subgroup is greater than that associated observed with the entire population. It may also be of interest to compare the response to treatment between two different subgroups. If σ differs between the groups, then the estimate of the number of patients that need to be enrolled in the trial must be calculated using equations 2.1 with N being the maximum of N1/P1 for the different subgroups.

[1040] Another issue in controls is the relative size of the treatment and control groups. In a prospectively designed clinical trial which selectively incorporates patients with haplotype “i” the number of patients in the control and treatment group will be essentially equivalent. If the control group is different, or if haplotypes are used for stratification but not inclusion, statistical corrections may need to be made for having populations of different size.

EXAMPLE 18

Stratification of Patients by Phenotype

[1041] The identification of genetic associations in Phase II or retrospective studies can be performed by stratifying patients by phenotype and analyzing the distribution of genotypes/haplotypes in the separate populations. A particularly important aspect of this analysis is that any gene may have only a partial effect on the observed outcome, meaning that there will be an association value (A) corresponding to the fraction of patients in a phenotypically-defined subgroup who exhibit that phenotype due to a specific genotype/phenotype.

[1042] It will be recognized to those skilled in the art that the fraction of individuals who exhibit a phenotype due to any specific allele will be less than 1 (i.e. A<1). This is true for several reasons. The observed phenotype may occur by random chance. The observed phenotype may be associated with environmental influences, or the observed phenotype may be due to different genetic effects in different individuals. Furthermore, the onstruction of haplotypes and analysis of recombination may not group all alleles with pheontypically-significant variances within a single haplotype or haplotype cluster. In this case, causative variances at a single locus may be associated with more than one haplotype or haplotype cluster and the association constant A for the locus would be A=A1+A2+. . . +An<1. It is likely that many phenotypes will be associated with multiple alleles at a given locus, and it is particularly important that statistical methods be sufficiently robust to identify association with a locus even if Ai is reduced by the presence of several causative alleles.

[1043] Statistical methods can be used to identify genetic effects on an observed outcome in patient groups stratified by phenotype, eg the presence or absence of the observed response. One such method entails determining the allele frequencies in two populations of patients stratified by an observed clinical outcome, for example efficacy or toxicity and performing a maximum likelihood analysis for the association between a given gene and the observed phenotype based on the allele frequencies and a range of values for A (the association constant between a specific allele and the observed outcome used to stratify patients).

[1044] This analysis is performed by comparing the observed gene frequencies in a patient population with an observed outcome to gene frequencies in a table in which the predicted frequencies of different alleles of the gene assuming different values of the association constant A for that allele. This table of predicted gene frequencies can be constructed by those skilled in the art based on the frequency of any specific allele in the normal population, the predicted inheritance of the effect (e.g. dominant or recessive) and the fraction of a subgroup with a specific outcome who would have that allele based on the association constant A.

[1045] For example, if a specific outcome was only observed in the presence of a specific allele of a gene, the expected frequency would be 1. If a specific outcome was never observed in the presence of a specific allele of a gene, the expected fequency would be 0. If there was no association between the allele and the observed outcome, the frequency of that allele among individuals with an observed outcome would be the same as in the general population. A statistical analysis can be performed to compare the observed allele frequencies with the predicted allele frequencies and determine the best fit or maximum likeihood of the association. For example, a chi square analysis will determine whether the observed outcome is statistically similar to predicted outcomes calculated for different modes of inheritance and different potential values of A. P values can then be calculated to determine the likelihood that any specific association is statistically significant. A curve can be calculated based on different values of A, and the maximal likelihood of an association determined from the peak of such a curve. Methods for chi square analysis are known to those in the art.

[1046] A multidimensional analysis can also be performed to determine whether an observed outcome is associated with more than one allele at a specific genetic locus. An example of this analysis considering the potential effects of two different alleles of a single gene is shown. It will be apparent to those skilled in the art that this analysis can be extended to n dimensions using computer programs.

[1047] This analysis can be used to determine the maximum likelihood that one or more alleles at a given locus are associated with a specific clinical outcome.

[1048] It will be apparent to those skilled in the art that critical issues in this analysis include the fidelity of the phenotypic association and identification of a control group. In particular, it may be useful to perform an identical analysis in patients receiving a placebo to eliminate other forms of bias which may contribute to statistical errors.

Other Embodiments

[1049] The invention described herein provides a method for identifying patients with a risk of developing drug-induced liver disease or hepatic dysfunction by determining the patients allele status for a gene listed in Tables 1 and 2 and providing a forecast of the patients ability to respond to a given drug treatment. In particular, the invention provides a method for determining, based on the presence or absence of a polymorphism, a patient's likely response to drug therapies as drug-induced liver disease or hepatic dysfunction. Given the predictive value of the described polymorphisms across two different classes of drug, having different mechanisms of action, the candidate polymorphism is likely to have a similar predictive value for other drugs acting through other pharmacological mechanisms. Thus, the methods of the invention may be used to determine a patient's response to other drugs including, without limitation, antihypertensives, anti-obesity, anti-hyperlipidemic, or anti-proliferative, antioxidants, or enhancers of terminal differentiation.

[1050] In addition, while determining the presence or absence of the candidate allele is a clear predictor determining the efficacy of a drug on a given patient, other allelic variants of reduced catalytic activity are envisioned as predicting drug efficacy using the methods described herein. In particular, the methods of the invention may be used to treat patients with any of the possible variances, e.g., as described in Table 3 of Stanton & Adams, application Ser. No. 09/300,747, supra.

[1051] In addition, while the methods described herein are preferably used for the treatment of human patient, non-human animals (e.g., pets and livestock) may also be treated using the methods of the invention.

[1052] All patents and publications mentioned in the specification are indicative of the levels of skill of those skilled in the art to which the invention pertains. All references cited in this disclosure are incorporated by reference to the same extent as if each reference had been incorporated by reference in its entirety individually.

[1053] One skilled in the art would readily appreciate that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those inherent therein. The methods, variances, and compositions described herein as presently representative of preferred embodiments are exemplary and are not intended as limitations on the scope of the invention. Changes therein and other uses will occur to those skilled in the art, which are encompassed within the spirit of the invention, are defined by the scope of the claims.

[1054] It will be readily apparent to one skilled in the art that varying substitutions and modifications may be made to the invention disclosed herein without departing from the scope and spirit of the invention. For example, using other compounds, and/or methods of administration are all within the scope of the present invention. Thus, such additional embodiments are within the scope of the present invention and the following claims.

[1055] The invention illustratively described herein suitably may be practiced in the absence of any element or elements, limitation or limitations which is not specifically disclosed herein. Thus, for example, in each instance herein any of the terms “comprising”, “consisting essentially of” and “consisting of” may be replaced with either of the other two terms. The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention that in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims.

[1056] In addition, where features or aspects of the invention are described in terms of Markush groups or other grouping of alternatives, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group or other group.

TABLE 1
Class	Pathway	Function	Name	OMIM	GID	Locus
Absorption	Gastrointestinal	Glycosidases	sucrase-isomaltase/S1	222900	NM_001041	3q25-q26
and	Drug Metabolism		maltase-glucoamylase/alpha-glucosidase/MGAM	154360	NM_004668	Chr.7
Distribution			lactase-phlorizin hydrolase/LPH/lactase/LCT	603202	NM_002299	2q21
			salivary amylase A/AMY1A	104700	NM_004038	1p21
			salivary amylase B/AMY1B	104701	******	1p21
			salivary amylase C/AMY1C	104702	******	1p22
			pancreatic amylase A/AMY2A	104650	X07057	1p21
			pancreatic amylase B/AMY2B	104660	******	1p21
		Proteases and	dipeptidylpeptidase IV/CD26/ADA complexing	102720	NM_001935	2q23
		Peptidases	protein 2/DPP4
			pepsinogen A/PGA/PG	169700	AH001519	11q13
			pepsinogen, group 3/PGA3	169710	******	11q13
				169740	J04443	6p21.3-
			pepsinogen C/PGC			q21.1
				147910	AH002853	19q13.2-
						q13.4
			chymotrypsin-like protease	118888	X71875	16q22.1
			trypsinogen 1/TRY1/protease, serine 1/PRSS1	276000	NM_002769	7q35
			trypsinogen 1/TRY2/protease, serine 2/PRSS2	601564	NM_002770	7q35
			trypsinogen 1/TRY3/protease, serine 3/PRSS3	******	NM_002771	******
			enterokinase 1/TRY3/protease, serine7/PRSS7	226200	NM_002772	21q21
			chymotrypsinogen 1/CTRB1	118890	NM_001906	16q23.2-
						q23.3
			carboxypeptidase A1/CPA1	114850	NM_001868	7q32-qter
			carboxypeptidase A2/CPA2	600688	NM_001869	******
			carboxypeptidase Z/CPZ	603105	NM_003652
			elastase 1/ELA1	130120	D00158	12q13
			renal microsomal dipeptidase/DPEP1 (b-lactam ring	179780	NM_004413 16q24.3
			hydrolysis)
			tripeptidyl peptidase II/TPP2	190470	NM_003291	13q32-
						q33
			protease inhibitor 1/alpha-1-antitrypsin/AAT/PI	107400	NM_000295	14q32.1
			protease inhibitor/alpha-1-antichymotrypsin/AACT	107280	NM_001085	14q32.1
			protease inhibitor 1 (alpha-1-antitrypsin)-like/PIL	107410	NM_006220	14q32.1
		Lipases	Carboxyl ester lipase (bile salt-stimulated	114840	M85201	9q34.3
			lipase)/CEL
			Carboxyl ester lipase-like (bile salt-stimulated lipase-	114841	NM_001808	9q34.3
			like)/CELL
			Pancreatic colipase/CLPS	120105	M95529	6pter-
						p21.1
			Pancreatic triglyceride lipase/PNLTP	246600	AH003527	10q26.1
			Lipoprotein lipase/LPL	238600	NM_000237	8p22
			Hepatic triglyceride lipase/LIPC	151670	AH005429
		Oxidases	salivary peroxidase/SAPX	170990	U39573	******
			alcohol dehydrogenases 6/ADH6	103735	NM_0006721	15q26
		Esterases	paraoxonase 2/PON2	602447	L48513	7q21.3
		Phosphatases	intestinal alkaline phosphatase/ALPI	171740	NM_00163	12q36.3-
						q37.1
			tissue non-specific alkaline phosphatase/liver	171760		1p36.1-
			alkaline phosphatase/ALPL		NM_000478	p34
	Drug Binding	Blood Transport	serum albumin/ALB	103600	NM_000477	4q11-q13
			alpha fetoprotein/AFP	104150	NM_001134	4q11-g13
			alpha albumin/afamin/AFM/ALB2	104145	NM_001133	4q11-q13
			vitamin D-binding protein/group-specific	139200	AH004448	4q12
			component/GC
			orosomucoid 1/alpha 1 acid glycoprotein/ORM1	138600	M13692	9q34.1-
						q34.3
			orosomucoid 2/alpha 1 acid glycoprotein, type	138610	NM_000608	9q34.1-
			2/ORM2			q34.3
			transthyretin (prealbumin, amyloidosis type I)/TTR	176300	NM_000371	18q11.2-
						q12.1
			thyroxin-binding globulin/TBG	314200	NM_000354	Xq22.2
			corticosteroid binding globulin precursor/CBG	122500	NM_001756	14q32.1
			sex hormone-binding globulin/SHBG	182205	X16349	17p13-
						p12
			mannose-binding lectin, soluble/MBL2	154545	NM_000242	10q11.2-
						q21
		Bile Acid	Hepatic fatty acid binding protein/FABP1	134650	******	2p11
		Binders	Intestinal fatty acid binding protein/FABP2	134640	NM_000134	4q28-q31
			Muscle fatty acid binding protein/mammary-derived	134651	NM_004102	1p33-p31
			growth inhibitor/MDGI/FABP3
			Adipocyte fatty acid binding protein/FABP4	600434	NM_001442	8q21
			Ileal fatty acid binding protein/FABP6	600422	U19869	5q23-q35
			Brain fatty acid binding protein/FABP7	602965	D88648	6q22-q23
			Adipocyte long chain fatty acid transport	600691	******	******
			protein/FATP
	Drug Uptake	ABC	Retina-specific ATP binding cassette	601691	NM_000350	1p21-p13
		Transporters	transporter/ABCR
			ATP binding cassette 1/ABC1	600046	AJ012376	9q22-q31
			ATP binding cassette 2/ABC2	600047	U18235	9q34
			ATP binding cassette 3/ABC3	601615	NM_001089	16p13.3
			ATP binding cassette 7/ABC7	300135	AB005289	Xq13.1-
						q13.3
			ATP binding cassette 8/ABC8	603076	AF0381752	1q22.3
			ATP-binding cassette 50/ABC50	603429	AF0273026	p21.33
			Placenta-specific ATP-binding cassette	603756	NM_004827	4q22
			transporter/ABCP
			cystic fibrosis transmembrane conductance	602421	NM_000492	7q31.2
			regulator/CFTR
			adrenoleukodystrophy/adrenomyeloneuropathy/ALD	300100	NM_000033	Xq28
			adrenoleukodystrophy related protein/ALDR	601081	U28150	12q11-
						q12
			sulfonylurca receptor (hyperinsulinemia)/SUR	600509	NM_000352	11p15.1
			peroxisomal membrane protein 1/PXMP1	170995	NM_002858	1p22-p21
			peroxisomal membrane protein 1-like/PXMP1L	603214	NM_005050	14q24.3
			antigen peptide transporter 1/MHC 1/TAP1	170260	NM_000593	6p21.3
			antigen peptide transporter 2/MHC 2/TAP2	170261	NM_000544	6p21.3
			multidrug resistance associated protein MRP1	158343	L05628	16p13.1
			multidrug resistance associated protein	601107	NM_000392	10q24
			MRP2/CMOAT
			ATP-binding cassette, sub-family C (CFTR/MRP),	******		******
			member 3/CMOAT2		NM_003786
			ATP-binding cassette, sub-family C (CFTR/MRP),	******	NM_005845	******
			member 4/MOATB
			ATP-binding cassette, sub-family C (CFTR/MRP),	******	NM_005688	******
			member 5/SMRP
			ATP-binding cassette, sub-family C (CFTR/MRP),	601439	NM_005691	******
			member 9/SUR2
			multidrug resistance protein MDR1	171050	X96395	7q21.1
			multidrug resistance protein MDR3/P-glycoprotein	602347	X06181	7q21.1
			3/PGY3
			anthracyleline resistance-related protein/ARA	603234	NM_001171	16p13.1
			bile salt export pump/BSEP	603201	NM_003742	2q24
			familial intrahepatic cholestasis 1/FIC1	602397	NM_005603	18q21
			Human sorcin/SRI	182520	L12387	7q21.1
		Solute	Solute carrier family 1, member 1/SLC1A1	133550	U08989	9p24
		Antiporters	(glutamate)
			Solute carrier family 1, member 2/SLC1A2	600300	U03505	11p13-
			(glutamate)			p12
			Solute carrier family 1, member 3/SLC1A3	600111	U03504	5p13
			(glutamate)
			Solute carrier family 1, member 4/SLC1A4	600229	NM_003038	2p15-p13
			(glutamate)
			Solute carrier family 1, member 5/SLC1A5 (neutral	109190	AF105230	19q13.3
			AA)
			Solute carrier family 1, member 6/SLC1A6	600637	NM_005071	******
			(glutamate)
			Solute carrier family 2, member 1/SLC2A1/SGLT1	182380	NM_006516	22q13.1
			(glucose)
			Solute carrier family 2, member 2/SLC2A2/GLUT2	138160	NM_006516	3q26.1-
			(glucose)			q26.3
			Solute carrier family 2, member 3/SLC2A3/GLUT3	138170	M20681	12p13.3
			(glucose)
			Solute carrier family 2, member 4/SLC2A4/GLUT4	138190	******	17p13
			(glucose)
			Solute carrier family 2, member 5/SLC2A5/GLUT5	138230	NM_003039	1p36.2
			(glucose)
			Solute carrier family 3 member 1/SLC3A1 (aa	104614	******	2p16.3
			transporter)
			Solute carrier family 5 member 1/SLC5A2 (glucose)	182381	******	16p11.2
			Solute carrier family 5 member 3/SLC5A3	600444	L38500	21q22
			Solute carrier family 5 member 6/SLC5A6 (folate,	604024	******	2p23
			biotin, lipoate)
			Solute carrier family 6 member 1/SLC6A1 (GABA)	137165	X54673	3p25-p24
			Solute carrier family 6 member 2/SLC6A2	163970	NM_001043	16q12.2
			(noradrenalin)
			Solute carrier family 6 member 3/SLC6A3	126455	L24178	5p15.3
			(dopamine)
			Solute carrier family 6 member 4/SLC6A4	182138	X70697	17q11.1-
			(serotonin)			q12
			Solute carrier family 6, member 5/SLC6A5 (glycine)	604159	NM_004211	******
			Solute carrier family 6, member 6/SLC6A6 (taurine)	186854	U16120	3p25-q24
			Solute carrier family 6, member 8/SLC6A8 (creatine)	300036	NM_005629	300036
			Solute carrier family 6, member 9/SLC6A9 (glycine)	601019	S70612	1p313
			Solute carrier family 6, member 10/SLC6A10	601294	******	16p11.2
			(creatine-testis)
			Solute carrier family 6, member 12/SLC6A12	603080	NM_003044	12p13
			(GABA-betaine)
			Solute carrier family 7, member 1/SLC7A1 (cationic	104615	******	13q12.3
			AA)
			Solute carrier family 7, member 2/SLC7A2 (cationic	601872	D29990	8p22
			AA)
			Solute carrier family 7, member 4/SLC7A4 (cationic	603752	******	22q11.2
			AA)
			Solute carrier family 7, member 5/SLC7A5 (neutral	600182	M80244	16q24.3
			AA)
			Solute carrier family 7, member 7/SLC7A7 (dibasic	603593	Y18474	14q11.2
			AA)
			Solute carrier family 7, member 9/SLC7A9 (neutral	604144	******	19q13.1
			AA)
			Solute carrier family 10, member 1/SLC10A1	182396	NM_003049	chr. 14
			(taurocholate)
			Solute carrier family 10, member 2/SLC10A2	601295	NM_000452	13q33
			(taurocholate)
			Solute carrier family 11, member 1/SLC11A1 (?)	600266	AH002806	2q35
			Solute carrier family 11, member 2/SLC11A2 (iron)	600523	L37347	12q13
			Solute carrier family 13, member 2/SLC13A2	604148	NM_003984	17p11.1-
			(dicarboxylic acids)			q11.1
			Solute carrier family 14, member 1/SLC14A1 (urea)	111000	******	18q11-
						q12
			Solute carrier family 14, member 2/SLC14A2 (urea)	601611	X96969	18q12.1-
						q21.1
			Solute carrier family 15, member 1/SLC15A1	600544	U13173	13q33-
			(peptides)			q34
			Solute carrier family 15, member 2/SLC15A2	602339	S78203	******
			(peptides)
			Solute carrier family 16, member 1/SLC16A1	600682	NM_003051	1p13.2-
			(monocarboxylic acids)			p12
			Solute carrier family 16, member 2/SLC16A2	300095	NM_006517	Xq13.2
			(monocarboxylic acids)
			Solute carrier family 16, member 3/SLC16A3	603877	NM_004207	******
			(monocarboxylic acids)
			Solute carrier family 16, member 4/SLC16A4	603878	******	******
			(monocarboxylic acids)
			Solute carrier family 16, member 5/SLC16A5	603879	NM_004695	******
			(monocarboxylic acids)
			Solute carrier family 16, member 6/SLC16A6	603880	NM_004694	******
			(monocarboxylic acids)
			Solute carrier family 16, member 7/SLC16A7	603654	AF049608	12q13
			(monocarboxylic acids)
			Solute carrier family 18, member 1/VAT1/SLC18A1	193001	L09118	10q25
			(monoamines)
			Solute carrier family 18, member 2/VAT2/SLC18A2	193002	******	8p21.3
			(monoamines)
			Solute carrier family 18, member 3/VAT3/SLC18A3	600336	NM_003055	10q11.2
			(monoamines)
			Solute carrier family 19, member 1/SLC19A1	600424	U19720	21q22.3
			(reduced folate)
			Solute carrier family 19, member 2/SLC19A2	603941	AF160186	1q23.2-
			(thiamine)			q23.3
			Solute carrier family 21, member 2/SLC21A2	601460	NM_005630	3q21
			(prostaglandin)
			Solute carrier family 21, member 3/SLC21A3	602883	NM_005075	12p12
			(organic anion)
			Solute carrier family 22, member 1/SLC22A1	602607	NM_003058	6q26
			(organic cation)
			Solute carrier family 22, member 1-like/SLC22A1L	602631	AF037064	11p15.5
			(organic cation)
			Solute carrier family 22, member 2/SLC22A2	602608	NM_003058	6q26
			(organic cation)
			Solute carrier family 22, member 4/SLC22A4	604190	NM_003059	Chr. 5
			(organic cation)
			Solute carrier family 22, member 5/SLC22A5	603377	NM_003060	5q33.1
			(camitine)
			Solute carrier family 25, member 1/SLC25A1	190315	X96924	22q11
			(tricarboxylic acids) (mitochondrial)
			Solute carrier family 25, member 11/SLC25A11	604165	NM_003562	17p13.3
			(oxoglutarate/malate) (mitochondrial)
			Solute carrier family 25, member 12/SLC25A12 (?)	603667	NM_003705	******
			(mitochondrial)
			Solute carrier family 25, member 13/SLC25A13 (?)	603859	******	7q21.3
			(mitochondrial)
			Solute carrier family 25, member 15/SLC25A15	603861	******	13q14
			(ornithine) (mitochondrial)
			Solute carrier family 25, member 16/SLC25A16	139080	M31659	10q21.3-
			(ADP/ATP) (mitochondrial)			q22.1
			Solute carrier family 29, member 1/SLC29A1/ENT1	602193	NM_004955	6p21.2-
			(nucleoside) (mitochondrial)			p21.1
			Solute carrier family 29, member 2/SLC29A2/ENT2	602110	X86681	11q13
			(nucleoside) (mitochondrial)
Phase I Drug	Monooxigenases	Flavin-	Flavin-containing monooxygenase 1/FMO1	136130	NM_002021	1q23-q25
Metabolism	(mixed function	containing	Flavin-containing monooxygenase 3/FMO3	136132	AH006707	1q23-q25
(oxidation and	oxidases)	Mono-	Flavin-containing monooxygenase 4/FMO4	136131	NM_001460	1q23-q25
reduction)		oxygenases	Flavin-containing monooxygenase 5/FMO5	603957	NM_001461	1q21.1
		P450	Aryl hydrocarbon receptor nuclear	126110	NM_001668	1q21
		Cytochromes	translocator/ARNT
			Aryl hydrocarbon receptor nuclear translocator-	602550	NM_001178	11p15
			like/ARNTL
			Aryl hydrocarbon receptor/AHR	600253	NM_001621	7p15
			Nuclear receptor subfamily 1, group I, member	603065	NM_003889	******
			2/NR1I2
			Constitutive androstane receptor, beta/orphan nuclear	603881	NM_005122	******
			hormone receptor/CAR
			Nuclear receptor subfamily 1, group H, member	600380	U07132	19q13.3
			2/NR1H2
			Retinoic acid receptor, alpha/RARA	180240	NM_000964	17q12
			Retinoic acid receptor, beta/RARB	180220	NM_000965	3p24
			Retinoic acid receptor, gamma/RARG	180190	M57707	12q13
			Retinoid X receptor alpha/RXRA	180245	NM_005693	9q34.3
			Retinoid X receptor beta/RXRB	180246	X66424	6p21.3
			Retinoid X receptor gamma/RXRG	180247	U38480	1q22-q23
			RAR-related orphan receptor A/RORA	600825	NM_002943	15q21-
						q22
			RAR-related orphan receptor B/RORB	600825	******	15q21-
						q22
			RAR-related orphan receptor C/RORC	602943	NM_005060	1q21
			cellular retinoic acid-binding protein, type	180231	******	1q21.3
			2/CRABP2
			glucocorticoid receptor/GRL	138040	NM_000176	5q31
			Peroxisome proliferative activated receptor,	170998	NM_005036	22q12-
			alpha/PPARA			q13.1
			Peroxisome proliferative activated receptor,	601487	NM_005037	3p25
			gamma/PPARG
			Peroxisome proliferative activated receptor,	600409	NM_006238	1q21.3
			delta/PPARD
			cytochrome P450, subfamily I, polypeptide 1 (aryl	108330	NM_000499	15q22-
			hydrocarbon oxidase)/CYP1A1			q24
			cytochrome P450, subfamily I, polypeptide 2	124060	AH002667	15q22-
			(phenacetin metabolism)/CYP1A2			qter
			cytochrome P450, subfamily IB, polypeptide 1	601771	NM_000104	2p22-p21
			(dioxin inducible)/CYP1B1
			cytochrome P450, subfamily II, polypeptide 1	123960	X13897	19q13.2
			(phenobarbital inducible)/CYP2A
			cytochrome P450, subfamily IIA, polypeptide 6	122720	NM_000762	19q13.2
			(coumarin-7-hydroxylase)/CYP2A6
			cytochrome P450, subfamily IIB (phenobarbital	123930	M29874	19q13.2
			inducible)/CYP2B
			cytochrome P450, subfamily IIC, polypeptide	601129	******	10q24
			8/CYP2C8
			cytochrome P450, subfamily IIC, polypeptide 9	601130	******	10q24
			(hydroxylation of tolbutamide)/CYP2C9
			Cytochromescytochrome P450, subfamily IIC, polypeptide	601131	******	10q25
			18/CYP2C18
			cytochrome P450, subfamily IIC, polypeptide 19	124020	NM_000769	10q24.1-
			(mephenytoin 4-hydroxylase)/CYP2C19			q24.3
			cytochrome P450, subfamily IID, polypeptide 6	124030	NM_000106	22q13.1
			(debrisoquine hydroxylation)/CYP2D6
			cytochrome P450, subfamily IIE (ethanol	124040	J02843	10q24.3-
			inducible)/CYP2E			qter
			cytochrome P450, subfamily IIF (ethoxycoumarin	124070	NM_000774	19q13.2
			monooxygenase), polypeptide 1/CYP2F1
			cytochrome P450, subfamily IIJ (arachidonate	601258	NM_000775	1p31.3-
			epoxygenase), polypeptide 2/CYP2J2			p31.2
			cytochrome P450, subfamily IIIA (niphedipine	124010	NM_000776	7q22.1
			oxidase), polypeptide 3/CYP3A3
			cytochrome P450, subfamily IVA (fatty acid W-	601310	NM_000778	Chr.1
			hydroxylase), polypeptide 11/CYP4A11
			cytochrome P450, subfamily IVB, polypeptide	124075	NM_000779	1p34-p12
			1/CYP4B1
			cytochrome P450, subfamily IVF (leukotriene B4-W-	601270	NM_000896	19p13.2
			hydroxylase), polypeptide 3/CYP4F3
			cytochrome P450, subfamily VIIA (cholesterol 7-a-	118455	M89803	8q11-q12
			hydroxylase), polypeptide 1/CYP7A1
			cytochrome P450, subfamily VIIB (oxysterol 7-a-	603711	AF029403	8q21.3
			hydroxylase), polypeptide 1/CYP7B1
			cytochrome P450, subfamily VIIIB (sterol 12-a-	602172	******	3p21.3-
			hydroxylase), polypeptide 1/CYP8B1			p22
			cytochrome P450, subfamily XIA (cholesterol side-	118485	NM_000781	15q23-
			chain cleavage)/CYP11A			q24
			cytochrome P450, subfamily XIB, polypeptide 2	124080	NM_000498	8q21
			(steroid 11-b-hydroxylase)/CYP11B2
			cytochrome P450, subfamily XIX (androgen	107910	NM_000103	15q21.1
			aromatase)/CYP19
			cytochrome P450, subfamily XXI (sterol 21-a-	201910	M13936	6p21.3
			hydroxylase)/CYP21
			cytochrome P450, subfamily XXIV (25-	600125	S67623	20q13.2-
			hydroxyvitamin D24-hydroxylase)/CYP24			q13.3
			cytochrome P450, subfamily XXVIA, polypeptide 1	602239	NM_000783	10q23-
			(retinoic acid hydroxylase)/CYP26A1			q24
			cytochrome P450, subfamily XXVIIA, polypeptide 1	213700	NM_000105	2q33-qter
			(25-hydroxyvitamin D-1-a-hydroxylase)/CYP27A1
			adrenodoxin/ferredoxin 1/FDX1/ADX	103260	NM_004109	11q22
			adrenodoxin reductase/ferredoxin:NADP(+)	103270	NM_004110	17q24-
			reductase/FDXR/ADXR			q25
			cytochrome P450, subfamily XXVIIB, polypeptide 1	264700	NM_000785	12q14
			(25-hydroxyvitamin D-1-a-hydroxylase)/CYP27B1
			cytochrome P450, subfamily XLVI (cholesterol 24-	604087	NM_006668	14q32.1
			hydroxylase)/CYP46
			cytochrome P450, subfamily LI (lanosterol 14-a-	601637	U51692	7q21.2-
			demethylase)/CYP51			q21.3
	General	General	Monoamine Oxidase A; MAOA	309850	M69226	Xp11.23
	Oxidases	Oxidases	Monoamine Oxidase B; MAOB	309860	M69177	Xp11.23
			Copper-containing amine oxidase/AOC3	603735	NM_003734	17q21
			Xanthine dehydrogenase/XDH	278300	NM_000379	2p23-p22
			tryptophan 2,3-dioxygenase/TDO2	191070	NM_005651	4q31-q32
			sulfite oxidase/SUOX	272300	NM_000456	******
	Dehydrogenases	Cofactor	molybdenum factor synthesis 1/MOCS1	603707	AJ224328	6p21.3
		Synthesis	molybdenum factor synthesis 2/MOCS2	603708	******	5q11
		Alcohol	alcohol dehydrogenases 1, alpha subunit/ADH1	103700	NM_000667	4q22
		Dehydrogenases	alcohol dehydrogenases 2, beta subunit/ADH2	103720	NM_000668	4q22
			alcohol dehydrogenases 3, gamma subunit/ADH3	103730	M12272	4q22
			alcohol dehydrogenases 4/pi isozyme/ADH4	103740	M15943	4q22
			alcohol dehydrogenases 5/chi isozyme/ADH5	103710	NM_000671	4q21-q25
			alcohol dehydrogenases 6/ADH6	103735	NM_000672	15q26
			alcohol dehydrogenases 7/ADH7	600086	AH006682	4q23-q24
		Aldehyde	aldehyde dehydrogenase 1/ALDH1 (liver cytosol)	100640	AH002598	9q21
		Dehydrogenases	aldehyde dehydrogenase 2/ALDH2 (liver	100650	K03001	12q24.2
			mitochondria)
			aldehyde dehydrogenase 3/acetaldehyde	100660	M74542	17p11.2
			dehydrogenase/ALDH3 (stomach)
			aldehyde dehydrogenase 5/acetaldehyde	100670	NM_000692	9p13
			dehydrogenase/ALDH5
			aldehyde dehydrogenase 5, member Al/succinic	271980	NM_001080	6p22
			semialdehyde dehydrogenase/ALDH5A1
			aldehyde dehydrogenase 6/acetaldehyde	600463	NM_000693	15q26
			dehydrogenase/ALDH6
			aldehyde dehydrogenase 7/acetaldehyde	600466	NM_000694	11q13
			dehydrogenase/ALDH7
			aldehyde dehydrogenase 8/ALDH8	601917	NM_000695	chr. 11
			aldehyde dehydrogenase 9/g-aminobutyraldehyde	602733	NM_000696	1q22-q23
			dehydrogenase/ALDH9
			aldehydedehydrogenase 10/ALDH10	270200	NM_000382	17p11.2
		Dihydro-	Dihydropyrimidine dehydrogenase (5-fluoroacil	274270	U09178	1p22
		pyrimidine	detoxification)
		Dehydrogenase
	Fatty Acid β-	Peroxisome	Peroxisome proliferative activated receptor,	170998	NM_005036	22q12-
	Oxidation	Proliferation	alpha/PPARA			q13.1
			Peroxisome proliferative activated receptor,	601487	NM_005037	3p25
			gamma/PPARG
			Peroxisome proliferative activated receptor,	180231	NM_006238	1q21.3
			delta/PPARD
		Peroxisome	peroxisome biogenesis factor 1/PEX1	602136	AB008112	7g21-q22
		Synthesis	peroxisomal membrane protein 3 (35kD, Zeliweger	170993	NM_000318	8q21.1
			syndrome)/PXMP3/PEX2
			peroxisomal biogenesis factor 3/PEX3	603164	NM_003630	******
			peroxisomal biogenesis factor 6/PEX6	601498	NM_000287	6p21.1
			peroxisomal biogenesis factor 7/PEX7	601757	NM_000288	6q22-q24
			peroxisomal biogenesis factor 10/PEX10	602859	******	******
			peroxisomal biogenesis factor 11A/PEX11A	603866	NM_003847	******
			peroxisomal biogenesis factor 11B/PEX11B	603867	NM_003846	******
			peroxisomal biogenesis factor 12/PEX12	601758	NM_000286
			peroxisomal biogenesis factor 13/PEX13	601789	U71374	2p15
			peroxisomal biogenesis factor 14/PEX14	601791	******	******
			peroxisomal farnesylated protein/PXF/peroxisomal	600279	NM_002857	1q22
			biogenesis factor 19/PEX19
		Coenzyme A	Fatty acid CoA Ligase, long chain 1/FACL1	152425	******	3q13
		Ligases	Fatty acid CoA Ligase, long chain 2/FACL2	152426	******	4q34-q35
			Fatty acid CoA Ligase, long chain 3/FACL3	602371	NM_004457	2q34-q35
			Fatty acid CoA Ligase, long chain 4/FACL4	300157	NM_004458	Xq22.3
			Fatty acid CoA Ligase, very long chain 1/FACVL1	603247	******	15q21.2
		Oxidation	Enoyl-CoA, hydratase/3-hydroxyacyl CoA	261515	NM_001966	3q27
			dehydrogenase/EHHADH
			hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA	600890	NM_000182	2p23
			thiolase/enoyl-CoA hydratase, alpha
			subunit/HADHA
			hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA	143450	NM_000183	2p23
			thiolase/enoyl-CoA hydratase, beta subunit/HADHB
			acyl-Coenzyme A oxidase 1/ACOX1 (peroxisomal)	264470	NM_004035	17q25
			acyl-Coenzyme A oxidase 2, branched chain/ACOX2	601641	NM_003500	13pl4.13
			(peroxisomal)
			acyl-Coenzyme A dehydrogenase, C-2 to C-3 short	201470	NM_000017	12q22-
			chain precursor/ACADS (mitochondrial)			qter
			acyl-Coenzyme A dehydrogenase, C-4 to C-12	201450	NM_000016	1p31
			straight chain/ACADM (mitochondrial)
			acyl-Coenzyme A dehydrogenase, long	201460	NM_001608	2q34-q35
			chain/ACADL (mitochondrial)
			hydroxyacyl-Coenzyme A dehydrogenase, type	602057	NM_004493	******
			II/HADH2
			enoyl-Coenzyme A hydratase 1/ECH1 (peroxisomal)	600696	NM_001398	19q13
	Reduction	Aldo-Keto	Aldo-keto reductase family 1, member	103830	NM_006066	1p33-p32
		Reductases	A1/dihydrodiol dehydrogenase/AKR1A1
			Aldo-keto reductase family 1, member	600449	NM_001353	10p15-
			C1/dihydrodiol dehydrogenase/AKR1C1			p14
			Aldo-keto reductase family 1, member	603966	NM_003739	10p15-
			C3/dihydrodiol dehydrogenase/AKR1C3			p14
			Aldo-keto reductase family 1, member	600451	******	10p15-
			C4/chlorodecone reductase/AKR1C4			p14
			Aldo-keto reductase family 7, member A2/aflatoxin	603418	NM_003689	******
			aldehyde reductase/AKR7A2
			Carbonyl reductase 1/CBR1	114830	NM_001757	21q22.12
			Carbonyl reductase 2/CBR2	******	******	chr. 11
			Carbonyl reductase 3/CBR3	603608	NM_001236	21q22.2
			Sepiapterin reductase (7,8-dihydrobiopterin:NADP+	182125	NM_003124	2p14-p12
			oxidoreductase)/SPR
		Quinone	Z-crystallin/quinone reductase/CRYZ	123691	L315211	p31-p22
		Oxidoreductases	Z-crystallin-like/quinone reductase-like/CRYZL1	603920	NM_005111	21q22.1
			NAD(P)H menadione oxidoreductase 1, dioxin-	125860	NM_000903	16q22.1
			inducible/NMOR1/diaphorase 4/DIA4
			NAD(P)H menadione oxidoreductase 2, dioxin-	160998	NM_000904	6pter-q12
			inducible/NMOR2
	Conjugation	Sulfate Unit	3-prime-phosphoadenosine 5-prime-phosphosulfate	603262	NM_005443	4q
		Activation	synthase 1/PAPSS1
			Phenol-preferring sulfotransferase, family 1A,	171150	NM_001055	16p12.1-
			member 1/SULT1A1			p11.2
			Phenol-preferring sulfotransferase, family 1A,	601292	NM_001054	16p12.1-
			member 2/SULT1A2			p11.2
			Phenol-preferring sulfotransferase, family 1A,	600641	L19956	16p11.2
			member 3/SULT1A3
			Sulfotransferase, family 1C, member 3/SULT1C1	602385	U66036	2q11.1-
						q11.2
			Dehydroepiandrosterone (DHEA)-preferring	125263	NM_003167	19q13.3
			sulfotransferase, family 2A, member 1/SULT2A1
			Sulfotransferase, family 2B, member 1/SULT2B1	604125	NM_004605	19q13.3
			Estrogen-preferring sulfotransferase/STE	600043	NM_005420	4q13.1
			N-deacetylase/N-sulfotransferase (heparan	600853	U18918	5q32-
			glucosaminyl)/NDST1			q33.3
			N-deacetylase/N-sulfotransferase (heparan	603268	NM_003635	10q22
			glucosaminyl)/NDST2
			N-deacetylase/N-sulfotransferase (heparan	603950	NM_004784	******
			glucosaminyl)/NDST3
			Carbohydrate sulfotransferase 1 (chondroitin	603797	NM_003654	11p11.2-
			6/keratan)/CHST1			p11.1
			Carbohydrate sulfotransferase 2 (chondroitin	603798	******	7q31
			6/keratan)/CHST2
			Carbohydrate sulfotransferase 3 (chondroitin	603799	NM_004273	******
			6/keratan)/CHST3
			Cerebroside sulfotransferase (3′-	602300	NM_004861	******
			phosphoadenylylsulfate:galactosylceramide 3′)/CST
			Heparan sulfate (glucosamine) 3-O-sulfotransferase	603244	NM_005114	******
			1/HS3ST1
			Heparan sulfate (glucosamine) 3-O-sulfotransferase	604056	NM_006043	16p12
			2/HS3ST2
			Heparan sulfate (glucosamine) 3-O-sulfotransferase	604057	NM_006042	17p12-
			3A1/HS3ST3A1			p11.2
			Heparan sulfate (glucosamine) 3-O-sulfotransferase	604058	NM_006041	17p12-
			3B1/HS3ST3B1			p11.2
			Heparan sulfate (glucosamine) 3-O-sulfotransferase	604059	AF105378	16p11.2
			4/HS3ST4
		Sulfhydrylation	Methylguanine methyltransferase (O6-alkylguanine	156569	M29971	10q26
			detoxification)
			thiosulfate thiotransferase/rhodanese/TST (cyanide	180370	D87292	22q11.2-
			detoxification)			qter
		UDP-	UDP glycosyltransferase 1/UGT1	191740	NM_001072	Chr. 12
		Glycosyltransfer	UDP glycosyltransferase family 2, member	600067	NM_001073	4q13
		ases	B4/UGT2B4
			UDP glycosyltransferase family 2, member	600068	NM_001074	1q14
			B7/UGT2B7
			UDP glycosyltransferase family 2, member	600070	NM_001075	******
			B10/UGT2B10
			UDP glycosyltransferase family 2, member	600069	U06641	4q13
			B15/UGT2B15
			UDP glycosyltransferase family 2, member	601903	NM_001077	1q14
			B17/UGT2B17
			UDP glycosyltransferase 8/UGT8	601291	U30930	4q26
			UDP-glucuronosyltransferase	218800	AJ005162	Chr.2
		Carbon Unit	methionine adenosyltransferase I, alpha/MAT1A	250850	NM_000429	10q22
		Activation for	methionine adenosyltransferase II, alpha/MAT2A	601468	NM_005911	2p11.2
		SAM
		Carbon Unit	Folate Receptor Alpha/FOLR1	136430	M28099	11q13.3-
		Activation for				g13.5
		Folate	Folate Receptor Beta/FOLR2	136425	AF000380	11q13.3-
						q13.5
			Folate Receptor Gamma/FOLR3	602469	Z32564	******
			Folate Transporter (SLC19A1)	600424	U19720	21q22.3
			Vitamin B12 binding protein	275350	NM_000355	22q11.2-
						qter
			folylpolyglutamate synthetase/FPGS	136510	M98045	9cen-q34
			gamma-glutamyl hydrolase/GGH	601509	U55206	******
			Methylenetetrahydrofolate reductase/MTHFR	236250	U09806	1p36.3
			Dihydrofolate reductase/DHFR	126060	J00140	5q11.2-
						q13.2
			5,10-methylenetetrahydrofolate dehydrogenase, 5,10-	172460	NM_005956	14q24
			methylenetetrahydrofolate cyclohydrolase, 10-
			formyltetrahydrofolate synthetase/MTHFD1
			5,10-methenyltetrahydrofolate synthetase (5-	604197	NM_006441	Chr. 15
			formyltetrahydrofolate cyclo-ligase)/MTHFS
			phosphoribosylglycinamide formyltransferase,	138440	NM_000819	21q22.1
			phosphoribosylglycinamide synthetase,
			phosphoribosylaminoimidazole synthetase/GART
			folate hydrolase 1/FOH1	600934	NP004467	11q14
			6-pyruvoyl tetrahydrobiopterin synthase/PTPS	261640	Q03393	11q22.3-
						q23.3
			serine hydroxymethyltransferase 1 (soluble)/SHMT1	182144	NM_004169	17p11.2
			senile hydroxymethyltransferase 2	138450	NM_005412	12q13
			(mitochondrial)/SHMT2
			Glycine aminotransferase/glycine cleavage T	238310	NM_000481	3p21.2-
			protein/GAT			p21.1
			5-methyltetrahydrofolate-homocysteine	156570	NM_000254	1q43
			methyltransferase/methionine synthase/MTR
			glutamate formiminotransferase/dihydrofolate	229100	******	******
			synthetase
		Methylation	catechol-O-methyltransferase/COMT	116790	NM_000754	22q11.2
			phenylethanolamine N-methyltransferase/PNMT	171190	NM_002686	17q21-
						q22
			nicotinamide N-methyltransferase/NNMT	600008	NM_006169	11q23.1
			Thiopurine methyltransferase (6-mercaptopurine	187680	U12387	6p22.3
			detoxification)
		Carbon Unit	pyruvate dehydrogenase E1-alpha subunit/PDHA1	312170	L48690	Xp22.2-
		Activation for				p22.1
		Acetyl-CoA	pyruvate dehydrogenase (lipoamide) beta/PDHB	179060	NM_000925	3p13-q23
			Acetyl-CoA pyruvate dehydrogenase complex, lipoyl-	245349	NM_003477	11p13
			containing component X/E3-binding protein/PDX1
			pyruvate dehydrogenase complex E3 subunit/DLD	246900	NM_000108	7q31-q32
		Acylation	sterol-O-acyl transferase 1/SOAT1	102642	L21934	1q25
			sterol-O-acyl transferase 2/SOAT2	601311	******	chr. 12
			N-acetyltransferase 1/arylamide acetylase 1/NAT1	108345	NM_000662	8p23.1-
						p21.3
			N-acetyltransferase 2/arylamide acetylase 2/NAT2	243400	NM_000015	8p23.1-
						p21.3
		Glutathione	Glutathione-S-transferase 6	138391	******	******
		Transferase
			Glutathione-S-transferase, alpha 1/GSTA1	138359	L13269	6p12.2
			Glutathione-S-transferase, alpha 2/GSTA2	138360	M15872	6p12.2
			Glutathione-S-transferase, kappa 1/GSTK1	602321	******	******
			Glutathione-S-transferase 1/MGST1 (microsomal)	138330	AH003674	Chr. 12
			Glutathione-S-transferase 2/MGST2 (microsomal)	601733	NM_002413	4q28-q31
			Glutathione-S-transferase, mu 1-like/GSTM1L	138270	******	Chr. 3
			Glutathione-S-transferase, mu 1/GSTM1	138350	J03817	1p13.3
			Glutathione-S-transferase, mu 2/GSTM2 (muscle)	138380	NM_000848	1p13.3
			Glutathione-S-transferase, mu 3/GSTM3 (brain)	138390	NM_000849	1p13.3
			Glutathione-S-transferase, mu 4/GSTM4	138333	NM_000850	1p13.3
			Glutathione-S-transferase, mu 5/GSTM5 (brain/lung)	138385	NM_000851	1p13.3
			Glutathione-S-transferase, pi/GSTP1	134660	NM_000852	11q13
			Glutathione-S-transferase, theta 1/GSTT1	600436	NM_000853	22q11.2
			Glutathione-S-transferase, theta 2/GSTT2	600437	NM_000854	22q11.3
			Glutathione-S-transferase, zeta 1 /maleylacctoactetate	603758	NM_001513	14q24.3
			isomerase/MAAI/GSTZ1
		γ-Glutamyl-	Gamma-glutamyltranspeptidase 1/GGT1	231950	J04131	22q11.1-
		transpeptidase				q11.2
			Gamma-glutamyltranspeptidase 2/GGT2	137181	AH002728	22q11.1
			Gamma-glutamyltransferase-like activity 1/GGTLA1	137168	NM_004121	******
	Catabolism	Esterases	paraoxonase 1/PON1 (arylesterase)	168820	AH004193	7q21.3
			paraoxonase 2/PON2	602447	L48513	7q21.3
			paraoxonase 3/PON3	602720	******	7q21.4
			esterase C/ESC (acetyl esterase)	133270
			esterase A4/ESA4	133220	******	11q13-
						q22
			esterase B/buteryl esterase/ESB (erythrocyte)	133260	******	******
			esterase B3/ESB3	133290	******	Chr. 16
			esterase A5/A7/acetylesterase/ESA5/ESA7 (brain)	133230	******	******
			acetylcholinesterase/ACHE	100740	M55040	7q22
			butyrylcholinesterase 1/serum cholinesterase	177400	NM_000055	3q26.1-
			1/BCHE1			q26.2
			butyrylcholinestarase 2/serum cholinesterase	177500	******	2q33-q35
			2/BCHE2
			carboxylesterase 1/serine esterase/CES1 (hepatic)	114835	SEG_HUM	16q13-
					CESTG	q22.1
			arylacetamide deacetylase/AADAC	600338	NM_001086	3q21.3-
						q25.2
		Thioesterase	acyl-CoA thioester hydrolase 1, long chain/acyl-CoA	602586	******	******
			thioesterase 1/ACT1
			acyl-CoA thioester hydrolase 2, long chain/acyl-CoA	602587	******	******
			thioesterase 2/ACT2
			esterase D/S-forinylglutathion hydrolase/ESC	133280	M13450	13q14.11
			(thioesterase)
		Amidase	aminoacylase 1/ACY1	104620	NM_000666	3p21.1
			laminoacylase 2/ACY2/aspartoacylase (Canavan	271900	NM_000049	17pter-
			disease)/ASPA			p13
			fatty acid amide hydrolase/FAAH	602935	NM_001441	1p34-p35
		Epoxide	epoxide hydrolase 1/EPHX1 (microsomal)	132810	NM_000120	1p11-qter
		Hydratases	epoxide hydrolase 2/EPHX2 (cytosolic)	1132811	******	8p21-p12
		Proteases	bleomycin hydrolase/BLMH	602403	NM_000386	17q11.2
Excretion	Canalicular	Transporters	Multidrug resistance protein MDR3/P-glycoprotein	602347	X06181	7q21.1
	Uptake and		3/PGY3
	Concentration		Familial intrahepatic cholestasis 1, (progressive,	602397	NM_005603	18q21
			Byler disease and benign recurrent) /FIC1
			Bile salt export pump/BSEP	603201	NM_003742	2q24
			Microsomal triglyceride transfer protein large	157147	NM_000253	4q22-q24
			subunit/MTP
			Solute carrier family 6, member 6/SLC6A6 (taurine)	186854	U16120	3p25-q24
			Solute carrier family 10, member 1/SLC10A1	182396	NM_003049	chr. 14
			(taurocholate)
			Solute carrier family 10, member 2/SLC10A2	601295	NM_000452	13q33
			(taurocholate)
			Solute carrier family 13, member 2/SLC13A2	604148	NM_003984	17p11.1-
			(dicarboxylic acids)			q11.1
			Solute carrier family 19, member, 1/SLC19A1	600424	U19720	21q22.3
			(reduced folate)
			Solute carrier family 21, member 3/SLC21A3	602883	NM_005075	12p12
			(organic anion)
			Solute carrier family 22, member 1/SLC22A2	602607	NM_003058	6q26
			(organic cation)
			multidrug resistance protein MDR1	171050	X96395	7q21.1
			multidrug resistance associated protein	601107	NM_000392	10q24
			MRP2/CMOAT
			multidrug resistance protein MDR3/P-glycoprotein	602347	X06181	7q21.1
			3/PGY3
		Bile Salt	Bile acid Coenzyme A: amino acid N-acyltransferase	602938	NM_001701	9q22.3
		Synthesis	(glycine N-choloyltransferase)/BAAT
			cytochrome P450, subfamily XLVI (cholesterol 24-	604087	NM_006668	14q32.1
			hydroxylase)/CYP46
			cytochrome P450, subfamily VIIA (cholesterol 7-a-	118455	M89803	8q11-q12
			hydroxylase), polypeptide 1/CYP7A1
			cytochrome P450, subfamily VIIB (oxysterol 7-a-	603711	AF029403	8q21.3
			hydroxylase), polypeptide 1/CYP7B1
			ATPase, Na+/K+ transporting, alpha 1	182310	NM_000701	1p13-p11
			polypeptide/ATP1A1
			ATPase, Na+/K+ transporting, alpha 1 polypeptide-	182360	NM_001676	13q12.1-
			like/ATP1A1L			q12.3
			ATPase, Na+/K+ transporting, alpha 2	182340	NM_000702	1q21-q23
			polypeptide/ATP1A2
			ATPase, Na+/K+ transporting, beta 1	182330	NM_001677	1q22-q25
			polypeptide/ATP1B1
			ATPase, Na+/K+ transporting, beta 2	182331	X16645	17p
			polypeptide/ATP1B2
			ATPase, Na+/K+ transporting, beta 3	601867	NM_001679	3q22-q23
			polypeptide/ATP1B3
			solute carrier family 4, bicarbonate/chloride anion	109270	NM_000342	17q21-
			exchanger, member 1/SLC4A1			q22
			solute carrier family 4, sodium bicarbonate	603345	NM_003759	4q21
			cotransporter, member 4/SLC4A4
			solute carrier family 4, sodium bicarbonate	603318	NM_003788	4q21
			cotransporter, member 5/SLC4A5
			Solute carrier family 9, member A2/SLC9A2	600530	NM_003048	2q11.2
			(sodium/hydrogen ion)
			Solute carrier family 9, member A3/SLC9A3	182307	******	5p15.13
			(sodium/hydrogen ion)
			chloride channel 5/CLCN5	300008	NM_000084	Xp11.22
			chloride channel, calcium activated, family member	603906	NM_001285	1p31-p22
			1/CLCA1
			chloride channel, calcium activated, family member	604003	NM_006536	******
			2/CLCA2
			cystic fibrosis transmembrane conductance	602421	NM_000492	7q31.2
			regulator/CFTR
			aquaporin 1/AQP1	107776	NM_000385	7p14
			aquaporin 3/AQP3	600170	NM_004925	9p13
		Bile Secretion	Cholecystokinin/CCK	118440	L00354	3pter-p21
			Cholecystokinin A receptor/CCKAR	118444	L13605	4p15.2-
						p15.1
			Cholecystokinin B receptor/CCKBR	118445	L08112	11p15.5-
						p15.4
	Renal Tubular	Deconjugating	Cytoplasmic cysteine conjugate-beta lyase/glutamine	600547	NM_004059	Chr.9
	Uptake and	Enzymes	transaminase K/CCBL1
	Concentration		Galactosamine (N-acetyl)-6-sulfate sulfatase	253000	NM_000512	16q24.3
			(Morquio syndrome)/GALNS
			Iduronate-2-sulfatase (Hunter syndrome)/IDS	309900	NM_000202	Xq28
			Arylsulfatase Alsteroid sulfatase/ARSA	250100	NM_000487	22q13.31-
						qter
			Arylsulfatase B/steroid sulfatase/ARSB	253200	NM_000046	5q11-q13
			Arylsulfatase C, isozyme s/steroid sulfatase/ARCS	308100	NM_000351	Xp22.32
			Arylsulfatase D/steroid sulfatase/ARSD	300002	******	Xp22.3
			Arylsulfatase F/steroid sulfatase/ARSE	300180	NM_000047	Xp22.3
			Arylsulfatase F/steroid sulfatase/ARSF	300003	NM_004042	Xp22.3
		Uptake and	renal transport of beta-amino acids/AABT	109660	******	Chr.21
		Reuptake	Solute carrier family 3 member 1/SLC3A1 (aa	104614	******	2p16.3
		Transporters	transporter)
			Solute carrier family 5 member 2/SLC5A5	182381	A56765	16p11.2
			(Na/glucose transporter)
			Solute carrier family 6, member 6/SLC6A6 (taurine)	186854	U16120	3p25-q24
			Solute carrier family 7, member 9/SLC7A9 (neutral	604144	******	19q13.1
			AA)
			Solute carrier family 13, member 2/SLC13A2	604148	NM_003984	17p11.1-
			(dicarboxylic acids)			q11.1
			solute carrier family 17 (sodium phosphate), member	182308	NM_005074	6p23-
			1/SLC17A1			p21.3
			Solute carrier family 22, member 1/SLC22A2	602607	NM_003058	6q26
			(organic cation)
			Solute carrier family 22, member 1-like/SLC22A1L	602631	AF037064	11p15.5
			(organic cation)
			Solute carrier family 22, member 4/SLC22A4	604190	NM_003059	Chr. 5
			(organic cation)
			Solute carrier family 22, member 5/SLC22A5	603377	NM_003060	5q33.1
			(carnitine)
			Solute carrier family 34, member 1/SLC34A1	182309	NM_003052	5q35
			(sodium phosphate)
		Acidosis	H+-ATPase beta 1 subunit /ATP6B1	267300	AH007312	2cen-q13
			solute carrier family 4, sodium bicarbonate	603345	NM_003759	4q21
			cotransporter, member 4/SLC4A4
			solute carrier family 4, sodium bicarbonate	603318	NM_003788	4q21
			cotransporter, member 5/SLC4A5
			carbonic anhydrase II/CA2	259730	NM_000067	8q22
			carbonic anhydrase IV/CA4	114760	NM_000717	17q23
			carbonic anhydrase XII/CA12	603263	AF051882	15q22
			solute carrier family 4, bicarbonate/chloride anion	109270	NM_000342	17q21-
			exchanger, member 1/SLC4A1			q22
			Solute carrier family 9, member A1/SLC9A1	107310	M81768	1p36.1-
			(sodium/hydrogen ion)			p35
			Solute carrier family 9, member A2/SLC9A2	600530	NM_003048	2q11.2
			(sodium/hydrogen ion)
			Solute carrier family 9, member A3/SLC9A3	182307	******	5p15.3
			(sodium/hydrogen ion)
		Lithosis	Solute carrier family 13, member 2/SLC13A2	604148	NM_003984	17p11.1-
			(dicarboxylic acids)			q11.1
		Sodium	3′(2′), 5′-bisphosphate nucleotidase 1/BPNT	604053	NM_006085	******
		Tolerance
		Urine	chloride channel 5/CLCN5	300008	NM_000084	Xp11.22
		Concentration	chloride channel Ka, kidney/CLCNKA	602024	NM_004070	1p36
			chloride channel Kb, kidney/CLCNKB	602023	NM_000085	1p36
			solute carrier family 12 (sodium/potassium/chloride	600839	NM_000338	15q15-
			transporters), member 1/SLC12A1			q21.1
			solute carrier family 12 (sodium/potassium/chloride	600840	NM_001046	5q23.3
			transporters), member 2/SLC12A2
			solute carrier family 12 (sodium/chloride	600968	NM_000339	16q13
			transporters), member 3/SLC12A3
			ATPase, Na+/K+ transporting, alpha 1	182310	NM_000701	1p13-p11
			polypeptide/ATP1A1
			ATPase, Na+/K+ transporting, alpha 1 polypeptide-	182360	NM_001676	13q12.1-
			like/ATP1A1L			q12.3
			ATPase, Na+/K+ transporting, alpha	2182340	NM_000702	1q21-q23
			polypeptide/ATP1A2
			ATPase, Na+/K+ transporting, beta 1	182330	NM_001677	1q22-q25
			polypeptide/ATP1B1
			ATPase, Na+/K+ transporting, beta 2	182331	X16645	17p
			polypeptide/ATP1B2
			ATPase, Na+/K+ transporting, beta 3	601867	NM_001679	3q22-q23
			polypeptide/ATP1B3
			arginine vasopressin receptor 2 (nephrogenic	304800	NM_000054	Xq28
			diabetes insipidus)/AVPR2
			aquaporin 1/AQP1	107776	NM_000385	7p14
			aquaporin 2/AQP2	107777	NM_000486	12q13
			aquaporin 3/AQP3	600170	NM_004925	9p113
			aquaporin 6/AQP6	601383	NM_001652	12q13
		Superoxide	Superoxide Dismutase 1/SOD1 (soluble)	147450	NM_000454	21q22.1
		Dismutase	Superoxide Dismutase 2/SOD2 (mitochondrial)	147460	X65965	6q25.3
			Superoxide Dismutase 3/SOD3 (extracellular)	185490	NM_003102	4pter-q21
Organ and	Protection from	Aldehyde	aldehyde dehydrogenase 1/ALDH1 (liver cytosol)	100640	AH002598	9q21
Tissue	Radical Damage	Dehydrogenase	aldehyde dehydrogenase 2/ALDH2 (liver	100650	K03001	12q24.2
Damage			mitochondria)
			aldehyde dehydrogenase 3/acetaldehyde	100660	M74542	17p11.2
			dehydrogenase/ALDH3 (stomach)
			aldehyde dehydrogenase 5/acetaldehyde	100670	NM_000692	9p13
			dehydrogenase/ALDH5
			aldehyde dehydrogenase 5, member Al/succinic	271980	NM_001080	6p22
			semialdehyde dehydrogenase/ALDHSA1
			aldehyde dehydrogenase 6/acetaldehyde	600463	NM_000693	15q26
			dehydrogenase/ALDH6
			aldehyde dehydrogenase 7/acetaldehyde	600466	NM_000694	11q13
			dehydrogenase/ALDH7
			aldehyde dehydrogenase 8/ALDH8	601917	NM_000695	chr. 11
			aldehyde dehydrogenase 9/g-aminobutyraldehyde	602733	NM_000696	1q22-q23
			dehydrogenase/ALDH9
			aldehyde dehydrogenase 10/ALDH10	270200	NM_000382	17p11.2
		Glutathione	glutathione synthetase/GSS	601002	NM_0001781	20q11.2
			glutathione peroxidase/GPX1	138320	M21304	3p21.3
			glutathione peroxidase GPX2	138319	X68314	14g24.1:
			glutathione peroxidase GPX3	138321	X58295	5q32-
						q33.1
			glutathione peroxidase GPX4	138322	X71973	19p13.3
			glutathione peroxidase GPX5	603435	AJ005277	******
			glutathione reductase	138300	X15722	8p21.1
		Metallothionein	metallothionein 1A/MT1A	156350	NM_005953	16q13
			metallothionein 1B	56349	AH001510	16q13
			metallothionein 1E	156351	M10942	16q13
			metallothionein 1F	156352	M10943	16q13
			metallothionein 1G	156353	J03910	16q13
			metallothionein 2A/MT2A	156360	NM_005953	16q13
			metallothionein 3	139255	NM_005954	16q13
		Miscellaneous	glucose-6-phosphate dehydrogenase/G6PD	305900	NM_000402	Xq28
			(mitochondrial)
			8-oxoguanine DNA glycosylase/OGG1	601982	NM_002542	3p26.2
			Peptide methionine sulfoxide reductase/MSRA	601250	******	******
			succinate dehydrogenase complex, subunit C,	602413	NM_003001	1q21
			integral membrane protein/SDHC
			phospholipase A2 group IB/PLA2G1B	172410	NM_000928	12q23-
						q24.1
			lipoprotein, Lp(a)/LPA	152200	NM_005577	6g27
			Catalase/CAT	115500	NM_001752	11p13
			thioredoxin-dependent peroxide reductase/TDPX1	600538	NM_005809	13q12
Immune	Mast Cell and T-	IgE Production	interleukin 4 receptor/IL4R	147781	X52425	16p12.1-
Response	Cell Response					p11.2
			interferon gamma/IFNG	147570	L07633	12q14
			mast cell growth factor/MGF	184745	NM_003994	12q22
		Mast Cell	interleukin 9 receptor/IL9R	300007	M84747	Xq28
		Proliferation	interleukin 3 receptor/IL3R)	308385	M74782	Xp22.3
		Degranulation	mast cell IgE receptor alpha polypeptide/FCER1A	147140	******	1q23
		Mast Cells	mast cell IgE receptor beta polypeptide/FCER1B	147138	NM_000139	11q13
			mast cell IgE receptor beta polypeptide/FCER1G	147139	NM_004106	1q23
			SH2-containing inositol 5-phosphatase/SHIP	601582	U57650	2q36-q37
			secretory granule proteoglycan peptide core/PRG1	177040	J03223	10q22.1
		Histamine	Histidine Decarboxylase	142704	M60445	15q21-
						q22
			Histamine receptor H1	600167	AF026261	3p21-p14
			Histamine receptor H2	142703	M64799	******
			Histamine N-methyltransferase	******	D16224	dir. 2
			Amine oxidase (copper-containing) 2/AOC2	602268	D88213	17q21
			Amine oxidase (copper-containing) 3/AOC3	603735	AF054985	17q21
		Serotonin	aromatic L-Amino Acid Decarboxylase/AADC	107930	M76180	7p11
			tryptophan hydroxylase/TPH	191060	X52836	11p15.3-
						p14
			14-3-3 protein ETA	113508	X78138	22q12
			14-3-3 protein ZETA	601288	M86400	2q25.2-
						p25.1
			14-3-3 protein BETA	601289	X57346	20q13.1
			14-3-3 protein SIGMA	601290	X57348	******
			serotonin 5-HT receptors 5-HT1A, G protein-coupled	109760	X57829	5q11.2-
						q13
			serotonin 5-HT receptors 5-HT1B, G protein-coupled	182131	M81590	6q13
			serotonin 5-HT receptors 5-HT1C, G protein-coupled	312861	U49516	Xq24
			serotonin 5-HT receptors 5-HT1D, G protein-coupled	182133	M81590	1p36.3-
						p34.3
			serotonin 5-HT receptors 5-HT1E, G protein-coupled	182132	M91467	6q14-q15
			serotonin 5-HT receptors 5-HT1F, G protein-coupled	182134	L05597	3p12
			serotonin 5-HT receptors 5-HT2A, G protein-coupled	182135	D87030	13q14-
						q21
			scrotonin 5-HT receptors 5-HT2B, G protein-coupled	601122	X77307	2q36.3-
						q37.1
			serotonin 5-HT receptors 5-HT2C, G protein-coupled	312861	U49516	Xq24
			serotonin transporter	182138	X70697	17q11.1-
						q12
			monoamine oxidase A/MAOA	309850	M69226	Xp11.23
			monoamine oxidase B MAOB	309860	M69177	Xp11.23
			serotonin N-Acetyltransferase/SNAT	600950	U40347	17q25
			tryptophan 2,3-dioxygenase/TDO2	191070	NM_00565	14q31-q32
		Neutrophil and	eotaxin precursor/small inducible cytokine, family A,	601156	U46572	17q21.1-
		Eosinophil	member 11/SCYA11			q21.2
		Chemotaxis	monocyte-derived-neutrophil chemotactic	146930	M26383	4q12-q13
			factor/interleukin 8/1L8
		Proteases	tryptase alpha/TPS1	191080	NM_003293	Chr. 16
			tryptase beta/TPS2	191081	NM_003294	Chr. 16
			chymase 1, mast cell/CMA1	118938	NM_001836	14g11.2
		Release of	phospholipase A2 group IIA/PLA2G2A	172411	NM_000300	1p35
		Membrane	phospholipase A2 group IB/PLA2G1B	172410	NM_000928	12q23-
		Lipids				q24.1
		(common to,	phospholipase A2 group X/PLA2G10	603603	******	16p13.1-
		leukotriene, and				p12
		prostaglandin	phospholipase A2 group IVA/PLA2G4A	600522	U08374	1q25
		pathways	phospholipase A2 group VI/PLA2G6	603604	AF064594	22q13.1
			phospholipase A2 group IVC/PLA2G4C	603602	******	chr. 19
			phosholipase A2 group IVC/PLA2G4C	603602	******	chr. 19
			phospholipase A2 group V/PLA2G5	601192	NM_000929	1p36-p34
			phospholipase C beta 3	600230	U26425	11q13
			lysosomal acid lipase	278000	NM_000235	10q24-
						q25
		Platelet	CDP-choline: alkylacetylglycerol	******	******	******
		Activating	cholinephosphotransferase
		Factor (PAF)	platelet activating factor receptor/PTAFR	173393	M88177	1p35-
						p34.3
			platelet activating factor acetylhydrolase 1/PAFAH1	601690	NM_005084	6p21.2-
						p12
			platelet activating factor acetylhydrolase, isoform	601545	NM_000430	17p13.13
			1B, alpha subunit/PAFAH1B1
			platelet activating factor acetylhydrolase, isoform	602508	NM_002572	11q23
			1B, beta subunit/PAFAH1B2
			platelet activating factor acetylhydrolase, isoform	603074	NM_002573	19q13.1
			1B, gamma subunit/PAFAH1B3
			platelet activating factor acetylhydrolase 2/PAFAH2	602344	NM_000437	******
		Leukotriene	arachidonate 5-lipoxygenase/ALOX5	152390	NM_000698	Chr.10
			arachidonate 5-lipoxygenase-activating	603700	NM_001629	13q12
			protein/FLAP/ALOX5AP
			leukotriene A4 hydrolase/LTA4H	151570	NM_000895	12q22
			leukotriene C4 synthase/LTC4S	246530	NM_000897	5q35
			Gamma-glutamyltranspeptidase 1/GGT1	231950	J04131	22q11.1-
						q11.2
			Gamma-glutamyltranspeptidase 2/GGT2	137181	AH002728	22q11.1
			Gamma-glutamyltransferase-like activity 1/GGTLA1	137168	NM_004121	******
			renal microsomal dipeptidase/DPEP1	179780	NM_004413	16q24.3
			cysteinyl leukotriene receptor 1/CYSLT1	300201	NM_006639	Xg13-g21
			leukotriene b4 receptor (chemokine receptor-like	601531	NM_000752	14q11.2-
			1)/LTB4R			q12
		Prostaglandins	prostaglandin endoperoxide synthetase	176805	AH001520	9q32-
			1/COX1/PTGS1			q33.3
			prostaglandin endoperoxide synthetase	600262	NM_000963	1q25.2-
			2/COX2/PTGS2			q25.3
			thromboxane A synthase 1/TBXAS1	274180	SEG_D3461	7q34
					3S
			prostaglandin D2 synthase	602598	M61900	******
			prostaglandin I2 synthase/prostacyclin	601699	SEG_D8339	20q13
			synthase/PTGIS		3S
			prostaglandin E receptor 1, EP1 subtype/PTGER1	176802	NM_000955	19p13.1
			prostaglandin E receptor 2, EP2 subtype/PTGER2	176804	******	5p13.1
			prostaglandin E receptor 3, EP3 subtype/PTGER3	176806	NM_000957	1p31.2
			prostaglandin E receptor 4, EP4 subtype/PTGER4	601586	NM_000958	5p13.1
			prostaglandin F receptor/PTGFR	600563	L24470	1p31.1
			prostaglandin F2 receptor negative	601204	U26664	1p13.1-
			regulator/PTGFRN			q21.3
			prostaglandin 12 receptor/PTGIR/prostacyclin	600022	SEG_HUMI	19q13.3
			receptor		P
			15-hydroxyprostaglandin dehydrogenase/HPGD	601688	NM_000860	4q34-q35
			aldo-keto reductase family 1, member C2/AKR1C2	600450	NM_001353	10p15-
						p14
		Formation of	myeloperoxidase/MPO	254600	J02694	17g23.1
		Reactive Drug	eosinophil peroxidase/EPX	131399	NM_000502	******
		Metabolites	calreticulin/CALR	109091	CALR	19p13.2
			calnexin/CANX	114217	L18887	5q35
			ceruloplasmin (ferroxidase)/CP	117700	NM_000096	3q21-q24
		Antigen	MHC class II transactivator/MHC2TA	600005	NM_000246	16p13
		Presentation	MHC class II HLA DR-alpha chain/HLA-DRA	142860	X83114	6p21.3
			MHC class II HLA DR-beta chain/HLA-DRB	142857	M11161	6p21.3
			MHC class II HLA DP-alpha chain/HLA-DPA	142880	M23905	6p21.3
			MHC class II HLA DP-beta chain/HLA-DPB	142858	AH002893	6p21.3
			MHC class II ULA DM-alpha chain/HLA-DMA	142855	NM_006120	6p21.3
			MHC class II HLA DM-beta chain/HLA-DMB	142856	NM_002118	6p21.3
			MHC class II HLA DQ-alpha chain/HLA-DQA	146880	M11124	6p21.3
			MHC class II HLA DQ-beta chain/HLA-DQB	******	M24364	6p21.3
			MHC class II HLA DN-alpha chain/HLA-DNA	142930	X02882	6p21.3
			MHC class II HLA DO-beta chain/HLA-DOB	600629	NM_002120	6p21.3
			MHC class II antigen gamma chain/CD74	142790	K01144	5g32
			antigen peptide transporter 1/MHC 1/TAP1	170260	NM_000593	6p21.3
			antigen peptide transporter 2/MHC 2/TAP2	170261	NM_000544	6p21.3
		T-Cell Receptor	T-cell antigen receptor, alpha subunit/TCRA	186880	Z24457	14q11.2
			T-cell antigen receptor, beta subunit/TCRB	186930	AF011643	7q35
			T-cell antigen receptor, gamma subunit/TCRG	186970	M17325	7p15-p14
			T-cell antigen receptor, delta subunit/TCRD	186810	L36384	14q11.2
			thymocyte antigen receptor complex CD3G, gamma	186740	NM_000073	11q23
			polypeptide (TiT3 complex)/CD3G
			thymocyte antigen receptor complex CD3D, delta	186790	NM_000732	11q23
			polypeptide (TiT3 complex)/CD3D
			thymocyte antigen receptor complex CD3E, epsilon	186830	NM_000733	11q23
			polypeptide (TiT3 complex)/CD3E
			thymocyte antigen receptor complex CD3Z, zeta	186780	NM_000734	1q22-q23
			polypeptide (TiT3 complex)/CD3Z
		T-Cell Receptor	ataxia telangiectasia mutated (includes	208900	NM_000051	11q22.3
		Rearrangement	complementation groups A, C and D)/ATM
			recombination activating gene 1/RAG1	179615	NM_000448	11p13
			recombination activating gene 2/RAG2	179616	M94633	11p13
			interleukin 7 receptor/IL7R	146661	NM_002185	5p13
			v-myb avian myeloblastosis viral oncogene	189990	NM_005375	6q22
			homolog/MYB
			core binding factor, alpha 1 subunit/CBFA1	600211	AH005498	6p21
			core-binding factor, beta subunit/PEBP2B/CBFB	121360	L20298	16q22
			ligase I, DNA, ATP-dependent/LIG1	126391	NM_000234	19q13.2-
						q13.3
			ligase IV, DNA, ATP-dependent/LIG4	601837	NM_002312	13q22-
						q34
			X-ray repair, complementing defect in Chinese	194364	******	2q35
			hamster/Ku antigen, 80 kD/KU80/XRCC5
			thyroid autoantigen, 70 kD/KU70/G22P1	152690	NM_001469	22q11-
		T-Cell				q13
		Expansion	T-cell antigen T4/CD4	186940	X87579	12pter-
						p12
			T-cell antigen CD8, alpha polypeptide (p32)/CD8A	186910	NM_001768	2p12
			T-cell antigen CD8, beta polypeptide/CD8B	186730	AH003859	2p12
			T-cell antigen CD28 (Tp44)/CD28	186760	NM_006139	2q33-q34
			cytotoxic T-lymphocyte-associated 4/CTLA4	123890	L15006	2q33
			CD80 antigen (CD28 antigen ligand 1, B7-1	112203	NM_005191	3q21
			antigen)/CD80
			CD86 antigen (CD28 antigen ligand 2, B7-2	601020	NM_006889	3q21
			antigen)/CD86
			T cell receptor-associated protein tyrosine kinase	176947	S69911	2q12
			ZAP-70/ZAP70
			leukocyte common antigen T200/CD45	151460	M23492	1q31-q32
			nuclear factor of activated T-cells, cytoplasmic	600489	NM_006162	18q23
			1/NFATC1
			nuclear factor of activated T-cells, cytoplasmic	600490	******	20q13.2-
			2/NFATC2			q13.3
			nuclear factor of activated T-cells, cytoplasmic	602698	L41066	16q13-
			3/NFATC3			q24
			nuclear factor of activated T-cells, cytoplasmic	602699	L41067	******
			4/NFATC4
			interleukin 2 receptor alpha/IL2RA	147730	X01057	10p15-
						p14
			interleukin receptor beta/IL2RLB	146710	M26062	22q11.2-
						q13
			interleukin 2 receptor gamma/IL2RG	308380	D11086	Xq13
			interleukin 6 receptor/IL6R	147880	X12830	1q21
			interleukin 9 receptor/IL9R	300007	M84747	Xq28
			interleukin receptor 13 alpha/IL13RA1	300119	S80963	Chr.X
			interleukin receptor 13 alpha2/1L13RA2	300130	X95302	Xq24
			interleukin 15 receptor alpha/IL15RA	601070	U31628	10p15-
						p14
			transforming growth factor/TGFB1	190180	M60315	19q13.1-
						q13.3
			transforming growth factor/TGFB2	190220	M19154	1q41
			transforming growth factor/TGFB3	190230	X14149	14q24
			tumor necrosis factor beta/TNFB/lymphotoxin	153440	NM_000595	6p21.3
			alpha/LTA
			tumor necrosis factor ligand superfamily, member	134638	NM_000639	1q23
			6/TNFSF6
			tumor necrosis factor receptor superfamily, member	134637	NM_000043	10q24.1
			6/TNFRSF6
			caspase 10, apoptosis-related cysteine	601762	NM_001230	2q33-q34
			protease/CASP10
	B-Cell Response	Receptors	B-cell antigen CD20/B-lymphocyte differentiation	112210	AH003353	11q13
			antigen B1/CD20
			B-cell antigen CD72/CD72	107272		9p
					NM_001782
			natural resistance-associated macrophage protein	600266	AH002806	2q35
			1/NRAMP1/solute carrier family 11, member
			1/SLC11A1
			natural resistance-associated macrophage protein	600523	AB015355	12q13
			2/NIRAMP2/solute carrier family 11, member
			1/SLC11A2
			T-lymphocyte antigen CDW52 (CAMPATH-1	114280	NM_001803	******
			antigen)/CDW52
			B-cell antigen CD22/CD22	107266	NM_001771	19q13.1
			B-cell antigen CD24/CD62 ligand/CD24	600074	X69397	6q21
			leukocyte antigen CD156/disintegrin and	602267	NM_001109	10q26.3
			metalloprotease domain 8/ADAM8/CD156
			platelet antigen CD151/platelet-endothelial cell	602243	NM_004357	11p15.5
			tetraspan antigen 3/PETA3/CD151
			antigen CD32/low-affinity receptor IIA for Fc	146790	NM_004001	1q21-q23
			fragment of IgG/FCGR2A/CD32
			activated leucocyte cell adhesion molecule/CD6	601662	NM_001627	3q13.1-
			ligand/ALCAM			q13.2
			lymphocyte antigen CD79A/immunoglobulin-	112205	NM_001783	19q13.2
			associated alpha/CD79A
			lymphocyte antigen CD79B/immunoglobulin-	147245	L27587	17q23
			associated beta/CD79B
		Signalling	regulator of G-protein signalling 1/RGS1	600323	NM_002922	1q31
		Immunoglobulin	immunoglobulin K light chain constant region	147200	******	2p12
		Light Chains	locus/IGKC
			immunoglobulin K light chain variable region	146980	K01322	2p12
			locus/IGKV
			immunoglobulin K light chain joining region	146970	******	2p12
			locus/IGKJ
			immunoglobulin L light chain constant region	147220	NM_006146	22q11.2
			locus/IGLC1
			immunoglobulin L light chainjoining region	147230	NM_006146	22q11.2
			locus/IGLJ
			immunoglobulin L light chain variable region	147240	NM_006146	22q11.2
			locus/IGLJ
		Immunoglobulin	immunoglobulin A heavy chain constant region locus	146900	******	14q32.33
		Heavy Chains	1/IGHA1
			immunoglobulin A heavy chain constant region locus	147000	******	14q32.33
			2/IGHA2
			immunoglobulin D heavy chain constant region	147170	******	14q32.33
			locus/IGHD
			immunoglobulin B heavy chain constant region	147180	******	14q32.33
			locus/IGHE
			immunoglobulin G heavy chain constant region locus	147100	******	14q32.33
			1/IGHG1
			immunoglobulin G heavy chain constant region locus	147110	******	14q32.33
			2/IGHG2
			immunoglobulin G heavy chain constant region locus	147120	******	14q32.33
			3/IGHG3
			immunoglobulin G heavy chain constant region locus	147130	******	14q32.33
			4/IGHG4
			immunoglobulin M heavy chain constant region	1147020	******	14q32.33
			locus/IGHM
			immunoglobulin heavy chain variable region locus	147070	X92279	14q32.33
			1/IGHV1
			immunoglobulin heavy chain variable region locus	600949	******	16p11
			2/IGHV2
			immunoglobulin heavy chain diversity region locus	146910	X97051	14q32.33
			1/IGHDY1
			immunoglobulin heavy chain diversity region locus	146990	L25544	15q11-
			2/IGHDY2			q12
			immunoglobulin heavy chain joining region	147010	******	14q32.33
			locus/IGHJ
		Immunoglobulin	recombination activating gene 1/RAG1	179615	NM_000448	11p13
		Gene	recombination activating gene 2/RAG2	179616	M94633	11p13
		Rearrangement	immunoglobulin kappa J region recombination signal	147183	L07872	9p13-p12
			binding protein/RBPJK/IGKJRB1
			Bruton agammaglobulinemia tyrosine kinase/BTK	300300	NM_000061	Xq21.3-
						q22
			interleukin 7 receptor/IL7R	146661	NM_002185	5p13
			interferon-gamma receptor 1/IFNGR1	107470	NM_000416	6q23-q24
			interferon-gamma receptor 2/IFNGR2	147569	NM_005534	21q22.1-
						q22.2
			interleukin 4 receptor precursor/IL4R	147781	NM_000418	16p12.1-
						p11.2
			interleukin 4 receptor precursor/IL4R	147781	NM_000418	16p12.1-
						11.2
			ligase I, DNA, ATP-dependent/LJG1	126391	NM_000234	19q13.2-
						q13.3
			ligase IV, DNA, ATP-dependent/LIG4	601837	NM_002312	13q22-
						q34
			X-ray repair, complementing defect in Chinese	194364	******	2q35
			hamster/Ku antigen, 80 kD/KU80/XRCC5
			thyroid autoantigen, 70 kD/KU70/G22P1	152690	NM_001469	22q11-
					q13
		Immunoglobulin	nuclear factor kappa-B DNA binding subunit	164011	M58603	4q23-q24
		Gene	1/NFKB1
		Transcription	nuclear factor kappa-B DNA binding subunit	164012	NM_002502	10q24
			2/NFKB2
			nuclear factor kappa-B subunit 3/NFKB3	164014	Z22949	11q12-
					q13
			nuclear factor of kappa light chain gene enhancer in	164008	******	14q13
			B cells, inhibitor alpha/NFKIBIA
			nuclear factor of kappa light chain gene enhancer in	603258	NM_002503	8p11.2
			B cells, inhibitor beta/NFKBIB
			YY1 transcription factor/YY1	600013	NM_003403	14q
			immunoglobulin transcription factor	147141	******	19p13.3
			1/ITF1/transcription factor 3/TCF3
			immunoglobulin transcription factor	602272	NM_003199	18q21.1
			2/ITF2/transcription factor 4/TCF4
			immunoglobulin mu binding protein 2/IGHMBP2	600502	NM_002180 11q13.2-
						q13.4
			transcription factor binding to IGHM enhancer	314310	NM_006521	Xp11.22
			3/TFE3
			homeobox protein OCT1/POU domain transcription	164175	NM_002697	1q22-q23
			factor 2,class 1/POU2F1
			homeobox protein OCT2/POU domain transcription	164176	M22596	Chr.19
			factor 2,class 2/POU2F2
			POU domain, class 2, associating factor 1/POU2AF1	601206	NM_006235	11q23.1
			inhibitor of DNA binding 1, dominant negative helix-	600349	NM_002165	20q11
			loop-helix protein/ID1
			inhibitor of DNA binding 2, dominant negative helix-	600386	NM_002166	2p25
			loop-helix protein/1D2
		Immunoglobulin	B-cell antigen CD40/tumor necrosis factor receptor	109535	NM_001250	20q12-
		Isotype	superfamily, member 5/CD40/TNFRSFS			q13.2
		Switching	paired box gene 5/B-cell lineage-specific activator	167414	******	9p13
			protein/BSAP/PAX5
			lymphocye function-associated antigen, type	153420	NM_001779	1p13
			3/LFA3/LEU7/CD58
			interleukin 10 receptor, alpha/IL10RA	146933	NM_001558	11q23.3
			lymphocyte antigen CD45/protein tyrosine	151460	NM_002838	1q31-q32
			phosphatase, receptor type, c
			polypeptide/PTPRC/CD45
			prostaglandin E receptor 1, EP1 subtype/PTGER1	176802	NM_000955	19p13.1
			prostaglandin E receptor 2, EP2 subtype/PTGER2	176804	******	5p13.1
			prostaglandin E receptor 3, EP3 subtype/PTGER3	176806	NM_000957	1p31.2
			prostaglandin E receptor 4, EP4 subtype/PTGER4	601586	NM_000958	5p13.1
			interleukin 13 receptor, alpha 1/IL13RA1	300119	NM_001560	Chr.X
			interleukin receptor 13 alpha2/IL13A2	300130	X95302	Xq24
			interferon-gamma receptor 1/IFNGR1	107470	NM_000416	6q23-q24
			interferon-gamma receptor 2/IFNGR2	147569	NM_005534	21q22.1-
						q22.2
			interleukin 5 receptor alpha/IL5RA	147851	M96652	3p26-p24:
			transforming growth factor, beta receptor I (activin A	190181	NM_004612	9q33-q34
			receptor type II-like kinase, 53kD)/TGFBR1
			transforming growth factor, beta receptor II(70-	190182	NM_003242	3p22
			80kD)/TGFBR2
			transforming growth factor, beta receptor III	600742	NM_003243	1p33-p32
			(betaglycan, 300kD)/TGFBR3
			X-ray repair, complementing defect in Chinese	194364	******	2q35
			hamster/Ku antigen, 80 kD/KU80/XRCC5
			thyroid autoantigen, 70 kD/KU70/G22P1	152690	NM_001469	22q11-
						q13
	Myeloid	Granulocyte,	granulocyte-macrophage colony stimulating factor	138960	NM_000758	5q31.1
	Differentiation	Macrophage,	2/CSF2
		Erythrocyte,	macrophage-specific colony-stimulating factor/CSF	1120420	AH005300	1p21-p13
		and Platelet	granulocyte colony stimulating factor 3/CSF3	138970	NM_000759	17q11.2-
		Differentiation				q12
			colony stimulating factor 1 receptor/CSFR1	164770	U63963	5q33.2-
						q33.3
			granulocyte-macrophage colony stimulating factor 2	306250	NM_006140	Xp22.32
			receptor, alpha, low-affinity/CSF2RA
			granulocyte-macrophage colony stimulating factor 2	138981	U18373	22q12.2-
			receptor, beta/CSF2RB			q13.1
			granulocyte-macrophage colony stimulating factor 2	425000	******	Yp11
			receptor, alpha, Y chromasomal/CSF2RY
			flt3 ligand/FMS-related tyrosine kinase 3	600007	U03858	19q13.3
			ligand/FLT3LG
			STAT induced STAT inhibitor 3/SSI-3	604176	NM_003955	******
			erythropoietin/EPO	133170	NM_000799	7q21
			erythropoietin receptor/EPOR	133171	NM_000121	19p13.3-
						p13.2
			Janus kinase 2 (a protein tyrosine kinase)/JAK2	147796	NM_004972	9p24
			STAM-like protein containing SH3 and ITAM	******	NM_005843	******
			domains 2/STAM2
			ribosomal protein S7/RPS7	603474	NM_001011	19g13.2
			signal transducer and activator of transcription	601511	NM_003152	17q11.2
			5A/STAT5A
			BCL-X/BCLX	600039	Z23115	******
			thrombopoietin (MLV oncogene ligand,	600044	NM_000460	3q26.3-
			megakaryocyte growth and development			q27
			factor)/THPO
			mycloproliferative leukemia virus	159530	NM_005373	1p134
			oncogene/MPL/thrombopoietin receptor/TPOR
			FMS-related tyrosine kinase 3/FLT3	136351	NM_004119	13q12

[1057]

TABLE 2
1
2

[1058]

TABLE 3

Hugo

GID

OMIM ID VGX Symbol

Description

Variance Start

Variance

D89078

D89078

601531

GEN-7

Leukotriene B4 receptor, cDNA

434A

545G

889G

1156G

1393C

1708A

1715G

1771T

2644C

434A

545G

889G

1156G

1393C

1708A

1715T

1771T

2644C

434A

545G

889C

1156G

1393C

1708A

1715G

1771T

2644C

434A

545G

889G

1156G

1393T

1708A

1771T

2644C

434A

545G

889G

1156G

1393C

1708C

1715G

1771T

2644C

434A

545G

889C

1156G

1393C

1705C

1715G

1771T

434A

545G

889G

1156G

1393C

1708C

1715G

1771T

2644T

434A

545G

889G

1156G

1393C

1708A

1771C

2644C

434A

545G

889G

1156G

1393C

1708A

1715G

1771T

2644T

434A

545A

889G

1156G

1393C

1715G

1771T

2644T

434T

889C

434T

545G

889G

1156G

1393C

1708A

1715G

1771T

434A

889G

1156C

1393C

1708A

434A

545G

889C

1156C

1393C

2644C

434A

545G

889G

1156G

1393C

1708A

1715G

1771C

2644C

434A

545G

889G

1156G

1393T

1708A

1771T

2644C

434A

545G

889C

1156G

1393C

1708C

1715G

1771T

2644T

434A

545G

889G

1156C

1393C

2644C

434A

889G

1156C

1393C

1708A

2644C

434T

545G

889G

1156G

1393C

1708A

1715G

1771T

2644C

434T

889C

1156C

434A

545G

889G

1156G

1393C

2644C

434A

545A

889G

1156G

1393C

1708C

1715G

1771T

2644T

434A

545G

889C

1156G

1393C

1708A

1715T

1771T

2644C

434

(−1284)A > T

5′

545

(−1173)G > A

5′

889

(−829)G > C

5′

1156

(−562)G > C

5′

1393

(−325)C > T

5′

1708

(−10)C > A

5′

1715

(−3)G > T

5′

1771

54T > C

Silent

2644

927T > C

Silent

2920

1203A > G

3′

J03459

J03459

151570

GEN-8

Leukotriene A4 hydrolase

79T

140G

323G

1511A

1912C

79T

140G

323A

1511A

1912C

79C

140G

323G

1511A

1912C

79T

140T

323G

1511A

1912C

79T

140G

1912T

140G

323G

1912C

79T

140G

323A

1511A

1912T

79

11T > C

I4T

140

72G > T

Silent

323

255G > A

Silent

1511

1443A > T

E481D

1912

1844C > T

3′

J03571

J03571

152390

GEN-9

Lipoxygenases: 5-lipoxygenase

(leukocytes)

304A

959C

304G

959A

304G

959C

304

270G > A

Silent

959

925C > A

P309T

2076

2042-2043delAC

3′

2328

2294C > T

3′

2376

2342T > G

3′

J05594

J05594

601688

GEN-E

Prostaglandin 15-OH

dehydrogenase (PGDH)

363T

436C

506C

540G

913C

950G

1236G

1448A

1780T

1800A

1972T

363T

436C

506C

540G

913C

1236T

1448G

1780C

1800A

1972T

363T

436C

506T

540G

913C

950G

1236G

1448A

1780T

1800A

1972T

363T

436C

506C

540G

913C

950A

1236T

1780T

1800A

1972T

363T

436C

506C

540G

913C

950G

1236G

1448G

1780T

1800A

1972C

363T

436C

506C

540A

913C

950G

1236G

1448G

1780T

1800A

1972T

363T

436C

506C

540G

913C

950G

1236T

1780T

1800A

1972T

363C

436C

506C

540G

913C

950G

1236G

1780T

1800A

363T

436C

506C

540G

913C

950G

1236G

1448G

1780T

1800T

1972T

363T

436C

506C

540G

913C

950G

1236G

1448G

1780T

1800A

1972T

363T

436C

506C

540G

913C

1236T

1780T

1800T

1972T

363T

436C

506C

540G

913C

950G

1236G

1448A

1780T

1800A

1972C

913C

950G

1236G

1448G

1780C

1800A

1972T

363T

436C

506T

540G

913C

950G

1236G

1448G

1780T

1800A

1972T

363T

436C

506C

540G

913G

950G

1236G

1780T

1800A

363T

436T

506C

540G

913C

950G

1236G

1780T

1800A

1972T

363T

436C

506C

540G

913C

950G

1236G

1780T

1800T

1972C

363T

436C

506C

540G

913C

950G

1236T

1448G

1780T

1800A

1972T

1236G

1448G

1780T

1800A

1972C

363T

436C

506C

540G

913C

950A

1236T

1448A

1780T

1800A

1972T

363T

436C

506C

540G

913C

950A

1236T

1448A

1780T

1800T

1972T

1236G

1448A

1780T

1800A

1972T

363T

436C

506C

540G

913C

950A

1236T

1448G

1780C

1800A

1972T

913C

950G

1236G

1448G

1780C

1800A

1972T

363T

436C

506C

540G

913C

950G

1236G

1448G

1780T

1800T

1972C

363T

436C

506C

540G

913G

950G

1236G

1448G

1780T

1800A

1972C

363T

436C

506C

540G

913C

950G

1236G

1448A

1780T

1800T

1972C

173

156A > G

Silent

363

346T > C

Y116H

436

419C > T

A140V

506

489C > T

Silent

540

523G > A

G175S

913

896C > G

3′

950

933G > A

3′

1236

1219G > T

3′

1448

1431G > A

3′

1780

1763T > C

3′

1800

1783A > T

3′

1972

1955T > C

3′

L24470

L24470

600563

GEN-O

PROSTAGLANDIN F RECEPTOR

1422T

1490T

1517A

1535G

1908T

2203C

2244A

2299A

1422C

1490C

1517A

1535A

1908C

2203A

2244A

2299A

1422T

1490T

1517A

1535A

1908C

2203A

2244A

2299A

1422T

1490C

1517A

1908T

2244A

2299A

1422T

15170

2244A

2299A

1422T

1490C

1517A

1535A

1908C

2203A

2244A

2299A

1422T

1490C

1517A

1535A

1908C

2203A

2244G

2299A

1422T

1517A

1535A

1908C

2203A

2244A

2299G

1422T

149CC

1517A

1535G

1908T

2203C

2244A

2299A

1422T

1490T

15170

1535G

1908T

2203C

2244A

2299A

1422T

1490T

1517A

2244A

2299A

1490C

1517A

1535A

1908C

2203A

2244A

2299A

1422T

1490T

1517A

1535A

1908C

2203A

2244A

2299G

1422T

1490T

1517A

1535A

1908T

2203C

2244A

2299A

1422

1185T > C

3′

1490

1253C > T

3′

1517

1280A > G

3′

1535

1298A > G

3′

1908

1671C > T

3′

2203

1966A > C

3′

2244

2007A > G

3′

2299

2062A > G

3′

M12959

M12959

186880

GEN-S

CD3 glycoprotein on T

lymphocytes

431

295T > G

S99A

1060

924T > C

3′

1129

993C > A

3′

1343

1207T > C

3′

1345

1209G > C

3′

1394

1258T > G

3′

1463

1327G > A

3′

M59979

M59979

176805

GEN-Z

Cyclooxygenase 1 COX1

128G

559A

1517T

1892A

128G

559C

644A

1517T

1892C

2169T

2296C

128G

559A

644A

1517T

1892C

2169T

2296C

128A

559A

644C

1517T

1892C

2169T

2296A

128G

559A

1517C

1892A

128G

559A

644C

1517T

1892C

2169T

2296A

128G

559A

644A

1517T

1892C

2169T

2296A

128G

559A

644C

1517T

1892C

2169T

2296C

128G

559A

644C

1517T

1892A

2169C

2296A

128G

559A

1517C

1892A

2169T

2296C

644A

1892C

128G

559A

644A

1517C

1892A

2169C

2296C

128A

559C

644A

1517T

1892C

2169T

2296C

128

123G > A

Silent

559

554A > C

K18ST

644

639C > A

Silent

1517

1512T > C

Silent

1892

1887C > A

3′

2030

2025G > A

3′

2169

2164T > C

3′

2296

2291A > C

3′

M90100

M90100

600262

GEN-1A

Cyclooxygenase 2 COX2

2159G

2339C

2409A

2983C

2159G

2339T

2409G

2983C

2159C

2339T

2409G

2159G

2339C

2409G

2983C

2159G

2409A

2983C

2159C

2339T

2409G

2983T

2159

2062G > C

3′

2186

2089-2094delATATTA

3′

2230

2133A > G

3′

2339

2242T > C

3′

2409

2312G > A

3′

2726

2629C > T

3′

2983

2886C > T

3′

U49516

1349516

312861

GEN-1Q

Serotonin 5-HT receptors 5-HT2C

63C

289A

313G

342A

2915C

2947A

63C

289G

313A

2915A

2947A

63C

289A

313A

342A

2915A

2947A

63T

289A

313A

342A

2915A

2947G

63C

289A

313A

342A

2915C

2947A

63C

289G

313A

342G

2915A

2947A

63C

289A

313A

342A

63

(−666)C > T

5′

289

(−440)A > G

5′

313

(−416)A > G

5′

342

(−387)A > G

5′

2915

2187A > C

3′

2947

2219A > G

3′

X06538

X06538

180240

GEN-1U

Retinoic Acid alpha receptor

1063C

1617T

1063T

1617C

1063C

1617C

1063

747C > T

Silent

1617

1301C > T

3′

J03037

J03037

259730

GEN-2I

Carbonic anhydrase II

627

562C > T

Silent

1334

1269A > C

3′

1487

1422A > C

3′

M14565

M14565

118485

GEN-30

“Cytochrome P450, subfamily XIA

(cholesterol side chain cleavage)”

947

903G > C

M301I

M15856

M15856

238600

GEN-33

Lipoprotein lipase

843C

1553C

1611G

2743T

2851A

2958G

3017T

3272C

843C

1553T

1611G

2743C

2851A

2958G

3017T

843C

1553T

1611G

2743C

2851A

2958G

3017C

843C

1553C

1611G

2743C

2851A

2958G

3017T

3272C

843C

1553C

1611A

2851A

2958G

3017T

843C

1553C

1611G

2743C

2958G

3017T

3272T

843T

1553C

1611G

2743C

2958G

3017T

843C

1553C

1611G

2743C

2958A

3017T

3272C

843C

1553C

1611G

2743C

2958G

3017C

1553C

1611G

2743C

2958G

3017T

3272T

843C

1553C

1611G

2743C

2958G

3017T

3272C

843C

1553C

1611A

2743T

2958G

3017T

3272T

843C

1553T

1611G

2743C

2851A

2958G

3017C

3272T

843C

1553C

1611G

2743C

2958A

3017T

3272C

843C

1553C

1611G

2743C

2851A

2958G

3017C

843C

1553C

1611A

2743T

2851A

2958G

3017T

3272T

843C

1553T

1611G

2743C

2851A

2958G

3017T

3272C

843

669C > T

Silent

1553

1379C > T

A460V

1611

1437G > A

3′

1973

1799T > C

3′

2428

2254T > A

3′

2743

2569T > C

3′

2851

2677A > G

3′

2958

2784G > A

3′

3017

2843T > C

3′

3272

3098T > C

3′

3343

3169T > C

3′

3447

3273C > T

3′

M16541

M16541

177400

GEN-35

Butyryicholinesterase

978

849G > C

E283D

1828

1699G > A

A567T

2127

1998A > G

3′

M21054

M21054

172410

GEN-3B

Phospholipase A-2 (PLA-2) lung

331

294G > A

Silent

400

363C > A

D121E

M26062

M26062

146710

GEN-3D

Interleukin 2 receptor beta

chain

2202C

2231A

2287A

2492T

2895A

881C

2202C

2231A

2287G

2492T

2895A

881C

2202G

2231A

2287G

2492T

2895A

881T

2202G

2231A

2287G

2492T

2895A

2202C

2231A

2287G

2492G

2895A

881C

2202C

2231C

2287G

2202G

2895G

881T

2202C

2231A

2287G

2492T

2895A

881C

2202C

2895G

881T

2202C

2231A

2287A

2492T

2895A

881C

2202C

2231C

2287G

2492T

2895A

881T

2202C

2231A

2287G

2492G

2895A

881T

2202C

2895A

881T

2202G

2895G

881

750C > T

Silent

2202

2071G > C

3′

2231

2100A > C

3′

2287

2156G > A

3′

2492

2361T > G

3′

2895

2764A > C

3′

3158

3027A > C

3′

M26383

M26383

146930

GEN-3E

Interleukin 8

919C

1237A

919A

1237T

919A

1237A

919C

1237T

259

185C > G

A62G

919

845A > C

3′

1237

1163A > T

3′

1281

1207A > G

3′

M29696

M29696

146661

GEN-3H

Interleukin 7 receptor

154C

219T

434G

1263C

154C

219T

434A

1263C

154C

434A

1263T

154C

219C

434A

1263C

154T

1263C

154C

219T

434G

1263T

154C

219T

434A

1263T

154T

219T

434G

1263C

154

132C > T

Silent

219

197C > T

T66I

434

412A > G

I138V

1088

1066G > A

V356I

1263

1241C > T

T414M

M29874

M29874

123930

GEN-3I

“Cytochrome P450, subfamily IIB

(phenobarbital-inducible), polypeptide 6”

2758

2752T > A

3′

2836

2830G > A

3′

2902

2896T > C

3′

M34986

M34986

133171

GEN-3O

Erythropoietin receptor

1138

1138C > G

P380A

M55040

M55040

100740

GEN-3Q

acetyloholinesterase

323C

1213C

1587C

323T

1213C1587C

1213C

1587T

1213C

1587C

1213A

1587T

1213C

1587T

323

167C > T

P56L

1154

998T > A

V333E

1213

1057C > A

H353N

1482

1326G > T

Silent

1587

1431C > T

Silent

1663

1507T > C

F503L

M58525

M58525

116790

GEN-3S

Catechol-O-methyltransferase

390T

418G

423G

676A

813C

676A

813T

390T

418G

423G

676G

813C

390C

418G

423G

676G

813C

390C

418G

423A

676A

813C

390T

418G

423A

676A

813C

390C

418G

423G

676A

813C

390T

418T

423G

676A

813C

676A

813T

390

186T > C

Silent

418

214G > T

A72S

423

2190 > A

Silent

612

408C > G

Silent

676

472A > G

M158V

813

609C > T

Silent

1031

827delC

3′

1039

835C > A

3′

M64592

M64592

120420

GEN-3X

Granulocyte colony-stimulating

factor

271

271T > G

Y91D

1533

1533C > T

Silent

M69177

M69177

309860

GEN-3Y

Monoamine oxidase B

1685G

1860A

1875C

1685G

1860A

1875T

1685G

1860G

1875T

1685T

1860A

1875T

1685

1608G > T

3′

1860

1783A > G

3′

1875

1798T > C

3′

M69226

M69226

309850

GEN-3Z

Monoamine oxidase A

941

891T > G

Silent

1373

1323G > A

Frame

1460

1410C > T

Silent

M80646

M80646

274180

GEN-40

Thromboxane synthase

654A

943A

1240C

654C

943A

1240G

654C

943G

1240C

654C

943A

1240C

654

483C > A

D161E

756

585G > C

Silent

943

772A > G

K258E

1240

1069C > G

L357V

M84747

M84747

300007

GEN-45

Interleukin 9 receptor

1273

1094G > A

R365H

U00672

U00672

146933

GEN-4A

Interleukin 10 receptor

536G

1033C

1112A

1699C

2148T

536A

1033T

1112G

1699C

2148T

536A

1033C

1112G

1699C

2148C

536A

1033C

1112G

1699C

2148T

536A

1033C

1112A

1699C

2148T

536A

1033C

1112G

1699T

536A

1033C

1112A

1699C

536A

1033C

1112G

1699T

2148C

1033C

1112G

1699C

2148T

536

475A > G

5159G

1033

972C > T

Silent

1112

1051G > A

G351R

1699

1638C > T

Silent

2148

2087T > C

3′

3377

3316A > C

3′

3524

3463A > G

3′

U08092

U08092

None

GEN-4C

Histamine N-methyltransferase

594

555G > A

Silent

978

939G > A

3′

1136

1097T > A

3′

U19487

U19487

176804

GEN-4I

“PROSTAGLANDIN E2 RECEPTOR, EP2

SUBTYPE”

85G

231A

1269C

1295C

1442A

85G

231T

1269C

1295C

1442A

85G

231A

1269C

1295T

1442G

85A

231A

1269C

1295C

1442A

85A

231A

1269G

1295C

1442A

85A

231A

1269C

1295T

1442G

85G

231A

1269G

1295T

1442G

85G

231A

1269C

1295T

1442A

85G

231T

1269C

1295T

1442G

85G

231A

1269C

1295C

1442G

85A

231A

1269C

1295T

1442A

85G

231A

1269G

1295C

1442G

231A

1269C

1295T

1442G

85G

231T

1269G

1295T

1442G

85A

231A

85

(−72)A > G

5′

231

75A > T

Silent

1269

1113C > G

3′

1295

1139C > T

3′

1442

1286A > G

3′

U31628

U31628

601070

GEN-4J

Interleukin 15 receptor alpha

chain

627A

892G

627C

892G

627G

892A

627A

892A

627

545A > C

N182T

892

810G > A

3′

1250

1168G > T

3′

U70136

U70136

600044

GEN-4R

Thrombopoletin

4138

4105G > T

A1369S

4141

4108T > A

F13701

U70867

U70867

601460

GEN-4S

prostaglandin transporter hPGT

301A

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3255G

3594T

301G

931A

1069A

1888C

2706T

2839A

2908A

3171G

3253A

3594T

301G

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3594A

301G

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253G

3594T

301A

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3255A

3594T

301A

931G

1069A

1888C

2014C

2706T

2839A

3171G

3255A

301G

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3255G

3594T

301G

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3255A

3594T

301G

931A

1069A

1888C

2323T

2706T

2839T

2908A

3171G

3253G

3594T

301G

931A

1069A

1888T

2014C

2323A

2706T

2839T

2908A

3253A

3255A

3594T

301G

1069A

1888C

2014C

2323A

2706T

2908G

3253A

3255A

3594T

931G

1069A

1888T

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3255A

3594T

301G

931A

1069A

1888C

2323T

2706T

2839T

2908A

3253A

3255A

3594T

301G

1069G

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3255G

3594T

301A

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253G

3255G

3594T

931A

1069G

1888C

2014C

2323A

2839T

2908A

3171G

3253A

3255A

3594T

301G

931G

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3255A

3594T

301G

931G

1069A

1888C

2706T

2839A

2908A

3171G

3253A

3594T

301G

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171A

3253A

3255A

3594T

301A

931G

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3255A

3594T

301A

931G

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171A

3253A

3594T

301G

931A

1069A

1888C

2014C

2323A

2706T

2839A

2908G

3171G

3253A

3255A

3594T

301G

931A

1069A

1888C

2014C

2323T

2706T

2839A

2908A

3171G

3253A

3594T

301G

931G

1069A

1888C

2014T

2323A

2706T

2839A

2908A

3171G

3253A

3594T

301G

931A

1069A

1888C

2014C

2323T

2706T

2839T

2908A

3171G

3253G

3255A

3594T

931A

1069G

1888C

2014C

2323A

2706G

2839T

2908A

3171G

3253A

3255A

3594T

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171A

3253G

3255A

3594T

2014C

2323A

2706T

2839T

2908A

3171G

3253G

3255A

3594T

301G

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3255G

3594A

301A

931A

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253G

3255A

3594T

301A

931G

1069A

1888C

2014C

2323A

2706T

2839A

3171G

3255A

301G

931G

1069A

1888C

2014C

2323T

2706T

2839A

3171G

3255A

2706T

2839T

2908A

3594T

301G

931A

1069G

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253G

3255G

3594T

301G

931A

1069A

1888C

2014C

2323T

2706T

2839T

2908A

3171G

3253A

3255A

3594T

301G

1069A

1888C

2014C

2323A

2706T

2839T

2908A

3171G

3253A

3255A

3594T

301

210G > A

Silent

931

840A > G

Silent

1069

978k > G

Silent

1888

1797C > T

Silent

2014

1923C > T

Silent

2323

2232k > T

3′

2706

2615T > G

3′

2839

2748T > A

3′

2908

2817k > G

3′

3171

3080k > G

3′

3253

3162k > G

3′

3255

3164k > G

3′

3594

3503T > k

3′

X03663

X03663

164770

GEN-51

Colony stimulating factor 1

receptor

1026C

1135G

1385A

2835C

3254T

3255C

3530C

1026C

1135G

1385A

2835C

3254T

3255C

3530A

1026T

1135G

1385A

2835C

3254T

3255A

3530C

1026T

1135G

1385A

2835G

3254T

3530C

1026C

1135G

1385A

2835G

3254T

3530C

1026C

1135G

1385A

2835C

3254C

3255A

3530C

1026T

1135G

1385A

2835C

3254C

3255A

3530C

1026T

1135G

1385A

2835C

3254T

3255C

3530C

1135G

1385G

2835C

3254T

3255A

3530C

1026C

1135G

1385G

2835C

3254T

3255C

3530C

1026C

1135A

2835C

3254T

3255C

1026C

1135G

1385A

2835C

3254T

3255A

3530C

1026C

1135A

1385G

2835C

3254T

3255C

3530C

1026C

1135G

1385A

2835G

3254T

3255A

3530C

1026C

1135A

1385G

2835C

3254C

3255A

3530C

1026T

1135G

1385A

2835G

3254T

3255C

3530C

1026T

1135G

1385G

2835C

3254T

3255A

3530C

1026T

1135G

1385G

2835C

3254T

3255C

3530C

1026T

1135A

1385G

2835C

3254C

3530C

1026C

1135G

1385G

2835C

3254C

3255A

3530C

1026

726T > C

Silent

1135

835G > A

V279M

1385

1085A > G

H362R

2835

2535C > G

Silent

3254

2954T > C

3′

3255

2955C > A

3′

3530

3230C > A

3′

3732

3432T > C

3′

3951

3651C > A

3′

X03884

X03884

186830

GEN-52

“CD3E antigen, epsilon

polypeptide (TiT3 complex)”

108

54C > T

Silent

726

672C > A

3′

1258

1204T > A

3′

X13589

X13589

107910

GEN-56

Aromatase (CYPl9) , cDNA

625C

914T

625C

914C

625A

914T

364

240A > G

Silent

625

501C > A

Silent

914

790C > T

R264C

1655

1531C > T

3′

1796

1672G > T

3′

X52425

X52425

147781

GEN-59

Interleukin 4 receptor

170

(−6)C > G

5′

398

223A > G

I75V

412

237C > T

Silent

676

501C > T

Silent

943

768C > G

Silent

1114

939T > C

Silent

1211

1036A > G

I346V

1374

1199A > C

E400A

1417

1242G > T

Silent

1474

1299T > C

Silent

1682

1507T > C

S503P

1730

1555C > T

Silent

1902

1727A > G

Q576R

2198

2023C > T

P675S

2572

2397T > C

Silent

2659

2484T > C

3′

2661

2486T > C

3′

2741

2566C > G

3′

2892

2717G > A

3′

3044

2869G > A

3′

3289

3114A > G

3′

3391

3216C > T

3′

3419

3244G > C

3′

X83861

X83861

176806

GEN-5H

Prostaglandin E receptor 3

(subtype EP3) {alternative products}

801G

825T

801A

825G

801G

825G

387

180C > G

Silent

801

594G > A

Silent

825

618G > T

Silent

K03001

K03001

100650

GEN-5N

Aldehyde dehydrogenase 2,

mitochondrial

656

656T > A

V219E

988

988G > C

V330L

1156

1156G > A

E386K

L78207

L78207

600509

GEN-5Q

Cell surface receptor for

sulfonylureas on pancreatic b cells

4019

3981A > G

Silent

M11220

M11220

138960

GEN-5R

Granulocyte colony-stimulating

factor

82C

382T

82T

382C

82C

382C

82

50C > T

S17F

382

350T > C

I117T

M59941

M59941

138981

GEN-62

−Granulocyte-macrophage (Colony

stimulating factor 2 receptor, beta, low-affinity)”

773C

847C

855T

881G

773G

847G

855T

881G

773C

847G

855T

881G

773G

855T

881A

773G

847C

855T

881G

773G

847C

855C

881G

773G

847G

855T

881A

773

745G > C

E249Q

847

819C > G

Silent

855

827T > C

L276P

881

853G > A

G285R

M73832

M73832

425000

GEN-63

GRANULOCYTE-MACROPHAGE COLONY-

STIMULATING FACTOR RECEPTOR ALPHA CHAIN PRECURSOR

488

470C > G

Frame

M74782

M74782

308385

GEN-64

“Interleukin 3 receptor, alpha

(low affininty)”

952

806C > T

T269I

1396

1250C > T

3′

M96652

M96652

147851

GEN-65

Interleukin 5 receptor alpha

101G

145T

101A

145T

101G

145C

101

(−149)G > A

5′

145

(−105)T > C

5′

883

634T > G

S212A

U73338

U73338

156570

GEN-69

Methionine Synthase

194C

284C

1136G

1252C

1334G

1699T

3207G

3209G

3885G

5444C

5551G

5573C

5659T

5678T

5934G

194C

1136G

1252C

1334G

1699T

3207G

3209C

5444C

5551G

5659T

5678T

5934A

194C

284C

1136A

1252C

1334G

1699T

3207G

3209G

3538A

3885G

3886C

5573C

5659T

5678T

5934A

194C

284C

1136G

1334G

1699T

3150A

3207T

3209G

5444C

5551G

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

5444C

5551G

5573T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

3538G

3885G

3886A

5444C

5551G

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150G

3207G

3209G

3538A

3885G

3886C

5444C

5551G

5573C

5659T

5678T

5874T

5934A

194C

284C

1136G

1252C

1334G

1699C

3207G

3209G

5444C

5551G

5573C

5659T

5678T

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

3538A

3885G

3886C

5444A

5551A

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

3538A

3886C

5444C

5551G

5573C

5678C

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

3885G

3886A

5444A

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

3538A

3886C

5444C

5551G

5573C

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

3538A

3885G

3886C

5444A

5551A

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252T

1334G

1699T

3150A

3207G

3209G

3538G

3885G

3886A

5444C

5551G

5S73C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1699T

3150A

3207G

3209G

3538G

3885G

5444C

5551A

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150G

3207G

3209G

3885G

5444C

5551G

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

5444C

5551A

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150G

3207G

3209G

3538G

3885G

3886C

5444C

5551G

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1334G

1699T

3150A

3207T

3209G

5444C

5551G

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3538A

3885G

3886C

5444A

5551A

5573T

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150G

3207G

3209G

5444C

5551G

5573C

5659T

5678T

5874T

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

3538A

3885G

3886C

5444C

5551G

5573C

5659C

5678C

5874C

5934A

194C

284C

1136G

1252C

1334G

1699C

3150G

3207G

3209G

5444C

5551G

5573C

5659T

5678T

5874T

5934A

194C

284C

1136G

1252C

1334G

1699T

3150G

3207G

3209G

3538A

3885G

3886C

5444A

5551A

5573C

5659T

5678T

5874T

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

3538G

3885G

3886A

5444A

5551A

5573C

5659T

5678T

5874C

5934A

194C

284C

1136C

1252C

1334G

1699T

3150G

3207G

3209G

5659C

5678C

5874T

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

5444C

5551G

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150G

3207G

3209G

3538G

3885G

3886C

5444C

5551G

5573G

5659T

5678T

5874T

5934A

194C

284C

1136G

1252C

1334G

1699T

3207G

3209G

5444A

5551A

5573C

5659C

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

5444A

5551A

5573C

5659T

5678T

5874C

5934A

194C

284C

113GA

1252C

1334G

1699T

3150G

3207G

3209G

3538A

3885G

3886C

5444A

5551A

5573C

5659T

5678T

5874T

5934A

194C

284C

1136G

1252C

1334G

1699T

3150G

3207G

3209G

3538G

3885G

3886C

5444C

5551G

5573C

5659T

5678T

5874T

5934G

194C

284T

1136G

252C

1334G

1699T

3150G

3207G

3209C

5444C

5551G

5573T

5659T

5678T

5874T

5934A

194C

284C

113GG

1252C

1334A

1699T

3150A

3207G

3209G

3538G

3885G

3886C

5444C

5551A

5573C

5659T

5678T

5874C

5934A

194C

284C

1136G

1252C

1334G

1699T

3150A

3207G

3209G

5444C

5551G

5573T

5659C

5678T

5874C

5934A

194

(−201)C > G

5′

284

(−111)C > T

5′

1136

742G > A

V248M

1158

764G > A

C255Y

1252

858C > T

Silent

1334

940G > A

D314N

1699

1305T > C

Silent

3150

2756A > G

D919G

3207

2813G > T

S938I

3209

2815G > C

G939R

3538

3144G > A

Silent

3885

3491G > A

R1164H

3886

3492C > A

Silent

5095

4701G > A

3′

5444

5050C > A

3′

5551

5157G > A

3′

5573

5179C > T

3′

5659

5265T > C

3′

5678

5284T > C

3′

5874

5480C > T

3′

5934

5540A > G

3′

6750

6356G > A

3′

X01057

X01057

147730

GEN-6B

Interleukin-2 receptor (IL-2R)

264G

696C

891G

264A

696C

891A

264G

696T

891A

264G

696C

891A

264

84G > A

Silent

696

516C > T

Silent

891

711A > G

Silent

M29882

M29882

107670

GEN-6R

Apolipoprotein A-II

26

17C > A

AGE

183

174G > A

Silent

192

183C > A

Silent

X07282

X07282

180220

GEN-72

RETINOIC ACID RECEPTOR BETA-2

1532G

1664A

1532A

16640

1532G

1664G

1532

1189G > A

G397R

1664

1321G > A

V4411

X52773

X52773

180245

GEN-74

Retinoid X receptor, alpha

140T

363A

140C

363A

140C

363G

140

65C > T

P22L

363

288A > G

Silent

1744

1669G > A

3′

X63522

X63522

180246

GEN-75

Retinoic X receptor beta,

partial cDNA

1331

1152T > C

Silent

D25418

D25418

600022

GEN-78

Prostaglandin I2 (prostacyclin)

receptor(IP)

250C

1075A1

562C

250C

1075A

1562G

1047G

1075C

1332C

250G

1047C

1075A

1332C

1562C

250G

1047C

1075C

1332C

1562C

250G

1047C

1075C

1332C

1562G

250C

726G

1047C

1075C

1332C

1562C

250G

1047C

1332T

1562C

250G

1075A

1332C

1562G

250C

1075A

1562C

250C

1047G

1075C

1332C

1562G

250C

1075A

1562G

250C

726A

1047C

1075C

1332C

1562C

250G

1047C

1075C

1332C

1562G

250G

1047C

1075C

1332C

1562C

250G

1075A

1332C

1562C

250G

1047C

1075C

1332T

250C

1047C

1075C

1332C

250C

1047C

1075C

1332C

1562C

250G

1047C

1075C

1332T

1562C

250C

1047C

1075C

1332C

1562G

250G

1075A

1332C

1562G

250

159G > C

Silent

726

635G > A

R212H

1047

956C > G

S319W

1075

984A > C

Silent

1332

1241C > T

3′

1562

1471C > G

3′

PTGER2

L28175

601586

GEN-7C

Prostaglandin E receptor 2

(subtype EP2), 53kD

547C

1268G

1725G

547T

1268G

1725A

547C

1268A

1725A

547C

1268G

1725A

547C

1268G

547

159C > T

Silent

611

223G > A

V75M

1268

880G > A

V294I

1725

1337A > G

Q446R

M16505

M16505

308100

GEN-7D

STERYL-SULFATASE PRECURSOR

2725

2505T > G

3′

4364

4144G > A

3′

4665

4445A > G

3′

5894

5674A > G

3′

M68874

M68874

600522

GEN-7K

Cytosolic phospholipase A2, cDNA

2743

2605G > A

3′

U07132

U07132

600380

GEN-7M

Orphan receptor

763

519G > A

Silent

1399

1155C > T

Silent

1726

1482G > C

3′

1952

1708C > G

3′

X77307

X77307

601122

GEN-7T

Serotonin 5-HT receptors 5-HT2B

99C

207G

677G

783C

894A

1307T

1369C

99C

207G

677G

783T

894A

1307C

1369C

99C

207G

677T

783C

894A

1307C

1369C

99C

207A

677G

783C

1307C

99T

207G

677G

783C

894A

99C

207G

677G

783C

894A

1307C

1369C

207A

783C

894C

207G

783C

894A

1307C

1369C

99C

207A

677G

783C

894C

1307C

1369T

99

44C > T

P15L

207

152G > A

G51E

677

622G > T

V208L

783

728C > T

A243V

894

839A > C

K280T

1307

1252C > T

R418W

1369

1314C > T

Silent

X57830

X57830

182135

GEN-7V

Serotonin receptor 5HT-2A, cDNA

1384

1239A > T

Silent

1499

1354C > T

H452Y

1962

1817A > C

3′

M11050

M11050

138040

GEN-7Y

Glucocorticoid receptor

1220A

1896C

1220A

1896T

1220G

1896C

1220

1088A > G

N363S

1896

1764C > T

Silent

2166

2034C > T

Silent

3353

3221T > G

3′

3398

3266T > G

3′

M24857

M24857

180190

GEN-80

Retinoic acid receptor, gamma

1694

1280C > T

S427L

U25029

U25029

138040

GEN-82

Glucocorticoid receptor alpha

335C

386T

1069C

335C

386C

1069C

335C

386T

1069T

335T

386T

1069C

335C

386T

335

335C > T

3′

386

386T > C

3′

1069

1069C > T

3′

J03817

J03817

138350

GEN-9D

Glutathione S-transferase M1

99

84T > C

Silent

543

528C > T

Silent

643

628T > A

S210T

728

713C > G

3′

902

887C > T

3′

K03191

K03191

108330

GEN-9E

Cytochrome 9450, subfamily I

(aromatic compound-inducible), polypeptide 1

1470

1384G > A

V462I

M63012

M63012

168820

GEN-9F

Paraoxonase 1

172

163A > T

M55L

M63509

M63509

138380

GEN-9G

Glutathione S-transferase M2

(muscle)

644

628A > T

T210S

M64082

M64082

136130

GEN-9H

Flavin-containing monooxygenas

1 (DIMETHYLANILINE MONOOXYGENASE)

1808C

1818G

1830G

1904T

1808T

1818G

1830G

1904C

1808C

1818G

1830A

1808T

1818G

1830G

1904T

1808C

1818A

1904T

1808C

1818G

1830G

1904C

1808C

1818A

1830A

1904T

1808T

1818A

1830A

1904T

1808C

1818G

1830A

1904T

1286

1188A > G

Silent

1808

1710C > T

3′

1818

1720G > A

3′

1830

1732G > A

3′

1904

1806C > T

3′

M96234

M96234

138333

GEN-9J

Glutathione S-transferase M4

797

534T > C

Silent

X03674

X03674

305900

GEN-9K

Glucose-6-phosphate

dehydrogenase

672G

1438T

672A

1438T

672G

1438C

503

33C > G

H11Q

589

119C > T

S40L

672

202G > A

V68M

846

376A > G

N126D

1438

968T > C

L323P

2215

1745T > C

3′

2242

1772T > C

3′

2341

1871G > A

3′

Y00498

Y00498

601129

GEN-9N

Cytochrome P450, subfamily IIC

(mephenytoin 4-hydroxylase)

678G

723A

678G

723G

678A

723A

431

389C > A

T130N

489

447T > C

Silent

491

449A > G

H150R

522

480G > T

K160N

525

483T > C

Silent

582

540C > T

Silent

583

541G > A

V181I

678

636G > A

Frame

723

681A > G

Silent

834

792C > G

I264M

999

957C > G

Silent

1539

1497T > C

3′

AB000410

AB000410

601982

GEN-9O

Human hOGG1 mRNA, complete cds

251G

682T

251T

682C

251G

682C

251

(−18)G > T

5′

682

414C > T

Silent

AF001437

AF001437

245349

GEN-9T

Dihydrolipoamide S-

acetyltransferase (E2 component of pyruvate dehydrogenase complex)

75

67T > C

C23R

116

108C > T

Silent

759

751T > G

S251A

806

798C > T

Silent

866

858T > C

Silent

2000

1992G > T

3′

2158

2150C > A

3′

D13811

D13811

238310

GEN-AA

Glycine cleavage system: Protein

T

277G

1073A

1083G

1773C

277T

1073G

1083G

1773C

277G

1073G

1083G

1773C

277G

1073G

1083G

1773T

277

148G > T

V50L

1073

944G > A

R315K

1083

954G > A

Silent

1773

1644C > T

3′

2037

1908C > T

3′

J03490

J03490

246900

GEN-C5

Dihydrolipoamide dehydrogenase

(E3 component of pyruvate dehydrogenase complex, 2-oxo-glutarate complex,

branched chain keto acid dehydrogenase complex)

1427C

1624A

1634T

1813A

2096T

1427T

1624T

1634G

1813A

2096T

1427C

1624A

1634G

1813A

2096C

1427C

1624T

1634T

1813A

2096T

1427C

1624T

1634G

1813A

2096T

1427C

1624A

1634G

1813A

2096T

1427C

1624A

1634G

1813G

1427T

1624A

l634G

1813A

2096T

1624A

1634G

1813G

2096C

1427C

1624T

1634G

1813A

2096C

1427C

1624A

1634G

1813G

2096C

1427C

1624T

1634G

1813G

2096C

1427C

1624A

1634G

2096T

1427

1351C > T

Silent

1569

1493A > C

N498T

1624

1548T > A

3′

1634

1558G > T

3′

1813

1737A > G

3′

2096

2020T > C

3′

J04031

J04031

172460

GEN-CB

Methenyltetrahydrofolate

cyclohydrolase

454G

969C

1614C

2011A

2358C

2368G

2486C

454G

969C

1614C

2011G

2358C

2368G

2486C

454G

969C

1614C

2011A

2358T

2368G

2486C

454A

969C

1614C

2011A

2358C

2368G

2486T

454A

969C

1614C

2011G

2358C

2368G

2486C

454G

969C

1614T

2011A

2358C

2368G

2486C

454A

969C

1614C

2011A

2358T

2368G

2486C

454G

969C

1614C

2011A

2358C

2368G

2486T

454G

969C

1614C

2011G

2358C

2486C

454A

969C

1614C

2011G

2358C

2368G

2486T

454A

969C

1614C

2011A

2358C

2368G

2486C

454G

969C

1614C

2011G

2358C

2368G

2486T

454A

969C

1614C

2011G

454G

969C

1614C

2011G

2358C

2368G

2486C

454A

969C

1614C

2011A

454G

969C

1614C

2011A

454G

969C

1614C

2011G

454

401G > A

R134K

969

916C > G

Q306E

1614

1561T > C

Silent

2011

1958G > A

R653Q

2335

2282C > T

T761M

2358

2305C > T

L769F

2368

2315G > A

R772H

2486

2433C > T

Silent

L11696

L11696

104614

GEN-D6

Solute carrier family 3

(cystine, dibasic and neutral amino acid transporters, activator of cystine,

dibasic and neutral amino acid transport), member 1

1897

1854G > A

M618I

2232

2189T > C

3′

L14754

L14754

600502

GEN-D9

DNA-binding protein (SMBP2)

2129

2080C > T

R694W

2365

2316C > T

Silent

3696

3647C > T

3′

3712

3663T > C

3′

3771

3722C > G

3′

L19067

L19067

164014

GEN-DE

TRANSCRIPTION FACTOR P65

1130G

1708A

1936G

2024C

1130A

1708A

1936C

1130G

1708A

1936C

2024C

1130G

1708G

1936C

2024C

1936C

2024C

1130A

1708A

1936C

2024T

1129

1091C > T

S364L

1130

1092G > A

Silent

1708

1670A > G

3′

1936

1898G > C

3′

2024

1986C > T

3′

L20298

L20298

121360

GEN-DH

Transcription Factor (CBFB)

2696

2696A > G

3′

L31801

L31801

600682

GEN-DQ

Solute carrier family 16

(monocarboxylic acid transporters) , member 1

1021G

1416A

1482A

1660T

1021A

1416A

1482A

1660T

1021G

1416A

1482T

1660G

1021G

1416G

1660T

1021G

1416A

1482T

1660T

1021G

1416G

1482A

1660T

1021

1009G > A

V337I

1416

1404A > G

I468M

1482

1470A > T

E490D

1660

1648T > G

3′

1772

1760G > C

3′

M16827

M16827

201450

GEN-EI

Acyl-Coenzyme A dehydrogenase,

C-4to C-12 straight chain

918C

1179G

918

900C > T

Silent

1179

1161A > G

Silent

1956

1938T > C

3′

M26393

M26393

201470

GEN-EW

Acyl-Coenzyme A dehydrogenase,

C-2 to C-3 short chain

353T

657G

1022T

353C

657G

1022C

1386A

353C

657A

1022C

1386A

657A

1022C

1292G

1386G

353C

657G

1022C

1292G

1386G

353C

657G

1022T

1292G

1386G

1022C

1292C

1386G

353T

657G

1022C

1292G

1386G

353C

657A

353C

657G

353T

657G

1022T

1292G

1386G

353T

1022C

1292C

1386G

353T

657G

1022T

353T

657A

1022T

1292G

1386G

353T

657A

1022C

353T

657A

353T

657A

1022C

1292C

1386G

353T

657G

353T

657A

1022C

1292G

1386G

353

321T > C

Silent

657

625G > A

G209S

1022

990C > T

Silent

1292

1260G > C

3′

1386

1354G > A

3′

1797

1765A > G

3′

M30938

M30938

194364

GEN-F5

ATP-DEPENDENT DNA HELICASE II,

86 KD SUBUNIT

1599G

2549T

2953A

1599G

2549T

2953C

1599A

2549T

2953A

3067A

1599A

2549T

2953C

3067A

1599A

2549C

2953A

3067A

1599G

2549C

2953A

1599A

2549T

2953C

3067G

1599A

2549C

2953A

3067G

1599A

2549T

2953A

1599G

2549T

2953A

3067G

1599A

2549T

2953C

1599

1572A > G

Silent

2549

2522T > C

3′

2953

2926C > A

3′

3037

3010G > A

3′

3067

3040G > A

3′

M31523

M31523

147141

GEN-F7

Transcription factor 3 (E2A

immunoglobulin enhancer binding factors E12/E47)

1321

1291G > A

G431S

1323

1293C > T

Silent

1332

1302G > A

Silent

1338

1308T > C

Silent

1608

1578C > G

Silent

4022

3992G > A

3′

4254

4224T > A

3′

M34479

M34479

179060

GEN-F9

Pyruvate dehydrogenase

(lipoamide) beta

109

109G > A

D37N

438

438A > G

Silent

1172

1172A > C

3′

1179

1179C > T

3′

1323

1323C > A

3′

1376

1376G > C

3′

1433

1433C > T

3′

M55531

M55531

138230

GEN-FF

Solute carrier family 2

(facilitated glucose transporter) , member 5

1208

1133T > G

V378G

1975

1900C > T

3′

1985

1910A > G

3′

M60761

M60761

156569

GEN-FL

O-6-methylguanine-DNA

methyltransferase

174T

265C

442A

174C

265T

442A

174C

265C

442A

174T

265T

442A

174C

265C

442G

174T

265T

174T

265T

442G

174

159C > T

Silent

264

249A > T

Silent

265

250C > T

L84F

442

427A > G

I143V

M81181

M81181

182331

GEN-G4

ATPase, Na+/K+ transporting,

beta 2 polypeptide

107

(−301)C > G

5′

1070

663C > A

Silent

1745

1338A > G

3′

1845

1438C > G

3′

1891

1484G > A

3′

1974

1567C > A

3′

2364

1957T > C

3′

M81768

M81768

107310

GEN-G6

Solute carrier family 9

(sodium/hydrogen exchanger)

3042

2989G > A

3′

M84739

M84739

109091

GEN-GB

CALRETICULTN PRECURSOR

1416

1308T > G

3′

1695

1587G > A

3′

M94859

M94859

114217

GEN-GP

Calnexin

79

(−17)C > T

5′

2678

2583C > T

3′

3011

2916G > T

3′

3527

3432T > G

3′

U09178

U09178

274270

GEN-HA

Dihydropyrimidine Dehydrogenase

166T

577A

635A

1452C

1557G

1575A

1708A

1977T

3432T

3652C

3730G

3925G

3937C

166T

577A

638A

1452C

1557A

1575A

1708A

1977T

3432T

3682C

3730G

3925G

3937C

166T

577G

638A

1452C

1557G

1575A

1708A

1977T

3432T

3682C

3730G

3937C

166T

577A

638A

1452C

1557G

1575A

1708A

1977T

3432T

3682C

3730G

3925A

3937C

166T

577A

638A

1452C

1557G

1575A

1708A

1977T

3432C

3682C

3730G

3937T

166C

577A

638A

1452C

1557G

1575A

1705A

1977T

3432T

3682C

3730G

3925G

3937C

166C

577A

635A

1452C

1557G

1575A

1708A

1977T

3432T

3682C

3730G

3925A

3937T

166T

577A

638A

1452C

1557G

1575A

1708A

1977T

3432C

3682C

3730G

3937C

166T

577A

638A

1452C

1557G

1575A

1708A

1977T

3432T

3682C

3730G

3925A

3937T

577A

638A

1452C

1557G

1575A

1977T

3432T

3652T

3730G

166C

577A

638A

1452C

1557G

1575A

1708A

1977C

3432T

3682C

3730G

577A

1452C

1557G

1575G

1708A

1977T

3432T

3682C

3730G

3925G

3937C

166T

577A

638A

1452C

1557G

1575A

1708G

1977T

3432T

3682C

3730G

3925A

3937C

638A

1452C

1557G

1575A

1708A

1977T

3432T

3682C

3730A

3925A

3937T

166C

577A

638A

1557G

1575A

1708A

1977T

3432T

3682C

3730G

3925A

3937C

166T

577A

638A

1452C

1557G

1575A

1708A

1977T

3432T

3682C

3730G

166T

577A

638A

1452C

1557A

1575A

1708G

1977T

3432C

3682C

3730G

166C

577G

166T

577A

638A

1452C

1557G

1575A

1708A

1977T

3432T

3682T

3730G

3925A

3937C

166T

3925A

3937T

166T

577G

3925G

3937C

166T

577A

1452C

1557G

1575A

1708G

1977T

3682C

3730G

3925A

3937C

166T

577A

1452C

1557G

1575A

1708A

1977T

3432T

3682C

3730G

3925A

3937C

166T

577G

166T

577A

638A

1452C

1557G

1575G

1708A

1977T

3432T

3682C

3730G

3925G

3937C

166T

577A

3925A

166T

577A

638A

1452C

1557G

1575A

1708G

1977T

3432T

3682C

3730A

166T

577A

3925A

3937T

166C

577A

166T

577A

638A

1452C

1557G

1575A

1708A

1977T

3432C

3682C

3730G

3925A

3937T

166T

577A

3925G

3937C

166T

577A

3925A

3937C

166T

577A

638A

1452C

1557G

1575A

1708G

1977T

3432T

3682C

3730G

166C

577A

638A

1452T

1557G

1575A

1708A

1977T

3432T

3682C

3720G

3925A

3937T

166T

577G

638A

1452C

1557G

1575A

1708A

1977T

3432T

3682C

3730G

3925A

3937C

166T

577A

3937G

166T

577G

638A

1452C

1557G

1575A

1708A

1977T

3432T

3682C

3730G

3925A

3937T

166

85T > C

C29R

577

496A > G

M166V

638

557A > G

Y186C

1452

1371C > T

Silent

1557

1476G > A

Silent

1575

1494A > G

Silent

1708

1627A > G

I543V

1977

1896C > T

Silent

3432

3351T > C

3′

3682

3601C > T

3′

3730

3649G > A

3′

3925

3844A > G

3′

3937

3856T > C

3′

U19720

U19720

600424

GEN-I1

Folate Transporter (SLC19A1)

175A

341C

1067A

1337C

1997T

2652T

175G

341C

791T

1067G

1337C

1997T

2582G

2617T

2652T

175A

341C

79lT

1067G

1337C

1997C

2582G

26l7T

2652T

175A

341C

791T

1067G

1337C

1997T

2617C

2652T

175G

341C

791C

1067A

1337C

1997T

2582T

2617C

2652T

175A

341C

791T

1067G

1337C

1997T

2582G

2617T

2652T

175G

341C

791C

1067G

1337C

1997T

2582T

2617C

2652T

341C

791C

1067G

1337G

1997T

2582G

2617T

2652T

175A

341C

791C

1067G

1997T

2582T

2617C

175G

341C

791T

1067G

1337C

1997T

2582G

2652T

175A

341C

791T

1067G

1337C

1997T

2582T

2617T

2652T

175A

341C

791T

1067G

1337C

1997T

2582G

2652T

175G

341C

791T

1067G

1337C

1997T

2582T

2617T

2652T

175A

341C

791T

1337A

1997T

2582G

2617T

2652C

175G

341C

791C

1067G

1337A

1997T

2582T

2617C

2652T

175G

341C

791C

1067G

1337C

1997T

2582G

2617T

2652T

175A

341C

791C

1337C

1997T

2582G

2617T

2652T

175A

341C

791C

1067A

1337C

1997T

2582T

2617C

2652T

175G

341C

791C

1067G

1337C

1997T

2582T

2617T

2652T

175A

34lC

791T

1067G

1337C

1997T

2582T

2617C

2652T

53

(−43)T > C

5′

175

80G > A

R27H

341

246C > G

Silent

791

696C > T

Silent

1067

972G > A

Silent

1337

1242C > A

Silent

1997

1902T > C

3′

2100

2005{circumflex over ( )}2006insG

3′

2582

2487T > G

3′

2617

2522C > T

3′

2652

2557T > C

3′

U36601

U36601

603268

GEN-IR

Heparan N-deacetylase/N-

sulfotransferase-2

2727

2700T > G

3′

2972

2945A > G

3′

U45730

U48730

601511

GEN-JA

Transcription Factor Stat5b

494

484T > C

Silent

496

486A > G

Silent

499

489A > G

Silent

502

492G > A

Silent

570

560G > C

G187A

573

563C > A

P188Q

1003

993G > A

Silent

1063

1053T > C

Silent

1066

1056G > A

Silent

1105

1095C > T

Silent

1159

1149C > T

Silent

1969

1959C > T

Silent

X02317

X02317

147450

GEN-KM

Superoxide dismutase 1 (Cu/Zn)

614

550A > C

tr,26 3′

+TL,30

X03747

X03747

182330

GEN-KR

ATFase, Na+/K+ transporting,

beta 1 polypeptide

447

321G > A

Silent

1516

1390G > T

3′

2182

2056C > T

3′

X13403

X13403

164175

GEN-L8

POU domain, class 2,

transcription factor 1

1298

1239T > C

Silent

1476

1417G > A

A473T

X16396

X16396

None

GEN-LC

Methenyltetrahydrofolate

dehydrogenase

608A

1259G

1284C

1392T

1397A

1480G

608A

1259G

1284T

1392T

1397A

1480G

1397G

1480A

608A

1259G

1284C

1392T

1397A

1480A

608A

1259G

1284T

1392T

1397A

1480A

608A

1259A

1284C

1392T

1397A

1480A

608A

1259G

1284C

1392C

1397A

1480A

608G

1259G

1397A

1480G

1397G

1480A

1397A

1480A

1397A

1480G

608

593A > G

N198S

1259

1244G > A

3′

1284

1269C > T

3′

1392

1377T > C

3′

1397

1382A > G

3′

1480

1465G > A

3′

X54199

X54199

138440

GEN-LS

Phosphoribosylglycinamide

formyltransferase, phosphoribosylglycinamide synthetase,

phosphoribosylaminoimidazole synthetase

1339A

1999G

2333A

1339A

1999C

2333G

2682T

1339A

1999C

2333A

2682T

1339G

1999C

2333A

2682T

1339A

1999G

2333A

2682T

1339A

1999C

2333A

1339A

1999G

2333A

2682C

168

90G > A

Silent

1339

1261G > A

V421I

1999

1921C > G

P641A

2333

2255A > G

D752G

2682

2604T > C

Silent

V00594

V00594

156360

GEN-P6

Human mRNA for metallothionein

from cadmium-treated cells

320

263G > C

3′

K01171

K01171

142860

GEN-PB

Human HLA-DR alpha-chain mRNA

297

283T > C

Silent

416

402C > A

Silent

665

651C > T

Silent

738

724G > T

V242L

748

734G > A

S245N

797

783A > G

3′

842

828A > G

3′

901

887G > A

3′

928

914T > A

3′

933

919T > A

3′

942

928C > T

3′

954

940G > A

3′

999

985T > G

3′

1035

1021A > C

3′

1077

1063C > T

3′

1091

1077C > G

3′

1154

1140A > C

3′

1171

1157T > A

3′

X02920

X02920

107400

GEN-PH

Human mRNA for alpha 1-

antitrypsin carboxyterminal region (aa 268-394)

107

107T > C

L36P

137

137G > A

S46N

195

195C > T

Silent

327

327A > C

E109D

X03069

X03069

142857

GEN-PI

Human mRNA for HLA-D class II

antigen DR1 beta chain

99

37A > G

T13A

104

42G > T

Silent

348

286C > A

L96I

361

299G > A

R100K

452

390G > A

Silent

459

397T > G

5133A

463

401A > G

K134R

471

409C > A

P137T

500

438C > T

Silent

508

446G > A

S149N

523

461G > C

G154A

547

485G > T

R162L

551

489C > T

Silent

552

490G > A

G164S

567

505G > A

A169T

573

511G > A

V171M

584

522A > G

Silent

593

531C > T

Silent

596

534G > C

Q178H

605

543T > C

Silent

632

570G > A

Silent

647

585G > A

Silent

686

624T > C

Silent

691

629C > T

T210M

692

630G > A

Silent

716

654A > T

R218S

721

659G > A

R220Q

756

694G > A

V232I

767

705C > T

Silent

800

738G > A

Silent

814

752G > A

R251K

847

785C > G

T262R

865

803A > G

3′

868

806C > A

3′

893

831C > T

3′

899

837T > C

3′

903

841A > G

3′

913

851A > G

3′

988

926G > A

3′

1004

942G > A

3′

1027

965C > T

3′

1105

1043G > C

3′

1128

1066C > T

3′

1139

1077C > T

3′

1140

1078G > C

3′

X03348

X03348

138040

GEN-PL

Human mRNA for beta-

glucocorticoid receptor (clone OB10)

3297

3165G > A

3′

X03438

X03438

138970

GEN-PM

Human mRNA for granulocyte

colony-stimulating factor (G-CSF)

586

555G > A

Silent

1235

1204C > T

3′

J04794

J04794

103830

GEN-PR

Human aldehyde reductase mLRNA,

complete cds

661

601C > A

Q201K

J05176

J05176

107280

GEN-PT

Human alpha-1-antichymotrypsin

mRNA, 3′ end

240A

327C

240A

327T

240G

327C

240

240A > G

Silent

327

327C > T

Silent

554

554T > C

V185A

U07989

U07989

147200

GEN-Q2

Human Burkitt′s lymphoma

immunoglobulin kappa light chain mRNA, partial cds

39

39T > A

Silent

307

307G > C

V103L

312

312A > G

Silent

568

568G > C

V190L

610

610G > A

V204I

D126l4

D126l4

153440

GEN-QD

Human mRNA for lymphotoxin (TNF-

beta), complete cds

319

179C > A

T60N

M12807

M12807

186940

GEN-QG

Human T-cell surface

glycoprotein T4 mRNA, complete cds

867C

868C

1098C

1416C

1443C

1526T

867A

868C

1098C

1416T

1443C

1526T

867A

868C

1098T

1416C

1443C

1526A

868C

1098C

1416C

1443T

1526T

867C

868C

1098T

1416T

1443C

1526T

867A

868C

1098C

1416C

1443C

1526T

867A

868C

1098T

1416T

1443C

1526T

867A

868T

1098C

1416C

1443C

1526T

867A

868C

1098C

1416C

1443C

1526A

868C

1098T

1416T

1443C

1526A

867C

868C

1098C

1416T

1443C

1526T

867C

868C

1098C

1416C

1443T

1526T

867A

868T

1098C

867A

868T

1098T

1416T

1443C

1526T

867

792A > C

K264N

868

793C > T

R265W

1098

1023C > T

Silent

1416

1341C > T

Silent

1443

1368C > T

Silent

1526

1451T > A

3′

M12824

M12824

186910

GEN-QH

Human 1-cell differentiation

antigen Leu-2/T8 mRNA, partial cds

1545

1458C > T

3′

1765

1678C > T

3′

X15722

X15722

138300

GEN-QR

Human mRNA for glutathione

reductase (EC 1.6.4.2)

432T

1044A

164T

432C

1044A

164C

236T

432C

1044A

164C

236C

432C

1044A

164T

236C

432C

1044G

164T

236C

432C

1044A

164T

236C

432T

1044A

164C

236C

432C

164

60C > T

Silent

236

132T > C

Silent

432

328C > T

R110C

1044

940A > G

I314V

M15872

M15872

138360

GEN-QS

Human glutathione S-transferase

2 (GST) mRNA, complete cds

16

(−40)G > A

5′

54

(−2)T > C

5′

84

29T > C

F10S

111

56C > T

T19I

170

115G > T

Frame

321

266G > A

R89K

376

321C > T

Silent

430

375G > A

Silent

622

567C > T

Silent

684

629A > C

E210A

701

646G > T

A216S

M24400

M24400

118890

GEN-R2

Human chymotrypsinogen mRNA,

complete cds

121

105G > A

Silent

231

215C > A

T72N

460

444C > T

Silent

649

633C > T

Silent

M24895

M24895

104660

GEN-R3

Homo sapiens alpha-amylase mRNA,

complete cds

193

147C > G

Silent

967

921A > G

Silent

1009

963G > C

Silent

1027

981T > A

Silent

1054

1008T > C

Silent

1093

1047T > A

Silent

1178

1132A > G

N378D

1191

1145T > C

I382T

1394

1348A > T

T4505

1474

1428T > C

Silent

1492

1446C > T

Silent

1504

1458C > T

Silent

1543

1497G > A

Silent

1579

1533A > G

Silent

1601

1555T > A

3′

M33491

M33491

191080

GEN-RD

Human tryptase-I mRNA, 3′ end

92

92C > T

+TL,22

S31L

392

392C > G

T131R

609

609G > A

Silent

707

707G > A

C236Y

730

730G > A

A244T

837

837T > G

3′

840

840G > T

3′

1008

1008T > C

3′

1050

1050C > T

3′

1060

1060A > G

3′

M55643

M55643

164011

GEN-RP

Human factor KBF1 mRNA, complete

cds

1231C

1324C

1917A

1231C

1324T

1917G

1231C

1324C

1917G

1231T

1324C

1917G

1231

1050C > T

Silent

1324

1143C > T

Silent

1917

1736G > A

R579K

1936

1755G > A

Silent

X59498

X59498

176300

GEN-RU

H. sapiens ttr mRNA for

transthyretin

92

71G > A

G24D

97

76G > A

G26S

177

156G > T

Silent

187

166G > A

A56T

292

271G > A

V91M

380

359C > T

S120F

X62468

X62468

147570

GEN-RW

H. sapiens mRNA for IFN-gamma

(pKC-0)

395

383G > A

G128E

M68867

M68867

180231

GEN-S1

Human cellular retinoic acid-

binding protein II (CRABP) mRNA, complete cds

604

506C > A

3′

J05096

J05096

182340

GEN-SL

alpha-subunit of Na+/K+ ATPase

isoform2

2364

2260T > G

S754A

5295

5191G > A

3′

PTPRC

Y00062

151460

GEN-SY

Human mRNA for T200 leukocyte

common antigen (CD45, LC-A)

3437

3291T > C

Silent

3441

3295G > A

V1099I

HLA-DQA1

X00033

146880

GEN-TO

Human RNA sequence of the human

DS glycoprotein alpha subunit from the HLA-D region of the major

histocompatibility complex (MHC)

41

22A > C

M8L

79

60T > C

Silent

145

126C > T

Silent

157

138C > G

Silent

162

143T > A

F48Y

165

146C > G

T495

227

208A > C

K70Q

243

224A > G

H75R

248

229C > T

L77F

298

279T > C

Silent

311

292C > G

L98V

334

315C > T

Silent

388

369A > G

Silent

559

540T > G

Silent

564

545C > A

A182D

607

588T > C

Silent

644

625G > A

A209T

646

627A > C

Silent

679

660T > C

Silent

688

669G > A

Silent

704

685G > A

V229M

721

702G > C

Silent

724

705C > T

Silent

730

711G > C

L237F

800

781A > G

3′

GPX1

Y00433

138320

GEN-TJ

Human mRNA for glutathione

peroxidase (EC 1.11.1.9.)

504

186G > A

Silent

610

292C > G

R98G

911

593C > T

P198L

1048

730A > C

3′

1110

792A > C

3′

V00494

V00494

103600

GEN-TL

Human messenger RNA for serum

albumin (HSA)

34

(−6)G > T

5′

36

(−4)C > G

5′

401

362G > A

G121E

431

392A > G

D131G

1090

1051T > C

Silent

1091

1052T > G

L351W

1531

1492A > C

T498P

1533

1494C > A

Silent

1637

1598T > C

F533S

1707

1668C > T

Silent

1719

1680G > A

Silent

1926

1887T > A

3′

X00497

X00497

142790

GEN-TN

Human mRNA for HLA-DR antigens

associated invariant chain (p33)

805

750A > G

3′

881

826A > G

3′

1144

1089C > G

3′

AJ004832

AJ004832

None

GEN-TO

Homo sapiens mRNA for neuropathy

target esterase

4153

3996G > A

3′

HLA-DPB1

X00532

142858

GEN-U2

Human mRNA for SB beta-chain

(clone pII-beta-7)

13

13T > A

S5T

91

91T > A

S31T

94

94C > T

R32C

151

151C > A

L51M

154

154C > A

Silent

158

158G > C

S53T

213

213G > A

Silent

281

281C > T

T94M

306

306C > T

Silent

341

341A > G

Q114R

353

353G > A

R118Q

488

488C > T

T163M

496

496T > C

S166P

524

524C > T

T175I

568

568A > G

R190G

600

600G > A

Silent

708

708C > G

3′

761

761G > A

3′

840

840G > A

3′

SPINK1

Y00705

167790

GEN-UA

Homo sapiens pstI mPNA for

pancreatic secretory inhibitor (expressed in neoplastic tissue)

332

272C > T

3′

TCRG

Y00790

186970

GEN-UC

Human mRNA for T-cell receptor

gamma-chain

492

456G > A

Silent

507

471A > G

Silent

528

492C > T

Silent

555

519A > T

Silent

559

523A > G

I175V

636

600C > T

Silent

676

640G > A

E214K

733

697A > G

I233V

849

813G > T

W271C

908

872C > T

T291M

970

934A > G

R312G

CBS

L00972

236200

GEN-UV

Human cystathionine-beta

synthase (CBS) mRNA

1022

1022T > C

3′

2001

2001C > T

3′

2278

2278G > A

3′

2358

2358G > C

3′

2524

2524T > C

3′

2545

2545C > T

3′

AB005659

AB005659

None

GEN-VR

Homo sapiens SMRP mRNA, complete

cds

1045C

1781T

2887C

4158C

4159G

4820G

1045T

1781T

4158C

4159A

1045C

1781T

2887T

4l58C

4l59A

4820G

1045C

1781T

2887C

4158T

4159G

4820G

1781G

2887C

4159G

1045C

1781T

2887C

4158C

4159G

4820A

1045C

1781T

2887C

4158C

4159A

4820G

1045T

1781T

2887C

4158C

4159G

4820A

1045C

1781T

2887T

4158C

4159G

1045C

2887C

4158C

4159A

4820A

1045C

1781G

2887C

4158T

4159G

1045C

1781T

2887C

4158C

4159A

4820A

1045T

1781T

4l58C

1045C

1781T

2887T

4158C

4159G

4820A

1045C

1781T

2887C

4158C

4159G

1045C

1781T

2887C

4158T

4159A

1045C

1781T

2887T

4158C

4159G

1045C

1781T

2887C

4158C

4159A

1045C

2887C

4158C

4159A

4820A

1045

309C > T

Silent

1781

1045T > G

S349A

2887

2151T > C

Silent

3642

2906C > T

3′

4158

3422C > T

3′

4159

3423A > G

3′

4820

4084G > A

3′

GSTM5

L02321

138385

GEN-WO

Human glutathione S-transferase

(GSTM5) rnPNA, complete cds

1406

1349T > C

3′

10  X02812

X02812

190180

GEN-XR

Human mRNA for transforming

growth factor-beta (TGF-beta)

870T

979C

1854C

979G

1854G

870C

979C

1854G

870T

979C

1854G

870C

979G

1854G

870T

979C

870

29C > T

P10L

979

138C > G

I46M

1632

791C > T

T264I

1807

966C > T

Silent

1854

1013G > C

S338T

1930

1089G > A

Silent

1942

1101C > T

Silent

2013

1172G > A

S391N

NMOR2

J02888

160998

GEN-XT

Human quinone oxidoreductase

(NQO2) mRNA, complete cds

505

330G > A

Silent

909

734G > C

3′

CBG

J02943

122500

GEN-Y2

Human corticosteroid binding

globulin mRNA, complete cds

106

71A > T

D24V

413

378T > C

Silent

971

936T > C

Silent

1229

1194G > A

Silent

SOD3

J02947

185490

GEN-Y3

Human extracellular-superoxide

dismutase (SOD3) mPNA, complete cds

746

677C > A

Frame

1042

973C > T

3′

HLA-DOB

X03066

600629

GEN-ZO

Human mRNA for HLA-D class II

antigen DO beta chain

32

(−25)G > A

5′

1147

1091C > T

3′

1299

1243A > G

3′

J03143

J03143

107470

GEN-ZK

Human interferon-gamma receptor

mRNA, complete cds

1098

1050T > G

Silent

K03195

K03195

138140

GEN-ZT

Human (HepG2) glucose

transporter gene mRNA, complete cds

1484

1305C > T

Silent

2120

1941G > C

3′

LIPC

J03540

151670

GEN-11J

Human hepatic lipase mRNA,

complete cds

469

465T > G

Silent

595

591A > G

Silent

648

644G > A

S215N

817

813C > T

Silent

1441

1437C > A

Silent

J03548

J03548

103260

GEN-11M

Human adrenodoxin mRNA, complete

cds

1099

967G > A

3′

1123

991T > C

3′

1222

1090G > C

3′

1254

1122G > A

3′

CYP1B1

U03688

601771

GEN-11Y

Human dioxin-inducible

cytochrome P450 (CYP1Bl) mPNA, complete cds

488

142C > G

R48G

701

355G > T

A119S

2673

2327G > T

3′

J03746

J03746

138330

GEN-11Z

Human glutathione S-transferase

mRNA, complete ccis

560G

598T

676T

560G

598T

676C

560A

598T

676T

560G

598G

676T

560

487A > G

3′

598

525T > G

3′

676

603T > C

3′

PNMT

J03727

171190

GEN-120

Human phenylethanolamine N-

methyltransferase mRNA,complete cds

462

456A > G

Silent

AB007448

AB007448

None

GEN-125

Homo sapiens mRNA for

polyspecific oraganic cation transporter, complete cds

1559

1413C > G

Silent

NNOR1

J03934

125860

GEN-12L

Human, NAD(P)H:menadione

oxidoreductase mRNA, complete cds

609C

1994T

609T

1994C

609C

1994C

609T

1994T

609

559C > T

P187S

1784

1734T > G

3′

1994

1944C > T

3′

AF007216

AF007216

603345

GEN-13L

Homo sapiens sodium bicarbonate

cotransporter (HNBC1) mRNA, complete cds

3332A

3666C

4194C

4240T

4633C

5283A

3332A

3666C

4194G

4633C

3332C

3666C

4194C

4240T

4633G

5283A

3332A

3666G

4194G

4633G

5283G

3332A

3666C

4194G

4240T

4633G

5283A

3332A

3666C

4194C

4633G

5283A

3332A

3666C

4194C

4240T

4633G

5283A

3332A

3666C

4194G

4240A

4633G

5283A

3666C

4194C

4240A

4633G

5283A

3332A

3666G

4194C

4240T

3332A

3666G

4194C

4240T

4633C

5283A

3666C

4194C

4240T

4633G

5283A

3332A

3666C

4194C

4240A

4633G

5283A

3332A

3666G

4194G

4240A

5283A

3332A

3666C

4194G

3666C

4194G

4240T

4633G

5283G

3332C

3666G

4194C

4240T

4633G

5283A

3666C

4194G

4240T

4633C

5283A

3332A

3666C

4194C

4240T

5283A

3332A

3666G

4194G

4240T

4633G

5283G

3332C

3666C

4194C

4240A

4633G

5283A

3332A

3666G

4194C

4240T

3332A

3666C

4194G

4240T

4633G

3332C

3666C

4194C

4240A

5283A

3332

3183C > A

3′

3666

3517C > G

3′

4194

4045C > G

3′

4240

4091T > A

3′

4633

4484G > C

3′

5283

5134A > G

3′

OBR

J04056

114830

GEN-13O

Human carbonyl reductase mRNA,

complete cds

1060

9670 > A

3′

CAT

X04076

115500

GEN-13P

Human kidney mRNA for catalase

796C

1237C

1325C

1387T

796C

1237T

1325T

1387C

796A

1237C

1325C

1387C

796C

1237T

1325C

1387C

796C

1237C

1325T

1387C

796C

1237C

1325C

1387C

51

(−20)T > C

5′

218

148C > T

L50F

796

726C > A

Silent

1237

11671 > C

Silent

1325

1255C > T

Silent

1387

1317C > T

Silent

2131

2061A > C

3′

AB000812

AB000812

602550

GEN-14E

Human mRNA for BMAL1b, complete

cds

1084

1044C > A

Silent

G22P1

J04611

152690

GEN-153

Human lupus p70 (Ku) autoantigen

protein mRNA, complete cds

1762

1729A > T

T577S

1812

1779T > G

Silent

1900

1867G > T

3′

HADHA

U04627

600890

GEN-155

Human 78 kDa gastrin-binding

protein mRNA, complete cds

474

474C > T

Silent

1507

1507G > A

V503M

L04751

L04751

601310

GEN-157

Human cytochrome p-450 4A

(CYP4A) mRNA, complete cds

1001

969C > T

Silent

1333

1301T > C

F434S

1406

1374T > C

Silent

1944

1912A > G

3′

1970

1938G > A

3′

2011

1979C > T

3′

2047

2015T > C

3′

2115

2083A > G

3′

RORA

U04897

600825

GEN-15R

Human orphan hormone nuclear

receptor RORalpha1 mRNA, complete cds

884T

1527A

1529A

884C

1527G

1529A

884C

1527A

1529A

884C

1527A

1529C

884

783C > T

Silent

1527

1426A > G

T476A

1529

1428A > C

Silent

GSTM3

J05459

138390

GEN-17O

Human glutathione transferase M3

(GSTM3) mRNA, complete cds

687

670G > A

V224I

AJ001838

AJ001838

603758

GEN-17S

Homo sapiens mRNA for

maleylacetoacetate isomerase

65

(−39)G > C

5′

197

94A > G

K32E

227

124G > A

G42R

348

245C > T

T82M

AF001945

AF001945

601691

GEN-17Z

Homo sapiens rim ABC transporter

(ABCR) mRNA, complete cds

2725

2644G > A

G882S

5136

5055C > T

Silent

DDH1

U05598

600450

GEN-184

Human dihydrodiol dehydrogenase

mRNA, complete cds

139A

179T

806A

139G

179T

139A

179A

806G

139A

179T

806G

139G

179T

806G

38

15C > T

Silent

139

116A > G

K39R

179

156A > T

Silent

282

259A > T

S87C

350

327C > T

Silent

365

342T > C

Silent

464

44lG > A

Silent

474

451A > G

M151V

532

509A > G

H170R

538

515T > A

L172Q

689

666T > C

Silent

806

783G > A

Silent

872

849G > T

Silent

952

929T > G

I310S

1020

997G > A

3′

1035

1012G > A

3′

1112

1089C > T

3′

EPHX2

L05779

132811

GEN-18A

Human cytosolic epoxide

hydrolase mRNA, complete cds

205A

348C

502G

632A

898G

1274C

1631C

1742A

1800T

205A

348C

502G

632A

898G

1274C

1313G

1631A

1742A

1800T

205A

348C

502G

632A

898A

1274C

1313G

1631C

1742G

1800C

205A

348C

502G

632A

898G

1274C

1313A

1631A

1742A

1800T

205A

348C

502G

632A

898G

1274C

1313A

1631C

1742G

1800C

205G

348C

502G

898G

1274C

1313G

1631A

1742A

1800T

205A

348T

502G

632A

898G

1274C

1313G

1631A

1742A

1800T

205A

348C

502G

632A

898A

1274C

1313G

1631C

1742G

1800T

205G

348C

502G

632A

898G

1274C

1313G

1631C

1742G

1800C

205A

348C

502A

632A

898G

1274C

1313G

1631A

1742A

1800T

348C

502G

632A

898G

1274T

1313G

1631C

1742A

205A

348C

502G

632C

898G

1274C

1313G

1631C

1742G

1800C

205G

348C

502G

632A

898G

1274T

1313G

1631C

1742G

1800C

205A

348C

502G

632A

898G

1274C

1313G

1631C

1742G

205A

348C

502G

632A

898G

1313G

1631C

1742G

1800T

205G

348C

502G

632A

898G

1631A

1742G

1800C

205A

348C

502G

632A

898G

1274C

1313G

1742A

1800T

205A

348C

502G

632A

898G

1274T

1313G

1631C

1742A

1800T

205A

348C

502G

632A

898G

1274C

1313G

1631C

1742G

1800C

205A

348C

502G

632A

898G

1274C

1313A

1631A

1742G

1800C

205A

348C

502G

632A

898G

1313G

1631C

1742G

1800C

205G

348C

502G

632C

898G

1274C

1313G

1631A

1742A

1800T

205A

348C

502G

632C

898A

1274C

1313G

1631A

1742G

1800C

205

164A > G

K55R

348

307C > T

R103C

502

461G > A

C154Y

632

591A > C

Silent

898

857G > A

R286Q

1274

1233C > T

Silent

1313

1272G > A

Silent

1631

1590A > C

Silent

1742

1701A > G

3′

1800

1759T > C

3′

U05875

U05875

147569

GEN-18J

Human clone pSK1 interferon

gamma receptor accessory factor-1 (AF-1) mRNA, complete cds

520C

685C

821C

839A

1192A

1664C

520C

685C

821C

839A

1192A

1664T

520C

685C

821C

839A

1192A

1664C

520C

685T

821C

839A

1192A

1664C

520C

685C

821C

839G

1192A

1664C

520T

685C

821C

839A

1192A

1664C

520C

685C

1192G

1664C

520C

685C

821G

839A

1192A

1664T

520C

685C

821G

839A

1192G

1664C

520T

685C

821G

839A

1192G

1664C

520T

685C

821G

839A

1192A

1664C

520

(−129)C > T

5′

685

37C > T

L13F

821

173C > G

T58R

839

191G > A

R64Q

1192

544A > G

K182E

1664

1016C > T

3′

2047

1399C > G

3′

2087

1439T > C

3′

XDH

U06117

278300

GEN-194

Human xanthine dehydrogenase

(XDH) mRNA, complete cds

3951

3888C > G

Silent

TCRD

X06557

186810

GEN-19M

Human mRNA for TCR-delta chain

1032

1014C > A

3′

GSTP1

X06547

134660

GEN-19N

Human mRNA for class Pi

glutathione S-transferase (GST-Pi; E.C.2.5.1.18)

156

150C > T

Silent

319

313A > G

I105V

347

341C > T

A114V

561

555C > T

Silent

EHHADH

L07077

261515

GEN-1DF

Human enyol-CoA: hydratase 3-

hydroxyacyl-CoA dehydrogenase (EHHADH) mRNA, complete cds with repeats

1812G

2060A

1812G

2060C

1812A

2060A

2151T

2240G

2700T

2960G

1812G

2060A

2151C

2240G

2700T

2960G

3268A

1812A

2060A

2151T

2240A

2700G

2960A

3268A

1812G

2060A

2151C

2240G

2700G

2960G

3268A

1225

1218G > A

Silent

1812

1805G > A

R602Q

1823

1816C > A

P606T

2060

2053C > A

Q685K

2151

2144T > C

L715S

2240

2233G > A

3′

2700

2693T > G

3′

2960

2953G > A

3′

3268

3261A > G

3′

L07592

L07592

600409

GEN-1E7

Human peroxisome proliferator

activated receptor mRNA, complete cds

251T

271G

317G

826T

1821T

2359C

2960C

251C

271G

317G

826C

1228C

2359C

2926A

251C

271G

317G

826C

1228C

1821T

2359C

2926G

251C

271G

317G

826C

1228C

1821T

2359T

2926G

251C

271G

317T

826C

1228C

2359C

2926A

251T

271G

317G

826T

1228C

1821C

2359C

2926G

2960T

251T

271A

317G

826T

1228C

1821T

2926G

2960T

251T

271G

317G

826C

1821T

2359C

2926A

251T

271G

317G

826T

1228C

1821T

2359C

2926G

2960T

251T

271G

317G

826T

1228C

1821T

2359T

2926G

2960T

251T

271A

317G

826T

1228T

1821T

2359C

2926G

251T

271G

317G

826T

1821T

2359C

2926A

2960G

251C

271G

317G

826C

1228C

1821T

2359T

2926G

2960T

251C

271G

317G

826C

1228C

2359C

2926A

251C

271G

317G

826C

1821T

2359C

2926G

251C

271G

317G

826C

1228C

1821T

2359C

2926G

2960C

251C

271G

317G

826C

1228C

1821T

2359C

2926G

251C

271G

317T

826C

1228C

2359C

2926A

251C

271G

317G

826C

1228C

2359C

2926G

251C

271G

317G

1821T

2359C

2926A

251T

271A

317G

826T

1821T

2359C

2926A

251C

271G

317G

1228C

1821T

2359C

2926G

2960C

251C

271G

317G

826C

1228C

2359C

2926G

2960C

251T

271G

317G

826C

1821T

2359C

2926A

251C

271G

317G

826C

1228C

1821T

2359T

2926G

2960C

251T

271G

317G

1228C

1821T

2359C

2926A

251T

271A

317G

826T

1228C

1821T

2359C

2926G

2960T

251

(−87)C > T

5′

271

(−67)G > A

5′

317

(−21)G > T

5′

826

489C > T

Silent

1228

891C > T

Silent

1821

1484T > C

3′

2359

2022C > T

3′

2926

2589A > G

3′

2960

2623T > C

3′

3119

2782C > G

3′

TGFER3

L07594

600742

GEN-1EA

Human transforming growth

factor-beta type III receptor (TGF-beta) mRNA, complete cds

1548

1200G > A

Silent

2370

2022C > T

Silent

3966

3618G > C

3′

SOD2

X07834

147460

GEN-1ES

Human mRNA for manganese

superoxide dismutase (EC 1.15.1.1)

44

40C > G

P14A

51

47T > C

V16A

198

194C > A

T65N

249

245T > C

I82T

ALDH6

U07919

600463

GEN-1F5

Human aldehyde dehydrogenase 6

mRNA, complete cds

2453

2401A > G

3′

3396

3344C > T

3′

3397

3345G > A

3′

LCT

X07994

603202

GEN-1F6

Human mRNA for lactase-phiorizin

hydrolase LPH (EC 3.2.1.23-62)

5845

5834C > G

3′

AB003791

AB003791

603797

GEN-1F9

Homo sapiens mRNA for keratan

sulfate Gal-6-sulfotransferase, complete cds

1617

1251G > A

3′

1643

1277G > A

3′

U08015

U08015

600489

GEN-1FD

Human NF-ATc mRNA, complete cds

530

291C > T

Silent

1094

855G > A

Silent

2222

1983G > A

Silent

2225

1986A > G

Silent

2295

2056A > C

S686R

X08006

X08006

124030

GEN-1FE

Human mRNA for cytochrome P450

db1

100C

281A

336C

635A

100T

281A

336T

386C

635G

886C

1457C

100C

281A

336C

386G

635G

886C

1457G

100T

336C

386C

635G

886C

1457C

100C

281A

336C

386C

635G

886T

1457C

100C

281A

336C

386C

635A

886T

1457C

100T

281G

336C

386C

635G

886C

1457C

100

100C > T

P34S

281

281A > G

H94R

336

336C > T

Silent

386

386G > C

R129P

408

408G > C

Silent

454

454delT

Frame

635

635G > A

G212E

692

692T > C

L231P

696

696T > C

Silent

775

775delA

Frame

801

801C > A

Silent

836

836T > A

M279K

854

854A > G

N2855

886

886C > T

R296C

1108

1108G > A

V370I

1401

1401G > C

Silent

1457

1457G > C

S486T

U08021

U08021

600008

GEN-1FG

Human nicotinamide N-

methyltransferase (NNMT) mPNA, complete cds

584

467C > G

P156R

613

496C > T

Silent

CCKBR

L08112

118445

GEN-1FL

Cholecystokinin-B/gastrin

receptor

456

456G > A

Silent

FACL1

L09229

152425

GEN-1GI

Human long-chain acyl-coenzyme A

synthetase (FACL1) mRNA, complete cds

487C

1648A

487A

1648G

487C

1648G

487

414C > A

Silent

1648

1575G > A

Silent

3026

2953G > A

3′

3083

3010G > A

3′

AF009746

AF009746

603214

GEN-1HZ

Homo sapiens peroxisomal

membrane protein 69 (PMP69) mRNA, complete cds

961

910G > A

A304T

1895

1844A > G

3′

2134

2083T > G

3′

FABP2

M10050

134640

GEN-1IE

Human liver fatty acid binding

protein (FABP) mRNA, complete cds

322

280G > A

A94T

Y10387

Y10387

None

GEN-1IU

H. sapiens mRNA for PAPS

synthetase

55

19T > C

Silent

999

963C > T

Silent

1981

1945G > A

3′

Y10659

Y10659

300119

GEN-1J6

H. sapiens IL-13Ra mRNA

1408A

1508G

1408A

1508A

1408G

1508G

1116

1073G > A

G358D

1408

1365A > G

3′

1508

1465G > A

3′

1685

1642A > G

3′

1889

1846C > T

3′

U10868

U10868

600466

GEN-1JF

Human aldehyde dehydrogenase

ALDH7 mRNA, complete cds

2681

2634T > C

3′

L21005

L11005

602841

GEN-1JU

Human aldehyde oxidase (hAOX)

mRNA, complete cds

1721A

2331T

2341A

3534A

3865C

1721A

2331T

2341G

3534A

3865C

1721T

2331T

2341A

3195T

3534A

3865C

1721T

2331T

2341G

3195C

3250C

3534A

3865C

1721T

2331T

3195C

3250C

3865T

1721T

2331T

2341A

3195C

3250C

3534G

3865C

1721T

2331T

2341A

3195C

3250C

3534A

3865C

1721T

2331G

2341A

3534A

3865C

1721T

2331G

2341A

1721A

2331T

2341G

3534A

3865C

1721T

2331T

2341A

3195T

3250T

3534A

3865C

1721T

2331T

2341G

1721T

2331T

2341G

3195C

3250C

3534G

3865T

1721A

2331T

2341A

3534A

3865C

1721

1591T > A

S531T

2331

2201T > G

V734G

2341

2211A > G

Silent

3195

3065C > T

A1022V

3250

3120C > T

Silent

3534

3404A > G

N1135S

3865

3735C > T

Silent

4284

4154C > A

3′

4447

4317G > C

3′

4525

4395T > G

3′

4675

4545G > A

3′

ADH3

M12272

103730

GEN-1LU

Homo sapiens alcohol

dehydrogenase class I gamma subunit (ADH3) mRNA, complete cds

1128

1048A > G

I350V

TPMT

U12387

187680

GEN-1LY

Human thiopurine

methyltransferase (TPMT) mRNA, complete cds

536G

795A

1085C

536G

795A

1085T

536G

795G

1085T

536A

1085T

536A

795G

1085T

536G

795A

536

460G > A

A154T

795

719A > G

Y240C

1085

1009T > C

3′

1336

1260C > T

3′

1373

1297G > A

3′

X12387

X12387

124010

GEN-1LZ

Cytochrome P-450, CYP3A4

1751T

1847C

2525A

1751T

1847A

2525A

1751T

1847C

2525T

1751A

1847C

2525A

44

(−26)G > C

5′

628

559A > T

T187S

646

577A > G

I193V

676

607T > C

F203L

823

754T > G

S252A

1361

1292T > C

I431T

1751

1682T > A

3′

1847

1778C > A

3′

2189

2120G > A

3′

2525

2456A > T

3′

X22530

X12530

112210

GEN-1MH

Human mRNA for B lymphocyte

antigen CD20 (B1, Bp35)

309C

1318A

309T

1318G

309C

1318G

131

38C > T

P13L

309

216C > T

Silent

1318

1225G > A

3′

GC

M12654

139200

GEN-1MN

Human serum vitamin D-binding

protein (hDBP) mRNA, complete cds

925

897T > C

Silent

1324

1296G > T

E432D

1335

1307C > A

T436K

1362

1334G > A

R445H

D13138

D13138

179780

GEN-1NW

Human mRNA for dipeptidase

566

523T > G

S175A

CRYZ

L13278

123691

GEN-1NZ

Homo sapiens zeta

crystallin/quinone reductase mRNA, complete cds

64

54G > A

Silent

902

892G > A

V298M

1229

1219A > G

3′

L13286

L13286

600125

GEN-103

Human mitochondrial 1,25-

dihydroxyvitamin D3 24-hydroxylase mRNA, complete cds

2031

1638G > A

3′

MDCR

L13385

601545

GEN-106

Homo sapiens (clone 71) Miller-

Dieker lissencephaly protein (LIS1) mRNA, complete cds

1467

1250C > T

3′

1868

1651C > T

3′

1917

1700C > T

3′

2962

2745G > T

3′

4589

4372G > A

3′

X13561

X13561

147910

GEN-10H

Human mRNA for preprokallikrein

(EC 3.4.21)

592A

603T

732C

603C

732T

592G

603C

732C

592A

603C

732C

592G

603C

732T

54

18G > T

Silent

441

405T > C

Silent

469

433G > C

E145Q

592

556A > G

K186E

603

567C > T

Silent

732

696C > T

Silent

ORM1

M13692

138600

GEN-1P5

Human alpha-1 acid glycoprotein

mRNA, complete cds

128

113A > G

Q38R

222

207C > T

Silent

273

258A > C

Silent

296

281C > A

T94N

514

499C > T

R167C

535

520G > A

V174M

654

639G > T

3′

X13930

X13930

122720

GEN-1Q3

Human CYP2A4 mRNA for P-450 IIA4

protein

60

51A > G

Silent

253

246T > C

Silent

272

263G > A

R88K

1072

1063G > A

V355M

1146

1137G > A

Silent

1485

1476G > T

Silent

1675

1666A > T

3′

1677

1668C > G

3′

1697

1688C > A

3′

TBG

M14091

314200

GEN-1QO

Human thyroxine-binding globulin

mRNA, complete cds

901

571G > A

D191N

1239

909G > T

L303F

ALPL

X14174

171760

GEN-1QR

Human mRNA for liver-type

alkaline phosphatase (EC 3.1.3.1)

730C

1187T

1276G

730C

1187T

1276A

2040C

730T

1187T

1276A

2040T

730C

1187T

1276A

2040T

730C

1187C

1276A

2040C

730C

1187C

1276G

730C

1276G

730C

1187C

1276G

2040T

730T

1187C

1276G

730C

1187C

1276G

730

330C > T

Silent

1187

787C > T

H263Y

1276

876G > A

Silent

2040

1640C > T

3′

U14510

U14510

602698

GEN-1RD

Human transcription factor NFATx

mRNA, complete cds

2128

2104A > C

M702L

2516

2492T > G

L831W

2720

2696C > G

A899G

2792

2768C > T

A923V

2828

2804C > G

A935G

2903

2879C > G

A960G

2967

2943G > A

Silent

3333

3309G > A

3′

3577

3553G > A

3′

SLC1A4

L14595

600229

GEN-1RI

Human

alanine/serine/cysteine/threonine transporter (ASCT1) mRNA, complete cds

159C

292G

495C

1305G

1323T

1378G

1773G

1972A

159C

292G

495G

1305G

1323C

1378G

1773G

1972A

159C

292G

495C

1305G

1323C

1378G

1773A

1972A

159C

292G

495G

1305G

1323C

1378G

1773G

1972T

159C

292C

495G

1305G

1323C

1378G

1773G

1972A

159G

292G

495G

1305G

1323C

1972A

159C

292G

495G

1305G

1323C

1378G

1773A

1972A

159C

495G

1305C

1323C

1378G

1773G

1972A

159C

292G

495G

1305G

1323C

1378A

1773G

1972A

159C

292G

495C

1305G

1323C

1378G

1773G

1972A

159C

292G

495G

1305G

1323C

1378A

1773A

1972A

159C

292C

495G

1305G

1323C

1378A

1773G

1972A

159G

292G

495G

1305G

1323C

1378A

1773A

1972A

159C

292G

495C

1305G

1323C

1378A

1773A

1972A

159C

292C

495G

1305C

1323C

1378G

1773G

1972A

159

(−25)C > G

5′

292

109C > G

R37G

495

312G > C

Silent

1305

1122G > C

Silent

1323

1140C > T

Silent

1378

1195A > G

1399V

1773

1590G > A

Silent

1972

1789A > T

3′

X14583

X14583

147240

GEN-1RJ

Human mRNA for Ig lambda-chain

131

107A > G

K36R

132

108G > A

Silent

164

140A > C

N47T

255

231G > T

Silent

381

357A > G

Silent

400

376C > G

L126V

412

3880 > A

G130S

450

426G > A

Silent

522

498C > T

Silent

540

5160 > C

K172N

553

529C > A

P177T

594

570A > G

Silent

624

600T > C

Silent

639

615T > C

Silent

659

635G > A

R212K

738

714A > C

3′

740

716A > T

3′

752

728C > A

3′

858

834C > G

3′

U14650

U14650

602243

GEN-1Rl

Human platelet-endothelial

tetraspan antigen 3 mRNA, complete cds

638

579A > G

Silent

1048

989G > A

3′

1171

1112T > C

3′

1263

1204G > C

3′

1301

1242C > T

3′

1351

1292T > C

3′

1389

1330A > T

3′

1404

1345G > A

3′

M14758

M14758

171050

GEN-1S6

P glycoprotein 1

1623A

3101G

3859C

1623A

3101G

3859T

1623G

3101A

1623G

3101G

3859C

1623A

3101T

3859C

1623G

3101T

3859T

1623G

3101T

3859C

1623A

3101T

3859T

1623G

3101G

3859T

3101T

3859T

1623G

3101A

3859T

3101G

3859C

1623

1199G > A

S400N

3101

2677G > A

A893T

3101

2677G > T

A893S

3859

3435C > T

Silent

4460

4036A > G

3′

GUSB

M15182

253220

GEN-1TH

Endo-beta-D-glucuronidase

1766

1740T > C

Silent

1972

1946C > T

P649L

ADH4

M15943

103740

GEN-1UM

Human class II alchohol

dehydrogenase (ADH4) pi subunit mRNA, complete cds

826

765G > T

Silent

1389

1328T > C

3′

HADHB

D16481

143450

GEN-1Y5

Human mRNA for mitochondrial 3-

ketoacyl-CoA thiolase beta-subunit of trifunctional protein, complete cds

871

825T > C

Silent

1607

1561G > C

3′

1908

1862A > C

3′

1911

1865A > C

3′

U16660

U16660

600696

GEN-1YD

Human peroxisomal enoyl-CoA

hydratase-like protein (HPXEL) mRNA, complete cds

149A

402G

149C

402G

149C

402A

149A

402A

149

122A > C

E41A

402

375G > A

Silent

676

649G > A

G217R

802

775C > G

P259A

877

850G > A

D284N

1157

1130G > A

3′

ELA1

M16631

130120

GEN-1YI

Human elastase 2 mRNA, complete

cds

510

489G > A

Silent

693

672G > A

Silent

X16699

X16699

124075

GEN-1YJ

Human mRNA for cytochrome P-

450HP

1064

1064T > G

F355C

X17042

X17042

177040

GEN-1ZN

Human mRNA for hematopoetic

proteoglycan core protein

324

300C > T

Silent

1021

997G > T

3′

IGHM

X17115

147020

GEN-1ZX

Human mRNA for IgM heavy chain

complete sequence

849

777T > C

Silent

1102

1030A > G

S344G

1107

1035G > A

Silent

1175

1103T > G

V368G

1212

1140C > T

Silent

1561

1489C > G

R497G

1692

1620G > T

Q540H

1816

1744G > C

V582L

2006

1934T > A

3′

CYP21

M17252

201910

GEN-201

Human cytochrome P450c2l mRNA,

3′ end

745C

749G

869C

1061T

1066G

1083A

745C

869C

1061T

1066G

1083G

745C

749C

569C

1061T

1066G

1083A

745C

749C

869C

1061C

1066G

745T

749C

869C

1061C

1083A

749C

569T

1061T

1066G

1053A

745T

749C

869C

1061T

1066G

1083A

745T

749C

569T

1061T

1066G

1083A

745C

869C

1061T

1066G

1083G

745C

749C

869C

1061C

1066G

1053G

745T

749C

869C

1061C

1066G

1083G

745T

749C

569C

1061C

1066A

1083A

745T

749C

869C

1061C

1066G

1053A

224

224G > A

R75H

330

330C > T

Silent

745

745T > C

3′

749

749C > G

3′

869

869C > T

3′

1061

1061T > C

3′

1066

1066G > A

3′

1083

1083A > G

3′

D17793

D17793

603966

GEN-20Q

Human mRNA for KIAA01l9 gene,

complete cds

66

15G > C

Q5H

141

90G > A

Silent

363

312A > G

Silent

980

929G > C

S310T

HSST

U17970

600853

GEN-20V

Human heparan sulfate N-

deacetylase/N-sulfotransferase mRNA, complete cds

2294

2066G > C

G689A

1317986

1317986

300036

GEN-20X

Human GABA/noradrenaline

transporter mRNA, complete cds

1161

1132G > A

V378M

1670

1641C > T

Silent

2034

2005G > A

V669M

2088

2059C > T

R687C

2150

2121C > T

Silent

2231

2202A > G

3′

AHR

L19872

600253

GEN-22N

Human AH-receptor mRNA, complete

cds

4722

4347G > A

3′

U19977

U19977

600688

GEN-22Q

Human preprocarboxypeptidase A2

(proCPA2) mRNA, complete cds

631

627C > T

Silent

AF019386

AF019386

603244

GEN-231

Homo sapiens heparan sulfate 3-

O-sulfotransferase-1 precursor (30ST1) mRNA, complete cds

79

(−40)C > G

5′

U20157

U20157

601690

GEN-234

Human platelet-activating factor

acetylhydrolase mRNA, complete cds

1297

1136T > C

V379A

M20681

M20681

138170

GEN-23O

Human glucose transporter-like

protein-III (GLUT3), complete cds

1550

1308C > T

Silent

3179

2937T > C

3′

3238

2996C > T

3′

3356

3114T > C

3′

3378

3136T > C

3′

3524

3282C > A

3′

3572

3330G > T

3′

SLC2A4

M20747

138190

GEN-23Q

Human insulin-responsive glucose

transporter (GLUT4) mRNA, complete cds

535C

1182G

1218T

535C

1182G

1218C

535T

1182G

1218C

535C

1182A

1218C

535

390C > T

Silent

1182

1037G > A

R346Q

1218

1073C > T

A358V

SOAT

L21934

102642

GEN-25C

Human acyl coenzyme

A:cholesterol acyltransferase mRNA, complete cds

92T

93T

121T

379A

490C

814C

2365C

2821C

2973A

3083G

92T

93T

121T

379G

490C

676T

814C

1993T

2170C

2365C

2821C

2973A

3083G

92T

93C

121T

379G

490C

676T

814C

1993C

2365C

2821C

92T

121C

379G

490C

676G

814C

1993C

2170C

2365C

2821C

2973A

3083G

92T

93T

121T

379G

490C

676T

814C

1993C

2170C

2821G

3083G

92T

93T

121T

379G

490C

676G

814C

1993C

2170C

2365C

2821C

2973A

3083G

92T

93T

121T

379G

490C

676T

814C

1993C

2170C

2365C

2821C

2973A

3083G

92T

93T

121T

490G

676G

814C

1993C

2170C

2365C

2821C

2973A

3083G

92T

93T

121T

379G

490C

676T

814C

1993C

2170T

2365C

2821C

2973A

3083G

92C

93T

121T

379G

490C

676G

814C

1993C

2170C

2821C

2973A

3083G

92T 121C

379G

490G

676G

814C

1993T

2170C

2365C

2821C

2973A

3083G

92C

93T

121T

379G

490C

814C

1993T

2170C

2365C

2821C

2973A

3083G

92T

93T

121T

379G

676G

1993C

2821G

2973A

3083G

92T

93T

121T

379G

490C

814C

1993C

2365C

2821C

2973A

3083T

92T

93T

121T

379G

490C

676T

1993T

2170C

2365T

2821C

2973A

3083G

92T

93T

121T

379G

490C

676T

814C

1993C

2365T

2821C

92T

93T

121T

379G

676T

1993C

2821G

2973A

3083G

92T

93C

121C

379G

490C

676G

814C

1993T

2170C

2365C

2821C

2973A

3083G

92T

93T

121T

379A

490G

676G

814C

1993C

2170C

2365C

2821C

2973A

3083G

490C

676G

814C

1993C

2365C

2821C

92T

93T

121T

379G

490C

676G

814C

1993T

2170C

2365C

2821C

2973A

3083G

490C

676G

814C

1993C

2170C

2365C

2821C

2973A

3083G

92C

93T

121T

379G

490C

676G

814C

1993T

2170C

2365C

2821C

2973A

3083G

92T

93T

121T

379G

490C

676G

814C

1993C

2170C

2365T

2821C

2973G

3083G

92T

93T

121T

379G

676T

1993C

2170C

2365C

2821C

2973A

3083G

92T

93T

121T

379A

490C

676G

814C

1993C

2170T

2365C

2821C

2973A

3083G

92T

93T

121T

379G

490C

676T

814C

1993C

2170T

2365C

2821C

2973A

3083T

92T

93C

121T

379G

490C

676T

814C

1993C

2170T

2365T

2821G

2973A

3083G

92T

93T

121T

379G

490C

676T

814T

1993T

2170C

2365T

2821C

2973A

3083G

92T

93T

121T

379G

490C

676T

814C

1993C

2170C

2365T

2821G

2973G

3083G

92T

93T

121T

379G

490C

676T

814C

1993C

2170T

2365T

2821C

92T

93T

121T

379G

490G

676G

814C

1993C

2170C

2365C

2821C

2973A

3083G

92T

93T

121T

379G

490G

676G

814C

1993C

2365T

2821G

2973G

3083G

92T

93C

121T

379G

490C

676T

814C

1993C

2170T

2365C

2821C

92C

93C

121T

379G

490C

676T

814C

1993C

2365T

2821C

2973A

3083G

676G

814C

1993C

2365T

2821G

92T

93T

121T

379G

490C

676G

814C

1993C

2170C

2365T

2821C

2973A

3083G

92

(−1305)T > C

5′

93

(−1304)T > C

5′

121

(−1276)T > C

5′

379

(−1018)A > A

5′

490

(−907)G > G

5′

676

(−721)G > G

5′

814

(−583)T > T

5′

1993

597C > T

Silent

2170

774C > T

Silent

2365

969C > T

Silent

2821

1425G > C

Silent

2973

1577G > A

R526Q

3083

1687G > T

3′

3537

2141T > C

3′

M22324

M22324

151530

GEN-25R

Human aminopeptidase N/CD13 mRNA

encoding aminopeptidase N, complete cds

1052

932C > T

A311V

2168

2048C > G

T683S

2375

2255G > A

S752N

2505

2385C > T

Silent

3053

2933G > C

3′

3299

3179A > G

3′

3405

3285C > T

3′

PLA2G2A

M22430

172411

GEN-25V

Human RASF-A PLA2 mRNA, complete

cds

116

(−20)G > T

5′

267

132C > T

Silent

AF026947

AF026947

603418

GEN-261

Homo sapiens aflatoxin aldehyde

reductase AFAR mRNA, complete cds

1013

936T > C

Silent

1078

1001A > G

3′

PRSS1

M22612

276000

GEN-26A

Human pancreatic trypsin 1

(TRY1) mRNA, complete cds

34

28G > T

V10L

61

55G > A

D19N

97

91G > A

E31K

198

192C > T

Silent

412

406G > T

G136C

492

486T > C

Silent

711

705C > T

Silent

744

738T > C

Silent

TAP2

Z22935

170261

GEN-26P

H. sapiens TAP2B mRNA, complete

CDS

1186G

1336T

1186T

1336C

1186G

1336C

1163

1135G > A

V379I

1186

1158G > T

Silent

1336

1308T > C

Silent

1840

1812G > A

Silent

2021

1993G > A

A665T

2087

2059C > T

Frame

2119

2091T > G

Silent

HRH1

AF026261

600167

GEN-26W

Histamine receptor H1

1068

1068A > G

Silent

HLA-DMB

Z23139

142856

GEN-277

H. sapiens RING7 mLRNA for HLA

class II alpha chain-like product

380

212G > A

S71N

1125

957C > T

3′

CYP51

U23942

601637

GEN-27K

Human lanosterol 14-demethylase

cytochrome P450 (CYP51) mRNA, complete cds

766

644G > A

C215Y

894

772C > T

R258C

912

790C > T

R264W

1476

1354C > T

R452C

1616

1494G > A

Silent

1836

1714C > A

3′

2283

2161G > T

3′

2445

2323T > C

3′

2507

2385G > A

3′

2556

2434T > A

3′

2665

2543G > A

3′

AF027302

AF027302

603429

GEN-27T

Homo sapiens TNF-alpha

stimulated ABC protein (ABC50) mPNA, complete cds

3075

2981T > C

3′

SLC6A3

L24178

126455

GEN-283

Homo sapiens dopamine

transporter mRNA, complete cds

169T

181C

244C

169G

181T

244C

169G

181C

244C

169T

181C

244T

169

150G > T

Silent

181

162C > T

Silent

244

225C > T

Silent

1917

1898C > T

3′

ADH2

M24317

103720

GEN-28A

Human class I alcohol

dehydrogenase (ADH2) beta-1 subunit mRNA, complete cds

817

787G > A

V263M

SLC5A1

M24847

182380

GEN-28S

Human Na+/glucose cotransporter

1 mRNA, complete cds

2226

2216C > T

3′

U25147

U25147

190315

GEN-294

Human citrate transporter

protein mRNA, nuclear gene encoding mitochondrial protein, complete cds

353

279T > C

Silent

L25259

L25259

601020

GEN-298

Human CTLA4 counter-receptor

(B7-2) mRNA, complete cds

1034

928G > A

A310T

EPHX1

L25878

132810

GEN-29Z

Homo sapiens p33/HEH epoxide

hydrolase (EPHX) mRNA, complete cds

460

337T > C

Y113H

480

357A > G

Silent

539

416A > G

H139R

1194

1071C > T

Silent

ATM

U26455

208900

GEN-2AT

Human phosphatidylinositol 3-

kinase homolog (ATM) mRNA, complete cds

1772G

2409C

2450G

2652G

5430A

5622C

793T

1772G

2409T

2450G

2652G

3210T

5430A

5622C

793C

1772G

2409T

2450G

2652G

3210T

5430A

5622C

793C

1772A

2409T

2450G

2652G

3210T

5430A

5622C

793C

1772G

2409T

2450A

3210T

793T

1772G

2409C

2450G

2652G

3210C

5430A

5622C

793C

1772G

2409T

2450A

2652C

3210T

5430G

5622T

793

534C > T

Silent

1772

1513G > A

DS0SN

2409

2150T > C

I717T

2450

2191G > A

V731I

2652

2393G > C

8798T

3210

2951T > C

L984P

5222

4963G > A

D1655N

5308

5049G > A

Silent

5430

5171A > G

3′

5622

5363C > T

3′

5626

5367T > G

3′

D26579

D26579

602267

GEN-2B1

Human mRNA for transmembrane

protein, complete cds

709

700G > A

D234N

909

900T > C

Silent

999

990C > T

Silent

1104

1095A > G

Silent

Z26649

Z26649

600230

GEN-2B5

Phospholipase C beta-3

437C

466G

952G

1342A

1578G

1624C

1794G

3168G

3466G

3673G

3718G

437C

466G

952G

1342A

1578A

1624C

1794G

3168G

3466G

3673G

3718G

437C

466G

952G

1578G

1624C

1794A

3168G

3466G

3673G

3718G

437C

466G

952A

1342A

1578G

1624T

1794G

3168G

3466G

3673G

3718G

437C

466G

952G

1342G

1578G

1624C

1794G

3168G

3466G

3673A

437T

466G

952G

1342G

3168G

3466G

3673G

3718A

437C

466G

952A

1342G

1578G

1794G

3168G

3466G

3718G

437C

466G

952G

1342G

1578G

1624C

1794G

3168T

3466G

3673G

437C

466A

952G

1342G

1578G

1624C

1794G

3168G

3466G

3673G

3718G

437C

466G

952G

1342G

1578G

1624C

1794G

3168G

3466G

3673G

3718A

437C

466G

952G

1342G

1578G

1624C

1794G

3168G

3466A

3718G

437C

466G

952G

1342G

1578G

1624C

1794G

3168G

3466G

3673G

3718G

437C

466G

952G

1342G

1578G

1624C

1794G

3168G

3466G

3673A

3718A

437C

466G

952G

1342G

3168G

3466G

3673G

3718A

437C

466G

952G

1342G

1578G

1624C

1794G

3168G

3466G

3673A

3718G

437C

466G

952A

1342G

1578G

1624T

1794G

3168G

3466G

3673A

3718G

437C

466G

952A

1342A

1578G

1624T

1794G

3168G

3466G

3673A

3718G

437C

466G

952G

1342A

1578G

1624C

1794A

3168G

3466G

3673G

3718G

437T

466G

952G

1342G

3168G

3466G

3673G

3718A

437C

466G

952G

1342A

1578G

1624C

1794G

3168G

3466G

3673A

3718G

466G

952A

1342A

1578G

1624T

1794G

3168G

3673G

3718G

466G

952G

1342G

1578G

1624C

1794G

3168G

3673G

3718G

437C

466G

952G

1342G

1578G

1624C

1794G

3168T

3466G

3673G

3718A

437

437C > T

3′

466

466G > A

3′

952

952G > A

3′

1342

1342G > A

3′

1578

1578G > A

3′

1624

1624C > T

3′

1794

1794G > A

3′

2664

2664C > T

3′

3168

3168G > T

3′

3466

3466G > A

3′

3673

3673G > A

3′

3718

3718G > A

3′

PRSS2

M27602

601564

GEN-2C7

Human pancreatic trypsinogen

(TRY2) mRNA, complete cds

29

23C > T

T8I

34

28G > T

V10F

61

55G > A

D19N

97

91G > A

E31K

198

192C > T

Silent

276

270G > A

Silent

U27699

U27699

603080

GEN-2C9

Human pephBGT-1 betaine-GABA

transporter mRNA, complete cds

1033C

2498G

2643G

2655C

2681G

2775A

2947C

3120T

3266A

1033C

2498G

2643G

2655C

2681G

2775G

2947T

3120C

3266G

1033T

2498G

2643G

2655C

2681G

2775G

2947T

3120C

3266A

1033T

2498A

2655C

2681G

2775A

2947C

3120T

3266A

1033C

2498A

2655C

2681G

2775A

2947C

3120T

3266A

1033T

2498G

2643G

2655C

2681G

2775A

2947C

3120T

3266A

1033C

2498G

2643G

2655C

2681G

2947T

3120T

3266A

2498G

2643G

2655T

2681G

2947C

3266A

1033T

2498G

2643G

2655C

2681G

2775G

2947C

3266A

2498G

2643G

2655C

2681A

2775A

2947C

3120T

3266A

1033T

2498G

2643G

2655C

2681G

2775G

2947T

3120T

3266A

1033C

2498G

2643G

2655C

2681G

2775G

2947C

3266A

1033T

2498G

2643G

2655C

2681G

2775A

3120C

3266A

1033C

2498G

2643G

2655C

2681G

2775G

2947T

3120C

3266A

1033T

2498G

2643G

2655C

2681G

2775G

2947C

3120T

3266A

1033C

2643G

2655C

2681G

2775A

2947C

3120T

3266A

1033C

2498A

2643A

2655C

2681G

2775A

2947C

3120T

3266A

1033C

2498G

2643G

2655C

2681G

2775G

2947T

3120T

3266A

1033C

2498G

2643G

2655C

2681G

2775G

2947C

3120C

3266A

1033C

2498G

2643G

2655C

2681G

2775G

2947C

3120T

3266A

1033C

2498A

2643G

2655C

2681G

2775G

2947C

3120T

3266A

1033C

2498G

2947T

3120C

3266A

1033T

2498G

2643G

2655C

2681A

2775A

2947C

3120T

3266A

1033T

2498A

2643A

2655C

2681G

2775A

2947C

3120T

3266A

1033T

2498G

2643G

2655C

2681G

2775A

2947T

3120C

3266A

1033T

2643G

2655C

2681G

2775A

2947C

3120T

3266A

1033T

2498G

2643G

2655T

2681G

2775G

2947C

3120C

3266A

1033

447T > C

Silent

2498

1912G > A

3′

2643

2057G > A

3′

2655

2069C > T

3′

2681

2095G > A

3′

2775

2189G > A

3′

2841

2255C > T

3′

2947

2361T > C

3′

3120

2534C > T

3′

3266

2680A > G

3′

CFTR

M28668

602421

GEN-2DF

Human cystic fibrosis mRNA,

encoding a presumed transmembrane conductance regulator (CFTR)

2729

2597G > A

C866Y

5826

5694T > C

3′

ADHS

M29872

103710

GEN-2EU

Human alcohol dehydrogenase

class III (ADH5) mRNA, complete cds

1029

1025G > A

S342N

1375

1371T > C

3′

AF028738

AF028738

602631

GEN-2F6

Homo sapiens imprinted multi-

membrane spanning polyspecific transporter-related protein (IMPT1) mRNA,

complete cds

34

(−209)A > C

5′

210

(−33)G > A

5′

229

(−14)A > G

5′

375

133T > G

F45V

875

633A > C

E211D

881

639A > G

Silent

883

641G > C

G214A

919

677A > G

K226R

927

685T > C

Silent

935

693A > G

Silent

1004

762A > G

Silent

1017

775A > C

K259Q

1106

864A > G

Silent

1119

877G > C

G293R

1124

882A > C

Silent

1166

924G > C

W308C

AF034374

AF034374

None

GEN-2GC

Homo sapiens molybdenum cofactor

biosynthesis protein A and molybdenum cofactor biosynthesis protein C mRNA,

complete cds

2628

1435C > A

3′

2677

1484C > G

3′

2742

1549A > T

3′

L32179

L32179

600338

GEN-2IW

Human arylacetamide deacetylase

mRNA, complete cds

1366

1281G > A

3′

NRAMP1

L32185

600266

GEN-2IY

Homo sapiens integral membrane

protein (NRAMP1) mRNA, complete cds

1399

1323C > T

Silent

ARSB

M32373

253200

GEN-2J0

Human arylsulfatase B (ASB)

mRNA, complete cds

1631

1072G > A

V358M

U32989

U32989

191070

GEN-2JH

Human tryptophan oxygenase (TDO)

mRNA, complete cds

991

927G > A

Silent

M33195

M33195

147139

GEN-2JR

Human Fc-epsilon-receptor gamma-

chain mRNA, complete cds

446

421T > G

3′

489

464T > C

3′

HLA-DQB1

M33907

142857

GEN-2KB

Human MHC class II HLA-DQB1

mRNA, complete cds

561

516T > C

Silent

641

596G > A

R199H

648

603C > T

Silent

695

650T > C

I217T

771

726G > C

Silent

780

735C > T

Silent

AF037335

AF037335

603263

GEN-2KJ

Homo sapiens carbonic anhydrase

precursor (CA 12) mRNA, complete cds

1551

1436G > T

3′

2442

2327C > T

3′

GSS

U34683

601002

GEN-2LF

Human glutathione synthetase

mRNA, complete cds

1467G

1482T

1467A

1482C

1467G

1482C

364

324G > A

Silent

1467

1427G > A

3′

1482

1442C > T

3′

U35735

U35735

111000

GEN-2MN

Human RACH1 (RACH1) mRNA,

complete cds

1006

838A > G

N280D

2619

2451T > C

3′

2706

2538T > C

3′

LIG1

M36067

126391

GEN-2MS

Human DNA ligase I mRNA,

complete cds

2526

2406T > C

Silent

M36712

M36712

186730

GEN-2NC

Human T lymphocyte surface

glycoprotein (CD8-beta) mPNA, complete cds

986G

1004A

1047A

986G

1004G

1047G

986G

1004A

1047G

986A

1004G

1047G

986

941G > A

3′

1004

959A > G

3′

1046

1001C > A

3′

1047

1002G > A

3′

1281

1236T > C

3′

1326

1281C > A

3′

U37143

U37143

601258

GEN-2NS

Human cytochrome P450

monooxygenase CYP2J2 mRNA, complete cds

338

333G > C

Silent

1545

1540C > T

3′

NRAMP2

137347

600523

GEN-2O6

Human integral membrane protein

(Nramp2) mRNA, partial

1092

1083C > T

Silent

GSTT2

L38503

600437

GEN-2PC

Homo sapiens glutathione S-

transferase theta 2 (GSTT2) mRNA, complete cds

203

203C > T

S68L

543

543C > T

Silent

ALCAM

L38608

601662

GEN-2PJ

Homo sapiens CD6 ligand (ALCAM)

mRNA, complete cds

1041C

1344T

1401A

1041C

1344C

1401A

1041C

1344T

1401G

1041T

1344T

1401A

1041T

1344T

1401G

1041

978C > T

Silent

1344

1281T > C

Silent

1401

1338G > A

Silent

L38928

L38928

604197

GEN-2PT

Homo sapiens 5,10-

methenyltetrahydrofolate synthetase mRNA, complete cds

617

604A > G

T202A

AF038007

AF038007

602397

GEN-2QG

Homo sapiens P-type ATPase FIC1

mRNA, partial cds

152C

829C

2873G

3495C

152A

829C

2873A

3495C

152A

3495T

152A

829C

2873G

3495C

152A

829A

2873G

3495C

152A

2873G

152

152A > C

N51T

829

829C > A

Silent

2873

2873G > A

R958Q

3495

3495C > T

Silent

AF038175

AF038175

603076

GEN-2QM

Homo sapiens clone 23819 white

protein homolog mRNA, partial cds

1100

1100G > A

3′

U40347

U40347

600950

GEN-2RK

Human serotonin N-

acetyltransferase mRNA, complete cds

382

148G > A

E50K

IDS

L40586

309900

GEN-2SB

Homo sapiens iduronate-2-

sulphatase (IDS) mRNA, complete cds

565

438C > T

Silent

L40992

L40992

600211

GEN-2SO

Homo sapiens (clone PEBP2aA1)

core-binding factor, runt domain, alpha subunit 1 (CBFA1) mRNA, 3′ end of cds

265

265G > A

V89I

SCYA11

U46573

601156

GEN-2WZ

Human eotaxin precursor mRNA,

complete cds

120

67G > A

A23T

554

501T > C

3′

ALDH10

L47162

270200

GEN-2XI

Human fatty aldehyde

dehydrogenase (FALDH) mRNA, complete cds

1609

1446A > T

Silent

Z47553

Z47553

603957

GEN-2XN

H. sapiens mRNA for flavin

containing monooxygenase 5 (FMO5)

1092

1011A > G

Silent

L48513

L48513

602447

GEN-2YD

Homo sapiens paraoxonase 2

(PON2) mRNA, complete cds

460

443C > G

A148G

598

581G > A

G194H

949

932G > C

C311S

D49737

D49737

602413

GEN-2Z7

Homo sapiens mRNA for cytochrome

b large subunit of complex II, complete cds

908

784G > A

3′

U50040

U50040

601582

GEN-2ZR

Human signaling inositol

polyphosphate 5 phosphatase SIP-110 mRNA, complete cds

196

180A > G

Silent

418

402C > G

Silent

2613

2597C > A

P866H

2638

2622G > A

Silent

2882

2866C > T

H956Y

3193

3177C > T

3′

3222

3206C > T

3′

3863

3847G > A

3′

U51478

U51478

601867

GEN-31Z

Human sodium/potassium-

transporting ATPase beta-3 subunit mRNA, complete cds

1099

1071G > C

3′

1121

1093T > C

3′

1133

1105G > T

3′

AF055025

AF055025

300095

GEN-32U

Homo sapiens clone 24776 mRNA

sequence

784

784A > G

3′

2021

2021A > T

3′

X52079

X52079

602272

GEN-33B

H. sapiens transcription factor

(ITF-2) mRNA, 3′ end

979

979T > G

S327A

1794

1794G > A

Silent

CTH

S52028

219500

GEN-33F

cystathionine gamma-lyase {clone

HCL-1} [human, liver, mRNA, 1194 nt]

1109

1076T > G

I359S

X52125

X52125

189990

GEN-33J

Human alternatively spliced c-

myb mRNA (clone = pMbm − 1)

1727C

2096G

2451G

1727T

2096G

2451G

1727T

2096A

2451G

1727T

2096G

2451C

1727

1530T > C

Silent

2096

1899G > A

Silent

2380

2183{circumflex over ( )}2184insA

3′

2451

2254G > C

3′

PDHA1

X52709

312170

GEN-33Y

Human mRNA for brain pyruvate

dehydrogenase (EC 1.2.4.1)

849

795A > G

Silent

1337

1283C > T

3′

1416

1362G > A

3′

CD22

X52785

107266

GEN-33Z

H. sapiens CD22 mRNA

1357

1323C > T

Silent

1531

1497C > T

Silent

U53347

U53347

109190

GEN-34A

Human neutral amino acid

transporter B mRNA, complete cds

272C

281T

337T

350C

895G

1447T

1777C

1789C

1976A

2074C

2153G

2527A

272C

337T

350C

895T

1447C

1777C

1789C

1976A

2074C

2153G

2527G

272C

281T

337T

350C

895G

1447C

1777C

1789C

1976A

2074C

2153C

2527G

272C

281T

337T

350C

895G

1447C

1777C

1789C

1976A

2074T

2153G

2527G

272C

281C

337T

350C

895G

1777C

1789C

1976A

2074C

2153G

2527G

272C

281T

337T

350C

895G

1447C

1777C

1789C

1976A

2074C

2153G

2527G

350T

895G

1447T

1777C

1789C

1976A

2074C

2153G

2527G

272T

895G

1777C

1789C

1976A

2074T

2153G

2527G

272C

337T

350C

895G

1777C

1789T

1976A

2074C

2527G

895G

1777T

1789C

1976K

2074C

2153G

2527G

272C

281T

337T

350C

895G

1447T

1777C

1789C

1976C

2074C

2153G

2527G

272C

281T

337T

350C

895G

1447T

1777C

1789C

1976A

2074C

2153G

2527G

272C

281T

337T

895G

1447C

1777C

1789C

1976A

2074C

2153G

2527A

337C

895G

1447C

1777C

1789C

1976A

2074C

2153G

2527G

272C

281T

337T

350C

895G

1447C

1777C

1789C

1976A

2074C

2153C

2527A

272C

281C

337T

350C

895T

1447C

1777C

1789C

1976A

2074C

2153G

2527G

272T

337C

350T

895G

1447C

1777C

1789C

1976A

2074C

2153G

2527G

272C

281C

337T

350C

895G

1447C

1777C

1789C

1976A

2074C

2153G

2527G

272C

281T

337T

895G

1447C

1777C

1789C

1976A

2074C

2153G

2527A

272T

281C

337C

350T

895T

1447C

1777C

1789C

1976A

2074C

2153G

2527G

350C

895G

1447C

1777C

1789C

1976A

2074C

2153G

2527G

272C

281T

337T

350C

895G

1447C

1777C

1789C

1976C

2074C

2153C

2527G

272T

281C

337C

350C

895G

1447C

1777C

1789C

1976A

2074C

2153G

2527G

272T

281C

337C

350T

895G

1447T

1777C

1789C

1976A

2074T

2153G

2527G

272T

281C

337C

350T

895G

1447C

1777C

1789C

1976A

2074C

2153G

2527G

272T

281C

337C

350T

895G

1447C

1777T

1789C

1976A

2074C

2153G

2527G

272T

281C

337C

350T

895G

1447T

1777C

1789C

1976A

2074C

2153G

2527G

272T

281C

337C

350T

895G

1447C

1777C

1789C

1976A

2074T

2153G

2527G

272C

281T

337T

895G

1447T

1777C

1789C

1976A

2074C

2153G

2527G

272C

337T

350C

895G

1447C

1777C

1789T

1976A

2074C

2153C

2527G

272

(−348)C > T

5′

281

(−339)T > C

5′

337

(−283)T > C

5′

350

(−270)C > T

5′

895

276G > T

Silent

1447

828C > T

Silent

1777

1158C > T

Silent

1789

1170C > T

Silent

1976

1357A > C

I453L

2074

1455T > C

Silent

2153

1534G > C

V512L

2527

1908G > A

3′

2868

2249A > T

3′

TPP2

M55169

190470

GEN-35U

Homo sapiens tripeptidyl

peptidase II mRNA, 3′ end

2681

2681T > G

F894C

3637

3637G > K

3′

U55206

U55206

601509

GEN-35Z

Homo sapiens human gamma-

glutamyl hydrolase (hGH) mRNA, complete cds

75T

150G

511C

703K

1161G

75T

150G

511C

703G

1161A

75T

150G

511T

703A

1161A

75C

703A

1161G

75C

511C

703A

1161A

75T

150G

511C

703A

1161A

75C

150G

511C

703A

1161A

75C

150A

511C

1161A

75T

150G

511T

703A

1161G

75C

150G

511T

703A

1161A

75C

150A

511C

703A

1161A

75C

150G

511T

703A

1161G

75T

511C

703A

75

16T > C

C6R

150

91G > A

A31T

511

452C > T

T151I

703

644A > G

N21SS

1161

1102A > G

3′

X56199

X56199

603881

GEN-36T

Human XIST, coding sequence ‘a’

mRNA (locus DXS399E)

1338

1338T > G

3′

X56549

X56549

134651

GEN-370

Human mRNA for muscle fatty-

acid-binding protein (FABP)

203A

342C

203A

342T

203G

342C

203

158A > G

K53R

342

297C > T

Silent

YWHAB

X57346

601289

GEN-37R

H. sapiens mRNA for HS1 protein

432

60C > A

Silent

1135

763T > C

3′

X57348

X57348

601290

GEN-37S

H. sapiens mRNA (clone 9112)

786

621C > T

Silent

1317

1152C > T

3′

1342

1177C > T

3′

X57522

X57522

170260

GEN-37W

H. sapiens RING4 cDNA

1319C

1465T

2193G

2534G

2598C

2650C

1465G

2193G

2534G

2598T

2650C

1319C

1465G

2193G

2534G

2598C

2650C

1319C

1465G

2193G

2534T

2598C

2650C

1319T

1465G

2534G

2598C

2650C

1319C

1465G

2193G

2534G

2598C

2650G

1465G

2534T

2598C

2650C

1319T

1465G

2193A

2534G

2598C

2650C

1465G

2193G

2534G

2598T

2650C

1319C

1465G

2534T

2598T

2650C

1207

1177A > G

I393V

1319

1289C > T

A430V

1465

1435G > T

G479C

2120

2090A > G

D697G

2193

2163G > A

Silent

2534

2504G > T

3′

2598

2568C > T

3′

2650

2620G > G

3′

X57819

X57819

147220

GEN-389

Human rearranged immunoglobulin

lambda light chain mRNA

499

499T > C

C167R

524

524G > A

Frame

545

545G > A

S182N

558

558A > C

Q186H

571

571G > A

E19lK

616

616C > T

Frame

639

639G > A

Silent

695

695A > G

Y232C

714

714C > T

3′

724

724C > T

3′

M57899

M57899

191740

GEN-38A

Human bilirubin UDP-

glucuronosyltransferase isozyme 1 mRNA, complete cds

226G

1213A

1443C

1444G

1828C

1956C

2057C

226G

1213A

1443C

1444G

1828C

1956C

2057G

226A

1213C

1443C

1444G

1828C

1956C

2057C

226G

1213A

1443T

1444G

1828T

1956C

2057G

226G

1213A

1443C

1444G

1828T

1956C

2057G

226G

1213A

1443C

1444A

1828C

1956C

2057C

1213A

1443C

1444G

1828T

2057C

226G

1213A

1443C

1444G

1828T

1956G

2057G

226A

1213A

1443C

1444G

1828C

1956C

2057C

226A

1213A

1443C

1444G

1828T

1956G

2057G

226A

1213A

1443C

1444G

1828T

1956C

2057C

226

211G > A

G71R

1213

1198A > C

N400H

1443

1428C > T

Silent

1444

1429G > A

A477T

1828

1813C > T

3′

1956

1941C > G

3′

2057

2042C > G

3′

GPX3

X58295

138321

GEN-38S

Human GPx-3 mRNA for plasma

glutathione peroxidase

821

773C > T

3′

979

931G > A

3′

1187

1139T > G

3′

1354

1306C > T

3′

1443

1395C > T

3′

1516

1468C > A

3′

1581

1533C > T

3′

PXMP1

X58528

170995

GEN-392

Human PMP7O mRNA for a

peroxisomal membrane protein

2375

2351C > T

3′

M58664

M58664

103000

GEN-395

Homo sapiens CD24 signal

transducer mRNA, complete cds

226

170C > T

A57V

570

514A > T

3′

1109

1053A > G

3′

1334

1278C > G

3′

1345

1289T > C

3′

1374

1318C > T

3′

1403

1347C > T

3′

1408

1352T > G

3′

1415

1359C > A

3′

1677

1621A > G

3′

BTK

X58957

300300

GEN-39A

H. sapiens atk mRNA for

agammaglobulinaemia tyrosine kinase

2228

2096A > C

3′

2304

2172A > G

3′

U59185

U59185

603878

GEN-39I

Human putative monocarboxylate

transporter (MCT) mRNA, complete cds

863G

972A

863A

972C

863A

972A

863

681A > G

Silent

972

790A > C

N264H

2351

2169A > G

3′

X60069

X60069

231950

GEN-3AJ

Human mRNA for pancreatic gamma-

glutamyltransferase

1060G

1173C

1310G

1399T

1598G

1641G

2148G

1060G

1173T

1310A

1399C

1598G

1641G

2148G

1060G

1173C

1310G

1399C

1598G

1641G

2148A

1060G

1173T

1310G

1399C

1598G

1641G

2148G

1060G

1173C

1310G

1399C

1598G

1641G

2148G

1060G

1173C

1310G

1399C

1598A

1641G

2148G

1060A

1173C

1310G

1399C

1598G

1641G

2148G

1060G

1173C

1310A

1399C

1598G

1641G

2148G

1060G

1173T

1399C

1641G

2148A

1060G

1173C

1310A

1399C

1598G

1641G

2148A

1060G

1173T

1399C

1598G

1641A

2148G

1060G

1173T

1310A

1399C

1641G

2148A

1060G

1173T

1310A

1399C

1598G

1641G

1173C

1310G

1399C

1598G

1641G

2148G

1060G

1173T

1310A

1399C

1598G

1641A

2148G

1060G

1173T

1310A

1399C

1598G

1641A

102

(−257)G > A

5′

336

(−23)C > T

5′

1060

702G > A

Silent

1173

815C > T

A272V

1310

952G > A

E318K

1399

1041C > T

Silent

1409

1051G > T

A3S1S

1482

1124C > T

T375M

1591

1233G > A

Silent

1598

1240G > A

G414R

1624

1266C > T

Silent

1637

1279C > A

P427T

1641

1283G > A

S428N

1651

1293C > T

Silent

1662

1304T > C

V435A

1783

1425A > G

Silent

1794

1436C > T

T479M

1795

1437G > A

Silent

1981

1623C > T

Silent

2007

1649C > T

T550M

2031

1673C > T

5558L

2047

1689C > T

Silent

2147

1789C > T

3′

2148

1790G > A

3′

2176

1818C > T

3′

2224

1866C > A

3′

TCN2

M60396

275350

GEN-3AX

Human transcobalamin II (TCII)

mRNA,complete cds

1164

1127C > T

S376L

1765

1728T > C

3′

U60519

U60519

601762

GEN-3AZ

Human apoptotic cysteine

protease Mch4 (Mch4) mRNA, complete cds

1246A

1355A

2606A

2651A

1246G

1355A

2605A

2606A

2651A

1246G

1355A

2605G

2606G

2651A

2246G

1355G

2605G

1246G

1355A

2605G

2606A

2651A

1246G

1355A

2605G

2606A

2651G

1246G

1355A

2605G

2606G

2651G

1246G

1355G

2605G

2606A

2651A

1246A

1355A

2605G

2606A

2651A

304

157G > A

E53K

324

177A > G

Silent

1246

1099G > A

V367I

1355

1208A > G

Y403C

2605

2458A > G

3′

2606

2459A > G

3′

2651

2504A > G

3′

X60592

X60592

109535

GEN-3B0

Human CDw40 mRNA for nerve

growth factor receptor-related B-lymphocyte activation molecule

418C

726G

418T

726C

418C

726C

418

371C > T

S124L

726

679C > G

P227A

NFKB2

X61498

164012

GEN-3BW

H. sapiens mRNA for NF-kB subunit

1375G

1814G

2203G

2218G

2254C

1375G

1814G

2203G

2218C

2254T

1375G

1814G

2203G

2218C

2254C

1375T

1814G

2203G

2218C

2254C

1375G

1814G

2203A

2218C

2254C

1375G

1814C

2203G

2218C

2254C

1375T

1814C

2203G

2218C

2254C

1375T

1814G

2203G

2218G

2254C

1375G

1814G

2203A

2218G

2254C

1375

1212GT

Silent

1814

1651G > C

D551H

2203

2040C > A

Silent

2218

2055C > G

Silent

2254

2091C > T

Silent

2457

2294C > T

P765L

M61855

M61855

601130

GEN-3C1

Human cytochrome P4502C9

(CYP2C9) mRNA, clone 25

442T

1087A

442C

1087A

442C

1087C

442

442C > T

3′

852

852T > A

3′

1085

1085A > G

3′

1087

1087C > A

3′

1437

1437T > A

3′

X62572

X62572

146790

GEN-3CL

H. sapiens RNA for Fc receptor,

PC23

967

967T > C

3′

1240

1240A > G

3′

1300

1300C > T

3′

1542

1542G > C

3′

1560

1560C > A

3′

1709

1709T > G

3′

1931

1931A > T

3′

2032

2032G > A

3′

2136

2136G > A

3′

2176

2176C > T

3′

2201

2201G > A

3′

X62744

X62744

142855

GEN-3CQ

Human RING6 mRNA for HLA class

II alpha chain-like product

541

496G > A

V166I

674

629G > A

R210H

750

705G > C

Silent

1081

1036A > T

3′

X63359

X63359

600070

GEN-3DC

H. sapiens UGT2BIO mRNA for udp

glucuronosyltransferase

2219T

2422A

2219T

2422G

2219C

2422G

1516

1506C > T

Silent

2219

2209T > C

3′

2422

2412G > A

3′

2714

2704G > A

3′

TCRB

X63456

186930

GEN-3DG

H. sapiens mRNA for T-cell

antigen receptor beta-chain

421

411G > C

K137N

496

486G > A

Silent

516

506T > A

F169Y

520

510C > T

Silent

580

570A > G

Silent

754

744C > T

Silent

805

795T > C

Silent

811

801G > C

Silent

813

803T > A

V268E

817

807C > T

Silent

860

850C > T

Silent

878

868C > A

L290M

X64177

X64177

156351

GEN-3EQ

H. sapiens mRNA for

metallothionein

63

40G > A

A14T

90

67A > G

K23E

125

102C > T

Silent

131

108T > C

Silent

168

145A > G

I49V

182

159G > A

Silent

CPA1

X67318

114850

GEN-3HJ

H. sapiens mRNA for

procarboxypeptidase A1

172

165G > C

Silent

498

491C > G

T164R

629

622G > A

A208T

X67699

X67699

114280

GEN-3HP

H. sapiens HES mRNA for CDw52

antigen

143

119G > A

S40N

147

123G > A

M41I

X68836

X68836

601468

GEN-31R

H. sapiens mRNA for S-

adenosylmethionine synthetase

857G

878T

857G

878C

857C

878T

240

175G > A

V59I

857

792G > C

Silent

878

813T > C

Silent

M69043

M69043

164008

GEN-3IZ

Homo sapiens MAD-3 mRNA encoding

IkB-like activity, complete cds

1050T

1174A

1050C

1174G

400

306T > C

Silent

1050

956T > C

3′

1119

1025G > A

3′

1174

1080A > G

3′

ARNT

M69238

126110

GEN-3JH

Human aryl hydrocarbon receptor

nuclear translocator (ARNT) mRNA, complete cds

623

567G > C

Silent

X71440

X71440

264470

GEN-3KS

H. sapiens mRNA for peroxisomal

acyl-CoA oxidase

949G

1333T

949C

1333C

949C

1333T

949

936G > C

M312I

1333

1320T > C

Silent

GPX4

X71973

138322

GEN-3L1

H. sapiens GPx-4 mRNA for

phospholipid hydroperoxide glutathione peroxidase

718

638T > C

3′

837

757C > A

3′

882

802A > C

3′

RGS1

X73427

600323

GEN-3M6

H. sapiens 1r20 mRNA for alpha

helical basic phosphoprotein

247

233C > T

A78V

MHC2TA

X74301

600005

GEN-3N5

H. sapiens mRNA for MHC class II

transactivator

1614

1499C > G

A500G

3759

3644G > A

3′

4422

4307T > C

3′

ALDH3

M74542

100660

GEN-3N9

Human aldehyde dehydrogenase

type III (ALDHIII) mRNA, complete cds

1616

1574A > G

3′

U34252

U34252

602733

GEN-3O5

Human gamma-aminobutyraldehyde

dehydrogenase mRNA, complete cds

1683G

2471A

1683A

2471A

1683G

2471C

1683

1306G > A

E436K

2417

2040G > A

3′

2471

2094A > C

3′

2674

2297A > C

3′

2676

2299A > C

3′

MTP

X75500

157147

GEN-3O7

H. sapiens mRNA for microsomal

triglyceride transfer protein

63C

148G

309G

407T

477C

521A

546T

754C

915G

957C

1175A

2049C

63C

148A

309G

477T

521A

546T

754C

915C

957C

1175A

2049C

63C

148G

309G

407T

477C

521G

546C

754C

915G

957C

1175A

2049C

63C

148G

309G

407C

477C

521A

546T

915G

957C

1175A

2049C

63C

148G

309G

407T

477C

754C

915G

957A

1175A

2049C

63C

148G

309G

407C

477C

546C

754C

915G

957C

1175A

2049C

63C

148G

309G

407T

546C

754C

915C

957C

1175A

2049C

63C

148G

309G

477T

521A

546T

754C

915G

957C

1175A 2049C

63C

148G

407C

521A

546T

754C

915C

957C

1175A

2049C

148G

309G

407T

477C

521A

546T

754C

915C

957C

1175A

2049C

63G

148G

309G

407T

477C

521A

754C

915G

957C

2049T

63G

148G

309G

407T

477C

521A

546T

754C

915G

957C

1175A

2049C

63C

148G

309G

407T

477C

521A

546T

754C

915G

957C

1175A

2049T

148G

309G

407T

521A

546T

754C

915C

957C

1175A

2049C

148G

309G

407T

477C

521A

546T

754C

915G

957C

1175C

2049C

63C

148G

309G

407C

477C

521A

546T

754C

915G

957C

1175A

2049T

63C

148G

309G

407T

477T

521A

546T

754C

915C

957C

1175A

2049C

63C

148G

309G

407T

477C

521A

546C

754C

915G

957C

1175A

2049C

63G

148G

309G

407C

477C

521A

546T

754C

915G

957A

1175C

2049C

63C

148G

309G

407C

477C

521G

546C

754C

915G

957C

1175A

2049C

63G

148G

309G

407T

477C

521A

546C

754C

915G

957C

1175C

2049T

63C

148G

309G

407T

477C

521G

546C

754C

915G

957C

2049C

63C

148G

309G

407T

477C

521A

546T

754C

915G

957A

1175C

2049C

63C

148A

309G

407C

477T

521A

546T

754C

915C

957C

1175A

2049C

63C

148G

309G

407T

477C

521A

546T

754C

915G

957C

1175A

63C

148G

309G

407C

477C

521A

546T

754C

915G

957C

1175A

2049C

63C

148G

309G

407C

477C

521A

546T

754C

915C

957C

1175A

2049C

63G

148G

309G

407T

477C

521A

546T

754C

915G

957C

1175C

2049C

63G

148G

309G

407T

477C

521A

546T

754C

915C

957C

1175A

2049C

63C

148G

309G

407T

477C

521G

546C

754C

915G

957A

1175A

2049C

63C

148G

309G

407C

477T

521A

546T

754C

915G

957C

1175A

2049C

63C

148G

309G

407T

477C

521G

546C

754C

915C

957C

1175A

2049C

63G

148G

309G

407T

477C

521A

546T

754C

915C

957C

1175C

2049C

63C

148G

309G

407C

477C

521A

546C

754C

915G

957C

1175A

2049C

63

39C > G

Silent

148

124G > A

V42I

309

285G > C

Q95H

407

383T > C

I128T

477

453T > C

Silent

521

497A > G

N166S

546

522T > C

Silent

754

730C > G

Q244E

915

891C > G

H297Q

957

933C > A

Silent

1175

1151A > C

D384A

1847

1823T > G

F608C

2049

2025C > T

Silent

3231

3207G > A

3′

X75535

X75535

600279

GEN-3O8

H. sapiens mRNA for PxF protein

1808

1798A > G

3′

3066

3056G > C

3′

3263

3253G > T

3′

U76368

U76368

601872

GEN-3OU

Human cationic amino acid

transporter-2A (ATRC2) mRNA, complete cds

1338C

1445A

1904G

1338T

1904C

1338C

1445C

1904G

1338A

1445A

1904C

1338A

1445C

1904G

1338T

1904G

1338A

1445A

1904G

1338T

1904C

1338T

1904G

1338

1144A > C

K382Q

1338

1144A > T

Frame

1445

1251A > C

Silent

1904

1710G > C

Silent

LIPA

X76488

278000

GEN-3P2

H. sapiens mRNA for lysosomal

acid lipase

191A

212G

2186C

2254T

2439C

191A

212A

2186C

2254A

2439C

191C

212G

2186C

2254T

2439C

191A

212G

2186C

2254T

2439T

191A

212G

2186G

2254T

2439C

191C

212G

2186C

2254A

2439C

191A

212A

2186C

2254T

2439C

191A

212A

2186C

2439T

191A

212G

2186C

2254A

2439C

191A

212A

2186C

2254A

2439T

191

46A > C

T16P

212

67G > A

G23R

967

822G > A

M274I

1531

1386C > T

3′

2186

2041C > G

3′

2254

2109A > T

3′

2439

2294C > T

3′

U76560

U76560

601757

GEN-3P4

Human peroxisome targeting

signal 2 receptor (Pex7) mRNA, complete cds

1326

1278T > G

3′

M77829

M77829

107776

GEN-3QJ

Human channel-like integral

membrane protein (CHIP28) mRNA, complete cds

172

134C > T

A45V

1249

1211C > G

3′

ID1

X77956

600349

GEN-3QL

H. sapiens Id1 mRNA

380

345G > A

Silent

382

347C > A

T116N

842

807A > C

3′

851

816G > A

3′

YWHAH

X78138

113508

GEN-3QU

H. sapiens 14-3-3 eta subtype

mRNA

953

753A > G

3′

960

760G > A

3′

1387

1187C > T

3′

S78203

S78203

602339

GEN-3QY

PEPT 2=H+/peptide cotransporter

[human, kidney, mRNA Partial, 2685 nt]

171C

1078C

1191A

1255C

1365C

1556G

2226C

2311G

171C

1078T

1191G

1255T

1365C

1556A

2226C

2311G

171C

1078C

1191G

1365C

2226C

2311G

171C

1078T

1191G

1255T

1365T

1556A

2226C

2311G

171C

1365C

2226T

2311G

171C

1078T

1191G

1255T

1365C

1556G

2226C

2311G

171T

1365C

2226C

2311G

1365C

2226C

2311A

171C

1078C

1191A

1255T

1365C

1556G

2226C

2311G

171C

1078C

1191A

1255C

1365C

1556G

2226T

2311G

1078C

1191A

1255C

1365C

1556G

2226C

2311A

171C

1078C

1191A

1255T

1365C

2226C

2311G

171T

1078C

1191A

1255C

1365C

1556G

2226C

2311G

171C

1078C

1191G

1255T

1365C

1556A

2226C

2311G

1078T

1191G

1255T

1365C

1556A

2226C

2311G

171

141C > T

Silent

1078

1048C > T

L350F

1191

1161A > G

Silent

1255

1225C > T

P409S

1365

1335C > T

Silent

1556

1526G > A

R509K

2226

2196C > T

3′

2311

2281G > A

3′

X79389

X79389

600436

GEN-3T7

H. sapiens GSTT1 mRNA

824

824T > C

3′

M80244

M80244

600182

GEN-3UJ

Human E16 mRNA, complete cds

1324A

1473C

1493G

1614G

1862G

1918A

2102T

2728G

2811C

2917G

2933C

2992G

3538T

3872G

1111C

1119C

1324T

1473C

1614G

1862G

1918C

2102T

2591A

2728G

2811C

2917G

2933C

2992A

1111C

1119C

1324T

1493G

1862G

1918C

2102T

2728G

2811C

2917G

2933A

2992G

3538T

3872G

1324T

1493G

1614G

1862G

2728T

2811C

2917G

2933C

3538T

3872G

1324A

1473G

1493G

1614G

1862G

2102T

2591A

2728G

2811C

2917G

2933C

2992G

3538T

3872G

1111C

1119C

1324T

1473C

1493G

1614G

2591G

2728G

2811C

2917G

2933C

2992G

3538C

3872G

1111C

1119C

1324T

1493G

1614G

1862T

1918C

2102T

2728G

2811C

2917G

2933C

2992G

3538T

1111C

1119C

1324T

1473C

1493G

1614A

1862G

1918C

2102T

2591G

2728G

2811C

2917G

2933C

2992G

3538T

1324A

1473C

1493G

1614G

1862G

1918C

2102T

2728G

2811C

2917G

2933C

2992G

3538T

3872G

1111C

1119C

1324T

1473C

1493G

1614G

1862G

2591G

2811C

2917G

2933C

3538T

3872A

1111C

1119C

1324T

1473C

1493G

1614G

1862G

1918C

2102T

2591G

2728G

2811C

2917G

2933C

2992G

3538T

3872G

1111C

1119C

1324T

1473C

1493G

1614G

1862G

1918C

2102T

2591G

2728G

2811C

2917A

2933C

2992G

1111C

1119C

1324T

1473C

1614G

1862G

1918C

2102T

2591A

2728G

2811C

2917G

2933C

2992G

3538T

1111T

1119C

1324T

1493G

1614G

1862G

1918C

2102T

2728G

2811C

2917G

2933C

2992G

3538T

3872G

1111C

1119C

1324T

1473G

1493G

1614G

1862G

1918C

2102T

2591A

2728G

2811T

2917G

2933C

2992G

1111C

1119T

1324T

1473C

1493G

1614G

1862G

1918C

2102T

2591G

2728G

2811C

2917G

2933C

2992G

3538T

3872G

1111C

1119C

1324T

1473G

1493G

1614G

1862G

1918C

2102T

2591A

2728G

2811C

2917G

2933C

2992G

3538T

1111C

1119C

1324T

1473C

1493G

1614G

1862T

1918C

2102T

2591G

2728G

2811C

2917G

2933C

2992G

3538T

1111C

1119C

1324T

1473C

1493G

1614G

1862G

1918C

2102T

2591G

2728G

2811C

2917G

2933C

2992G

1111C

1119C

1324T

1473C

1493G

1614A

1862G

1918C

2102T

2591G

2728G

2811C

2917G

2933A

2992G

3538T

3872G

1324A

1473C

1493G

1614G

1862G

1918A

2102T

2591A

2728G

2811C

2917G

2933C

2992G

3538T

3872G

1111C

1119C

1324T

1473C

1493G

1614G

2591G

2728G

2811C

2917G

2933C

2992G

3538C

3872G

1111C

1119C

1324T

1473C

1493A

1614G

1862G

1918C

2102T

2591A

2728G

2811C

2917G

2933C

2992G

3538T

3872A

1111C

1119C

1324T

1473C

1493G

1614A

1862G

1918C

2102T

2591G

2728G

2811C

2917G

2933C

2992G

3538T

1111C

1119C

1324T

1473C

1493G

1614G

1862G

1918C

2102A

2591G

2728G

2811C

2917G

2933C

2992G

1324A

1473C

1493G

1614G

1862G

1918C

2102T

2591G

2728G

2811C

2917G

2933C

2992G

3538T

3872G

1111C

1119C

1324T

1473C

1493G

1614G

1862G

1918C

2102T

2591A

2728G

2811C

2917G

2933C

2992A

1111C

1119C

1324T

1473C

1493G

1614G

1862G

1918C

2102T

2591G

2728G

2811C

2917A

2933C

2992G

1111C

1119C

1324T

1473G

1493G

1614G

1862G

1918C

2102T

2591A

2728G

2811C

2917G

2933C

2992G

1111C

1119C

1324T

1473C

1493G

1614G

1862G

1918A

2102A

2591G

2728T

2811C

2917G

2933C

2992A

3538T

3872A

1324T

1473G

1493G

1614G

1862G

1918C

2102T

2591A

2728T

2811C

2917G

2933C

2992G

3538T

3872G

1111C

1119C

1324T

1473G

1493G

1614G

1862G

1918C

2102T

2591A

2728G

2811T

2917G

2933C

2992G

1111C

1119C

1324T

1473C

1493G

1614G

1862G

1918C

2102T

2591G

2728G

2811C

2917G

2933C

2992G

3538T

1324A

1473G

1493G

1614G

1862G

1918C

2102T

2591A

2728G

2811C

2917G

2933C

2992G

3538T

3872G

1111T

1119C

1324T

1473C

1493G

1614G

1862G

1918C

2102T

2591G

2728G

2811C

2917G

2933C

2992G

3538T

3872G

202

(−109)G > C

5′

520

210T > C

Silent

1111

801C > T

3′

1119

809C > T

3′

1185

875G > A

3′

1324

1014T > A

3′

1473

1163C > G

3′

1493

1183A > G

3′

1614

1304G > A

3′

1692

1382C > T

3′

1862

1552G > T

3′

1918

1608C > A

3′

2102

1792T > A

3′

2591

2281A > G

3′

2728

2418G > T

3′

2811

2501C > T

3′

2917

2607G > A

3′

2933

2623C > A

3′

2992

2682A > G

3′

3138

2828G > C

3′

3538

3228T > C

3′

3872

3562G > A

3′

M80462

M80462

112205

GEN-3US

Human MB-1 mRNA, complete cds

241

205G > A

V69I

CD79B

M80461

147245

GEN-3UT

Human B29 mRNA, complete cds

795

781C > T

3′

804

790C > A

3′

1033

1019C > T

3′

U81375

U81375

602193

GEN-3VO

Human placental equilibrative

nucleoside transporter 1 (hENT1) mRNA, complete cds

1466G

1989G

1996C

2045T

1466G

1989A

1996C

1466A

1989G

1996C

2045C

1466G

1989G

1996T

2045C

1466G

1989G

1996C

2045C

1466A

1996C

1466G

1996C

1466G

1989A

1996C

2045C

1466

1288G > A

A430T

1989

1811G > A

3′

1996

1818C > T

3′

2045

1867T > C

3′

M81590

M81590

182131

GEN-3VZ

Serotonin receptor 5HT-1B, cDNA

190C

432T

922C

190C

432T

922G

190T

432T

922C

190C

432G

922G

190

129C > T

Silent

432

371T > G

F124C

922

861G > C

Silent

1241

1180G > A

3′

IGHG3

X81695

147120

GEN-3W4

H. sapiens rearranged IgG VH-D-

JH-Hinge-CH2-CH3 region

528

528C > T

3′

534

534C > T

3′

594

594T > C

3′

601

601A > C

3′

668

668A > T

3′

726

726T > C

3′

796

796G > A

3′

804

804G > A

3′

827

827A > T

3′

828

828C > T

3′

842

842C > T

3′

849

849G > T

3′

853

853A > C

3′

900

900T > C

3′

905

905G > A

3′

916

916G > A

3′

957

957G > C

3′

963

963G > A

3′

970

970G > A

3′

973

973C > G

3′

999

999C > T

3′

1002

1002T > C

3′

1012

1012A > C

3′

1045

1045G > A

3′

1050

1050C > T

3′

1073

1073C > G

3′

1075

1075C > T

3′

1088

1088A > T

3′

1092

1092G > A

3′

1113

1113C > T

3′

1130

1130G > C

3′

1137

1137G > A

3′

M81757

M81757

603474

GEN-3W6

H. sapiens S19 ribosomal protein

mRNA, complete cds

276

254A > T

N85I

338

316G > A

A106T

496

474G > A

3′

U81800

U81800

603878

GEN-3WB

Homo sapiens monocarboxylate

transporter (MCT3) mRNA, complete cds

1485

1423C > T

3′

1624

1562G > C

3′

X82321

X82321

600538

GEN-3WT

H. sapiens mRNA for thiol-

specific antioxidant

304

304G > A

G102R

422

422G > T

W141L

640

640C > G

3′

655

655C > T

3′

U83411

U83411

603105

GEN-3Y1

Homo sapiens carboxypeptidase Z

precursor, mRNA, complete cds

1683

1644C > A

Silent

1788

1749G > A

Silent

2007

1968A > G

3′

2013

1974G > A

3′

ARSE

X83573

300180

GEN-3Y8

Homo sapiens APSE gene, complete

CDS

1759

1692C > T

Silent

1795

1728G > A

Silent

AF085690

AF085690

None

GEN-3YE

Homo sapiens multidrug

resistance-associated protein 3 (MRP3) mRNA, complete cds

3978C

4064T

4386C

4545A

3926G

3978T

4064C

4386C

4545A

3926G

3978T

4064C

4386C

4545G

3926A

3978C

4064C

4386C

4545A

3926G

3978C

4064C

4386C

4545A

3926G

4064C

4386T

4545A

3926G

3978C

4064C

4386C

4545G

3926G

3978C

4064C

4386T

4545A

3978C

4064T

4386C

4545A

3926G

3978T

4064C

4386C

3926

3890G > A

R1297H

3978

3942C > T

Silent

4064

4028C > T

A1343V

4386

4350C > T

Silent

4545

4509A > G

Silent

5119

5083A > C

3′

TGFBR2

M85079

190182

GEN-3ZS

Human TGF-beta type II receptor

mRNA, complete cds

1334

999A > G

Silent

2045

1710A > C

3′

PXMP3

M85038

170993

GEN-3ZU

Human 35kD peroxisomal membrane

protein mRNA, complete cds

102

(−164)A > C

5′

CEL

M85201

114841

GEN-404

Human cholesterol esterase mRNA,

complete cds

566

558T > C

Silent

1306

1298G > A

S433N

1826

1818C > T

Silent

YWHAZ

M86400

601288

GEN-40Y

Human phospholipase A2 mRNA,

complete cds

1653

1569T > A

3′

2599

2515C > G

3′

2619

2535A > C

3′

2656

2572A > C

3′

2745

2661C > T

3′

2761

2677A > C

3′

X86681

X86681

602110

GEN-41E

H. sapiens mRNA for nucleolar

protein, HNP36

1537C

1645T

1796G

1537C

1645C

1725G

1796G

1915A

1537C

1645C

1725G

1796A

1915A

1537T

1645C

1796G

1537C

1645C

1725A

1796G

1915C

1537T

1645C

1725G

1796G

1915A

1537C

1645T

1725G

1796G

1915A

1537

1152C > T

3′

1645

1260C > T

3′

1725

1340G > A

3′

1796

1411G > A

3′

1915

1530A > C

3′

D87292

D87292

180370

GEN-42Y

Human mRNA for rhodanese,

complete cds

816

768C > T

Silent

946

898G > A

3′

D87845

D87845

602344

GEN-44C

Human mRNA for platelet-

activating factor acetylhydrolase 2, complete cds

2299

2096G > A

3′

2332

2129A > G

3′

D88308

D88308

603247

GEN-44Z

Homo sapiens mRNA for very-long

chain acyl-CoA synthetase, complete cds

498

276C > T

Silent

D90041

D90041

108345

GEN-464

Human liver arylamine N-

acetyltransferase (EC 2.3.1.5) gene

591

445G > A

V149I

1240

1094C > A

3′

AAC2

D90040

243400

GEN-465

Human mRNA for arylamine N-

acetyltransferase (EC 2.3.1.5)

232

191G > A

R64Q

323

282C > T

Silent

844

803A > G

K268R

M90656

M90656

230450

GEN-46P

Human gamma-glutamylcysteine

synthetase (GCS) mRNA, complete cds

620

528A > G

Silent

X90999

X90999

138760

GEN-477

H. sapiens mRNA for Glyoxalase II

950

914A > G

3′

U91521

U91521

601758

GEN-47E

Human peroxin 12 (HsPEX12) mRNA,

complete cds

1747

1597A > G

3′

2066

1916G > C

3′

X92106

X92106

602403

GEN-47S

H. sapiens mRNA for bleomycin

hydrolase

681C

1405A

1576T

681G

1405G

1576C

681G

1405A

1576C

681C

1405G

1576C

681C

1405A

1576C

681

603C > G

Silent

1405

1327A > G

I443V

1576

1498C > T

3′

U92314

U92314

604125

GEN-47U

Homo sapiens hydroxysteroid

sulfotransferase SULT2B1a (HSST2) mRNA, complete cds

1146

771C > T

Silent

1164

789C > T

Silent

1278

903T > C

Silent

PNLIP

M93285

246600

GEN-48N

Pancreatic lipase (PNLIP)

(Dietary supplement)

646

646G > T

V216L

X95190

X95190

601641

GEN-49Y

H. sapiens mRNA for Branched

chain Acyl-CoA Oxidase

1394

1302C > T

Silent

1934

1842C > A Silent

X96395

X96395

601107

GEN-4AM

H. sapiens mRNA for canalicular

multidrug resistance protein

1286G

2971G

3144T

4525T

4564C

4581G

2971G

3144T

4564T

1286G

2971A

3144T

4525C

4564C

4581G

1286A

2971G

3144T

4525C

4564C

4581G

1286G

2971G

3144T

4525C

4564C

4581G

1286G

2971G

3144C

4525C

4564C

4581G

1286G

2971G

3144T

4525C

4564C

4581A

2971G

3144T

4525C

4564C

4581G

1286A

2971G

3144T

4525T

4564T

4581A

848

811C > T

A271S

1286

1249G > A

V417I

2971

2934G > A

Silent

3144

3107T > C

Il036T

4525

4488C > T

Silent

4564

4527C > T

Silent

4581

4544G > A

C1515Y

ABC3

X97l87

601615

GEN-4BI

H. sapiens mRNA for ABC-C

transporter

4671

4324G > T

V1442F

5075

4728G > A

Silent

ID2

M97796

600386

GEN-4C3

Human helix-loop-helix protein

(Id-2) mRNA, complete cds

402

294C > G

Silent

M98045

M98045

136510

GEN-4C3

Homo sapiens folylpolyglutamate

synthetase mRNA, complete cds

802C

1747G

1900T

802T

1747G

802C

1747G

1900C

802

732C > T

Silent

1747

1677G > T

3′

1900

1830T > C

3′

1912

1842G > A

3′

1995

1925C > G

3′

L05628

L05628

158343

GEN-4D9

Human multidrug resistance-

associated protein (MRP) mRNA, complete cds

1258C

1264A

3369G

4198G

4648C

1258C

1264G

3369A

3976C

4648C

1258C

3369G

3976C

4198G

4648T

1258T

1264G

3369G

3976C

4198G

4648C

1258C

1264G

3369G

3976C

4198G

4648C

1258T

1264G

3369G

3976C

4198A

4648C

1258C

1264G

3369G

3976C

4198A

4648C

1258T

1264G

3369G

3976C

4198A

1258C

1264A

3369G

3976T

4198G

4648C

1258C

1264A

3369G

3976C

4198G

4648T

3369G

3976C

4198G

4648T

1258C

1264G

3369A

3976C

4198A

4648C

1258

1062T > C

Silent

1264

1068G > A

Silent

3369

3173G > A

R1058Q

3976

3780C > T

Silent

4198

4002G > A

Silent

4648

4452C > T

Silent

Z34897

Z34897

600167

GEN-4DE

H. sapiens mRNA for H1 histamine

receptor

1068A

1087T

1135G

1139T

1249T

1068A

1087T

1135G

1139C

1249T

1087C

1135G

1068A

1087T

1135A

1139C

1249T

1068A

1087T

1135G

1139C

1249A

1068G

1087T

1135G

1139C

1249T

1068A

1139C

1249T

10680

1087C

1135G

1068

1068A > G

Silent

1087

1087T > C

S363P

1135

1135G > A

G379R

1139

1139C > T

S380F

1249

1249T > A

L417M

PTGIR

D38128

600022

GEN-4DH

Human IP gene for prostacyclin

receptor, exon 3

203C

231A

203C

231C

203G

231C

203

203C > G

3′

231

231C > A

3′

M64799

M64799

162020

GEN-4DN

Histamine receptor H2

543

543G > A

Silent

L11931

L11931

182144

GEN-4DT

Human cytosolic serine

hydroxymethyltransferase (SHMT) mRNA, complete cds

1444C

1523C

1541C

1444C

1523G

1541T

1444T

1523C

1541T

1444C

1523C

1541T

1444T

1523G

1541T

1444T

1523C

1541C

1444T

1523G

1541C

1444T

1541C

1444C

1541C

1444T

1541T

1444

1420C > T

L474F

1523

1499C > G

3′

1541

1517C > T

3′

L22647

L22647

176802

GEN-4DZ

Human prostaglandin receptor ep1

subtype mRNA, complete cds

841

767A > G

H256R

LTC4S

U11552

246530

GEN-4E1

Human leukotriene-C4 synthase

mRNA, complete cds

468

382G > A

A128T

ATP1A1

D00099

182310

GEN-4E8

Homo sapiens mRNA for Na,K-

ATPase alpha-subunit, complete cds

1059A

1428G

2538T

3324C

3375G

3397A

3408C

1059A

1428A

2538T

3324C

3375G

3397G

1059A

1428G

2538T

3324C

3375G

3397G

3408C

1428G

2538T

3324C

3375A

3397G

3408C

1059C

1428G

2538T

3324T

3397G

3408C

1059A

1428G

2538C

3324C

3375G

3397G

3408C

1059C

1428G

2538T

3324C

3375G

3397G

3408C

1059C

1428G

2538T

3324C

3375A

3397G

3408C

1059A

1428A

2538T

3324C

3375G

3397G

3408A

1059C

1428A

2538T

3324C

3375G

3397G

3408C

1059

741A > C

Silent

1428

1110G > A

Silent

2056

1738A > G

I580V

2538

2220T > C

Silent

3324

3006C > T

Silent

3375

3057G > A

Silent

3397

3079G > A

3′

3408

3090C > A

3′

3505

3187C > A

3′

3538

3220G > T

3′

DHFR

J00140

126060

GEN-4E9

Human dihydrofolate reductase

gene

666T

721A

729T

829C

666A

829C

666T

721T

729T

829C

666T

721A

729T

829T

666A

721T

729C

829C

721T

829T

666

624T > A

3′

721

679T > A

3′

729

687T > C

3′

829

787C > T

3′

HTR1E

M91467

182132

GEN-4EE

Serotonin 5-HT receptors 5-HT1E

964G

1097C

1188G

964A

1097C

1188G

964A

1097C

1188A

964A

1097T

1188G

964

398A > G

K133R

1097

531C > T

Silent

1188

622G > A

A208T

L05597

L05597

182134

GEN-4EV

Serotonin 5-HT receptors 5-HT1F

824

600T > C

Silent

1010 786{circumflex over ( )}787insAATAAAATTCAT [H262Q;262{circumflex over ( )}263insIKFI]

SLC18A3

U09210

600336

GEN-4F3

Human vesicular acetylcholine

transporter mRNA, complete cds

838T

1057G

1369A

1567C

2080G

2199G

2349G

1269G

2080G

2349G

838C

1057G

1369A

1567C

2080G

2199G

2349G

1057G

1369A

1567C

2199G

2349T

838T

1057G

1369A

1567C

2080T

2199G

2349G

838C

1057G

1369A

1567C

2080T

2199G

2349T

838C

1057C

1369G

1567G

2080G

2199A

2349G

838

396T > C

Silent

1057

615G > C

Silent

1369

927A > G

Silent

1567

1125C > G

Silent

2080

1638G > T

3′

2199

1757G > A

3′

2349

1907G > T

3′

U09806

U09806

236250

GBN-4FZ

Human methylenetetrahydrofolate

reductase mRNA, partial cds

120C

519C

668C

1059T

1308T

120C

464T

519C

1059C

1308T

1784A

120C

464T

519T

668C

1059C

1289A

1308C

1784G

120C

464T

519C

668C

1059C

1289A

1308C

1784G

120C

464T

519T

1059C

1289A

1308T

1784G

120T

464T

519C

668C

1308T

1784G

120C

464T

668T

1059C

1289C

1308T

1784G

120C

464T

519C

668T

1059C

1289A

1308T

1784G

120C

464T

519C

668C

1059C

1289C

1308T

1784G

120C

464T

519C

668C

1059C

1289A

1308T

1784G

120T

464T

519C

668C

1059T

1289C

1308T

1784G

120C

464G

519C

668C

1059T

1289C

1308T

1784A

120T

464T

519C

668C

1059T

1289C

1308T

1784A

120C

464T

519T

668T

1059C

1289C

1308T

1784G

120T

464T

519T

668C

1059T

1289C

1308T

1784G

120C

668C

1059C

1289C

1308T

1784G

120C

464T

519T

668C

1059C

1289A

1308T

1784G

120C

464T

519C

668C

1059C

1289C

1308T

1784A

120C

464T

5l9T

668C

1059C

1784G

120

120T > C

Silent

464

464T > G

M155R

519

519C > T

Silent

668

668C > T

A223V

1059

1059T > C

Silent

1289

1289C > A

3′

1308

1308T > C

3′

1784

1784G > A

3′

U08989

U08989

133550

GEN-CBZ

Human glutamate transporter

mRNA, complete cds

684

519C > T

Silent

1617

1452T > C

Silent

CYP11B2

D13752

124080

GEN-CCD

Human CYP11B2 gene for steroid

18-hydroxylase, complete cds

1600

1593G > A

3′

AB004854

AB004854

603608

GEN-KV6

Homo sapiens mRNA for carbonyl

reductase 3, complete cds

730

730G > A

V244M

AJ005162

AJ005162

600067

GEN-KVT

Homo sapiens mRNA for UDP-

glucuronosyltransferase

519G

1845T

519A

1845T

519G

1845C

519

486G > A

Silent

1845

1812T > C

3′

1915

1882A > C

3′

AB005289

AB005289

300135

GEN-KVU

Homo sapiens mRNA for ABC

transporter 7 protein,complete cds

2137

2069A > T

H690L

L02932

L02932

170998

GEN-KW4

Human peroxisome proliferator

activated receptor mRNA, complete cds

207

(−10)T > C

5′

648

432G > A

Silent

J04132

J04132

186780

GEN-KXY

Human T cell receptor zeta-chain

mRNA, complete cds

1403

1329G > C

3′

1410

1336A > T

3′

AJ000730

AJ000730

603752

GEN-KY4

Homo sapiens mRNA for cationic

amino acid transporter 3

1126G

2021T

2051G

1126A

2021C

2051G

1126A

2021T

2051A

1126A

2021T

2051G

1126G

2021C

2051A

1126A

2021C

2051A

195

117G > A

Silent

1126

1048G > A

A350T

2021

1943C > T

3′

2051

1973A > G

3′

L05148

L05148

176947

GEN-KYC

Human protein tyrosine kinase

related mRNA sequence

1886

1886G > A

3′

AB001325

AB001325

600170

GEN-KYP

Human AQP3 gene for aquaporine 3

(water channel), partail cds

1203

1143G > A

3′

Y08639

Y08639

601972

GEN-KZ7

H. sapiens mRNA for nuclear

orphan receptor ROR-beta

126C

846A

126T

846G

126C

846G

126

(−469)C > T

5′

846

252G > A

Silent

AB014679

AB014679

603798

GEN-L22

Homo sapiens GN6ST mRNA for N-

acetylglucosamine-6-O-sulfotransferase (GlcNAc6ST), complete cds

1578

1189G > T

V397L

2335

1946T > C

3′

AB015050

AB015050

603377

GEN-L2D

Homo sapiens mRNA for OCTN2,

complete cds

1101

978G > A

Silent

AB017546

AB017546

601791

GEN-L3J

Homo sapiens Pex14 mPNA for

peroxisomal membrane anchor protein, complete cds

104

99G > A

Silent

AF012390

AF011390

603345

GEN-L59

Homo sapiens pancreas sodium

bicarbonate cotransporter mRNA, complete cds

1131C

3108T

3434A

3647T

3767C

4294G

4345A47330

1131C

3108C

3434A

3647T

3767C

4345T

4733G

1131C

3434A

3767G

4294G

4345T

4733G

3434C

3647T

3767C

4345T

4733C

1131C

3108T

3434A

3647T

3767C

4294G

4345T

4733C

1131C

3108T

3434A

3647T

3767C

4294G

4345T

4733G

1131T

3108T

3647T

3767C

4345T

4733G

2131C

3647C

3767C

4345T

4733C

1131C

3108T

3434C

3647T

3767C

4345T

4733G

1131C

3108T

3434A

3647T

3767C

4294C

4345T

4733G

3108C

4294G

1131C

3108C

3434A

3647C

3767C

4294G

4345T

4733C

1131T

3108T

3434C

3647T

3767C

4294G

4345T

4733G

1131C

3108C

3434A

3647C

3767G

4294C

4345T

4733G

1131C

3108T

3434A

3647T

3767C

4345T

4733G

1131C

3108T

3434C

3647C

3767G

4294C

4733G

1131T

3108C

3434A

3647T

3767C

4294G

4345T

4733C

2131C

3108T

3434C

3647T

3767C

4294C

1131C

3108T

3434C

3647T

3767C

4294G

4345T

4733G

1131C

3108T

3434C

3647T

3767C

4294C

4345T

4733G

1131C

3108C

3434A

3647C

3767G

4294G

4345T

4733G

1131

1014C > T

Silent

3108

2991T > C

Silent

3434

3317A > C

3′

3647

3530T > C

3′

3767

3650C > G

3′

4294

4177G > C

3′

4345

4228T > A

3′

4733

4616G > C

3′

U16997

U16997

602943

GEN-L5O

Human orphan receptor ROR gamma

mRNA, complete cds

1545

1476C > G

I492M

U21943

U21943

602883

GEN-L97

Human organic anion transporting

polypeptide (OATP) mRNA, complete cds

1964A

2183A

2229A

1964A

2183A

2229G

2295A

1964T

2183C

2229G

2295A

1964A

2183C

2229G

2295C

1964A

2183C

2229G

2295A

1964A

2183A

2229A

2295C

1964A

2183A

2229G

2295C

1964

1911A > T

Silent

2183

2130C > A

3′

2229

2176G > A

3′

2295

2242A > C

3′

AJ225089

AJ225089

603281

GEN-L99

Homo sapiens mRNA for 2′-5′

oligoadenylate synthetase 59 kDa isoform

1724

1718C > T

3′

1738

1732G > T

3′

D26480

D26480

None

GEN-LBX

Human mRNA for leukotriene B4

omega-hydroxylase, complete cds

75T

320A

847C

872T

1085G

1115A

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147G

75T

320A

847A

872T

1115G

1121G

1275C

1596C

1795A

2020G

2043G

2147C

320A

847C

1115G

1121G

1275G

1596C

1780G

1795G

2020G

2043G

2147G

75T

320A

847C

872T

1085A

1115G

1121G

1275C

1780G

1795A

2043G

2147G

75T

320A

847C

872C

1085G

1115A

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147G

75T

320A

847C

1115G

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147C

75T

320A

847C

1085G

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847C

1115A

1121G

1275G

1596C

1780A

1795A

2020G

2043G

2147C

75T

320A

847A

1085G

1115A

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147G

75T

320A

847C

1115G

1121G

1275G

1596C

1780A

1795A

2020G

2043G

2147G

75T

320A

847C

1085G

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147G

75T

320A

847A

872C

1085G

1121G

1275C

1596C

1795A

2020G

2043G

2147C

75T

320A

847C

872C

1085A

1115G

1121G

1275C

1780G

1795A

2043G

2147G

75G

320A

847C

1085A

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

75T

320A

847C

1085G

1115A

1121G

1275C

1596C

1780G

1795A

2020G

2043T

2147G

75T

320A

847C

1085A

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75G

847C

1115G

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147G

75T

320A

872C

1121G

1275A

1596C

1780G

1795A

2020G

2043G

75T

320A

847C

1085G

1115A

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147G

75T

320A

847C

1115G

1121A

1275C

1596C

1780A

1795A

2020G

2043G

2147C

75T

320A

847C

1085G

1115A

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847C

872T

1085A

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847A

872C

1085G

1115A

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147C

75T

320A

847C

872C

1085A

1115A

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147C

75T

320A

847A

872C

1085G

1115A

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147G

75T

320A

847A

872C

1085G

1115A

1121G

1275A

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847C

872T

1085G

1115A

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847C

872C

1085G

1115A

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847C

872T

1085A

1115G

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147C

75T

320A

847C

872T

1085G

1115A

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147G

75T

320A

847C

872T

1085G

1115G

1121G

1275C

1596C

1780G

1795G

2020G

2043G

2147G

75T

320A

847C

872C

1085A

1115G

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147G

75T

320A

847A

872C

1085A

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847A

872C

1085A

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147G

75T

320A

847C

872T

1085A

1115G

1121G

1275C

1596A

1780G

1795A

2020A

2043G

2147G

75T

320A

847C

872T

1085G

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147G

75T

320A

847C

872C

1085G

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847C

872T

1085A

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847C

872C

1085A

1115G

1121A

1275C

1596C

1780A

1795A

2020G

2043G

2147C

75T

320A

847C

872C

1085G

1115A

1121G

1275C

1596C

1780G

1795A

2020G

2043T

2147G

75T

320A

847C

872C

1085G

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147G

75T

320A

847A

872T

1085G

1115A

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847C

872C

1085A

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147G

75T

320A

847A

872T

1085G

1115G

1121G

1275C

1596C

1780G

1795A

2020G

2043G

2147C

75T

320A

847C

872T

1085G

1115G

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147C

75T

320A

847C

872T

1085A

1115A

1121G

1275C

1596C

1780G

1795a

2020G

2043G

2147C

75T

320A

847C

872T

1085G

1115G

1121G

1275C

1596C

1780A

1795A

2020G

2043G

2147G

75

34T > G

W12G

320

279A > C

Silent

847

806C > A

A269D

872

83lT > C

Silent

1085

1044A > G

Silent

1115

1074G > A

Silent

1121

1080A > G

Silent

1275

1234C > A

Silent

1439

13980 > A

Silent

1596

l555C > A

L5l9M

1780

1739G > A

3′

1795

1754A > G

3′

2003

1962C > T

3′

2020

1979G > A

3′

2043

2002G > T

3′

2147

2106C > G

3′

AJ130718

AJ130718

603593

GEN-LDO

Homo sapiens mRNA for

glycoprotein-associated amino acid transporter y+LAT1

791T

953T

1820A

791C

953C

1820A

791C

953C

1820G

791T

953C

1820G

791C

953T

1820A

791C

953T

1820G

791T

953T

1820G

791T

953C

1820A

791

498C > T

Silent

953

660T > C

Silent

1820

1527G > A

Silent

AF031416

AF031416

603258

GEN-LDU

Homo sapiens IkB kinase beta

subunit mRNA, complete cds

2028

2028G > A

M676I

AF058921

AF058921

603602

GEN-LJY

Homo sapiens cytosolic

phospholipase A2-gamma mRNA, complete cds

1972

1663G > A

3′

1989

1680A > T

3′

AF060502

AF060502

602859

GEN-LL7

Homo sapiens peroxisome assembly

protein PEX10 mRNA, complete cds

1186

1154G > A

3′

AF070548

AF070548

604165

GEN-LNS

Homo sapiens clone 24408 2-

oxoglutarate carrier protein mRNA, complete cds

1224

1113C > T

3′

1483

1372A > C

3′

AF071202

AF071202

None

GEN-LP3

Homo sapiens ABC transporter

MOAT-B (MOAT-B) mRNA, complete cds

674G

1027G

1084A

1612C

2384G

2827G

2959C

2962C

3445T

3463G

4131G

674T

1084G

1612C

2384A

2827G

2959C

2962C

3445T

3463A

4131T

674G

1027G

1084A

1612C

2384G

2827G

2959C

2962C

3445T

3463A

4131T

674G

1084A

1612C

2384G

2827A

2959T

2962C

3445T

3463G

4131T

674T

1027G

1084G

1612C

2384G

2827G

2959C

2962C

3445T

3463A

674G

1027G

1084A

2384G

2827G

2959C

2962C

3445T

3463G

4131T

674G

1027G

1084G

1612C

2384G

2827G

2959C

2962C

3445T

3463A

4131G

674T

1027G

1084G

1612C

2384G

2959T

2962C

3445T

4131T

674G

1027G

2384G

2827G

2959C

2962C

3445C

3463A

674G

1027G

2384A

2959C

3445T

674G

1027G

1084G

1612C

2384G

2827G

2959C

2962C

3445T

3463A

4131T

674G

1027G

1084A

1612C

2384G

2827G

2959C

2962C

3445T

3463A

4131G

674G

1027G

1084G

2384G

2827A

2959C

3445T

4131T

674G

1084G

1612C

2384G

2827A

2959T

2962C

3445T

3463G

4131T

674G

1027G

1612T

2384G

2827G

2959C

2962C

3445T

3463A

4131G

674G

1027T

1612C

2384G

2827G

2959C

2962C

3445T

3463A

674G

1027G

1084G

1612C

2384G

2827G

2959C

2962C

3445T

3463G

674T

1027G

1084G

1612C

2384G

2827G

2959C

2962C

3445T

3463A

4131G

674G

1027G

1084A

1612T

2384A

2827A

2959C

2962T

3445T

3463G

4131G

674T

1027G

1084G

1612C

2384G

2827G

2959C

2962C

3445T

3463A

4131T

674G

1027G

1084A

1612C

2384G

2827G

2959C

2962C

3445T

3463G

4131T

674G

1027T

1612C

2384G

2827G

2959C

2962C

3445T

3463A

4131G

674G

1027G

1084G

1612C

2384G

2827G

2959C

2962C

3445T

3463G

4131G

674G

1027G

1084A

1612T

2384G

2827G

2959C

2962C

3445T

3463A

4131G

674T

1027G

1084G

1612C

2384G

2827A

2959T

2962C

3445T

3463G

4131T

674G

1027G

1084A

2384G

2827G

2959C

2962C

3445T

3463G

4131T

674T

1027G

1612C

2384G

2827G

2959C

2962C

3445T

3463A

4131G

674G

1027T

1084A

1612C

2384G

2827A

2959T

2962C

3445T

3463G

4131T

674G

1027T

1084G

1612C

2384G

2827A

2959T

2962C

3445T

3463G

4131G

674G

1027G

1084A

1612T

2384G

2827G

2959C

2962C

3445C

3463A

4131G

674G

1027G

1084A

1612C

2384G

2827A

2959T

2962C

3445T

3463G

4131G

674G

1027T

1084A

1612C

2384G

2827G

2959C

2962C

3445T

3463G

4131T

674G

1027G

1084A

1612T

2384G

2827G

2959C

2962C

3445T

3463G

4131T

674T

1027T

1084G

1612C

2384A

2827G

2959C

2962C

3445T

3463A

4131T

674G

1027T

1084G

1612C

2384G

2827A

2959T

2962C

3445T

3463G

4131T

674G

1027G

1084A

1612T

2384G

2827G

2959C

2962C

3445T

3463A

674G

1027G

1084A

1612C

2384G

2827A

2959T

2962C

3445T

3463G

4131T

674G

1027G

1084G

1612T

2384G

2827A

2959C

2962T

3445T

3463G

4131T

674

559G > T

G187W

1027

912G > T

K304N

1084

969C > A

Silent

1612

1497T > C

Silent

2384

2269G > A

E757K

2827

2712G > A

Silent

2959

2844C > T

Silent

2962

2847C > T

Silent

3445

3330T > C

Silent

3463

3348A > G

Silent

4131

4016T > G

3′

U79745

U79745

603880

GEN-LPT

Homo sapiens monocarboxylate

transporter homologue MCT6 mRNA, complete cds

775T

816A

1476G

775A

816A

1476G

775T

816C

1476G

775A

816C

1476G

775A

816C

1476A

775

610A > T

I204F

816

651A > C

E217D

1476

1311G > A

Silent

2095

1930G > A

3′

D89053

D89053

602371

GEN-LRT

Homo sapiens mRNA for Acyl-CoA

synthetase 3, complete cds

2035A

2432G

2035C

2432A

2035A

2432A

2035

1893A > C

E631D

2432

2290A > G

3′

X90908

X90908

600422

GEN-LSA

H. sapiens mRNA for I-15P (I-

BABP) protein

364

236C > T

T79M

X97868

X97868

300003

GEN-LTH

H. sapiens mRNA for

arylsulphatase

1652

1582T > C

Y528H

AF093771

AF093771

603756

GEN-LTJ

Homo sapiens mitoxantrone

resistance protein 1 mRNA, partial sequence

528

528G > A

3′

X98333

X98333

602608

GEN-LTN

H. sapiens mRNA for organic

cation transporter, kidney

201

57G > A

Silent

NM_005094

NM_005094

600691

GEN-LU3

Homo sapiens fatty acid

transport protein 4 (FATP4) mRNA

168C

591A

168T

591G

168C

591G

168

168C > T

Silent

591

591G > A

Silent

U24253

U24253

136510

GEN-LUE

Human folylpolyglutamate

synthetase (FPGS) gene, exons 5-11, and partial cds

1424C

1649A

1678C

1912C

1424C

1649A

1678C

1912T

1424C

1649G

1678C

1912C

1424A

1649G

1678C

1912C

1424C

1649G

1678T

1912C

1424A

1649G

2554A

1424C

1649A

1678C

1912T

1424C

1649G

1678C

1912C

1424C

1649A

1678C

1912C

1424C

1649G

1678T

1912C

1424

1424C > A

Genomic

1649

1649G > A

Genomic

1678

1678C > T

Genomic

1912

1912C > T

Genomic

2554

2554A > G

Genomic

U24252

U24252

136510

GEN-LUF

Folylpolyglutamate synthetase,

promoter and exons 1-4

263A

266G

527C

1139G

1217C

1647C

2017G

2037G

2282T

2309A

263A

266G

527C

1037G

1139G

1217C

1647C

1955A

2017G

2037G

2282C

2309A

263G

266T

527C

1647C

1955A

2017G

2037G

2282C

2309A

263A

266T

527C

1037G

1139G

1217C

1647C

1955A

2017A

2037G

2282C

2309A

263A

527C

1037G

1139G

1217T

1647C

1955A

2017G

2282C

2309A

263A

527C

1037G

1139G

1217T

1647C

1955A

2017G

2282C

2309G

263A

266T

527C

1037G

1139G

1217C

1647C

1955A

2017G

2037G

2282C

2309A

266G

527C

1037A

1139G

1217C

1647C

1955A

2017G

2189A

2282C

266G

527C

1037G

1139G

1217C

1647C

1955A

2017A

2189A

2282C

263A

266G

527C

1037G

1139A

1217C

1955A

2017G

2037G

2282C

2309A

263A

527C

1139G

1217C

1647C

1955G

2017A

2037G

2282C

2309A

263A

266G

527C

1139G

1217C

1647C

1955G

2017G

2037G

2282C

2309A

263A

266G

527G

1037G

1139G

1217C

1647T

1955A

2017G

2037G

2282C

2309A

263A

266G

527C

1037G

1139G

1217C

1647C

1955A

2017G

2037A

2282C

2309A

263A

266G

527C

1037G

1139G

1217C

1647T

1955A

2017G

2037G

2282C

2309A

266T

527C

1037G

1139G

1217T

1647C

1955A

2017G

2189G

2282C

266T

527C

1037G

1139G

1217C

1647C

1955A

2017A

2189A

2282C

263A

266G

527C

1037A

1139G

1217C

1647C

1955G

2017G

2037G

2282C

2309A

266G

527C

1037G

1139G

1217C

1647T

1955A

2017G

2189A

2282C

263A

266T

527C

1037G

1139G

1217C

1647C

1955A

2017A

2037G

2282C

2309A

266T

527C

1037G

1139G

1217T

1647C

1955A

2017G

2189A

2282C

263A

266G

1037A

1139G

1217C

1647C

1955G

2017G

2037G

2282C

2309A

266T

527C

1037A

1139G

1217C

1647C

1955G

2017G

2189A

2282C

266G

527C

1037G

1139G

1217C

1647C

1955A

2017A

2189A

2282C

263A

266G

527C

1037G

1139G

1217T

1647C

1955A

2017G

2037G

2282C

2309G

263A

266G

527G

1037G

1139G

1217C

1647T

1955A

2017G

2037G

2282C

2309A

266G

527C

1037A

1139G

1217C

1647C

1955A

2017G

2189A

2282C

263A

266G

527C

1037G

1139G

1217C

1647T

1955A

2017G

2037G

2282C

2309A

263A

266G

527C

1037A

1139A

1217C

1647T

1955G

2017G

2037G

2282C

2309A

263A

266T

527C

1037A

1139G

1217C

1647C

1955G

2017A

2037G

2282C

2309A

263A

266G

527C

1037G

1139A

1217C

1647T

1955A

2017G

2037G

2282C

2309A

266G

527C

1037G

1139A

1217C

1647T

1955A

2017G

2189A

2282C

266G

527C

1037A

1139G

1217C

1647C

1955G

2017G

2189A

2282C

266G

527C

1037A

1139G

1217C

1647C

1955G

20170

2189A

2282T

263A

266G

527C

1647C

1955G

2017G

2037G

2282C

2309A

266G

527C

1037G

1139G

1217C

1647C

1955A

2017G

2189G

2282C

266G

527C

1037G

1139G

1217C

1647C

1955A

2017G

2189A

2282C

263A

266T

527C

1037A

1139G

1217C

1647C

1955G

2017G

2037G

2282C

2309A

266T

527C

1037G

1139G

1217C

1647C

1955A

2017G

2189A

2282C

263A

266G

527C

1037A

1139G

1217C

1647C

1955G

2017G

2037G

2282T

2309A

266G

527C

1037A

1139A

1217C

1647T

1955G

2017G

2189A

2282C

263

263A > G

Genomic

266

266G > T

Genomic

527

527C > G

Genomic

1037

1037A > G

Genomic

1139

1139G > A

Genomic

1217

1217C > T

Genomic

1647

1647C > T

Genomic

1955

1955G > A

Genomic

2017

2017G > A

Genomic

2037

2037G > A

Genomic

2189

2189A > G

Genomic

2282

2282C > T

Genomic

2309

2309A > G

Genomic

U92868

U92868

600424

GEN-LUK

Homo sapiens reduced folate

carrier (RFC1) gene, exons 1a, 1c and 1b

441G

498C

579G

599G

431A

441A

498C

579G

599G

431A

441A

498T

431G

441G

498C

579G

599G

441G

498C

579G

599G

431A

441A

498T

579C

599G

431

431A > G

Genomic

441

441A > G

Genomic

498

498G > T

Genomic

579

579G > C

Genomic

599

599G > C

Genomic

X96751

X96751

114835

GEN-LUL

Carboxylesterase I, promoter

235C

328T

975A

984G

235T

328T

939G

975A

984G

235T

328T

939T

975A

984G

235T

328C

939T

975A

984G

235T

328T

939T

975G

984G

235T

328T

939T

975A

984C

235C

328C

939T

975A

984G

235T

328T

939G

975G

984G

235C

328T

939G

975A

984G

235

235T > C

Genomic

258

258{circumflex over ( )}insC

Genomic

328

328T > C

Genomic

939

939G > T

Genomic

975

975A > G

Genomic

984

984G > C

Genomic

L06484

L06484

100740

GEN-LUM

Human acetylcholinesterase

(ACHE) gene, exons 1-2, and promoter region

700C

748C

1274G

1534T

1609C

1690G

1779A

400C

1274G

1534C

1609G

1690G

1744A

1779G

1803G

114A

400C

748C

1274G

1534C

1609G

1690G

1744C

1779G

1803G

700C

1274C

1609C

1690G

1779G

400C

1274G

1534C

1609G

1690G

1744C

1779A

1803G

114C

400C

748C

1274G

1534T

1609G

1690G

1744C

1779A

1803G

114C

400C

748C

1274G

1534T

1609G

1690G

1744C

1779A

1803A

700C

748C

1274G

1534T

1609C

1690A

1779G

700C

748T

1274G

1534T

1609C

1690G

1779G

700C

748C

1274G

1534T

1609C

1690G

1779G

114C

400T

1274G

1534T

1609G

1690G

1744C

1779A

114C

400T

748T

1274G

1534T

1609G

1690G

1744C

1779G

1803G

700T

748C

1274G

1609C

1690G

114C

400C

748C

1274G

1534C

1609G

1690G

1744C

1779G

1803G

114C

400C

748C

1274G

1534T

1609G

1690G

1744C

1779G

1803G

114A

400C

748C

1274G

1534C

1609G

1690A

1744C

1779G

1803G

1274C

1609G

1690G

1744C

1779G

1803G

700C

748C

1274G

1534C

1609C

1690G

1779G

114A

400C

748C

1274G

1534C

1609G

1690G

1744C

1779G

1803G

700C

748C

1274G

1534C

1609C

1690G

1779A

700C

748T

1274C

1534C

1609C

1690G

1779G

114C

400C

748C

1274G

1534T

1609G

1690G

1744C

1779G

1803G

114C

400C

1274G

1534T

1609G

1690G

1744C

1779A

1803A

114C

400C

748C

1274G

1534C

1609G

1690G

1744C

1779A

1803G

700C

748C

1274G

1534T

1609C

1690A

1779G

700C

748T

1274G

1534C

1609C

1690G

1779G

114A

400C

748C

1274G

1534C

1609G

1690G

1744C

1779A

1803G

114C

400C

748C

1274G

1534C

1609G

1690G

1744C

1779G

1803G

114C

400T

748T

1274C

1534T

1609G

1690G

1744C

1779G

1803G

700T

748C

1274G

1534C

1609C

1690G

1779A

748C

1274G

1534T

1609G

1690G

1744C

1779A

1803A

114A

400C

748C

1274G

1534C

1609G

1690A

1744C

1779G

1803G

114C

400C

748C

1274C

1534T

1609G

1690G

1744C

1779A

1803A

400T

1274G

1534T

1609G

1690G

1744C

1779G

1803G

400C

748C

1274G

1534C

1609G

1690G

1744C

1779A

1803G

700C

748C

1274G

1534T

1609C

1690G

1779A

700C

748C

1274G

1534C

1609G

1690G

1779A

400T

748T

1274G

1534T

1609G

1690A

1744C

1779G

1803G

114C

400C

748C

1274G

1534T

1609G

1690G

1744C

1779A

1803G

700C

748T

1274G

1534C

1609G

1690A

1779G

700C

748T

1274G

1534T

1609G

1690G

1779A

114C

400T

748T

1274G

1534T

1609G

1690G

1744C

1779A

1803G

700C

748T

1274G

1534T

1609G

1690G

1779G

700C

748C

1274G

1534T

1609G

1690G

1779G

114C

400T

748T

1274G

1534T

1609G

1690G

1744C

1779G

1803G

114A

400C

1274G

1534C

1609G

1690G

1744C

1779A

1803G

400C

748C

1534C

1609G

1690G

1744C

1779A

1803G

400C

748C

1274G

1534T

1609G

1690G

1744C

1779A

1803A

114C

400T

748C

1274G

1534T

1609G

1690G

1744C

1779A

1803G

1534C

1690G

1744A

1779G

1803G

114

114C > A

Genomic

400

400C > T

Genomic

700

700C > T

Genomic

748

748C > T

Genomic

1274

1274G > C

Genomic

1534

1534T > C

Genomic

1609

1609G > C

Genomic

1690

1690G > A

Genomic

1744

1744C > A

Genomic

1779

1779A > G

Genomic

1803

1803A > G

Genomic

L42812

L42812

100740

GEN-LUN

Homo sapiens

acetylcholinesterase (ACHE) gene, exons 2-6

261C

1871C

2384C

2710G

2831G

3541G

4047C

261C

1871T

2309G

2384C

2710A

2831G

3290C

3541G

4047C

261C

1871C

2309G

2384C

2710A

2831G

3290C

3541G

4047C

261C

1871C

2384C

2710A

2831G

3290C

3541A

4047C

261C

1871C

2309A

2384C

2710A

2831A

3290C

3541G

4047A

1871T

2309A

2384C

261C

1871C

2309A

2384C

2710A

2831G

3290C

3541G

4047C

261T

1871C

2309G

2384C

2710A

2831G

3290C

3541G

4047C

261C

1871C

2309A

2384C

2710A

2831G

3290G

3541G

4047C

261C

1871C

2309G

2384T

2710A

2831G

3290C

3541G

4047C

261C

1871C

2309A

2384C

2710A

2831A

3290C

3541G

4047C

261C

1871C

2309A

2384C

2710G

2831G

3290C

3541G

4047C

261C

1871C

2309A

2384C

3290C

3541G

4047C

1871C

2384T

261C

1871C

2309A

2384C

2710A

2831G

3290G

4047C

1871C

2309G

2384C

261C

1871C

2309A

2384C

2710G

2831G

3290G

3541G

4047C

1871T

2309A

2384C

261C

1871T

2309A

2384C

3290C

3541G

4047C

261C

1871T

2309A

2384C

2710A

2831G

3290C

3541G

4047C

261C

1871C

2309A

2384C

2710A

2831G

3290C

3541A

4047C

261

261C > T

Genomic

1871

1871C > T

Genomic

2309

2309G > A

Genomic

2384

2384C > T

Genomic

2710

2710A > G

Genomic

2831

2831G > A

Genomic

3290

3290C > G

Genomic

3541

3541G > A

Genomic

4047

4047C > A

Genomic

U10554

U10554

118490

GEN-LUP

Exon R of choline

acetyltransferase and complete cds for vesicular acetylcholine transporter

574

574T > C

Genomic

607

607C > T

Genomic

1679

1679T > A

Genomic

1980

1980C > T

Genomic

2081

2081A > G

Genomic

2265

2265A > G

Genomic

2338

2338A > C

Genomic

2372

2372A > G

Genomic

2440

2440T > C

Genomic

2894

2894C > T

Genomic

3512

3512C > T

Genomic

3650

3650C > T

Genomic

3666

3666G > C

Genomic

3779

3779G > C

Genomic

4270

4270C > G

Genomic

4320

4320C > T

Genomic

4848

4848C > A

Genomic

4960

4960G > A

Genomic

5041

5041G > A

Genomic

5163

5163C > T

Genomic

5331

5331T > C

Genomic

5392

5392G > C

Genomic

5440

5440C > T

Genomic

5443

5443C > A

Genomic

5751

5751T > C

Genomic

5970

5970G > C

Genomic

X56585

X56585

118490

GEN-LUU

Human gene for choline

acetyltransferase (EC 2.3.1.6), partial

1728G

1860C

2266T

2295C

2422T

2753G

2881C

3697A

5435T

5606A

1728G

1860C

2266T

2295C

2422C

2753G

2881C

3697A

5435T

5606A

1728G

1860C

2266C

2295C

2422T

3961C

4571C

4798A

4804G

4822C

1728G

1860C

2266C

2295C

2422C

2753G

2881C

3697A

5435T

5606A

1728G

1860C

2266T

2295T

2422C

2753G

2881C

3697G

5435T

5606A

1728G

1860C

2266T

2295T

2422C

2753G

2881C

3697A

5435T

5606A

1728G

1860C

2266C

2295C

2422T

4571C

4798A

4804C

4822C

1728A

1860C

2266T

2295C

3961C

4571C

4798A

4804G

4822C

1728G

1860T

2295C

2422T

2753G

5435T

5606A

1728G

1860T

2266C

2295T

2422T

2753G

2881G

3697G

5435T

5606A

1728G

1860C

2266T

2295C

2422T

2753G

2881C

3697A

5435T

5606A

1728G

1860C

2266T

2295T

2422T

2753G

2881C

3697G

5435T

5606A

1728A

1860C

2266T

2295C

2422T

4571C

4798A

4804C

4822C

1728G

1860C

2266C

2295C

2422T

3961C

4571C

4798A

4804G

4822C

1728G

1860C

2266C

2295C

2422T

2753A

2881G

3697A

5435T

5606A

1728G

1860C

2266C

2295T

2422T

2753G

2881G

3697G

5435T

5606A

1728A

1860C

2266T

2295C

2422T

1728G

1860C

2266T

2295C

2422T

3961G

4571C

4798A

4804G

4822C

1728A

1860C

2266T

2295C

2422T

2753G

2881C

5435T

5606A

1728A

1860C

2266T

2295T

2422T

2753G

2881C

3697G

5435T

5606A

1728G

1860C

2266C

2295C

2422C

2753G

2881C

3697A

5435T

5606A

1728A

1860C

2266C

2295C

2422T

3961C

4571C

4798A

4804G

4822C

1728G

1860C

22660

2295C

2422T

3961C

4571C

4798A

4804C

4822C

1728A

1860C

2266T

2295C

2422T

3961C

4571C

4798A

4804G

4822C

1728A

2266T

2295C

2422T

2753G

2881C

3697A

5435T

5606A

1728G

1860C

2266T

2295C

2422C

2753G

2881C

3697A

5435T

5606A

1728G

1860C

2266C

2295T

2422T

3961G

4571T

4798A

4804G

4822C

1728G

1860C

2266T

2295T

2422C

2753G

2881C

3697G

5435T

5606A

2266G

2295C

2422T

3961C

4571C

4798A

4804G

4822C

1728A

1860C

2266C

2295C

2422T

2753G

2881C

3697A

5435T

5606A

1728G

1860T

2266C

2295C

2422T

1728A

1860C

2266T

2295C

2422T

2753G

2881C

3697A

5435T

5606A

1728G

1860C

2266T

2295T

2422T

3961G

4571C

4798A

4804G

4822T

1728G

1860C

2266C

2295C

2422T

3961G

4571C

4798A

4804G

4822C

1728G

1860C

2266C

2295C

2422T

2753G

2881G

3697A

5435T

5606A

1728G

1860C

2266T

2295T

2422T

3961C

4571C

4798A

4804G

4822C

1728G

1860T

2266C

2295C

2422T

2753G

2881G

5435T

5606A

1728G

1860C

2266C

2295C

2422T

2753G

2881C

3697A

5435T

5606G

1728G

1860C

2266T

2295T

2422C

3961G

4571C

4798A

4804G

4822T

2728A

1860C

2266T

2295C

2422T

2753G

2881C

3697A

5435G

5606A

1728A

1860C

2266T

2295T

2422T

3961G

4571C

4798A

4804G

4822C

1728G

1860C

2266C

2295C

2422C

3961C

4571C

4798A

4804G

4822C

1728G

1860C

2266C

2295C

2422T

2753G

2881C

3697A

5435T

5606A

1728

1728G > A

Genomic

1860

1860C > T

Genomic

2266

2266C > T

Genomic

2295

2295C > T

Genomic

2422

2422T > C

Genomic

2753

2753G > A

Genomic

2881

2881G > C

Genomic

3697

3697A > G

Genomic

3961

3961C > G

Genomic

4571

4571C > T

Genomic

4798

4798G > A

Genomic

4804

4804G > C

Genomic

4822

4822C > T

Genomic

5435

5435T > G

Genomic

5606

5606A > G

Genomic

M96015

M96015

118490

GEN-LUZ

Human choline acetyltransferase

gene, alternate exon 1 including 5′ end of cds

445G

564G

1206T

1258G

1537G

1839T

2021C

445G

564G

1206G

1258G

1537G

1839T

2021C

1206T

1258A

445G

564G

1206G

1258A

1537G

1839G

2021C

564G

1258G

1537G

1839T

2021G

445G

1206G

1537A

1839T

2021C

445G

564G

1206G

1258A

1839T

2021G

445G

564G

1206G

1258A

1537G

1839T

2021C

445T

564G

1206T

1258G

1537G

2021C

1206T

1258G

1206T

1258A

445T

564G

1206G

1258G

1537G

1839T

2021G

445G

564G

1206G

1258A

1537A

1839T

2021C

445G

564A

1206G

1258G

1537A

1839T

2021C

445T

564G

1206G

1258G

1537G

2021G

445G

564G

1206G

1258G

1537G

1839G

2021C

445G

564G

1206T

1258G

1537G

1839G

2021C

1206G

1258A

445G

564G

1206G

1258G

1537G

1839G

2021G

445T

564G

1206G

1258G

1537G

1839T

2021C

445

445G > T

Genomic

564

564G > A

Genomic

1206

1206T > G

Genomic

1258

1258G > A

Genomic

1537

1537G > A

Genomic

1839

1839T > G

Genomic

2021

2021C > G G

enomic

L10819

L10819

171150

GEN-LVD

Homo sapiens aryl

sulfotransferase mRNA, complete cds

191

153C > T

Silent

200

162G > A

Silent

230

192T > C

Silent

242

204G > A

Silent

267

229A > G

M77V

295

257C > T

A86V

330

292G > A

D98N

338

300G > A

Silent

638

600C > G

Silent

676

638A > G

H213R

940

902G > A

3′

1011

973T > C

3′

L19956

L19956

600641

GEN-LVE

Human aryl sulfotransferase

mRNA, complete cds

243

105A > G

Silent

284

146C > T

549F

X78282

X78282

601292

GEN-LVF

H. sapiens mRNA for aryl

sulfotransferase (ST1A2)

895

895T > C

3′

L11695

L11695

190181

GEN-MDJ

Human activin receptor-like

kinase (ALK-5) mRNA, complete cds

1657

1581G > A

3′

U03858

U03858

600007

GEN-MDM

Fms-related tyrosine kinase 3

ligand

683

600C > T

Silent

1016

933T > C

3′

M37815

M37815

186760

GEN-MDZ

Human T-cell membrane

glycoprotein CD28 mRNA

327G

1061A

327A

1061A

327G

1061C

327

105G > A

Silent

1061

839A > C

3′

X57303

X57303

104615

GEN-MEB

H. sapiens REC1L mRNA

474C

573C

582C

630G

1026G

1059G

1185C

1332C

1401C

1551G

1656T

1672A

1747G

474G

582C

630G

1026G

1059G

1185C

1332C

1401C

1551G

1656T

1672A

1747A

474G

573C

582C

630G

1026G

1059G

1185C

1332C

1401G

1551C

1656C

1672A

1747G

474G

573C

582C

630G

1026G

1059G

1185C

1332T

1401C

1551G

1656T

1672A

1747G

573C

582C

630G

1026G

1059G

1185T

1332C

1401C

1551G

1656T

1672A

1747G

474G

573C

582C

630G

1026G

1059G

1185C

1332C

1401G

1551G

1656C

1672A

1747G

573G

1059G

1185C

1332C

1401G

1551G

1747G

474G

573C

582C

630G

1026G

1059G

1185C

1332C

1401C

1656C

1672A

1747G

573C

1059A

1185C

1332C

1401G

1551G

1747G

474G

573C

582C

630G

1026G

1059A

1185C

1332C

1401C

1551G

1656T

1672A

1747G

474G

573C

582C

630G

1026G

1059G

1185C

1332C

1401C

1551G

1656T

1672A

1747G

573C

582C

630G

1026G

1059A

1185C

1332T

1401C

1551G

1656T

1672A

1747G

474G

573G

582C

630G

1026G

1059G

1185C

1332C

1401C

1551G

1656T

1672A

1747G

573C

582C

630G

1026A

1059G

1185C

1332C

1401C

1551C

1656T

1672A

1747G

474G

573G

582C

630G

1026G

1059G

1185C

1332C

1401C

1551C

1656C

1672A

1747G

474C

573C

582C

630G

1026G

1059A

1185C

1332C

1401C

1551G

1656T

1672A

1747G

573G

582C

630G

1026G

1059G

1185C

1332C

1401C

1551C

1656C

1672A

1747G

474C

573C

582C

630G

1026G

1059G

1185T

1332C

1401C

1551G

1656T

1672A

1747G

474G

573G

582C

630G

1026G

1059G

1185C

1332C

1401C

1551G

1656C

1672A

1747G

573G

582C

630G

1059G

1185C

1332C

1401C

1551C

1656C

1672A

1747G

474G

573C

582C

630G

1026G

1059G

1185C

1332C

1401C

1551C

1656C

1672A

1747G

573G

582C

630G

1026G

1059A

1185C

1332C

1401C

1551G

1656T

1672A

1747G

573C

582C

630G

1026G

1059A

1185C

1332T

1401C

1551G

1656T

1672A

1747G

474G

573C

582C

630G

1026G

1059G

1185C

1332C

1401C

1551G

1656T

1747G

573G

582T

630A

1059A

1185C

1332C

1401G

1551G

1656C

1672G

1747G

573C

582C

630G

1026G

1059A

1185C

1332C

1401C

1551G

1656T

1672A

1747G

573G

582C

630G

1059G

1185C

1332C

1401G

1551G

1656T

1672A

1747G

474G

573G

582C

630G

1026G

1059G

1185C

1332C

1401C

1551G

1656T

1672A

1747A

573C

582C

630G

1026A

1059G

1185C

1332C

1401C

1551G

1656T

1672A

1747G

474

324C > G

Silent

573

423C > G

Silent

582

432C > T

Silent

630

480C > A

Silent

1026

876G > A

Silent

1059

909G > A

Silent

1185

1035T > C

Silent

1332

1182C > T

Silent

1401

1251C > G

Silent

1551

1401C > C

Silent

1656

1506T > C

Silent

1672

1522A > G

I508V

1747

1597G > A

A533T

M68895

M68895

103735

GEN-MH7

Human alcohol dehydrogenase 6

gene, complete cds

547

454G > A

V152M

AF117815

AF117815

603708

GEN-MII

Homo sapiens molybdopterin

synthase small and large subunit (MOCO1) bicistronic mRNA, complete cds

1159

1159T > C

3′

U23143

U23143

138450

GEN-MIY

Human mitochondrial serine

hydroxymethyltransferase gene, nuclear encoded mitochondrion protein, complete

cds

506

506T > G

F169C

U68162

U68162

159530

GEN-MJM

Human thrombopoietin receptor

(MPL) gene

830G

2749C

3008G

830A

3008G

830G

2749A

3008G

830G

2749C

3008A

830G

2749C

830A

2749A

3008G

830G

2749A

830

764G > A

G255E

2749

2683C > A

3′

3008

2942G > A

3′

X98332

X98332

602607

GEN-MMA

H. sapiens mRNA for organic

cation transporter, liver

228T

1294A

228C

1294G

228C

1294A

228T

1294G

228

156C > T

Silent

630

558C > T

Silent

1294

1222A > G

M408V

AF058056

AF058056

603654

GEN-MNJ

Homo sapiens monocarboxylate

transporter 2 (hMCT2) mRNA, complete cds

1460T

1510C

1460A

1510C

1460A

1510T

200

73G > A

A25T

203

76G > A

A26T

588

461G > A

S154N

1460

1333T > A

S445T

1510

1383C > T

Silent

U30930

U30930

601291

GEN-MOS

UDP glycosyltransferase 8 (UDP-

galactose ceramide galactosyltransferase)

1256A

1619G

1758A

2150T

1256A

1619G

1758G

2150C

1256A

1619A

1758A

2150C

1256G

1619G

1758A

2150C

1256A

1619G

1758A

2150C

1256A

1619G

2150C

1256

741A > G

Silent

1619

1104G > A

M368I

1758

1243A > G

K415E

2150

1635C > T

3′

U06088

U06088

253000

GEN-MP3

Human N-acetylgalactosamine 6-

sulphatase (GALNS) gene

1936

1936C > T

3′

2180

2180G > A

3′

2221

2221G > A

3′

AF039400

AP039400

603906

GEN-MQY

Homo sapiens calcium-dependent

chloride channel-1 (hCLCA1) mRNA, complete cds

996

645T > A

Silent

2787

2436T > C

Silent

K01612

K01612

None

GEN-MT4

Dihydrofolate reductase,

promoter

527A

1120C

1124G

1678G

164A

169A

278T

287G

372A

380A

527G

879C

1120T

1124G

1135G

1229G

1678C

164C

169A

278T

287G

372A

380A

527A

879C

1120C

1124G

1135G

1229G

1678C

164C

287G

372A

1120C

1124G

1229A

1678C

164C

169A

278T

287G

372A

380A

527G

879C

1120C

1124G

1135G

1229G

1678C

169A

278T

287A

372A

380A

527G

1120C

1124G

1135G

1229G

169A

278T

287G

372A

380A

527G

918G

925A

1124G

1135G

1229G

1678G

264C

169A

278T

287G

372A

380A

1120C

1124A

1135G

1229G

1678C

169A

278T

287G

372G

380A

918C

925A

1124G

1135G

1229G

1678C

164C

169A

278T

287G

372A

380A

527G

918C

925A

1120C

1124G

1135A

1229A

1678C

169G

278C

380T

527A

918G

925G

1678C

264A

169A

278T

287G

372A

380A

527G

879C

1120C

1124G

1135G

1229G

1678C

264C

169A

278T

287G

372G

380A

527A

918C

925A

1120C

1124G

1135G

1229G

1678C

169A

278T

380A

527A

918G

925A

1120C

1124G

1135G

1229A

1678C

164C

169A

278T

287G

372A

380A

527A

1120C

1124A

1135G

1229G

1678C

164A

169A

278T

287A

372A

380A

527G

112CC

1124G

1135G

1229G

1678G

169A

278T

380A

527A

918C

925A

1120C

1124G

1135A

1229A

1678C

169A

278T

380A

527A

918C

925A

1120C

1124G

1135A

1229G

1678G

164C

169A

278T

287G

372A

380A

527G

918G

925A

1120C

1124G

1135G

1229G

1678G

169A

278T

380A

527G

918C

925A

1120C

1124G

1135A

1229G

1678C

164C

169A

278T

287G

372A

380A

527A

879C

1120C

1124G

1135G

1229G

1678C

164C

169A

278T

287G

372A

380A

527G

879C

1120C

1124G

1135G

1229G

1678C

164C

169A

278T

287G

372A

380A

527A

918G

925A

1120C

1124G

1135G

1229G

1678C

918C

925G

1120C

1124G

1135A

1229A

1678C

164C

169A

278T

287G

372A

380A

527A

879T

1120C

1124A

1135G

1229G

1678C

169A

278T

380A

527A

918C

925G

1120C

1124G

1135A

1229G

1678C

164

164C > A

Genomic

169

169G > A

Genomic

278

278C > T

Genomic

287

287G > A

Genomic

372

372A > G

Genomic

380

38CT > A

Genomic

527

527A > G

Genomic

879

879C > T

Genomic

918

918G > C

Genomic

925

925G > A

Genomic

1120

1120C > T

Genomic

1124

1124G > A

Genomic

1135

1135A > G

Genomic

1229

1229A > G

Genomic

1678

1678C > G

Genomic

AF005216

AF005216

147796

+L,23  GEN-MT5

Homo sapiens receptor-associated

tyrosine kinase (JAK2) mRNA,complete cds

144C

298T

491C

874A

983T

1641C

1668G

2423T

2620A

2984A

144C

491T

874G

983C

1641C

1668G

2423T

2620A

144C

298T

491C

874G

983T

1641C

1668A

2423T

262CA

2984A

144C

298C

491C

874G

983C

1641C

1668G

2423T

2620A

2984G

144C

298T

491C

874G

983T

1641C

1668G

2423C

2620A

144C

491C

874G

983C

1641G

1668G

2423T

2620A

144C

298T

491C

874G

1641C

1668G

2423T

2620C

2984A

144C

298T

491C

874G

983C

1641C

1668G

2423T

2620A

2984A

144C

298T

491C

874G

983C

1641C

1668G

2423T

2620A

2984G

144C

298T

491C

874G

983T

1641C

1668G

2423T

2620A

2984A

144T

298C

491C

874G

983C

1641C

1668G

2423T

2620A

2984G

144C

298T

491C

874G

983T

1641C

1668G

2620A

2984G

144C

298T

491C

874G

983T

1641C

1668G

2423C

2620A

144C

298T

491C

874G

983C

1641C

1668G

2423T

2620A

2984A

144C

298T

491C

874G

983T

1641C

1668G

2423T

2620C

2984A

144C

298T

491T

874G

983C

1641C

1668G

2423T

2620A

2984A

144C

298T

491C

874G

983C

2423T

2620A

2984G

144T

298T

491C

874G

983C

1641C

1668G

2423T

2620A

2984G

144C

298T

874G

983T

1641C

1668G

2423T

2620A

2984A

144C

298T

491C

874G

983T

2423T

2620A

2984A

144

144C > T

3′

298

298C > T

3′

491

491C > T

3′

874

874G > A

3′

983

983C > T

3′

1641

1641C > G

3′

1668

1668G > A

3′

2423

2423T > C

3′

2620

2620A > C

3′

2984

2984A > G

3′

AJ005200

AJ005200

None

GEN-MT6

Homo sapiens MRP2 gene, promoter

region

211G

1206T

211A

1206C

211G

1206C

211

211A > G

Genomic

1206

1206C > T

Genomic

Y08062

Y08062

None

GEN-MT8

Organic anion transporter,

promoter

310T

689G

726G

799G

908T

1449T

1470G

310C

689G

726G

799G

908T

1449T

1470A

310C

689G

726G

799H

908T

1449C

1470G

310C

689A

908T

1449T

1470G

310C

689G

726A

799A

908T

1449T

1470G

310C

689G

726G

799G

908T

1449T

1470G

310C

726A

799A

908A

1449T

1470G

310C

689A

726A

799A

908A

1449T

1470G

310C

689G

726A

799A

908T

310C

689G

726A

799A

908T

1449T

1470A

310C

689G

726A

799A

908T

1449C

1470G

310C

689A

726A

799A

908T

1449T

1470G

310

310C > T

Genomic

689

689G > A

Genomic

726

726G > A

Genomic

799

799G > A

Genomic

908

908T > A

Genomic

1449

1449T > C

Genomic

1470

1470G > A

Genomic

1702

1702{circumflex over ( )}insA

Genomic

U08374

U08374

600522

GEN-MT9

Human cytosolic phospholipase A2

(cPLA2) gene, promoter region

588

588T > C

Genomic

AF120161

AF120161

None

GEN-MTB

Retinoic X receptor beta,

promoter and genomic

107

107C > G

Genomic

218

218C > T

Genomic

2230

2230T > A

Genomic

2352

2352T > C

Genomic

3148

3148A > C

Genomic

3148

3148A > T

Genomic

3459

3459T > C

Genomic

3558

3558C > T

Genomic

3713

3713C > G

Genomic

5462

5462C > T

Genomic

5667

5667C > T

Genomic

5865

5865G > A

Genomic

6041

6041T > C

Genomic

6544

6544C > T

Genomic

6604

6604C > A

Genomic

7048

7048G > T

Genomic

7266

7266T > A

Genomic

7279

7279T > G

Genomic

7412

7412A > T

Genomic

7804

7804T > A

Genomic

7833

7833T > C

Genomic

7834

7834A > C

Genomic

Z29336

Z29336

None

GEN-MTC

Superoxide dismutase 1 (Cu/Zn),

promoter

161G

402T

161A

402C

161G

402C

161

161G > A

Genomic

402

402C > T

Genomic

Z35286

Z35286

None

GEN-MTD

Multidrug resistance protein 3

(MDR3), promoter

286C

459T

481C

796T

1966C

1985G

2002T

286C

510A

796C

1966T

1985C

2002T

286C

459T

510A

1966T

1985G

2002T

286C

481C

510A

1966T

2002C

286C

481C

510A

796C

1966C

1985G

2002T

286C

459C

481C

510A

796C

1966T

1985G

2002T

286C

459T

481C

510A

796C

1885G

1966C

1985C

2002T

286C

459T

481C

510A

796T

1885G

1966C

1985C

2002T

286T

459T

481C

796T

1966C

1985G

2002T

286C

459T

481C

510A

796C

1885C

1966C

1985C

2002C

286C

459T

481C

510A

796T

1885C

1966C

1985C

2002T

286C

459T

481C

510A

1885C

1966T

1985C

2002C

286C

459T

481C

510A

796T

1885G

1966C

1985G

2002C

286C

796C

1885C

1966C

1985C

2002C

286C

459T

481A

510A

796T

1966T

1985G

2002T

286T

459T

481C

510G

796T

1966C

1985G

2002T

286C

459T

481C

510A

796T

1885G

1966C

1985G

2002T

286C

459C

481C

510A

796C

1966T

1985G

2002T

286C

459T

481A

510A

796C

1885G

1966T

1985C

2002T

286C

459C

481C

510A

796C

1885G

1966C

1985G

2002T

286C

459T

481C

510A

796T

1885G

1966C

1985C

2002C

286

286C > T

Genomic

459

459T > C

Genomic

481

481C > A

Genomic

510

510A > G

Genomic

796

796C > T

Genomic

1885

1885C > G

Genomic

1966

1966C > T

Genomic

1985

1985C > G

Genomic

2002

2002T > C

Genomic

M38191

M38191

None

GEN-MTE

5-lipoxygenase, promoter

84G

137G

168G

351G

559G

940G

943G

1000G

1085G

1285T

84G

137G

168A

351G

559G

940G

943G

1000G

1085C

1285T

1310C

84G

137G

168G

351G

559T

940G

943G

1000G

1085C

1285T

1310C

84G

137G

168G

351G

559G

940A

943G

1000G

1085C

1285T

1310C

84G

137G

168G

351G

559G

940G

943G

1000G

1085C

1285T

1310C

84A

137A

168G

351A

559T

940G

943G

1000A

1085C

1285C

1310C

84G

137G

168G

351G

559G

940G

943A

1000G

1085C

1285T

1310C

84A

137A

168G

351G

559T

940G

943G

1000A

1085C

1285C

1310C

84G

137G

168G

351G

559T

940A

943G

1000G

1085C

1285T

1310C

84A

137A

168G

351G

559G

940G

943G

1000A

1085C

1285C

1310C

84G

137G

168G

351G

559T

940A

943G

1000G

1085G

1285T

1310C

84G

137G

168G

351G

559G

940G

943G

1000G

1085G

1285T

1310T

559T

940G

943G

1000G

1085C

1285T

1310C

84

84G > A

Genomic

137

137G > A

Genomic

168

168G > A

Genomic

351

351G > A

Genomic

472

472-477de1GTTAAA

Genomic

559

559G > T

Genomic

940

940G > A

Genomic

943

943G > A

Genomic

1000

1000G > A

Genomic

1085

1085C > G

Genomic

1285

1285T > C

Genomic

1310

1310C > T

Genomic

AL049595

AL049595

None

GEN-MTI

Serotonin receptor 5HT-1B,

promoter

73G

287C

295G

302T

462C

793T

900T

1001A

73G

287C

295G

302T

462C

793T

900G

73G

287C

295G

302C

462A

793T

900G

1001A

302C

462A

900G

1001T

73A

287C

295G

302T

462C

793T

900T

1001A

73G

287T

302T

462C

793T

900T

1001A

302C

462A

900G

1001T

73G

287C

295G

302T

462C

793G

900T

1001A

73A

287C

295G

302C

462A

793T

900G

1001A

73G

287C

295G

302T

462C

793T

900G

1001T

73G

287T

295G

302T

462C

900T

1001A

73G

287T

295T

302T

462C

793T

900T

1001A

73

73A > G

Genomic

287

287C > T

Genomic

295

295G > T

Genomic

302

302T > C

Genomic

462

462C > A

Genomic

793

793T > G

Genomic

900

900T > G

Genomic

1001

1001A > T

Genomic

U50136

U50136

246530

GEN-MTJ

Leukotriene C4 synthase,

promoter and genomic

375G

1003A

1279G

1342C

1544T

2154G

2169C

2406G

2742C

2779G

3252T

3416G

3486C

3603C

3689G

4010A

375G

1003A

1279G

1342C

1544G

1902A

2154G

2169C

2406G

2742C

2779G

2940C

3252T

3416G

3486C

3603T

3689G

4010A

375G

1003A

1279G

1342C

1544T

1902A

2169C

2406G

2742C

2779G

2940C

3252G

3416G

3486C

3603T

3689G

4010A

375G

1279G

1342C

1902A

2154G

2169T

2779G

2940C

3252T

3416G

3486C

3689G

1003A

1279A

1342C

1544T

1902A

2154G

2169C

2406G

2742C

2779G

2940C

3252T

3416G

3486C

3603T

3689G

4010A

375G

1003A

1279G

1342C

1544T

1902A

2154G

2169C

2406A

2742C

2779G

2940C

3252T

3416G

3486C

3603T

3689G

4010A

375G

1003A

1279G

1342T

1544T

1902A

2154G

2169C

2406G

2742C

2779G

2940C

3252T

3416G

3486C

3603C

3689A

4010A

375G

1003A

1279G

1342C

1544T

1902A

2154T

2169C

2406G

2742C

2779G

2940C

3252T

3416G

3486C

3603T

3689G

4010A

375A

1003A

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742C

2779G

2940C

3252T

3416G

3486C

3603T

3689G

4010A

375G

1003C

1279G

1342T

1544T

1902A

2154G

2169C

2406G

2742C

2779G

2940C

3252T

3416G

3486C

3603C

3689A

4010A

375G

1003A

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742C

2779G

2940C

3252T

3416A

3486C

3603T

3689G

4010A

375G

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2779G

2940C

3252T

3416G

3486T

3689G

375G

1003A

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742C

2779G

2940A

3252T

3416G

3486C

3603T

3689G

4010A

375G

1003A

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742C

2779G

2940C

3252T

3416G

3486C

3603T

3689G

4010A

375G

1003C

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742T

2940G

3252T

3416G

3486C

3603C

3689G

375G

1279G

1342C

1902A

2154G

2169T

2406A

2742T

2779G

2940C

3252T

3416G

3486C

3603C

3689G

4010G

375G

1003C

1279G

1342C

1544T

1902A

2154T

2169C

2406G

2742T

2779G

2940C

3252T

3416G

3486C

375G

1003C

1279G

1342T

1544T

1902G

2154G

2169C

2406G

2940C

3252T

3486C

3603C

3689A

4010A

375G

1003C

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742T

2779G

2940C

3252T

3416G

3486C

3603C

3689G

4010G

375G

1003C

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742T

2779G

2940C

3252T

3416G

3486T

3603C

3689G

4010G

375G

1003C

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742T

2779A

2940C

3252T

3416G

3486C

3603C

3689G

4010G

375G

1003A

1279G

1342C

1544T

1902A

2154T

2169C

2406G

2742C

2779G

2940C

3252G

3416G

3486C

3603T

3689G

4010A

375G

1003A

1279G

1342C

1544T

2154G

2169C

2406G

2742C

2779G

3252T

3416G

3486C

3603C

3689G

4010A

375A

1003C

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742T

2779G

2940C

3252T

3416G

3486C

3603C

3689G

4010G

375G

1003C

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742T

2940C

3252T

3416G

3486G

3603C

3689G

375A

1003C

1279G

1342C

1544T

1902A

2154G

2169C

2406G

2742T

2779G

2940C

3252T

3416G

3486T

3603C

3689G

4010G

375G

1279A

1342C

1544T

1902A

2154G

2169C

2406G

2742C

2779G

3252T

3416G

3486C

3603T

3689G

4010A

375

375G > A

Genomic

1003

1003A > C

Genomic

1279

1279G > A

Genomic

1342

1342C > T

Genomic

1544

1544T > G

Genomic

1902

1902A > G

Genomic

2154

2154G > T

Genomic

2169

2169C > T

Genomic

2406

2406G > A

Genomic

2742

2742C > T

Genomic

2779

2779G > A

Genomic

2940

2940C > A

Genomic

2252

3252T > G

Genomic

3416

3416G > A

Genomic

3486

3486C > T

Genomic

3603

3603T > C

Genomic

3689

3689G > A

Genomic

4010

4010A > G

Genomic

M60470

M60470

603700

GEN-MTL

5-lipoxygenase activating

protein (FLAP) gene, promoter and exon 1

185G

188T

336T

41GT

524G

840G

862A

185G

188T

336T

415T

524G

840G

862C

185G

188T

336G

840G

185A

188T

336T

415T

524G

840G

185G

188T

336T

415T

524G

840A

862A

185G

188C

336T

415T

524G

862A

185G

188C

336T

415T

524G

840A

862A

185G

188T

336G

415A

524A

840G

862C

185A

188T

336T

415T

524G

840G

862C

185G

188T

336T

415T

524G

840A

862C

185

185G > A

Genomic

188

188T > C

Genomic

336

336T > G

Genomic

415

415T > A

Genomic

524

524G > A

Genomic

840

840G > A

Genomic

862

862C > A

Genomic

M63259

M63259

603700

GEN-MTM

Human 5-lipoxygenase activating

protein (FLAP) gene, exon 2

65G

113G

353C

446T

65A

113T

353A

446T

65A

113T

353A

446C

65A

113T

353C

446T

65G

113T

353C

446T

65

65G > A

Genomic

113

113T > G

Genomic

353

353G > A

Genomic

353

353G > C

Genomic

446

446T > C

Genomic

M63262

M63262

603700

GEN-MTN

Human 5-lipoxygenase activating

protein (FLAP) gene, exon 5

533

533A > G

Genomic

M63261

M63261

603700

GEN-MTO

Human 5-lipoxygenase activating

protein (FLAP) gene, exon 4

440

440C > T

Genomic

M63260

M63260

603700

GEN-MTP

Human 5-lipoxygenase activating

protein (FLAP) gene, exon 3

419

419G > A

Genomic

U65080

U65080

None

GEN-MTR

Leukotriene A4 hydrolase,

promoter

213G

454C

455C

526G

643T

671T

808C

1115A

1654A

1754G

213G

454C

455G

526G

643T

671T

1115A

1654A

1754G

454C

455C

526C

643T

671T

808G

1115C

1654A

1754G

213G

454C

455C

643T

671T

808C

1115A

1654A

1754A

454C

455G

526C

643T

671T

808G

1115C

1654A

1754G

213G

455C

526C

643T

671T

808G

1115C

1654G

213G

454C

455C

526G

643T

671T

808C

1115C

1654A

1754G

213C

454C

455C

526C

643T

808C

1115C

1654A

1754A

213G

454C

455C

526C

643T

671T

808G

1115A

1654A

1754G

213G

454C

455C

643T

671T

1115C

1654A

1754A

213G

454T

455C

526G

643T

671T

1115A

213C

454C

455C

808C

1115A

1654A

1754A

213G

454C

455C

526G

643T

671T

808C

1115A

1654A

1754G

213C

454T

455C

643T

671T

1115C

213C

454C

455C

526C

643A

671A

808C

1115C

1654A

1754A

213C

454C

455C

526C

643T

671T

808C

1115C

1654A

1754G

213G

454C

455C

526G

643T

671T

808G

1115C

1654A

1754G

213G

454T

455C

526C

643T

671T

808G

1115C

1654G

1754A

213G

454T

455C

526G

643T

671T

808G

1115A

1654G

1754A

213G

454C

455C

526C

643T

671T

808G

1115C

1654A

1754G

213G

454C

455C

526C

643T

671T

808C

1115A

1654A

1754A

213G

454T

455C

526G

643T

671T

808G

1115C

1654G

1754A

213C

454C

455C

526C

643T

671T

808G

1115C

1654A

1754G

213G

454C

455C

526C

643T

671T

808C

1115C

1654A

1754A

213C

454C

455C

526C

643A

671A

808C

1115A

1654A

1754A

213G

454C

455C

526G

643T

671T

808C

1115A

1654A

1754A

213G

454C

455G

526G

643T

671T

808G

1115A

1654A

1754G

213C

454T

455C

526G

643T

671T

808G

1115C

1654G

1754A

213G

454C

455C

526G

643T

671T

808G

1115C

1654A

1754A

213C

454C

455C

526C

643T

671T

808C

1115C

1654A

1754A

213G

454C

455G

526C

643T

671T

808G

1115C

1654A

1754G

213

213G > C

Genomic

454

454C > T

Genomic

455

455C > G

Genomic

526

526C > G

Genomic

643

643T > A

Genomic

671

671T > A

Genomic

808

808C > G

Genomic

1115

1115C > A

Genomic

1654

1654A > G

Genomic

1754

1754A > G

Genomic

S77127

S77127

None

GEN-MTU

Superoxide dismutase 2

(manganese), promoter and genomic

333G

485C

745G

888A

333G

485C

745C

888A

333G

485C

745G

888G

333G

485A

745G

333A

485A

745C

888A

333G

485A

745C

888A

333G

485A

745G

888A

333G

485A

745G

888G

333

333G > A

Genomic

485

485A > C

Genomic

745

745C > G

Genomic

888

888A > G

Genomic

AF088893

AF088893

None

GEN-MTX

Homo sapiens retinoic acid

receptor alpha (RARA) gene, exon 7

97A

355C

97G

355T

97

97A > G

Genomic

355

355C > T

Genomic

AF088890

AF088890

None

GEN-MU0

Homo sapiens retinoic acid

receptor alpha (RARA) gene, exon 3

502

502C > G

Genomic

AF088895

AF088895

None

GEN-MU1

Homo sapiens retinoic acid

receptor alpha (RARA) gene, exon 9 and complete cds

197

197T > C

Genomic

AF088888

AF088888

None

GEN-MU4

Retinoic acid receptor alpha,

promoter and exon 1

992C

1020T

1157C

992C

1020C

1157C

992C

1020C

1157G

992G

1020C

1157C

992

992C > G

Genomic

1020

1020C > T

Genomic

1157

1157C > G

Genomic

AF091582

AF091582

603201

GEN-MU6

Homo sapiens bile salt export

pump (BSEP) mRNA, complete cds

933T

1083A

1457C

2155A

2260T

3733T

4328G

4460A

4512G

933T

1083G

1457T

2155A

2260T

3733T

4328G

4460A

4512G

933T

1083G

1457T

2l55A

2260T

3733T

4328A

4460G

4512A

933T

1083A

1457C

2155G

2260T

3733T

4328G

4460A

4512G

933T

1457C

2155A

2260T

4328A

4460A

933T

1083A

1457T

2155A

2260T

3733T

4328A

4460G

4512A

933T

1083A

1457C

2155A

2260T

3733T

4328G

4512A

933T

1083A

1457T

2155G

2260T

3733T

4328A

4460G

4512A

933C

1083A

1457C

2155A

2260T

3733T

4328G

4512G

933T

1083A

1457C

2155A

226CC

3733T

4328A

4460G

4512A

933T

1083A

1457T

2155A

2260T

3733T

4328G

4512A

933T

1083G

1457T

2155A

2260T

3733T

4328G

4460G

4512A

933T

1083A

1457C

2155A

2260T

3733T

4328A

4460G

4512A

933T

1083A

1457T

3733A

4328G

933T

1083G

1457C

2155G

2260T

3733T

933T

1083A

1457T

2155A

2260T

3733T

4328G

4460A

4512G

933C

1083A

1457C

2155A

2260T

3733T

4328G

4512A

933T

1457C

2155A

2260T

4328A

4460A

4512A

933T

1083A

1457T

2155A

2260T

3733T

4328G

4460G

4512A

933T

1083A

1457C

2155A

2260T

3733T

4328G

4460G

45l2A

1083A

1457C

2155A

2260T

3733T

4328A

4460G

4512A

1083A

1457C

3733T

4328G

4460A

4512G

1083A

1457C

2155A

2260T

3733T

4328G

4460A

4512G

933C

1083A

1457C

2155A

2260T

3733T

4328G

4460G

4512A

933T

1083A

1457T

2155A

2260T

3733T

4328G

4460A

4512A

933T

1083A

1457C

3733T

4328G

4460G

4512A

4328A

4460G

4512A

933T

1083A

1457T

3733A

4328G

4460G

4512A

1083A

1457C

2155A

2260T

3733T

4328G

4460G

4512A

933T

1083G

1457C

2155G

2260T

3733T

4328G

4460A

4512G

933T

1083A

1457C

3733T

4328G

4460A

4512G

933

807T > C

Silent

1083

957A > G

Silent

1457

1331T > C

V444A

2155

2029A > G

M677V

2260

2134T > C

Silent

3733

3607T > A

S1203T

4328

4202G > A

3′

4460

4334A > G

3′

4512

4386G > A

3′

AC004590

AC004590

None

GEN-MU7

Multidrug resistance-associated

protein 3 (MRP3), promoter

56A

149A

690A

735G

1325G

1764G

1879C

1958T

2527G

2832T

2881T

149A

690A

1764G

1879G

1958C

2527G

2881C

56A

149A

690C

1764G

1879C

1958C

2527G

56A

149A

690A

735A

1325G

1764G

1879C

1958C

2527G

2832T

2881T

56A

149A

690A

735A

1325G

1764G

1879C

1958C

2527G

2832T

2881C

56A

149A

690A

735A

1325G

1764G

1879C

1958C

2527A

2832T

2881T

56A

149A

690A

735G

1764G

1879C

1958C

2527G

2832A

56A

149G

690A

735G

1325G

1764G

1879C

1958C

2527G

2832T

2881T

56A

149A

690A

735A

1325G

1764A

1879C

1958C

2527G

2832T

56A

149A

690A

735G

1325G

1764G

1879C

1958C

2527G

2832T

2881T

56G

149A

690A

1325G

1764G

1879C

1958C

2527G

2832T

2881T

56G

149A

690A

735G

1325A

1764G

1879G

1958C

2527G

2832A

2881C

149A

690A

735G

1325A

1764G

1879C

1958C

2527G

2832A

2881C

56A

149A

690A

735G

1325A

1764G

1879C

1958C

2527G

2832A

2881C

56G

149A

690A

735G

1325G

1764G

1879C

1958C

2527G

2832T

2881T

56A

149A

690A

735A

1325G

1764A

1879C

1958C

2527G

2832T

2881C

56A

149A

690C

735G

1325A

1764G

1879C

1958C

2527G

2832A

2881C

56A

149A

690A

735G

1325G

1764G

1879C

1958C

2527G

2832T

2881C

56

56A > G

Genomic

149

149A > G

Genomic

690

690A > C

Genomic

735

735A > G

Genomic

1325

1325G > A

Genomic

1764

1764G > A

Genomic

1879

1879C > G

Genomic

1958

1958C > T

Genomic

2527

2527G > A

Genomic

2832

2832T > A

Genomic

2881

2881C > T

Genomic

AC007022

AC007022

None

GEN-MU8

Serotonin receptor 5-HT2C,

promoter

198G

260A

498T

872C

198C

260G

498C

872A

260A

498C

198G

260G

498C

872C

198G

260G

872C

198C

260A

498C

872A

198C

260A

498T

872A

198

198C > G

Genomic

260

260G > A

Genomic

498

498C > T

Genomic

872

872A > C

Genomic

U01824

U01824

None

GEN-MU9

Human glutamate/aspartate

transporter II mRNA, complete cds

754A

1372G

754G

1372G

754G

1372A

754A

1372A

754

576G > A

Silent

1372

1194A > G

Silent

S70609

S70609

601019

GEN-MUB

glycine transporter type 1b

[human, substantia nigra, mENA, 2364 nt]

927G

1556G

927

694G > A

A232T

1217

984C > T

Silent

1556

1323G > A

Silent

X87816

X87816

None

GEN-MUC

Cystathionine-beta-synthase,

promoters

173G

181T

231A

571G

683A

173A

181T

231C

571G

173G

181T

231C

571G

683C

173G

181T

231C

571C

173G

181T

231C

571G

683A

173G

181C

231C

571G

683A

571G

683C

173A

181T

231C

571G

683C

173G

181T

231C

571C

683C

173

173G > A

Genomic

181

181T > C

Genomic

231

231C > A

Genomic

571

571G > C

Genomic

683

683A > C

Genomic

L12178

L12178

308380

GEN-MUF

Human interleukin 2 receptor

gamma chain (IL2RG) gene, exon 1 and promoter region

270G

720A

270C

720A

270G

720T

270

270G > C

Genomic

720

720A > T

Genomic

L19546

L19546

308380

GEN-MUG

Interleukin-2 receptor gamma

chain, genomic sequence (not including promoter)

377G

389C

885G

389G

885G

377G

389G

885A

377T

389G

885A

377G

389C

885A

377G

389G

885G

377

377T > G

Genomic

389

389G > C

Genomic

885

885A > G

Genomic

AF185589

AF185589

124010

GEN-MVA

Homo sapiens cytochrome P450 3A4

(CYP3A4) gene, promoter region

355

355A > G

Genomic

763

763A > G

Genomic

3113

3113T > C

Genomic

3262

3262G > T

Genomic

3782

3782A > G

Genomic

3798

3798C > T

Genomic

4245

4245A > G

Genomic

4257

4257G > A

Genomic

4394

4394G > A

Genomic

4587

4587C > A

Genomic

5414

5414G > C

Genomic

5458

5458A > G

Genomic

5546

5546A > G

Genomic

5730

5730T > C

Genomic

6303

6303T > G

Genomic

6559

6559G > A

Genomic

6577

6577G > C

Genomic

7293

7293A > T

Genomic

8653

8653C > T

Genomic

9304

9304C > T

Genomic

9315

9315C > T

Genomic

9318

9318C > T

Genomic

9335

9335T > C

Genomic

9338

9338T > C

Genomic

9340

9340G > T

Genomic

9825

9825G > G

Genomic

10180

10180A > G

Genomic

10186

10186A > G

Genomic

U04636

U04636

600262

GEN-MVG

Cyclooxygenase 2, genomic

sequence (not including promoter)

227

227T > C

Genomic

322

322C > T

Genomic

671

671C > G

Genomic

774

774C > G

Genomic

841

841T > G

Genomic

848

848T > A

Genomic

1080

1080C > G

Genomic

1167

1167C > T

Genomic

1290

1290G > T

Genomic

1379

1379T > C

Genomic

1639

1639G > T

Genomic

1985

1985T > c

Genomic

2016

2016C > A

Genomic

2033

2033C > G

Genomic

2191

2191C > G

Genomic

2231

2231C > T

Genomic

2315

2315G > C

Genomic

3068

3068A > G

Genomic

3121

3121T > A

Genomic

3250

3250G > A

Genomic

3810

3810C > T

Genomic

3989

3989T > G

Genomic

4065

4065T > G

Genomic

4383

4383G > A

Genomic

4461

4461G > A

Genomic

4505

4505T > C

Genomic

4719

4719T > C

Genomic

4900

4900T > C

Genomic

5106

5106G > A

Genomic

5310

5310T > C

Genomic

5593

5593G > T

Genomic

5756

5756C > T

Genomic

6106

6106G > A

Genomic

6251

6251G > A

Genomic

6429

6429T > A

Genomic

6438

6438T > C

Genomic

7150

7150G > C

Genomic

7959

7959C > T

Genomic

A2002455

AB002455

601270

GEN-MVH

Leukotriene B4 omega-hydroxylase

(CYP4F3), promoter

424G

685T

709G

1335A

1423A

1612C

1840C

2103T

2433G

2476C

2506G

2559A

2673T

424A

685T

709G

1335A

1423A

1612C

1840C

2103C

2433T

2476C

2506G

2559G

2673C

424G

685T

709G

1335A

1423A

1612C

1840C

2103C

2433T

2476C

2506G

2559G

2673C

424G

685T

709G

1335A

1612C

1840C

2103T

2433G

2476C

2506G

2673C

685T

709G

1335A

1612T

1840C

2103T

2433C

2476C

2506G

424A

685T

709G

1335G

1423A

1612C

1840C

2103C

2433T

2476C

2506G

2559G

2673C

685T

709G

1612C

1840T

2103C

2433T

2476C

2506G

2559G

2673C

424A

685T

709G

1335A

1423A

1612C

1840C

2103T

2433G

2476C

2506G

2673T

424A

685T

709G

1335A

1612C

1840C

2103T

2433T

2476C

2506G

2559G

2673C

685T

709G

1335A

1423G

1612C

1840C

2103C

2433T

2476C

2506G

2559G

2673C

424G

685T

709A

1335A

1423A

1612C

1840C

2103T

2433G

2476C

2506G

424G

685T

709G

1335A

1423G

1612C

1840C

2103T

2433G

2476C

2506G

2559A

2673T

424A

685T

709G

1612C

1840C

2103C

2433T

2476T

2506G

2559G

2673C

424A

685T

709G

1335A

1423A

1612C

1840C

2103T

2433T

2476C

2506G

2673T

685T

709G

1335G

1612C

1840C

2103T

2476C

2506G

685T

709G

1335A

1423G

1612C

1840C

2103C

2433T

2476C

2506G

2673T

424G

685T

709G

1335A

1612C

1840C

2103T

2433T

2476C

2506G

2559G

2673C

424G

685C

709G

1335A

1423G

1612C

2476C

424G

685T

709A

1335A

1423A

1612C

1840C

2103T

2433G

2476C

2506G

2559G

2673C

424G

685C

709G

1335A

1423G

1612C

1840T

2103C

2433T

2476C

2506T

2559G

2673C

424A

685T

709G

1335A

1423A

1612C

1840C

2103T

2433G

2476C

2506G

2559A

2673T

424A

685T

709G

1335A

1423G

1612C

1840C

2103T

2433T

2476C

2506G

2559G

2673C

424A

685T

709G

1335A

1423G

1612C

1840C

2103C

2433T

2476C

2506G

2559A

2673T

424G

685T

709G

1335A

1423A

1612C

1840C

2103T

2433T

2476C

2506G

2559G

2673C

424A

685T

709G

1335A

1423A

1612C

1840C

2103T

2433T

2476C

2506G

2559G

2673T

424G

685T

709G

1335G

1423G

1612C

1840T

2103C

2433T

2476C

2506G

2559G

2673C

424A

685T

709G

1335G

1423G

1612C

1840C

2103C

2433T

2476C

2506G

2559G

2673C

424A

685T

709G

1335A

1423A

1612C

1840C

2103T

2433T

2476C

2506G

2559G

2673C

424G

685T

709G

1335A

1423G

1612C

1840C

2103C

2433T

2476C

2506G

2559G

2673C

424A

685T

709G

1335G

1423G

1612C

1840C

2103C

2433T

2476T

2506G

2559G

2673C

424A

685T

709G

1335G

1423A

1612C

1840C

2103T

2433T

2476C

2506G

2559G

2673C

424G

685T

709A

1335G

1423A

1612C

1840C

2103T

2433G

2476C

2506G

2559A

2673T

424G

685T

709G

1335A

1423A

1612C

1840C

2103T

2433G

2476C

2506G

2559G

2673C

424A

685T

709G

1335A

1423A

1612T

1840C

2103T

2433G

2476C

2506G

2559G

2673C

424G

685T

709G

1335A

1423G

1612C

1840C

2103T

2433G

2476C

2506G

2559G

2673C

424

424A > G

Genomic

685

685T > C

Genomic

709

709G > A

Genomic

1335

1335G > A

Genomic

1423

1423A > G

Genomic

1612

1612C > T

Genomic

1840

1840C > T

Genomic

2103

2103T > C

Genomic

2433

2433G > T

Genomic

2476

2476C > T

Genomic

2506

2506G > T

Genomic

2559

2559A > G

Genomic

2673

2673T > C

Genomic

AF209389

AF209389

124010

GEN-MVI

Homo sapiens cytochrome P450

IIIA4 (CYP3A4) gene, exons 1 through 13 and complete cds

732

732T > C

Genomic

755

755C > T

Genomic

1870

1870A > G

Genomic

1925

1925A > G

Genomic

2253

2253G > C

Genomic

2444

2444A > G

Genomic

2523

2523A > G

Genomic

3136

3136C > T

Genomic

3352

3352G > A

Genomic

4768

4768A > T

Genomic

4808

4808G > T

Genomic

7208

7208T > A

Genomic

7445

7445A > G

Genomic

11923

11923G > A

Genomic

13115

13115T > G

Genomic

15105

15105T > C

Genomic

15746

15746C > T

Genomic

15871

15871T > G

Genomic

15955

15955T > A

Genomic

16095

16095T > C

Genomic

16149

16149G > A

Genomic

16363

16363C > T

Genomic

17890

17890C > T

Genomic

17997

17997C > G

Genomic

18651

18651T > G

Genomic

19100

19100A > T

Genomic

20178

20178T > C

Genomic

20338

20338G > A

Genomic

22651

22651G > A

Genomic

23187

23187C > T

Genomic

23489

23489G > C

Genomic

AJ012376

AJ012376

600046

GEN-MVJ

Homo sapiens mRNA for ATP

binding cassette transporter-1 (ABC-1)

876C

888G

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876C

888A

1980C

2260A

2589A

3099T

3304C

3456C

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876C

888G

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580A

6666G

876C

1980C

2251G

2260A

2413G

2589A

2754G

3099T

3456G

3573A

3624A

4162C

4221G

4230G

4700A

6580G

6666G

876C

888G

2251G

2260A

2413G

2589G

2754G

3099T

3304C

3573A

4162C

4221G

4230G

4700A

6666A

2251G

2260A

2413G

2589G

2754G

3099T

3304C

3456C

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876C

888G

1980C

2251G

2260A

2413G

2754A

3099T

3304C

3456G

3573A

4700A

6580G

6666G

1980C

2413A

2589G

2754G

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876C

2251A

2260A

2413G

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

6580G

6666G

876C

888A

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580G

6666G

876C

888G

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162T

4221G

4230G

4700G

6580A

6666G

876C

1980C

2251G

2260A

2413G

2589A

2754G

3099G

3304C

3456G

3573A

3624G

4162C

4221G

4230G

6580G

6666G

876C

1980C

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3573A

3624G

4162G

4221G

4230T

6580G

6666G

876C

888G

2251G

2260A

2413G

2589G

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580G

6666G

876C

888G

1980C

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580G

6666G

876C

888G

1980A

2251G

2260A

2413G

2589G

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221A

4230G

4700G

6580G

6666G

876C

888A

1980C

2260A

2589A

3099T

3304C

3456C

3624G

4162C

4221G

4230G

4700G

6580G

6666G

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221A

4230G

4700G

6580G

6666G

876C

888G

2251G

2260A

2413G

2589G

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4230G

4700A

6666G

876C

888G

1980A

2413A

2589G

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6666G

876C

2251G

2260A

2413G

2754G

3099T

3304T

3456G

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

1980A

2413A

2589G

2754G

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876C

2260C

2413G

2589A

2754G

3099T

3304G

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580G

6666G

876C

888G

1980C

2251G

2260A

2413G

2589G

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580G

6666G

876C

888G

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580A

6666G

876C

888G

2251G

2260A

2413G

2589G

2754G

3099T

3304C

3456C

3573A

3624A

4162C

4221G

4230G

4700A

6666A

876C

888A

2251A

2260C

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580G

6666G

876C

888A

1980C

2251G

2260A

2413G

2589A

2754G

3099G

3304C

3456G

3573A

3624G

4162C

4221G

4230G

6580G

6666G

876C

888G

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580G

6666G

876C

888A

1980A

2251A

2260A

2413G

2589G

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

1980C

2413A

2589G

2754G

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

1980A

2413A

2589G

2754G

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876C

888A

1980C

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456C

3573A

3624G

4162C

4221G

4230T

4700A

6580G

6666G

876C

888A

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580G

6666G

876C

888G

1980C

2251G

2260A

2413G

2589G

2754A

3099T

3304C

3456G

3573A

3624A

4700A

6580G

6666G

876C

888G

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162T

4221G

4230G

4700G

6580A

6666G

876C

888A

1980C

2251G

2260A

2413G

2589A

2754G

3099T

3304T

3456G

3573A

3624A

4162C

4221G

4230G

4700A

6580G

6666G

1980C

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221A

4230G

4700G

6580G

6666G

876C

888A

2251A

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4221G

6580G

6666G

876C

888A

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700A

876C

888G

2251A

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4221G

6580G

6666G

876C

888G

2251G

2260A

2413G

2589G

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700G

6580G

6666G

876T

888G

1980A

2413A

2589G

2754G

3099T

3304C

3456G

4700G

65800

6666G

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456G

3573A

3624G

4700A

6580G

6666G

2251G

2260A

24l3G

2589G

2754G

3099T

3304C

3456C

3573G

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876C

888G

2251G

2260A

2413G

2589G

2754G

3099T

3304C

3456G

3573A

3624G

4162C

4221G

4230G

4700A

6666G

876C

888A

1980C

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456C

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876C

888G

2251G

2260A

2413G

2589G

2754G

3099T

3304T

3456G

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876C

888G

1980A

2413A

2589G

2754G

3099T

3304C

3456G

3573A

36240

4162C

4221G

4230G

4700G

6666G

876C

888A

1980C

2251A

2260A

2413A

2589A

2754A

3099T

3304C

3456C

3573G

3624G

4162C

4221G

4230G

4700G

6580G 6666G

2251G

2260A

2413G

2589A

2754G

3099T

3304C

3456C

3573A

3624G

4162C

4221G

4230G

4700A

6580G

6666G

876

756C > T

Silent

888

768G > A

Silent

1980

1860C > A

Silent

2251

2131G > A

V711M

2260

2l40A > C

T714P

2413

2293G > A

V765I

2589

2469A > G

I823M

2754

2634G > A

Silent

3099

2979T > G

Silent

3304

3184C > T

Silent

3456

3336G > C

E1112D

3573

3453A > G

Silent

3624

3504G > A

Silent

4162

4042C > T

L1348F

4221

4101G > A

Silent

4230

4110G > T

Q1370H

4700

4580G > A

R1527K

6580

6460G > A

D2154N

6666

6546G > A

Silent

M30795

M30795

107910

GEN-MVM

Aromatase (CYP19), promoter

747C

825C

881C

747T

825C

881C

747C

825G

881C

747C

825C

881T

747

747C > T

Genomic

825

825C > G

Genomic

881

881C > T

Genomic

AF044206

AF044206

600262

GEN-MVP

Homo sapiens cyclooxygenase

(COX-2) gene, promoter and exon 1

190

190T > C

Genomic

270

270T > C

Genomic

498

498A > G

Genomic

534

534G > A

Genomic

540

540C > T

Genomic

684

684G > A

Genomic

1177

1177A > G

Genomic

1341

1341A > G

Genomic

1381

1381T > C

Genomic

1438

1438A > T

Genomic

1648

1648T > C

Genomic

1902

1902T > A

Genomic

1904

1904G > T

Genomic

2474

2474C > T

Genomic

2486

2486G > C

Genomic

2538

2538G > A

Genomic

2615

2615A > G

Genomic

2827

2827T > C

Genomic

2926

2926T > G

Genomic

3065

3065G > C

Genomic

3141

3141C > T

Genomic

3161

3161A > G

Genomic

3658

3658T > G

Genomic

3683

3683G > A

Genomic

3739

3739C > A

Genomic

3749

3749C > T

Genomic

4295

4295A > C

Genomic

4296

4296G > A

Genomic

4816

4816C > A

Genomic

4969

4969T > C

Genomic

5402

5402A > G

Genomic

5615

5615A > C

Genomic

5990

5990A > G

Genomic

6376

6376G > C

Genomic

6978

6978C > G

Genomic

7079

7079C > G

Genomic

NM_006639

NM_006639

None

GEN-MVT

Homo sapiens cysteinyl

leukotriene receptor 1 (CYSLT1) mRNA

927

927C > T

Silent

AL096870

AL096870

601531

GEN-MWO

Leukotriene B4 receptor,

promoter and genomic

399

399G > A

Genomic

579

579A > G

Genomic

755

755T > G

Genomic

1191

1191A > G

Genomic

1270

1270C > T

Genomic

1874

1874A > C

Genomic

1944

1944A > G

Genomic

2107

2107C > T

Genomic

2155

2155C > T

Genomic

2383

2383G > A

Genomic

3256

3256A > G

Genomic

4250

4250G > A

Genomic

4486

4486C > G

Genomic

4753

4753C > G

Genomic

5098

5098C > T

Genomic

5209

5209A > T

Genomic

5626

5626A > T

Genomic

6314

6314A > C

Genomic

7903

7903G > T

Genomic

8032

8032G > A

Genomic

8381

8381A > C

Genomic

8573

8573A > C

Genomic

9134

9134C > T

Genomic

9224

9224G > A

Genomic

9493

9493G > T

Genomic

9524

9524G > A

Genomic

10576

10576G > C

Genomic

10756

10756T > G

Genomic

11025

11025G > C

Genomic

11163

11163T > C

Genomic

11206

11206C > T

Genomic

11398

11398C > A

Genomic

11710

11710G > C

Genomic

11766

11766G > T

Genomic

11823

11823A > G

Genomic

11948

11948A > G

Genomic

12149

12149G > A

Genomic

X02612

X02612

None

GEN-MW2

Cytochrome P450 CYP1A1, promoter

and genomic

546T

547C

573G

588T

658G

1136G

1987G

3617C

6305A

6817C

6819A

6879G

7597T

7786C

547C

573G

658G

1136G

1987G

3617T

6305A

6817C

6819A

6879G

7597C

7786C

547C

573G

658G

1136G

1987G

3617T

6305A

6817C

6819G

6879G

7597C

7786C

546C

547C

573G

588T

658G

1136G

3617C

6305A

6817C

6819A

6879G

7597T

7786A

546C

547C

573G

588T

658G

1136G

1987G

3617C

6305A

6817C

6819A

6879G

7597T

7786C

547C

573G

658G

1136G

1987G

3617T

6305A

6817C

6819A

6879G

7597T

7786C

547G

573G

658G

1136G

1987G

3617T

6305A

6817C

6819A

6879G

7597T

7786A

546C

547C

573G

588T

658G

1136G

1987T

3617C

6305A

6817C

6819A

6879A

7597T

7786C

546C

547C

573A

1136G

1987G

6305A

6817G

6819A

6879G

7597T

7786C

547C

573G

588G

658G

1136G

1987G

3617T

6305A

6817C

6819G

6879G

7597T

7786C

546C

547C

573G

588T

658G

1136A

3617C

6305A

6817C

6819A

6879G

7597T

7786A

546C

547C

573G

588T

658G

1136G

1987T

3617C

6305A

6817C

6819A

6879G

7597T

7786C

546C

547C

573G

658G

1136G

6305G

546C

547C

573G

588G

658G

1136G

1987G

3617C

6305A

6817C

6819A

6879G

7786C

547C

573G

588T

658G

1136G

6305A

6817C

6819G

6879G

7597T

7786C

547C

573G

658G

1136G

1987G

3617T

6305A

6817C

6819A

6879G

7597T

7786A

547C

658G

1136G

6305A

6817A

6819A

6879G

7597T

7786C

547C

573G

588G

658G

1136G

1987G

3617T

6305A

6817C

6819G

6879G

7597T

7786C

547C

573G

658G

1136G

1987G

3617T

6305A

6817C

6819G

6879G

7597C

7786C

546C

547C

573G

588G

658G

1136G

6305C

547C

573G

658G

1136G

1987G

3617T

6305A

6817C

6819A

6879G

547G

573G

658G

1136G

1987G

3617T

6305A

6817C

6819A

6879G

7597T

7786A

546C

547C

573G

588G

658G

1136G

1987G

3617T

6305A

6817C

6819G

6879G

546C

547C

573A

588T

658A

1136G

1987G

3617T

6305A

6817C

6819A

6879G

7597T

7786C

546C

547C

573G

588T

658G

1136A

1987T

3617C

6305A

6817C

6819A

6879G

7597T

7786A

546C

547C

573G

588G

658G

1136G

1987G

3617T

6305A

6817C

6819A

6879G

7597T

7786C

547C

573G

588T

658G

1136G

1987G

3617T

6305A

6817C

6819A

6879G

7597T

7786C

547C

573G

588T

658G

1136G

1987G

3617C

6305A

6817C

6819A

6879G

7597T

7786C

573G

658G

1136G

1987G

3617C

6305A

6817C

6819A

6879G

546C

547C

573G

588G

658G

1136G

1987G

3617C

6305A

6817C

6819A

6879G

7597C

7786C

547C

573A

588G

658G

1136G

1987G

6305A

6817A

6819A

6879G

7597T

7786C

547C

573A

588G

658G

1136G

1987G

6305A

6817C

6819A

6879G

7597T

7786C

547C

573G

588T

658G

1136G

1987G

3617T

6305A

6817C

6819G

6879G

7597T

7786C

546

546C > T

Genomic

547

547C > G

Genomic

573

573G > A

Genomic

588

588T > G

Genomic

658

658G > A

Genomic

1136

1136G > A

Genomic

1987

1987T > G

Genomic

3437

3437{circumflex over ( )}insC

Genomic

3617

3617C > T

Genomic

6305

6305A > C

Genomic

6817

6817C > A

Genomic

6819

6819G > A

Genomic

6879

6879G > A

Genomic

7597

7597T > C

Genomic

7786

7786C > A

Genomic

J02843

J02843

None

GEN-MW4

Cytochrome P450 CYP2E1, promoter

and genomic

373

373T > G

Genomic

582

582C > T

Genomic

637

637C > G

Genomic

646

646C > T

Genomic

1051

1051C > T

Genomic

1172

1172G > C

Genomic

1179

1179A > G

Genomic

1262

1262T > A

Genomic

1312

1312T > G

Genomic

1409

1409C > A

Genomic

1435

1435T > G

Genomic

1483

1483C > G

Genomic

1532

1532G > C

Genomic

1772

1772C > T

Genomic

1800

1800T > C

Genomic

1896

1896A > G

Genomic

2019

2019T > C

Genomic

2413

2413T > C

Genomic

2473

2473A > G

Genomic

2492

2492T > A

Genomic

2754

2754G > T

Genomic

3372

3372G > A

Genomic

3811

3811C > A

Genomic

3858

3858C > T

Genomic

4182

4182T > C

Genomic

4236

4236C > G

Genomic

4504

4504C > T

Genomic

4565

4565G > A

Genomic

4574

4574G > A

Genomic

4703

4703C > G

Genomic

5155

5155G > A

Genomic

5657

5657C > T

Genomic

5737

5737C > T

Genomic

5926

5926T > C

Genomic

6103

6103C > T

Genomic

6300

6300G > A

Genomic

6418

6418G > A

Genomic

6497

6497G > A

Genomic

6609

6609C > T

Genomic

6629

6629T > C

Genomic

6741

6741T > A

Genomic

6999

6999G > A

Genomic

7257

7257C > T

Genomic

7275

7275C > G

Genomic

7310

7310G > T

Genomic

7353

7353C > T

Genomic

7520

7520G > A

Genomic

7592

7592G > A

Genomic

7669

7669T > C

Genomic

7728

7728T > C

Genomic

7915

7915C > T

Genomic

7934

7934C > T

Genomic

7935

7935A > G

Genomic

8449

8449G > A

Genomic

8526

8526C > T

Genomic

8610

8610C > T

Genomic

8619

8619A > G

Genomic

8835

8835A > G

Genomic

8841

8841C > A

Genomic

8857

8857G > T

Genomic

8873

8873G > A

Genomic

9638

9638A > G

Genomic

9787

9787T > C

Genomic

9940

9940G > A

Genomic

10101

10101T > C

Genomic

10171

10171C > T

Genomic

10456

10456T > A

Genomic

10491

10491A > G

Genomic

10622

10622C > T

Genomic

10698

10698C > T

Genomic

10869

10869C > A

Genomic

11138

11138C > A

Genomic

11195

11195A > G

Genomic

11279

11279G > A

Genomic

11449

11449G > A

Genomic

12454

12454G > T

Genomic

12569

12569C > T

Genomic

12720

12720C > G

Genomic

12757

12757A > T

Genomic

12811

12811C > G

Genomic

12945

12945C > T

Genomic

13369

13369A > G

Genomic

13593

13593T > C

Genomic

13656

13656G > A

Genomic

13680

13680C > T

Genomic

13733

13733G > A

Genomic

14070

14070C > T

Genomic

14089

14089A > T

Genomic

14094

14094G > A

Genomic

X17059

X17059

None

GEN-MWB

N-acetyltransferase 1, genomic

sequence (not including promoter)

163T

401A

405T

163T

401A

405A

885G

899G

1000G

1528A

1535A

163A

401A

885G

899G

1000G

1528A

1535A

163T

401A

405A

885G

899G

1000A

1528T

1535C

163T

401A

405A

885G

899G

1000G

1528T

1535A

163T

401T

405A

1000G

1528T

1535A

163T

401A

405A

885G

899G

1000G

1528T

1535C

163T

401A

405T

163A

401A

405T

885G

899G

1000G

1528A

1535A

163T

401T

405A

885A

899A

1000G

1528T

1535A

163

(−278)T > A

Genomic

401

(−40)A > T

Genomic

405

(−36)A > T

Genomic

885

445G > A

Genomic

899

459G > A

Genomic

1000

560G > A

Genomic

1528

1088T > A

Genomic

1535

1095A > C

Genomic

U22027

U22027

None

GEN-MWD

Cytochrome P450 CYP2A6, promoter

and genomic

841G

2936A

5090G

5262A

841A

934G

2936G

5090G

5262G

841G

934G

2612C

2936G

5090G

5262G

841A

934G

2612C

2936A

5090G

5262G

841G

934G

2612C

2936A

5090G

5262G

841G

934G

2612A

2936A

5090G

5262G

841G

934G

2612C

2936A

5090A

5262G

841G

934A

2612C

2936A

5090G

5262A

841G

2612C

2936G

5090G

5262G

5090G

5262G

841G

2612A

2936A

5090A

5262G

841G

934G

2612C

2936A

5090A

5262A

2612C

2936A

5090G

5262A

841A

934G

2612C

2936G

5090G

5262G

841G

934A

2612A

2936A

5090G

5262A

841G

2612A

2936G

5090G

5262G

841A

2612A

841G

934G

2612C

2936A

5090G

5262A

841G

934G

2612A

2936G

5090G

5262G

841

841G > A

Genomic

934

934G > A

Genomic

2612

2612C > A

Genomic

2936

2936G > A

Genomic

3900

3900A > C

Genomic

4416

4416C > T

Genomic

5090

5090G > A

Genomic

5262

5262G > A

Genomic

NM_004695

NM_004695

None

GEN-MWE

Homo sapiens solute carrier

family 16 (monocarboxylic acid transporters), member 5 (SLC16A5) mRNA

913

853A > G

I28EV

NM_004211

NM_004211

None

GEN-MWG

Homo sapiens solute carrier

family 6 (neurotransmitter transporter, glycine), member 5 (SLC6A5) mRNA

2678

23970 > T

3′

D13305

D13305

None

GEN-MWW

Human mRNA for brain

cholecystokinin receptor

1945A

1973G

1945C

1973G

1945C

1973A

1945A

1973A

1945

1753C > A

3′

1973

1781A > G

3′

NM_000966

NM_000966

None

GEN-MX8

Homo sapiens retinoic acid

receptor, gamma (RARG) mRNA

1426C

1627A

2202T

2328G

1426C

1627A

2202C

2328C

1426C

1627G

2202T

2328C

1426C

1627A

2202T

2328C

1426T

2202T

2328C

1426C

2202T

2328C

1426C

1627G

2202T

2328G

1426

1280C > T

S427L

1627

1481A > G

3′

2202

2056T > C

3′

2328

21820 > G

3′

NM_000176

NM_000176

None

GEN-MXL

Homo sapiens nuclear receptor

subfamily 3, group C, member 1 (NR3C1), mRNA

1220A

1896C

2430T

3642C

4346G

1220A

1896T

2430T

3642C

4346A

4654A

1220A

1896C

2430T

3642C

4346A

4654A

1220A

1896C

2430C

3642C

4346A

4654A

1220G

1896C

2430T

3642C

4346A

4654A

1220A

1896C

2430T

3642T

4346A

4654A

1220A

1896C

2430T

3642C

4346G

4654G

1220

1088A > G

N363S

1896

1764C > T

Silent

2430

2298T > C

Silent

3642

3510C > T

3′

4346

4214A > G

3′

4654

4522A > G

3′

NM_000367

NM_000367

None

GEN-MXO

Homo sapiens thiopurine 5-

methyltransferase (TPMT) mRNA

784A

1074T

2108C

784G

1074T

2108C

784A

1074T

2108G

784A

1074C

2108C

784

719A > G

Y240C

1074

1009T > C

3′

2108

2043C > G

3′

NM_001085

NM_001085

None

GEN-MXS

Homo sapiens alpha-1-

antichymotrypsin (AACT) mRNA

401

390C > T

Silent

NM_001045

NM_001045

None

GEN-MXT

Homo sapiens solute carrier

family 6 (neurotransmitter transporter, serotonin), member 4 (SLC6A4) mRNA

2293G

2406T

2428G

2293A

2406C

2428G

2293A

2406T

2428T

2293A

2406T

2428G

2293

2221A > G

3′

2406

2334T > C

3′

2428

2356G > T

3′

M83181

M83181

None

GEN-MY7

Serotonin receptor 5HT-1A,

complete cds

661G

685G

778C

661G

685G

778A

661T

685G

778C

661G

685A

778C

661

270G > T

Silent

685

294G > A

Silent

778

387C > A

Silent

NM_000054

NM_000054

None

GEN-MYY

Homo sapiens arginine

vasopressin receptor 2 (nephrogenic diabetes insipidus) (AVPR2) mRNA

675C

878G

1162G

675T

878G

1162G

675C

878A

1162G

675C

878G

1162A

675

440C > T

A147V

878

643G > A

V215M

1162

927A > G

Silent

NM_001080

NM_001080

None

GEN-MZO

Homo sapiens Succinic

semialdehyde dehydrogenase (SSADH) mPLNA

1664

1664C > T

3′

1861

1861C > A

3′

AC000111

AC000111

None

GEN-MZM

Human BAC clone 068P20 from

7q31-q32, complete sequence

1287C

1517A

2330A

2349G

2406G

2582G

2616A

3354G

3383G

3772C

4283A

1287C

2349G

2406A

2582A

2616A

3383G

4283T

1287C

1517A

2349G

2582G

2616A

3354C

3383G

3772C

4283T

1517A

2349G

2406G

2582G

2616A

3772T

4283T

1287T

1517A

2330G

2349G

2406A

2582G

2616A

3354C

3383T

4283T

1287C

1517T

2330A

2349G

2406A

2582G

3354G

3383G

3772C

4283T

1287C

2349G

2406A

2582G

2616A

3383G

3772T

4283T

1287C

1517A

2349A

2582G

2616A

3383T

3772C

4283T

1287C

1517A

2330A

2349G

2406G

2582G

2616A

3354G

3383G

3772C

4283T

1287C

2330G

2349G

2582G

2616A

3354G

3383G

3772C

4283T

1287C

1517T

2330A

2349G

2406A

2582G

2616A

3354G

3383T

3772C

4283T

1287C

1517A

2330A

2349G

2406A

2582G

2616A

3354G

3383T

3772C

4283T

1287C

1517A

2330G

2349G

2406A

2582G

2616A

3354C

3383T

3772T

4283T

1287C

1517A

2330G

2349G

2406A

2582G

2616A

3354C

3383T

3772C

4283T

1287T

1517A

2349G

2406G

2582G

2616A

3772C

4283T

1287C

1517A

2330A

2349G

2406A

2582G

2616A

3354G

3383G

3772C

4283T

1287C

1517A

2330G

2349G

2406G

2582G

2616A

3354G

3383T

3772C

4283T

1287C

1517A

2330G

2349G

2406G

2582G

2616A

3354C

3383T

3772C

4283T

1287T

1517A

2330A

2349G

2406G

2582G

2616A

3354G

3383G

3772T

4283T

1287C

1517T

2330A

2349G

2406A

2582G

2616A

3354G

3383G

3772T

4283T

2330G

2349G

2406A

2582G

2616A

3354C

3383T

3772T

4283T

1287C

1517A

2330G

2349G

2406G

2582G

2616A

3354C

3383T

3772C

4283A

1287C

1517A

2330A

2349G

2406G

2582G

2616A

3354C

3383T

3772C

4283T

1287C

1517T

2330G

2349G

2406G

2582G

2616A

3354C

3383G

3772T

4283T

1287C

1517T

2330G

2349G

2406G

2582G

2616A

3354G

3383G

3772C

4283T

1287C

1517A

2330G

2349G

2406G

2582G

2616A

3354C

3383G

3772C

4283T

1517A

2330G

2349G

2406A

2582G

3772T

1287C

1517A

2330A

2349A

2406G

2582G

2616A

3354G

3383T

3772C

4283T

1287C

1517T

2330G

2349G

2406A

2582A

2616A

3354C

3383G

3772T

4283T

2330G

2349G

3383G

4283T

1287T

1517A

2330G

2349G

2406A

2582G

2616A

3354C

3383T

3772C

4283T

1287C

1517T

2330A

2349G

2406A

2582G

2616G

3354G

3383G

3772C

4283T

472 472-486delTTTGCTTCAAOAATA  Genomic

1287

1287C > T

Genomic

1517

1517A > T

Genomic

2330

2330A > G

Genomic

2349

2349G > A

Genomic

2406

2406G > A

Genomic

2519

2519-2521delCTT

Genomic

2582

2582G > A

Genomic

2616

2616A > G

Genomic

3354

3354G > C

Genomic

3383

3383G > T

Genomic

3772

3772C > T

Genomic

4283

4283T > A

Genomic

GEN-199-NT_001649_6

NT_001649

None

GEN-N08

Multidrug resistance

protein 3 exon 7

84

84T > C

Genomic

GEN-199-NT_001649_0

NT_001649

None

GEN-N0A

Multidrug resistance

protein 3 exon 1

311

311G > A

Genomic

GEN-LV1-NT_000648_11

NT_000648

None

GEN-N0E

histidine

decarboxylase (HDC) exon 12

471

471T > C

Genomic

GEN-4CS-NT_000568_11

NT_000568

None

GEN-N12

prostate-specific

membrane antigen (PSM) exon 12

41

41T > G

Genomic

GEN-2O6-NT_001987_0

NT_001987

None

GEN-N1R

Integral membrane

protein (Nramp2) exon 1

229C

236C

1176C

229C

236T

1176A

229C

236C

1176A

229T

236C

229T

236C

1176A

229T

236C

229

229C > T

Genomic

236

236C > T

Genomic

1176

1176C > A

Genomic

GEN-3L1-NT_000876_6

NT_000876

None

GEN-N1X

phospholipid

hydroperoxide glutathione peroxidase exon 7

38C

117G

176C

293G

380C

38C

117G

176C

293G

380T

38G

117G

176T

293A

380T

38G

117A

293G

380T

38G

117G

176T

293G

380T

38G

117G

176C

293G

380T

38G

117A

176C

293G

380T

38

38G > C

Genomic

117

117G > A

Genomic

176

176T > C

Genomic

293

293G > A

Genomic

380

380T > C

Genomic

GEN-3L1-NT_000876_2

NT_000876

None

GEN-N20

phospholipid

hydroperoxide glutathione peroxidase exon 3

45

45T > C

Genomic

GEN-3L1-NT_000876_0

NT_000876

None

GEN-N22

phospholipid

hydroperoxide glutathione peroxidase exon 1

805A

822C

1008T

1084G

1115G

805A

819A

822C

1008C

1084G

1115A

805A

822C

1008C

1084A

1115G

805A

819G

822C

1008C

1084G

1115G

805T

819A

1008C

1084G

1115G

805A

819A

822C

1008C

1084G

1115G

805T

819A

822T

1008C

1084G

1115G

805A

819G

822C

1008T

1084G

1115G

819A

1008T

1084G

1115G

805A

819G

822C

1008C

1084A

1115G

805

805A > T

Genomic

819

819A > G

Genomic

822

822C > T

Genomic

1008

1008C > T

Genomic

1084

1084G > A

Genomic

1115

1115G > A

Genomic

GEN-3L1-NT_000876_1

NT_000876

None

GEN-N23

phospholipid

hydroperoxide glutathione peroxidase exon 2

225

225T > C

Genomic

GEN-KV0-NT_000562_0

NT_000562

None

GEN-N2A

Tryptophan hydroxylase

exon 1

827

827A > T

Genomic

GEN-LV1-NT_000648_8

NT_000648

None

GEN-N2Y

histidine decarboxylase

(HDC) exon 9

160

160T > C

Genomic

GEN-LV1-NT_000648_2

NT_000648

None

GEN-N30

histidine decarboxylase

(HDC) exon 3

253

253T > C

Genomic

GEN-LV-NT_000648_3

NT_000648

None

GEN-N31

histidine decarboxylase

(HDC) exon 4

256

256G > C

Genomic

GEN-LV1-NT_000648_0

NT_000648

None

GEN-N32

histidine decarboxylase

(HDC) exon 1

1133

1133C > T

Genomic

GEN-LV1-NT_000648_7

NT_000648

None

GEN-N35

histidine decarboxylase

(HDC) exon 8

200

200C > T

Genomic

GEN-LV1-NT_000648_4

NT_000648

None

GEN-N36

histidine decarboxylase

(HDC) exon 5

287

287T > C

Genomic

GEN-LU5-NT_001817_17

NT_001817

None

GEN-N3G

Cytosolic

phospholipase A2 exon 18

446C

587A

446T

587G

446C

587G

446

446C > T

Genomic

587

587G > A

Genomic

GEN-MJW-NT_002940_5

NT_002940

None

GEN-N3M

Chloride channel 5 exon

6 (complement)

215

215C > T

Genomic

GEN-MJW-NT_002940_0

NT_002940

None

GEN-N3P

Chloride channel 5 exon

1 (complement)

218

218G > C

Genomic

GEN-PS-NT_000840_8

NT_000840

None

GEN-N3S

myeloperoxidase exon 9

(complement)

47

47C > T

Genomic

GEN-PS-NT_000840_7

NT_000840

None

GEN-N3T

myeloperoxidase exon 8

(complement)

81

81C > T

Genomic

GEN-PS-NT_000840_1

NT_000840

None

GEN-N3Z

myeloperoxidase exon 2

(complement)

192C

207A

192A

207G

192C

207G

192A

207A

192

192C > A

Genomic

207

207G > A

Genomic

GEN-PS-NT_000840_0

NT_000840

None

GEN-N40

myeloperoxidase exon 1

(complement)

123C

319A

507C

897C

123C

319A

507T

897T

123C

319A

507T

897C

123T

319A

507T

897T

123C

319G

507T

123C

319G

507T

897T

123

123C > T

Genomic

319

319A > G

Genomic

507

507T > C

Genomic

897

897C > T

Genomic

GEN-5Q-NT_000564_1

NT_000564

None

GEN-N48

Cell surface receptor

for sulfonylureas exon 2

158

158T > C

Genomic

GEN-5Q-NT_000564_3

NT_000564

None

GEN-N4A

Cell surface receptor

for sulfonylureas exon 4

280

280C > T

Genomic

GEN-PS-NT_000840_11

NT_000840

None

GEN-N4D

myeloperoxidase exon 12

(complement)

1005T

1021G

1129T

1005T

1021G

1129G

1005C

1021G

1129T

1005T

1021A

1129T

1005

1005T > C

Genomic

1021

1021G > A

Genomic

1129

1129T > G

Genomic

GEN-PS-NT_000840_10

NT_000840

None

GEN-N4E

myeloperoxidase exon 11

(complement)

82

82G > A

Genomic

GEN-LU5-NT_001817_8

NT_001817

None

GEN-N4X

Cytosolic phospholipase

A2 exon 9

80

80G > A

Genomic

GEN-2DF-NT_000476_26

NT_000476

None

GEN-N4Z

Cystic fibrosis exon

27

1111A

1594A

1792A

1111A

1551C

1594G

1792G

1111A

1551T

1594G

1792A

1111G

1551C

1594G

1792A

1111A

1551C

1594G

1792A

1111A

1551C

1594A

1792A

1111

1111A > G

Genomic

1551

1551C > T

Genomic

1594

1594G > A

Genomic

1792

1792A > G

Genomic

GEN-2DF-NT_000476_16

NT_000476

None

GEN-N59

Cystic fibrosis exon

17

219

219T > C

Genomic

GEN-2DF-NT_000476_10

NT_000476

None

GEN-N5F

Cystic fibrosis exon

11

115A

291G

325A

115G

291G

325A

115A

291A

325A

115A

291G

325G

115

115G > A

Genomic

291

291G > A

Genomic

325

325A > G

Genomic

GEN-106-NT_000744 _9

NT_000744

None

GEN-N5R

“Platelet-activating

factor acetylhydrolase, isoform Ib, alpha subunit (45kD) exon 10”

190C

1306A

190T

1306G

190C

1306G

190

190C > T

Genomic

1306

1306A > G

Genomic

GEN-106-NT_000744_4

NT_000744

None

GEN-N5U

“Platelet-activating

factor acetylhydrolase, isoform Ib, alpha subunit (45kD) exon 5”

295

295C > T

Genomic

GEN-5Q-NT_000564_13

NT_000564

None

GEN-N6M

Cell surface receptor

for sulfonylureas exon 14

123

123G > A

Genomic

GEN-5Q-NT_000564_17

NT_000564

None

GEN-N6Q

Cell surface receptor

for sulfonylureas exon 18

35G

50T

35A

50C

35G

50C

35

35G > A

Genomic

50

50T > C

Genomic

GEN-SQ-NT_000564_15

NT_000564

None

GEN-N6S

Cell surface receptor

for sulfonylureas exon 16

97

97C > T

Genomic

GEN-SQ-NT_000564_22

NT_000564

None

GEN-N6X

Cell surface receptor

for sulfonylureas exon 23

292

292C > T

Genomic

GEN-SQ-NT_000564_32

NT_000564

None

GEN-N77

Cell surface receptor

for sulfonylureas exon 33

216

216G > T

Genomic

GEN-SQ-NT_000564_30

NT_000564

None

GEN-N79

Cell surface receptor

for sulfonylureas exon 31

162

162G > A

Genomic

GEN-SQ-NT_000564_37

NT_000564

None

GEN-N7A

Cell surface receptor

for sulfonylureas exon 38

216

216G > C

Genomic

GEN-3DE-NT_001504_0

NT_001504

None

GEN-N7W

platelet activating

factor acetylhydrolase IB gamma-subunit exon 1 (compliment)

483

483G > C

Genomic

GEN-2DF-NT_000476_3

NT_000476

None

GEN-N89

Cystic fibrosis exon 4

184

184G > A

Genomic

GEN-2DF-NT_000476_6

NT_000476

None

GEN-N8C

Cystic fibrosis exon 7

236

236C > T

Genomic

GEN-2O6-NT_001987_11

NT_001987

None

GEN-N8O

Integral membrane

protein (Nramp2) exon 12

82

82T > A

Genomic

GEN-3S-NT_001039_3

NT_001039

None

GEN-N8W

Catechol-O

methyltransferase exon 4

183T

211G

216G

296A

183C

211G

216G

296G

183C

211G

216A

296G

183T

211G

216G

296G

211T

216G

296G

183C

211T

216G

296G

183

183T > C

Genomic

211

211G > T

Genomic

216

216G > A

Genomic

296

296G > A

Genomic

GEN-4CS-NT_000568_9

NT_000568

None

GEN-N8Y

prostate-specific

membrane antigen (PSM) exon 10

38G

234T

38A

234C

38G

234C

38A

234T

38

38G > A

Genomic

234

234T > C

Genomic

GEN-4CS-NT_000568_0

NT_000568

None

GEN-N91

prostate-specific

membrane antigen (PSM) exon 1

195A

476C

595G

195A

476C

595A

195G

476T

595A

195G

476C

595A

476C

595A

195

195A > G

Genomic

476

476C > T

Genomic

595

595A > G

Genomic

GEN-4CS-NT_000568_7

NT_000568

None

GEN-N94

prostate-specific

membrane antigen (PSM) exon 8

84

84T > G

Genomic

214

214T > C

Genomic

GEN-4CS-NT_000568_5

NT_000568

None

GEN-N96

prostate-specific

membraneantigen (PSM) exon 6

147A

192C

193G

147C

192T

193G

147C

l92C

193G

147A

192C

193A

147

147C > A

Genomic

192

192C > T

Genomic

193

193G > A

Genomic

GEN-3MB-NT_001220_1

NT_001220

None

GEN-N9M

short-chain alcohol

dehydrogenase (XH98G2) exon 2

791

791G > C

Genomic

GEN-21W-AC068647_4

AC068647

None

GEN-N16

Homo sapiens chromosome

3 clone RP11-64D22, WORKING DRAFT SEQUENCE, 8 unordered pieces

334

334C > T

Genomic

774

774C > T

Genomic

GEN-1P5-AL356796_0

AL356796

None

GEN-NKT

Homo sapiens chromosome

9 clone RP11-82I1, *** SEQUENCING IN PROGRESS ***, 28 unordered pieces

723

723A > G

Genomic

773

773T > A

Genomic

GEN-GO-AC006994_3

AC006994

None

GEN-O1O

Homo sapiens BAC clone

RP11-396J8 from 2, complete sequence

173

173G > A

Genomic

GEN-1MN-L10641_3

L10641

None

GEN-O2H

Human vitamin D-binding

protein (GC) gene, complete cds

315

315C > T

Genomic

GEN-1MN-L10641_0

L10641

None

GEN-O2I

Human vitamin D-binding

protein (GC) gene, complete cds

238

1238C > T

Genomic

GEN-1MN-L10641_1

L10641

None

GEN-O2J

Human vitamin D-binding

protein (GC) gene, complete cds

1359

1359C > G

Genomic

1733

1733C > T

Genomic

1755

1755G > A

Genomic

GEN-1MN-L10641_6

L10641

None

GEN-O2K

Human vitamin D-binding

protein (GC) gene, complete cds

169

169A > G

Genomic

GEN-1MN-L10641_7

L10641

None

GEN-O2L

Human vitamin D-binding

protein (GC) gene, complete cds

146

146A > G

Genomic

GEN-1MN-L10641_4

L10641

None

GEN-O2M

Human vitamin D-binding

protein (GC) gene, complete cds

193

193A > G

Genomic

434

434T > C

Genomic

GEN-1MN-L10641_9

L10641

None

GEN-O2P

Human vitamin D-binding

protein (GC) gene, complete cds

455

455T > G

Genomic

GEN-3B-AC003982_1

AC003982

None

GEN-O5V

Homo sapiens PAC clone

166H1 from 12q, complete sequence

240

240C > T

Genomic

GEN-22Q-AC024085_10

AC024085

None

GEN-O5W

Human Chromosome 7

clone RP11-190G13, complete sequence

472

472A > G

Genomic

GEN-1ET-AP002027_1

AP002027

None

GEN-OTL

Homo sapiens genomic

DNA, chromosome 4q22-q24, clone:496L13, complete sequence

337

337G > A

Genomic

GEN-3B6-AC008063_4

AC008063

None

GEN-OU5

Homo sapiens BAC clone

RP11-178A14 from 2, complete sequence

170

170A > G

Genomic

GEN-3B6-AC008063_1

AC008063

None

GEN-OUA

Homo sapiens BAC clone

RP11-178A14 from 2, complete sequence

417

417C > A

Genomic

GEN-3AX-AC005006_8

AC005006

None

GEN-OUO

Homo sapiens clone RP1-

56J10, complete sequence

243

243C > T

Genomic

288

288C > T

Genomic

605

605C > T

Genomic

GEN-MQ1-AL021068_0

AL021068

None

GEN-OXZ

Homo sapiens DNA

sequence from PAC 206D15 on chromosome 1q24. contains a Reduced Folate Carrier

protein (RFC) LIKE gene, a mitochondrial ATP Synthetase protein 8 (ATP8, MTATP8)

LIKE pseudogene, an unknown gene and the last exon of the JEM1 gene coding for

179

179T > C

Genomic

243

243G > T

Genomic

680

680T > C

Genomic

790

790C > G

Genomic

GEN-MQY-AL122002_5

AL122002

None

GEN-OY2

Human DNA sequence from

clone RP4-651E10 on chromosome 1p22.3-31.1, complete sequence

45

45C > A

Genomic

273

273A > T

Genomic

467

467T > C

Genomic

GEN-MQY-AL122002_7

AL122002

None

GEN-OY4

Human DNA sequence from

clone RP4-651E10 on chromosome 1p22.3-31.1, complete sequence

276

276A > G

Genomic

GEN-MQY-AL122002_3

AL122002

None

GEN-OY8

Human DNA sequence from

clone RP4-651E10 on chromosome 1p22.3-31.1, complete sequence

179

179G > A

Genomic

GEN-PB-Z84814_0

Z84814

None

GEN-OZA

Human DNA sequence from PAC

172K2 on chromosome 6 contains HLA CLASS II DRA pseudogene, DRB3*01012 genes,

DRB9 pseudogene butyrophilin precursor and ESTs

2093

2093A > G

Genomic

GEN-PB-Z84814_1

Z84814

None

GEN-OZB

Human DNA sequence from PAC

172K2 on chromosome 6 contains HLA CLASS II DRA pseudogene, DRB3*01012 genes,

DRB9 pseudogene butyrophilin precursor and ESTs

361

361G > A

Genomic

434

434G > A

Genomic

618

618T > C

Genomic

727

727C > T

Genomic

784

784A > G

Genomic

790

790G > A

Genomic

874

874G > T

Genomic

GEN-PB-Z84814_2

Z84814

None

GEN-OZC

Human DNA sequence from PAC

172K2 on chromosome 6 contains HLA CLASS II DRA pseudogene, DRB3*01012 genes,

DRB9 pseudogene butyrophilin precursor and ESTs

165

165T > C

Genomic

GEN-QB-AC034228_0

AC034228

None

GEN-OZF

Homo sapiens chromosome 5

clone CTD-2198K16, WORKING DRAFT SEQUENCE, 19 unordered pieces

1228

1228T > G

Genomic

1608

1608C > T

Genomic

1728

1728G > A

Genomic

2396

2396A > G

Genomic

2469

2469A > G

Genomic

GEN-KYP-AL356218_1

AL356218

None

GEN-P1K

Homo sapiens chromosome

9 clone RP11-311H10, *** SEQUENCING IN PROGRESS ***, 20 unordered pieces

102

102C > G

Genomic

GEN-9K-L44140_3

L44140

None

GEN-P22

Homo sapiens chromosome X

region from filamin (FLN) gene to glucose-6-phosphate dehydrogenase (G6PD) gene,

complete cds's

276

276C > T

Genomic

GEN-9K-L44140_5

L44140

None

GEN-P24

Homo sapiens chromosome X

region from filamin (FLN) gene to glucose-6-phosphate dehydrogenase (G6PD) gene,

complete cds's

1149

1149G > A

Genomic

GEN-EY-AC003043_0

AC003043

None

GEN-P92

Homo sapiens chromosome

17, clone HRPC1067MG, complete sequence

1488

1488T > C

Genomic

GEN-PH-AL132708_1

AL132708

None

GEN-PM6

Human chromosome 14 DNA

sequence *** IN PROGRESS *** BAC R-34911 of library RPCI-11 from chromosome 14

of Homo sapiens (Human), complete sequence

1632

1632C > T

Genomic

GEN-2KB-Z80898_4

Z80898

None

GEN-PND

Human DNA sequence from

cosmid E1448 on chromosome 6p21.3 contains NRC class II HLA-DQB1

73

73C > T

Genomic

114

114G > A

Genomic

GEN-2KB-Z80898_0

Z80898

None

GEN-PNE

Human DNA sequence from

cosmid E1448 on chromosome 6p21.3 contains NRC class II HLA-DQB1

207

207T > C

Genomic

GEN-2KB-Z80898_3

Z80898

None

GEN-PNH

Human DNA sequence from

cosmid E1448 on chromosome 6p21.3 contains MHC class II HLA-DQB1

371

371C > A

Genomic

GEN-KV6-AP001725_1

AP001725

None

GEN-PNI

Homo sapiens genomic

DNA, chromosome 21q, section 69/105

93

93C > T

Genomic

180

180A > G

Genomic

352

352G > A

Genomic

GEN-9J-AC011780_0

AC011780

None

GEN-PPJ

Homo sapiens clone RP11

15H8, WORKING DRAFT SEQUENCE, 31 unordered pieces

421

421A > G

Genomic

702

702T > C

Genomic

GEN-9J-AC011780_6

AC011780

None

GEN-PPL

Homo sapiens clone RP11-

15H8, WORKING DRAFT SEQUENCE, 31 unordered pieces

299

299C > T

Genomic

303

303T > C

Genomic

GEN-4R-AL133553_10

AL133553

None

GEN-PQM

Human DNA sequence from

clone GS1-174L6 on chromosome 1, complete sequence

175

175A > G

Genomic

362

362C > T

Genomic

365

365G > A

Genomic

GEN-170-AL158847_2

AL158847

None

GEN-PQR

Homo sapiens chromosome

1 clone RP4-735C1, *** SEQUENCING IN PROGRESS ***, 20 unordered pieces

417

417C > T

Genomic

GEN-1QL-AL157871_2

AC011450

None

GEN-PRN

Homo sapiens chromosome

19 clone CTC-30107, complete sequence

99

99G > C

Genomic

GEN-25R-AC018988_7

AC018988

None

GEN-PS1

Homo sapiens chromosome

15 clone RP11-233C13 map 15, WORKING DRAFT SEQUENCE, 23 unordered pieces

335

335G > A

Genomic

367

367G > C

Genomic

GEN-25R-AC018988_5

AC018988

None

GEN-PS3

Homo sapiens chromosome

15 clone RP11-233C13 map 15, WORKING DRAFT SEQUENCE, 23 unordered pieces

115

115G > A

Genomic

121

121A > C

Genomic

346

346G > C

Genomic

GEN-25R-AC018988_2

AC018988

None

GEN-PS4

Homo sapiens chromosome

15 clone RP11-233C13 map 15, WORKING DRAFT SEQUENCE, 23 unordered pieces

1104

1104G > A

Genomic

1111

1111T > C

Genomic

GEN-2SV-AL096870_0

AL096870

None

GEN-Q9E

Human chromosome 14 DNA

sequence *** IN PROGRESS *** BAC R-93459 of library RPCI-11 from chromosome 14

of Homo sapiens (Human), complete sequence

932

932G > C

Genomic

GEN-3G-AC013599_1

AC013599

None

GEN-QAN

Homo sapiens clone RP11-

9N17, WORKING DRAFT SEQUENCE, 27 unordered pieces

168

168T > C

Genomic

191

191G > A

Genomic

222

222G > T

Genomic

GEN-1HZ--AC005519_7

AC005519

None

GEN-QBH

Homo sapiens PAC clone

RP5-919J22 from 14q24.3, complete sequence

180

150T > C

Genomic

[1059]

TABLE 4
AAC2	D90040	243400	GEN-465	Human mRNA for
arylamine N-acetyltransferase (EC 2.3.1.5)
231	190C>T	R64W
232	191G>A	R64Q
382	341T>C	I114T
522	481C>T	Silent
631	590G>A	R197Q
844	803A>G	K268R
898	857A>G	E286G
1062	1021T>C	3′
AB000410	AB000410	601982	GEN-9O	Human hOGG1
mRNA, complete cds
246	(−23)A>G	5′
251	(−18)G>T	5′
562	294G>A	Silent
AB005659	AB005659	None	GEN-VR	Homo sapiens SMRP
mRNA, complete cds
4820	4084G>A	3′
AB017546	AB017546	601791	GEN-L3J	Homo sapiens Pex14
mRNA for peroxisomal membrane anchor protein, complete cds
161	156T>C	Silent
ADH3	M12272	103730	GEN-1LU	Homo sapiens alcohol
dehydrogenase class I gamma subunit (ADH3) mRNA, complete cds
1128	1048A>G	I350V
AF001437	AF001437	245349	GEN-9T	Dihydrolipoamide S-
acetyltransferase (E2 component of pyruvate dehydrogenase complex)
2000	1992G>T	3′
AF001945	AF001945	601691	GEN-17Z	Homo sapiens rim
ABC transporter (ABCR) mRNA, complete cds
1492	1411G>A	E471K
2336	2255G>T	S752I
2646	2565G>A	Frame
2669	2588G>C	G863A
2872	2791G>A	V931M
3164	3083C>T	A1028V
3187	3106G>A	E1036K
3292	3211ˆ 3212insGT	Frame
4284	4203C>A	Silent
4364	4283C>T	T1428M
4813	4732G>A	G1578R
5684	5603A>T	N18681
5763	5682G>C	Silent
5963	5882G>A	G1961E
6160	6079C>T	L2027F
6229	6148G>C	V2050L
6330	6249C>T	Silent
6366	6285T>C	Silent
6610	6529G>A	D2177N
6774	6693C>T	Silent
AF007216	AF007216	603345	GEN-13L	Homo sapiens sodium
bicarbonate cotransporter (HNBC1) mRNA, complete cds
1043	894A>C	R298S
1678	1529G>A	R510H
AF009746	AF009746	603214	GEN-1HZ	Homo sapiens
peroxisomal membrane protein 69 (PMP69) mRNA, complete cds
2060	2009T>C	3′
AF027302	AF027302	603429	GEN-27T	Homo sapiens TNF-
alpha stimulated ABC protein (ABC50) mRNA, complete cds
3075	2981T>C	3′
AF038007	AF038007	602397	GEN-2QG	Homo sapiens P-type
ATPase FIC1 mRNA, partial cds
941	941G>T	G314V
AF044206	AF044206	600262	GEN-MVP	Homo sapiens
cyclooxygenase (COX-2) gene, promoter and exon 1
6978	6978C>G	Genomic
7150	7150T>G	Genomic
AF055025	AF055025	300095	GEN-32U	Homo sapiens clone
24776 mRNA sequence
1579	1579A>G	3′
AF058921	AF058921	603602	GEN-LJY	Homo sapiens
cytosolic phospholipase A2-gamma mRNA, complete cds
1989	1680A>T	3′
AF185589	AF185589	124010	GEN-MVA	Homo sapiens
cytochrome P450 3A4 (CYP3A4) gene, promoter region
10282	10282A>G	Genomic
AJ001838	AJ001838	603758	GEN-17S	Homo sapiens mRNA
for maleylacetoacetate isomerase
197	94A>G	K32E
ARSB	M32373	253200	GEN-2J0	Human arylsulfatase B
(ASB) mRNA, complete cds
182	(−378)G>C	5′
182	(−378)G>T	5′
ATM	U26455	208900	GEN-2AT	Human
phosphatidylinositol 3-kinase homolog (ATM) mRNA, complete cds
1286	1027A>C	S343R
1772	1513G>A	D505N
1773	1514A>T	D505V
2450	2191G>A	V731I
3075	2816G>C	G939A
ATP1A1	D00099	182310	GEN-4E8	Homo sapiens mRNA
for Na,K-ATPase alpha-subunit, complete cds
3375	3057G>A	Silent
AVPR2	AF030626	304800	GEN-2LO	Vasopressin receptor
V2
137	105G>A	Silent
157	125C>T	A42V
212	180G>T	Silent
472	440C>T	A147V
1025	993C>T	Silent
1145	1113G>A	Silent
1165	1133T>C	3′
CAT	X04076	115500	GEN-13P	Human kidney mRNA
for catalase
51	(−20)T>C	5′
1325	1255C>T	Silent
CBG	J02943	122500	GEN-Y2	Human corticosteroid
binding globulin mRNA, complete cds
1229	1194G>A	Silent
CBR	J04056	114830	GEN-13O	Human carbonyl
reductase mRNA, complete cds
1060	967G>A	3′
CBS	L00972	236200	GEN-UV	Human cystathionine-
beta synthase (CBS) mRNA
1022	1022T>C	3′
1403	1403T>C	3′
CFTR	M28668	602421	GEN-2DF	Human cystic fibrosis
mRNA, encoding a presumed transmembrane conductance regulator
(CFTR)
125	(−8)G>C	5′
156	24G>A	Silent
223	91C>T	R31C
263	131A>T	D44V
345	213T>C	Silent
356	224G>A	R75Q
492	360G>A	Silent
545	413T>C	L138P
676	544A>G	S182G
741	609C>T	Silent
1047	915C>T	Silent
1059	927C>G	Silent
1096	964G>A	V322M
1104	972C>G	Silent
1184	1052C>G	T351S
1191	1059A>C	Q353H
1296	1164G>T	Silent
1572	1440T>C	Silent
1650	1518C>G	I506M
1651	1519A>G	I507V
1655	1523T>G	F508C
1773	1641A>T	Silent
1859	1727G>C	G576A
2092	1960A>G	S654G
2134	2002C>T	R668C
2209	2077T>C	F693L
2238	2106C>G	Silent
2553	2421A>G	I807M
2691	2559T>C	Silent
2694	2562T>G	Silent
2839	2707T>C	Y903H
2858	2726G>T	S909I
2901	2769C>T	Silent
3212	3080T>C	I1027T
3309	3177A>G	Silent
3332	3200C>T	A1067V
3333	3201C>T	Silent
3336	3204C>T	Silent
3384	3252A>G	Silent
3417	3285A>T	Silent
3471	3339T>C	Silent
3690	3558A>G	Silent
3726	3594G>T	Silent
3791	3659C>T	T12201
3939	3807C>T	Silent
4029	3897A>G	Silent
4050	3918C>T	Silent
4404	4272C>T	Silent
4521	4389G>A	Silent
CYP11B2	D13752	124080	GEN-CCD	Human CYP11B2
gene for steroid 18-hydroxylase, complete cds
295	288T>C	Silent
511	504C>T	Silent
525	518A>G	K173R
672	665A>C	N222T
750	743T>C	I248T
778	771T>G	F257L
832	825C>T	Silent
849	842A>G	N281S
880	873G>A	Silent
898	891G>A	Silent
1023	1016T>C	I339T
1155	1148A>T	E383V
1164	1157T>C	V386A
1177	1170G>A	Silent
1310	1303G>A	G435S
1322	1315C>T	H439Y
1360	1353C>T	Silent
1466	1459T>G	F487V
1600	1593G>A	3′
CYP1B1	U03688	601771	GEN-11Y	Human dioxin-
inducible cytochrome P450 (CYP1B1) mRNA, complete cds
1640	1294G>C	V432L
1693	1347T>A	D449E
1704	1358A>G	N453S
2096	1750C>G	3′
2316	1970T>A	3′
CYP51	U23942	601637	GEN-27K	Human lanosterol 14-
demethylase cytochrome P450 (CYP51) mRNA, complete cds
2283	2161G>T	3′
D12614	D12614	153440	GEN-QD	Human mRNA for
lymphotoxin (TNF-beta), complete cds
319	179C>A	T60N
D13811	D13811	238310	GEN-AA	Glycine cleavage
system: Protein T
254	125A>G	H42R
268	139G>A	G47R
312	183delC	Frame
935	806G>A	G269D
955	826G>C	D276H
1088	959G>A	R320H
D17793	D17793	603966	GEN-20Q	Human mRNA for
KIAA0119 gene, complete cds
980	929G>C	S310T
D26480	D26480	None	GEN-LBX	Human mRNA for
leukotriene B4 omega-hydroxylase, complete cds
2147	2106C>G	3′
D49737	D49737	602413	GEN-2Z7	Homo sapiens mRNA
for cytochrome b large subunit of complex II, complete cds
908	784G>A	3′
D87030	D87030	None	GEN-4EZ	Serotonin receptor
5HT-2A, 5′ upstream region
178	178G>A	3′
DDH1	U05598	600450	GEN-184	Human dihydrodiol
dehydrogenase mRNA, complete cds
126	103C>T	Silent
149	126A>T	Silent
179	156A>T	Silent
1020	997G>A	3′
ESD	M13450	133280	GEN-1O7	Human esterase D
mRNA, 3′ end
614	614G>A	G205E
FABP2	M10050	134640	GEN-1IE	Human liver fatty acid
binding protein (FABP) mRNA, complete cds
202	160G>A	A54T
FACL1	L09229	152425	GEN-1GI	Human long-chain
acyl-coenzyme A synthetase (FACL1) mRNA, complete cds
3026	2953G>A	3′
GC	M12654	139200	GEN-1MN	Human serum vitamin
D-binding protein (hDBP) mRNA, complete cds
45	17T>C	V6A
GPX1	Y00433	138320	GEN-TJ	Human mRNA for
glutathione peroxidase (EC 1.11.1.9.)
273	(−46)C>T	5′
911	593C>T	P198L
GPX3	X58295	138321	GEN-38S	Human GPx-3 mRNA
for plasma glutathione peroxidase
1354	1306C>T	3′
GPX4	X71973	138322	GEN-3L1	H.sapiens GPx-4
mRNA for phospholipid hydroperoxide glutathione peroxidase
738	658C>T	3′
GSS	U34683	601002	GEN-2LF	Human glutathione
synthetase mRNA, complete cds
1737	1697C>T	3′
GSTM3	J05459	138390	GEN-17O	Human glutathione
transferase M3 (GSTM3) mRNA, complete cds
687	670G>A	V224I
GSTP1	X06547	134660	GEN-19N	Human mRNA for
class Pi glutathione S-transferase (GST-Pi; E.C.2.5.1.18)
319	313A>G	I105V
561	555C>T	Silent
GSTT2	L38503	600437	GEN-2PC	Homo sapiens
glutathione S-transferase theta 2 (GSTT2) mRNA, complete cds
888	888G>T	3′
GUSB	M15182	253220	GEN-1TH	Endo-beta-D-
glucuronidase
698	672C>T	Silent
1170	1144C>T	R382C
1882	1856C>T	A619V
1972	1946C>T	P649L
HADHB	D16481	143450	GEN-1Y5	Human mRNA for
mitochondrial 3-ketoacyl-CoA thiolase beta-subunit of trifunctional
protein, complete cds
228	182G>A	R61H
786	740G>A	R247H
834	788A>G	D263G
HLA-	X00532	142858	GEN-U2	Human mRNA for
DPB1
SB beta-chain (clone pII-beta-7)
568	568A>G	R190G
HTR1E	M91467	182132	GEN-4EE	Serotonin 5-HT
receptors 5-HT1E
1097	531C>T	Silent
1351	785C>T	S262F
ID1	X77956	600349	GEN-3QL	H.sapiens Id1 mRNA
851	816G>A	3′
J03037	J03037	259730	GEN-2I	Carbonic anhydrase II
627	562C>T	Silent
J03143	J03143	107470	GEN-ZK	Human interferon-
gamma receptor mRNA, complete cds
395	347C>A	Frame
J03490	J03490	246900	GEN-C5	Dihydrolipoamide
dehydrogenase (E3 component of pyruvate dehydrogenase complex,
2-oxo-glutarate complex, branched chain keto acid dehydrogenase
complex)
1624	1548T>A	3′
2088	2012T>C	3′
2096	2020T>C	3′
2142	2066G>T	3′
J03571	J03571	152390	GEN-9	Lipoxygenases:
5-lipoxygenase (leukocytes)
55	21C>T	Silent
304	270G>A	Silent
1762	1728A>T	Silent
J03810	J03810	138160	GEN-C9	Solute carrier family 2
(facilitated glucose transporter), member 2
339	301G>A	V101I
367	329C>T	T110I
699	661C>T	Silent
1475	1437C>T	Silent
1544	1506G>A	Silent
J03817	J03817	138350	GEN-9D	Glutathione S-
transferase M1
1008	993C>T	3′
J04031	J04031	172460	GEN-CB	Methenyltetrahydro-
folate cyclohydrolase
931	878G>A	R293H
3009	2956A>C	3′
J05176	J05176	107280	GEN-PT	Human alpha-1-
antichymotrypsin mRNA, 3′ end
170	170T>C	L57P
J05594	J05594	601688	GEN-E	Prostaglandin 15-OH
dehydrogenase (PGDH)
1448	1431G>A	3′
K01171	K01171	142860	GEN-PB	Human HLA-DR
alpha-chain mRNA
416	402C>A	Silent
K03191	K03191	108330	GEN-9E	Cytochrome P450,
subfamily I (aromatic compound-inducible), polypeptide 1
1470	1384G>A	V462I
2220	2134T>C	3′
K03195	K03195	138140	GEN-ZT	Human (HepG2)
glucose transporter gene mRNA, complete cds
943	764A>C	K255T
2120	1941G>C	3′
L02932	L02932	170998	GEN-KW4	Human peroxisome
proliferator activated receptor mRNA, complete cds
896	680T>C	V227A
978	762G>C	Silent
1019	803T>C	V268A
1442	1226G>C	R409T
1651	1435C>T	3′
L05597	L05597	182134	GEN-4EV	Serotonin 5-HT
receptors 5-HT1F
147	(−78)C>T	5′
752	528C>T	Silent
1007	783T>A	Silent
1330	1106A>T	3′
L10819	L10819	171150	GEN-LVD	Homo sapiens aryl
sulfotransferase mRNA, complete cds
676	638A>G	H213R
705	667A>G	M223V
L11695	L11695	190181	GEN-MDJ	Human activin
receptor-like kinase (ALK-5) mRNA, complete cds
1644	1568A>G	3′
1657	1581G>A	3′
L11696	L11696	104614	GEN-D6	Solute carrier family 3
(cystine, dibasic and neutral amino acid transporters, activator of cystine,
dibasic and neutral amino acid transport), member 1
2232	2189T>C	3′
L14754	L14754	600502	GEN-D9	DNA-binding protein
(SMBP2)
244	195C>A	Silent
244	195C>G	Silent
390	341T>C	V114A
390	341T>G	V114G
L19067	L19067	164014	GEN-DE	TRANSCRIPTION
FACTOR P65
2024	1986C>T	3′
2310	2272G>T	3′
L24470	L24470	600563	GEN-O	PROSTAGLANDIN F
RECEPTOR
2203	1966A>C	3′
2299	2062A>G	3′
L38928	L38928	604197	GEN-2PT	Homo sapiens 5,10-
methenyltetrahydrofolate synthetase mRNA, complete cds
617	604A>G	T202A
L40904	L40904	601487	GEN-2SK	H. sapiens peroxisome
proliferator activated receptor gamma, complete cds
206	34C>G	P12A
426	254C>A	P85Q
L42812	L42812	100740	GEN-LUN	Homo sapiens
acetylcholinesterase (ACHE) gene, exons 2-6
1092	1092T>A	Genomic
1151	1151C>A	Genomic
1871	1871C>T	Genomic
3290	3290C>G	Genomic
L48513	L48513	602447	GEN-2YD	Homo sapiens
paraoxonase 2 (PON2) mRNA, complete cds
460	443C>G	A148G
949	932G>C	C311S
L78207	L78207	600509	GEN-5Q	Cell surface receptor
for sulfonylureas on pancreatic b cells
245	207T>C	Silent
2315	2277C>T	Silent
3857	3819G>A	Silent
LIG1	M36067	126391	GEN-2MS	Human DNA ligase I
mRNA, complete cds
630	510A>C	Silent
LIPC	J03540	151670	GEN-11J	Human hepatic lipase
mRNA, complete cds
648	644G>A	S215N
676	672C>G	Silent
1323	1319G>A	S440N
1441	1437C>A	Silent
M10901	M10901	138040	GEN-2W	Corticosteroid nuclear
receptor b
198	66G>A	Silent
200	68G>A	R23K
325	193T>G	F65V
936	804C>T	Silent
1220	1088A>G	N363S
1226	1094A>G	N365S
2024	1892-1893delAG	Frame
2054	1922A>T	D641V
2372	2240T>G	I747S
2391	2259A>C	L753F
2430	2298T>C	Silent
3691	3559T>C	3′
4172	4040G>T	3′
4654	4522A>G	3′
M11050	M11050	138040	GEN-7Y	Glucocorticoid
receptor
198	66G>A	Silent
200	68G>A	R23K
325	193T>G	F65V
936	804C>T	Silent
1220	1088A>G	N363S
1226	1094A>G	N365S
3134	3002G>T	3′
3669	3537A>G	3′
M12959	M12959	186880	GEN-S	CD3 glycoprotein on T
lymphocytes
1249	1113C>G	3′
1343	1207T>C	3′
1345	1209G>C	3′
1394	1258T>G	3′
1463	1327G>A	3′
M14565	M14565	118485	GEN-30	“Cytochrome P450,
subfamily XIA (cholesterol side chain cleavage)”
984	940G>A	E314K
M14758	M14758	171050	GEN-1S6	P glycoprotein 1
978	554T>G	V185G
979	555T>A	Silent
4460	4036A>G	3′
M15856	M15856	238600	GEN-33	Lipoprotein lipase
136	(−39)T>C	5′
280	106G>A	D36N
438	264T>A	Frame
447	273G>A	Frame
474	300C>A	Frame
480	306A>C	R102S
511	337T>C	W113R
571	397C>T	Frame
680	506G>A	G169E
722	548A>G	D183G
770	596C>G	S199C
781	607G>A	A203T
795	621C>G	D207E
818	644G>A	G215E
836	662T>C	I221T
839	665G>A	G222E
867	693C>G	D231E
875	701C>T	P234L
916	742delG	Frame
983	809G>A	R270H
985	811T>A	S271T
1003	829G>A	D277N
1036	862G>A	A288T
1127	953A>G	N318S
1255	1081G>A	A361T
1282	1108G>A	V370M
1348	1174C>G	L392V
1401	1227G>A	Frame
1453	1279G>A	A427T
1508	1334G>A	C445Y
1595	1421C>G	Frame
1973	1799T>C	3′
M15872	M15872	138360	GEN-QS	Human glutathione S-
transferase 2 (GST) mRNA, complete cds
170	115G>T	Frame
M16541	M16541	177400	GEN-35	Butyrylcholinesterase
422	293A>G	D98G
557	428G>A	G143D
568	439C>T	Frame
596	467A>G	Y156C
961	832A>C	T278P
1201	1072T>A	L358I
1306	1177G>A	G393R
1382	1253G>T	G418V
1549	1420T>G	F474V
1564	1435G>T	Frame
1703	1574A>T	E525V
1756	1627C>T	R543C
1828	1699G>A	A567T
2127	1998A>G	3′
M16827	M16827	201450	GEN-EI	Acyl-Coenzyme A
dehydrogenase, C-4 to C-12 straight chain
1179	1161A>G	Silent
1956	1938T>C	3′
M19154	M19154	190220	GEN-R0	Human transforming
growth factor-beta-2 mRNA, complete cds
1990	1523C>A	3′
1990	1523C>T	3′
1997	1530A>T	3′
M21054	M21054	172410	GEN-3B	Phospholipase A-2
(PLA-2) lung
160	123C>T	Silent
259	222T>C	Silent
303	266A>C	N89T
304	267C>A	N89K
331	294G>A	Silent
M22324	M22324	151530	GEN-25R	Human amino-
peptidase N/CD13 mRNA encoding aminopeptidase N, complete cds
3053	2933G>C	3′
M26393	M26393	201470	GEN-EW	Acyl-Coenzyme A
dehydrogenase, C-2 to C-3 short chain
353	321T>C	Silent
543	511C>T	R171W
1022	990C>T	Silent
1292	1260G>C	3′
1797	1765A>G	3′
M27819	M27819	109270	GEN-EY	“Solute carrier family
4, anion exchanger, member 1 (MEDIATES EXCHANGE OF
INORGANIC ANIONS ACROSS THE MEMBRANE)”
1038	924G>A	Silent
1353	1239C>T	Silent
1363	1249C>T	Silent
1437	1323G>A	Silent
1438	1324A>T	I442F
1870	1756A>T	M586L
2067	1953C>T	Silent
2214	2100C>T	Silent
2698	2584G>A	V862I
3119	3005G>A	3′
3158	3044C>A	3′
M29474	M29474	179615	GEN-MIU	Human recombination
activating protein (RAG-1) gene, complete cds
579	467C>T	A156V
M29882	M29882	107670	GEN-6R	Apolipoprotein A-II
256	247C>T	Silent
M30938	M30938	194364	GEN-F5	ATP-DEPENDENT
DNA HELICASE II, 86 KD SUBUNIT
3150	3123T>A	3′
M31523	M31523	147141	GEN-F7	Transcription factor 3
(E2A immunoglobulin enhancer binding factors E12/E47)
1332	1302G>A	Silent
M33195	M33195	147139	GEN-2JR	Human Fc-epsilon-
receptor gamma-chain mRNA, complete cds
446	421T>G	3′
M34479	M34479	179060	GEN-F9	Pyruvate
dehydrogenase (lipoamide) beta
1323	1323C>A	3′
M55040	M55040	100740	GEN-3Q	acetylcholinesterase
1154	998T>A	V333E
1213	1057C>A	H353N
1587	1431C>T	Silent
M55531	M55531	138230	GEN-FF	Solute carrier family 2
(facilitated glucose transporter), member 5
51	(−25)G>A	5′
M57899	M57899	191740	GEN-38A	Human bilirubin UDP-
glucuronosyltransferase isozyme 1 mRNA, complete cds
2057	2042C>G	3′
M58525	M58525	116790	GEN-3S	Catechol-o-
methyltransferase
390	186T>C	Silent
418	214G>T	A72S
612	408C>G	Silent
676	472A>G	M158V
M58664	M58664	103000	GEN-395	Homo sapiens CD24
signal transducer mRNA, complete cds
226	170C>T	A57V
M59941	M59941	138981	GEN-62	“Granulocyte-
macrophage (Colony stimulating factor 2 receptor, beta, low-affinity)”
730	702C>T	Silent
773	745G>C	E249Q
1306	1278C>T	Silent
1835	1807C>A	P603T
1968	1940G>T	G647V
1972	1944G>A	Silent
1982	1954G>A	V652M
2428	2400G>A	Silent
M59979	M59979	176805	GEN-Z	Cyclooxygenase 1
COX1
328	323G>A	R108Q
644	639C>A	Silent
714	709C>A	L237M
956	951T>C	Silent
1081	1076A>G	K359R
1332	1327A>G	I443V
1404	1399A>G	K467E
1446	1441G>A	V481I
1924	1919T>C	3′
1966	1961T>C	3′
2055	2050T>G	3′
M60761	M60761	156569	GEN-FL	O-6-methylguanine-
DNA methyltransferase
174	159C>T	Silent
210	195G>C	W65C
265	250C>T	L84F
442	427A>G	I143V
493	478G>A	G160R
548	533A>G	K178R
582	567G>A	Silent
M61855	M61855	601130	GEN-3C1	Human cytochrome
P4502C9 (CYP2C9) mRNA, clone 25
442	442C>T	3′
1087	1087C>A	3′
M63012	M63012	168820	GEN-9F	Paraoxonase 1
584	575A>G	Q192R
M64799	M64799	162020	GEN-4DN	Histamine receptor H2
398	398T>C	V133A
525	525A>T	K175N
620	620A>G	K207R
649	649A>G	N217D
692	692A>G	K231R
802	802G>A	V268M
M65105	M65105	163970	GEN-14	Norepinephrine
transporter
897	837C>G	Silent
935	875A>C	N292T
1126	1066G>C	V356L
1165	1105G>C	A369P
1347	1287G>A	Silent
1429	1369G>C	A457P
1702	1642T>C	Y548H
M69043	M69043	164008	GEN-3IZ	Homo sapiens MAD-3
mRNA encoding IkB-like activity, complete cds
1050	956T>C	3′
1174	1080A>G	3′
M69177	M69177	309860	GEN-3Y	Monoamine oxidase B
1538	1461C>T	Silent
M69226	M69226	309850	GEN-3Z	Monoamine oxidase A
435	385A>C	Silent
936	886C>T	Frame
941	891T>G	Silent
1076	1026A>T	Silent
1460	1410C>T	Silent
1609	1559A>G	K520R
M74096	M74096	201460	GEN-G0	Acyl-Coenzyme A
dehydrogenase, long chain
913	908G>C	S303T
M76180	M76180	107930	GEN-16	L-aromatic amino acid
decarboxylase
118	49G>A	V17M
698	629C>T	P210L
718	649A>G	M217V
M80646	M80646	274180	GEN-40	Thromboxane synthase
654	483C>A	D161E
658	487C>A	L163I
943	772A>G	K258E
952	781A>G	R261G
1120	949C>A	Q317K
1166	995T>C	I332T
1240	1069C>G	L357V
1340	1169G>T	G390V
1444	1273C>T	R425C
1459	1288G>A	A430T
1476	1305C>T	Silent
M81590	M81590	182131	GEN-3VZ	Serotonin receptor
5HT-1B, cDNA
190	129C>T	Silent
922	861G>C	Silent
M81757	M81757	603474	GEN-3W6	H.sapiens S19
ribosomal protein mRNA, complete cds
1	(−22)C>A	5′
45	23A>C	D8A
45	23A>T	D8V
M81768	M81768	107310	GEN-G6	Solute carrier family 9
(sodium/hydrogen exchanger)
3080	3027T>C	3′
M90100	M90100	600262	GEN-1A	Cyclooxygenase 2
COX2
1306	1209T>C	Silent
1560	1463A>G	E488G
1629	1532T>C	V511A
1852	1755C>A	Silent
2409	2312G>A	3′
M94859	M94859	114217	GEN-GP	Calnexin
3011	2916G>T	3′
MBL	X15422	154545	GEN-1U2	Human mRNA for
mannose-binding protein C
226	161G>A	G54D
235	170G>A	G57E
MDCR	L13385	601545	GEN-1O6	Homo sapiens (clone
71) Miller-Dieker lissencephaly protein (LIS1) mRNA, complete cds
239	22C>T	Frame
275	58C>T	R20C
663	446A>G	H149R
716	499T>C	S167P
1034	817C>T	Frame
5175	4958C>A	3′
MHC2TA	X74301	600005	GEN-3N5	H.sapiens mRNA for
MHC class II transactivator
1614	1499C>G	A500G
NMOR1	J03934	125860	GEN-12L	Human, NAD(P)H:
menadione oxidoreductase mRNA, complete cds
609	559C>T	P187S
PDHA1	X52709	312170	GEN-33Y	Human mRNA for
brain pyruvate dehydrogenase (EC 1.2.4.1)
1337	1283C>T	3′
PLA2G2A	M22430	172411	GEN-25V	Human RASF-A
PLA2 mRNA, complete cds
231	96G>C	Silent
267	132C>T	Silent
278	143-144delGT	Frame
643	508C>T	3′
700	565G>C	3′
PNMT	J03727	171190	GEN-120	Human
phenylethanolamine N-methyltransferase mRNA, complete cds
462	456A>G	Silent
568	562A>T	S188C
638	632T>A	L211H
656	650T>A	L217Q
767	761G>A	R254H
832	826T>A	W276R
PTAFR	M76674	173393	GEN-3P9	Platelet-activating
factor receptor
696	671C>A	A224D
S77127	S77127	None	GEN-MTU	Superoxide dismutase
2 (manganese), promoter and genomic
1183	1183C>T	Genomic
1735	1735T>C	Genomic
4903	4903G>A	Genomic
7939	7939G>A	Genomic
SLC12A3	U44128	600968	GEN-CCX	Human thiazide-
sensitive Na-Cl cotransporter (hTSC) mRNA, complete cds
1884	1884G>A	Silent
2142	2142C>T	Silent
2625	2625C>T	Silent
SLC2A4	M20747	138190	GEN-23Q	Human insulin-
responsive glucose transporter (GLUT4) mRNA, complete cds
378	233C>G	T78S
535	390C>T	Silent
SLC6A1	X54673	137165	GEN-358	H.sapiens GAT1
mRNA for GABA transporter
240	6G>A	Silent
885	651G>T	Silent
SLC6A3	L24178	126455	GEN-283	Homo sapiens
dopamine transporter mRNA, complete cds
133	114C>T	Silent
169	150G>T	Silent
181	162C>T	Silent
729	710G>A	R237Q
1234	1215G>A	Silent
1750	1731C>T	Silent
SOD3	J02947	185490	GEN-Y3	Human extracellular-
superoxide dismutase (SOD3) mRNA, complete cds
760	691C>G	R231G
TAP2	Z22935	170261	GEN-26P	H.sapiens TAP2B
mRNA, complete CDS
1690	1662G>A	Silent
1746	1718A>G	D573G
2021	1993G>A	A665T
TCN2	M60396	275350	GEN-3AX	Human transcobalamin
II (TCII) mRNA, complete cds
813	776C>G	P259R
1623	1586C>A	3′
1635	1598C>A	3′
TGFBR3	L07594	600742	GEN-1EA	Human transforming
growth factor-beta type III receptor (TGF-beta) mRNA, complete cds
150	(−199)G>A	5′
150	(−199)G>C	5′
3957	3609A>C	3′
3966	3618G>C	3′
TPMT	U12387	187680	GEN-1LY	Human thiopurine
methyltransferase (TPMT) mRNA, complete cds
314	238G>C	A80P
536	460G>A	A154T
720	644G>A	R215H
795	719A>G	Y240C
TRP2	M55169	190470	GEN-35U	Homo sapiens
tripeptidyl peptidase II mRNA, 3′ end
3637	3637G>A	3′
U00672	U00672	146933	GEN-4A	Interleukin 10 receptor
3524	3463A>G	3′
U04636	U04636	600262	GEN-MVG	Cyclooxygenase 2,
genomic sequence (not including promoter)
671	671C>G	Genomic
841	841T>G	Genomic
2191	2191C>G	Genomic
4719	4719T>C	Genomic
5310	5310T>C	Genomic
6551	6551A>G	Genomic
6620	6620T>C	Genomic
6843	6843C>A	Genomic
7330	7330T>C	Genomic
7401	7401G>A	Genomic
U06088	U06088	253000	GEN-MP3	Human N-
acetylgalactosamine 6-sulphatase (GALNS) gene
708	708T>C	Silent
U08092	U08092	None	GEN-4C	Histamine
N-methyltransferase
353	314T>C	I105T
978	939G>A	3′
U09178	U09178	274270	GEN-HA	Dihydropyrimidine
Dehydrogenase
143	62G>A	R21Q
166	85T>C	C29R
784	703C>T	R235W
1084	1003G>C	V335L
1237	1156G>T	Frame
1682	1601G>A	S534N
1708	1627A>G	I543V
2275	2194G>A	V7321I
2738	2657G>A	R886H
3002	2921A>T	D974V
3064	2983G>T	V995F
U09806	U09806	236250	GEN-4FZ	Human
methylenetetrahydrofolate reductase mRNA, partial cds
120	120T>C	Silent
473	473G>A	R158Q
550	550C>T	Frame
668	668C>T	A223V
1059	1059T>C	Silent
1289	1289C>A	3′
1308	1308T>C	3′
U10417	U10417	601295	GEN-1IX	Homo sapiens ileal
sodium-dependent bile acid transporter (SLC10-A2) mRNA, complete cds
1109	511G>T	A171S
1326	728T>C	L243P
1383	785C>T	T262M
U14510	U14510	602698	GEN-1RD	Human transcription
factor NFATx mRNA, complete cds
3564	3540A>C	3′
U14650	U14650	602243	GEN-1RL	Human platelet-
endothelial tetraspan antigen 3 mRNA, complete cds
1263	1204G>C	3′
U16660	U16660	600696	GEN-1YD	Human peroxisomal
enoyl-CoA hydratase-like protein (HPXEL) mRNA, complete cds
149	122A>C	E41A
U19487	U19487	176804	GEN-4I	“PROSTAGLANDIN
E2 RECEPTOR, EP2 SUBTYPE”
1442	1286A>G	3′
U36601	U36601	603268	GEN-IR	Heparan N-
deacetylase/N-sulfotransferase-2
2727	2700T>G	3′
2972	2945A>G	3′
U37519	U37519	601917	GEN-2OF	Human aldehyde
dehydrogenase (ALDH8) mRNA, complete cds
1871	1255C>T	3′
U49516	U49516	312861	GEN-1Q	Serotonin 5-HT
receptors 5-HT2C
796	68G>C	C23S
2831	2103T>G	3′
U50040	U50040	601582	GEN-2ZR	Human signaling
inositol polyphosphate 5 phosphatase SIP-110 mRNA, complete cds
2882	2866C>T	H956Y
U68162	U68162	159530	GEN-MJM	Human thrombopoietin
receptor (MPL) gene
218	152C>T	A51V
547	481G>A	E161K
U73338	U73338	156570	GEN-69	Methionine Synthase
6750	6356G>A	3′
U83411	U83411	603105	GEN-3Y1	Homo sapiens
carboxypeptidase Z precursor, mRNA, complete cds
1788	1749G>A	Silent
X02612	X02612	None	GEN-MW2	Cytochrome P450
CYP1A1, promoter and genomic
6819	6819G>A	Genomic
7569	7569T>C	Genomic
X02812	X02812	190180	GEN-XR	Human mRNA for
transforming growth factor-beta (TGF-beta)
870	29C>T	P10L
915	74C>G	P25R
1632	791C>T	T264I
X02920	X02920	107400	GEN-PH	Human mRNA for
alpha 1-antitrypsin carboxyterminal region (aa 268-394)
195	195C>T	Silent
327	327A>C	E109D
X03348	X03348	138040	GEN-PL	Human mRNA for
beta-glucocorticoid receptor (clone OB10)
198	66G>A	Silent
200	68G>A	R23K
325	193T>G	F65V
936	804C>T	Silent
1220	1088A>G	N363S
1226	1094A>G	N365S
3134	3002G>T	3′
3669	3537A>G	3′
X03438	X03438	138970	GEN-PM	Human mRNA for
granulocyte colony-stimulating factor (G-CSF)
1180	1149C>T	3′
X03663	X03663	164770	GEN-51	Colony stimulating
factor 1 receptor
3206	2906A>G	Y969C
3807	3507G>C	3′
X03747	X03747	182330	GEN-KR	ATPase, Na+/K+
transporting, beta 1 polypeptide
1773	1647C>T	3′
X08006	X08006	124030	GEN-1FE	Human mRNA for
cytochrome P450 db1
100	100C>T	P34S
124	124G>A	G42R
137	137ˆ 138insT	Frame
271	271C>A	L91M
281	281A>G	H94R
294	294C>G	Silent
336	336C>T	Silent
408	408G>C	Silent
454	454delT	Frame
505	505G>T	Frame
635	635G>A	G212E
775	775delA	Frame
839	839-841delAGA	K281del
840	840-842delGAA	K281del
886	886C>T	R296C
971	971A>C	H324P
1203	1203G>A	Silent
1262	1262T>C	L421P
1457	1457G>C	S486T
X13561	X13561	147910	GEN-1OH	Human mRNA for
preprokallikrein (EC 3.4.21)
469	433G>C	E145Q
592	556A>G	K186E
614	578T>A	V193E
X13589	X13589	107910	GEN-56	Aromatase (CYP19),
cDNA
914	790C>T	R264C
1218	1094G>A	R365Q
1247	1123C>T	R375C
1347	1223delC	Frame
1427	1303C>T	R435C
1434	1310G>A	C437Y
X13930	X13930	122720	GEN-1Q3	Human CYP2A4
mRNA for P-450 IIA4 protein
488	479A>T	H160L
X14583	X14583	147240	GEN-1RJ	Human mRNA for Ig
lambda-chain
611	587C>T	A196V
X52079	X52079	602272	GEN-33B	H.sapiens transcription
factor (ITF-2) mRNA, 3′ end
1794	1794G>A	Silent
X52425	X52425	147781	GEN-59	Interleukin 4 receptor
1902	1727A>G	Q576R
3289	3114A>G	3′
3391	3216C>T	3′
X54199	X54199	138440	GEN-LS	Phosphoribosylglycin-
amide formyltransferase, phosphoribosylglycinamide synthetase,
phosphoribosylaminoimidazole synthetase
1339	1261G>A	V421I
2333	2255A>G	D752G
X57522	X57522	170260	GEN-37W	H.sapiens RING4
cDNA
1207	1177A>G	I393V
X57829	X57829	112203	GEN-4EW	Serotonin receptor
5HT-1A, coding sequence except for stop codon
47	47C>T	P16L
64	64G>A	G22S
82	82A>G	I28V
294	294G>A	Silent
551	551C>T	P184L
659	659G>T	R220L
818	818G>A	G273D
X57830	X57830	182135	GEN-7V	Serotonin receptor
5HT-2A, cDNA
247	102T>C	Silent
661	516C>T	Silent
734	589A>G	I197V
1485	1340C>T	A447V
1499	1354C>T	H452Y
1681	1536G>C	3′
X60592	X60592	109535	GEN-3B0	Human CDw40
mRNA for nerve growth factor receptor-related B-lymphocyte activation
molecule
437	390G>T	Silent
X62572	X62572	146790	GEN-3CL	H.sapiens RNA for Fc
receptor, PC23
487	487G>A	3′
1240	1240A>G	3′
X63359	X63359	600070	GEN-3DC	H.sapiens UGT2BIO
mRNA for udp glucuronosyltransferase
2714	2704G>A	3′
X70697	X70697	182138	GEN-4DI	H.sapiens mRNA for
serotonin transporter
277	167G>C	G56A
X75535	X75535	600279	GEN-3O8	H.sapiens mRNA for
PxF protein
1808	1798A>G	3′
X79389	X79389	600436	GEN-3T7	H.sapiens GSTT1
mRNA
824	824T>C	3′
X83861	X83861	176806	GEN-5H	Prostaglandin E
receptor 3 (subtype EP3) {alternative products}
179	(−29)C>T	5′
318	111C>G	Silent
712	505A>T	M169L
825	618G>T	Silent
1518	1311T>C	3′
1593	1386A>G	3′
X92106	X92106	602403	GEN-47S	H.sapiens mRNA for
bleomycin hydrolase
1405	1327A>G	I443V
XDH	U06117	278300	GEN-194	Human xanthine
dehydrogenase (XDH) mRNA, complete cds
745	682C>T	Frame
2630	2567delC	Frame
Y10387	Y10387	None	GEN-1IU	H.sapiens mRNA for
PAPS synthetase
1981	1945G>A	3′
Z30643	Z30643	602024	GEN-MMQ	H.sapiens mRNA for
chloride channel (putative) 2139bp
1711	1689G>A	Silent

Claims

1. A method for selecting a treatment for a patient suffering from a disease disorder or condition, comprising determining whether cells of said patient contain at least one variance in a gene from Tables 1, 3 and 4, wherein the presence or the absence of said variance in said gene is indicative of the effectiveness or safety of said treatment for said disease, disorder, or condition.

2. The method of claim 1, wherein said disease, disorder, or condition is selected from the group consisting of drug-induced diseases, disorders, or toxicities consisting of blood dyscrasias, cutaneous toxicities, systemic toxicities, central nervous system toxicities, hepatic toxicities, cardiovascular toxicities, pulmonary toxicities, and renal toxicities.

3. The method of claim 1, wherein the presence of said at least one variance is indicative that said treatment will be effective for said patient.

4. The method of claim 1, wherein the presence of said variance is indicative that said treatment will be ineffective or contra-indicated for said patient.

5. The method of claim 1, wherein said at least one variance comprises a plurality of variances.

6. The method of claim 5, wherein said plurality of variances comprise a haplotype or haplotypes.

7. The method of claim 1, wherein said selecting a treatment further comprises identifying a compound differentially active in a patient bearing a form of said gene containing said at least one variance.

8. The method of claim 7, wherein said compound is a compound listed in a Table herein or that belongs to the same chemical class as a compound listed in said Table.

9. The method of claim 1, wherein said selecting a treatment further comprises eliminating or excluding a treatment, wherein said presence or absence of said at least one variance is indicative that said treatment will be ineffective or contra-indicated.

10. The method of claim 1, wherein said treatment comprises a first treatment and a second treatment, said method comprising the steps of: identifying a said first treatment effective to treat said disease, disorder, or condition; and identifying a said second treatment which reduces a deleterious effect or promotes efficacy of said first treatment.

11. The method of claim 1, wherein said selecting a treatment further comprises selecting a method of administration of a compound effective to treat said disease, disorder or condition, wherein said presence or absence of said at least one variance is indicative of the appropriate method of administration for said compound.

12. The method of claim 11, wherein said selecting a method of administration comprises selecting a suitable dosage level or frequency of administration of a compound.

13. The method of claim 1, further comprising determining the level of expression of said gene or the level of activity of a protein containing a polypeptide expressed from said gene, wherein the combination of the determination of the presence or absence of said at least one variance and the determination of the level of activity or the level of expression provides a further indication of the effectiveness of said treatment.

14. The method of claim 1, further comprising determining the at least one of sex, age, racial origin, ethnic origin, and geopraphic origin of said patient, wherein the combination of the determination of the presence or absence of said at least one variance and the determination of the sex, age, racial origin, ethnic origin, and geopraphic origin of said patient provides a further indication of the effectiveness of said treatment.

15. The method of claim 1, wherein said disease, disorder, or condition is selected from the group consisting of drug-induced diseases, disorders, or toxicities consisting of blood dyscrasias, cutaneous toxicities, systemic toxicities, central nervous system toxicities, hepatic toxicities, cardiovascular toxicities, pulmonary toxicities, and renal toxicities.

16. The method of claim 1, wherein the detection of the presence or absence of said at least one variance comprises amplifying a segment of nucleic acid including at least one of said variances.

17. The method of claim 16, wherein said segment of nucleic acid is 500 nucleotides or less in length.

18. The method of claim 16, wherein said segment of nucleic acid is 100 nucleotides or less in length.

19. The method of claim 16, wherein said segment of nucleic acid is 45 nucleotides or less in length.

20. The method of claim 16, wherein said segment includes a plurality of variances.

21. The method of claim 17, wherein amplification preferentially occurs from one of the two strands of a chromosome.

22. The method of claim 17, wherein said segment of nucleic acid is at least 500 nucleotides in length.

23. The method of claim 1, wherein the detection of the presence or absence of said at least one variance comprises contacting nucleic acid comprising a variance site with at least one nucleic acid probe, wherein said at least one probe preferentially hybridizes with a nucleic acid sequence including said variance site and containing a complementary base at said variance site under selective hybridization conditions.

24. The method of claim 1, wherein the detection of the presence or absence of said at least one variance comprises sequencing at least one nucleic acid sequence.

25. The method of claim 1, wherein the detection of the presence or absence of said at least one variance comprises mass spectrometric determination of at least one nucleic acid sequence.

26. The method of claim 1, wherein the detection of the presence or absence of said at least one variance comprises determining the haplotype of a plurality of variances in a gene.

27. A method for selecting a method of treatment, comprising comparing at least one variance in at least one gene from Tables 1, 3 and 4 in a patient suffering from a disease or condition with a list of variances in said at least one gene indicative of the effectiveness of at least one method of treatment.

28. The method of claim 27, wherein said list comprises at least 5 variances.

29. The method of claim 27, wherein said at least one variance comprises a plurality of variances.

30. The method of claim 27, wherein said list of variances comprises a plurality of variances.

31. The method of claim 27, wherein at least one said method of treatment comprises the administration of a compound effective against said disease or condition to a patient.

32. The method of claim 31, wherein said compound is selected from the group consisting of agonists, antagonists, blockers, partial agonists, partial antagonists, inhibitors, activators, modulators, negative antagonists, inverse agonists, mimetics, or factors that elicit pharmacological activity on a gene product of at least one gene or gene pathway listed in Table 1.

33. The method of claim 27, wherein the presence or absence of at least one variance or haplotype in said gene is indicative that said treatment will be effective in said patient.

34. The method of claim 27, wherein the presence or absence of at least one variance in said gene is indicative that said treatment will be ineffective or contra-indicated.

35. The method of claim 27, wherein said treatment is a first treatment and the presence or absence of at least one variance in said gene is indicative that a second treatment will be beneficial to reduce a deleterious effect or promotes efficacy of said first treatment.

36. The method of claim 27, wherein said at least one method of treatment is a plurality of methods of treatment.

37. The method of claim 36, wherein said selecting comprises determining whether any of said plurality of methods of treatment will be more effective than at least one other of said plurality of methods of treatment.

38. The method of claim 27, wherein said disease is from the group consisting of drug-induced diseases, disorders, or toxicities consisting of blood dyscrasias, cutaneous toxicities, systemic toxicities, central nervous system toxicities, hepatic toxicities, cardiovascular toxicities, pulmonary toxicities, and renal toxicities.

39. A method for selecting a method of administration of to a patient suffering from a condition or disease for a compound or compounds effective to treat said condition or disease, comprising determining whether at least one variance in a gene from Tables 1, 3 and 4 is present or absent in cells of said patient, wherein said presence or absence of said at least one variance is indicative of an appropriate method of administration for said compound.

40. The method of claim 39, wherein said at least one variance is a plurality of variances.

41. The method of claim 39, wherein said selecting a method of administration comprises selecting a dosage level or frequency or frequency of administration of said compound.

42. The method of claim 39, wherein said compound is selected from the group consisting of agonists, antagonists, blockers, partial agonists, partial antagonists, inhibitors, activators, modulators, negative antagonists, inverse agonists, mimetics, or factors that elicit pharmacological activity on a gene product of at least one gene or gene pathway listed in Table 1.

43. The method of claim 39, wherein said disease is from the group consisting of drug-induced diseases, disorders, or toxicities consisting of blood dyscrasias, cutaneous toxicities, systemic toxicities, central nervous system toxicities, hepatic toxicities, cardiovascular toxicities, pulmonary toxicities, and renal toxicities.

44. A method for selecting a patient for administration of a method of treatment, comprising comparing the presence or absence of at least one variance or haplotype in a gene from Tables 1, 3 and 4 in cells of a patient suffering from a disease or condition with a list of variances in said at least one gene, wherein the presence or absence of said at least one variance or haplotype in said cells is indicative that said treatment will be effective, more effective, less effective, ineffective, or contra-indicated in said patient; and determining whether said patient will receive said method of treatment based on the presence or absence of said at least one variance in said cells.

45. The method of claim 44, wherein said list comprises at least 5 variances.

46. The method of claim 44, wherein said method of treatment comprises administration of a compound effective against said disease or condition.

47. The method of claim 46, wherein said compound is selected from the group consisting of agonists, antagonists, blockers, partial agonists, partial antagonists, inhibitors, activators, modulators, negative antagonists, inverse agonists, mimetics, or factors that elicit pharmacological activity on a gene product of at least one gene or gene pathway listed in Table 1.

48. A method for identifying the presence or absence of at least one form of a gene from Tables 1, 3 and 4 in cells of an individual, comprising: determining the presence or absence of at least one variance in said gene in said cells.

49. The method of claim 48, wherein said said at least one variance is a plurality of variances.

50. The method of claim 49, wherein said plurality of variances comprises a haplotype.

51. The method of claim 50, wherein said individual suffers from a disease or condition.

52. The method of claim 50, wherein the presence or absence of said at least one variance is indicative of the effectiveness of a therapeutic treatment in a patient having cells containing said at least one variance.

53. The method of claim 50, wherein said determining comprises amplifying a segment of nucleic acid including a site of at least one variance.

54. The method of claim 50, wherein said determining comprises contacting a nucleic acid sequence containing a variance site corresponding to a said variance with a probe which specifically binds under selective binding conditions to a nucleic acid sequence comprising at least one said variance.

55. The method of claim 50, wherein the detection of the presence or absence of said at least one variance comprises sequencing at least one nucleic acid sequence.

56. The method of claim 50, wherein the detection of the presence or absence of said at least one variance comprises mass spectrometric determination of at least one nucleic acid sequence.

57. The method of claim 50, wherein the detection of the presence or absence of said at least one variance comprises determining the haplotype of a plurality of variances in a gene.

58. A pharmaceutical composition comprising a compound which has a differential effect in patients having at least one copy of a particular form of an identified gene from Tables 1, 3 and 4; and a pharmaceutically acceptable carrier or excipient or diluent, wherein said composition is preferentially effective to treat a patient with cells comprising a form of said gene comprising at least one variance.

59. The method of claim 58, wherein said composition is adapted to be preferentially effective based on the unit dosage, presence of additional active components, complexing of said compound with stabilizing components, or inclusion of components enhancing delivery or slowing excretion of said compound.

60. The composition of claim 58, wherein said compound is deleterious to patients having said at least one copy or in patients not having said at least one copy, but not in both.

61. The composition of claim 58, wherein said patient suffers from a disease or condition selected from the group consisting of drug-induced diseases, disorders, or toxicities consisting of blood dyscrasias, cutaneous toxicities, systemic toxicities, central nervous system toxicities, hepatic toxicities, cardiovascular toxicities, pulmonary toxicities, and renal toxicities.

62. The pharmaceutical composition of claim 58, wherein said composition is subject to a regulatory restriction or recommendation for use of a diagnostic test determining the presence or absence of at least one variance or haplotype in said gene.

63. The pharmaceutical composition of claim 58, wherein said pharmaceutical composition is subject to a regulatory limitation or recommendation restricting or recommending restriction of the use of said pharmaceutical composition to patients having at least one copy of a form of a gene comprising at least one variance.

64. The pharmaceutical composition of claim 58, wherein said pharmaceutical composition is subject to a regulatory limitation or recommendation indicating said pharmaceutical composition is not to be used in patients having at least one copy of a form of a gene comprising at least one variance.

65. The pharmaceutical composition of claim 58, wherein said pharmaceutical composition is packaged, and the packaging includes a label or insert restricting or recommending the restriction of the use of said pharmaceutical composition to patients having at least one copy of a form of a gene comprising at least one variance or haplotype.

66. The pharmaceutical composition of claim 58, wherein said pharmaceutical composition is packaged, and said packaging includes a label or insert requiring or recommending the use of a test to determine the presence or absence of at least one variance in cells of a said patient.

67. A nucleic acid probe comprising a nucleic acid sequence 7 to 200 nucleotide bases in length that specifically binds under selective binding conditions to a nucleic acid sequence comprising at least one variance in a gene from Tables 1, 3 and 4, or a sequence complementary thereto or an RNA equivalent.

68. The probe of claim 67, wherein said probe comprises a nucleic acid sequence 500 nucleotide bases or fewer in length.

69. The probe of claim 67, wherein said nucleic acid sequence is 100 or fewer nucleotide bases in length.

70. The probe of claim 67, wherein said nucleic acid sequence is 25 or fewer nucleotide bases in length.

71. The probe of claim 67, wherein said probe comprises DNA.

72. The probe of claim 67, wherein said probe comprises DNA and at least one nucleic acid analog.

73. The probe of claim 67, wherein said probe comprises peptide nucleic acid (PNA).

74. The probe of claim 67, further comprising a detectable label.

75. The probe of claim 74, wherein said detectable label is a fluorescent label.

76. A method for determining a genotype of an individual, comprising analyzing at least one nucleic acid sequence from cells of said individual using mass spectrometric analysis, wherein said nucleic acid sequence is a portion of a gene from Tables 1, 3 and 4 or a sequence complementary thereto.

77. The method of claim 76, wherein said analyzing a nucleic acid sequence comprises determining the presence or absence of a variance in said gene.

78. The method of claim 76, wherein said analyzing a nucleic acid sequence comprises determining the nucleotide sequence of said at least one nucleic acid sequence.

79. The method of claim 76, wherein said at least one nucleic acid sequence is 500 nucleotides or less in length.

80. The method of claim 76, wherein said at least one nucleic acid sequence comprises at least one variance site in said gene.

81. An isolated, purified or enriched nucleic acid sequence of 15 to 500 nucleotides in length, comprising at least one variance site, wherein said sequence has the base sequence of a portion of an allele of a gene from Tables 1, 3 and 4.

82. The nucleic acid sequence of claim 81, wherein said nucleic acid sequence is 15 to 100 nucleotide bases in length.

83. The nucleic acid sequence of claim 81, wherein said nucleic acid sequence is 15 to 25 nucleotide bases in length.

84. A method for determining whether a compound has differential effects on cells containing at least one different form of a gene from Tables 1, 3 and 4, comprising: contacting a first cell and a second cell with said compound, wherein said first cell and said second cell differ in the presence or absence of at least one variance in said gene; and determining whether the responses of said first cell and said second cell to said compound differ, wherein the difference in said response is due to the presence or absence of said at least one variance.

85. The method of claim 84, wherein said at least one variance comprises a haplotype.

86. The method of claim 84, wherein at least one of said first cell and said second cell are contacted in vivo.

87. The method of claim 85, wherein at least one of said first cell and said second cell are contacted in vitro.

88. The method of claim 87, wherein at least one of said first cell and said second cell is contacted in vivo in a plurality of patients suffering from a disease or condition.

89. A method of treating a patient suffering from a condition or disease, comprising: a) determining whether cells of said patient contain a form of a gene from Tables 1, 3 and 4 which comprises at least one variance, wherein the presence or absence of said at least one variance is indicative that a treatment will be effective in said patient; and b) administering said treatment to said patient.

90. The method of claim 89, wherein said gene is listed in Table 1 or is a gene in a pathway listed in Table 1 herein.

91. The method of claim 89, wherein said disease or condition is selected from the group consisting of drug-induced diseases, disorders, or toxicities consisting of blood dyscrasias, cutaneous toxicities, systemic toxicities, central nervous system toxicities, hepatic toxicities, cardiovascular toxicities, pulmonary toxicities, and renal toxicities.

92. The method of claim 89, wherein said at least one variance is a plurality of variances.

93. The method of claim 89, wherein the presence of said at least one variance is indicative that said treatment will be effective in said patient.

94. The method of claim 93, wherein said treatment comprises the administration of a compound preferentially active for said condition or disease in a said patient having said at least one variance in said gene.

95. The method of claim 94, wherein said compound is selected from the group consisting of agonists, antagonists, blockers, partial agonists, partial antagonists, inhibitors, activators, modulators, negative antagonists, inverse agonists, mimetics, or factors that elicit pharmacological activity on a gene product of at least one gene or gene pathway listed in Table 1.

96. The method of claim 89, wherein the presence of said at least one variance in said gene is indicative of an appropriate dosage or frequency of administration of a compound in said treatment.

97. A method of treating a patient suffering from a disease or condition, comprising: a) comparing the presence or absence of at least one variance in a gene from Tables 1, 3 and 4 in cells of a patient suffering from said disease or condition with a list of variances in said gene indicative of the effectiveness of at least one method of treatment; b) selecting a method of treatment from said at least one method of treatment, wherein the presence or absence of at least one of said at least one variance is indicative that said method of treatment will be effective in said patient; and c) administering said method of treatment to said patient.

98. The method of claim 97, wherein said at least one gene comprises a gene listed in Table 1 or comprises a gene in a pathway listed in Table 1 herein.

99. The method of claim 97, wherein said condition or disease is a condition or disease listed in the Detailed Description, Examples, or Tables herein.

100. The method of claim 97, further comprising determining the presence or absence of said at least one variance in cells of said patient.

101. The method of claim 97, wherein said at least one variance comprises a plurality of variances.

102. The method of claim 97, wherein said list of variances comprises a plurality of variances.

103. The method of claim 102, wherein said plurality of variances comprises a haplotype or haplotypes.

104. The method of claim 97, wherein said method of treatment comprises the administration of a compound effective against said disease or condition.

105. The method of claim 97, wherein said treatment is a first treatment and the presence or absence of at least one variance in said gene is indicative that a second treatment will be beneficial to reduce a deleterious effect or promotes efficacy of said first treatment.

106. The method of claim 97, wherein said at least one method of treatment is a plurality of methods of treatment.

107. The method of claim 97, wherein said disease or condition is selected from the group consisting of drug-induced diseases, disorders, or toxicities consisting of blood dyscrasias, cutaneous toxicities, systemic toxicities, central nervous system toxicities, hepatic toxicities, cardiovascular toxicities, pulmonary toxicities, and renal toxicities.

108. A method of treating a patient suffering from a disease or condition, comprising: a) comparing the presence or absence of at least one variance in a gene from Tables 1, 3 and 4 in cells of a patient suffering from said disease or condition with a list of variances in said gene indicative of the effectiveness of at least one method of treatment; b) eliminating or excluding a method of treatment from said at least one method of treatment, wherein the presence or absence of at least one of said at least one variance is indicative that said method of treatment will be ineffective or contra-indicated in said patient; c) selecting an alternative method of treatment effective to treat said cardiovascular or renal disease or condition; and d) administering said alternative method of treatment to said patient.

109. The method of claim 108, further comprising determining the presence or absence of said at least one variance in cells of said patient.

110. The method of claim 108, wherein said gene is listed in Table 1 or is a gene in a pathway listed in Table 1 herein.

111. The method of claim 108, wherein said disease or condition is a disease or condition listed in the Detailed Description, Examples, or Tables herein.

112. A method for producing a pharmaceutical composition, comprising: a) identifying a compound which has differential activity against a disease or condition in patients having at least one variance in a gene from Tables 1, 3 and 4; b) compounding said pharmaceutical composition by combining said compound and a pharmaceutically acceptable carrier or excipient or diluent in a manner adapted to be preferentially effective in patients having said at least one variance.

113. A method for producing a pharmaceutical agent, comprising: a) identifying a compound which has differential activity against a disease or condition in patients having at least one variance in a gene from Tables 1, 3 and 4; and b) synthesizing said compound in an amount sufficient to provide a pharmaceutical effect in a patient suffering from said cardiovascular or renal disease or condition.

114. A method for determining whether a variance in a gene from Tables 1, 3 and 4 provides variable patient response to a method of treatment for a disease or condition, comprising: determining whether the response of a first patient or set of patients suffering from said disease or condition differs from the response of a second patient or set of patients suffering from said disease or condition; and determining whether the presence or absence of at least one variance in said gene differs between said first patient or set of patient and said second patient or set of patients, wherein correlation of said presence or absence of at least one variance and the response of said patient to said treatment is indicative that said at least one variance provides variable patient response.

115. The method of claim 114, further comprising identifying at least one variance in a said gene.

116. The method of claim 114, wherein a plurality of pairwise comparisons of treatment response and the presence or absence of at least one variance are performed for a plurality of patients.

117. The method of claim 114, wherein said determining whether the presence or absence of at least one variance in at least one gene comprises comparing the response of at least one patient homozygous for said at least one variance with at least one patient homozygous for the alternative form of said at least one variance.

118. The method of claim 114, wherein said determining whether the presence or absence of said at least one variance in at least one gene comprises comparing the response of at least one patient heterozygous for said at least one variance with the response of at least one patient homozygous for said at least one variance.

119. The method of claim 114, wherein it is previously known that patient response to said method of treatment is variable.

120. The method of claim 114, wherein said disease or condition is a disease or condition listed in the Detailed Description, Examples, or Tables herein.

121. The method of claim 114, wherein said disease or condition is selected from the group consisting of drug-induced diseases, disorders, or toxicities consisting of blood dyscrasias, cutaneous toxicities, systemic toxicities, central nervous system toxicities, hepatic toxicities, cardiovascular toxicities, pulmonary toxicities, and renal toxicities.

122. The method of claim 114, wherein said method of treatment comprises administration of a compound effective to treat said disease or condition.

123. A method of treating a disease, condition, or a drug-induced disease in a patient, comprising a) selecting a patient whose cells comprise an allele of a gene from Tables 1, 3 and 4, wherein said allele comprises at least one variance correlated with more effective treatment of said disease or condition; and b) altering the level of activity in cells of said patient of a product of said allele, wherein said altering provides a therapeutic effect.

124. A method for determining a method of treatment effective to treat a disease or condition in a sub-population of patients, comprising altering the level of activity of a product of an allele of a gene from Tables 1, 3 and 4; and determining whether said alteration provides a differential effect related to reducing or alleviating a disease or condition as compared to at least one alternative allele, wherein the presence of a said differential effect is indicative that said altering the level of activity comprises an effective treatment for said disease or condition in said sub-population.

125. A method for performing a clinical trial or study, comprising selecting or stratifying subjects using a variance or variances or haplotypes from one or more genes specified in Tables 1, 3 or 4.

126. The method of claim 125, wherein differential efficacy, tolerance, or safety of a treatment in a subset of patients who have a particular variance, variances, or haplotype in a gene or genes from Tables 1, 3 and 4, comprising conducting a clinical trial and using a statistical test to assess whether a relationship exists between efficacy, tolerance, or safety with the presence or absence of any of said variances or haplotype in one or more of said genes, wherein results of said clinical trial or study are indicative whether a higher or lower efficacy, tolerance, or safety of said treatment in said subset of patients is associated with any of said variance or variances or haplotype in one or more of said gene.

127. The method of claim 125 wherein normal subjects or patients are prospectively stratified by genotype in different genotype-defined groups, including the use of genotype as a enrollment criterion, using a variance, variances or haplotypes from Tables 1, 3 or 4, and subsequently a biological or clinical response variable is compared between the different genotype-defined groups.

128. The method of claim 125 wherein the normal subjects or patients in a clinical trial or study are stratified by a biological or clinical response variable in different biologically or clinically-defined groups, and subsequently the frequency of a variance, variances or haplotypes from Tables 1, 3 or 4 is measured in the different biologically or clinically defined groups.

129. The method of claim 127 or 128 where the normal subjects or patients in a clinical trial or study are stratified by at least one demographic characteristic selected from the groups consisting of sex, age, racial origin, ethnic origin, or geographic origin.

130. The method of claim 125, wherein said determining comprises assigning said patient to a group to receive said method of treatment or to a control group.

131. A method for determining whether a variance in a gene provides variable patient response to a method of treatment for a disease or condition, comprising: determining whether the response of a first patient or set of patients suffering from a disease, condition, or drug-induced disease differs from the response of a second patient or set of patients suffering from said disease or condition; determining whether the presence or absence of at least one variance in a gene from Tables 1, 3 and 4 differs between said first patient or set of patients and said second patient or set of patients; wherein correlation of said presence or absence of at least one variance and the response of said patient to said treatment is indicative that said at least one variance provides variable patient response.

132. A method for treating a patient at risk for a disease or diagnosed with a disease or disorder or a drug induced disease, comprising identifying a said patient and determining the patient's genotype allele status for a gene from Tables 1, 3 and 4; determining a treatment protocol using the patient's genotype status to provide a prediction of the efficacy and safety of a therapy in light of said disease or an associated condition.

133. A method for identifying a patient for participation in a clinical trial of a therapy for the treatment of a disease or a drug-associated disease or disorder, comprising identifying a patient with a disease risk and determining the patient's genotype, allele status for an identified gene from Tables 1, 3 and 4.

134. The method of claim 123, further comprising determining the patient's allele status and selecting those patients having at least one wild type allele of said gene as candidates likely to be affected by a drug-induced disease or condition.

135. A method for treating a patient at risk for a disease condition, comprising identifying a patient with a risk for said disease; determining the genotypic allele status of the patient for at least one gene from Tables 1, 3 and 4; and converting the genotypic allele status into a treatment protocol that comprises a comparison of the genotypic allele status determination with the allele frequency of a control population, thereby allowing a statistical calculation of the patient's risk for having said disease or condition.

136. A method for treating a patient at risk for or diagnosed with having a disease or condition, comprising identifying a said patient; determining the gene allele load status of the patient for at least one gene from Tables 1, 3 and 4 and converting the gene allele load status into a treatment protocol that includes a comparison of the allele status determinations with the allele frequency of a control population, thereby allowing a statistical calculation of the patient's risk for having having said disease or condition.

137. A method for improving the safety of candidate therapies associated with having a disease or condition, comprising comparing the relative safety of the candidate therapeutic intervention in patients having different alleles in one or more than one of the genes listed in Tables 1, 3 and 4, thereby identifying subsets of patients with differing safety of the candidate therapeutic intervention.

138. A kit for determination of the presence or absence of at least one sequence variance in a gene identified in any of Tables 1, 3, and 4, comprising at least one probe that preferentially hybridizes with a nucleic acid sequence corresponding to a portion of said gene or at least one primer comprising a nucleic acid sequence corresponding to a portion of said gene or a sequence complementary thereto or both said at least one probe and said at least one primer.

139. A method for determining whether there is a genetic component to intersubject variation in a surrogate treatment response, comprising: a. administering said treatment to a group of related normal subjects and a group of unrelated normal subjects; b. measuring a surrogate pharmacodynamic or pharmacokinetic drug response variable in said subjects; c. performing a statistical test measuring the variation in response in said group of related normal subjects and, separately in said group or unrelated normal subjects; and d. comparing the magnitude or pattern of variation in response or both between said groups to determine if the responses of said groups are different, using a predetermined statistical measure of difference, wherein a difference in response between said groups is indicative that there is a genetic component to intersubject variation in said surrogate treatment response.

140. The method of claim 129, wherein the size of the related and unrelated groups is set in order to achieve a predetermined degree of statistical power.

141. A method for evaluating the combined contribution of two or more variances to a surrogate drug response phenotype in subjects, comprising: a. genotyping a set of unrelated subjects participating in a clinical trial or study of a compound for two or more variances to identify subjects with specific genotypes, wherein said two or more specific genotypes define two or more genotype-defined groups; b. administering a drug to subjects with two or more of said specific genotypes; c. measuring a surrogate pharmacodynamic or pharmacokinetic drug response variable in said subjects; d. performing statistical tests to measure response in said groups separately, wherein said statistical tests provide a measurement of variation in response with each said group; and e. comparing the magnitude or pattern of variation in response or both between said groups to determine if said groups are different using a predetermined statistical measure of difference.

142. The method of claim 141, wherein said clinical trial or study is a Phase I clinical trial or study.

143. The method of claim 141, wherein said specific genotypes are homozygous genotypes for two variances.

144. The method of claim 141, wherein the comparison is between groups of subjects differing in three or more variances.

145. A method for providing contract research services to a client, comprising: a. enrolling subjects in a clinical drug trial or study unit for the purpose of genotyping said subjects in order to assess the contribution of one or more variances or haplotypes to variation in drug response; b. genotyping said subjects to determine the status of one or more variances in said subjects; c. administering a compound to said subjects and measuring a surrogate drug response variable; d. comparing responses between two or more genotype-defined groups of said subjects to determine whether there is a genetic component to the interperson variability in response to said compound; and e. reporting the results of said clinical drug trial or study unit to a contracting entity.

146. The method of claim 145, wherein said clinical drug trial or study unit is a Phase I drug trial or study unit.

147. The method of claim 145, wherein at least some of the subjects have disclosed that they are related to each other and said comparing includes comparison of groups of related individuals.

148. The method of claim 147, wherein the related individuals are encouraged to participate by compensation in proportion to the number of their relatives participating.

149. A method for recruiting a clinical trial or study population for studies of the influence of one or more variances or haplotypes on drug response, comprising soliciting subjects to participate in said clinical trial or study; obtaining consent of said subjects for participation in said clinical trial or study; and obtaining additional related subjects for participation in said clinical trial by compensating one or more of the related subjects for said participation at a level based on the number of related subjects participating or based on participation of at least a minimum specified number of related subjects.