METHODS OF IDENTIFYING AUTISM SPECTRUM DISORDER

Information

  • Patent Application
  • 20230313301
  • Publication Number
    20230313301
  • Date Filed
    August 18, 2021
    3 years ago
  • Date Published
    October 05, 2023
    a year ago
Abstract
Described herein are methods of identifying one or more epigenetic modifications in a nucleic acid sequence of a sample of a subject's father to identify a risk of autism spectrum disorder (ASD), to diagnose ASD early in a subject based at least in part of the epigenetic modification identified in the nucleic acid sequence of the sample of the subject's father. Also disclosed herein are methods of treating a subject with autism or ASD.
Description
INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.


BACKGROUND

Autism spectrum disorder (ASD) is a complex neurological disorder involving deficits in communication, social behaviors and stereotypic movements. The prevalence of ASD in 1975 was reported as 1 in 5000 and then in 2009 as 1 in 110. The American Centers for Disease Control and Prevention reported a 1 in 88 prevalence in 2012 and then a 1 in 68 in 2014. Although improved diagnosis and current awareness have played a role in this increase, particularly in the first couple decades (1975-2000), the increase in the last two decades is thought to be due to environmental and molecular factors. This is supported by twin studies and numerous environmental studies. Genetic studies using genome-wide association studies (GWAS) have identified multiple genetic mutations, but they are correlated with a small percentage of the autism patients. Combining genetic mutations and altered epigenetics appear to improve this association. Many specific toxicants and factors have been suggested to be involved, but generally more extensive analysis is required. Environmental factors are now believed to be involved in the etiology of autism, however, the specific environmental factors, molecular processes, and etiology of autism remain to be fully elucidated.


SUMMARY

Disclosed herein are methods, comprising: obtaining a sperm sample from a human male subject; isolating deoxyribonucleic acid (DNA) from the sample; determining a methylation level of a differential DNA methylation region (DMR) comprised in the isolated DNA; and comparing the methylation level of the DMR to a reference level of a corresponding reference DMR. In some embodiments, the comparing can comprise comparing employing a computer comprising a computer processor and computer readable memory comprising computer readable instructions contained thereon In some embodiments, the determining can comprise a methylated DNA immunoprecipitation (MeDIP), a sequencing, a bisulfite treatment, a bisulfite conversion, a deamination of an unmethylated cytosine base, employing an array, or any combination of these. In some embodiments, about 90 to about 1000 distinct DMRs can be detected and compared; and the about 90 to about 1000 distinct DMRs can be selected from the DMRs in Table 3. In some embodiments, about 200 to about 1000 distinct DMRs, about 300 to about 1000 distinct DMRs, about 400 to about 1000 distinct DMRs, about 500 to about 1000 distinct DMRs, about 600 to about 1000 distinct DMRs, about 700 to about 1000 distinct DMRs, about 800 to about 1000, or about 900 to about 1000 distinct DMRs can be detected. In some embodiments, the method can comprise sequencing, and the sequencing can comprise sequencing by synthesis, ion semiconductor sequencing, single molecule real time sequencing, nanopore sequencing, next-generation sequencing, or any combination thereof. In some embodiments, the detected DMRs can comprise DMRs from at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 19, 20, 21, 22, or 23, chromosomes; or the detected DMRs are DMRs are from at least about: 1-23, 2-23, 3-23, 4-23, 5-23, 6-23, 7-23, 8-23, 9-23, 10-23, 11-23, 12-23, 13-23, 14-23, 15-23, 16-23, 17-23, 18-23, 19-23, 20-23, 21-23, 22-23 chromosomes. In some embodiments, the sperm sample can be obtained from a human male subject at least about: 1 day, 2, days, 3 days, 4 days, 5 days, 6 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 11 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years, 18 years, 19 years, 20 years, 21 years, 22 years, 23 years, 24 years, 25 years, 26 years, 27 years, 28 years, 29 years, 30 years, 31 years, 32 years, 33 years, 34 years, 35 years, 36 years, 37 years, 38 years, 39 years, 40 years, 41 years, 42 years, 43 years, 44 years, 45 years, 46 years, 47 years, 48 years, 49 years, 50 years, 51 years, 52 years, 53 years, 54 years, 55 years, 56 years, 57 years, 58 years, 59 years, 60 years, 61 years, 62 years, 63 years, 64 years, 65 years, 66 years, 67 years, 68 years, 69 years, 70 years, 71 years, 72 years, 73 years, 74 years, 75 years, 76 years, 77 years, 78 years, 79 years, 80 years, 81 years, 82 years, 83 years, 84 years, 85 years, 86 years, 87 years, 88 years, 89 years, 90 years, 91 years, 92 years, 93 years, 94 years, 95 years, 96 years, 97 years, 98 years, 99 years, or 100 years of age. In some embodiments, the sperm sample can be obtained from a human male subject of an age ranging from about 15 years to about 80 years of age. In some embodiments, DMRs that are determined and compared, individually, can range from about 100 to about 17000 adjacent nucleotides. In some embodiments, at least a plurality of the DMRs that are determined and compared can comprise a CpG density of less than about 10 CpG per 100 nucleotides. In some embodiments, at least a plurality of the DMRs that are determined and compared can comprise a CpG density of less than about 3 CpG per 100 nucleotides. In some embodiments, at least about: 50, 60, or 70 percent of the DMRs that are determined and compared can be hypermethylated when compared, individually, to individual reference methylation levels of corresponding individual reference DMRs. In some embodiments, at least about: 30, 40, or 50 percent of the DMRs that are determined and compared can be hypomethylated when compared, individually, to individual reference methylation levels of corresponding individual reference DMRs. In some embodiments, a method can further comprise, determining with a computer, a risk of an offspring of the human male subject having a disease or condition. In some embodiments, a method can further comprise, determining with a computer, a severity of autism spectrum disorder of an offspring of the human male subject. In some embodiments, a method can further comprise, determining with a computer, a severity of autism spectrum disorder of the human male subject. In some embodiments, a disease or condition can comprise autism or autism spectrum disorder. In some embodiments, a disease or condition can be selected from the group consisting of a disease related to autism or a neurodegenerative disease, such as Asperger's syndrome. In some embodiments, a method can further comprise performing a further analysis using a computer. In some embodiments, a further analysis can comprise a principle component analysis (PCA), a dendrogram analysis, a machine learning analysis, or any combination thereof. In some embodiments, a further analysis can generate data points, and the data points in the further analysis can be grouped into two spatially distinct categories — a first category which can indicate the subject or an offspring of the subject is at increased risk of having a disease or condition and second category which can indicate the subject or the offspring of the subject is not at increased risk of having the disease or condition.


Also disclosed herein are method, comprising: obtaining a sperm sample from a human male subject; isolating deoxyribonucleic acid (DNA) from the sample; determining a methylation level of a differential DNA methylation region (DMR) comprised in the isolated DNA; and comparing the methylation level of the DMR to a reference level of a corresponding reference DMR. In some embodiments, the comparing can comprise comparing employing a computer comprising a computer processor and computer readable memory comprising computer readable instructions contained thereon. In some embodiments, the determining can comprise a methylated DNA immunoprecipitation (MeDIP), a sequencing, a bisulfite treatment, a bisulfite conversion, a deamination of an unmethylated cytosine base, employing an array, or any combination of these. In some embodiments, a number of determined DMRs can be sufficient to determine, from a process comprising the comparing and employing a computer, whether the human male subject, or an offspring of the human male subject, has or is at increased risk of having autism or autism spectrum disorder, or determine a severity of autism spectrum disorder. In some embodiments, about 90 to about 1000 distinct DMRs can be determined and compared. In some embodiments, about 90 to about 1000 distinct DMRs can be determined and compared, and the about 90 to about 1000 distinct DMRs can be selected from the DMRs in Table 3. In some embodiments, the method can further comprise treating a human male subject or an offspring thereof. In some embodiments, the method can comprise treating the offspring of a human male subject. In some embodiments, treating the offspring can comprise treating at least one cell, treating a human male subject, or treating a sperm cell of the human male subject or a male offspring of the human male subject. In some embodiments, the offspring is less than about 2 years old. In some embodiments, treating can comprise administering an applied behavior analysis, a cognitive behavior therapy, an educational therapy, a joint attention therapy, a nutritional therapy, an occupational therapy, a physical therapy, a social skills training, a speech language therapy, an antipsychotic drug or a salt thereof, risperidone or a salt thereof, aripiprazole or a salt thereof, a selective serotonin re-uptake inhibitor or a salt thereof, citalopram or a salt thereof, escitalopram or a salt thereof, fluoxetine or a salt thereof, fluvoxamine or a salt thereof, paroxetine or a salt thereof, sertraline or a salt thereof, dapoxetine or a salt thereof, indalpine or a salt thereof, zimelidine or a salt thereof, alaproclate or a salt thereof, centpropazine or a salt thereof, femoxetine or a salt thereof, omiloxetine or a salt thereof, panuramine or a salt thereof, seproxetine or a salt thereof, venlafaxine or a salt thereof, clomipramine or a salt thereof, methylphenidate or a salt thereof, mixed amphetamine salts, a psychoactive medication or a salt thereof, a stimulant or a salt thereof, a valproic acid or a salt thereof, phenytoin or a salt thereof, clonazepam or a salt thereof, carbamazepine or a salt thereof, a social skills therapy, speech therapy, supplementing a vitamin or a salt thereof, a mineral or a salt thereof, or both, a restricted diet, a risperidone or a salt thereof, or any combination thereof. In some embodiments, treating can comprise administering a therapeutically effective amount of a pharmaceutical formulation to the subject. In some embodiments, a pharmaceutical formulation can comprise a pharmaceutically acceptable: excipient, diluent, or carrier. In some embodiments, a pharmaceutical formulation can be in unit dose form. In some embodiments, a pharmaceutical formulation can be administered orally, intranasally, by inhalation, sublingually, by injection, by a transdermally, intravenously, subcutaneously, intramuscularly, in an eye, in an ear, in a rectum, intrathecally, or any combination thereof. In some embodiments, a pharmaceutical formulation can be administered in an amount ranging from about 0.0001 to about 100,000 mg of pharmaceutical formulation per kg of subject body weight or offspring of subject body weight. In some embodiments, a method can further comprise transmitting data, a result, or both, via an electronic communication medium.


Also disclosed herein are kits comprising at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13 14, 14, 16, 17, 18, 19, 20, 30, 40, 50, 60,70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 distinct primers or pairs of primers, each distinct primer or pairs of primers comprising a distinct sequence complementary to a distinct DMR sequence present in Table 3; and a container. In some embodiments, the distinct primers or pairs of primers can each further comprise a unique barcode.


Also disclosed herein are kits comprising at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13 14, 14, 16, 17, 18, 19, 20, 30, 40, 50, 60,70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 distinct probes, each distinct probe complementary to a distinct DMR sequence present in Table 3; and a container. In some embodiments, distinct probes can further comprise at least one of: a fluorophore, a chromophore, a barcode, or any combination thereof. In some embodiments, each probe can comprise a unique: fluorophore, chromophore, barcode, or any combination thereof. In some embodiments, the probes or the primers may not be bound to an array or a microarray. In some embodiments, the probes or the primers can be bound to an array or a microarray. In some embodiments, wherein the probes, the primers, or both comprise DNA.


Also disclosed herein are methods, comprising: obtaining a sperm sample from a human male subject; isolating deoxyribonucleic acid (DNA) from the sample; fragmenting the DNA; isolating fragmented methylated DNA. In some embodiments, a method can comprise determining a methylation level of a differential DNA methylation region (DMR) comprised in the isolated fragmented methylated DNA; and comparing the methylation level of the DMR to a reference level of a corresponding reference DMR. In some embodiments, the comparing can comprise comparing employing a computer comprising a computer processor and computer readable memory comprising computer readable instructions contained thereon. In some embodiments, the determining can comprise amplification of the isolated fragmented methylated DNA, sequencing the isolated fragmented methylated DNA, an amplicon thereof, or both, employing an array, or any combination of these. In some embodiments, about 100 to about 1000 distinct DMRs can be detected and compared. In some embodiments, the about 100 to about 1000 distinct DMRs can be selected from the DMRs in Table 3. In some embodiments, isolating the fragmented methylated DNA can comprise methylated DNA immunoprecipitation (MeDIP).


Also disclosed herein are methods, comprising: obtaining a sperm sample from a human male subject; isolating deoxyribonucleic acid (DNA) from the sample; fragmenting the DNA; isolating fragmented methylated DNA. In some embodiments, a method can comprise determining a methylation level of a differential DNA methylation region (DMR) comprised in the isolated fragmented methylated DNA; and comparing the methylation level of the DMR to a reference level of a corresponding reference DMR. In some embodiments, the comparing can comprise comparing employing a computer comprising a computer processor and computer readable memory comprising computer readable instructions contained thereon. In some embodiments, the determining can comprise amplification of the isolated fragmented methylated DNA, sequencing the isolated fragmented methylated DNA, an amplicon thereof, or both, employing an array, or any combination of these. In some embodiments, a number of determined DMRs are sufficient to determine, from a process comprising the comparing and employing a computer, whether the human male subject, or an offspring of the human male subject, has or may be at increased risk of having autism or autism spectrum disorder, or determine a severity of autism spectrum disorder. In some embodiments, isolating the fragmented methylated DNA can comprise methylated DNA immunoprecipitation (MeDIP).





BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein), of which:



FIG. 1 shows DMR identifications. FIG. 1A shows autism case versus control sperm DMR analysis. The number of DMRs found using different p-value cutoff thresholds. The all window column shows all DMRs. The multiple window column shows the number of DMRs containing at least two adjacent significant windows and the number of DMRs with each specific number of significant windows at a p-value threshold of 1e-05. FIG. 1B shows autism case versus control patient DMR analysis. The DMR locations on the individual chromosomes. All DMRs at a p-value threshold of p<1e-05 are shown with the arrowhead (triangles) and clusters of DMRs with the black boxes. FIG. 1C shows DMR CpG density in the autism case versus control patient DMRs. The number of DMRs at different CpG densities. All DMRs at a p-value threshold of p<1e-05. FIG. 1D shows autism case versus control patient DMR lengths in kilobases. All DMRs at a p-value threshold of 1e-05 are shown.



FIG. 2 shows DMR associated genes. FIG. 2A shows DMR associated gene categories. DMRs at a p-value threshold p<1e-05 are shown. FIG. 2B shows DMR associated genes and autism. The paternal offspring autism susceptible DMRs previously shown to correlate with autism and associated neurodegenerative disease are presented. DMR associated genes from the current study were compared to genes associated with autism in the published literature using Pathway Studio software (Elsivier, Inc.). Those that were in common are depicted. FIG. 2C shows autism case versus control DMR PCA. PCA analysis for DMRs at p<1e-05. The first three principal components used and samples color code index indicated. The underlying data is the RPKM read depth for all DMRs.



FIG. 3 shows a permutation analysis. The number of DMR for autism case versus control patient comparison for all permutation analyses. The vertical red line shows the number of DMR found in the original analysis. All DMRs are defined using an edgeR p-value threshold of p<1e-05.





DETAILED DESCRIPTION
Overview

While various embodiments of the disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed.


Early diagnosis and intervention for Autism Spectrum Disorder (ASD) can be significantly beneficial to the development of the ASD individual and lessens the burden on the families and direct caregivers. The identification of a predictive epigenetic biomarker for ASD from the father's sperm may provide physicians and parents with information that can drive earlier identification of ASD and better care. Presence of an ASD methylation signature in paternal sperm cells may encourage parents and physicians to seek early testing and intervention for children in the early years of life.


ASD has increased over ten-fold over the past several decades, and appears predominantly associated with paternal transmission. Although genetics is anticipated to be a component of ASD etiology, environmental epigenetics is now thought to be an important factor. Epigenetic alterations, such as DNA methylation have been correlated with ASD. The current study was designed to identify a DNA methylation signature in sperm as a potential biomarker to identify paternal offspring autism susceptibility. Sperm samples were obtained from fathers, many undergoing in vitro fertilization (IVF) procedures, that have children with or without autism, and the sperm then assessed for alterations in DNA methylation. Differential DNA methylation regions (DMRs) were identified in the sperm of fathers with autistic children in comparison to those without ASD children. The genomic features and genes associated with the DMRs were identified. The potential sperm DMR biomarker/diagnostic was validated with a blinded test set of individuals. Observations demonstrate a highly significant and reproducible set of 800 DMRs in sperm that can act as a biomarker for paternal offspring autism susceptibility. Ancestral or early life paternal exposures that alter germline epigenetics is anticipated to be a molecular component of ASD etiology.


Although there is both paternal and maternal transmission of ASD, the prevalence of paternal transmission can be higher in most populations. One of the main factors proposed to be involved can be paternal age, with an increased percentage of 28% between 40-49 years and nearly 70% when greater than 50 years of age. Increased paternal age has been associated with epigenetic DNA methylation alterations in sperm, with specific genes associated with autism, and with offspring abnormal behavior. Paternal age associated DNA methylation alterations have been shown to impact offspring health and disease susceptibility. In addition to paternal age effects, ancestral and early life exposures to toxicants, abnormal nutrition and stress can also impact sperm DNA methylation to affect disease susceptibility of offspring. The current disclosure can be directed to examine the father's sperm epigenetics (DNA methylation) in families with or without autistic children.


The prevalence of Autism Spectrum Disorder (ASD) in the United States has doubled since 2000 and currently affects 1 in 59 children, with boys being four-times more likely to be diagnosed (1 in 37 boys are diagnosed with ASD). The increase in ASD prevalence can be due to a combination of factors including increased awareness of the condition in schools and medical environments, and better diagnosis guidelines. Additionally, biological factors such as an increased trend of rising paternal age and improved survival of babies born prematurely have been linked to increased ASD rates. Every individual with ASD can be affected differently however some common challenges associated with ASD include non-verbal communication, difficulty with social engagement, trouble with emotional connection or understanding, and restrictive and repetitive behavior patterns. There is no cure for ASD and, especially for the more severe cases, it is considered a life-long disorder that can place significant emotional and financial burdens on families. A higher incidence of depression and decreased quality of family-life has been associated with familial caregivers of ASD children. Additionally, the burden on caregivers associated with ASD children is persistent from childhood to adolescence and often all the way to adulthood (REF). Medical costs of children with ASD are 4-6 times higher than children without ASD, while behavioral therapies cost families ˜$50,000 per child per year. In 2011 the total cost per year for children with ASD in the US was estimated to cost between $11.5 billion and $60.9 billion. These costs are estimated to grow to $461 billion per year by 2025. Methods and platforms described herein include development of an epigenetic test that may be utilized by a rheumatologist to order prior to prescribing a therapeutic, that can predict which TNF inhibitor a patient is most likely to respond to—and thus may eliminate a trial and error approach for treatment and may ease the debilitating symptoms of RA sufferers. Methods and platforms described herein may use epigenetics as a tool for diagnosis of chronic diseases (such as autoimmune diseases) and prediction of therapeutic response.


Recent research has shown dramatic benefits for early diagnosis and treatment of ASD. Early behavior, communication, and social therapies can greatly improve the associated skills leading to reduced care needs and financial burden through adolescence and adulthood. The skills taught from Early and Intensive Behavioral Intervention (EIBI) have been shown to last for more than a decade and lead to significantly decreased symptoms of ASD. To maximize these benefits intervention needs to start as early as possible, before a child's brain has fully developed. ASD can be diagnosed as early as 12-18 months, and EIBI at these ages has been shown to have the most dramatic benefits. Unfortunately, the average age of diagnosis in the US can be 4-5 years old, after the child's brain has already significantly developed and created permanent connections. Better awareness of ASD risk factors and symptoms can be important for early intervention and improving long-term outcomes for language, daily living skills, social behavior, and cognition.


The cause of ASD is unknown, however, research has identified both genetic and environmental factors that are associated with increased occurrences of ASD. Increased risk has been linked to families with a history of ASD, increased paternal age, prenatal chemical exposures, and preterm birth. Developing a reliable test for ASD prediction in offspring can both help uncover the causes and empower parents to seek earlier diagnosis and treatment.


With advancing molecular diagnostic tools, the identification of novel genetic and epigenetic markers for ASD is becoming a realistic option. Significant funding has driven research on the genetic basis of, and diagnostics for, ASD. Through large-scale genome-wide-analyses several genetic variants have been shown to substantially contribute to the susceptibility of ASD, however these fall short of being predictive for most of the population. Disclosed herein are methods of detecting and treating autism, ASD and similar disorders.


Definitions

Unless otherwise indicated, open terms for example “contain,” “containing,” “include,” “including,” and the like mean comprising.


The singular forms “a”, “an”, and “the” are used herein to include plural references unless the context clearly dictates otherwise. Accordingly, unless the contrary is indicated, the numerical parameters set forth in this application are approximations that may vary depending upon the desired properties sought to be obtained by the present disclosure.


The term “about” or “approximately” as used herein when referring to a measurable value such as an amount or concentration and the like, is meant to encompass variations of 20%, 10%, 5%, 1%, 0.5%, or even 0.1% of the specified amount. For example, “about” can mean plus or minus 10%, per the practice in the art. Alternatively, “about” can mean a range of plus or minus 20%, plus or minus 10%, plus or minus 5%, or plus or minus 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, within 5-fold, or within 2-fold, of a value. Where particular values can be described in the application and claims, unless otherwise stated the term “about” meaning within an acceptable error range for the particular value should be assumed. Also, where ranges, subranges, or both, of values can be provided, the ranges or subranges can include the endpoints of the ranges or subranges. The terms “substantially”, “substantially no”, “substantially not”, “substantially free”, and “approximately” can be used when describing a magnitude, a position or both to indicate that the value described can be within a reasonable expected range of values. For example, a numeric value can have a value that can be +/−0.1% of the stated value (or range of values), +/−1% of the stated value (or range of values), +/−2% of the stated value (or range of values), +/−5% of the stated value (or range of values), +/−10% of the stated value (or range of values), etc. Any numerical range recited herein can be intended to include all sub-ranges subsumed therein.


As used herein, the terms “treating, ” “ treatment” and the like are used herein to mean obtaining a desired pharmacologic and/or physiologic effect. The effect may be prophylactic in terms of completely or partially preventing a disease, disorder, or condition or sign or symptom thereof, and/or may be therapeutic in terms of a partial or complete cure for a disease or condition. In some instances, a disease or condition can comprise Autism Spectrum Disorder, Autism, or any combination thereof. In some instances, an individual can be treated therapeutically, such therapeutic treatment can cause a partial or a complete cure for the disease or disorder. In some cases, therapeutic treatment can comprise a pharmaceutical composition disclosed herein, a behavioral therapy (e.g. psychological therapy), or a combination of both. In some instances, a treatment can reverse an adverse effect attributable to the disease or disorder. In some cases, treating can comprise treating the offspring of a male subject. In some instances, treating can comprise treating at least one cell, treating a human male subject, or treating a sperm cell of the human male subject or a male offspring of the human male subject. In some cases, treating can comprise treating an offspring that is less than about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 years of age. In some cases, treating can comprise treating an offspring that is more than about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 years of age. In some cases, the treatment can stabilize the disease or disorder. In some cases, the treatment can delay progression of the disease or disorder. In some instances, the treatment can cause regression of the disease or disorder. In some cases, a treatment's effect can be measured. In some cases, measurements can be compared before and after administration of the composition. For example, a subject can have Autism Diagnostic Observation Schedule (ADOS) and its Severity Score recorded before therapy and compared to the ADOS after treatment to show improvement in ASD. In some instances, measurements can be compared to a standard.


An “effective amount” can be an amount of a therapeutic agent sufficient to achieve an intended purpose. An effective amount of a composition to treat or ameliorate a disease (e.g. ASD) can be an amount of the composition sufficient to reduce or remove the symptoms of the disorder.


Unless otherwise indicated, some instances herein contemplate numerical ranges. When a numerical range is provided, unless otherwise indicated, the range includes the range endpoints. Unless otherwise indicated, numerical ranges include all values and subranges therein as if explicitly written out. Unless otherwise indicated, any numerical ranges and/or values herein, following or not following the term “about,” can be at 85-115% (i.e., plus or minus 15%) of the numerical ranges and/or values.


Epigenetics, as used herein, generally refers to “molecular factors and processes around DNA that regulate genome activity independent of DNA sequence and are mitotically stable.” The molecular factors and processes currently known are DNA methylation, histone modifications, chromatin structural changes, non-coding RNA, and RNA methylation. When the epigenetic alterations become programmed in the germ cells (sperm or egg), they have the ability to promote the epigenetic transgenerational inheritance of disease and phenotypic alterations. Environmental factors that promote these early life epigenetic alterations have the ability to promote epigenetic inheritance to subsequent generations, and dramatically increase disease susceptibility and prevalence. The current study was designed to use a genome-wide approach and develop a potential paternal sperm biomarker for offspring with autism susceptibility.


The term “subject,” as used herein, generally refers to any individual that has, may have, or may be suspected of having a disease condition (e.g., Autism Spectrum Disorder (ASD)). The subject may be an animal. The animal can be a mammal, such as a human, non-human primate, a rodent such as a mouse or rat, a dog, a cat, pig, sheep, or rabbit. Animals can be fish, reptiles, or others Animals can be neonatal, infant, adolescent, or adult animals. The subject may be a living organism. The subject may be a human. Humans can be greater than or equal to 1, 2, 5, 10, 20, 30, 40, 50, 60, 65, 70, 75, 80 or more years of age. A human may be from about 18 to about 90 years of age. A human may be from about 18 to about 30 years of age. A human may be from about 30 to about 50 years of age. A human may be from about 50 to about 90 years of age. The subject may have one or more risk factors of a condition and be asymptomatic. The subject may be asymptomatic of a condition. The subject may have one or more risk factors for a condition. The subject may be symptomatic for a condition. The subject may be symptomatic for a condition and have one or more risk factors of the condition. The subject may have or be suspected of having a disease, such as ASD. The subject may be a patient being treated for a disease, such as ASD. The subject may be predisposed to a risk of developing a disease such as ASD. The subject may be in remission from a disease, such as ASD. The subject may not have ASD. The subject may be healthy.


The term “sample,” as used herein, generally refers to any sample of a subject (such as a blood sample, a plasma sample, a urine sample, a sperm sample, a vaginal swab, a sweat sample, a saliva sample, a biological fluid sample, a cell-free sample, a tissue sample, a buccal swab, or a nasal swab). Genomic data may be obtained from the sample. A sample may be a sample suspected or confirmed of having a disease or condition such as ASD. A sample may be a sample removed from a subject, such as a tissue brushing, a swabbing, a tissue biopsy, an excised tissue, a fine needle aspirate, a tissue washing, a cytology specimen, a bronchoscopy, or any combination thereof.


The term “increased risk” in the context of developing or having ASD, as used herein, generally refers to an increased risk or probability associated with the occurrence of ASD in a subject. An increased risk of developing ASD can include a first occurrence of the condition in a subject or can include subsequent occurrences, such as a second, third, fourth, or subsequent occurrence. An increased risk of developing ASD can include a) a risk of developing the condition for a first time, b) a risk of developing the condition in the future, c) a risk of being predisposed to developing the condition in the subject's lifetime, or d) a risk of being predisposed to developing the condition as an infant, adolescent, or adult.


As used herein, a “biosimilar” or a “biosimilar product” may refer to a biological product that is licensed based on a showing that it is substantially similar to an FDA-approved biological product, known as a reference product, and has no clinically meaningful differences in terms of safety and effectiveness from the reference product. Only minor differences in clinically inactive components may be allowable in biosimilar products. A “biosimilar” of an approved reference product/biological drug refers to a biologic product that is similar to the reference product based upon data derived from (a) analytical studies that demonstrate that the biological product is highly similar to the reference product notwithstanding minor differences in clinically inactive components; (b) animal studies (including the assessment of toxicity); and/or (c) a clinical study or studies (including the assessment of immunogenicity and pharmacokinetics or pharmacodynamics) that are sufficient to demonstrate safety, purity, and potency in one or more appropriate conditions of use for which the reference product is licensed and intended to be used and for which licensure is sought for the biological product. In some embodiments, the biosimilar biological product and reference product utilize the same mechanism or mechanisms of action for the condition or conditions of use prescribed, recommended, or suggested in the proposed labeling, but only to the extent the mechanism or mechanisms of action are known for the reference product. In some embodiments, the condition or conditions of use prescribed, recommended, or suggested in the labeling proposed for the biological product have been previously approved for the reference product. In some embodiments, the route of administration, the dosage form, and/or the strength of the biological product are the same as those of the reference product. In some embodiments, the facility in which the biological product is manufactured, processed, packed, or held may meet standards designed to assure that the biological product continues to be safe, pure, and potent. The reference product may be approved in at least one of the U.S., Europe, or Japan. In some embodiments, a response rate of human subjects administered the biosimilar product can be 50%-150% of the response rate of human subjects administered the reference product. For example, the response rate of human subjects administered the biosimilar product can be 50%-100%, 50%-110%, 50%-120%, 50%-130%, 50%-140%, 50%-150%, 60%-100%, 60%-110%, 60%-120%, 60%-130%, 60%-140%, 60%-150%, 70%-100%, 70%-110%, 70%-120%, 70%-130%, 70%-140%, 70%-150%, 80%-100%, 80%-110%, 80%-120%, 80%-130%, 80%-140%, 80%-150%, 90%-100%, 90%-110%, 90%-120%, 90%-130%, 90%-140%, 90%-150%, 100%-110%, 100%-120%, 100%-130%, 100%-140%, 100%-150%, 110%-120%, 110%-130%, 110%-140%, 110%-150%, 120%-130%, 120%-140%, 120%-150%, 130%-140%, 130%-150%, or 140%-150% of the response rate of human subjects administered the reference product. In some embodiments, a biosimilar product and a reference product can utilize the same mechanism or mechanisms of action for the condition or conditions of use prescribed, recommended, or suggested in the proposed labeling, but only to extent the mechanism or mechanisms are known for the reference product. To obtain approval for biosimilar drugs, studies and data of structure, function, animal toxicity, pharmacokinetics, pharmacodynamics, immunogenicity, and clinical safety and efficacy may be needed. A biosimilar may also be known as a follow-on biologic or a subsequent entry biologic. In some embodiments, a biosimilar product may be substantially similar to the reference product notwithstanding minor different in clinically inactive components.


As used herein, a “interchangeable biological product” may refer to a biosimilar of an FDA-approved reference product and may meet additional standards for interchangeability. In some embodiments, an interchangeable biological product can, for example, produce the same clinical result as the reference product in any given subject. In some embodiments, an interchangeable product may contain the same amount of the same active ingredients, may possess comparable pharmacokinetic properties, may have the same clinically significant characteristics, and may be administered in the same way as the reference compound. In some embodiments, an interchangeable product can be a biosimilar product that meets additional standards for interchangeability. In some embodiments, an interchangeable product can produce the same clinical result as a reference product in all the reference product's licensed conditions of use. In some embodiments, an interchangeable product can be substituted for the reference product by a pharmacist without the intervention of the health care provider who prescribed the reference product. In some embodiments, when administered more than once to an individual, the risk in terms of safety or diminished efficacy of alternating or switching between use of the biological product and the reference product is not greater than the risk of using the reference product without such alternation or switch. In some embodiments, an interchangeable product can be a regulatory agency approved product. In some embodiments, a response rate of human subjects administered the interchangeable product can be 80%-120% of the response rate of human subjects administered the reference product. For example, the response rate of human subjects administered the interchangeable product can be 80%-100%, 80%-110%, 80%-120%, 90%-100%, 90%-110%, 90%-120%, 100%-110%, 100%-120%, or 110%-120 of the response rate of human subjects administered the reference product.


The term “sequencing” as used herein, may comprise high-throughput sequencing, next-gen sequencing, Maxam-Gilbert sequencing, massively parallel signature sequencing, Polony sequencing, 454 pyrosequencing, pH sequencing, Sanger sequencing (chain termination), Illumina sequencing, SOLiD sequencing, Ion Torrent semiconductor sequencing, DNA nanoball sequencing, Heliscope single molecule sequencing, single molecule real time (SMRT) sequencing, nanopore sequencing, shot gun sequencing, RNA sequencing, Enigma sequencing, sequencing-by-hybridization, sequencing by synthesis, sequencing-by-ligation, or any combination thereof. The sequencing output data may be subject to quality controls, including filtering for quality (e.g., confidence) of base reads. Exemplary sequencing systems include 454 pyrosequencing (454 Life Sciences), Illumina (Solexa) sequencing, SOLiD (Applied Biosystems), and Ion Torrent Systems' pH sequencing system. In some cases, a nucleic acid of a sample may be sequenced without an associated label or tag. In some cases, a nucleic acid of a sample may be sequenced, the nucleic acid of which may have a label or tag associated with it.


Methods of Detection and Treatment

Methods described herein can be used to detect a neurodegenerative disease, autism or autism spectrum disorder. In some embodiments, a method can comprise obtaining a sperm sample from a human male subject; isolating deoxyribonucleic acid (DNA) from the sample; determining a methylation level of a differential DNA methylation region (DMR) comprised in the isolated DNA; and comparing the methylation level of the DMR to a reference level of a corresponding reference DMR. In some instances, DNA can be fragmented. In some instances, a methylation level can comprise hypomethylation, hypermethylation, or no change in methylation. In some cases, comparing can comprise comparing employing a computer comprising a computer processor and computer readable memory comprising computer readable instructions contained thereon. In some cases, the determining can comprise a methylated DNA immunoprecipitation (MeDIP), a sequencing, a bisulfite treatment, a bisulfite conversion, a deamination of an unmethylated cytosine base, employing an array, or any combination of these. In some instances, MeDIP can be used to isolate methylated DNA from a sample. In some cases, determining can comprise amplification of an isolated fragmented methylated DNA, sequencing the isolated fragmented methylated DNA, an amplicon thereof, or both, employing an array (e.g. a microarray), or any combination of these. In some cases, a number of determined DMRs can be sufficient to determine, from a process comprising comparing and employing a computer, whether the human male subject, or an offspring of the human male subject, has or is at increased risk of having autism or autism spectrum disorder, or determine a severity of autism spectrum disorder. In some cases, about 90 to about 1000 distinct DMRs are detected and compared. In the distinct DMRs can be selected from the DMRs in Table 3. In some cases, about 200 to about 1000 distinct DMRs, about 300 to about 1000 distinct DMRs, about 400 to about 1000 distinct DMRs, about 500 to about 1000 distinct DMRs, about 600 to about 1000 distinct DMRs, about 700 to about 1000 distinct DMRs, about 800 to about 1000, or about 900 to about 1000 distinct DMRs can be detected. In some cases, more than 1000 distinct DMRs can be detected, for example about: 1500, 2000, 2500, 3000, 3500, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500 or more distinct DMRs can be detected. In some cases, about: 1000 to about 2000, 2000 to about 3000, 3000 to about 5000, 4000 to about 7000, 5000 to about 7500, 6000 to about 8500, or 8500 to about 10000 distinct DMRs can be detected. In some cases, less than about 200 distinct DMRs can be detected for example about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200 distinct DMRs can be detected. In some cases, about: 1 to about 10, 10 to about 50, 25 to about 75, 40 to about 100, 80 to about 140, 120 to about 180, or 140 to about 200 distinct DMRs can be detected.


In some embodiments, the detected DMRs can comprise DMRs from at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 19, 20, 21, 22, or 23, chromosomes. In some cases, the detected DMRs can be DMRs are from at least about: 1-23, 2-23, 3-23, 4-23, 5-23, 6-23, 7-23, 8-23, 9-23, 10-23, 11-23, 12-23, 13-23, 14-23, 15-23, 16-23, 17-23, 18-23, 19-23, 20-23, 21-23, 22-23 chromosomes. In some cases, the detected DMRs can be detected from any part of a genome. In some cases, the detected DMRs can be from a specific part of the genome, for example a specific chromosome. In some cases, the DMRs that are determined and compared, individually, range from about: 10 to about 1000, 25 to about 1500, 50 to about 500, 1000 to about 2500, 100 to about 17000, 2500 to about 7500, 5000 to about 20000, 7500 to about 15000 or 10000 to about 25000 adjacent nucleotides. In some embodiments, at least a plurality of the DMRs that can be determined and compared comprise a CpG density of less than about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 CpG per 100 nucleotides. In some embodiments, at least a plurality of the DMRs that can be determined and compared comprise a CpG density of more than about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 CpG per 100 nucleotides.


In some embodiments about: 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, or 85 percent of the DMRs that are determined and compared can be hypermethylated when compared, individually, to individual reference methylation levels of corresponding individual reference DMRs. In some embodiments about: 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, or 85 percent of the DMRs that are determined and compared can be hypomethylated when compared, individually, to individual reference methylation levels of corresponding individual reference DMRs.


In some embodiments, a method can comprise, determining with a computer, a risk of an offspring of a human male subject having a disease or condition. In some cases, a disease or condition can comprise autism or autism spectrum disorder. In some cases, a disease or condition can be a neurodegenerative disease such as Asperger's syndrome or any disease or condition related to autism or autism spectrum disorder. In some cases, a method can comprise determining with a computer, a severity of autism spectrum disorder of an offspring of a human male subject. In some cases, a method can comprise using a computer for further analysis. In some cases, further analysis can comprise a principle component analysis (PCA), a dendrogram analysis, a machine learning analysis, or any combination thereof. In some cases, further analysis can generate data points, and the data points can be grouped into two spatially distinct categories—a first category which indicates the subject or an offspring of the subject is at increased risk of having a disease or condition and second category which indicates the subject or the offspring of the subject is not at increased risk of having a disease or condition. In some cases, a method can comprise transmitting data, a result or both via an electronic communication medium.


In some embodiments, a cell (e.g. a sperm sample) can be obtained from a subject. In some cases, a subject can be a human male or a human female subject. In some cases, a cell can be a stem cell, a cartilage cell, a bone cell, a blood cell, a muscle cell, a fat cell, a skin cell, a nerve cell, an endothelial cell, an epithelial cell, a sex cell, a pancreatic cell, a cancer cell, or any combination thereof. In some cases, a cell can be a sperm cell. In some cases, a cell sample can be obtained from a subject at least about: 1 day, 2, days, 3 days, 4 days, 5 days, 6 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 11 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years, 18 years, 19 years, 20 years, 21 years, 22 years, 23 years, 24 years, 25 years, 26 years, 27 years, 28 years, 29 years, 30 years, 31 years, 32 years, 33 years, 34 years, 35 years, 36 years, 37 years, 38 years, 39 years, 40 years, 41 years, 42 years, 43 years, 44 years, 45 years, 46 years, 47 years, 48 years, 49 years, 50 years, 51 years, 52 years, 53 years, 54 years, 55 years, 56 years, 57 years, 58 years, 59 years, 60 years, 61 years, 62 years, 63 years, 64 years, 65 years, 66 years, 67 years, 68 years, 69 years, 70 years, 71 years, 72 years, 73 years, 74 years, 75 years, 76 years, 77 years, 78 years, 79 years, 80 years, 81 years, 82 years, 83 years, 84 years, 85 years, 86 years, 87 years, 88 years, 89 years, 90 years, 91 years, 92 years, 93 years, 94 years, 95 years, 96 years, 97 years, 98 years, 99 years, or 100 years of age. In some cases, a sample can be obtained from a subject who can be about: 1 day to about 1 week old, 1 week to about 5 weeks old, 5 weeks to about 12 months old, 1 year to about 6 years old, 6 years to about 100 years old, 6 years to about 12 years old, 12 years to about 60 years old, 15 years to about 80 years old, 20 years to about 70 years old, or 30 years to about 120 years old.


An epigenetic modification may comprise a 5-methylated base, such as a 5-methylated cytosine (5-mC). An epigenetic modification may comprise a 5-hydroxymethylated base, such as a 5-hydroxymethylated cytosine (5-hmC). An epigenetic modification may comprise a 5-formylated base, such as a 5-formylated cytosine (5-fC). An epigenetic modification may comprise a 5-carboxylated base or a salt thereof, such as a 5-carboxylated cytosine (5-caC). A nucleic acid sequence may comprise an epigenetic modification. A nucleic acid sequence may comprise a plurality of epigenetic modifications. A nucleic acid sequence may comprise an epigenetic modification positioned within a CG site, a CpG island, a CpG desert (i.e., a nucleotide sequence region with lower CpG density) or a combination thereof. A nucleic acid sequence may comprise different epigenetic modifications, such as a methylated base, a hydroxymethylated base, a formylated base, a carboxylic acid containing base or a salt thereof, a plurality of any of these, or any combination thereof.


The use of a sperm epigenetic biomarkers for paternal offspring autism susceptibility could be used in an assisted reproduction setting. Although genetic tests are common in assisted reproduction and preimplantation diagnostics, epigenetic analysis may be less common. Sperm DNA methylation diagnostics have been proposed for use in assisted reproduction. The availability of a sperm DNA methylation biomarker for offspring autism susceptibility would allow improved clinical management and early treatment options to be considered. A genome-wide analysis of DNA methylation alterations in sperm from fathers with or without autistic children was used to identify a potential sperm epigenetic biomarker for paternal offspring autism susceptibility.



FIG. 1B shows data linking paternal-sperm DNA methylation to ASD risk in offspring. In a cohort of fathers with offspring diagnosed with ASD and a matching control group of fathers with healthy offspring, a consistent and unique DNA methylation pattern was found at 223 locations (p=1e-06) throughout the genome.


Referring to FIG. 2C, it shows control and study participant/subjects methylation data separated and clustered into their own cohorts in an unsupervised statistical analysis, PCA plot, showing evidence of a unique methylation pattern between groups. Further, FIG. 2A outlines the gene categories that had differential methylation patterns in the study samples versus the control samples where changes in DNA methylation significantly change in genes associated with transcription, signaling and metabolism.


In some embodiments, a method can comprise treating a disease or condition. In some cases, a method can comprise treating a male subject or the offspring of the male subject thereof. In some cases, the method can comprise treating at least one cell such as a sperm cell. In some cases, the method can comprise treating a human male subject or the offspring of the human male subject. In some cases, treating can comprise administering a therapy. In some cases, a therapy can comprise a applied behavior analysis, a cognitive behavior therapy, an educational therapy, a joint attention therapy, a nutritional therapy, an occupational therapy, a physical therapy, a social skills training, a social skills therapy, speech therapy, a speech language therapy, or any combination thereof. In some cases, treating can comprise administering an antipsychotic drug or a salt thereof, risperidone or a salt thereof, aripiprazole or a salt thereof, a selective serotonin re-uptake inhibitor or a salt thereof, citalopram or a salt thereof, escitalopram or a salt thereof, fluoxetine or a salt thereof, fluvoxamine or a salt thereof, paroxetine or a salt thereof, sertraline or a salt thereof, dapoxetine or a salt thereof, indalpine or a salt thereof, zimelidine or a salt thereof, alaproclate or a salt thereof, centpropazine or a salt thereof, femoxetine or a salt thereof, omiloxetine or a salt thereof, panuramine or a salt thereof, seproxetine or a salt thereof, venlafaxine or a salt thereof, clomipramine or a salt thereof, methylphenidate or a salt thereof, mixed amphetamine salts, a psychoactive medication or a salt thereof, a stimulant or a salt thereof, a valproic acid or a salt thereof, phenytoin or a salt thereof, clonazepam or a salt thereof, carbamazepine or a salt thereof, risperidone or a salt thereof, an attention-deficit/hyperactivity disorder medication, an amphetamine mixed salts or any combination thereof. In some cases, treating can comprise administering clozapine or a salt thereof, haloperidol or a salt thereof, oxytocin or a salt thereof, secretin or a salt thereof, bumetanide or a salt thereof, memantine or a salt thereof, rivastigmine or a salt thereof, mirtazapine or a salt thereof, melatonin or a salt thereof. In some cases, treatment can comprise supplementing a vitamin or a salt thereof, a mineral or a salt thereof, or both, a restricted diet, or any combination thereof. In some cases, treating can comprise administering a therapeutically effective amount of a pharmaceutical formulation (e.g. pharmaceutical composition) to a subject. In some cases, a pharmaceutical formulation can be administered in unit dose form. In some case a pharmaceutical formulation can be administered orally, intranasally, by inhalation, sublingually, by injection, by a transdermally, intravenously, subcutaneously, intramuscularly, in an eye, in an ear, in a rectum, intrathecally, or any combination thereof.


In some embodiments, a pharmaceutical formulation can be administered in an amount ranging from about: 0.0001 mg to about 100,000 mg, 0.001 mg to about 10,000 mg, 0.01 mg to about 1,000 mg, 0.1 mg to about 100 mg, or about 1 mg to about 10 mg of pharmaceutical formulation per kg of subject body weight or offspring of subject body weight.


In some embodiments, compositions disclosed herein can be in unit dose forms or multiple dose forms. For example, a pharmaceutical composition described herein can be in unit dose form. Unit dose forms, as used herein, refer to physically discrete units suitable for administration to human or non-human subjects (e.g. pets) and packaged individually. Each unit dose can contain a predetermined quantity of an active ingredient(s) that may be sufficient to produce the desired therapeutic effect in association with pharmaceutical carriers, diluents, excipients or any combination thereof. Examples of unit dose forms can include ampules, syringes, and individually packaged tablets and capsules.


In some embodiments, a composition disclosed herein can be formulated as a pharmaceutical composition. In some cases, a composition can comprise an excipient, a diluent, a carrier or any combination thereof. In some cases, the compositions can be made by mixing a composition described herein, and a pharmaceutically acceptable excipient. An excipient can be an excipient described in the Handbook of Pharmaceutical Excipients, American Pharmaceutical Association (1986).


Non-limiting examples of suitable excipients can include a buffering agent, a preservative, a stabilizer, a binder, a compaction agent, a lubricant, a chelator, a dispersion enhancer, a disintegration agent, a flavoring agent, a sweetener, a coloring agent or any combination thereof. In some instances, the excipient comprising one or more of cellulose, disodium hydrogen phosphate, hydroxypropyl cellulose, hypromellose, lactose, mannitol, or sodium lauryl sulfate. In some instances, the compositions further comprise glyceryl monostearate 40-50, hydroxypropyl cellulose, hypromellose, magnesium stearate, methacrylic acid copolymer type C, polysorbate 80, sugar spheres, talc, or triethyl citrate. In some instances, a composition can further comprise carnauba wax, crospovidone, diacetylated monoglycerides, ethylcellulose, hydroxypropyl cellulose, hypromellose phthalate, magnesium stearate, mannitol, sodium hydroxide, sodium stearyl fumarate, talc, titanium dioxide, or yellow ferric oxide. In some instances, a composition can further comprise calcium stearate, crospovidone, hydroxypropyl methylcellulose, iron oxide, mannitol, methacrylic acid copolymer, polysorbate 80, povidone, propylene glycol, sodium carbonate, sodium lauryl sulfate, titanium dioxide, and triethyl citrate. Examples of carriers for the composition include any degradable, partially degradable or non-degradable and generally biocompatible polymer, e.g., polystirex, polypropylene, polyethylene, polacrilex, poly-lactic acid (PLA), polyglycolic acid (PGA) and/or poly-lactic polyglycolic acid (PGLA), e.g., in the form or a liquid, matrix, or bead. In some instances, a binder can comprise starches, pregelatinized starches, gelatin, polyvinylpyrolidone, cellulose, methylcellulose, sodium carboxymethylcellulose, ethylcellulose, polyacrylamides, polyvinyloxoazolidone, polyvinylalcohols, C12-C18 fatty acid alcohol, polyethylene glycol, polyols, saccharides, oligosaccharides or any combination thereof.


In some embodiments, a pharmaceutical composition can comprise a diluent. Non-limiting examples of diluents can include water, glycerol, methanol, ethanol, and other similar biocompatible diluents. In some cases, a diluent can be an aqueous acid such as acetic acid, citric acid, maleic acid, hydrochloric acid, phosphoric acid, nitric acid, sulfuric acid, or similar In other cases, a diluent can be selected from a group comprising alkaline metal carbonates such as calcium carbonate; alkaline metal phosphates such as calcium phosphate; alkaline metal sulphates such as calcium sulphate; cellulose derivatives such as cellulose, microcrystalline cellulose, cellulose acetate; magnesium oxide, dextrin, fructose, dextrose, glyceryl palmitostearate, lactitol, choline, lactose, maltose, mannitol, simethicone, sorbitol, starch, pregelatinized starch, talc, xylitol and/or anhydrates, hydrates and/or pharmaceutically acceptable derivatives thereof or combinations thereof.


In some embodiments, a salt can include, but are not limited to, metal salts such as sodium salt, potassium salt, cesium salt and the like; alkaline earth metals such as calcium salt, magnesium salt and the like; organic amine salts such as triethylamine salt, pyridine salt, picoline salt, ethanolamine salt, triethanolamine salt, dicyclohexylamine salt, N, N′-dibenzylethylenediamine salt and the like; inorganic acid salts such as hydrochloride, hydrobromide, phosphate, sulphate and the like; organic acid salts such as citrate, lactate, tartrate, maleate, fumarate, mandelate, acetate, dichloroacetate, trifluoroacetate, oxalate, formate and the like; sulfonates such as methanesulfonate, benzenesulfonate, p-toluenesulfonate and the like; and amino acid salts such as arginate, asparginate, glutamate and the like. In some cases, a salt can comprise a pharmaceutically acceptable salt. In some instances, a salt of a polypeptide or derivative thereof or a compound can be a Zwitterionic salt


Administration disclosed herein to a subject in need of treatment can be achieved by, for example and not by way of limitation, oral administration, topical administration, intravenous administration, inhalation administration, or any combination thereof. In some cases, delivery can include injection, catheterization, gastrostomy tube administration, intraosseous administration, ocular administration, otic administration, transdermal administration, oral administration, rectal administration, nasal administration, intravaginal administration, intracavernous administration, transurethral administration, sublingual administration, or a combination thereof. Delivery can include direct application to the affect tissue or region of the body. Delivery can include a parenchymal injection, an intra-thecal injection, an intra-ventricular injection, or an intra-cisternal injection. A composition provided herein can be administered by any method. A method of administration can be by intraarterial injection, intracisternal injection, intramuscular injection, intraparenchymal injection, intraperitoneal injection, intraspinal injection, intrathecal injection, intravenous injection, intraventricular injection, stereotactic injection, subcutaneous injection, epidural, or any combination thereof. Delivery can include parenteral administration (including intravenous, subcutaneous, intrathecal, intraperitoneal, intramuscular, intravascular or infusion administration). In some embodiments, delivery can comprise a nanoparticle, a liposome, an exosome, an extracellular vesicle, an implant, or a combination thereof. In some cases, delivery can be from a device. In some instances, delivery can be administered by a pump, an infusion pump or a combination thereof. In some cases, delivery can be by an enema, an eye drop, a nasal spray, an ear drop, or any combination thereof.


In some embodiments, a healthcare provider can administer a composition herein to a subject in need thereof. In some cases, a healthcare provider or the subject can administer the method of detecting a DMR.


Administration of a composition or therapy disclosed herein can be performed for a duration of at least about at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 300, 400, 500, 600, 700, 800, 900, 1000 days consecutive or nonconsecutive days. In some cases, the composition or therapy can be administered for life. In some cases, administration of the composition or therapy described herein can be from about 1 to about 30 days, from about 1 to about 60 days, from about 1 to about 90 days, from about 1 to about 300 days, from about 1 to about 3000 days, from about 30 day to about 90 days, from about 60 days to about 900 days, from about 30 days to about 900 days, or from about 90 days to about 1500 days. In some cases, administration of the composition described herein can be from about: 1 week to about 5 weeks, 1 month to about 12 months, 1 year to about 3 years, 2 years to about 8 years, 3 years to about 10 years, 10 years to about 50 years, 15 years to about 40 years, 25 years to about 100 years, 30 years to about 75 years, 60 years to about 110 years, or about 50 years to about 130 years.


Administration of a composition or therapy disclosed herein can be performed for a duration of at least about: 1 week, at least about 1 month, at least about 1 year, at least about 2 years, at least about 3 years, at least about 4 years, at least about 5 years, at least about 6 years, at least about 7 years, at least about 8 years, at least about 9 years, at least about 10 years, at least about 15 years, at least about 20 years, or for life. Administration can be performed repeatedly over a lifetime of a subject, such as once a day, once a week, or once a month for the lifetime of a subject Administration can be performed repeatedly over a substantial portion of a subject's life, such as once a day, once a week, or once a month for at least about: 1 year, 5 years, 10 years, 15 years, 20 years, 25 years, 30 years, or more.


Administration of composition or therapy disclosed herein can be performed at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 times a in a 24-hour period. In some instances, administration can comprise administration of a pharmaceutical formulation, a supplement, a therapy or any combination thereof. In some cases, administration of a composition can be performed continuously throughout a 24-hour period. In some cases, administration of composition or therapy disclosed herein can be performed at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or 21 times a week. In some cases, administration of a composition or therapy disclosed herein can be performed at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, or more times a month. In some cases, a composition can be administered as a single dose or as divided doses. In some cases, the compositions described herein can be administered at a first time point and a second time point. In some cases, a composition can be administered such that a first administration can be administered before the other with a difference in administration time of about: 1 hour, 2 hours, 4 hours, 8 hours, 12 hours, 16 hours, 20 hours, 1 day, 2 days, 4 days, 7 days, 2 weeks, 4 weeks, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year or more.


In some cases, a subject may have diagnosed prior to treatment. In some cases, a method described herein can further comprise diagnosing a subject.


Kits

Also described herein are kits comprising distinct primers or pairs of primers and a container. In some cases, a kit can comprise about more than about: 1, 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13 14, 14, 16, 17, 18, 19, 20, 30, 40, 50, 60,70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900 or 2000 distinct primers or pairs of primers. In some cases, a kit can comprise about less than about: 1, 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13 14, 14, 16, 17, 18, 19, 20, 30, 40, 50, 60,70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900 or 2000 distinct primers or pairs of primers. In some cases, each distinct primer or pairs of primers can comprise a distinct sequence complementary to a distinct DMR sequence or a region comprising a distinct DMR sequence present in Table 3. In some cases, a kit can comprise about: 1, 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13 14, 14, 16, 17, 18, 19, 20, 30, 40, 50, 60,70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900 or 2000 distinct probes. In some cases, a distinct probe can be complementary to a distinct DMR sequence or region comprising a DMR sequence in Table 3. In some cases, a probe can comprise at least one of a fluorophore, a chromophore, a barcode or a combination thereof. In some cases, a primer or a pair of primers can comprise a unique barcode. In some cases, a probe, a primer, or a pair of primers may not be bound do an array or a microarray. In some cases, a probe, a primer, or a pair of primers can be bound do an array or a microarray. In some cases, a probe and/or primer can comprise a nucleic acid. In some cases, a nucleic acid can comprise DNA.


EXAMPLES


Example 1
Results

Paternal males with children affected by autism (case) or without (control) were recruited and paternal sperm samples were collected at the Andrology Laboratory of IVIRMA Clinic in Valencia, Spain. The sperm sample was collected upon enrollment. Thirty-two patients were enrolled, which included thirteen in the control group, thirteen in the autism case group, and six for the blinded test group. The differences (mean±SD) between the semen analysis for both control and case group are shown in Table 1. Observations from the groups showed no significant difference in sperm volume, concentration, or sperm concentration between the groups. Progressive sperm motility was greater in the autism case group, with no difference in non-progressive sperm motility, Table 1. The motile percentage was higher in the control group, and no difference was observed in the total motile sperm count. One of the control samples IVI 14 had a very high sperm count of 396.62 million that was outside two standard deviations of the mean (2±SD), so the analysis was redone without this sample. When the IVI 14 sample was not used in the analysis, the total sperm number was increased in the autism case group (p<0.02), and the total motile sperm count (Time) was increased in the autism case group (p<0.017), as well as the progressive spermatozoa (%) (p<0.019) and immotile % (p<0.019) parameters. The ethnicity of all the fathers was Caucasian. The date of the patient sperm collection, age of the father, and age of the case father at pregnancy are all provided in Table 1. All the autistic children were males. The human subjects' approval was obtained prior to the initiation of the study and approved by the Ethics Committee of IVIRMA Valencia, with code, #1311-VLC-136-FC.









TABLE 1







Sperm Samples and Clinical Analysis









Paternal Sperm Analysis









Study Sample

TOTAL





















FATHER
FATHER

OFFSPRING






MOTILE




AGE
AGE
COLLECTION
AUTISM


TOTAL OF
PROGRESSIVE
NON-PROGRESSIVE

SPERM



AGE
(yrs) UPON
(yrs) AT
DATE
CASE/
VOLUME
CONCENTRATION
SPERMATOZOA
SPERMATOZOA
SPERMATOZOA
INMOTILE
COUNT


SAMPLE
(yrs)
COLLECTION
PREGNANCY
OF SAMPLE
CONTROL
(mL)
(mill/mL)
(mill)
(%)
(%)
(%)
(Time)






















IVI 1
42
42
31
Jul. 27, 2015
CASE
2.2
83
182.6
58
11
31
105.91


IVI 2
44
45
43
Jul. 28, 2015
CASE
1.5
38
57
42
11
47
23.94


IVI 3
41
41
38
Jul. 29, 2015
CASE
3
68
204
44
11
45
89.76


IVI 4
38
38
34
Jul. 29, 2015
CASE
3.4
66
224.4
35
11
54
78.54


IVI 5
37
37
33
Jul. 30, 2015
CASE
1.4
10
14
50
14
36
7


IVI 6
39
49
40
Aug. 4, 2015
CASE
2.2
23
50.6
31
26
43
15.69


IVI 7
41
41
31
Aug. 12, 2015
CASE
6
47
282
69
14
17
194.58


IVI 8
42
42
32
Aug. 14, 2015
CASE
2.5
9
227.5
60
15
25
136.5


IVI 9
45
45
35
Sep. 9, 2015
CASE
3.5
87
304.5
50
13
37
152.25


IVI 10

39
31
Sep. 28, 2015
CASE
3
33.3
99.9
44
9
47
43.96


IVI 11

45
39
Dec. 21, 2015
CASE
4
120
480
50
12
38
240


IVI 12
35
37
24
Sep. 5, 2017
CASE
5.4
36.3
196.02
43
3
54
84.29


IVI 13
46
46
35
Mar. 7, 2016
CASE
4.2
17.6
73.92
60
17
23
44.35


IVI 14
40
40

Oct. 11, 16
CONTROL
2.8
141.65
396.62
55
10
35
218.14


IVI 15
41
41

Oct. 11, 2016
CONTROL
1
34.3
34.3
31
18
51
10.63


IVI 16
46
46

Oct. 17, 2016
CONTROL
2.2
5.5
12.1
29
3
68
3.51


IVI 17
44
44

Oct. 20, 2016
CONTROL
4.8
10.5
50.4
42
12
46
21.17


IVI 18
38
38

Oct. 21, 2016
CONTROL
3.2
1.6
5.12
28
13
59
1.43


IVI 19
36
36

Nov. 3, 2016
CONTROL
6.8
4
95.2
32
3
65
30.46


IVI 20
41
41

Mar. 22, 2017
CONTROL
2.2
91.8
201.96
49
14
37
98.96


IVI 21
42
42

May 23, 2017
CONTROL
1.4
42.1
58.94
37
23
40
21.81


IVI 22
37
37

Sep. 6, 2017
CONTROL
4.2
5
226.8
52
9
39
117.94


IVI 23
43
43

Sep. 6, 2017
CONTROL
1.8
13
23.4
23
20
57
5.38


IVI 24
54
54

Sep. 15, 2017
CONTROL
2.5
52
130
48
7
45
62.4


IVI 25
38
38

Sep. 15, 2017
CONTROL
5.5
1.7
9.35
11
8
81
1.03


IVI 26
43
43

Sep. 22, 2017
CONTROL
1.5
96
144
35
17
48
50.4














Mean ± SD Offspring Autism Case
3 ± 1
55 ± 33
184 ± 128
49 ± 11
13 ± 5
38 ± 12
94 ± 71


Mean ± SD Offspring Control
3 ± 2
43 ± 44
107 ± 114
36 ± 13
12 ± 6
52 ± 14
49 ± 63


Statistical Comparison (Case vs. Control), Not Significant
NS
NS
NS
P < 0.01
NS
P < 0.01
NS


(NS) p > 0.05









Individual patient sperm samples from the collection upon enrollment were prepared for sperm analysis, and an aliquot taken, and flash frozen with liquid nitrogen and stored at −20 C. until shipment on dry ice for the epigenetic analysis. The DNA was extracted from the sperm then fragmented for a methylated DNA immunoprecipitation (MeDIP) analysis in order to identify differential DNA methylated regions (DMRs). The MeDIP is a genome-wide analysis examining 95% of the genome comprising low density CpG regions in comparison to the less than 5% of the genome of high density regions and CpG islands. The MeDIP DNA was then prepared for next generation DNA sequencing by creating individual patient sequencing libraries. Samples were then sequenced for bioinformatic analysis, as described in the Supplemental Methods section. A comparison of the sequences between the control (non-autism children) and case (autism children) patient sperm samples identified DMRs, FIG. 1A. At a p-value of p<1e-05 there were 805 DMRs identified with the majority being a single 1 kb window with fewer (i.e. six) having multiple adjacent 1 kb windows involved. The DMRs at a number of p-values are presented for p<001 to p<1e-07, FIG. 1A. The DMRs at p<1e-05 were used for subsequent data analysis, and a list of these DMRs with various genomic features are presented in Table 3. Observations demonstrate that males with autistic children have a sperm DMR signature that is distinct from males without autistic children (control).









TABLE 3







Exemplary list of DMRs


DMR List Case vs. Control p < 1e−05























# Sig



CpG




DMR Name
Chr
Start
Stop
Length
Win
minP
maxLFC
CpG #
Density
Gene Annotation
Gene Category





















DMR1: 386001
1
386001
389000
3000
1
6.10E−06
0.8139908
31
1.033
AL732372.2



DMR1: 517001
1
517001
518000
1000
1
3.58E−06
0.9372704
10
1
AL732372.2; RF00026


DMR1: 824001
1
824001
825000
1000
1
2.83E−07
−1.2835435
6
0.6
AL669831.3; AL669831.4;












FAM87B; LINC01128; LINC00115


DMR1: 2656001
1
2656001
2667000
11000
3
1.10E−08
1.0405831
267
2.427
TTC34


DMR1: 2668001
1
2668001
2683000
15000
3
4.42E−08
0.8755558
363
2.42
TTC34


DMR1: 3571001
1
3571001
3572000
1000
1
1.60E−06
0.9307299
10
1
MEGF6
Growth Factors & Cytokines


DMR1: 3920001
1
3920001
3921000
1000
1
2.77E−06
0.6803401
8
0.8
LINC01134


DMR1: 6258001
1
6258001
6259000
1000
1
5.33E−09
0.6549455
24
2.4
GPR153; ACOT7
Metabolism


DMR1: 6522001
1
6522001
6523000
1000
1
9.22E−06
0.3394789
35
3.5
PLEKHG5; NOL9
Signaling; Transcription


DMR1: 7222001
1
7222001
7224000
2000
1
7.86E−07
−0.4398214
34
1.7
CAMTA1; RNU1-8P
Transcription


DMR1: 7966001
1
7966001
7967000
1000
1
4.45E−07
−0.571204
18
1.8
PARK7
Development


DMR1: 9642001
1
9642001
9644000
2000
1
1.02E−06
0.705812
33
1.65
PIK3CD; PIK3CD-AS1
Signaling


DMR1: 15866001
1
15866001
15867000
1000
1
4.64E−07
0.6211895
14
1.4
SPEN
Transcription


DMR1: 19808001
1
19808001
19811000
3000
1
8.97E−06
−0.4319503
33
1.1
TMCO4; RNF186; AL391883.1


DMR1: 24159001
1
24159001
24162000
3000
1
3.90E−06
0.7355099
43
1.433
IFNLR1
Receptor


DMR1: 24226001
1
24226001
24228000
2000
1
1.52E−06
0.6173313
26
1.3


DMR1: 26621001
1
26621001
26622000
1000
1
5.27E−06
0.7579737
69
6.9


DMR1: 28784001
1
28784001
28785000
1000
1
8.21E−06
0.5491186
25
2.5


DMR1: 28907001
1
28907001
28909000
2000
1
9.03E−07
0.5713446
44
2.2
EPB41


DMR1: 29480001
1
29480001
29481000
1000
1
6.96E−07
0.670635
10
1
AL671862.1


DMR1: 30010001
1
30010001
30011000
1000
1
7.09E−07
−0.5386825
20
2
LINC01648


DMR1: 33804001
1
33804001
33806000
2000
1
3.15E−07
−0.5887044
12
0.6
CSMD2
Unknown


DMR1: 41502001
1
41502001
41503000
1000
1
9.96E−06
−0.5315307
9
0.9
HIVEP3
Transcription


DMR1: 46799001
1
46799001
46800000
1000
1
8.31E−06
−0.5854623
13
1.3
CYP4B1
Metabolism


DMR1: 54305001
1
54305001
54307000
2000
1
6.42E−06
−0.4431958
30
1.5
SSBP3
Translation


DMR1: 59530001
1
59530001
59532000
2000
1
2.16E−06
−0.7317057
9
0.45
FGGY
Signaling


DMR1: 61708001
1
61708001
61709000
1000
1
2.21E−06
0.8628242
6
0.6
TM2D1
Unknown


DMR1: 69382001
1
69382001
69383000
1000
1
7.36E−06
−0.5941512
10
1


DMR1: 70268001
1
70268001
70269000
1000
1
5.34E−06
0.5314653
12
1.2
ANKRD13C


DMR1: 72494001
1
72494001
72495000
1000
1
8.03E−06
−0.625482
1
0.1


DMR1: 74449001
1
74449001
74450000
1000
1
1.94E−06
−0.4828113
8
0.8
FPGT-TNNI3K; TNNI3K
Transcription


DMR1: 79458001
1
79458001
79459000
1000
1
2.48E−06
−0.5341593
9
0.9


DMR1: 79677001
1
79677001
79678000
1000
1
4.17E−06
−0.6074266
4
0.4


DMR1: 89368001
1
89368001
89369000
1000
1
6.02E−06
−0.6720726
6
0.6
GBP6
Signaling


DMR1: 97504001
1
97504001
97506000
2000
1
4.69E−06
−0.8910787
12
0.6
DPYD
Metabolism


DMR1: 103356001
1
103356001
103358000
2000
1
7.28E−08
−0.6163035
13
0.65


DMR1: 109039001
1
109039001
109040000
1000
1
5.02E−08
0.6790758
18
1.8
WDR47; BX679664.1; RANP5


DMR1: 109276001
1
109276001
109277000
1000
1
7.42E−07
0.8406167
15
1.5
CELSR2; PSRC1
Cytoskeleton


DMR1: 109537001
1
109537001
109538000
1000
1
2.85E−06
0.6361615
13
1.3
GPR61; AL355310.3
Signaling


DMR1: 112075001
1
112075001
112077000
2000
1
8.92E−07
0.7714121
5
0.25


DMR1: 115225001
1
115225001
115227000
2000
1
4.15E−06
−0.5591425
21
1.05


DMR1: 115384001
1
115384001
115385000
1000
1
2.98E−06
−0.9033439
1
0.1


DMR1: 116921001
1
116921001
116923000
2000
1
6.04E−06
−0.4536816
20
1
PTGFRN


DMR1: 117487001
1
117487001
117488000
1000
1
7.49E−08
−0.670835
6
0.6
MAN1A2; AL157902.2
Metabolism


DMR1: 154605001
1
154605001
154607000
2000
1
9.58E−06
1.088414
23
1.15
ADAR
Transcription


DMR1: 156166001
1
156166001
156167000
1000
1
3.15E−06
0.8921821
8
0.8
SEMA4A
Development


DMR1: 156171001
1
156171001
156174000
3000
1
5.84E−06
0.5794197
48
1.6
SEMA4A
Development


DMR1: 158269001
1
158269001
158270000
1000
1
2.47E−06
−0.5083161
10
1
HMGN1P5


DMR1: 161306001
1
161306001
161307000
1000
1
6.21E−07
1.1274576
35
3.5
MPZ; SDHC
Extracellular Matrix


DMR1: 164199001
1
164199001
164200000
1000
1
4.12E−06
−0.4587256
7
0.7


DMR1: 166045001
1
166045001
166047000
2000
1
5.38E−06
−0.586517
11
0.55
RNA5SP64


DMR1: 170280001
1
170280001
170281000
1000
1
2.39E−06
−0.4550262
6
0.6
LINC01142


DMR1: 173100001
1
173100001
173101000
1000
1
1.46E−06
−0.5881991
5
0.5


DMR1: 173156001
1
173156001
173157000
1000
1
1.79E−07
−0.4918186
6
0.6


DMR1: 174089001
1
174089001
174090000
1000
1
3.08E−07
−0.5918141
5
0.5
RPL30P1


DMR1: 176750001
1
176750001
176751000
1000
1
7.81E−07
0.8580497
10
1
PAPPA2


DMR1: 178399001
1
178399001
178400000
1000
1
6.92E−06
−0.4565437
8
0.8
RASAL2
Signaling


DMR1: 193063001
1
193063001
193064000
1000
1
9.19E−06
−0.6469959
6
0.6
UCHL5; SCARNA18B; RO60
Protease


DMR1: 199919001
1
199919001
199920000
1000
1
7.47E−06
−0.4324768
10
1
AL445687.2


DMR1: 201006001
1
201006001
201008000
2000
1
1.27E−06
−0.3748639
22
1.1
KIF21B
Cytoskeleton


DMR1: 201696001
1
201696001
201697000
1000
1
5.80E−06
−0.5161451
23
2.3
NAV1; IPO9-AS1


DMR1: 204002001
1
204002001
204003000
1000
1
4.95E−06
0.9292689
10
1


DMR1: 205229001
1
205229001
205231000
2000
1
2.97E−07
−0.7229921
46
2.3
TMCC2; AC093422.2
Unknown


DMR1: 206732001
1
206732001
206734000
2000
1
7.16E−06
−0.3181144
39
1.95
MAPKAPK2
Signaling


DMR1: 207843001
1
207843001
207844000
1000
1
6.98E−06
−0.4149983
12
1.2
MIR29B2CHG


DMR2: 204061001
2
204061001
204062000
1000
1
1.33E−09
−0.7914435
9
0.9
AC009965.2


DMR2: 206667001
2
206667001
206669000
2000
1
3.27E−08
−0.7418124
15
0.75
DYTN


DMR2: 208215001
2
208215001
208216000
1000
1
8.26E−06
0.6347569
15
1.5
RPSAP27; TPT1P2


DMR2: 211716001
2
211716001
211717000
1000
1
2.11E−06
0.4703591
30
3
ERBB4
Signaling


DMR2: 216710001
2
216710001
216713000
3000
1
1.59E−07
−0.5847288
39
1.3
AC007563.2


DMR2: 218561001
2
218561001
218562000
1000
1
4.87E−07
−0.4202055
12
1.2
USP37; CNOT9
Protease


DMR2: 226806001
2
226806001
226808000
2000
1
7.02E−07
−0.6563554
15
0.75
IRS1; AC010735.2; AC010735.1
Unknown


DMR2: 231272001
2
231272001
231273000
1000
1
2.75E−06
0.5428854
20
2
ARMC9


DMR2: 235963001
2
235963001
235965000
2000
1
3.57E−07
−0.5756016
36
1.8
AGAP1
Signaling


DMR3: 304001
3
304001
305000
1000
1
2.59E−06
−0.662561
7
0.7
CHL1; RPS8P6
Extracellular Matrix


DMR3: 1034001
3
1034001
1035000
1000
1
1.10E−07
−0.9882
5
0.5


DMR3: 9344001
3
9344001
9345000
1000
1
7.73E−07
0.5947837
25
2.5
SRGAP3; AC026191.1;
Signaling












PGAM1P4; THUMPD3-AS1


DMR3: 12368001
3
12368001
12370000
2000
1
3.30E−06
0.7976002
17
0.85
PPARG
Receptor


DMR3: 14334001
3
14334001
14335000
1000
1
2.11E−07
−0.5358308
12
1.2


DMR3: 15593001
3
15593001
15596000
3000
1
8.85E−06
0.6773648
45
1.5
HACL1; BTD
Metabolism


DMR3: 19593001
3
19593001
19594000
1000
1
9.42E−06
−0.379851
9
0.9


DMR3: 20600001
3
20600001
20601000
1000
1
6.74E−07
−0.5380999
10
1
SGO1-AS1


DMR3: 32765001
3
32765001
32767000
2000
1
5.67E−06
1.1020184
36
1.8
CNOT10


DMR3: 34173001
3
34173001
34174000
1000
1
8.35E−06
−0.733452
4
0.4
LINC01811


DMR3: 46373001
3
46373001
46376000
3000
1
3.79E−07
0.7776259
30
1
AC098613.1; CCR5
Growth Factors & Cytokines


DMR3: 48201001
3
48201001
48202000
1000
1
9.15E−06
0.5055743
29
2.9
MIR4443


DMR3: 50192001
3
50192001
50193000
1000
1
6.23E−06
1.3249619
25
2.5
SEMA3F; GNAT1
Growth Factors & Cytokines;













Signaling


DMR3: 51110001
3
51110001
51111000
1000
1
6.32E−06
−0.5088342
4
0.4
DOCK3
Signaling


DMR3: 54078001
3
54078001
54079000
1000
1
9.12E−06
−0.9024898
10
1


DMR3: 59570001
3
59570001
59572000
2000
1
1.87E−06
0.5355753
16
0.8


DMR3: 64181001
3
64181001
64183000
2000
1
2.93E−06
−0.5960076
13
0.65
PRICKLE2; PRDX3P4; PRICKLE2-
Cytoskeleton












AS3


DMR3: 68392001
3
68392001
68394000
2000
1
4.11E−06
−0.5435105
15
0.75
FAM19A1


DMR3: 73021001
3
73021001
73022000
1000
1
5.31E−06
0.9559614
6
0.6
PPP4R2; RNU7-19P


DMR3: 74120001
3
74120001
74121000
1000
1
2.34E−08
−1.4543578
5
0.5


DMR3: 78490001
3
78490001
78491000
1000
1
7.61E−09
−0.6011024
11
1.1


DMR3: 87649001
3
87649001
87651000
2000
1
2.10E−06
−0.8331352
8
0.4
AC108749.1


DMR3: 88836001
3
88836001
88837000
1000
1
9.61E−07
−0.9823663
6
0.6


DMR3: 89157001
3
89157001
89158000
1000
1
2.80E−07
−1.0778311
8
0.8
EPHA3
Receptor


DMR3: 91375001
3
91375001
91376000
1000
1
3.91E−07
1.1716095
3
0.3
ABBA01000935.2


DMR3: 91549001
3
91549001
91554000
5000
2
7.47E−07
1.0363492
79
1.58


DMR3: 93705001
3
93705001
93714000
9000
3
3.57E−08
0.8443159
153
1.7


DMR3: 100255001
3
100255001
100256000
1000
1
1.04E−07
1.0378051
5
0.5
TBC1D23


DMR3: 100289001
3
100289001
100290000
1000
1
7.50E−06
0.8356997
9
0.9
TBC1D23


DMR3: 110714001
3
110714001
110715000
1000
1
5.50E−06
−0.9557768
5
0.5


DMR3: 117669001
3
117669001
117670000
1000
1
1.41E−06
−0.7118383
10
1
AC092691.1; AC092691.2;












LINC02024


DMR3: 122419001
3
122419001
122420000
1000
1
2.99E−07
0.6840997
40
4
FAM162A; WDR5B; AC083798.2;
Unknown; Metabolism












AC083798.1; KPNA1


DMR3: 129294001
3
129294001
129295000
1000
1
3.67E−07
0.6314323
16
1.6
HMCES


DMR3: 130403001
3
130403001
130405000
2000
1
3.20E−06
−0.8355836
9
0.45
COL6A5


DMR3: 134740001
3
134740001
134741000
1000
1
1.67E−06
0.5250261
21
2.1
EPHB1
Receptor


DMR3: 139029001
3
139029001
139030000
1000
1
8.83E−07
−0.4752568
9
0.9
MRPS22; PRR23B
Transcription


DMR3: 143232001
3
143232001
143233000
1000
1
9.77E−06
−0.7744006
3
0.3


DMR3: 151846001
3
151846001
151847000
1000
1
7.08E−06
−0.8926749
13
1.3
AADACL2-AS1


DMR3: 155545001
3
155545001
155546000
1000
1
4.90E−06
−0.4916974
7
0.7
PLCH1
Metabolism


DMR3: 159021001
3
159021001
159022000
1000
1
3.82E−07
−1.0881578
2
0.2
IQCJ-SCHIP1; IQCJ


DMR3: 168445001
3
168445001
168449000
4000
1
1.36E−06
−0.8900465
22
0.55
EGFEM1P


DMR3: 170743001
3
170743001
170744000
1000
1
4.53E−06
−0.6188246
7
0.7
AC026316.4; SLC7A14-












AS1; AC026316.2


DMR3: 181107001
3
181107001
181108000
1000
1
9.79E−06
−0.5737711
13
1.3
SOX2-OT


DMR3: 184810001
3
184810001
184811000
1000
1
5.88E−06
0.774526
8
0.8
VPS8


DMR3: 185345001
3
185345001
185346000
1000
1
5.97E−06
−0.3851038
11
1.1
MAP3K13
Signaling


DMR3: 185844001
3
185844001
185845000
1000
1
3.88E−06
−0.6385158
11
1.1


DMR3: 188581001
3
188581001
188582000
1000
1
6.75E−07
−0.4045768
8
0.8
LPP
Cytoskeleton


DMR3: 192432001
3
192432001
192434000
2000
1
2.43E−06
−0.5818597
20
1
FGF12
Growth Factors & Cytokines


DMR3: 196945001
3
196945001
196946000
1000
1
9.74E−06
0.5237731
20
2
NCBP2; NCBP2-AS1; NCBP2-
Transcription; Metabolism












AS2; PIGZ


DMR4: 3759001
4
3759001
3760000
1000
1
6.31E−06
−0.5600381
29
2.9
LINC02600; ADRA2C
Receptor


DMR4: 6951001
4
6951001
6953000
2000
1
1.99E−06
−0.4179546
26
1.3
TBC1D14
Signaling


DMR4: 7448001
4
7448001
7451000
3000
1
9.05E−06
0.8706796
45
1.5
SORCS2; MIR4274
Receptor


DMR4: 9862001
4
9862001
9863000
1000
1
2.44E−06
−0.708581
8
0.8
SLC2A9
Metabolism


DMR4: 10066001
4
10066001
10067000
1000
1
5.57E−07
−0.5455646
11
1.1
AC005674.2; WDR1
Unknown


DMR4: 12097001
4
12097001
12098000
1000
1
7.77E−06
−0.8038681
4
0.4


DMR4: 13176001
4
13176001
13177000
1000
1
1.61E−06
−0.5710104
8
0.8


DMR4: 16890001
4
16890001
16891000
1000
1
5.75E−07
−0.7572532
10
1
LDB2
Transcription


DMR4: 18209001
4
18209001
18212000
3000
1
2.17E−07
−0.6757687
11
0.367


DMR4: 49222001
4
49222001
49223000
1000
1
1.02E−06
0.5127587
6
0.6
AC118282.2; AC118282.4


DMR4: 49224001
4
49224001
49226000
2000
1
4.26E−07
0.611876
15
0.75
AC118282.4; SNX18P23


DMR4: 52666001
4
52666001
52667000
1000
1
3.84E−06
0.5704389
2
0.2
USP46; USP46-AS1
Proteolysis


DMR4: 52973001
4
52973001
52974000
1000
1
5.63E−06
−0.6395201
5
0.5
SCFD2
Unknown


DMR4: 53467001
4
53467001
53469000
2000
1
5.10E−06
−0.7313163
22
1.1
FIP1L1; AC058822.1; LNX1
Cytoskeleton


DMR4: 63099001
4
63099001
63100000
1000
1
1.24E−06
−0.6469376
4
0.4


DMR4: 72705001
4
72705001
72706000
1000
1
1.95E−06
0.7554899
12
1.2


DMR4: 75551001
4
75551001
75552000
1000
1
6.14E−07
0.5856654
18
1.8
THAP6; ODAPH
Transcription


DMR4: 76025001
4
76025001
76026000
1000
1
3.02E−06
−0.5129691
7
0.7
ART3; CXCL10; CXCL11
Metabolism; Growth Factors&













Cytokines


DMR4: 87346001
4
87346001
87347000
1000
1
6.15E−07
0.7495587
15
1.5
AC108516.2; HSD17B11
Metabolism


DMR4: 88979001
4
88979001
88980000
1000
1
5.00E−06
0.7908192
8
0.8
FAM13A


DMR4: 94591001
4
94591001
94593000
2000
1
1.71E−06
−0.7403106
15
0.75
PDLIM5
Cytoskeleton


DMR4: 98055001
4
98055001
98057000
2000
1
2.10E−06
−0.8233235
13
0.65
STPG2; DUTP8
Development


DMR4: 98282001
4
98282001
98284000
2000
1
2.73E−07
−0.5139398
14
0.7
RAP1GDS1
Signaling


DMR4: 99064001
4
99064001
99066000
2000
1
1.24E−06
0.8542533
22
1.1
METAP1; AC019131.2; ADH5
Protease; Metabolism


DMR4: 100090001
4
100090001
100091000
1000
1
1.58E−06
−0.6226641
4
0.4
AC097460.1


DMR4: 100204001
4
100204001
100205000
1000
1
1.90E−06
0.9039055
21
2.1
AC097460.1; AP001961.1


DMR4: 104066001
4
104066001
104067000
1000
1
2.28E−07
−0.8211243
9
0.9


DMR4: 107484001
4
107484001
107485000
1000
1
3.40E−07
−0.6626713
5
0.5


DMR4: 122751001
4
122751001
122752000
1000
1
1.91E−06
−0.5261898
6
0.6
BBS12


DMR4: 128916001
4
128916001
128917000
1000
1
7.29E−06
−0.905045
6
0.6
SCLT1
Metabolism


DMR4: 140013001
4
140013001
140015000
2000
1
6.49E−06
−0.8846538
14
0.7
MAML3
EST


DMR4: 143686001
4
143686001
143687000
1000
1
2.54E−06
−0.5027995
10
1
AC107223.1; FREM3
Transport


DMR4: 147213001
4
147213001
147215000
2000
1
2.20E−06
−0.8128533
8
0.4


DMR4: 153111001
4
153111001
153112000
1000
1
2.55E−06
−0.6121456
14
1.4


DMR4: 164321001
4
164321001
164322000
1000
1
2.74E−06
−0.7249916
5
0.5
1-Mar
Metabolism


DMR4: 165109001
4
165109001
165111000
2000
1
9.92E−06
0.7641435
19
0.95
TMEM192
Unknown


DMR4: 184480001
4
184480001
184481000
1000
1
1.01E−06
−0.4423459
6
0.6
IRF2; AC099343.2; AC099343.3
Transcription


DMR4: 188636001
4
188636001
188637000
1000
1
4.63E−06
0.681319
15
1.5
LINC01060; AC093909.3


DMR5: 696001
5
696001
699000
3000
1
2.05E−06
−0.6667814
48
1.6
TPPP
Cytoskeleton


DMR5: 1461001
5
1461001
1463000
2000
1
6.49E−06
−1.7427536
53
2.65
LPCAT1
Metabolism


DMR5: 3128001
5
3128001
3130000
2000
1
3.94E−06
−0.6158025
51
2.55


DMR5: 4492001
5
4492001
4493000
1000
1
7.33E−08
−0.7392452
15
1.5
AC106799.2


DMR5: 20445001
5
20445001
20447000
2000
1
4.42E−06
−0.5958123
13
0.65
CDH18
Cytoskeleton


DMR5: 27224001
5
27224001
27225000
1000
1
1.94E−08
−0.8761978
5
0.5
PURPL


DMR5: 32070001
5
32070001
32071000
1000
1
1.02E−06
0.6963497
17
1.7
PDZD2


DMR5: 37976001
5
37976001
37978000
2000
1
5.29E−06
−0.7481109
16
0.8
AC034226.1


DMR5: 39122001
5
39122001
39123000
1000
1
8.38E−06
−0.4437573
7
0.7
FYB1


DMR5: 44758001
5
44758001
44759000
1000
1
9.05E−06
−0.4511642
9
0.9
MRPS30-DT; AC093297.1


DMR5: 46433001
5
46433001
46436000
3000
1
4.69E−06
−0.5484316
37
1.233


DMR5: 54849001
5
54849001
54850000
1000
1
9.09E−08
−0.7133944
8
0.8
AC112198.2; AC112198.1


DMR5: 56154001
5
56154001
56155000
1000
1
2.30E−06
0.5844628
17
1.7
ANKRD55; RNA5SP184


DMR5: 57379001
5
57379001
57380000
1000
1
1.46E−07
−0.5826456
10
1


DMR5: 626750017
5
62675001
62676000
1000
1
4.01E−10
−0.5902725
6
0.6


DMR5: 65984001
5
65984001
65985000
1000
1
3.74E−06
−0.5831688
12
1.2
ERBIN


DMR5: 75179001
5
75179001
75180000
1000
1
2.07E−06
−0.5246361
6
0.6
ANKRD31


DMR5: 79263001
5
79263001
79264000
1000
1
1.58E−06
0.5879923
18
1.8
JMY


DMR5: 80306001
5
80306001
80308000
2000
1
3.68E−07
0.6320542
34
1.7
AC026410.1; RF00322; AC026410.2


DMR5: 87198001
5
87198001
87199000
1000
1
9.41E−06
0.4671994
12
1.2
LINC01949


DMR5: 94045001
5
94045001
94046000
1000
1
4.21E−06
0.8850949
3
0.3
FAM172A
Unknown


DMR5: 102044001
5
102044001
102045000
1000
1
1.42E−06
−0.4686804
8
0.8


DMR5: 105574001
5
105574001
105575000
1000
1
9.89E−06
0.7868771
12
1.2


DMR5: 105598001
5
105598001
105599000
1000
1
7.36E−06
−0.6168524
7
0.7


DMR5: 122279001
5
122279001
122280000
1000
1
9.77E−06
−0.8489853
3
0.3


DMR5: 126188001
5
126188001
126189000
1000
1
1.50E−08
−1.0559611
8
0.8
AC116362.1; LINC02039


DMR5: 126715001
5
126715001
126716000
1000
1
3.10E−06
0.5800275
7
0.7


DMR5: 133134001
5
133134001
133136000
2000
1
8.76E−06
−0.55855
10
0.5


DMR5: 134757001
5
134757001
134758000
1000
1
6.46E−09
1.082406
12
1.2
CAMLG; DDX46
Transcription


DMR5: 135090001
5
135090001
135091000
1000
1
7.16E−06
−0.6150929
3
0.3
C5orf66


DMR5: 135780001
5
135780001
135781000
1000
1
5.96E−06
1.1879249
25
2.5
SLC25A48


DMR5: 138562001
5
138562001
138564000
2000
1
1.95E−06
0.5819006
26
1.3
HSPA9; SNORD63B; SNORD63
Signaling


DMR5: 139469001
5
139469001
139470000
1000
1
2.22E−06
−0.5321188
10
1
AC142391.1; ECSCR; SMIM33;












TMEM173


DMR5: 139858001
5
139858001
139860000
2000
1
1.17E−06
0.4361855
21
1.05
NRG2; AC008667.2
Signaling


DMR5: 141114001
5
141114001
141115000
1000
1
4.39E−07
−0.4926117
6
0.6
AC244517.2; AC244517.1; PCDHB4
Cytoskeleton


DMR5: 160271001
5
160271001
160272000
1000
1
1.26E−06
−0.533452
19
1.9
CCNJL
Signaling


DMR5: 164170001
5
164170001
164171000
1000
1
4.13E−06
−0.6355043
8
0.8
AC008662.1


DMR5: 165244001
5
165244001
165245000
1000
1
2.19E−06
−0.5904441
6
0.6
LINC01938


DMR5: 173415001
5
173415001
173417000
2000
1
3.69E−06
−0.427507
15
0.75
AC016573.1


DMR5: 173729001
5
173729001
173730000
1000
1
3.40E−06
−0.8118145
12
1.2
LINC01484


DMR5: 175062001
5
175062001
175063000
1000
1
6.94E−07
−0.4382482
11
1.1


DMR5: 177954001
5
177954001
177955000
1000
1
8.84E−06
0.5201743
14
1.4
AC106795.1; AC106795.3; AC106795.2


DMR5: 179132001
5
179132001
179138000
6000
1
8.10E−06
0.9730536
168
2.8
ADAMTS2
Protease


DMR5: 179969001
5
179969001
179970000
1000
1
5.15E−06
0.6155863
15
1.5
RNF130; AC010285.1; AC010285.3
Metabolism


DMR5: 180779001
5
180779001
180780000
1000
1
3.90E−06
−0.4453688
12
1.2
MGAT1
Metabolism


DMR6: 1402001
6
1402001
1404000
2000
2
2.66E−06
−0.4116056
34
1.7
FOXF2
Transcription


DMR6: 3924001
6
3924001
3925000
1000
1
3.73E−06
−0.5157897
13
1.3
AL590004.2; AL590004.1


DMR6: 6430001
6
6430001
6431000
1000
1
9.25E−06
−0.5263292
5
0.5
LY86-AS1


DMR6: 6718001
6
6718001
6721000
3000
1
1.16E−06
−0.8640551
34
1.133
AL031123.1


DMR6: 11738001
6
11738001
11741000
3000
1
6.40E−06
−0.7211799
27
0.9
ADTRP
Development


DMR6: 12921001
6
12921001
12922000
1000
1
1.52E−06
0.9800264
2
0.2
PHACTR1
Signaling


DMR6: 13133001
6
13133001
13134000
1000
1
7.27E−06
0.602249
51
5.1
PHACTR1
Signaling


DMR6: 14622001
6
14622001
14623000
1000
1
5.64E−06
−0.4587547
22
2.2


DMR6: 19698001
6
19698001
19699000
1000
1
6.34E−07
−0.3771312
17
1.7
AL022068.1


DMR6: 21055001
6
21055001
21056000
1000
1
2.87E−06
−1.1051213
3
0.3
CDKAL1
Cell Cycle


DMR6: 24039001
6
24039001
24041000
2000
1
8.51E−06
−0.9233759
7
0.35


DMR6: 28926001
6
28926001
28927000
1000
1
8.45E−06
0.8705091
15
1.5
TRIM27
Metabolism


DMR6: 29618001
6
29618001
29619000
1000
1
1.19E−06
−0.636553
6
0.6
GABBR1
Receptor


DMR6: 38496001
6
38496001
38497000
1000
1
1.42E−06
−0.7941924
7
0.7
BTBD9
Unknown


DMR6: 38624001
6
38624001
38625000
1000
1
2.89E−07
−0.6006061
15
1.5
BTBD9
Unknown


DMR6: 43546001
6
43546001
43547000
1000
1
6.01E−06
−1.0896769
5
0.5
POLR1C; XPO5; AL355802.1; RF00426
Translation; Receptor


DMR6: 43856001
6
43856001
43857000
1000
1
4.81E−07
−1.1220187
6
0.6
LINC02537; AL157371.1


DMR6: 49550001
6
49550001
49551000
1000
1
9.52E−07
0.8714945
49
4.9
C6orf141


DMR6: 52624001
6
52624001
52625000
1000
1
6.99E−06
0.7442294
16
1.6


DMR6: 79220001
6
79220001
79221000
1000
1
4.37E−06
0.6615165
9
0.9
HMGN3
Epigenetic


DMR6: 89472001
6
89472001
89473000
1000
1
7.35E−06
−0.7215289
11
1.1
ANKRD6; RN7SL11P


DMR6: 91335001
6
91335001
91336000
1000
1
4.31E−06
−0.5591004
7
0.7


DMR6: 97192001
6
97192001
97193000
1000
1
4.66E−06
1.016525
3
0.3
MMS22L


DMR6: 99166001
6
99166001
99168000
2000
1
9.50E−07
−0.6259245
9
0.45
BDH2P1


DMR6: 99861001
6
99861001
99862000
1000
1
8.83E−09
−1.0353924
3
0.3


DMR6: 102673001
6
102673001
102675000
2000
1
9.49E−08
−0.6446693
12
0.6


DMR6: 104679001
6
104679001
104680000
1000
1
2.88E−06
0.7593947
6
0.6
AL356967.1


DMR6: 110799001
6
110799001
110801000
2000
1
1.52E−06
−0.6955559
14
0.7
CDK19
Signaling


DMR6: 119104001
6
119104001
119105000
1000
1
1.35E−06
1.016355
8
0.8
AL137009.1; FAM184A
Unknown


DMR6: 119412001
6
119412001
119414000
2000
1
6.30E−06
−0.7922276
6
0.3


DMR6: 119589001
6
119589001
119590000
1000
1
3.17E−06
−0.4975542
3
0.3


DMR6: 122964001
6
122964001
122965000
1000
1
8.16E−06
−1.0593466
1
0.1


DMR6: 124759001
6
124759001
124761000
2000
1
5.75E−07
0.6666712
21
1.05
NKAIN2
Transport


DMR6: 135329001
6
135329001
135332000
3000
1
3.71E−06
−0.869098
25
0.833
AHI1
Development


DMR6: 150690001
6
150690001
150691000
1000
1
9.60E−06
−0.5202252
10
1
PLEKHG1
Signaling


DMR6: 151058001
6
151058001
151059000
1000
1
2.29E−06
0.6011066
16
1.6
MTHFD1L; AL133260.2
Metabolism


DMR6: 154006001
6
154006001
154007000
1000
1
3.02E−06
−0.7391696
3
0.3
OPRM1
Receptor


DMR6: 156358001
6
156358001
156360000
2000
1
3.95E−06
−0.435247
27
1.35
AL512658.2


DMR6: 159658001
6
159658001
159659000
1000
1
4.31E−07
−0.510362
5
0.5


DMR6: 159707001
6
159707001
159709000
2000
1
2.36E−07
−0.6397778
13
0.65
SOD2; HNRNPH1P1
Metabolism


DMR6: 160945001
6
160945001
160948000
3000
1
2.05E−06
−0.4429917
33
1.1
AL139393.1


DMR6: 167422001
6
167422001
167424000
2000
1
5.02E−07
0.7600747
143
7.15


DMR7: 1273001
7
1273001
1279000
6000
1
5.10E−06
0.5570609
200
3.333
AC073094.1


DMR7: 2902001
7
2902001
2904000
2000
1
1.27E−07
0.7713864
35
1.75
CARD11
Unknown


DMR7: 7648001
7
7648001
7650000
2000
1
5.29E−07
−0.6083148
20
1
RPA3; UMAD1; AC007161.3
Cytoskeleton


DMR7: 17916001
7
17916001
17918000
2000
1
1.28E−06
−0.6276699
13
0.65
SNX13
Signaling


DMR7: 26081001
7
26081001
26083000
2000
1
1.79E−06
−0.7509067
14
0.7


DMR7: 28416001
7
28416001
28418000
2000
1
4.34E−06
−0.53156
16
0.8
CREB5
Transcription


DMR7: 30512001
7
30512001
30514000
2000
1
8.02E−06
−0.4580246
20
1
GGCT; AC005154.5; GARS-DT;












AC005154.2


DMR7: 32601001
7
32601001
32602000
1000
1
1.07E−06
−0.6311666
8
0.8
DPY19L1P1


DMR7: 35347001
7
35347001
35348000
1000
1
6.54E−06
0.8059713
12
1.2


DMR7: 35750001
7
35750001
35751000
1000
1
3.61E−06
−0.456453
5
0.5
SEPT7-AS1


DMR7: 37123001
7
37123001
37124000
1000
1
2.27E−07
−0.5588348
5
0.5
ELMO1; RPS17P13
Signaling


DMR7: 37689001
7
37689001
37691000
2000
1
9.39E−06
−0.4653141
19
0.95
GPR141; EPDR1
Receptor


DMR7: 38672001
7
38672001
38673000
1000
1
8.17E−06
−0.5160182
12
1.2


DMR7: 40481001
7
40481001
40482000
1000
1
4.84E−07
1.0367547
9
0.9
SUGCT
EST


DMR7: 42733001
7
42733001
42734000
1000
1
5.91E−09
−0.6650931
10
1


DMR7: 42927001
7
42927001
42929000
2000
1
6.42E−06
0.7891598
19
0.95
AC010132.3; PSMA2; AC010132.2;












MRPL32


DMR7: 44024001
7
44024001
44026000
2000
1
3.41E−06
0.7581505
18
0.9
POLR2J4; AC004951.4; AC017116.2;












RASA4CP


DMR7: 44804001
7
44804001
44805000
1000
1
7.50E−06
0.6919024
13
1.3
PPIA


DMR7: 46116001
7
46116001
46117000
1000
1
1.39E−06
−0.5695176
8
0.8


DMR7: 50060001
7
50060001
50061000
1000
1
2.21E−06
−0.6013637
5
0.5
ZP BP
Development


DMR7: 50814001
7
50814001
50815000
1000
1
5.01E−06
0.5820185
11
1.1


DMR7: 53275001
7
53275001
53276000
1000
1
2.40E−06
0.6137477
9
0.9


DMR7: 65521001
7
65521001
65522000
1000
1
6.72E−06
−0.8485457
9
0.9
AC114501.2


DMR7: 68057001
7
68057001
68058000
1000
1
8.28E−06
0.7511854
9
0.9


DMR7: 72788001
7
72788001
72791000
3000
1
9.28E−06
0.5741053
42
1.4
TYW1B


DMR7: 73698001
7
73698001
73699000
1000
1
3.21E−06
0.5988557
12
1.2
BUD23; STX1A
Metabolism; Transport


DMR7: 74011001
7
74011001
74012000
1000
1
1.47E−06
0.7238667
18
1.8


DMR7: 74096001
7
74096001
74097000
1000
1
5.46E−06
0.5571859
22
2.2
LIMK1
Signaling


DMR7: 98826001
7
98826001
98827000
1000
1
7.36E−06
−0.4244376
31
3.1


DMR7: 103693001
7
103693001
103695000
2000
1
6.89E−06
−0.7201378
12
0.6
RELN
Protease


DMR7: 110708001
7
110708001
110712000
4000
1
4.02E−08
−0.7197439
41
1.025
IMMP2L
Protease


DMR7: 110843001
7
110843001
110845000
2000
1
6.88E−07
−0.7812022
15
0.75
IMMP2L
Protease


DMR7: 130079001
7
130079001
130080000
1000
1
3.97E−06
0.5701097
13
1.3
KLHDC10


DMR7: 131148001
7
131148001
131150000
2000
1
3.71E−07
−0.6301175
19
0.95
MKLN1
Signaling


DMR7: 133296001
7
133296001
133297000
1000
1
4.30E−06
−0.815133
3
0.3
EXOC4; MIR6133
Transport


DMR7: 142062001
7
142062001
142063000
1000
1
5.33E−07
−0.8628877
12
1.2
MGAM
Metabolism


DMR7: 142930001
7
142930001
142931000
1000
1
2.48E−06
−0.547
15
1.5
TRPV5; LLCFC1
Transport


DMR7: 146096001
7
146096001
146097000
1000
1
2.61E−06
0.8492238
39
3.9


DMR7: 157874001
7
157874001
157875000
1000
1
1.39E−06
−0.3499685
27
2.7
PTPRN2; AC011899.3
Signaling


DMR8: 482001
8
482001
483000
1000
1
4.98E−07
−0.6810516
17
1.7
FBXO25; AC083964.2; TDRP


DMR8: 1242001
8
1242001
1244000
2000
1
1.88E−07
−0.4707801
44
2.2
DLGAP2; AC110288.1
Receptor


DMR8: 1824001
8
1824001
1826000
2000
2
5.56E−10
1.0155084
96
4.8
AC019257.8; MIR596; ARHGEF10
Protease


DMR8: 5650001
8
5650001
5654000
4000
1
1.64E−07
−0.5336835
46
1.15
AC084768.1


DMR8: 6559001
8
6559001
6560000
1000
1
1.10E−06
0.8944343
14
1.4
MCPH1; ANGPT2
DNA Repair; Signaling


DMR8: 7690001
8
7690001
7691000
1000
1
2.61E−07
1.0332821
14
1.4
AC084121.3; AC084121.4; AC084121.2


DMR8: 10430001
8
10430001
10432000
2000
1
2.86E−06
−0.4179805
33
1.65
MSRA; AC104964.3
Metabolism


DMR8: 12623001
8
12623001
12625000
2000
1
2.45E−07
−1.1402102
29
1.45
AC068587.4; RPS3AP35


DMR8: 18621001
8
18621001
18623000
2000
1
5.96E−06
0.6731904
20
1
PSD3
Signaling


DMR8: 22162001
8
22162001
22164000
2000
1
1.60E−06
−0.5803564
39
1.95
LGI3; SFTPC; BMP1; AC105206.1
Receptor; Unknown; Protease


DMR8: 25681001
8
25681001
25683000
2000
1
1.05E−06
−0.4786134
10
0.5
AC009623.1


DMR8: 26563001
8
26563001
26565000
2000
1
8.10E−07
−0.3632575
14
0.7
DPYSL2
Metabolism


DMR8: 35097001
8
35097001
35098000
1000
1
7.68E−06
−0.9679379
6
0.6


DMR8: 38869001
8
38869001
38870000
1000
1
3.10E−06
1.0914025
9
0.9


DMR8: 48530001
8
48530001
48531000
1000
1
2.53E−06
−0.9712279
6
0.6


DMR8: 51871001
8
51871001
51872000
1000
1
8.41E−06
−0.5646483
6
0.6
PCMTD1
Metabolism


DMR8: 53301001
8
53301001
53302000
1000
1
8.87E−06
−0.8694708
5
0.5


DMR8: 59382001
8
59382001
59383000
1000
1
9.68E−08
−0.6404769
11
1.1


DMR8: 67401001
8
67401001
67402000
1000
1
2.11E−06
0.7281808
7
0.7
AC011037.1


DMR8: 71115001
8
71115001
71116000
1000
1
9.76E−06
0.8768569
13
1.3
AC015687.1


DMR8: 76198001
8
76198001
76199000
1000
1
3.52E−08
−0.6295675
5
0.5


DMR8: 81328001
8
81328001
81329000
1000
1
6.17E−07
−0.4740234
6
0.6


DMR8: 84728001
8
84728001
84730000
2000
1
1.43E−06
−0.5480921
10
0.5
RALYL
Transcription


DMR8: 85216001
8
85216001
85217000
1000
1
1.62E−06
−0.5208883
7
0.7
E2F5; C8orf59; CA13; AC011773.3
Transcription; Metabolism


DMR8: 86444001
8
86444001
86446000
2000
1
4.72E−07
−0.8015767
9
0.45
WWP1
Proteolysis


DMR8: 88448001
8
88448001
88449000
1000
1
1.24E−06
0.8389684
10
1
AC090578.1


DMR8: 88646001
8
88646001
88647000
1000
1
9.72E−08
−0.8520037
3
0.3
AC090578.1


DMR8: 91548001
8
91548001
91550000
2000
1
1.28E−07
−1.1503793
21
1.05
AC103409.1


DMR8: 95873001
8
95873001
95875000
2000
1
8.32E−06
−0.6286364
8
0.4


DMR8: 96325001
8
96325001
96326000
1000
1
2.37E−09
−0.6699008
6
0.6
PTDSS1
Metabolism


DMR8: 97331001
8
97331001
97332000
1000
1
9.10E−06
−0.5777146
10
1


DMR8: 114583001
8
114583001
114585000
2000
1
3.44E−07
−0.5416655
17
0.85


DMR8: 118551001
8
118551001
118553000
2000
1
6.80E−07
−0.5612178
17
0.85
SAMD12
Unknown


DMR8: 118685001
8
118685001
118687000
2000
1
3.20E−07
−0.6557276
15
0.75
SAMD12-AS1


DMR8: 120190001
8
120190001
120192000
2000
1
8.49E−06
−0.5971808
10
0.5
COL14A1
Cytoskeleton


DMR8: 122964001
8
122964001
122966000
2000
1
1.56E−06
−0.7544156
9
0.45
ZHX2
Transcription


DMR8: 124750001
8
124750001
124751000
1000
1
5.93E−06
0.7510927
14
1.4


DMR8: 125639001
8
125639001
125641000
2000
1
7.56E−07
−0.5941291
27
1.35
AC016074.2


DMR8: 127990001
8
127990001
127991000
1000
1
3.65E−06
−0.5753967
7
0.7
PVT1; RNU1-106P


DMR8: 133091001
8
133091001
133093000
2000
1
1.34E−07
−0.4136718
30
1.5
TG; SLA
Signaling


DMR8: 133250001
8
133250001
133251000
1000
1
6.01E−06
−0.6032632
18
1.8
NDRG1
Transcription


DMR8: 136037001
8
136037001
136039000
2000
1
2.28E−07
1.0371973
23
1.15
LINC02055


DMR8: 142072001
8
142072001
142073000
1000
1
6.37E−07
0.8235722
13
1.3


DMR9: 24711001
9
24711001
24712000
1000
1
1.13E−06
−1.0081227
3
0.3


DMR9: 25308001
9
25308001
25310000
2000
1
5.33E−07
0.8622406
19
0.95


DMR9: 27371001
9
27371001
27372000
1000
1
6.39E−06
−0.4952696
11
1.1
MOB3B
Signaling


DMR9: 31375001
9
31375001
31376000
1000
1
3.62E−08
−0.4665531
10
1
LINC01243


DMR9: 62708001
9
62708001
62710000
2000
1
4.99E−06
0.7495045
39
1.95
FKBP4P7


DMR9: 65560001
9
65560001
65561000
1000
1
7.15E−06
−0.6755017
7
0.7


DMR9: 72375001
9
72375001
72377000
2000
1
5.56E−07
0.6838577
33
1.65
ZFAND5
Transcription


DMR9: 82141001
9
82141001
82142000
1000
1
3.98E−06
0.8524656
32
3.2
AL162726.3; AL158047.1


DMR9: 87814001
9
87814001
87816000
2000
1
1.99E−07
−0.9849743
9
0.45
AL772337.4; FBP2P1; ELF2P3; NPAP1P7


DMR9: 88286001
9
88286001
88287000
1000
1
3.02E−08
0.6938888
18
1.8


DMR9: 90190001
9
90190001
90192000
2000
1
9.63E−06
−1.0380804
7
0.35


DMR9: 90411001
9
90411001
90413000
2000
1
6.89E−06
−0.5865592
13
0.65
LINC01508


DMR9: 94745001
9
94745001
94747000
2000
1
3.85E−06
−0.5994547
27
1.35
C9orf3
Proteolysis


DMR9: 98603001
9
98603001
98606000
3000
1
6.91E−06
−0.7573327
44
1.467
GABBR2; SEPT7P7
Receptor


DMR9: 100217001
9
100217001
100218000
1000
1
9.55E−06
0.8744344
11
1.1
INVS


DMR9: 102024001
9
102024001
102027000
3000
1
6.18E−06
−0.8691895
11
0.367


DMR9: 116117001
9
116117001
116118000
1000
1
8.04E−07
−0.6406795
7
0.7


DMR9: 121919001
9
121919001
121920000
1000
1
9.38E−06
−0.6209179
4
0.4
TTLL11; TTLL11-IT1
Cytoskeleton


DMR9: 124096001
9
124096001
124097000
1000
1
2.94E−06
0.5522826
18
1.8


DMR9: 125187001
9
125187001
125189000
2000
1
1.44E−06
0.8385809
58
2.9
PPP6C; RPSAP76
Signaling


DMR9: 128010001
9
128010001
128012000
2000
1
9.26E−06
0.532175
31
1.55


DMR9: 128486001
9
128486001
128487000
1000
1
2.59E−06
−1.0404849
3
0.3
ODF2
Cytoskeleton


DMR9: 129113001
9
129113001
129114000
1000
1
3.34E−07
0.587374
23
2.3
CRAT; PTPA
Metabolism


DMR9: 134443001
9
134443001
134446000
3000
1
9.49E−07
0.9415373
53
1.767
RXRA
Receptor


DMR9: 137140001
9
137140001
137142000
2000
1
1.23E−06
−0.7865738
51
2.55
GRIN1
Receptor


DMR10: 5881001
10
5881001
5883000
2000
1
1.72E−06
0.645441
33
1.65
ANKRD16; FBH1


DMR10: 6852001
10
6852001
6853000
1000
1
7.94E−06
1.0386449
9
0.9
LINC00707; AL392086.2


DMR10: 6930001
10
6930001
6931000
1000
1
6.57E−06
−0.8284854
4
0.4
AL392086.1


DMR10: 7527001
10
7527001
7528000
1000
1
3.74E−06
−0.4612989
7
0.7
AL445070.1


DMR10: 19091001
10
19091001
19092000
1000
1
1.35E−07
−0.5344647
8
0.8
MALRD1


DMR10: 20290001
10
20290001
20292000
2000
1
2.52E−07
0.7460668
15
0.75
PLXDC2
Binding Protein


DMR10: 20821001
10
20821001
20822000
1000
1
1.28E−09
−0.7634124
6
0.6
NEBL
Cytoskeleton


DMR10: 26085001
10
26085001
26087000
2000
1
7.85E−06
−0.7895772
9
0.45
MYO3A
Cytoskeleton


DMR10: 27491001
10
27491001
27492000
1000
1
1.51E−08
−0.6822888
7
0.7


DMR10: 27628001
10
27628001
27629000
1000
1
3.66E−06
−0.6216715
13
1.3


DMR10: 34836001
10
34836001
34837000
1000
1
7.09E−06
−0.3568398
15
1.5


DMR10: 37507001
10
37507001
37508000
1000
1
3.45E−06
1.0486368
16
1.6


DMR10: 43225001
10
43225001
43226000
1000
1
1.77E−06
−0.3470552
12
1.2
RASGEF1A
Signaling


DMR10: 49630001
10
49630001
49633000
3000
1
2.75E−06
−0.3770004
37
1.233
CHAT
Metabolism


DMR10: 49806001
10
49806001
49807000
1000
1
1.26E−06
0.7055622
18
1.8
RPL21P89


DMR10: 54518001
10
54518001
54519000
1000
1
9.94E−06
−0.4557692
9
0.9
PCDH15; AL353784.1
Extracellular Matrix


DMR10: 57997001
10
57997001
57998000
1000
1
2.56E−06
−0.5250618
6
0.6


DMR10: 64949001
10
64949001
64951000
2000
1
9.17E−07
−0.5648882
6
0.3


DMR10: 69258001
10
69258001
69260000
2000
1
7.13E−07
−0.5566237
14
0.7
HKDC1; AL596223.2; HK1
Signaling


DMR10: 70813001
10
70813001
70814000
1000
1
6.98E−06
−0.4802403
11
1.1
SGPL1
Metabolism


DMR10: 72197001
10
72197001
72199000
2000
1
2.65E−06
0.5785468
22
1.1
ASCC1; RPL15P14
Binding Protein


DMR10: 73625001
10
73625001
73626000
1000
1
1.86E−06
0.7193911
55
5.5
USP54; AC073389.1; AC073389.3;












MYOZ1


DMR10: 76218001
10
76218001
76222000
4000
1
5.21E−06
−0.653641
30
0.75
LRMDA


DMR10: 77091001
10
77091001
77092000
1000
1
9.93E−07
−0.6685994
14
1.4
KCNMA1
Metabolism


DMR10: 79033001
10
79033001
79035000
2000
1
1.90E−06
−0.2902581
32
1.6
ZMIZ1-AS1


DMR10: 79325001
10
79325001
79328000
3000
1
5.79E−06
−0.3867976
21
0.7
ZMIZ1
Metabolism


DMR10: 86365001
10
86365001
86366000
1000
1
4.36E−07
0.767855
62
6.2
GRID1
Receptor


DMR10: 91469001
10
91469001
91472000
3000
1
7.56E−06
−0.8092263
13
0.433
HECTD2; AL161798.1
EST


DMR10: 100809001
10
100809001
100810000
1000
1
2.62E−06
0.6844549
15
1.5
PAX2
Transcription


DMR10: 101619001
10
101619001
101622000
3000
1
1.67E−06
1.2099467
40
1.333
DPCD; FBXW4; RNU6-1165P
Unknown


DMR10: 101661001
10
101661001
101663000
2000
1
4.42E−08
−0.5988523
19
0.95
FBXW4
Unknown


DMR10: 103389001
10
103389001
103392000
3000
1
8.61E−06
0.6316328
44
1.467
TAF5; ATP5MD; MIR1307; PDCD11
Transcription; Apoptosis


DMR10: 106795001
10
106795001
106796000
1000
1
4.54E−06
−0.6829713
7
0.7
SORCS1
Receptor


DMR10: 113356001
10
113356001
113357000
1000
1
6.23E−06
−0.9072859
7
0.7
RNU7-165P


DMR10: 117499001
10
117499001
117501000
2000
1
8.78E−06
−0.6012907
30
1.5
AC005871.2; EMX2OS


DMR10: 118021001
10
118021001
118023000
2000
1
5.68E−08
−0.4882908
13
0.65
RAB11FIP2; AC022395.1


DMR10: 125943001
10
125943001
125944000
1000
1
4.47E−06
1.042737
12
1.2
FANK1


DMR10: 133012001
10
133012001
133013000
1000
1
7.74E−06
−0.4565435
49
4.9


DMR11: 3911001
11
3911001
3913000
2000
1
3.62E−06
0.633939
9
0.45
STIM1; RF00409
Signaling


DMR11: 6281001
11
6281001
6282000
1000
1
9.32E−06
0.5672051
16
1.6
CCKBR
Receptor


DMR11: 14276001
11
14276001
14278000
2000
1
5.87E−08
0.5808539
17
0.85
SPON1; SPON1-AS1; RRAS2
Growth Factors & Cytokines;













Signaling


DMR11: 19359001
11
19359001
19360000
1000
1
2.69E−08
−0.6964187
8
0.8
NAV2
Development


DMR11: 22614001
11
22614001
22615000
1000
1
5.04E−07
−0.72362
5
0.5
FANCF


DMR11: 24281001
11
24281001
24283000
2000
1
4.83E−06
−0.5355246
17
0.85


DMR11: 24960001
11
24960001
24962000
2000
1
9.40E−07
−0.490419
11
0.55
LUZP2


DMR11: 31576001
11
31576001
31578000
2000
1
8.37E−06
−0.4835536
17
0.85
ELP4
Transcription


DMR11: 33571001
11
33571001
33573000
2000
1
4.44E−06
0.7680501
21
1.05
KIAA1549L


DMR11: 35771001
11
35771001
35772000
1000
1
3.97E−07
0.5739109
14
1.4
TRIM44


DMR11: 40588001
11
40588001
40589000
1000
1
8.95E−06
−0.3503883
9
0.9
LRRC4C
Extracellular Matrix


DMR11: 46022001
11
46022001
46025000
3000
1
6.14E−08
0.7306804
24
0.8
PHF21A
Metabolism


DMR11: 49397001
11
49397001
49398000
1000
1
5.12E−06
−0.6419734
4
0.4
TYRL; CBX3P8


DMR11: 56338001
11
56338001
56340000
2000
1
7.61E−06
−0.816767
16
0.8
FAM8A2P; OR8K2P; OR8K1
Receptor


DMR11: 61100001
11
61100001
61101000
1000
1
3.13E−06
0.6428472
2
0.2
CD5
Receptor


DMR11: 62013001
11
62013001
62014000
1000
1
6.72E−06
−0.8931881
12
1.2
AP003733.1


DMR11: 63013001
11
63013001
63014000
1000
1
2.77E−06
−0.6064386
10
1
SLC22A8
Transport


DMR11: 71288001
11
71288001
71289000
1000
1
5.82E−06
−0.6601629
7
0.7


DMR11: 72648001
11
72648001
72650000
2000
1
3.42E−07
−0.4059606
18
0.9
PDE2A; AP003065.1
Signaling


DMR11: 76945001
11
76945001
76946000
1000
1
1.93E−06
−0.7038751
7
0.7
ACER3; AP002498.1
Metabolism


DMR11: 78428001
11
78428001
78431000
3000
1
1.93E−06
−0.6269496
17
0.567
GAB2; AP003086.2; AP003086.1;
Receptor; Transcription












NARS2


DMR11: 79350001
11
79350001
79352000
2000
1
9.99E−07
−0.3244548
21
1.05
TENM4
Signaling


DMR11: 79876001
11
79876001
79877000
1000
1
3.53E−07
1.447963
6
0.6


DMR11: 93175001
11
93175001
93176000
1000
1
9.74E−06
−0.42818
12
1.2
SLC36A4; AP003072.5
Transport


DMR11: 94281001
11
94281001
94282000
1000
1
1.64E−07
−0.8646871
9
0.9
AP002784.1


DMR11: 95592001
11
95592001
95593000
1000
1
4.67E−07
−0.730889
7
0.7
AP000820.2


DMR11: 97733001
11
97733001
97737000
4000
1
3.85E−06
−0.6907272
10
0.25


DMR11: 106275001
11
106275001
106276000
1000
1
3.84E−06
−0.7619225
6
0.6


DMR11: 109564001
11
109564001
109565000
1000
1
6.72E−07
−0.6557736
2
0.2
AP003049.2


DMR11: 111872001
11
111872001
111873000
1000
1
4.27E−06
−0.6308238
8
0.8
ALG9; AP001781.2; GNG5P3; FDXACB1;
Metabolism












C11orf1


DMR11: 112401001
11
112401001
112403000
2000
1
2.73E−06
−0.4219308
18
0.9
AP003063.1


DMR11: 120177001
11
120177001
120179000
2000
1
3.39E−07
1.0832786
18
0.9
TRIM29; AP000679.1
Metabolism


DMR11: 121501001
11
121501001
121502000
1000
1
4.35E−06
−0.7893128
6
0.6
SORL1
Receptor


DMR11: 128972001
11
128972001
128973000
1000
1
1.59E−06
−0.5045185
10
1
ARHGAP32
Signaling


DMR12: 4746001
12
4746001
4748000
2000
1
3.52E−07
0.697345
14
0.7
AC005833.1; GALNT8
Metabolism


DMR12: 5142001
12
5142001
5143000
1000
1
3.22E−06
−0.4060174
17
1.7


DMR12: 7100001
12
7100001
7105000
5000
1
7.21E−06
−0.4409029
58
1.16
C1R; C1RL; C1RL-AS1
Immune; Protease


DMR12: 9415001
12
9415001
9416000
1000
1
8.47E−06
0.4767515
21
2.1
AC141557.1; AC141557.2; DDX12P


DMR12: 9579001
12
9579001
9580000
1000
1
6.05E−06
−1.3024967
8
0.8
AC092821.3; AC092821.1


DMR12: 10651001
12
10651001
10653000
2000
1
1.93E−08
−0.7747836
15
0.75
STYK1
Receptor


DMR12: 13564001
12
13564001
13565000
1000
1
5.08E−06
−0.9862011
35
3.5
GRIN2B
Receptor


DMR12: 19669001
12
19669001
19670000
1000
1
2.29E−07
−0.8077415
3
0.3
AEBP2
Transcription


DMR12: 20061001
12
20061001
20062000
1000
1
5.69E−06
−0.5184235
8
0.8
LINC02398


DMR12: 27229001
12
27229001
27230000
1000
1
9.20E−06
−0.4982085
3
0.3


DMR12: 32916001
12
32916001
32917000
1000
1
3.67E−06
−0.5129993
6
0.6


DMR12: 43584001
12
43584001
43585000
1000
1
1.56E−06
−0.5114725
10
1


DMR12: 47009001
12
47009001
47010000
1000
1
9.47E−06
−0.5548508
9
0.9


DMR12: 49758001
12
49758001
49759000
1000
1
1.67E−06
−0.6080902
10
1
TMBIM6
Apoptosis


DMR12: 53498001
12
53498001
53500000
2000
1
1.79E−06
0.8645961
61
3.05
MAP3K12; AC023509.3; TARBP2;
Signaling; Transcription












ATF7; NPFF


DMR12: 55811001
12
55811001
55812000
1000
1
6.24E−06
−0.5246924
9
0.9
SARNP; AC023055.1; ORMDL2;
Transcription; Translation; Protein












DNAJC14
Binding


DMR12: 62445001
12
62445001
62446000
1000
1
2.93E−07
−0.7140059
4
0.4


DMR12: 68739001
12
68739001
68740000
1000
1
6.63E−06
0.8115642
20
2
NUP107; SLC35 E3
Transport


DMR12: 71103001
12
71103001
71104000
1000
1
7.41E−06
1.1122277
6
0.6
AC025575.1; AC025575.2


DMR12: 75480001
12
75480001
75482000
2000
1
2.99E−06
−0.5648618
21
1.05
GLIPR1; AC121761.1; KRR1
Transcription


DMR12: 81780001
12
81780001
81781000
1000
1
3.89E−06
−1.1812637
4
0.4


DMR12: 86917001
12
86917001
86919000
2000
1
5.12E−06
−0.6624892
13
0.65


DMR12: 89311001
12
89311001
89314000
3000
1
5.48E−06
0.6391671
34
1.133
LINC02458; MRPS6P4


DMR12: 99154001
12
99154001
99155000
1000
1
4.73E−06
−0.4861876
17
1.7
ANKS1B
Receptor


DMR12: 101460001
12
101460001
101461000
1000
1
6.30E−06
0.5985655
16
1.6
RNU5E-5P


DMR12: 102110001
12
102110001
102112000
2000
1
8.81E−06
−0.9481755
13
0.65
NUP37; PARPBP
Metabolism


DMR12: 102176001
12
102176001
102177000
1000
1
4.75E−06
0.7412656
9
0.9
PARPBP


DMR12: 102498001
12
102498001
102499000
1000
1
4.99E−06
−0.4957777
2
0.2


DMR12: 105630001
12
105630001
105632000
2000
1
2.23E−06
−0.4978292
15
0.75


DMR12: 106215001
12
106215001
106216000
1000
1
2.79E−06
0.4783249
19
1.9


DMR12: 109267001
12
109267001
109269000
2000
1
4.92E−06
−0.3766094
33
1.65
ACACB; FOXN4
Metabolism; Transcription


DMR12: 110325001
12
110325001
110327000
2000
1
1.30E−07
−0.9564508
18
0.9
ATP2A2
Metabolism


DMR12: 110336001
12
110336001
110337000
1000
1
5.90E−06
−0.7809265
7
0.7
ATP2A2
Metabolism


DMR12: 110441001
12
110441001
110445000
4000
1
4.96E−07
0.7889969
75
1.875
AC144548.1; ARPC3; GPN3
Cytoskeleton; Transcription


DMR12: 112178001
12
112178001
112179000
1000
1
3.32E−06
0.868337
21
2.1
HECTD4


DMR12: 114323001
12
114323001
114324000
1000
1
4.39E−06
−0.6522382
11
1.1


DMR12: 114875001
12
114875001
114879000
4000
1
3.57E−06
0.5413825
50
1.25


DMR12: 119809001
12
119809001
119810000
1000
1
3.51E−06
−0.5548171
11
1.1
CIT
Signaling


DMR12: 121514001
12
121514001
121516000
2000
1
3.39E−07
0.8815815
34
1.7
KDM2B


DMR12: 123452001
12
123452001
123453000
1000
1
1.90E−06
−0.4962994
9
0.9
SNRNP35
Translation


DMR12: 124005001
12
124005001
124006000
1000
1
8.23E−06
0.9747191
10
1
ZNF664; RFLNA; AC068790.8;
Transcription












AC068790.1


DMR12: 125102001
12
125102001
125104000
2000
1
1.21E−06
0.725551
42
2.1
AACS
Metabolism


DMR12: 125211001
12
125211001
125212000
1000
1
4.67E−06
−0.6246062
9
0.9
TMEM132B
Unknown


DMR12: 128045001
12
128045001
128046000
1000
1
5.88E−06
0.5920729
15
1.5


DMR12: 128609001
12
128609001
128610000
1000
1
5.10E−06
0.7145798
6
0.6
TMEM132C
Unknown


DMR12: 128663001
12
128663001
128666000
3000
1
1.78E−06
−0.5331325
66
2.2
TMEM132C
Unknown


DMR12: 128711001
12
128711001
128714000
3000
1
7.13E−06
0.7826136
51
1.
TMEM132C
Unknown


DMR12: 131432001
12
131432001
131436000
4000
1
8.59E−06
1.0151738
177
4.425
AC073578.4


DMR12: 132316001
12
132316001
132317000
1000
1
4.95E−06
−0.4315296
31
3.1
GALNT9
Metabolism


DMR12: 132731001
12
132731001
132734000
3000
1
1.20E−06
0.6993745
151
5.033
PGAM5; RNA5SP379; ANKLE2


DMR13: 19292001
13
19292001
19293000
1000
1
2.74E−06
−0.6175243
12
1.2
ANKRD26P3


DMR13: 21960001
13
21960001
21962000
2000
1
9.31E−06
−0.6041032
13
0.65


DMR13: 26666001
13
26666001
26667000
1000
1
3.98E−06
0.5971087
16
1.6
WASF3
Cytoskeleton


DMR13: 26944001
13
26944001
26945000
1000
1
3.80E−06
−0.5491039
5
0.5


DMR13: 28996001
13
28996001
28999000
3000
1
3.18E−07
−0.5977335
29
0.967
MTUS2
Cytoskeleton


DMR13: 36497001
13
36497001
36498000
1000
1
3.79E−06
1.036701
5
0.5
HIST1H2APS6


DMR13: 44088001
13
44088001
44089000
1000
1
2.34E−06
−0.6920414
8
0.8


DMR13: 45282001
13
45282001
45284000
2000
1
2.40E−06
0.7220002
18
9
GTF2F2
Transcription


DMR13: 56816001
13
56816001
56818000
2000
1
1.38E−06
−0.6806105
7
0.35


DMR13: 59560001
13
59560001
59561000
1000
1
6.94E−06
−0.5272228
3
0.3


DMR13: 59957001
13
59957001
59958000
1000
1
7.18E−07
−0.6158558
4
0.4
DIAPH3
Cytoskeleton


DMR13: 60867001
13
60867001
60869000
2000
1
1.10E−08
−0.7810444
3
0.15


DMR13: 65751001
13
65751001
65752000
1000
1
1.98E−06
−0.9786599
2
0.2


DMR13: 67510001
13
67510001
67511000
1000
1
9.08E−06
−0.6254222
9
0.9


DMR13: 69426001
13
69426001
69430000
4000
1
5.58E−06
0.5709394
20
0.5


DMR13: 73229001
13
73229001
73231000
2000
1
3.07E−08
0.9013651
18
0.9
RNY1P8


DMR13: 75109001
13
75109001
75112000
3000
1
7.76E−06
−0.4875661
21
0.7
RNU6-38P


DMR13: 79970001
13
79970001
79972000
2000
1
1.69E−06
−0.6902922
11
0.55
AL158064.1


DMR13: 95689001
13
95689001
95690000
1000
1
2.64E−06
−0.4309865
4
0.4
DNAJC3; MTND5P2; MTND6P18;
Protein Binding












MTCYBP3


DMR13: 98266001
13
98266001
98267000
1000
1
6.40E−06
0.6814442
15
1.5
FARP1
Signaling


DMR13: 99311001
13
99311001
99316000
5000
1
5.06E−07
−0.4679861
64
1.28
UBAC2; GPR183
Signaling


DMR13: 105201001
13
105201001
105202000
1000
1
4.30E−07
−1.2078866
4
0.4


DMR13: 107931001
13
107931001
107932000
1000
1
4.44E−07
0.7650271
5
0.5


DMR13: 113470001
13
113470001
113471000
1000
1
2.93E−06
−0.5195481
15
1.5
DCUN1D2; DCUN1D2-
Proteolysis












AS; RNU1-16P


DMR14: 24027001
14
24027001
24028000
1000
1
8.41E−06
−0.5667869
3
0.3
AL136419.1; DHRS4L1
Metabolism


DMR14: 24415001
14
24415001
24416000
1000
1
1.05E−06
−0.4544707
28
2.8
NYNRIN
Transcription


DMR14: 26918001
14
26918001
26919000
1000
1
8.53E−06
−1.0542583
9
0.9
AL110292.1; MIR4307HG; MIR4307


DMR14: 27063001
14
27063001
27064000
1000
1
9.70E−06
−0.675709
12
1.2
AL110292.1


DMR14: 30687001
14
30687001
30689000
2000
1
4.95E−07
−0.5831677
17
0.85
SCFD1; UBE2CP1
Receptor


DMR14: 34790001
14
34790001
34792000
2000
1
6.58E−06
0.6923428
19
0.95
BAZ1A
Metabolism


DMR14: 48570001
14
48570001
48572000
2000
1
4.86E−06
−0.7430836
17
0.85


DMR14: 52035001
14
52035001
52037000
2000
1
2.24E−06
−0.6898251
19
0.95
NID2
Extracellular Matrix


DMR14: 63549001
14
63549001
63551000
2000
1
4.26E−06
0.4715623
40
2
PPP2R5E; AL136038.3
Signaling


DMR14: 66714001
14
66714001
66716000
2000
1
2.42E−06
−0.4104103
12
0.6
GPHN
Receptor


DMR14: 67093001
14
67093001
67094000
1000
1
1.31E−06
−0.9221725
3
0.3
GPHN
Receptor


DMR14: 69838001
14
69838001
69840000
2000
1
2.99E−07
0.5105971
14
0.7


DMR14: 76610001
14
76610001
76611000
1000
1
2.87E−07
1.1097524
6
0.6
AC008050.1


DMR14: 79029001
14
79029001
79031000
2000
1
3.07E−07
−0.7369459
10
0.5
NRXN3
Receptor


DMR14: 80091001
14
80091001
80092000
1000
1
4.87E−06
−0.4813456
7
0.7


DMR14: 86746001
14
86746001
86748000
2000
1
2.02E−06
−0.6001955
11
0.55


DMR14: 88290001
14
88290001
88292000
2000
1
2.92E−06
−0.6365923
21
1.05
KCNK10
Transport


DMR14: 90106001
14
90106001
90110000
4000
1
7.56E−07
0.6835915
76
1.9
KCNK13; GLRXP2
Metabolism


DMR14: 90379001
14
90379001
90380000
1000
1
5.95E−06
−0.4452028
8
0.8
AL512791.2


DMR14: 92547001
14
92547001
92548000
1000
1
6.70E−06
0.9411342
6
0.6
RIN3
Signaling


DMR14: 95446001
14
95446001
95448000
2000
1
3.08E−07
−0.5229343
30
1.5
SYNE3


DMR14: 96645001
14
96645001
96648000
3000
1
7.46E−07
−0.6205166
10
0.333
RN7SKP108


DMR14: 99448001
14
99448001
99449000
1000
1
1.12E−06
−0.4549786
13
1.3
SETD3; RNU6-91P


DMR14: 101827001
14
101827001
101828000
1000
1
9.70E−06
0.7856008
12
1.2
PPP2R5C; AL137779.1
Signaling


DMR14: 103701001
14
103701001
103702000
1000
1
1.39E−06
0.4950851
36
3.6
KLC1; AL049840.2; AL049840.3;
Cytoskeleton; Transcription












AL049840.4; XRCC3


DMR14: 106189001
14
106189001
106190000
1000
1
3.29E−06
0.7841305
11
1.1
SLC20A1P2; IGHV1-18; IGHV3-19


DMR15: 30831001
15
30831001
30833000
2000
1
3.79E−07
−0.4597954
32
1.6
HERC2P10


DMR15: 34840001
15
34840001
34843000
3000
1
8.34E−06
−0.630889
23
0.767
AC018868.2; AQR
Transcription


DMR15: 36684001
15
36684001
36688000
4000
1
6.49E−06
−0.4764924
26
0.65
C15orf41
EST


DMR15: 40174001
15
40174001
40175000
1000
1
3.70E−07
0.7124244
14
1.4
BUB1B
Signaling


DMR15: 40422001
15
40422001
40423000
1000
1
1.30E−06
0.6713205
48
4.8
IVD
Metabolism


DMR15: 42047001
15
42047001
42048000
1000
1
6.07E−08
−0.8507646
8
0.8
PLA2G4E
Metabolism


DMR15: 42282001
15
42282001
42283000
1000
1
3.90E−06
1.0894247
10
1
TMEM87A; GANC
Unknown; Metabolism


DMR15: 43596001
15
43596001
43599000
3000
1
1.34E−06
−0.5614992
29
0.967
PPIP5K1; CKMT1B; STRC; RNU6-554P
Signaling; Extracellular Matrix


DMR15: 44253001
15
44253001
44256000
3000
1
8.70E−06
−0.4641334
29
0.967


DMR15: 49400001
15
49400001
49401000
1000
1
5.26E−06
−0.6751511
5
0.5
FAM227B


DMR15: 52365001
15
52365001
52366000
1000
1
1.70E−06
−0.6316008
7
0.7
MYO5A
Cytoskeleton


DMR15: 53743001
15
53743001
53744000
1000
1
5.22E−06
0.9684616
20
2
WDR72


DMR15: 61121001
15
61121001
61124000
3000
1
3.76E−06
−1.1658575
35
1.167
RORA
Receptor


DMR15: 62743001
15
62743001
62744000
1000
1
6.34E−06
−0.8823453
9
0.9
TLN2
Cytoskeleton


DMR15: 62963001
15
62963001
62966000
3000
1
9.65E−08
−0.6067509
39
1.3
AC079328.1


DMR15: 64230001
15
64230001
64231000
1000
1
4.83E−06
0.4355004
21
2.1
CSNK1G1; AC087632.1
Signaling


DMR15: 66241001
15
66241001
66242000
1000
1
1.99E−09
−0.6412479
13
1.3
MEGF11
Extracellular Matrix


DMR15: 68771001
15
68771001
68772000
1000
1
2.89E−06
−0.4701824
5
0.5
ANP32A
Signaling


DMR15: 70433001
15
70433001
70434000
1000
1
1.79E−07
−0.5189136
18
1.8


DMR15: 70529001
15
70529001
70532000
3000
1
6.97E−06
−0.3902115
25
0.833


DMR15: 71213001
15
71213001
71214000
1000
1
8.58E−07
−1.1917199
6
0.6
THSD4
Extracellular Matrix


DMR15: 75185001
15
75185001
75186000
1000
1
7.13E−06
0.5836484
17
1.7
RPL36AP45; C15orf39
Growth Factors & Cytokines


DMR15: 77892001
15
77892001
77893000
1000
1
4.85E−06
0.5930174
16
1.6
CSPG4P13


DMR15: 77963001
15
77963001
77964000
1000
1
3.35E−06
−0.6939627
8
0.8
AC104758.5


DMR15: 78209001
15
78209001
78211000
2000
1
6.59E−06
0.5237414
36
1.8
ACSBG1
Metabolism


DMR15: 78624001
15
78624001
78626000
2000
1
7.74E−06
−0.360405
29
1.45
CHRNA3; CHRNB4; AC067863.1
Receptor


DMR15: 78920001
15
78920001
78921000
1000
1
3.45E−06
0.984213
8
0.8
CTSH
Protease


DMR15: 86743001
15
86743001
86744000
1000
1
7.44E−06
−0.6118818
10
1
AGBL1
Signaling


DMR15: 87023001
15
87023001
87026000
3000
1
8.07E−09
−0.7509167
9
0.3
AGBL1
Signaling


DMR15: 91262001
15
91262001
91264000
2000
1
2.19E−06
−0.3980937
28
1.4
SV2B
Binding Protein


DMR15: 98978001
15
98978001
98980000
2000
1
1.95E−06
0.7588471
34
1.7
AC036108.1; PGPEP1L
Protease


DMR15: 101548001
15
101548001
101550000
2000
1
2.53E−06
−0.4412748
38
1.9


DMR16: 3791001
16
3791001
3792000
1000
1
8.93E−07
−0.4352327
24
2.4
CREBBP
Transcription


DMR16: 5472001
16
5472001
5475000
3000
1
2.42E−06
0.5231262
35
1.167
RBFOX1
Unknown


DMR16: 6397001
16
6397001
6399000
2000
1
4.39E−06
−0.8114704
16
0.8
RBFOX1
Unknown


DMR16: 6851001
16
6851001
6852000
1000
1
4.59E−06
−0.563282
11
1.1
RBFOX1
Unknown


DMR16: 6971001
16
6971001
6972000
1000
1
2.10E−06
−0.3482249
14
1.4
RBFOX1
Unknown


DMR16: 11992001
16
11992001
11995000
3000
1
1.99E−06
0.5223881
37
1.233
SNX29
Cytoskeleton


DMR16: 12633001
16
12633001
12634000
1000
1
4.43E−06
0.6979455
18
1.8


DMR16: 19052001
16
19052001
19053000
1000
1
1.12E−06
0.6511852
15
1.5
TMC7; AC099518.4
Transport


DMR16: 24437001
16
24437001
24440000
3000
1
5.38E−06
−0.7692339
19
0.633


DMR16: 27243001
16
27243001
27244000
1000
1
4.77E−06
−0.4181808
16
1.6
NSMCE1


DMR16: 28616001
16
28616001
28617000
1000
1
1.97E−06
−0.4645907
22
2.2
SULT1A1; AC020765.5
Metabolism


DMR16: 31116001
16
31116001
31117000
1000
1
1.98E−06
0.6873786
60
6
BCKDK; KAT8; AC135050.5; AC135050.6
Signaling; Epigenetic


DMR16: 46538001
16
46538001
46540000
2000
1
5.53E−06
−0.4745983
13
0.65
ANKRD26P1


DMR16: 55737001
16
55737001
55738000
1000
1
3.91E−06
−0.4457135
7
0.7
CES1P2


DMR16: 56968001
16
56968001
56969000
1000
1
3.35E−06
−0.4016815
14
1.4
CETP
Binding Protein


DMR16: 66282001
16
66282001
66283000
1000
1
8.56E−07
−0.9281135
4
0.4


DMR16: 68303001
16
68303001
68305000
2000
1
1.43E−06
0.642697
29
1.45
SLC7A6; SLC7A6OS; PRMT7
Metabolism


DMR16: 75102001
16
75102001
75103000
1000
1
8.16E−06
−0.3433842
21
2.1
ZNRF1; AC099508.1; LDHD
Transcription; Metabolism


DMR16: 75181001
16
75181001
75182000
1000
1
4.54E−06
−0.7466281
6
0.6
ZFP1


DMR16: 76252001
16
76252001
76255000
3000
1
4.04E−06
−0.4643694
35
1.167
AC010528.1


DMR16: 77339001
16
77339001
77340000
1000
1
8.60E−07
−0.785832
8
0.8
ADAMTS18
Protease


DMR16: 87824001
16
87824001
87825000
1000
1
3.42E−06
−0.5329661
20
2
SLC7A5; MIR6775
Metabolism


DMR16: 87970001
16
87970001
87973000
3000
1
5.79E−06
−0.3894862
42
1.4
BANP
Transcription


DMR16: 89402001
16
89402001
89403000
1000
1
5.94E−06
0.4520241
28
2.8
ANKRD11
EST


DMR16: 89713001
16
89713001
89714000
1000
1
4.47E−06
0.5205058
32
3.2
VPS9D1; VPS9D1-AS1; ZNF276
Transcription


DMR17: 963001
17
963001
964000
1000
1
8.05E−10
0.5073656
27
2.7
NXN; AC036164.1
Signaling


DMR17: 6433001
17
6433001
6434000
1000
1
2.06E−06
−0.3925399
25
2.5
AIPL1
Protein Binding


DMR17: 6984001
17
6984001
6985000
1000
1
1.41E−06
0.6759975
13
1.3
ALOX12-AS1; AC027763.2; AC040977.2;












AC040977.1


DMR17: 7838001
17
7838001
7839000
1000
1
2.42E−07
1.1202783
12
1.2
DNAH2; KDM6B
Cytoskeleton; Transcription


DMR17: 8909001
17
8909001
8911000
2000
1
1.70E−06
−0.5784843
34
1.7
PIK3R5
Signaling


DMR17: 9707001
17
9707001
9708000
1000
1
9.47E−06
0.6569972
22
2.2
USP43


DMR17: 10595001
17
10595001
10597000
2000
1
5.43E−06
0.8541415
19
0.95
MYHAS; AC005323.1


DMR17: 11763001
17
11763001
11765000
2000
1
1.26E−08
−0.736115
21
1.05
DNAH9
Cytoskeleton


DMR17: 11863001
17
11863001
11865000
2000
1
3.77E−07
−0.5703788
13
0.65
DNAH9; AC005209.1
Cytoskeleton


DMR17: 16245001
17
16245001
16246000
1000
1
7.66E−06
0.4462797
23
2.3
PIGL
Metabolism


DMR17: 18278001
17
18278001
18280000
2000
1
1.94E−06
0.5858592
38
1.9
AC127537.1; TOP3A
Transcription


DMR17: 28031001
17
28031001
28033000
2000
1
4.47E−06
−0.6934768
18
0.9
AC090287.1; RF00156; NLK
Signaling


DMR17: 30727001
17
30727001
30728000
1000
1
7.92E−07
−0.4150466
20
2
SUZ12P1; AC127024.7; AC127024.3; AC12 7024.2


DMR17: 33222001
17
33222001
33223000
1000
1
1.04E−06
−0.8983157
1
0.1
ASIC2
Transport


DMR17: 34755001
17
34755001
34756000
1000
1
2.17E−06
−0.4877424
12
1.2


DMR17: 35682001
17
35682001
35684000
2000
1
6.58E−06
0.9483375
25
1.25
AP2B1
Transport


DMR17: 36222001
17
36222001
36224000
2000
1
7.79E−09
0.9564993
26
1.3
CCL4L2; AC243829.5


DMR17: 36740001
17
36740001
36741000
1000
1
4.32E−07
−0.4195057
18
1.8


DMR17: 40506001
17
40506001
40508000
2000
1
8.91E−06
0.634816
18
0.9
TNS4; AC004585.1
Signaling


DMR17: 40846001
17
40846001
40847000
1000
1
4.02E−06
−0.4223231
8
0.8
TMEM99; AC004231.3


DMR17: 42436001
17
42436001
42439000
3000
1
3.26E−06
0.5821806
46
1.533
RNU7-97P


DMR17: 43109001
17
43109001
43112000
3000
1
3.20E−06
0.5820137
54
1.8
BRCA1
Transcription


DMR17: 44496001
17
44496001
44497000
1000
1
2.48E−06
0.8977599
12
1.2
GPATCH8; AC103703.1


DMR17: 50774001
17
50774001
50776000
2000
1
3.92E−07
0.6612705
43
2.15
ANKRD40CL; MIR8059; AC005921.4


DMR17: 55556001
17
55556001
55558000
2000
1
7.06E−07
−0.5026078
24
1.2
AC105021.1; GARSP1; RNU6-1249P


DMR17: 60736001
17
60736001
60738000
2000
1
2.22E−07
0.5793683
19
0.95
BCAS3; AC110602.1
Transcription


DMR17: 61238001
17
61238001
61239000
1000
1
1.97E−06
0.8992406
19
1.9
BCAS3
Transcription


DMR17: 63464001
17
63464001
63465000
1000
1
2.09E−06
−0.6950074
5
0.5
AC005828.5; PPIAP55


DMR17: 64998001
17
64998001
64999000
1000
1
2.28E−06
0.494202
18
1.8


DMR17: 67168001
17
67168001
67169000
1000
1
8.02E−07
0.7367953
11
1.1
HELZ
Transcription


DMR17: 67631001
17
67631001
67634000
3000
1
7.11E−07
−0.5059349
61
2.033
PITPNC1; AC079331.1


DMR17: 68913001
17
68913001
68914000
1000
1
1.15E−08
−0.6930886
6
0.6
ABCA8
Receptor


DMR17: 73378001
17
73378001
73379000
1000
1
1.80E−06
0.5528568
16
1.6
SDK2
Development


DMR17: 73741001
17
73741001
73742000
1000
1
2.61E−06
−0.4850082
19
1.9
AC125421.1; LINC00469


DMR17: 75194001
17
75194001
75195000
1000
1
4.19E−06
0.5828536
45
4.5


DMR17: 75537001
17
75537001
75538000
1000
1
3.32E−07
0.621861
18
1.8
LLGL2
Development


DMR17: 76900001
17
76900001
76902000
2000
1
4.88E−06
−0.3985727
36
1.8
MGAT5B
Metabolism


DMR17: 77598001
17
77598001
77599000
1000
1
8.09E−06
0.7931789
16
1.6
AC021683.5


DMR17: 78336001
17
78336001
78337000
1000
1
4.25E−06
0.5847373
15
1.5
AC087645.2


DMR18: 3760001
18
3760001
3762000
2000
1
9.46E−06
−0.4058592
28
1.4
DLGAP1; AP002478.2
Signaling


DMR18: 8749001
18
8749001
8750000
1000
1
3.17E−06
−0.5126872
11
1.1
MTCL1


DMR18: 9902001
18
9902001
9903000
1000
1
5.82E−06
0.4980064
23
2.3
AC006238.1


DMR18: 10568001
18
10568001
10569000
1000
1
5.04E−07
1.172583
10
1


DMR18: 21429001
18
21429001
21430000
1000
1
4.35E−06
0.6792457
5
0.5
GREB1L; AC015878.1


DMR18: 22336001
18
22336001
22337000
1000
1
1.66E−08
−0.5445338
17
1.7


DMR18: 26821001
18
26821001
26822000
1000
1
1.04E−06
1.0010425
2
0.2
AQP4-AS1; AC018371.1; AC018371.2


DMR18: 31938001
18
31938001
31939000
1000
1
1.18E−06
1.0044881
5
0.5
TRAPPC8; RNU6-1050P; AC009831.1


DMR18: 35725001
18
35725001
35727000
2000
1
1.72E−06
−0.5902583
8
0.4
AC090229.1


DMR18: 55250001
18
55250001
55252000
2000
1
7.32E−06
−0.6984778
24
1.2
TCF4
Transcription


DMR18: 56496001
18
56496001
56498000
2000
1
3.03E−06
−0.5625773
14
0.7


DMR18: 56968001
18
56968001
56969000
1000
1
5.66E−06
−0.4509659
5
0.5
WDR7
Unknown


DMR18: 58343001
18
58343001
58344000
1000
1
4.45E−06
0.6257183
15
1.5
NEDD4L
Protease


DMR18: 62436001
18
62436001
62438000
2000
1
6.86E−06
−0.7114954
34
1.7
ACTBP9


DMR18: 63599001
18
63599001
63601000
2000
1
9.61E−06
−0.8856543
15
0.75
SERPINB13
Protease


DMR18: 67261001
18
67261001
67263000
2000
1
7.94E−07
−0.7200381
13
0.65


DMR18: 67302001
18
67302001
67303000
1000
1
6.31E−07
−1.1893104
6
0.6


DMR18: 73132001
18
73132001
73133000
1000
1
1.40E−06
−0.5854772
3
0.3


DMR18: 74204001
18
74204001
74205000
1000
1
4.98E−06
0.8467968
10
1
AC090398.1


DMR18: 78053001
18
78053001
78054000
1000
1
5.01E−06
−0.4758
1
1.7


DMR19: 603001
19
603001
605000
2000
1
6.20E−06
0.7706511
75
3.75
HCN2
Metabolism


DMR19: 862001
19
862001
864000
2000
1
7.70E−06
0.8872191
103
5.15
ELANE; CFD; MED16
Protease; Immune; Transcription


DMR19: 3230001
19
3230001
3231000
1000
1
6.08E−08
−0.5573396
10
1
CELF5
Translation


DMR19: 7500001
19
7500001
7504000
4000
1
7.11E−06
0.6057991
151
3.775
PEX11G; TEX45; AC008878.1


DMR19: 7599001
19
7599001
7601000
2000
1
4.15E−06
0.6055752
22
1.1
CAMSAP3


DMR19: 7830001
19
7830001
7831000
1000
1
9.26E−06
0.6855874
85
8.5
EVI5L


DMR19: 10197001
19
10197001
10199000
2000
1
3.21E−06
0.7194797
31
1.55
DNMT1
Epigenetic


DMR19: 13626001
19
13626001
13627000
1000
1
6.20E−08
0.5860472
15
1.5
CACNA1A
Transport


DMR19: 13800001
19
13800001
13801000
1000
1
7.08E−06
0.4843254
41
4.1
ZSWIM4; AC020916.2
Transcription


DMR19: 16948001
19
16948001
16950000
2000
1
2.06E−07
0.7652186
21
1.05
CPAMD8


DMR19: 18085001
19
18085001
18087000
2000
1
1.27E−06
−0.5950983
29
1.45
IL12RB1


DMR19: 21043001
19
21043001
21044000
1000
1
4.89E−06
−0.427829
7
0.7
ZNF430
Transcription


DMR19: 27955001
19
27955001
27956000
1000
1
8.25E−06
−0.5328916
6
0.6
AC006504.7; AC005357.2


DMR19: 29076001
19
29076001
29078000
2000
1
5.28E−06
−0.2891072
27
1.35


DMR19: 31144001
19
31144001
31145000
1000
1
1.69E−06
−0.4941799
8
0.8
AC020912.1; TSHZ3
Transcription


DMR19: 33107001
19
33107001
33110000
3000
1
7.18E−06
−0.4886915
60
2
GPATCH1


DMR19: 36978001
19
36978001
36980000
2000
1
8.79E−06
−0.5872276
23
1.15
ZNF568
Transcription


DMR19: 39861001
19
39861001
39862000
1000
1
1.19E−07
0.7759882
8
0.8
FCGBP
Extracellular Matrix


DMR19: 42889001
19
42889001
42891000
2000
1
1.02E−06
−0.5300016
34
1.7
PSG1
Extracellular Matrix


DMR19: 44922001
19
44922001
44924000
2000
1
9.88E−06
0.4064002
42
2.1
AC011481.4; APOC1; APOC1P1
Transport


DMR19: 46318001
19
46318001
46319000
1000
1
1.24E−07
0.8315254
15
1.5
HIF3A; AC007193.2
Transcription


DMR19: 50906001
19
50906001
50907000
1000
1
6.16E−06
0.6470385
8
0.8
KLKP1; KLK4
Protease


DMR20: 9582001
20
9582001
9583000
1000
1
6.46E−06
−0.7489359
6
0.6
PAK5; AL353612.1


DMR20: 11768001
20
11768001
11770000
2000
1
1.93E−06
−0.6202546
14
0.7


DMR20: 13703001
20
13703001
13704000
1000
1
4.88E−08
0.8215851
13
1.3


DMR20: 15075001
20
15075001
15076000
1000
1
4.59E−06
−0.6158566
7
0.7
MACROD2


DMR20: 18989001
20
18989001
18990000
1000
1
5.57E−06
−0.8119199
7
0.7


DMR20: 22597001
20
22597001
22600000
3000
1
1.82E−06
−0.5801319
29
0.967
LNCNEF


DMR20: 23455001
20
23455001
23456000
1000
1
3.68E−07
−0.587544
11
1.1
CST11; RF00019; AL109954.2
Signaling


DMR20: 26117001
20
26117001
26118000
1000
1
7.19E−06
−0.4663103
12
1.2
NCOR1P1


DMR20: 34042001
20
34042001
34043000
1000
1
9.08E−06
0.4914578
16
1.6
RALY; MIR4755
Transcription


DMR20: 36082001
20
36082001
36084000
2000
1
5.12E−06
0.8193762
22
1.1
AL035420.1; HMGB3P2; AL035420.2;












EPB 41L1


DMR20: 39079001
20
39079001
39080000
1000
1
3.50E−06
0.9926674
17
1.7


DMR20: 47404001
20
47404001
47405000
1000
1
1.44E−06
0.5621608
14
1.4
LINC01754


DMR20: 47941001
20
47941001
47942000
1000
1
2.38E−07
−0.484401
21
2.1
AL357558.2


DMR20: 48875001
20
48875001
48877000
2000
1
3.41E−06
−0.6567095
32
1.6


DMR20: 52445001
20
52445001
52447000
2000
1
1.88E−06
−0.3016192
23
1.15
LINC01524; AL109610.1


20: 53996001
20
53996001
53997000
1000
1
2.25E−07
−0.4277728
11
1.1
BCAS1


20: 55763001
20
55763001
55764000
1000
1
5.23E−06
−0.5593399
7
0.7


20: 55944001
20
55944001
55945000
1000
1
2.32E−06
1.1446512
8
0.8


20: 61483001
20
61483001
61484000
1000
1
4.29E−08
−0.5607195
16
1.6
CDH4
Extracellular Matrix


20: 64093001
20
64093001
64095000
2000
1
4.08E−06
0.8474669
26
1.3
OPRL1; LKAAEAR1; MYT1
Receptor; Transcription


21: 6152001
21
6152001
6153000
1000
1
8.97E−06
−0.4006685
2
2.1


21: 6342001
21
6342001
6344000
2000
1
9.96E−06
−0.5381794
17
0.85
CU633906.2


21: 10741001
21
10741001
10743000
2000
1
6.11E−06
−0.5337132
16
0.8


21: 17965001
21
17965001
17966000
1000
1
2.16E−06
0.6491482
4
0.4
CHODL


21: 19680001
21
19680001
19681000
1000
1
5.08E−07
−0.7726042
5
0.5


21: 23640001
21
23640001
23641000
1000
1
2.15E−06
−0.9618059
6
0.6


21: 23835001
21
23835001
23836000
1000
1
3.71E−08
1.1225167
11
1.1


21: 26824001
21
26824001
26825000
1000
1
2.31E−06
0.8413207
26
2.6


21: 31508001
21
31508001
31509000
1000
1
8.63E−06
−0.4623201
19
1.9
TIAM1
Transcription


21: 33493001
21
33493001
33494000
1000
1
5.79E−06
0.5975765
29
2.9
AP000302.1; DNAJC28; GART
Transcription; Metabolism


21: 38356001
21
38356001
38357000
1000
1
9.45E−08
0.4944057
22
2.2


21: 43583001
21
43583001
43585000
2000
1
3.04E−07
−0.5202146
12
0.6
HSF2BP


21: 46148001
21
46148001
46149000
1000
1
5.34E−07
−0.5375053
17
1.7
FTCD; FTCD-AS1


22: 17210001
22
17210001
17212000
2000
1
8.66E−06
0.4740007
51
2.55
ADA2; FAM32BP
Transcription


22: 18386001
22
18386001
18388000
2000
1
5.01E−06
0.7548866
11
0.55
FAM230J


22: 29607001
22
29607001
29609000
2000
1
9.53E−06
0.4201027
30
1.5
NF2; RPEP4
Cytoskeleton


22: 34327001
22
34327001
34328000
1000
1
1.63E−06
−1.0055776
4
0.4


22: 39704001
22
39704001
39706000
2000
1
3.50E−07
0.5982911
49
2.45


22: 46325001
22
46325001
46329000
4000
1
6.89E−06
0.428583
91
2.275
GTSE1; TRMU


22: 49957001
22
49957001
49958000
1000
1
5.09E−07
−0.4575814
23
2.3
PIM3; MIR6821
Epigenetic


X: 1364001
x
1364001
1369000
5000
1
4.56E−06
0.9289401
494
9.88
IL3RA
Receptor


X: 13651001
X
13651001
13653000
2000
1
6.31E−06
1.0192184
30
1.5
TCEANC
Transcription


X: 16276001
X
16276001
16277000
1000
1
5.26E−06
−0.6609142
4
0.4


X: 17491001
X
17491001
17492000
1000
1
3.02E−07
−0.6759206
10
1
NHS


X: 22781001
X
22781001
22782000
1000
1
7.68E−06
−0.8046573
5
0.5
PTCHD1-AS


X: 23695001
X
23695001
23697000
2000
1
2.79E−06
0.8706407
42
2.1
PRDX4; ACOT9
Electron Transport; Metabolism


X: 24165001
X
24165001
24166000
1000
1
2.98E−06
0.9300575
13
1.3
ZFX
Transcription


X: 31900001
x
31900001
31901000
1000
1
3.63E−06
−0.9599591
13
1.3
DMD
Development


X: 44792001
X
44792001
44793000
1000
1
5.94E−06
0.6076124
11
1.1


X: 46370001
X
46370001
46372000
2000
1
3.53E−06
1.0238952
34
1.7


X: 46581001
X
46581001
46582000
1000
1
2.05E−08
0.6105555
27
2.7
CHST7
Metabolism


X: 51310001
x
51310001
51312000
2000
1
9.77E−06
−0.9422977
18
0.9


X: 52488001
X
52488001
52489000
1000
1
2.93E−06
−0.5199447
18
1.8
BX510359.8; BX510359.7; RBM22P6;












XAGE1A


X: 53469001
X
53469001
53470000
1000
1
8.43E−06
0.9332251
7
0.7
VTRNA3-1P


X: 72115001
X
72115001
72116000
1000
1
1.31E−06
−0.8874394
13
1.3
NHSL2


X: 73379001
X
73379001
73380000
1000
1
9.24E−06
−0.8777041
5
0.5


X: 91549001
X
91549001
91550000
1000
1
3.45E−06
0.8262897
21
2.1


X: 97607001
X
97607001
97609000
2000
1
2.72E−06
−0.7725409
24
1.2
DIAPH2; DIAPH2-AS1
Cytoskeleton


X: 118476001
X
118476001
118479000
3000
1
3.48E−07
0.7506308
56
1.867


X: 123148001
X
123148001
123149000
1000
1
8.23E−06
0.9915717
5
0.5


X: 123827001
X
123827001
123831000
4000
1
3.22E−06
0.5590727
108
2.7


X: 125538001
X
125538001
125539000
1000
1
4.60E−07
−1.1559361
3
0.3


X: 127483001
X
127483001
127484000
1000
1
1.80E−06
−1.0343179
7
0.7


X: 130717001
X
130717001
130718000
1000
1
1.53E−06
−0.4949376
5
0.5
ENOX2
Transcription


X: 135215001
X
135215001
135216000
1000
1
9.76E−06
−0.939075
13
1.3
AC234771.2


X: 141265001
X
141265001
141266000
1000
1
1.11E−08
0.9501535
14
1.4
RBMX2P2


X: 151196001
X
151196001
151197000
1000
1
7.27E−06
−1.0056685
6
0.6


X: 155134001
X
155134001
155135000
1000
1
1.52E−08
−1.1075893
6
0.6
BX293995.1; MTCP1


X: 155830001
X
155830001
155832000
2000
1
2.05E−07
−0.5866864
13
0.65
AMD1P2


Y: 26328001
Y
26328001
26329000
1000
1
7.16E−06
0.7192894
36
3.6
PPP1R12BP1









The genomic features of the offspring autism susceptibility DMRs were investigated. The chromosomal locations of the DMRs at p<1e-05 within the human genome are presented in FIG. 1B. The arrowheads (triangles) indicate the individual DMRs, and the black boxes represent a cluster of DMRs. The DMRs are present on all chromosomes. The CpG density of the DMRs is generally less than 10 CpG per 100 bp with 1-3 CpG predominant for the paternal offspring autism susceptibility DMRs, FIG. 1C. The size of the DMRs was predominantly 1-3 kb for the sperm DMRs, FIG. 1D. Additional genomic features are presented in Table 3. The log-fold-change (LFC) in Table 3 demonstrated approximately 60% of the DMRs have an increase in DNA methylation, and the rest a decrease in DNA methylation. Therefore, the majority of the sperm DMRs had low CpG density, termed a CpG desert, and were 1 kb in length with both an increase or decrease in DNA methylation.


The paternal offspring autism susceptibility sperm DMR associated genes and corresponding gene functional categories were determined, as presented in Table 3. The functional categories corresponding to each DMR associated gene are summarized in FIG. 2A. The signaling, transcription, and metabolism functional categories are predominant. This reflects that these gene functional categories have the highest number of genes within them. A comparison of previously identified genes associated with neurodegenerative disease and autism with the DMR associated genes of this study are summarized in FIG. 2B. These autism-associated genes have previously been shown to be regulated or involve genetic mutations within autism patients and the gene symbols, descriptions and associated references are presented in Table 4. The DMR associated genes were also used in a gene pathway or gene set analysis to identify associated pathways. Interestingly, the top pathway or gene set identified was autism and the majority of the subsequent pathways with greater than three genes were all neurodevelopmental or neuro-pathology associated pathways, listed in Table 2. All those gene sets were found to be significant and a list of the specific DMR associated genes are provided in Table 2. Therefore, the DMR associated genes did correlate well with previously identified autism and neurodevelopment associated genes.









TABLE 2







DMR Associated Gene Pathways or Gene Sets













Pathway or
Total # of
Pathology

Percent




GeneSet Name
Neighbors
Gene Set Pathway
Overlap
Overlap
Overlapping Genes
p-value
















Protein regulators
313
autism
19
6
RBFOX1, CD5, CCR5, GRIN1,
9.02E−05


ofautism




GPHN, SLC7A5, SNTG2,







RELN, ARHGAP32, OPRM1,







DLGAP2, DIAPH3, CACNA1A,







KCNMA1, TSHZ3, SLC25A12,







NRXN3, SEMA3F, RORA


Protein regulators
616
intellectualdisability
28
4
SRGAP3, ERBB4, DMD, TG, AHI1,
3.42E−04


ofintellectual




ANKRD11, CHL1, ST3GAL5,


disability




BCKDK, CDK19, DLGAP2, NARS2,







PHF21A, KDM2B, CAMTA1,







MCPH1, CREBBP, ARHGEF10,







TCF4, LIMK1, STIM1, RELN,







PRMT7, DPYSL2, SORL1, LRMDA,







CDH18, EXOC4


Protein regulators
273
neurodevelopmental
16
5
RBFOX1, SRGAP3, GRIN1,
4.70E−04


of

disorder


LIMK1, AHI1, DOCK3, ANKS1B,


neurodevelopmentaldisorder




ELP4, ANKRD11, RELN, DPYSL2,







ST3GAL5, AGAP1, MCPH1,







CREBBP, TCF4


Protein regulators
538
psychiatricdisorder
25
4
GRIN2B, PDE2A, ERBB4,
5.12E−04


ofpsychiatric




AHI1, CCKBR, ANKS1B,


disorder




DLGAP1, CCDC141, OPRM1,







PDLIM5, GABBR1, SORCS2,







CREBBP, ARHGEF10, TCF4,







DNMT1, RBFOX1, GPHN,







USP46, RELN, DPYSL2,







ADRA2C, IMMP2L, NRXN3, ALK


Protein regulators
18
intellectual
4
21
GRIN2B, GRIN1, DMD, RELN
5.95E−04


ofintellectual

impairment


impairment


Protein regulators of
86
estrogen
8
9
KDM6B, PPARG, ERBB4,
6.90E−04


estrogen receptor-

receptor-


PIK3CD, NDRG1, CREBBP,


positive breast cancer

positive


SLC7A5, STIM1




breast cancer


Protein regulators
147
behavioral
10
6
GRIN2B, GRIN1, ERBB4, PARK7,
1.80E−03


ofbehavioral

disorder


STX1A, RORA, DNMT1,


disorder




TMEM173, AP2B1, OPRM1


Protein regulators
14
severe
3
20
PAX2, AIPL1, MERTK
3.39E−03


ofsevere visual

visual


impairment

impairment
















TABLE 4







DMR associated Gene and Protein Regulators of Autism









Gene

Literature References


Name
Gene Description
PMID





SLC25A12
solute carrier family 25 member 12
19913066; 16205742;




19360665; 19913066;




18180767


NRXN3
neurexin 3
23306218


SEMA3F
semaphorin 3F
30635860


RORA
RAR related orphan receptor A
27179922; 26625251;




1336


RBFOX1
RNA binding fox-1 homolog 1
18329129; 17503474


CD5
CD5 molecule
28979127


CCR5
C-C motif chemokine receptor 5
28986277



(gene/


GRIN1
glutamate ionotropic receptor
31299220



NMDA ty


GPHN
gephyrin
25149987


SLC7A5
solute carrier family 7 member 5
27912058


SNTG2
syntrophin gamma 2
17292328; 17292328


RELN
reelin
15749247; 15560956;




19359144; 25450950;




28966264; 26285919;




28966264; 15820235;




12192627


ARHGAP32
Rho GTPase activating protein 32
30045817


OPRM1
opioid receptor mu 1
21525276


DLGAP2
DLG associated protein 2
28407363


DIAPH3
diaphanous related formin 3
20308993; 20308993


CACNA1A
calcium voltage-gated channel
28799511; 26566276;



subunit
26566276; 25735478;




26566276


KCNMA1
potassium calcium-activated
17236127



channel s


TSHZ3
teashirt zinc finger homeobox 3
27668656









The final analysis examines the statistical significance and validation of the DMRs for the paternal offspring autism susceptibility. Initially, a permutation analysis was performed on the DMRs to demonstrate the DMRs were not due to background variation in the data and randomly generated. The permutation analysis shows the number of DMRs generated from the control versus autism case comparison was significantly greater than the DMRs generated from random subsets within the analysis, in FIG. 3. The dashed line to the right indicates the comparison DMRs versus the low numbers from the random subset comparison. Another analysis involved a cross validation of the DMRs and demonstrated approximately 80% accuracy in the confirmation of the DMRs to assess autism susceptibility. A principal component analysis (PCA) of the control male sperm without an autistic child versus the male sperm with an autistic child is presented in FIG. 2C. A clear separation of the DMR principal components is seen between the groups. This demonstrates a distinction between the DMR principal components. The current disclosure provides that epigenetic biomarkers exist and may be used to diagnose that a father may have an autistic child.


The frequency of autism in the population has dramatically increased over tenfold the past several decades. This increase appears to be due in part to increased diagnosis efficiency from 1975 to the early 2000's, as well as greater public awareness of the disease. The more recent increase in the last couple of decades suggests environmental factors and exposures also have a role in autism prevalence. Although many suggestions have been made on specific toxicants and factors being involved, more extensive analysis and better understanding of autism etiology can be needed to understand this increase in autism frequency. An example is the suggestion assisted reproduction and in vitro fertilization are involved but follow up studies demonstrated no risk of ASD in children born after assisted reproduction. One factor that has been correlated with autism is paternal age and sperm DNA methylation alterations. Previous studies have shown a hypermethylation of sperm DNA can be associated with male infertility, abnormal sperm parameters, and increasing age. Therefore, the majority of DMR involve an increase in DNA methylation when associated with infertility or age. The current study demonstrated 60% of the DMRs have an increase in DNA methylation and 40% of DMRs decreases in DNA methylation, as listed in Table 3. Therefore, a mixture of an increase and decrease in methylation can be observed, which can be distinct from the sperm hypermethylation observed in male infertility and aging. Since all the paternal patients were fertile and generally younger ages, the current study observations appear to be distinct from infertility and aging DNA hypermethylation. Therefore, the current disclosure was designed to identify a sperm epigenetic biomarker to assess a father's ability to transmit autism susceptibility to his offspring.


Altered germline epigenetics has been shown to impact offspring health later in life, and if permanently programmed, to promote the epigenetic transgenerational inheritance of disease and pathology to subsequent generations. Since sperm or egg epigenetics can impact the zygote epigenetics and transcription following fertilization, as well as the subsequent stem cell population in the early embryo epigenetics and transcription, all subsequently derived somatic cells also have the potential to have an altered cell type specific epigenomes and transcriptomes later in development. This molecular alteration has been shown to be associated with adult somatic cell epigenetics, transcriptomes, and associated diseases. The ability of an ancestral or early life exposure to impact the germline epigenetics to then subsequently impact the offspring epigenetics and susceptibility to develop pathology and disease has been established, and may be anticipated to be a component of autism etiology as well.


The application of a sperm molecular diagnostic can be used in an assisted reproduction setting. Routine semen analysis and genetic testing can be used in most in vitro fertilization clinical settings. Although epigenetic analysis is not as routine, the proposal for such analysis may be made. The analysis of male infertility using sperm DNA methylation alterations has been developed. Epigenetic alterations (DNA methylation) in sperm have been shown to associate in fathers of families with autistic children. That study used a targeted array-based approach that focused on high density CpG islands that constitute approximately 1% of the genome, but does demonstrate such an analysis can be feasible. The current disclosure provides a genome-wide approach to identify altered DNA methylation for paternal sperm and offspring autism susceptibility.


Although genetics may be involved in autism etiology, genome-wide association studies (GWAS) have demonstrated generally less than 1% of the patients with a specific disease, such as neurodegenerative disease, have a correlated genetic mutation. ASD can be similar with only a few percent correlation with associated genetic mutations. An additional molecular mechanism to consider for ASD disease etiology involves epigenetics. The current study uses a more genome-wide approach to investigate sperm DNA methylation in fathers with or without autistic children. A procedure to assess DNA methylation alterations in low density CpG regions that constitute over 95% of the human genome was used in comparison to the high density CpG procedures previously used. A highly significant and reproducible signature of differential DNA methylation regions (DMRs) was identified comparing the sperm from fathers with or without autistic children. The genomic features of the DMRs were identified and demonstrated generally 1 kb lengths and low density CpG regions. The DMR associated genes were identified, and a number of previously identified autism linked genes were present (FIG. 2B, Tables 2 and 4). In regard to the autism sperm DMR biomarkers, a strong separation in a principal component analysis (PCA) was observed. In addition to this validation, the permutation and cross validation analysis demonstrated the robustness and sensitivity of the analysis. The observations demonstrate the paternal sperm epigenetic analysis can be effective at identifying offspring susceptibility for autism. The current disclosure provides a diagnostic for autism susceptibility may be developed.


Although a reproducible epigenetic signature was identified for paternal transmission of susceptibility of autism children was identified and statistically significant, a limitation of the current study may be the low number of samples used for the analysis. Expanded clinical trials are required with increase numbers, greater ethnic diversity, and more thorough assessment of the impacts of paternal age. The impacts of these variables need to be elucidated to improve and expand the accuracy of the analysis. The expanded clinical trial with greater numbers and diverse subpopulations may be important to develop a diagnostic. However, the current study does provide a diagnostic may be developed.


Applications of the paternal offspring autism susceptibility biomarker / diagnostic may potentially improve the health care for ASD patients. This would allow IVF patients to assess risk and determine management procedures. Importantly, this would allow clinicians to plan the offspring's clinical management options more efficiently. Potential preventative treatments could be considered to reduce the severity of the autism spectrum disorder. The availability of the assay could also be used in a research setting to facilitate the identification of environmental factors potentially involved in the ASD etiology. Therefore, potential therapeutic and preventative options not previously considered could be taken.


The current disclosure identified a genome-wide signature of DNA methylation sites that are associated with the paternal transmission of offspring autism susceptibility. The current disclosure provides the proof of concept for the assay and biomarkers. Therefore, the identification of offspring susceptibility can be assessed, allowing better clinical management of ASD. The potential for therapy options may be expanded to improve health care for ASD. Such epigenetic biomarkers are anticipated to exist for many disease and pathology conditions, which may facilitate the future preventative medicine strategies for health care.


Methods
Clinical Sample Collection

A single center (IVIRMA Valencia, Spain) prospective and open clinical study was performed. The participant approval was obtained prior to the clinical sample collection. The study protocols were approved by the Institutional Review Board 1311-VLC-136-FC. The semen was analyzed as described in the Supplemental Methods. Samples were immersed in liquid nitrogen and then stored at −20 C. prior to analysis.


Epigenetic Analysis, Statistics and Bioinformatics

Sperm DNA was isolated as previously described 15. Methylated DNA immunoprecipitation (MeDIP), followed by next generation sequencing (MeDIP-Seq) was performed. MeDIP-Seq, sequencing libraries, next generation sequencing, and bioinformatics analysis were performed as described, and are found in the Supplemental Methods. The statistical analysis and validation protocols were performed as previously described, and are found in the Supplemental Methods. All molecular data has been deposited into the public database at NCBI (GEO # pending), and R code computational tools are available at GitHub (https://github.com/skinnerlab/MeDIP-seq) and www.skinner.wsu.edu.


Example 2

Further studies are conducted to increase the sensitivity of the prediction model of DNA methylation signature in father's sperm that is predictive of ASD in context of more study and control participants. The commercialization of a validated DNA methylation signature to predict the susceptibility of a father having offspring with ASD would be instrumental in increasing the rates of early diagnosis and therapeutic interventions. With this test, expecting parents at higher risk or concerned about a potential ASD diagnosis for their child can better understand their potential of having a child with ASD and drive more vigilant developmental assessments and diagnosis.


Example 3

More studies are conducted to transition the examination of these biomarkers to a more commercial platform. The current discovery research and algorithmic model was developed using methylation immunoprecipitation (MeDIP) technology. A scalable and more cost-effective platform to conduct the ASD prediction test from the father's sperm using targeted sequencing technology is implemented. An at-home semen collection kit provided to expecting fathers by a couple's obstetrician. The fathers may collect a semen sample and ship the sample directly to a lab for processing and analysis. Results may be provided to the ordering obstetrician, similar to the standard data-flow for paternal carrier testing. Additional research, may focus on 1) integrating the refined and scalable test into a fully regulated, CAP accredited and CLIA certified, workflow and 2) developing an appropriate physician and patient facing report. A report of this type may require significant input from both patients and physicians due to the sensitivities of an ASD prediction. Subsequent research may interrogate the existence of any of the paternal methylation patterns, or other unique methylation patterns, in young children diagnosed with ASD. This subsequent research may hopefully lead to commercialization of a newborn screening diagnostic for ASD.


Patient Cohort Distribution

About 60 fathers between the ages of 30-45 who have a single offspring with the diagnosis of ASD, level 1, 2, or 3, no known family history of ASD, and no identified genetic diagnosis of ASD are participating in the study. ASD diagnosis is required from a comprehensive diagnostic evaluation following the criteria and standardization provided by the American Psychiatric Association's Diagnostic and Statistical Manual, Fifth Edition (DSM-5). Additionally, for this study, diagnosis is required by a qualified Pediatric Psychologist, Pediatric Physiatrist, Pediatric neurologist, or Developmental Pediatrician. Currently participants with a known family history of ASD, or an identified genetic diagnosis may be excluded to remove the variable of genetic inheritance into this study. In the cases of familial inheritance or germline genetic mutations, it is likely that the DNA code plays a more significant role in ASD risk than DNA methylation. In addition to the diagnosis, all co-occurring conditions associated with the ASD individuals as well as basic anthropometric measurements (i.e. height, weight, sex, age etc.) of father and offspring may be collected.


Further semen samples are collected from the subjects and processed. Processing includes:

    • 1. Counting of sperm under a microscope to determine concentration, following the World Health Organization guidelines for sperm counting
    • 2. Isolation of sperm cells from somatic cells
    • 3. DNA extraction from a pure sperm cell population
    • 4. DNA fragmentation into 200-400 base pairs
    • 5. Methylated DNA immunoprecipitation (MeDIP) to capture methylated genomic regions
    • 6. DNA purification
    • 7. High-Throughput DNA Illumina DNA sequencing


Data Analysis

Bioinformatic analysis of sequencing results may first be done blinded to cohort type. Reads from each sample may be mapped back to HG19 human genome. Utilizing the R programming language, the differential sequencing coverage as well as the relative DNA methylation coverage between samples are calculated. Samples are re-identified and analyzed for consistent and reproducible patterns that are predictive of offspring with an ASD diagnosis. A process for high-fidelity analysis that includes is utilized:

    • 1. Filtering out locations with inadequate coverage.
    • 2. Aligning all reads to the human genome to identify methylated regions of DNA.
    • 3. Quantifying how many sequencing reads fall into each genomic window of differential methylation.
    • 4. Comparing the methylated regions between samples.
    • 5. Cohort Analysis identifying any statistical difference in the DNA methylation patterns between samples.


The model using sequencing data is retained. The model for application on targeted sequencing data is updated and tuned to accommodate nuances in the data that arise from the data being generated on a different platform. The model is adjusted to compensate for any differences that are present between MeDIP and sequencing data. Further, the model is continuously updated using larger numbers of samples when more samples are collected over time.


Sample Preparation

All validation steps required under the regulatory guidance of CAP/CLIA are followed for sample preparation, sequencing, and analysis. Effort may be focused on the amplification of previously identified 223 genomic regions (ranging in size from 500 -2000 kb).


The samples are thawed and subjected to somatic cell lysis to ensure the elimination of any potentially contaminating non-sperm cells followed by DNA extraction. For somatic cell lysis, the thawed samples are washed in 14 ml of PBS followed by two washes in 14 ml of distilled water. The sample are then centrifuged, and the resulting pellet incubated for a minimum of 60 minutes a 4° C. in 14 ml of a somatic cell lysis buffer (0.1% SDS, 0.5% Triton X-100 in DEPC H2O). Following somatic cell lysis, sperm DNA is isolated using a sperm-specific modification to a column-based extraction protocol using the DNeasy DNA isolation kit (Qiagen, Valencia CA). Extracted sperm DNA is bisulfite converted with EZ-96 DNA Methylation-Gold kit (Zymo Research, Irvine CA).


Targeted amplification and sequencing of the differentially methylated genomic regions are completed using ThermoFisher's Ion Ampliseq technology which includes QuantStudio real-time PCR (amplification) and the Ion Torrent S5 (next generation sequencing). Due to the bisulfite converted state of the sperm DNA, primer design requires a manual design service provided by ThermoFisher. As bisulfite conversion changes unmethylated cytosines to uracil, primers need to be designed to bind to regions that do not contain base-pairs that may be converted to uracil. Additionally, bisulfite converted DNA requires three-times the number of primers compared to native DNA in order to effectively amplify the genomic regions. The proper primer design can be important to get the depth of amplification needed for analysis. Additionally, due to the subtle changes of methylation, we require 1000× depth of coverage for each site.


While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the disclosure be limited by the specific examples provided within the specification. While the disclosure has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. Furthermore, it shall be understood that all aspects of the disclosure are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the disclosure. It is therefore contemplated that the disclosure shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims
  • 1. A method, comprising: obtaining a sperm sample from a human male subject;isolating deoxyribonucleic acid (DNA) from the sample;determining a methylation level of a differential DNA methylation region (DMR) comprised in the isolated DNA; andcomparing the methylation level of the DMR to a reference level of a corresponding reference DMR;wherein:the comparing comprises comparing employing a computer comprising a computer processor and computer readable memory comprising computer readable instructions contained thereon;the determining comprises a methylated DNA immunoprecipitation (MeDIP), a sequencing, a bisulfite treatment, a bisulfite conversion, a deamination of an unmethylated cytosine base, employing an array, or any combination of these;wherein about 90 to about 1000 distinct DMRs are detected and compared; andthe about 90 to about 1000 distinct DMRs are selected from the DMRs in Table 3.
  • 2. The method of claim 1, wherein about 200 to about 1000 distinct DMRs, about 300 to about 1000 distinct DMRs, about 400 to about 1000 distinct DMRs, about 500 to about 1000 distinct DMRs, about 600 to about 1000 distinct DMRs, about 700 to about 1000 distinct DMRs, about 800 to about 1000, or about 900 to about 1000 distinct DMRs are detected.
  • 3. The method of claim 1, comprising sequencing, and wherein the sequencing comprises sequencing by synthesis, ion semiconductor sequencing, single molecule real time sequencing, nanopore sequencing, next-generation sequencing, or any combination thereof.
  • 4. The method of claim 1, wherein the detected DMRs comprise DMRs from at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 19, 20, 21, 22, or 23, chromosomes; orthe detected DMRs are DMRs are from at least about: 1-23, 2-23, 3-23, 4-23, 5-23, 6-23, 7-23, 8-23, 9-23, 10-23, 11-23, 12-23, 13-23, 14-23, 15-23, 16-23, 17-23, 18-23, 19-23, 20-23, 21-23, 22-23 chromosomes.
  • 5. The method of claim 1, wherein the sperm sample is obtained from a human male subject at least about: 1 day, 2, days, 3 days, 4 days, 5 days, 6 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 11 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years, 18 years, 19 years, 20 years, 21 years, 22 years, 23 years, 24 years, 25 years, 26 years, 27 years, 28 years, 29 years, 30 years, 31 years, 32 years, 33 years, 34 years, 35 years, 36 years, 37 years, 38 years, 39 years, 40 years, 41 years, 42 years, 43 years, 44 years, 45 years, 46 years, 47 years, 48 years, 49 years, 50 years, 51 years, 52 years, 53 years, 54 years, 55 years, 56 years, 57 years, 58 years, 59 years, 60 years, 61 years, 62 years, 63 years, 64 years, 65 years, 66 years, 67 years, 68 years, 69 years, 70 years, 71 years, 72 years, 73 years, 74 years, 75 years, 76 years, 77 years, 78 years, 79 years, 80 years, 81 years, 82 years, 83 years, 84 years, 85 years, 86 years, 87 years, 88 years, 89 years, 90 years, 91 years, 92 years, 93 years, 94 years, 95 years, 96 years, 97 years, 98 years, 99 years, or 100 years of age.
  • 6. The method of claim 1, wherein the sperm sample is obtained from a human male subject of an age ranging from about 15 years to about 80 years of age.
  • 7. The method of claim 1, wherein the DMRs that are determined and compared, individually, range from about 100 to about 17000 adjacent nucleotides.
  • 8. The method of claim 1, wherein at least a plurality of the DMRs that are determined and compared comprise a CpG density of less than about 10 CpG per 100 nucleotides.
  • 9. The method of claim 8, wherein at least a plurality of the DMRs that are determined and compared comprise a CpG density of less than about 3 CpG per 100 nucleotides.
  • 10. The method of claim 2, wherein at least about: 30, 40, 50, 60, or 70 percent of the DMRs that are determined and compared are hypermethylated when compared, individually, to individual reference methylation levels of corresponding individual reference DMRs.
  • 11. (canceled)
  • 12. The method of claim 1, wherein the method further comprises, determining with a computer, a risk of an offspring of the human male subject having a disease or condition.
  • 13. The method of claim 1, wherein the method further comprises, determining with a computer, a severity of autism spectrum disorder of an offspring of the human male subject.
  • 14. The method of claim 1, wherein the method further comprises, determining with a computer, a severity of autism spectrum disorder of the human male subject.
  • 15. The method of claim 12, wherein the disease or condition comprises autism or autism spectrum disorder.
  • 16. The method of claim 12, wherein the disease or condition is selected from the group consisting of disease related to autism or neurodegenerative disease, such as Asperger's syndrome.
  • 17. The method of claim 1, further comprising performing a further analysis using a computer
  • 18. The method of claim 17, wherein the further analysis comprises a principle component analysis (PCA), a dendrogram analysis, a machine learning analysis, or any combination thereof.
  • 19. The method of claim 17, wherein the further analysis generates data points, and wherein the data points in the further analysis are grouped into two spatially distinct categories—a first category which indicates the subject or an offspring of the subject is at increased risk of having a disease or condition and second category which indicates the subject or the offspring of the subject is not at increased risk of having the disease or condition.
  • 20. A method, comprising: obtaining a sperm sample from a human male subject;isolating deoxyribonucleic acid (DNA) from the sample;determining a methylation level of a differential DNA methylation region (DMR) comprised in the isolated DNA; andcomparing the methylation level of the DMR to a reference level of a corresponding reference DMR;wherein:the comparing comprises comparing employing a computer comprising a computer processor and computer readable memory comprising computer readable instructions contained thereon;the determining comprises a methylated DNA immunoprecipitation (MeDIP), a sequencing, a bisulfite treatment, a bisulfite conversion, a deamination of an unmethylated cytosine base, employing an array, or any combination of these; andwherein a number of determined DMRs are sufficient to determine, from a process comprising the comparing and employing a computer, whether the human male subject, or an offspring of the human male subject, has or is at increased risk of having autism or autism spectrum disorder, or determine a severity of autism spectrum disorder.
  • 21. The method of claim 20, wherein about 90 to about 1000 distinct DMRs are determined and compared.
  • 22. (canceled)
  • 23. The method of claim 20, further comprising treating the human male subject or an offspring thereof.
  • 24. The method of claim 23, comprising treating the offspring of the human male subject, wherein the offspring comprises at least one cell, treating the human male subject, or treating a sperm cell of the human male subject or a male offspring of the human male subject.
  • 25. The method of claim 23, comprising treating the offspring of the human male subject, wherein the offspring is less than about 2 years old.
  • 26. The method of claim 23, wherein the treating comprises administering an applied behavior analysis, a cognitive behavior therapy, an educational therapy, a joint attention therapy, a nutritional therapy, an occupational therapy, a physical therapy, a social skills training, a speech language therapy, an antipsychotic drug or a salt thereof, risperidone or a salt thereof, aripiprazole or a salt thereof, a selective serotonin re-uptake inhibitor or a salt thereof, citalopram or a salt thereof, escitalopram or a salt thereof, fluoxetine or a salt thereof, fluvoxamine or a salt thereof, paroxetine or a salt thereof, sertraline or a salt thereof, dapoxetine or a salt thereof, indalpine or a salt thereof, zimelidine or a salt thereof, alaproclate or a salt thereof, centpropazine or a salt thereof, femoxetine or a salt thereof, omiloxetine or a salt thereof, panuramine or a salt thereof, seproxetine or a salt thereof, venlafaxine or a salt thereof, clomipramine or a salt thereof, methylphenidate or a salt thereof, mixed amphetamine salts, a psychoactive medication or a salt thereof, a stimulant or a salt thereof, a valproic acid or a salt thereof, phenytoin or a salt thereof, clonazepam or a salt thereof, carbamazepine or a salt thereof, a social skills therapy, speech therapy, supplementing a vitamin or a salt thereof, a mineral or a salt thereof, or both, a restricted diet, a risperidone or a salt thereof, or any combination thereof.
  • 27. The method of claim 24, wherein the treating comprises administering a therapeutically effective amount of a pharmaceutical formulation to the subject.
  • 28. The method of claim 27, wherein the pharmaceutical formulation comprises a pharmaceutically acceptable: excipient, diluent, or carrier.
  • 29. The method of claim 27, wherein the pharmaceutical formulation is in unit dose form.
  • 30. The method of claim 27, wherein the pharmaceutical formulation is administered orally, intranasally, by inhalation, sublingually, by injection, by a transdermally, intravenously, subcutaneously, intramuscularly, in an eye, in an ear, in a rectum, intrathecally, or any combination thereof.
  • 31. The method of claim 27, wherein the pharmaceutical formulation is administered in an amount ranging from about 0.0001 to about 100,000 mg of pharmaceutical formulation per kg of subject body weight or offspring of subject body weight.
  • 32. The method of claim 1, further comprising transmitting data, a result, or both, via an electronic communication medium.
  • 33. A kit comprising at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13 14, 14, 16, 17, 18, 19, 20, 30, 40, 50, 60,70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 distinct primers or pairs of primers, each distinct primer or pairs of primers comprising a distinct sequence complementary to a distinct DMR sequence present in Table 3; and a container.
  • 34. A kit comprising at least about: 1, 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13 14, 14, 16, 17, 18, 19, 20, 30, 40, 50, 60,70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 distinct probes, each distinct probe complementary to a distinct DMR sequence present in Table 3; and a container.
  • 35. The kit of claim 34, wherein the distinct probes further comprises at least one of fluorophore, a chromophore, a barcode, or any combination thereof.
  • 36. The kit of claim 35, wherein each probe comprises a unique: fluorophore, a chromophore, barcode, or any combination thereof.
  • 37. The kit of claim 33, wherein the distinct primers or pairs of primers each further comprise a unique barcode.
  • 38. The kit of claim 33, wherein the probes or the primers are not bound to an array or a microarray.
  • 39. The kit of claim 33, wherein the probes or the primers are bound to an array or a microarray.
  • 40. The kit of claim 33, wherein the probes, the primers, or both comprise DNA.
  • 41. A method, comprising: obtaining a sperm sample from a human male subject;isolating deoxyribonucleic acid (DNA) from the sample;fragmenting the DNA;isolating fragmented methylated DNA;determining a methylation level of a differential DNA methylation region (DMR) comprised in the isolated fragmented methylated DNA; andcomparing the methylation level of the DMR to a reference level of a corresponding reference DMR;wherein:the comparing comprises comparing employing a computer comprising a computer processor and computer readable memory comprising computer readable instructions contained thereon;the determining comprises: amplification of the isolated fragmented methylated DNA, sequencing the isolated fragmented methylated DNA, an amplicon thereof, or both, employing an array, or any combination of these;wherein about 90 to about 1000 distinct DMRs are detected and compared; andthe about 90 to about 1000 distinct DMRs are selected from the DMRs in Table 3.
  • 42. The method of claim 41, wherein the isolating the fragmented methylated DNA comprises methylated DNA immunoprecipitation (MeDIP).
  • 43. A method, comprising: obtaining a sperm sample from a human male subject;isolating deoxyribonucleic acid (DNA) from the sample;fragmenting the DNA;isolating fragmented methylated DNA;determining a methylation level of a differential DNA methylation region (DMR) comprised in the isolated fragmented methylated DNA; andcomparing the methylation level of the DMR to a reference level of a corresponding reference DMR;wherein:the comparing comprises comparing employing a computer comprising a computer processor and computer readable memory comprising computer readable instructions contained thereon;the determining comprises: amplification of the isolated fragmented methylated DNA, sequencing the isolated fragmented methylated DNA, an amplicon thereof, or both, employing an array, or any combination of these; andwherein a number of determined DMRs are sufficient to determine, from a process comprising the comparing and employing a computer, whether the human male subject, or an offspring of the human male subject, has or is at increased risk of having autism or autism spectrum disorder, or determine a severity of autism spectrum disorder.
  • 44. The method of claim 43, wherein the isolating the fragmented methylated DNA comprises methylated DNA immunoprecipitation (MeDIP).
CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser. No. 63/055,199, filed Jul. 22, 2020, which is incorporated herein by reference in its entirety.

PCT Information
Filing Document Filing Date Country Kind
PCT/US2021/046422 8/18/2021 WO
Provisional Applications (1)
Number Date Country
63055199 Jul 2020 US