Comethyl: A network-based methylome approach to investigate the multivariate nature of health and disease

AbstractHealth outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social, and environmental factors. DNA methylation patterns reflect such multi-variate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing (WGBS) enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis (WGCNA) of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth, and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing gene regions with brain glial functions. Comethyl is expected to be useful in uncovering the multi-variate nature of health disparities for a variety of common disorders. Comethyl is available at github.com/cemordaunt/comethyl.Description of the AuthorsCharles E. Mordaunt, Ph.D. developed Comethyl while a postdoctoral fellow in the department of Medical Microbiology and Immunology at UC Davis. He is currently a Computational Biologist at GSK.Julia S. Mouat is a doctoral student in the Integrative Genetics and Genomics graduate group at UC Davis with interests in health disparities and intergenerational epigenetic risk factors for autism spectrum disorders.Rebecca J. Schmidt, Ph.D. is an Associate Professor of Public Health Sciences at UC Davis, with expertise in the use of epigenetics in epidemiology and neurodevelopmental disorders.Janine M. LaSalle, Ph.D. is a Professor of Medical Microbiology and Immunology, Co-Director of the Perinatal Origins of Disparities Center, and Deputy Director of the Environmental Health Sciences Center at UC Davis, with expertise in epigenomics and neurodevelopmental disorders.

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Polygenic risk scores for major psychiatric and neurodevelopmental disorders contribute to sleep disturbance in childhood: Adolescent Brain Cognitive Development (ABCD) Study

Translational Psychiatry ◽

10.1038/s41398-021-01308-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kazutaka Ohi ◽

Ryo Ochi ◽

Yoshihiro Noda ◽

Masataka Wada ◽

Shunsuke Sugiyama ◽

...

Keyword(s):

Cognitive Development ◽

Anxiety Disorders ◽

Sleep Disturbance ◽

Neurodevelopmental Disorders ◽

Association Studies ◽

Autism Spectrum ◽

Polygenic Risk Score ◽

Common Symptom ◽

Genome Wide Association Studies ◽

Polygenic Risk

AbstractSleep disturbance is a common symptom of psychiatric and neurodevelopmental disorders and, especially in childhood, can be a precursor to various mental disorders. However, the genetic etiology of mental illness that contributes to sleep disturbance during childhood is poorly understood. We investigated whether the polygenic features of psychiatric and neurodevelopmental disorders are associated with sleep disturbance during childhood. We conducted polygenic risk score (PRS) analyses by utilizing large-scale genome-wide association studies (GWASs) (n = 46,350–500,199) of five major psychiatric and neurodevelopmental disorders (autism spectrum disorder, schizophrenia, attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and bipolar disorder) and, additionally, anxiety disorders as base datasets. We used the data of 9- to 10-year-olds from the Adolescent Brain Cognitive Development study (n = 9683) as a target dataset. Sleep disturbance was assessed based on the Sleep Disturbance Scale for Children (SDSC) scores. The effects of PRSs for these psychiatric and neurodevelopmental disorders on the total scores and six subscale scores of the SDSC were investigated. Of the PRSs for the five psychiatric and neurodevelopmental disorders, the PRSs for ADHD and MDD positively correlated with sleep disturbance in children (ADHD: R2 = 0.0033, p = 6.19 × 10−5, MDD: R2 = 0.0042, p = 5.69 × 10−6). Regarding the six subscale scores of the SDSC, the PRSs for ADHD positively correlated with both disorders of initiating and maintaining sleep (R2 = 0.0028, p = 2.31 × 10−4) and excessive somnolence (R2 = 0.0023, p = 8.44 × 10−4). Furthermore, the PRSs for MDD primarily positively correlated with disorders of initiating and maintaining sleep (R2 = 0.0048, p = 1.26 × 10−6), followed by excessive somnolence (R2 = 0.0023, p = 7.74 × 10−4) and sleep hyperhidrosis (R2 = 0.0014, p = 9.55 × 10−3). Despite high genetic overlap between MDD and anxiety disorders, PRSs for anxiety disorders correlated with different types of sleep disturbances such as disorders of arousal or nightmares (R2 = 0.0013, p = 0.011). These findings suggest that greater genetic susceptibility to specific psychiatric and neurodevelopmental disorders, as represented by ADHD, MDD, and anxiety disorders, may contribute to greater sleep problems among children.

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Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

Genome Biology ◽

10.1186/s13059-021-02398-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Daniel L. McCartney ◽

Josine L. Min ◽

Rebecca C. Richmond ◽

Ake T. Lu ◽

Maria K. Sobczyk ◽

...

Keyword(s):

Dna Methylation ◽

Genome Wide Association Study ◽

Association Studies ◽

Genome Wide Association ◽

Biological Aging ◽

Genome Wide Association Studies ◽

Genetic Loci ◽

Biomarkers Of Aging ◽

Genome Wide ◽

Shared Genetic

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

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Investigation of gene–environment interactions in relation to tic severity

Journal of Neural Transmission ◽

10.1007/s00702-021-02396-y ◽

2021 ◽

Author(s):

Mohamed Abdulkadir ◽

Dongmei Yu ◽

Lisa Osiecki ◽

Robert A. King ◽

Thomas V. Fernandez ◽

...

Keyword(s):

Tourette Syndrome ◽

Association Studies ◽

Autism Spectrum ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Linear Regression Models ◽

Compulsive Disorder ◽

Gene Environment ◽

Tic Severity

AbstractTourette syndrome (TS) is a neuropsychiatric disorder with involvement of genetic and environmental factors. We investigated genetic loci previously implicated in Tourette syndrome and associated disorders in interaction with pre- and perinatal adversity in relation to tic severity using a case-only (N = 518) design. We assessed 98 single-nucleotide polymorphisms (SNPs) selected from (I) top SNPs from genome-wide association studies (GWASs) of TS; (II) top SNPs from GWASs of obsessive–compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD); (III) SNPs previously implicated in candidate-gene studies of TS; (IV) SNPs previously implicated in OCD or ASD; and (V) tagging SNPs in neurotransmitter-related candidate genes. Linear regression models were used to examine the main effects of the SNPs on tic severity, and the interaction effect of these SNPs with a cumulative pre- and perinatal adversity score. Replication was sought for SNPs that met the threshold of significance (after correcting for multiple testing) in a replication sample (N = 678). One SNP (rs7123010), previously implicated in a TS meta-analysis, was significantly related to higher tic severity. We found a gene–environment interaction for rs6539267, another top TS GWAS SNP. These findings were not independently replicated. Our study highlights the future potential of TS GWAS top hits in gene–environment studies.

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Immunity and mental illness: findings from a Danish population-based immunogenetic study of seven psychiatric and neurodevelopmental disorders

European Journal of Human Genetics ◽

10.1038/s41431-019-0402-9 ◽

2019 ◽

Vol 27 (9) ◽

pp. 1445-1455 ◽

Cited By ~ 10

Author(s):

Ron Nudel ◽

Michael E. Benros ◽

Morten Dybdahl Krebs ◽

Rosa Lundbye Allesøe ◽

Camilla Koldbæk Lemvigh ◽

...

Keyword(s):

Intellectual Disability ◽

Protective Effect ◽

Neurodevelopmental Disorders ◽

Association Studies ◽

Human Leukocyte ◽

Population Based ◽

Autism Spectrum ◽

Small Samples ◽

Hla Association ◽

Hla Genes

AbstractHuman leukocyte antigen (HLA) genes encode proteins with important roles in the regulation of the immune system. Many studies have also implicated HLA genes in psychiatric and neurodevelopmental disorders. However, these studies usually focus on one disorder and/or on one HLA candidate gene, often with small samples. Here, we access a large dataset of 65,534 genotyped individuals consisting of controls (N = 19,645) and cases having one or more of autism spectrum disorder (N = 12,331), attention deficit hyperactivity disorder (N = 14,397), schizophrenia (N = 2401), bipolar disorder (N = 1391), depression (N = 18,511), anorexia (N = 2551) or intellectual disability (N = 3175). We imputed participants’ HLA alleles to investigate the involvement of HLA genes in these disorders using regression models. We found a pronounced protective effect of DPB1*1501 on susceptibility to autism (p = 0.0094, OR = 0.72) and intellectual disability (p = 0.00099, OR = 0.41), with an increased protective effect on a comorbid diagnosis of both disorders (p = 0.003, OR = 0.29). We also identified a risk allele for intellectual disability, B*5701 (p = 0.00016, OR = 1.33). Associations with both alleles survived FDR correction and a permutation procedure. We did not find significant evidence for replication of previously-reported associations for autism or schizophrenia. Our results support an implication of HLA genes in autism and intellectual disability, which requires replication by other studies. Our study also highlights the importance of large sample sizes in HLA association studies.

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Genetics and Epigenetics in Asthma

International Journal of Molecular Sciences ◽

10.3390/ijms22052412 ◽

2021 ◽

Vol 22 (5) ◽

pp. 2412

Author(s):

Polyxeni Ntontsi ◽

Andreas Photiades ◽

Eleftherios Zervas ◽

Georgina Xanthou ◽

Konstantinos Samitas

Keyword(s):

Dna Methylation ◽

Association Studies ◽

Airway Remodeling ◽

Methylation Pattern ◽

Atopic Asthma ◽

Genome Wide Association Studies ◽

Childhood Onset ◽

Gene Markers ◽

Asthmatic Patients ◽

Asthma Susceptibility

Asthma is one of the most common respiratory disease that affects both children and adults worldwide, with diverse phenotypes and underlying pathogenetic mechanisms poorly understood. As technology in genome sequencing progressed, scientific efforts were made to explain and predict asthma’s complexity and heterogeneity, and genome-wide association studies (GWAS) quickly became the preferred study method. Several gene markers and loci associated with asthma susceptibility, atopic and childhood-onset asthma were identified during the last few decades. Markers near the ORMDL3/GSDMB genes were associated with childhood-onset asthma, interleukin (IL)33 and IL1RL1 SNPs were associated with atopic asthma, and the Thymic Stromal Lymphopoietin (TSLP) gene was identified as protective against the risk to TH2-asthma. The latest efforts and advances in identifying and decoding asthma susceptibility are focused on epigenetics, heritable characteristics that affect gene expression without altering DNA sequence, with DNA methylation being the most described mechanism. Other less studied epigenetic mechanisms include histone modifications and alterations of miR expression. Recent findings suggest that the DNA methylation pattern is tissue and cell-specific. Several studies attempt to describe DNA methylation of different types of cells and tissues of asthmatic patients that regulate airway remodeling, phagocytosis, and other lung functions in asthma. In this review, we attempt to briefly present the latest advancements in the field of genetics and mainly epigenetics concerning asthma susceptibility.

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Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽

10.1098/rsob.180031 ◽

2018 ◽

Vol 8 (5) ◽

pp. 180031 ◽

Cited By ~ 23

Author(s):

Shani Stern ◽

Sara Linker ◽

Krishna C. Vadodaria ◽

Maria C. Marchetto ◽

Fred H. Gage

Keyword(s):

Autism Spectrum Disorder ◽

Bipolar Disorder ◽

Major Depression ◽

Personalized Medicine ◽

Psychiatric Disorders ◽

Association Studies ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Genome Wide Association Studies ◽

Patient Reported

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

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Children’s Greenness Exposure and IQ-Associated DNA Methylation: A Prospective Cohort Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18147429 ◽

2021 ◽

Vol 18 (14) ◽

pp. 7429

Author(s):

Kyung-Shin Lee ◽

Yoon-Jung Choi ◽

Jin-Woo Cho ◽

Sung-Ji Moon ◽

Youn-Hee Lim ◽

...

Keyword(s):

Dna Methylation ◽

Cohort Study ◽

Literature Review ◽

Cognitive Ability ◽

Prospective Cohort Study ◽

Prospective Cohort ◽

Association Studies ◽

The Body ◽

Genome Wide Association Studies ◽

Regulatory Pathways

Epigenetics is known to be involved in regulatory pathways through which greenness exposure influences child development and health. We aimed to investigate the associations between residential surrounding greenness and DNA methylation changes in children, and further assessed the association between DNA methylation and children’s intelligence quotient (IQ) in a prospective cohort study. We identified cytosine-guanine dinucleotide sites (CpGs) associated with cognitive abilities from epigenome- and genome-wide association studies through a systematic literature review for candidate gene analysis. We estimated the residential surrounding greenness at age 2 using a geographic information system. DNA methylation was analyzed from whole blood using the HumanMethylationEPIC array in 59 children at age 2. We analyzed the association between greenness exposure and DNA methylation at age 2 at the selected CpGs using multivariable linear regression. We further investigated the relationship between DNA methylation and children’s IQ. We identified 8743 CpGs associated with cognitive ability based on the literature review. Among these CpGs, we found that 25 CpGs were significantly associated with greenness exposure at age 2, including cg26269038 (Bonferroni-corrected p ≤ 0.05) located in the body of SLC6A3, which encodes a dopamine transporter. DNA methylation at cg26269038 at age 2 was significantly associated with children’s performance IQ at age 6. Exposure to surrounding greenness was associated with cognitive ability-related DNA methylation changes, which was also associated with children’s IQ. Further studies are warranted to clarify the epigenetic pathways linking greenness exposure and neurocognitive function.

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An approach to gene-based testing accounting for dependence of tests among nearby genes

10.1101/2021.05.24.445494 ◽

2021 ◽

Author(s):

Ronald J Yurko ◽

Kathryn Roeder ◽

Bernie Devlin ◽

Max G'Sell

Keyword(s):

Multiple Testing ◽

Association Studies ◽

Autism Spectrum ◽

P Value ◽

Genome Wide Association Studies ◽

Strongly Correlated ◽

Test Statistics ◽

Test Statistic ◽

Genome Wide ◽

Insight Into

In genome-wide association studies (GWAS), it has become commonplace to test millions of SNPs for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive p-value thresholding (AdaPT), guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.

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Insights into the genomics of affective disorders

Medizinische Genetik ◽

10.1515/medgen-2020-2003 ◽

2020 ◽

Vol 32 (1) ◽

pp. 9-18

Author(s):

Andreas J. Forstner ◽

Per Hoffmann ◽

Markus M. Nöthen ◽

Sven Cichon

Keyword(s):

Bipolar Disorder ◽

Major Depression ◽

Affective Disorders ◽

Association Studies ◽

Phenotypic Variability ◽

Genetic Research ◽

Autism Spectrum ◽

Risk Scores ◽

Depression Symptoms ◽

Genome Wide Association Studies

Abstract Affective disorders, or mood disorders, are a group of neuropsychiatric illnesses that are characterized by a disturbance of mood or affect. Most genetic research in this field to date has focused on bipolar disorder and major depression. Symptoms of major depression include a depressed mood, reduced energy, and a loss of interest and enjoyment. Bipolar disorder is characterized by the occurrence of (hypo)manic episodes, which generally alternate with periods of depression. Formal and molecular genetic studies have demonstrated that affective disorders are multifactorial diseases, in which both genetic and environmental factors contribute to disease development. Twin and family studies have generated heritability estimates of 58–85 % for bipolar disorder and 40 % for major depression. Large genome-wide association studies have provided important insights into the genetics of affective disorders via the identification of a number of common genetic risk factors. Based on these studies, the estimated overall contribution of common variants to the phenotypic variability (single-nucleotide polymorphism [SNP]-based heritability) is 17–23 % for bipolar disorder and 9 % for major depression. Bioinformatic analyses suggest that the associated loci and implicated genes converge into specific pathways, including calcium signaling. Research suggests that rare copy number variants make a lower contribution to the development of affective disorders than to other psychiatric diseases, such as schizophrenia or the autism spectrum disorders, which would be compatible with their less pronounced negative impact on reproduction. However, the identification of rare sequence variants remains in its infancy, as available next-generation sequencing studies have been conducted in limited samples. Future research strategies will include the enlargement of genomic data sets via innovative recruitment strategies; functional analyses of known associated loci; and the development of new, etiologically based disease models. Researchers hope that deeper insights into the biological causes of affective disorders will eventually lead to improved diagnostics and disease prediction, as well as to the development of new preventative, diagnostic, and therapeutic strategies. Pharmacogenetics and the application of polygenic risk scores represent promising initial approaches to the future translation of genomic findings into psychiatric clinical practice.

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A systematic analysis of genetically regulated differences in gene expression and the role of co-expression networks across 16 psychiatric disorders and substance use phenotypes

10.1101/2021.01.28.428688 ◽

2021 ◽

Author(s):

Zachary F Gerring ◽

Jackson G Thorp ◽

Eric R Gamazon ◽

Eske M Derks

Keyword(s):

Gene Expression ◽

Mental Health ◽

Substance Use ◽

Prefrontal Cortex ◽

Psychiatric Disorders ◽

Developmental Disorders ◽

Association Studies ◽

Genetic Correlations ◽

Autism Spectrum ◽

Genome Wide Association Studies

ABSTRACTGenome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed “mental health phenotypes”) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

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