METHODS OF PREDICTING COMPLICATION AND SURGERY IN CROHN'S DISEASE

Abstract
The present invention relates to prognosing, diagnosing and treating an aggressive form of Crohn's disease characterized by rapid progression to complication and/or surgery from the time of diagnosis. In one embodiment, the prognosis, diagnosis and treatment is based upon the presence of one or more genetic risk factors.
Description
FIELD OF THE INVENTION

The invention relates generally to the field of inflammatory disease, specifically to Crohn's disease and progression to complication and/or surgery.


BACKGROUND

All publications herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.


Crohn's disease (CD) and ulcerative colitis (UC), the two common forms of idiopathic inflammatory bowel disease (IBD), are chronic, relapsing inflammatory disorders of the gastrointestinal tract. Each has a peak age of onset in the second to fourth decades of life and prevalences in European ancestry populations that average approximately 100-150 per 100,000 (D. K. Podolsky, N Engl J Med 347, 417 (2002); E. V. Loftus, Jr., Gastroenterology 126, 1504 (2004)). Although the precise etiology of IBD remains to be elucidated, a widely accepted hypothesis is that ubiquitous, commensal intestinal bacteria trigger an inappropriate, overactive, and ongoing mucosal immune response that mediates intestinal tissue damage in genetically susceptible individuals (D. K. Podolsky, N Engl J Med 347, 417 (2002)). Genetic factors play an important role in IBD pathogenesis, as evidenced by the increased rates of IBD in Ashkenazi Jews, familial aggregation of IBD, and increased concordance for IBD in monozygotic compared to dizygotic twin pairs (S. Vermeire, P. Rutgeerts, Genes Immun 6, 637 (2005)). Moreover, genetic analyses have linked IBD to specific genetic variants, especially CARD15 variants on chromosome 16q12 and the IBD5 haplotype (spanning the organic cation transporters, SLC22A4 and SLC22A5, and other genes) on chromosome 5q31 (S. Vermeire, P. Rutgeerts, Genes Immun 6, 637 (2005); J. P. Hugot et al., Nature 411, 599 (2001); Y. Ogura et al., Nature 411, 603 (2001); J. D. Rioux et al., Nat Genet 29, 223 (2001); V. D. Peltekova et al., Nat Genet 36, 471 (2004)). CD and UC are thought to be related disorders that share some genetic susceptibility loci but differ at others.


Thus, there is a need in the art to identify environmental factors, serological profiles, genes, allelic variants and/or haplotypes that may assist in explaining the genetic risk, diagnosing and/or predicting susceptibility for or protection against inflammatory bowel disease.





BRIEF DESCRIPTION OF THE FIGURES


FIG. 1 depicts, in accordance with an embodiment herein, survival distribution for subgroups of SC1 (model 1) for survival for complication.



FIG. 2 depicts, in accordance with an embodiment herein, survival distribution for subgroups of SC2 (model 2) for survival for complication.



FIG. 3 depicts, in accordance with an embodiment herein, survival distribution across models for stratum 1 for survival for complication.



FIG. 4 depicts, in accordance with an embodiment herein, survival distribution across models for stratum 2 for survival for complication.



FIG. 5 depicts, in accordance with an embodiment herein, survival distribution across models for stratum 3 for survival for complication.



FIG. 6 depicts, in accordance with an embodiment herein, survival distribution for subgroups of SS1 (model 1) for survival for surgery.



FIG. 7 depicts, in accordance with an embodiment herein, survival distribution for subgroups of SS2 (model 2) for survival for surgery.



FIG. 8 depicts, in accordance with an embodiment herein, survival distribution for subgroups of SS3 (model 3) for survival for surgery.



FIG. 9 depicts, in accordance with an embodiment herein, survival distribution for subgroups of SS4 (model 4) for survival for surgery.



FIG. 10 depicts, in accordance with an embodiment herein, survival distribution across models for stratum 1 for survival for surgery.



FIG. 11 depicts, in accordance with an embodiment herein, survival distribution across models for stratum 2 for survival for surgery.



FIG. 12 depicts, in accordance with an embodiment herein, survival distribution across models for stratum 3 for survival for surgery.





SUMMARY OF THE INVENTION

Various embodiments include a method of prognosing Crohn's disease in an individual, comprising obtaining a sample from the individual, assaying the sample for the presence or absence of one or more genetic risk variants, and prognosing an aggressive form of Crohn's disease based on the presence of one or more genetic risk variants, where the one or more genetic risk variants are selected from the genetic loci of 8q24, 16p11, Bromodomain and WD repeat domain containing 1 (BRWD1) and/or Tumor necrosis factor superfamily member 15 (TNFSF15). In another embodiment, the presence of each genetic risk variant has an additive effect on rapidity of Crohn's disease progression from a relatively less severe case of Crohn's disease to a relatively more severe case of Crohn's disease. In another embodiment, the one or more genetic risk variants comprise SEQ. ID. NO.: 1, SEQ. ID. NO.: 2, SEQ. ID. NO.: 3, SEQ. ID. NO.: 4, SEQ. ID. NO.: 5 and/or SEQ. ID. NO.: 6. In another embodiment, the aggressive form of Crohn's disease is characterized by one or more phenotypes associated with complications. In another embodiment, the aggressive form of Crohn's disease is characterized by one or more phenotypes associated with conditions requiring surgery. In another embodiment, the aggressive form of Crohn's Disease is characterized by a rapid progression from a relatively less severe case of Crohn's disease to a relatively more severe case of Crohn's disease. In another embodiment, the individual has previously been diagnosed with inflammatory bowel disease (IBD). In another embodiment, the individual is a child 17 years old or younger. In another embodiment, the aggressive form of Crohn's disease comprises internal penetrating and/or stricture. In another embodiment, the aggressive form of Crohn's disease comprises a high expression of anti-neutrophil cytoplasmic antibody (ANCA) relative to levels found in a healthy individual. In another embodiment, the presence of one or more genetic risk variants is determined from an expression product thereof.


Other embodiment include a method of prognosing Crohn's disease in an individual, comprising obtaining a sample from the individual, assaying the sample for the presence or absence of one or more genetic risk variants, and prognosing a form of Crohn's disease associated with a complication based on the presence of one or more genetic risk variants, where the one or more genetic risk variants is selected from the group consisting of SEQ. ID. NO.: 7, SEQ. ID. NO.: 8, SEQ. ID. NO.: 9, SEQ. ID. NO.: 10, SEQ. ID. NO.: 11, SEQ. ID. NO.: 12, SEQ. ID. NO.: 13, SEQ. ID. NO.: 14, SEQ. ID. NO.: 15, SEQ. ID. NO.: 16, SEQ. ID. NO.: 17, SEQ. ID. NO.: 18, SEQ. ID. NO.: 19, SEQ. ID. NO.: 20, SEQ. ID. NO.: 21, and/or SEQ. ID. NO.: 22. In another embodiment, the complication comprises internal penetrating and/or stricturing disease.


Other embodiments include a method of prognosing Crohn's disease in an individual, comprising obtaining a sample from the individual, assaying the sample for the presence or absence of one or more genetic risk variants, and prognosing a form of Crohn's disease associated with one or more conditions that require a treatment by surgery, where the one or more genetic risk variants is selected from the group consisting of SEQ. ID. NO.: 23, SEQ. ID. NO.: 24, SEQ. ID. NO.: 25, SEQ. ID. NO.: 26, SEQ. ID. NO.: 27, SEQ. ID. NO.: 28, SEQ. ID. NO.: 29, SEQ. ID. NO.: 30, SEQ. ID. NO.: 31, SEQ. ID. NO.: 32, SEQ. ID. NO.: 33, SEQ. ID. NO.: 34, SEQ. ID. NO.: 35, SEQ. ID. NO.: 36, SEQ. ID. NO.: 37, SEQ. ID. NO.: 38, SEQ. ID. NO.: 39, SEQ. ID. NO.: 40, SEQ. ID. NO.: 41, SEQ. ID. NO.: 42, SEQ. ID. NO.: 43, SEQ. ID. NO.: 44, SEQ. ID. NO.: 45, SEQ. ID. NO.: 46, SEQ. ID. NO.: 47, SEQ. ID. NO.: 48, SEQ. ID. NO.: 49, SEQ. ID. NO.: 50, SEQ. ID. NO.: 51, and/or SEQ. ID. NO.: 52. In another embodiment, the treatment by surgery comprises small-bowel resection, colectomy and/or colonic resection.


Various embodiments include a method of treating Crohn's disease in an individual, comprising prognosing an aggressive form of Crohn's disease in the individual based on the presence of one or more genetic risk variants, and treating the individual, where the one or more genetic risk variants are selected from the genetic loci of 8q24, 16p11, Bromodomain and WD repeat domain containing 1 (BRWD1) and/or Tumor necrosis factor superfamily member 15 (TNFSF15). In another embodiment, treating the individual comprises exposing the individual to a treatment that ameliorates the symptoms of Crohn's disease on the basis that the subject tests positive for one or more genetic risk variants. In another embodiment, treating the individual comprises administering a surgical procedure associated with treating an aggressive form of Crohn's disease. In another embodiment, treating the individual comprises performing on the individual a small-bowel resection, colectomy and/or colonic resection. In another embodiment, the presence of each genetic risk variant has an additive effect on rapidity of Crohn's disease progression from a relatively less severe case of Crohn's disease to a relatively more severe case of Crohn's disease. In another embodiment, the one or more genetic risk variants comprise SEQ. ID. NO.: 1, SEQ. ID. NO.: 2, SEQ. ID. NO.: 3, SEQ. ID. NO.: 4, SEQ. ID. NO.: 5 and/or SEQ. ID. NO.: 6. In another embodiment, the one or more genetic risk variants comprise SEQ. ID. NO.: 7, SEQ. ID. NO.: 8, SEQ. ID. NO.: 9, SEQ. ID. NO.: 10, SEQ. ID. NO.: 11, SEQ. ID. NO.: 12, SEQ. ID. NO.: 13, SEQ. ID. NO.: 14, SEQ. ID. NO.: 15, SEQ. ID. NO.: 16, SEQ. ID. NO.: 17, SEQ. ID. NO.: 18, SEQ. ID. NO.: 19, SEQ. ID. NO.: 20, SEQ. ID. NO.: 21, and/or SEQ. ID. NO.: 22. In another embodiment, the one or more genetic risk variants comprise SEQ. ID. NO.: 23, SEQ. ID. NO.: 24, SEQ. ID. NO.: 25, SEQ. ID. NO.: 26, SEQ. ID. NO.: 27, SEQ. ID. NO.: 28, SEQ. ID. NO.: 29, SEQ. ID. NO.: 30, SEQ. ID. NO.: 31, SEQ. ID. NO.: 32, SEQ. ID. NO.: 33, SEQ. ID. NO.: 34, SEQ. ID. NO.: 35, SEQ. ID. NO.: 36, SEQ. ID. NO.: 37, SEQ. ID. NO.: 38, SEQ. ID. NO.: 39, SEQ. ID. NO.: 40, SEQ. ID. NO.: 41, SEQ. ID. NO.: 42, SEQ. ID. NO.: 43, SEQ. ID. NO.: 44, SEQ. ID. NO.: 45, SEQ. ID. NO.: 46, SEQ. ID. NO.: 47, SEQ. ID. NO.: 48, SEQ. ID. NO.: 49, SEQ. ID. NO.: 50, SEQ. ID. NO.: 51, and/or SEQ. ID. NO.: 52. In another embodiment, the individual is a child 17 years old or younger.


Other embodiments include a method of diagnosing susceptibility to Crohn's disease in an individual, comprising obtaining a sample from the individual, assaying the sample for the presence or absence of one or more genetic risk variants, and diagnosing susceptibility to Crohn's disease in the individual based on the presence of one or more genetic risk variants, where the one or more genetic risk variants are located at the genetic loci of 8q24, 16p11, and/or Bromodomain and WD repeat domain containing 1 (BRWD1). In another embodiment, the one or more genetic risk variants comprise SEQ. ID. NO.: 1, SEQ. ID. NO.: 2, SEQ. ID. NO.: 3, SEQ. ID. NO.: 4, SEQ. ID. NO.: 5 and/or SEQ. ID. NO.: 6. In another embodiment, the one or more genetic risk variants comprise SEQ. ID. NO.: 7, SEQ. ID. NO.: 8, SEQ. ID. NO.: 9, SEQ. ID. NO.: 10, SEQ. ID. NO.: 11, SEQ. ID. NO.: 12, SEQ. ID. NO.: 13, SEQ. ID. NO.: 14, SEQ. ID. NO.: 15, SEQ. ID. NO.: 16, SEQ. ID. NO.: 17, SEQ. ID. NO.: 18, SEQ. ID. NO.: 19, SEQ. ID. NO.: 20, SEQ. ID. NO.: 21, and/or SEQ. ID. NO.: 22. In another embodiment, the one or more genetic risk variants comprise SEQ. ID. NO.: 23, SEQ. ID. NO.: 24, SEQ. ID. NO.: 25, SEQ. ID. NO.: 26, SEQ. ID. NO.: 27, SEQ. ID. NO.: 28, SEQ. ID. NO.: 29, SEQ. ID. NO.: 30, SEQ. ID. NO.: 31, SEQ. ID. NO.: 32, SEQ. ID. NO.: 33, SEQ. ID. NO.: 34, SEQ. ID. NO.: 35, SEQ. ID. NO.: 36, SEQ. ID. NO.: 37, SEQ. ID. NO.: 38, SEQ. ID. NO.: 39, SEQ. ID. NO.: 40, SEQ. ID. NO.: 41, SEQ. ID. NO.: 42, SEQ. ID. NO.: 43, SEQ. ID. NO.: 44, SEQ. ID. NO.: 45, SEQ. ID. NO.: 46, SEQ. ID. NO.: 47, SEQ. ID. NO.: 48, SEQ. ID. NO.: 49, SEQ. ID. NO.: 50, SEQ. ID. NO.: 51, and/or SEQ. ID. NO.: 52. In another embodiment, the individual is a child 17 years old or younger.


Other features and advantages of the invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, which illustrate, by way of example, various embodiments of the invention.


DESCRIPTION OF THE INVENTION

All references cited herein are incorporated by reference in their entirety as though fully set forth. Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Singleton et al., Dictionary of Microbiology and Molecular Biology 3rd ed., J. Wiley & Sons (New York, N.Y. 2001); March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 5th ed., J. Wiley & Sons (New York, N.Y. 2001); and Sambrook and Russel, Molecular Cloning: A Laboratory Manual 3rd ed., Cold Spring Harbor Laboratory Press (Cold Spring Harbor, N.Y. 2001), provide one skilled in the art with a general guide to many of the terms used in the present application.


One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials described.


“IBD” as used herein is an abbreviation of inflammatory bowel disease.


“CD” as used herein is an abbreviation of Crohn's Disease.


“UC” as used herein is an abbreviation of ulcerative colitis.


“ANCA” as used herein refers to anti-neutrophil cytoplasmic antibody.


As used herein, “SNP” means single nucleotide polymorphism.


“GWAS” as used herein is an abbreviation of genome wide associations.


“Antibody sum” as used herein refers to the number of positive antibody markers per individual.


“Antibody quartile score” as used herein refers to the quartile score for each antibody level.


“Quartile sum score” as used herein refers to the sum of quartile scores for all types of antibody tested.


“Complication” as used herein refers to a severe form of Crohn's disease that may be associated with an internal penetrating and/or stricturing disease phenotype, or conditions that require surgical procedures associated with the treatment of Crohn's disease due to unresponsiveness to non surgical treatments.


“Surgery” as used herein refers to a surgical procedure related to Inflammatory Bowel Disease or Crohn's disease, including small-bowel resections, colectomy and colonic resection.


“Progressive” Crohn's disease or “aggressive” Crohn's disease as used herein refers to a condition that may be characterized by the rapid progression from an uncomplicated to complicated phenotype in a Crohn's disease patient. Complicated phenotypes of Crohn's disease patients may include, for example, the development of internal penetrating, stricturing disease and/or perianal penetrating. This is in contrast to an uncomplicated phenotype that may be characterized, for example, by nonpenetrating and/or nonstricturing.


Various survival studies are described herein. The survival studies utilized a cohort at time of diagnosis of Crohn's disease (time zero) and then followed them forward to complication and/or surgery phenotypes, with time from diagnosis to complication and/or surgery measured in months. A genetic risk variant and/or risk marker with a 0.05 or less significance value in survival outcome is indicative of a statistically significant association with surgery and/or complication phenotype.


As used herein, the term “biological sample” means any biological material from which nucleic acid molecules can be prepared. As non-limiting examples, the term material encompasses whole blood, plasma, saliva, cheek swab, or other bodily fluid or tissue that contains nucleic acid.


As disclosed herein, the inventors examined 34 SNPs to look at the association with surgery in 173 pediatric patients with Crohn's Disease. The outcome was any Crohn's Disease surgery. Specifically, SNPs were found by multivariate analysis to be independently associated with surgery. Additionally, survival analysis was used to determine whether specific SNPs were associated with faster progression to surgery, where survival analysis as a predictive model showed that as patients were determined to have more of the significant genes, the progression to surgery was faster. Some of the genetic loci found to be significant include 8q24, 16p11, BRWD1 and TNFSF15.


As further disclosed herein, the inventors performed genome-wide association studies (GWAS) to determine the association between the presence of SNPs in an individual with Crohn's disease and the result of complication and/or surgery. Stepwise variable selection was then applied to logistic regression models (3 for complication and 5 for surgery) including SNPs selected from GWAS, gender, age, disease location, ANCA and antibody sum/quartile score as predictors. Survival analyses for complication and surgery were performed with the Cox Regression model. First, in order to select significant SNPs, genome-wide survival analyses were performed with a Cox regression model, in which each SNP was a predictor. Second, stepwise variable selection was applied to Cox regression models (3 models for complication and 5 models for surgery) using SNPs, gender, age, disease location, ANCA, and antibody sum/antibody quartile score as predictors. Third, the survival functions obtained by the Kaplan-Meier (KM) estimator among subgroups of patients were compared, which were subgrouped with 25% quartile and 75% quartile of the genetic risk score calculated from the selected model in the second step for each regression model (group 1 if risk score ≦25% quartile, group 2 if 25% quartile <risk score <75% quartile, and group 3 if risk score ≧75% quartile). Finally, for each subgroup, the survival functions were compared across the models. For all 3 complication models, the survival functions obtained by the KM estimator were significantly different among subgroups of patients. For all 3 subgroups, the survival functions across the 3 models were statistically indistinguishable with a significance level of 0.05. As further disclosed herein, for all 5 surgery models, the survival functions obtained by the KM estimator were significantly different among subgroups of patients. For all 3 subgroups, the survival functions across the 5 models were statistically indistinguishable with a significance level of 0.05.


In one embodiment, the present invention provides a method of prognosing Crohn's Disease in an individual by determining the presence or absence of one or more risk factors, where the presence of one or more risk factors is indicative of an aggressive form of Crohn's Disease. In another embodiment, the aggressive form of Crohn's Disease is characterized by a fast progression from a relatively less severe form of Crohn's disease to a relatively more severe case of Crohn's disease. In another embodiment, the aggressive form of Crohn's Disease is characterized by conditions requiring surgical treatment associated with treating the Crohn's disease. In another embodiment, the one or more risk factors are described in Tables 1-6 herein. In another embodiment, the risk factors include one or more genetic and serological or demographic or disease location or disease behavior risk factors. In another embodiment the disease behavior risk factor is stricture or penetration. In another embodiment a serological risk factor is ASCA. In another embodiment the disease location risk factor is the ileal, colonic or ileocolonic form of Crohn's disease, or a combination thereof. In another embodiment the demographic risk factors are gender and/or age.


In another embodiment, the presence of each additional risk factor has an additive effect on the rate of progression. In another embodiment, the individual is a child 17 years old or younger.


In one embodiment, the present invention provides a method of diagnosing susceptibility to Crohn's Disease in an individual by determining the presence or absence of one or more risk factors described in Tables 1-6 herein, where the presence of one or more risk factors described in Tables 1-6 herein is indicative of susceptibility to Crohn's disease in the individual. In another embodiment, the risk factors include one or more genetic and serological or demographic or disease location or disease behavior risk factors. In another embodiment the disease behavior risk factor is stricture or penetration. In another embodiment a serological risk factor is ASCA. In another embodiment the disease location risk factor is the ileal, colonic or ileocolonic form of Crohn's disease, or a combination thereof. In another embodiment the demographic risk factors are gender and/or age. In another embodiment, the Crohn's Disease is associated with a complicated and/or conditions associated with the need for surgery phenotypes. In another embodiment, the individual is a child 17 years old or younger.


In another embodiment, the present invention provides a method of treating Crohn's Disease in an individual by determining the presence of one or more risk factors and treating the individual. In another embodiment, the one or more risk factors are described in Tables 1-6 herein. In another embodiment, the risk factors include one or more genetic and serological or demographic or disease location or disease behavior risk factors. In another embodiment the disease behavior risk factor is stricture or penetration. In another embodiment a serological risk factor is ASCA. In another embodiment the disease location risk factor is the ileal, colonic or ileocolonic form of Crohn's disease, or a combination thereof. In another embodiment, the demographic risk factors are gender and/or age. In another embodiment, the individual is a child.


A variety of methods can be used to determine the presence or absence of a variant allele or haplotype or serological profile. As an example, enzymatic amplification of nucleic acid from an individual may be used to obtain nucleic acid for subsequent analysis. The presence or absence of a variant allele or haplotype may also be determined directly from the individual's nucleic acid without enzymatic amplification.


Analysis of the nucleic acid from an individual, whether amplified or not, may be performed using any of various techniques. Useful techniques include, without limitation, polymerase chain reaction based analysis, sequence analysis and electrophoretic analysis. As used herein, the term “nucleic acid” means a polynucleotide such as a single or double-stranded DNA or RNA molecule including, for example, genomic DNA, cDNA and mRNA. The term nucleic acid encompasses nucleic acid molecules of both natural and synthetic origin as well as molecules of linear, circular or branched configuration representing either the sense or antisense strand, or both, of a native nucleic acid molecule.


The presence or absence of a variant allele or haplotype may involve amplification of an individual's nucleic acid by the polymerase chain reaction. Use of the polymerase chain reaction for the amplification of nucleic acids is well known in the art (see, for example, Mullis et al. (Eds.), The Polymerase Chain Reaction, Birkhauser, Boston, (1994)).


A TaqmanB allelic discrimination assay available from Applied Biosystems may be useful for determining the presence or absence of a variant allele. In a TaqmanB allelic discrimination assay, a specific, fluorescent, dye-labeled probe for each allele is constructed. The probes contain different fluorescent reporter dyes such as FAM and VICTM to differentiate the amplification of each allele. In addition, each probe has a quencher dye at one end which quenches fluorescence by fluorescence resonant energy transfer (FRET). During PCR, each probe anneals specifically to complementary sequences in the nucleic acid from the individual. The 5′ nuclease activity of Taq polymerase is used to cleave only probe that hybridize to the allele. Cleavage separates the reporter dye from the quencher dye, resulting in increased fluorescence by the reporter dye. Thus, the fluorescence signal generated by PCR amplification indicates which alleles are present in the sample. Mismatches between a probe and allele reduce the efficiency of both probe hybridization and cleavage by Taq polymerase, resulting in little to no fluorescent signal. Improved specificity in allelic discrimination assays can be achieved by conjugating a DNA minor grove binder (MGB) group to a DNA probe as described, for example, in Kutyavin et al., “3′-minor groove binder-DNA probes increase sequence specificity at PCR extension temperature, “Nucleic Acids Research 28:655-661 (2000)). Minor grove binders include, but are not limited to, compounds such as dihydrocyclopyrroloindole tripeptide (DPI).


Sequence analysis also may also be useful for determining the presence or absence of a variant allele or haplotype.


Restriction fragment length polymorphism (RFLP) analysis may also be useful for determining the presence or absence of a particular allele (Jarcho et al. in Dracopoli et al., Current Protocols in Human Genetics pages 2.7.1-2.7.5, John Wiley & Sons, New York; Innis et al., (Ed.), PCR Protocols, San Diego: Academic Press, Inc. (1990)). As used herein, restriction fragment length polymorphism analysis is any method for distinguishing genetic polymorphisms using a restriction enzyme, which is an endonuclease that catalyzes the degradation of nucleic acid and recognizes a specific base sequence, generally a palindrome or inverted repeat. One skilled in the art understands that the use of RFLP analysis depends upon an enzyme that can differentiate two alleles at a polymorphic site.


Allele-specific oligonucleotide hybridization may also be used to detect a disease-predisposing allele. Allele-specific oligonucleotide hybridization is based on the use of a labeled oligonucleotide probe having a sequence perfectly complementary, for example, to the sequence encompassing a disease-predisposing allele. Under appropriate conditions, the allele-specific probe hybridizes to a nucleic acid containing the disease-predisposing allele but does not hybridize to the one or more other alleles, which have one or more nucleotide mismatches as compared to the probe. If desired, a second allele-specific oligonucleotide probe that matches an alternate allele also can be used. Similarly, the technique of allele-specific oligonucleotide amplification can be used to selectively amplify, for example, a disease-predisposing allele by using an allele-specific oligonucleotide primer that is perfectly complementary to the nucleotide sequence of the disease-predisposing allele but which has one or more mismatches as compared to other alleles (Mullis et al., supra, (1994)). One skilled in the art understands that the one or more nucleotide mismatches that distinguish between the disease-predisposing allele and one or more other alleles are preferably located in the center of an allele-specific oligonucleotide primer to be used in allele-specific oligonucleotide hybridization. In contrast, an allele-specific oligonucleotide primer to be used in PCR amplification preferably contains the one or more nucleotide mismatches that distinguish between the disease-associated and other alleles at the 3′ end of the primer.


A heteroduplex mobility assay (HMA) is another well known assay that may be used to detect a SNP or a haplotype. HMA is useful for detecting the presence of a polymorphic sequence since a DNA duplex carrying a mismatch has reduced mobility in a polyacrylamide gel compared to the mobility of a perfectly base-paired duplex (Delwart et al., Science 262:1257-1261 (1993); White et al., Genomics 12:301-306 (1992)).


The technique of single strand conformational, polymorphism (SSCP) also may be used to detect the presence or absence of a SNP and/or a haplotype (see Hayashi, K., Methods Applic. 1:34-38 (1991)). This technique can be used to detect mutations based on differences in the secondary structure of single-strand DNA that produce an altered electrophoretic mobility upon non-denaturing gel electrophoresis. Polymorphic fragments are detected by comparison of the electrophoretic pattern of the test fragment to corresponding standard fragments containing known alleles.


Denaturing gradient gel electrophoresis (DGGE) also may be used to detect a SNP and/or a haplotype. In DGGE, double-stranded DNA is electrophoresed in a gel containing an increasing concentration of denaturant; double-stranded fragments made up of mismatched alleles have segments that melt more rapidly, causing such fragments to migrate differently as compared to perfectly complementary sequences (Sheffield et al., “Identifying DNA Polymorphisms by Denaturing Gradient Gel Electrophoresis” in Innis et al., supra, 1990).


Other molecular methods useful for determining the presence or absence of a SNP and/or a haplotype are known in the art and useful in the methods of the invention. Other well-known approaches for determining the presence or absence of a SNP and/or a haplotype include automated sequencing and RNAase mismatch techniques (Winter et al., Proc. Natl. Acad. Sci. 82:7575-7579 (1985)). Furthermore, one skilled in the art understands that, where the presence or absence of multiple alleles or haplotype(s) is to be determined, individual alleles can be detected by any combination of molecular methods. See, in general, Birren et al. (Eds.) Genome Analysis: A Laboratory Manual Volume 1 (Analyzing DNA) New York, Cold Spring Harbor Laboratory Press (1997). In addition, one skilled in the art understands that multiple alleles can be detected in individual reactions or in a single reaction (a “multiplex” assay). In view of the above, one skilled in the art realizes that the methods of the present invention may be practiced using one or any combination of the well known assays described above or another art-recognized genetic assay.


Similarly, there are many techniques readily available in the field for detecting the presence or absence of serological markers, polypeptides or other biomarkers, including protein microarrays. For example, some of the detection paradigms that can be employed to this end include optical methods, electrochemical methods (voltametry and amperometry techniques), atomic force microscopy, and radio frequency methods, e.g., multipolar resonance spectroscopy. Illustrative of optical methods, in addition to microscopy, both confocal and non-confocal, are detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, and birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry).


Similarly, there are any number of techniques that may be employed to isolate and/or fractionate biomarkers. For example, a biomarker may be captured using biospecific capture reagents, such as antibodies, aptamers or antibodies that recognize the biomarker and modified forms of it. This method could also result in the capture of protein interactors that are bound to the proteins or that are otherwise recognized by antibodies and that, themselves, can be biomarkers. The biospecific capture reagents may also be bound to a solid phase. Then, the captured proteins can be detected by SELDI mass spectrometry or by eluting the proteins from the capture reagent and detecting the eluted proteins by traditional MALDI or by SELDI. One example of SELDI is called “affinity capture mass spectrometry,” or “Surface-Enhanced Affinity Capture” or “SEAC,” which involves the use of probes that have a material on the probe surface that captures analytes through a non-covalent affinity interaction (adsorption) between the material and the analyte. Some examples of mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these.


Alternatively, for example, the presence of biomarkers such as polypeptides may be detected using traditional immunoassay techniques. Immunoassay requires biospecific capture reagents, such as antibodies, to capture the analytes. The assay may also be designed to specifically distinguish protein and modified forms of protein, which can be done by employing a sandwich assay in which one antibody captures more than one form and second, distinctly labeled antibodies, specifically bind, and provide distinct detection of, the various forms. Antibodies can be produced by immunizing animals with the biomolecules. Traditional immunoassays may also include sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays.


Prior to detection, biomarkers may also be fractionated to isolate them from other components in a solution or of blood that may interfere with detection. Fractionation may include platelet isolation from other blood components, sub-cellular fractionation of platelet components and/or fractionation of the desired biomarkers from other biomolecules found in platelets using techniques such as chromatography, affinity purification, 1D and 2D mapping, and other methodologies for purification known to those of skill in the art. In one embodiment, a sample is analyzed by means of a biochip. Biochips generally comprise solid substrates and have a generally planar surface, to which a capture reagent (also called an adsorbent or affinity reagent) is attached. Frequently, the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there.


One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials described. For purposes of the present invention, the following terms are defined below.


EXAMPLES

The following examples are provided to better illustrate the claimed invention and are not to be interpreted as limiting the scope of the invention. To the extent that specific materials are mentioned, it is merely for purposes of illustration and is not intended to limit the invention. One skilled in the art may develop equivalent means or reactants without the exercise of inventive capacity and without departing from the scope of the invention.


Example 1
Associations with Outcome of Surgery
Table 1

Using a GWAS top hits and using Crohn's Disease surgery as an outcome, 34 SNPs were tested to look at the association with surgery in 173 children. Table 1 lists five (5) SNPs that, out of the 34 initially tested, demonstrated the strongest association with the outcome of surgery when individually tested after the initial genome wide association analysis. The first column of Table 1 lists the SNPs, the second column lists the p-value of association, and the third column lists the odds ratio (95% confidence limits) for the increased risk of surgery for those patients with the minor allele in the respective gene.













TABLE 1









rs1551398(8q24)
0.0082
3.3 (1.36, 8.1)



rs1968752(16p11)
0.0044
0.32 (0.15, 0.69)



rs2836878(21q22/BRWD1)
0.08
0.5 (0.2, 1.1) 



rs4574921(TNFSF15)
0.06
0.44 (0.2, 1.0) 



rs8049439(16p11)
0.003
0.31 (0.15, 0.67)










The third column in Table 1, or “risk factor” column, interprets the alleles in the context of the results deciphered and referenced in Tables 2-4 below. In Table 1, the results were rearranged so that each allele tested was the specific combination of alleles that increased risk. Note that in Table 1, some of the odds ratios were larger than 1, where for example rs1551398 the odds ratio is 3.3. For others the odds ratio were less than 1, such as for example rs1969752 where the risk is 0.32. An odds ratio of less than 1 means that the particular test is showing a decreased risk, such as in this case a decreased risk for the minor allele. These were re-arranged so that each SNP would be showing an increase in risk. A decreased risk for the minor allele would mean an increased risk for the major allele.


Finally, all of the SNPs were put into a single statistical model and tested together, with the result being that four of the SNPs remained significant while the rs8049439 SNP does not remain in the model. This is not a surprising result given that rs8049439 is in the same gene as the SNP rs1968752. Each is significant when tested individually, but only one is needed when these are tested together.


Example 2
Multivariate Analysis Demonstrated 4 SNPs Independently Associated with Surgery Outcome
Table 2

Table 2 describes multivariate analysis demonstrating the four SNPs referenced below as independently associated with surgery outcome. For example in Table 2 below, for rs15513982c, the presence of “12” or “22” increases the likelihood of requiring surgery in the individual by 1.18 with a significance of 0.121. The alleles are referenced in Table 6 below, where for example, the presence of the minor allele (which is “G” if using the top strand, and “C” if using the forward strand), increases the likelihood for surgery by 1.18. Similarly, for example in Table 2 below, for rs1968752, an individual homozygous for the major allele (or “A” for both top and forward strand) increases the likelihood of surgery by 1.2 with a significance of 0.0035. Table 2 uses an estimation of the maximum likelihood of the effect.









TABLE 2







Analysis of Maximum Likelihood Estimates
















Wald






Standard
Chi-
Pr >


Parameter
DF
Estimate
Error
Square
ChiSq















Intercept
1
−4.1426
0.697
35.3235
<.0001


rs1551398_2c(12/22
1
1.1807
0.4705
6.2983
0.0121


vs. 11)


rs1968752_11(11
1
1.2173
0.4169
8.525
0.0035


vs. 12/22)


rs2836878_11(11
1
0.8441
0.4291
3.8697
0.0492


vs. 12/22)


rs4574921_11(11
1
1.119
0.4726
5.6071
0.0179


vs. 12/22)









Example 3
Odds Ratio Estimates
Table 3

Table 3 demonstrates how the risk factors may increase the odds ratio (compared to Table 2 above which is estimating likelihood) for going to surgery using the Wald test. For example, a subject having the presence of the minor allele for rs1551398 has an odds ratio of requiring surgery of 3.2.













TABLE 3









95% Wald Confidence



Effect
Point Estimate
Limits





















Rs1551398
3.257
1.295
8.189



Rs1968752_11
3.378
1.492
7.649



Rs2836878
2.326
1.003
5.393



Rs4574921_11
3.062
1.213
7.731










Example 4
Survival Analysis for Time to Surgery
Table 4

Table 4 below describes the use of survival analysis to determine whether certain SNPs were associated with faster progression to Crohn's Disease surgery. The common allele is designated as “1”, and the rare allele is designated as “2.”


















TABLE 4







rs1968752
11
62
12
50
80.65
Log-
0.0177
0.37(12/22
0.02








Rank

vs. 11)



12/22
117
9
108
92.31
Wilcoxon
0.0118


rs8049439
11
66
13
53
80.3
Log-
0.004
 0.3(12/22
0.008








Rank

vs. 11)



12/22
113
8
105
92.92
Wilcoxon
0.0113


rs11174631
11
154
14
140
90.91
Log-
0.0319
 2.6(12/22
0.04








Rank

vs. 11)



12/22
25
7
18
72
Wilcoxon
0.5321









Example 5
Survival Analysis Predictive Model
Table 5

Table 5 below uses survival analysis regarding the question of whether risk factors are counted, does the patient progress to surgery faster. The risk factor column is the count of the risk alleles referenced in Table 6 below; the overall significance is shown in the right most column. The total shows how many subjects had risk alleles; failed is the number that required surgery; censored is the number that did not require surgery but that had the date when they were last known to not have surgery. As demonstrated below, survival analysis as a predictive model showed that as patients had more genes, then the progression to surgery was faster (0 vs. 4 genes). The four (4) genes were the same as those found in the multivariate analysis referenced above.














TABLE 5





riskfactor
total
failed
censored
% censored
logrank




















0
10
0
10
100%
<0.0001


1
36
0
36
100%


2
79
10
69
87%


3
43
6
37
86%


4
11
5
6
54%









Example 6
Corresponding Alleles for Six (6) SNPs Referenced Herein
Table 6

Table 6 describes the referenced alleles for the listed SNPs, where the top strand designates the actual allele used in the analysis herein, and the forward strand designates the same allele on the reference genome assembly number 36 as referenced in the National Center for Biotechnology Information (NCBI).













TABLE 6









Top Strand
Forward Strand












Minor
Major
(dbsnp)














Allele
Allele
Minor
Major



SNPid
(“2”)
(“1”)
Allele
Allele
Risk Factor





rs1551398
G
A
C
T
Presence of minor


(SEQ. ID.




allele


NO.: 1)


rs1968752
A
C
A
C
Homozygous for


(SEQ. ID.




major allele


NO.: 2)


rs2836878
A
G
A
G
Homozygous for


(SEQ. ID.




major allele


NO.: 3)


rs4574921
G
A
C
T
Homozygous for


(SEQ. ID.




major allele


NO.: 4)


rs8049439
G
A
C
T
Presence of minor


(SEQ. ID.




allele


NO.: 5)


rs11174631
A
G
C
T
Presence of minor


(SEQ. ID.




allele


NO.: 6)









Example 7
Additional Genome-Wide Association Studies

Genome-wide association studies (GWAS) were performed to determine the association between disease phenotypes (complication and surgery) and single nucleotide polymorphisms (SNPs). Then, stepwise variable selection was applied to logistic regression models (3 models for complication and 5 models for surgery) incorporating: SNPs selected from GWAS, gender, age, disease location, ANCA and antibody sum/antibody quartile score as predictors.


Example 8
Significant SNPs (p<5×10−5) Selected from GWAS with Complication

For complication, Table 7 shows 16 SNPs with p-values less than 5×10−5 were selected throughout the GWAS. SNPs rs7181301, rs11223560, rs2245872, rs261827, rs12909385, rs4787664, rs11009506, rs7672594, rs1781873, rs17771939, rs10180293, rs4833624, rs12512646, rs6413435, rs1889926, and rs4305427 are described herein as SEQ. ID. NOS.: 7-22, respectively.









TABLE 7







List of Significant SNPs (p < 5 × 10−5) selected from GWAS with Complication













Obs
CHR
SNP
BP
OR
STAT
P
















1
15
rs7181301
96440815
3.2440
4.662
.000003137


2
11
rs11223560
133066609
1.9330
4.374
.000012180


3
1
rs2245872
37704373
1.9750
4.347
.000013810


4
1
rs261827
239136994
1.9660
4.318
.000015730


5
15
rs12909385
55484367
2.0650
4.238
.000022590


6
16
rs4787664
23958740
0.3960
−4.234
.000022940


7
10
rs11009506
34063503
0.4937
−4.223
.000024150


8
4
rs7672594
120467991
1.9380
4.206
.000026030


9
19
rs1781873
21269271
0.5245
−4.204
.000026230


10
8
rs17771939
94328281
0.4497
−4.103
.000040850


11
2
rs10180293
206330821
0.3500
−4.100
.000041300


12
4
rs4833624
120804945
1.9030
4.097
.000041890


13
4
rs12512646
120805181
1.9030
4.097
.000041890


14
19
rs6413435
18358137
2.1750
4.094
.000042490


15
1
rs1889926
65470767
2.0270
4.093
.000042620


16
3
rs4305427
68750047
1.8530
4.075
.000045970









Example 9
Selection of 3 Logistic Regression Models

Next, 3 logistic regression models were considered in order to measure the strength of association between the response of complication (Yes/No) and the predictors. The first model included: 16 SNPs, gender, age, and disease location. The second model included: 16 SNPs, gender, age, disease location, ANCA, and antibody quartile score. The third model included: 16 SNPs, gender, age, disease location, ANCA, and antibody sum. After stepwise variable selection, primary associations with complication were determined.


Example 10
Model 1
Logistic Regression of Complication with 16 SNPs Selected, Sex1, Age, and sb1

As indicated in Table 8, in the first model, 14 out of 16 SNPs, gender, age and disease location were determined to be statistically significant.









TABLE 8a







Analysis of Maximum Likelihood Estimates















Standard
Wald



Parameter
DF
Estimate
Error
Chi-Square
Pr > ChiSq





rs7181301
1
1.1091
0.3011
13.5657
0.0002


rs11223560
1
0.0536
0.2382
12.8386
0.0003


rs2245872
1
0.6269
0.2085
9.0386
0.0026


rs261827
1
−0.7731
0.3323
5.4136
0.0200


rs12909385
1
−0.8385
0.2790
9.0297
0.0027


rs11009506
1
−0.6072
0.2039
0.8695
0.0029


rs1781873
1
0.8734
0.2439
12.8222
0.0003


rs17771939
1
−0.7792
0.2309
11.3921
0.0007


rs10180293
1
0.9107
0.2031
20.1041
<.0001


rs4833624
1
0.5907
0.2298
6.6096
0.0101


rs12512646
1
−1.8591
0.3335
31.0658
<.0001


rs6413435
1
−0.8896
0.2771
10.3050
0.0013


rs1889926
1
−0.6911
0.2471
7.8193
0.0052


rs4305427
1
1.3481
0.4186
10.3705
0.0013


sex1
1
−0.8994
0.2913
9.5327
0.0020


age_at_dx2
1
1.0368
0.2977
12.1312
0.0005


sb1
1
1.2903
0.3765
11.7450
0.0006










Hosmer and Lemeshow Goodness-of-Fit Test









Chi-Square
DF
Pr > ChiSq





3.5183
8
0.8378





AUC = 0.906













TABLE 8b







Odds Ratio Estimates














95% Wald





Point
Confidence



Effect
Estimate
Limits
















rs7181301
3.032
1.680
5.470



rs11223560
2.348
1.472
3.745



rs2245872
1.072
1.244
2.817



rs261827
0.462
0.241
0.885



rs12909385
0.432
0.250
0.747



rs11009506
0.545
0.365
0.813



rs1781873
2.395
1.485
3.863



rs17771939
0.459
0.292
0.721



rs10100293
2.486
1.670
3.702



rs4833624
1.805
1.151
2.832



rs12512646
0.156
0.081
0.300



rs6413435
0.411
0.239
0.707



rs1889926
0.501
0.309
0.813



rs4305427
3.850
1.695
8.745



sex1
0.407
0.230
0.720



age_at_dx2
2.820
1.574
5.054



sb1
3.634
1.737
7.60










Example 11
Model 2
Logistic Regression of Complication with 16 SNPs Selected, sex1, Age at Diagnosis, sb1, anca p1, and Antibody Quartile

As indicated in Table 9, in the second model, 14 out of 16 SNPs, gender, age, disease location, ANCA, and antibody quartile score were determined to be statistically significant.









TABLE 9a







Analysis of Maximum Likelihood Estimates















Standard
Wald



Parameter
DF
Estimate
Error
Chi-Square
Pr > Chisq





rs7181381
1
0.9923
0.3242
9.3684
0.0022


rs11223560
1
0.8874
0.2577
11.8598
0.0006


rs2245872
1
0.6265
0.2358
7.1581
0.0075


rs261827
1
−0.7985
0.3761
4.5083
0.0337


rs12909385
1
−1.1616
0.3098
14.1305
0.0002


rs11009586
1
−0.8349
0.2349
12.6354
0.0004


rs1781873
1
0.9181
0.2639
11.8927
0.0006


rs17771939
1
−0.8549
0.2465
12.0254
0.0005


rs10188293
1
1.0455
0.2291
20.0239
<.0001


rs4833624
1
0.6598
0.2565
6.6143
0.0101


rs12512646
1
−2.1169
0.3715
32.4764
<.0001


rs6413435
1
−0.9961
0.3021
10.8723
0.0010


rs1889926
1
−0.8970
0.2768
10.5001
0.0012


rs4385427
1
1.1535
0.4372
6.9619
0.0083


sex1
1
−0.9212
0.3193
8.3234
0.0039


age_at_dx2
1
1.0503
0.3278
10.4647
0.0012


anca_P1
1
−1.5651
0.4747
10.8730
0.0010


ab_quar1
1
1.0654
0.1933
30.3832
<.0001










Hosmer and Lemeshow Goodness-of-Fit Test









Chi-Square
DF
Pr > ChiSq





7.1251
8
0.5232





AUC = 0.938













TABLE 9b







Odds Ratio Estimates














95% Wald





Point
Confidence



Effect
Estimate
Limits
















rs7181381
2.697
1.429
5.892



rs11223588
2.429
1.466
4.825



rs2245872
1.875
1.183
2.972



rs281827
0.450
0.215
0.940



rs12989385
0.313
0.171
0.574



rs11009506
0.434
0.274
0.688



rs1701873
2.485
1.481
4.168



rs17771939
0.425
0.262
0.690



rs10186293
2.845
1.816
4.457



rs4833624
1.934
1.178
3.198



rs12512646
0.120
0.058
0.243



rs6413435
0.369
0.284
0.668



rs1889925
0.408
0.237
0.702



rs4385427
3.169
1.345
7.466



sex1
0.398
0.213
0.744



age_at_dx2
2.887
1.519
5.488



anca_P1
0.289
0.082
0.530



ab_quar1
2.902
1.987
4.239










Example 12
Model 3
Logistic Regression of Complication with 16 SNPs Selected, sex1, Age at Diagnosis, sb1, anca p1, and Antibody Sum

As indicated in Table 10, in the third model, 14 out of 16 SNPs, gender, age, disease location, ANCA, and antibody sum were determined to be statistically significant.









TABLE 10a







Analysis of Maximum Likelihood Estimates















Standard
Wald



Parameter
DF
Estimate
Error
Chi-Square
Pr > Chisq





rs7181381
1
1.0739
0.3277
10.7356
0.0011


rs11223560
1
0.8708
0.2568
11.5812
0.0007


rs2245872
1
0.6764
0.2316
0.5768
0.0034


rs261827
1
−0.6401
0.3668
3.8462
0.0009


rs12909385
1
−1.0195
0.3878
11.0258
0.0009


rs11009586
1
−0.6543
0.2283
0.2149
0.0042


rs1761873
1
0.8869
0.2617
11.5338
0.0007


rs17771939
1
−0.8878
0.2486
12.7512
0.0004


rs10180293
1
1.0645
0.2298
21.4536
<.0001


rs4833624
1
0.7220
0.2579
7.8399
0.0051


rs12512646
1
−1.8675
0.3693
25.5759
<.0001


rs6413435
1
−0.8736
0.3822
0.3581
0.0038


rs1889926
1
−0.7832
0.2717
0.3072
0.0039


rs4305427
1
1.1488
0.4495
0.5386
0.0106


sex1
1
−0.8954
0.3206
7.7986
0.0052


age_at_dx2
1
1.0866
0.3278
9.4278
0.0021


sb1
1
0.8180
0.4864
4.6514
0.0441


anca_P1
1
−1.3505
0.4672
0.3542
0.0038


ab_sum
1
0.6831
0.1412
23.4165
<.0001










Hosmer and Lemeshow Goodness-of-Fit Test









Chi-Square
DF
Pr > ChiSq





4.9462
8
0.7633





AUC = 0.929













TABLE 10b







Odds Ratio Estimates














95% Wald





Point
Confidence



Effect
Estimate
Limits
















rs7181301
2.927
1.540
5.564



rs11223560
2.389
1.444
3.952



rs2245872
1.971
1.252
3.103



rs261827
0.527
0.257
1.082



rs12909385
0.361
0.198
0.659



rs11009586
0.520
0.332
0.813



rs1781873
2.432
1.456
4.063



rs17771939
0.412
0.253
0.670



rs10180293
2.899
1.848
4.549



rs4833624
2.053
1.242
3.412



rs12512646
0.155
0.075
0.319



rs6413435
0.417
0.231
0.755



rs1883926
0.457
0.268
0.778



rs4305427
3.154
1.307
7.613



sex1
0.408
0.218
0.766



age_at_dx2
2.736
1.439
5.203



sb1
2.266
1.022
5.026



anca_P1
0.259
0.104
0.647



ab_sum
1.980
1.501
2.611










Example 13
Significant SNPs (p<5×10−5) Selected from GWAS with Surgery

As indicated in Table 11, for surgery, 30 significant SNPs were selected with p-values less than 5×10−5. SNPs rs6491069, rs12100242, rs7575216, rs9742643, rs7333546, rs10825455, rs187783, rs261804, rs501691, rs2993493, rs1749969, rs7157738, rs1325607, rs2018454, rs1403146, rs261827, rs487675, rs12386815, rs2928686, rs1168566, rs2698174, rs16842384, rs705308, rs12909385, rs724685, rs9864383, rs11845504, rs898716, rs7181301, and rs913735 are described herein as SEQ. ID. NOS.: 23-52, respectively.









TABLE 11







List of Significant SNPs (p < 5 × 10−5) selected from GWAS with Surgery













Obs
CHR
snp
BP
OR
STAT
P
















1
13
rs6491069
25050039
2.6550
4.805
.000001545


2
13
rs12100242
25078845
2.5750
4.712
.000002456


3
2
rs7575216
39257514
3.3980
4.683
.000002832


4
13
rs9742643
25026096
2.6140
4.681
.000002857


5
13
rs7333546
24949574
2.4770
4.587
.000004506


6
10
rs10825455
56496449
3.6210
4.530
.000005886


7
1
rs187783
239119745
2.0080
4.530
.000005888


8
1
rs261804
239134094
1.9980
4.510
.000006489


9
1
rs501691
65516415
2.4290
4.505
.000006628


10
1
rs2993493
3010106
2.3910
4.475
.000007605


11
1
rs1749969
65500587
2.4150
4.468
.000007886


12
14
rs7157738
37944754
0.2567
−4.457
.000008296


13
1
rs1325607
65523648
2.3660
4.445
.000008792


14
19
rs2018454
15873612
2.2490
4.390
.000011360


15
3
rs1403146
6698888
0.4707
−4.371
.000012380


16
1
rs261827
239136994
1.9488
4.234
.000022960


17
1
rs487675
183067888
0.4671
−4.188
.000028120


18
8
rs12386815
136027851
2.0230
4.173
.000030130


19
8
rs2928686
23477641
1.9670
4.165
.000031180


20
14
rs1168566
37957632
0.3417
−4.151
.000033170


21
18
rs2698174
66897090
2.8540
4.149
.000033390


22
2
rs16842384
209650323
1.9410
4.145
.000033940


23
7
rs705308
97533299
0.4995
−4.135
.000035480


24
15
rs12909385
55484367
2.0620
4.119
.000038000


25
1
rs724685
65499104
2.1800
4.118
.000038200


26
3
rs9864383
113264489
1.8730
4.115
.000038780


27
14
rs11845504
37965784
0.3454
−4.111
.000039470


28
10
rs898716
14165659
2.0110
4.099
.000041430


29
15
rs7181301
96440815
2.7270
4.091
.000043000


30
14
rs913735
37951124
0.3393
−4.072
.000046680









Five logistic regression models with the response of surgery (Yes/No) and the predictors were considered. In the first model, the following variables were included: 30 SNPs, gender, age, and disease location. In the second model, the following variables were included: 30 SNPs, gender, age, disease location, ANCA, and antibody quartile score. In the third model, the following variables were included: 30 SNPs, gender, age, disease location, ANCA, antibody quartile score, internal penetrating, and stricture. In the fourth model, the following variables were included 16 SNPs, gender, age, disease location, ANCA, and antibody quartile score. In the fifth model, the following variables were included: 16 SNPs, gender, age, disease location, ANCA, antibody quartile score, internal penetrating, and stricture. After applying stepwise variable selection, primary associations with the response variable, surgery, were determined.


Example 14
Model 1
Logistic Regression of Surgery with 30 SNPs Selected, sex1, Age at Diagnosis 2, and sb1

As indicated in Table 12, in the first model, 17 out of 30 SNPs, and disease location were statistically significant.









TABLE 12a







Analysis of Maximum Likelihood Estimates















Standard
Wald



Parameter
DF
Estimate
Error
Chi-Square
Pr > ChiSq





Intercept
1
5.0724
2.3025
4.8532
0.0276


rs9742643
1
1.0303
0.2833
13.2306
0.0003


rs10825455
1
−0.7561
0.2518
9.0209
0.0027


rs261804
1
1.0697
0.2238
22.8398
<.0001


rs2993493
1
−0.7851
0.4032
3.7918
0.0515


rs1749969
1
−0.9655
0.3172
9.2655
0.0023


rs1325607
1
−1.1166
0.3855
8.3903
0.0038


rs1403146
1
−0.9719
0.2404
16.3451
<.0001


rs261827
1
−1.0055
0.2567
15.3366
<.0001


rs487675
1
−0.3229
0.2425
11.5155
0.0007


rs12386815
1
−0.9665
0.3995
5.8525
0.0156


rs16842384
1
−0.9109
0.2991
9.2727
0.0023


rs705308
1
3.3659
0.8530
15.3910
<.0001


rs12909385
1
1.1371
0.6592
2.9750
0.0046


rs11845504
1
−0.7177
0.2545
7.9539
0.0048


rs898716
1
−1.4424
0.4229
11.6328
0.0006


rs7181301
1
1.4879
0.3961
14.1106
0.0002


rs913735
1
−0.6918
0.2729
6.4266
0.0112


sb1
1
1.7672
0.4093
18.6413
<.0001










Hosmer and Lemeshow Goodness-of-Fit Test









Chi-Square
DF
Pr > ChiSq





5.6000
8
0.6919





AUC = 0.925













TABLE 12b







Odds Ratio Estimates













Point
95% Wald




Effect
Estimate
Confidence Limits
















rs9742643
2.802
1.608
4.082



rs10325455
0.469
0.287
0.769



rs261804
2.915
1.880
4.528



rs2933493
0.456
0.207
1.885



rs1749969
0.381
0.205
0.789



rs1325607
0.327
0.154
0.697



rs1403146
0.373
0.236
0.686



rs261827
0.366
0.221
0.685



rs487675
0.439
0.273
0.786



rs12386815
0.388
0.174
0.832



rs16842384
0.402
0.224
0.723



rs705303
28.959
5.389
155.623



rs12909385
3.118
0.856
11.358



rs11845504
0.468
0.296
0.803



rs898716
0.236
0.103
0.541



rs7181301
4.428
2.037
0.623



rs913735
0.501
0.293
0.855



sb1
5.855
2.625
13.059










Example 15
Model 2
Logistic Regression of Surgery with 30 SNPs Selected, sex1, Age at Diagnosis2, sb1, anca p1 and Antibody Quartile 1

As indicated in Table 13, in the second model, 16 out of 30 SNPs, disease location, ANCA, and antibody quartile score were statistically significant.









TABLE 13a







Analysis of Maximum Likelihood Estimates















Standard
Wald



Parameter
DF
Estimate
Error
Chi-Square
Pr > ChiSq





Intercept
1
0.3430
2.0602
16.3997
<.0001


rs12100242
1
−1.0226
0.2973
11.8330
0.0005


rs10825455
1
−1.0556
0.2856
13.6590
0.0002


rs261804
1
0.6613
0.3033
4.7525
0.0293


rs501691
1
0.5934
0.3249
3.3363
0.0670


rs2993493
1
−1.0127
0.4429
5.2278
0.0222


rs1749969
1
−1.0052
0.3479
0.3499
0.0039


rs1325607
1
−1.2141
0.4225
0.2570
0.0041


rs1403140
1
−0.9187
0.2563
12.8481
0.0003


rs261827
1
−1.1034
0.2752
16.0814
<.0001


rs487675
1
−0.9426
0.2628
12.0659
0.0003


rs12386815
1
−1.1928
0.4211
0.0232
0.0046


rs2698174
1
−1.2826
0.3178
16.2873
<.0001


rs705308
1
2.0876
0.4645
20.2015
<.0001


rs898716
1
−1.2787
0.4520
0.0030
0.0047


rs7181301
1
1.2469
0.4273
0.5133
0.0035


rs913735
1
−0.6716
0.2966
5.1255
0.0236


sb1
1
1.4063
0.4483
10.2042
0.0014


anca_P1
1
−0.9295
0.4477
4.3101
0.0379


ab_quar1
1
0.0798
0.2059
18.2549
<.0001










Hosmer and Lemeshow Goodness-of-Fit Test









Chi-Square
DF
Pr > ChiSq





2.6755
8
0.9530





AUC = 0.940













TABLE 13b







Odds Ratio Estimates













Point
95% Wald




Effect
Estimate
Confidence Limits
















rs12100242
0.360
0.201
0.644



rs10825455
0.348
0.199
0.609



rs261804
1.937
0.069
3.511



rs501691
1.810
0.958
3.422



rs2993493
0.363
0.152
0.865



rs1749969
0.366
0.185
0.724



rs1325607
0.297
0.130
0.680



rs1403146
0.399
0.241
0.659



rs261827
0.332
0.193
0.569



rs487675
0.398
0.233
0.652



rs12386815
0.383
0.133
0.693



rs2698174
0.277
0.149
0.517



rs785308
0.065
3.245
20.044



rs898716
0.278
0.115
0.675



rs7181301
3.479
1.506
0.040



rs913735
0.511
0.286
0.914



sb1
4.081
1.722
9.672



anca_P1
0.395
0.164
0.949



ab_quar1
2.410
1.610
3.609










Example 16
Model 3
Logistic Regression of Surgery with 30 SNPs Selected, sex1, Age at Diagnosis2, sb1, anca p1, Antibody Quartile 1, Stricture 1, and ip1

As demonstrated in Table 14, in the third model, 15 out of 30 SNPs, antibody quartile score, internal penetrating, and stricture were statistically significant.









TABLE 14a







Analysis of Maximum Likelihood Estimates















Standard
Wald



Parameter
DF
Estimate
Error
Chi-Square
Pr > ChiSq





Intercept
1
4.9758
2.4784
4.0307
0.0447


rs6491069
1
2.1774
1.0160
4.5930
0.0321


rs7575216
1
−3.0946
1.2437
6.1916
0.0120


rs10825455
1
−1.0364
0.3235
10.2636
0.0014


rs261804
1
0.8382
0.2606
10.3478
0.0013


rs2993493
1
−0.9862
0.4897
4.0558
0.0440


rs1749969
1
−1.0281
0.3993
0.6304
0.0100


rs1325607
1
−1.0502
0.4859
4.6724
0.0307


rs1403146
1
−0.3196
0.2898
0.0009
0.0047


rs261827
1
−1.0228
0.3157
10.4969
0.0012


rs487675
1
−0.9786
0.2799
12.2197
0.0005


rs12386815
1
−0.9141
0.4571
3.9993
0.0455


rs2698174
1
−1.2727
0.3486
13.3267
0.0003


rs785308
1
2.3357
0.5514
17.9452
<.0001


rs7181301
1
1.2855
0.4564
7.9330
0.0049


rs913735
1
−1.1026
0.3481
10.0342
0.0015


ab_quar1
1
0.7188
0.2266
10.0573
0.0015


stricture1
1
2.7013
0.4226
40.8556
<.0001


ip1
1
1.9157
0.5121
13.9936
0.002










Hosmer and Lemeshow Goodness-of-Fit Test









Chi-Square
DF
Pr > ChiSq





3.9729
8
0.8596





AUC = 0.960













TABLE 14b







Odds Ratio Estimates













Point
95% Wald




Effect
Estimate
Confidence Limits
















rs6491869
0.023
1.205
64.638



rs7575216
0.045
0.004
0.518



rs10825455
0.355
0.188
0.669



rs261804
2.312
1.387
3.853



rs2993493
0.373
0.143
0.974



rs1749969
0.358
0.164
0.782



rs1325607
0.350
0.135
0.907



rs1403146
0.441
0.250
0.777



rs261827
0.360
0.194
0.668



rs487675
0.376
0.217
0.651



rs12386815
0.401
0.164
0.932



rs2698174
0.200
0.141
0.955



rs705308
10.337
3.508
30.461



rs7181301
3.617
1.478
0.847



rs913735
0.332
0.168
0.657



ab_quar1
2.052
1.316
3.199



stricture1
14.898
6.587
34.109



ip1
6.792
2.489
18.930










Example 17
Model 4
Logistic Regression of Surgery with 30 SNPs Selected, sex1, Age at Diagnosis 2, sb1, anca p1, and Antibody Sum

As demonstrated in Table 15, in the fourth model, 17 out of 30 SNPs, disease location, ANCA, and antibody sum were statistically significant.









TABLE 15a







Analysis of Maximum Likelihood Estimates















Standard
Wald



Parameter
DF
Estimate
Error
Chi-Square
Pr > ChiSq





Intercept
1
0.4807
1.9985
18.0074
<.0001


rs9742643
1
1.0930
0.3089
12.5188
0.0004


rs10825455
1
−1.0907
0.2890
14.2429
0.0002


rs261804
1
0.6599
0.2991
4.8690
0.0273


rs501691
1
0.6255
0.3241
3.7246
0.0536


rs2993493
1
−0.9194
0.4416
4.3349
0.0373


rs1749969
1
−0.9184
0.3430
7.1708
0.0074


rs1325607
1
−1.2065
0.4189
8.2937
0.0040


rs1403146
1
−1.0123
0.2577
15.4330
<.0001


rs261827
1
−1.0659
0.2764
14.8709
0.0001


rs487675
1
−0.0561
0.2573
11.0698
0.0009


rs12386815
1
−1.2401
0.4158
8.8951
0.0029


rs2698174
1
−1.1881
0.3266
13.2361
0.0003


rs705308
1
2.1105
0.4805
19.2958
<.0001


rs11845504
1
−0.4644
0.2754
2.8436
0.0917


rs898716
1
−1.4547
0.4623
9.9016
0.0017


rs7181301
1
1.3742
0.4276
10.3287
0.0013


rs913735
1
−0.7096
0.2998
5.6013
0.0179


sb1
1
1.4676
0.4396
11.1446
0.0008


anca_P1
1
−1.0562
0.4430
5.6828
0.0171


ab_sum
1
0.5304
0.1458
13.2379
0.0003










Hosmer and Lemeshow Goodness-of-Fit Test









Chi-Square
DF
Pr > ChiSq





4.8880
8
0.7695





AUC = 0.940













TABLE 15b







Odds Ratio Estimates













Point
95% Wald




Effect
Estimate
Confidence Limits
















rs9742643
2.983
1.628
5.466



rs10825455
0.336
0.191
0.592



rs261804
1.935
1.077
3.477



rs501691
1.869
0.990
3.528



rs2993493
0.399
0.168
0.948



rs1749969
0.399
0.204
0.782



rs1325607
0.299
0.132
0.680



rs1403146
0.363
0.219
0.602



rs261827
0.344
0.200
0.592



rs487675
0.425
0.257
0.703



rs12386815
0.289
0.128
0.654



rs2698174
0.305
0.161
0.578



rs705308
0.253
3.218
21.165



rs11845504
0.628
0.366
1.078



rs898716
0.233
0.094
0.578



rs7181301
3.952
1.709
0.136



rs913735
0.492
0.273
0.885



sb1
4.339
1.833
10.269



anca_P1
0.348
0.146
0.829



ab_sum
1.700
1.277
2.262










Example 18
Model 5
Logistic Regression of Surgery with 30 SNPs Selected, sex1, Age at Diagnosis, sb1, anca p1, Antibody Sum, Stricture1, and ip1

As indicated in Table 16, in the fifth model, 15 out of 30 SNPs, antibody sum, internal penetrating, and stricture were statistically significant.









TABLE 16a







Analysis of Maximum Likelihood Estimates















Standard
Wald



Parameter
DF
Estimate
Error
Chi-Square
Pr > ChiSq





Intercept
1
5.6515
2.3696
5.6884
0.0171


rs6491069
1
2.3223
0.9716
5.7134
0.0168


rs7579216
1
−2.9085
1.1932
9.3420
0.0148


rs10825455
1
−1.0239
0.3229
10.0561
0.0015


rs261804
1
0.8842
0.2594
11.6139
0.0007


rs2993493
1
−0.8840
0.4757
3.4529
0.0631


rs1749969
1
−0.9685
0.3946
6.0235
0.0141


rs1329607
1
−1.0257
0.4795
4.5760
0.0324


rs1403146
1
−0.8829
0.2859
9.5328
0.0020


rs261827
1
−1.0102
0.3148
10.3004
0.0013


rs487675
1
−0.0331
0.2726
11.7189
0.0006


rs12386815
1
−0.9113
0.4469
4.1578
0.0414


rs2698174
1
−1.2875
0.3497
13.8742
0.0002


rs705308
1
2.2974
0.5546
17.1582
<.0001


rs7181301
1
1.3132
0.4518
8.4487
0.0037


rs913735
1
−1.1052
0.3484
10.0611
0.0015


ab_sum
1
0.4456
0.1671
7.1145
0.0076


stricture1
1
2.7412
0.4228
42.0421
<.0001


ip1
1
1.9216
0.5117
14.1165
0.0002










Hosmer and Lemeshow Goodness-of-Fit Test









Chi-Square
DF
Pr > ChiSq





8.7486
8
0.3639





AUC = 0.958













TABLE 16b







Odds Ratio Estimates













Point
95% Wald




Effect
Estimate
Confidence Limits
















rs6491869
10.199
1.519
68.477



rs7975216
0.055
0.005
0.566



rs10825455
0.359
0.191
0.676



rs261804
2.421
1.456
4.026



rs2993493
0.413
0.163
1.050



rs1749969
0.389
0.175
0.823



rs1325607
0.359
0.140
0.918



rs1403146
0.414
0.236
0.724



rs261827
0.364
0.196
0.675



rs487675
0.393
0.231
0.671



rs12386815
0.402
0.167
0.965



rs2698174
0.275
0.140
0.543



rs705308
9.948
3.355
29.501



rs7181301
3.718
1.534
0.013



rs913735
0.331
0.167
0.656



ab_sum
1.561
1.125
2.166



stricture1
19.505
6.770
35.507



ip1
6.639
2.508
18.644










Example 19
Survival Analysis

In order to examine the disease phenotypes (complication and surgery) and the time to reach the disease status, a survival analysis was performed with a Cox regression model. First, in order to select significant SNPs, genome-wide survival analyses were performed with a Cox regression model, in which each SNP was a predictor. Second, stepwise variable selection was applied to Cox regression models (3 models for complication and 5 models for surgery) using SNPs selected, gender, age, disease location, ANCA, and antibody sum/antibody quartile score as predictors. Third, the survival functions obtained by the Kaplan-Meier (KM) estimator among subgroups of patients were compared, which were subgrouped with 25% quartile and 75% quartile of the genetic risk score calculated from the selected model in the second step for each regression model (group1 if risk score ≦25% quartile, group 2 if 25% quartile <risk score <75% quartile, and group3 if risk score ≧75% quartile). Finally, for each subgroup, the survival functions were compared across the models.


Example 20
Survival Analysis for Complication

For complication, 50 SNPs with p-values less than 5×10−5 were selected throughout the genome-wide survival analyses. 3 Cox regression models were considered as follows; In model 1, the following variables were used: 50 SNPs, gender, age, and disease location. In model 2, the following variables were used: 50 SNPs, gender, age, disease location, ANCA, and antibody quartile score. In model 3, the following variables were used: 50 SNPs, gender, age, disease location, ANCA, and antibody sum. For each model, stepwise variable selection determined statistically significant predictors, as indicated in Table 17.


In the first model, 14 out of 50 SNPs, gender, and disease location were statistically significant. In the second model, 14 out of 50 SNPs, gender, disease location, and ANCA. In the third model, the results were the same as the model. For all 3 models, the survival functions obtained by the Kaplan-Meier (KM) estimator were significantly different among subgroups of patients (FIGS. 1,2). For all 3 subgroups, the survival functions across 3 models were statistically indistinguishable with a significance level of 0.05.


Tables 17-22 below indicate the results of the survival analysis for complication. As described herein, statistically significant predictors were identified for each model and used to determine a genetic risk score. The genetic risk score was then used to determine quartile subgroups. The column headings “minimum”, “median” and “maximum” in tables 17 and 23 refer to risk scores. The column headings “25% quartile” and “75% quartile” in tables 17 and 23 refer to boundaries for subgroups. The column heading “variable” in tables 17 and 23 refer to the model tested, ie. SC1 (model 1) or SC2 (model 2). The column heading “stratum” in each model refers to the range of risk scores within each group. The column heading “gp” in each model refers to the group number (ie. gpsc1 is group sc1 aka group 1). The column heading “N” in tables each model refers to the number of subjects used to calculate the results. The column heading “Failed” in tables 18-22 refers to the number of subjects experiencing complication. The column heading “Failed” in tables 23-30 refer to the number of subjects undergoing surgery. The column heading “Censored” in tables 18-22 indicates the number of subjects that did not experience complication as of a known date. The column heading “Censored” in tables 23-30 indicates the number of subjects that did not experience surgery as of a known date. The column headings “% Censored” and “Median” in tables 17-30 describe standard statistical manipulations of the data in each model.









TABLE 17







Survival for Complication












Variable
Minimum
Median
Maximum
25% Quartile
75% Quartile





sc1
9
14
18
12
15


sc2
9
15
19
13
16









Example 21
Survival for Complication Model 1
Summary of the Number of Censored and Uncensored Values And Test of Equality Over Strata









TABLE 18a







Model: SC1


Summary of the Number of Censored and Uncensored Values


















% Cen-



Stratum
gpsc1
N
Failed
Censored
sored
Median
















1(sc1 <= 12)
1
190
20
170
89.47
32.0


2(12 < sc1 < 15)
2
176
23
153
86.93
31.5


3(sc1 >= 15)
3
97
36
61
62.89
31.0


Total

463
79
384
82.94
















TABLE 18b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square







Log-Rank
32.6525
2
<.0001



Wilcoxon
31.1405
2
<.0001



−2Log(LR)
26.9305
2
<.0001










Example 22
Survival for Complication Model 2
Summary of the Number of Censored and Uncensored Values and Test of Equality Over Strata









TABLE 19a







Model: SC2


Summary of the Number of Censored and Uncensored Values


















% Cen-



Stratum
gpsc2
N
Failed
Censored
sored
Median
















1(sc2 <= 13)
1
229
26
203
88.65
32.0


2(13 < sc2 < 16)
2
164
28
136
82.93
31.5


3(sc2 >= 16)
3
70
25
45
64.29
30.5


Total

463
79
384
82.94
















TABLE 19b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square
















Log-Rank
22.3261
2
<.0001



Wilcoxon
17.2221
2
0.0002



−2Log(LR)
18.6671
2
<.0001










Example 23
Survival for Complication Stratum 1
Analysis Across Models









TABLE 20a







Across Models for Stratum 1


Summary of the Number of Censored and Uncensored Values












Stratum
gp1
N
Failed
Censored
% Censored





1
1
190
20
170
89.47


2
2
229
26
203
88.65


Total

419
46
373
89.02
















TABLE 20b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square







Log-Rank
0.0593
1
0.8075



Wilcoxon
0.0332
1
0.8555



−2Log(LR)
0.0492
1
0.8245










Example 24
Survival for Complication Stratum 2
Analysis Across Models









TABLE 21a







Across Models for Stratum 2


Summary of the Number of Censored and Uncensored Values












Stratum
gp2
N
Failed
Censored
% Censored





1
1
176
23
153
86.93


2
2
164
28
136
82.93


Total

340
51
289
85.00
















TABLE 21b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square







Log-Rank
0.8536
1
0.3555



Wilcoxon
1.2619
1
0.2613



−2Log(LR)
0.9108
1
0.3399










Example 25
Survival for Complication Stratum 3
Analysis Across Models









TABLE 22a







Across Models for Stratum 3


Summary of the Number of Censored and Uncensored Values












Stratum
gp3
N
Failed
Censored
% Censored















1
1
97
36
61
62.89


2
2
70
25
45
64.29


Total

167
61
106
63.47
















TABLE 22b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square







Log-Rank
0.0023
1
0.9621



Wilcoxon
0.0271
1
0.8693



−2Log(LR)
0.0008
1
0.9779










Example 26
Survival Analysis for Surgery

For surgery, 75 SNPs were selected throughout the genome-wide survival analyses with the p-value (10−5). Similarly to the complication, 5 Cox regression models were considered. In model 1, the following variables were used: 75 SNPs, gender, age, and disease location. In model 2, the following variables were used: 75 SNPs, gender, age, disease location, ANCA, and antibody quartile score. In model 3, the following variables were used: 75 SNPs, gender, age, disease location, ANCA, antibody quartile score, internal penetrating, and stricture. In model 4, the following variables were used: 75 SNPs, gender, age, disease location, ANCA, and antibody quartile score. In model 5, the following variables were used: 75 SNPs, gender, age, disease location, ANCA, antibody quartile score, internal penetrating, and stricture. For each model, stepwise variable selection. In the first model, 12 out of 75 SNPs, age, and disease location were statistically significant. In the second model: 11 out of 75 SNPs, disease location, and antibody quartile were statistically significant. In the third model, 7 out of 75 SNPs, internal penetrating, and stricture, were statistically significant. In the fourth model, 15 out of 75 SNPs, disease location, and antibody sum were statistically significant. For all 5 models, the survival functions obtained by the Kaplan-Meier (KM) estimator indicated significant differences among subgroups of patients. For all 3 subgroups, the survival functions across the 5 models were statistically indistinguishable, with a significance level of 0.05.









TABLE 23







Survival for Surgery












Variable
Minimum
Median
Maximum
25% Quartile
75% Quartile















ss1
2
5
11
4
6


ss2
3
6
13
5
7.5


ss3
1
3
8
2
4


ss4
7
11
20
10
12









Example 27
Survival for Surgery Model 1
Summary of the Number of Censored and Uncensored Values and Test of Equality Over Strata









TABLE 24a







SS1 Model


Summary of the Number of Censored and Uncensored Values


















%



Stratum
gpss1
N
Failed
Censored
Censored
Median
















1(ss1 >= 4)
1
430
33
397
92.33
33


2(4 < ss1 < 6)
2
53
20
33
62.26
34


3(ss1 >= 6)
3
53
33
20
37.74
26


Total

536
86
450
83.96
















TABLE 24b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square
















Log-Rank
181.4000
2
<.0001



Wilcoxon
130.1560
2
<.0001



−2Log(LR)
99.0692
2
<.0001










Example 28
Survival for Surgery Model 2
Summary of the Number of Censored and Uncensored Values and Test of Equality Over Strata









TABLE 25a







SS2 Model


Summary of the Number of Censored and Uncensored Values


















% Cen-



Stratum
gpss2
N
Failed
Censored
sored
Median
















1(ss2 >= 5)
1
423
29
394
93.14
34


2(5 < ss2 < 7.5)
2
83
37
46
55.42
30


3(ss2 >= 7.5)
3
30
20
10
33.33
24


Total

536
86
450
83.96
















TABLE 25b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square







Log-Rank
198.0272
2
<.0001



Wilcoxon
134.8483
2
<.0001



−2Log(LR)
111.3678
2
<.0001










Example 29
Survival for Surgery Model 3
Summary of the Number of Censored and Uncensored Values and Test of Equality Over Strata









TABLE 26a







SS3 Model


Summary of the Number of Censored and Uncensored Values


















%



Stratum
gpss2
N
Failed
Censored
Censored
Median
















1(ss3 >= 2)
1
346
22
324
93.64
35


2(2 < ss3 < 4)
2
105
23
82
78.10
30


3(ss3 >= 4)
3
85
41
44
51.76
29


Total

536
86
450
83.96
















TABLE 26b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square
















Log-Rank
120.8535
2
<.0001



Wilcoxon
97.2703
2
<.0001



−2Log(LR)
83.8218
2
<.0001










Example 30
Survival for Surgery Model 4
Summary of the Number of Censored and Uncensored Values and Test of Equality Over Strata









TABLE 27a







SS4 Model


Summary of the Number of Censored and Uncensored Values


















% Cen-



Stratum
gpss2
N
Failed
Censored
sored
Median
















1(ss3 >= 10)
1
456
39
417
91.45
33


2(10 < ss3 < 12)
2
38
21
17
44.74
32


3(ss3 >= 12)
3
42
26
16
38.10
24


Total

536
86
450
83.96
















TABLE 27b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square
















Log-Rank
171.1712
2
<.0001



Wilcoxon
138.5943
2
<.0001



−2Log(LR)
93.0443
2
<.0001










Example 31
Survival for Surgery Stratum 1
Analysis Across Models









TABLE 28a







Across Models for Stratum 1


Summary of the Number of Censored and Uncensored Values












Stratum
gp1
N
Failed
Censored
% Censored















1
1
430
33
397
92.33


2
2
423
29
394
93.14


3
3
346
22
324
93.64


4
4
456
39
417
91.45


Total

1655
123
1532
92.57
















TABLE 28b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square







Log-Rank
2.1519
3
0.5415



Wilcoxon
2.2926
3
0.5139



−2Log(LR)
1.9439
3
0.5841










Example 32
Survival for Surgery Stratum 2
Analysis Across Models









TABLE 29a







Across Models for Stratum 2


Summary of the Number of Censored and Uncensored Values












Stratum
gp2
N
Failed
Censored
% Censored















1
1
53
20
33
62.26


2
2
83
37
46
55.42


3
3
143
44
99
69.23


4
4
143
44
99
69.23


Total

422
145
277
65.64
















TABLE 29b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square







Log-Rank
7.7332
3
0.0519



Wilcoxon
2.9542
3
0.3987



−2Log(LR)
5.7950
3
0.1220










Example 33
Survival for Surgery Stratum 3
Analysis Across Models









TABLE 30a







Across Models for Stratum 3


Summary of the Number of Censored and Uncensored Values












Stratum
gp3
N
Failed
Censored
% Censored















1
1
53
33
20
37.74


2
2
30
20
10
33.33


3
3
85
41
44
51.76


4
4
42
26
16
38.10


Total

210
120
90
42.86
















TABLE 30b







Test of Equality over Strata












Test
Chi-Square
DF
Pr > Chi-Square







Log-Rank
7.0961
3
0.0689



Wilcoxon
4.2355
3
0.2371



−2Log(LR)
5.5109
3
0.1380










Various embodiments of the invention are described above in the Detailed Description. While these descriptions directly describe the above embodiments, it is understood that those skilled in the art may conceive modifications and/or variations to the specific embodiments shown and described herein. Any such modifications or variations that fall within the purview of this description are intended to be included therein as well. Unless specifically noted, it is the intention of the inventor that the words and phrases in the specification and claims be given the ordinary and accustomed meanings to those of ordinary skill in the applicable art(s).


The foregoing description of various embodiments of the invention known to the applicant at this time of filing the application has been presented and is intended for the purposes of illustration and description. The present description is not intended to be exhaustive nor limit the invention to the precise form disclosed and many modifications and variations are possible in the light of the above teachings. The embodiments described serve to explain the principles of the invention and its practical application and to enable others skilled in the art to utilize the invention in various embodiments and with various modifications as are suited to the particular use contemplated. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed for carrying out the invention.


While particular embodiments of the present invention have been shown and described, it will be obvious to those skilled in the art that, based upon the teachings herein, changes and modifications may be made without departing from this invention and its broader aspects and, therefore, the appended claims are to encompass within their scope all such changes and modifications as are within the true spirit and scope of this invention. Furthermore, it is to be understood that the invention is solely defined by the appended claims. It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations).


Accordingly, the invention is not limited except as by the appended claims.

Claims
  • 1. A method of prognosing Crohn's disease in an individual, comprising: obtaining a sample from the individual;assaying the sample for the presence or absence of one or more genetic risk variants; andprognosing an aggressive form of Crohn's disease based on the presence of one or more genetic risk variants,wherein the one or more genetic risk variants are selected from the genetic loci of 8q24, 16p11, Bromodomain and WD repeat domain containing 1 (BRWD1) and/or Tumor necrosis factor superfamily member 15 (TNFSF15).
  • 2. The method of claim 1, wherein the presence of each genetic risk variant has an additive effect on rapidity of Crohn's disease progression from a relatively less severe case of Crohn's disease to a relatively more severe case of Crohn's disease.
  • 3. The method of claim 1, wherein the one or more genetic risk variants comprise SEQ. ID. NO.: 1, SEQ. ID. NO.: 2, SEQ. ID. NO.: 3, SEQ. ID. NO.: 4, SEQ. ID. NO.: 5 and/or SEQ. ID. NO.: 6.
  • 4. The method of claim 1, wherein the aggressive form of Crohn's disease is characterized by one or more phenotypes associated with complications.
  • 5. The method of claim 1, wherein the aggressive form of Crohn's disease is characterized by one or more phenotypes associated with conditions requiring surgery.
  • 6. The method of claim 1, wherein the aggressive form of Crohn's Disease is characterized by a rapid progression from a relatively less severe case of Crohn's disease to a relatively more severe case of Crohn's disease.
  • 7. The method of claim 1, wherein the individual has previously been diagnosed with inflammatory bowel disease (IBD).
  • 8. The method of claim 1, wherein the individual is a child 17 years old or younger.
  • 9. The method of claim 1, wherein the aggressive form of Crohn's disease comprises internal penetrating and/or stricture.
  • 10. The method of claim 1, wherein the aggressive form of Crohn's disease comprises a high expression of anti-neutrophil cytoplasmic antibody (ANCA) relative to levels found in a healthy individual.
  • 11. The method of claim 1, wherein the presence of one or more genetic risk variants is determined from an expression product thereof.
  • 12. A method of prognosing Crohn's disease in an individual, comprising: obtaining a sample from the individual;assaying the sample for the presence or absence of one or more genetic risk variants; andprognosing a form of Crohn's disease associated with a complication based on the presence of one or more genetic risk variants,wherein the one or more genetic risk variants is selected from the group consisting of SEQ. ID. NO.: 7, SEQ. ID. NO.: 8, SEQ. ID. NO.: 9, SEQ. ID. NO.: 10, SEQ. ID. NO.: 11, SEQ. ID. NO.: 12, SEQ. ID. NO.: 13, SEQ. ID. NO.: 14, SEQ. ID. NO.: 15, SEQ. ID. NO.: 16, SEQ. ID. NO.: 17, SEQ. ID. NO.: 18, SEQ. ID. NO.: 19, SEQ. ID. NO.: 20, SEQ. ID. NO.: 21, and/or SEQ. ID. NO.: 22.
  • 13. The method of claim 12, wherein the complication comprises internal penetrating and/or stricturing disease.
  • 14. A method of prognosing Crohn's disease in an individual, comprising: obtaining a sample from the individual;assaying the sample for the presence or absence of one or more genetic risk variants; andprognosing a form of Crohn's disease associated with one or more conditions that require a treatment by surgery;wherein the one or more genetic risk variants is selected from the group consisting of SEQ. ID. NO.: 23, SEQ. ID. NO.: 24, SEQ. ID. NO.: 25, SEQ. ID. NO.: 26, SEQ. ID. NO.: 27, SEQ. ID. NO.: 28, SEQ. ID. NO.: 29, SEQ. ID. NO.: 30, SEQ. ID. NO.: 31, SEQ. ID. NO.: 32, SEQ. ID. NO.: 33, SEQ. ID. NO.: 34, SEQ. ID. NO.: 35, SEQ. ID. NO.: 36, SEQ. ID. NO.: 37, SEQ. ID. NO.: 38, SEQ. ID. NO.: 39, SEQ. ID. NO.: 40, SEQ. ID. NO.: 41, SEQ. ID. NO.: 42, SEQ. ID. NO.: 43, SEQ. ID. NO.: 44, SEQ. ID. NO.: 45, SEQ. ID. NO.: 46, SEQ. ID. NO.: 47, SEQ. ID. NO.: 48, SEQ. ID. NO.: 49, SEQ. ID. NO.: 50, SEQ. ID. NO.: 51, and/or SEQ. ID. NO.: 52.
  • 15. The method of claim 14, wherein the treatment by surgery comprises small-bowel resection, colectomy and/or colonic resection.
  • 16. A method of treating Crohn's disease in an individual, comprising: prognosing an aggressive form of Crohn's disease in the individual based on the presence of one or more genetic risk variants; andtreating the individual,wherein the one or more genetic risk variants are selected from the genetic loci of 8q24, 16p11, Bromodomain and WD repeat domain containing 1 (BRWD1) and/or Tumor necrosis factor superfamily member 15 (TNFSF15).
  • 17. The method of claim 16, wherein treating the individual comprises exposing the individual to a treatment that ameliorates the symptoms of Crohn's disease on the basis that the subject tests positive for one or more genetic risk variants.
  • 18. The method of claim 16, wherein treating the individual comprises administering a surgical procedure associated with treating an aggressive form of Crohn's disease.
  • 19. The method of claim 16, wherein treating the individual comprises performing on the individual a small-bowel resection, colectomy and/or colonic resection.
  • 20. The method of claim 16, wherein the presence of each genetic risk variant has an additive effect on rapidity of Crohn's disease progression from a relatively less severe case of Crohn's disease to a relatively more severe case of Crohn's disease.
  • 21. The method of claim 16, wherein the one or more genetic risk variants comprise SEQ. ID. NO.: 1, SEQ. ID. NO.: 2, SEQ. ID. NO.: 3, SEQ. ID. NO.: 4, SEQ. ID. NO.: 5 and/or SEQ. ID. NO.: 6.
  • 22. The method of claim 16, wherein the one or more genetic risk variants comprise SEQ. ID. NO.: 7, SEQ. ID. NO.: 8, SEQ. ID. NO.: 9, SEQ. ID. NO.: 10, SEQ. ID. NO.: 11, SEQ. ID. NO.: 12, SEQ. ID. NO.: 13, SEQ. ID. NO.: 14, SEQ. ID. NO.: 15, SEQ. ID. NO.: 16, SEQ. ID. NO.: 17, SEQ. ID. NO.: 18, SEQ. ID. NO.: 19, SEQ. ID. NO.: 20, SEQ. ID. NO.: 21, and/or SEQ. ID. NO.: 22.
  • 23. The method of claim 16, wherein the one or more genetic risk variants comprise SEQ. ID. NO.: 23, SEQ. ID. NO.: 24, SEQ. ID. NO.: 25, SEQ. ID. NO.: 26, SEQ. ID. NO.: 27, SEQ. ID. NO.: 28, SEQ. ID. NO.: 29, SEQ. ID. NO.: 30, SEQ. ID. NO.: 31, SEQ. ID. NO.: 32, SEQ. ID. NO.: 33, SEQ. ID. NO.: 34, SEQ. ID. NO.: 35, SEQ. ID. NO.: 36, SEQ. ID. NO.: 37, SEQ. ID. NO.: 38, SEQ. ID. NO.: 39, SEQ. ID. NO.: 40, SEQ. ID. NO.: 41, SEQ. ID. NO.: 42, SEQ. ID. NO.: 43, SEQ. ID. NO.: 44, SEQ. ID. NO.: 45, SEQ. ID. NO.: 46, SEQ. ID. NO.: 47, SEQ. ID. NO.: 48, SEQ. ID. NO.: 49, SEQ. ID. NO.: 50, SEQ. ID. NO.: 51, and/or SEQ. ID. NO.: 52.
  • 24. The method of claim 16, wherein the individual is a child 17 years old or younger.
  • 25. A method of diagnosing susceptibility to Crohn's disease in an individual, comprising: obtaining a sample from the individual;assaying the sample for the presence or absence of one or more genetic risk variants; anddiagnosing susceptibility to Crohn's disease in the individual based on the presence of one or more genetic risk variants,wherein the one or more genetic risk variants are located at the genetic loci of 8q24, 16p11, and/or Bromodomain and WD repeat domain containing 1 (BRWD1).
  • 26. The method of claim 25, wherein the one or more genetic risk variants comprise SEQ. ID. NO.: 1, SEQ. ID. NO.: 2, SEQ. ID. NO.: 3, SEQ. ID. NO.: 4, SEQ. ID. NO.: 5 and/or SEQ. ID. NO.: 6.
  • 27. The method of claim 25, wherein the one or more genetic risk variants comprise SEQ. ID. NO.: 7, SEQ. ID. NO.: 8, SEQ. ID. NO.: 9, SEQ. ID. NO.: 10, SEQ. ID. NO.: 11, SEQ. ID. NO.: 12, SEQ. ID. NO.: 13, SEQ. ID. NO.: 14, SEQ. ID. NO.: 15, SEQ. ID. NO.: 16, SEQ. ID. NO.: 17, SEQ. ID. NO.: 18, SEQ. ID. NO.: 19, SEQ. ID. NO.: 20, SEQ. ID. NO.: 21, and/or SEQ. ID. NO.: 22.
  • 28. The method of claim 25, wherein the one or more genetic risk variants comprise SEQ. ID. NO.: 23, SEQ. ID. NO.: 24, SEQ. ID. NO.: 25, SEQ. ID. NO.: 26, SEQ. ID. NO.: 27, SEQ. ID. NO.: 28, SEQ. ID. NO.: 29, SEQ. ID. NO.: 30, SEQ. ID. NO.: 31, SEQ. ID. NO.: 32, SEQ. ID. NO.: 33, SEQ. ID. NO.: 34, SEQ. ID. NO.: 35, SEQ. ID. NO.: 36, SEQ. ID. NO.: 37, SEQ. ID. NO.: 38, SEQ. ID. NO.: 39, SEQ. ID. NO.: 40, SEQ. ID. NO.: 41, SEQ. ID. NO.: 42, SEQ. ID. NO.: 43, SEQ. ID. NO.: 44, SEQ. ID. NO.: 45, SEQ. ID. NO.: 46, SEQ. ID. NO.: 47, SEQ. ID. NO.: 48, SEQ. ID. NO.: 49, SEQ. ID. NO.: 50, SEQ. ID. NO.: 51, and/or SEQ. ID. NO.: 52.
  • 29. The method of claim 25, wherein the individual is a child 17 years old or younger.
PCT Information
Filing Document Filing Date Country Kind 371c Date
PCT/US10/30359 4/8/2010 WO 00 12/15/2011
Provisional Applications (1)
Number Date Country
61167752 Apr 2009 US