Epigenome-wide association of liver methylation patterns and complex metabolic traits in mice
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE67507 | GEO | 2015/04/02
SECONDARY ACCESSION(S): PRJNA280119
REPOSITORIES: GEO
Similar Datasets
Project description:Heritable epigenetic factors can contribute to complex disease etiology. In this study we examine, on a global scale, the contribution of DNA methylation to complex traits that are precursors to heart disease, diabetes and osteoporosis. We profiled DNA methylation patterns in the liver using bisulfite sequencing in 90 mouse inbred strains, genome-wide expression levels, proteomics, metabolomics and sixty-eight clinical traits, and performed epigenome-wide association studies (EWAS). We found associations with numerous clinical traits including bone mineral density, plasma cholesterol, insulin resistance, gene expression, protein and metabolite levels. A large proportion of associations were unique to EWAS and were not identified using GWAS. Methylation levels were regulated by genetics largely in cis, but we also found evidence of trans regulation, and we demonstrate that genetic variation in the methionine synthase reductase gene Mtrr affects methylation of hundreds of CpGs throughout the genome. Our results indicate that natural variation in methylation levels contributes to the etiology of complex clinical traits. Reduced representation bisulfite sequencing in mouse strains using liver genomic DNA
2015-04-02 | E-GEOD-67507 | biostudies-arrayexpress
Project description:Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation. 675 whole blood derived DNA samples (353 schizophrenia cases and 322 controls) representing phase 1 of our meta-analysis. Bisulfite converted DNA from these samples were hybridized to the Illumina Infinium 450k Human Methylation Beadchip v1.0.
2016-08-01 | E-GEOD-80417 | biostudies-arrayexpress
Project description:Abstract Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation. 847 whole blood derived DNA samples (414 schizophrenia cases and 433 controls) representing phase 2 of our meta-analysis. Bisulfite converted DNA from these samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip v1.0.
2016-08-01 | E-GEOD-84727 | biostudies-arrayexpress
Project description:Background: Epigenome-wide association studies (EWAS) using measurements of blood DNA methylation are performed to identify associations of methylation changes with environmental and lifestyle exposures, and with risk of developing disease. However, little is known about the variation of methylation markers in the population and their stability over time, both important factors in the design and interpretation of EWAS. Methods: We estimated the intraclass correlation coefficient (ICC) for each probe on the Illumina 450K methylation array in paired samples collected approximately six years apart from 92 participants in the Breakthrough Generations Study. We also evaluated relationships with age, reproductive and hormonal history, weight, alcohol intake and smoking. Results: Approximately 17% of probes on the 450K array had an ICC>0.50 and were considered stable variable methylated probes (stable-VMPs). Stable-VMPs were enriched for probes located in "shores" bordering CpG islands, and at approximately 1.3kb downstream from the transcription start site in the transition between the unmethylated promoter and methylated gene body. Both cross-sectional and longitudinal data analyses provided strong evidence for associations between changes in methylation levels and ageing. Smoking-related probes at 2q37.1 and AHRR were stable-VMPs and, as previously reported, related to time since quitting smoking. We also observed an excess of associations between methylation and weight changes beyond those expected by chance. Conclusion: Our results provide support for the use of WBC DNA methylation as a biomarker of exposure in EWAS. Larger studies, preferably with repeated measures over time, will be required to establish associations between specific probes and exposures.
2014-11-12 | GSE61151 | GEO
Project description:Abstract Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.
2016-08-01 | GSE84727 | GEO
Project description:Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.
2016-08-01 | GSE80417 | GEO
Project description:Purpose: We studied dioxin exposures and DNA methylation in sperm in 37 men from the Air Force Health Study Methods: We compared methylation levels in subjects with no, low, medium, and high dioxin levels using EWAS and looking at regions of DNA. We compared our results to those from a Russian study Results: No loci were found to be significantly associated with dioxin exposure in the EWAS when controlling for false discovery rate. Region H19 was found to have significant associations. Five of our loci of interest overlapped with significant findings from the Russian study
2019-10-24 | GSE139307 | GEO
Project description:An epigenome-wide association study (EWAS) was performed on buccal cells from monozygotic-twins (MZ) reared together as children, but who live apart as adults. Cohorts of twin pairs were used to investigate associations between neighborhood walkability and objectively measured physical activity (PA) levels. Due to dramatic cellular epigenetic sex differences, male and female MZ twin pairs were analyzed separately to identify differential DNA methylation regions (DMRs). A priori comparisons were made on MZ twin pairs discordant on body mass index (BMI), PA levels, and neighborhood walkability. In addition to direct comparative analysis to identify specific DMRs, a weighted gene coexpression network analysis (WGCNA) was performed to identify DNA methylation sites associated with the physiological traits of interest. The pairs discordant in PA levels had epigenetic alterations that correlated with reduced metabolic parameters (i.e., BMI). The DNA methylation sites are associated with over fifty genes previously found to be specific to vigorous PA, metabolic risk factors, and sex. Combined observations demonstrate that behavioral factors, such as physical activity, appear to promote systemic epigenetic alterations that impact metabolic risk factors. The epigenetic DNA methylation sites and associated genes identified provide insight into PA impacts on metabolic parameters and the etiology of obesity.
2022-12-01 | GSE216387 | GEO
Project description:Folate metabolism is important for the generation of methyl groups required for cellular methylation reactions. Mice with a hypomorphic mutation in the gene Mtrr have disrupted folate metabolism and show transgenerational inheritance of developmental abnormalities. We sought to explore if DNA methylation patterns in the sperm of Mtrr mice may be disrupted. Therefore we performed MeDIP-seq on sperm collect from males with the Mtrr mutation (either heterozygous (Mtrr +/gt) or homozygous mutants (Mtrr gt/gt)), males ancestrally exposed to the Mtrr mutation (Mtrr +/+) and C57Bl/6J control males. C57Bl/6J males were used as controls as they have the same genetic background as the mice with Mtrr mutation.
2021-04-06 | E-MTAB-8533 | biostudies-arrayexpress
Project description:In this study, we examined the association of DNA methylation with metabolic traits in humans using adipose tissue samples from the Metabolic Syndrome in Men (METSIM) cohort. The METSIM cohort has been thoroughly characterized for longitudinal clinical data of metabolic traits including a 3-point oral glucose tolerance test, cardiovascular disorders, diabetes complications, drug and diet questionnaire, as well as high density genotyping, and genome-wide expression in adipose. We performed epigenome-wide association studies on clinical traits using reduced representation bisulfite sequencing data and identified 61 signifiant associations for metabolic syndrome traits, corresponding to 25 unique loci. These associations include previously known genes, FASN, RXRA, MSH2, and MSH6, as well as 22 loci harboring 18 new candidate genes for diabetes and obesity in humans.
2017-01-01 | GSE87893 | GEO