Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease.
ABSTRACT: Identifying methylation quantitative trait loci (meQTLs) and integrating them with disease-associated variants from genome-wide association studies (GWAS) may illuminate functional mechanisms underlying genetic variant-disease associations. Here, we perform GWAS of >415 thousand CpG methylation sites in whole blood from 4170 individuals and map 4.7 million cis- and 630 thousand trans-meQTL variants targeting >120 thousand CpGs. Independent replication is performed in 1347 participants from two studies. By linking cis-meQTL variants with GWAS results for cardiovascular disease (CVD) traits, we identify 92 putatively causal CpGs for CVD traits by Mendelian randomization analysis. Further integrating gene expression data reveals evidence of cis CpG-transcript pairs causally linked to CVD. In addition, we identify 22 trans-meQTL hotspots each targeting more than 30 CpGs and find that trans-meQTL hotspots appear to act in cis on expression of nearby transcriptional regulatory genes. Our findings provide a powerful meQTL resource and shed light on DNA methylation involvement in human diseases.
Project description:DNA methylation at CpG sites is both heritable and influenced by environment, but the relative contributions of each to DNA methylation levels are unclear. We conducted a heritability analysis of CpG methylation in human CD4+ cells across 975 individuals from 163 families in the Genetics of Lipid-lowering Drugs and Diet Network (GOLDN). Based on a broad-sense heritability (H2) value threshold of 0.4, we identified 20,575 highly heritable CpGs among the 174,445 most variable autosomal CpGs (SD > 0.02). Tests for associations of heritable CpGs with genotype at 2,145,360 SNPs using 717 of 975 individuals showed that ~74% were cis-meQTLs (< 1 Mb away from the CpG), 6% of CpGs exhibited trans-meQTL associations (>1 Mb away from the CpG or located on a different chromosome), and 20% of CpGs showed no strong significant associations with genotype (based on a p-value threshold of 1e-7). Genes proximal to the genotype independent heritable CpGs were enriched for functional terms related to regulation of T cell activation. These CpGs were also among those that distinguished T cells from other blood cell lineages. Compared to genes proximal to meQTL-associated heritable CpGs, genotype independent heritable CpGs were moderately enriched in the same genomic regions that escape erasure during primordial germ cell development and could carry potential for generational transmission.
Project description:Emerging evidence emphasizes the strong impact of regulatory genomic elements in neurodevelopmental processes and the complex pathways of brain disorders. The present genome-wide quantitative trait loci analyses explore the cis-regulatory effects of single-nucleotide polymorphisms (SNPs) on DNA methylation (meQTL) and gene expression (eQTL) in 110 human hippocampal biopsies. We identify cis-meQTLs at 14,118 CpG methylation sites and cis-eQTLs for 302 3'-mRNA transcripts of 288 genes. Hippocampal cis-meQTL-CpGs are enriched in flanking regions of active promoters, CpG island shores, binding sites of the transcription factor CTCF and brain eQTLs. Cis-acting SNPs of hippocampal meQTLs and eQTLs significantly overlap schizophrenia-associated SNPs. Correlations of CpG methylation and RNA expression are found for 34 genes. Our comprehensive maps of cis-acting hippocampal meQTLs and eQTLs provide a link between disease-associated SNPs and the regulatory genome that will improve the functional interpretation of non-coding genetic variants in the molecular genetic dissection of brain disorders.
Project description:Oxidative stress has been related to type 2 diabetes (T2D) and cardiovascular disease (CVD), the leading global cause of death. Contributions of environmental factors such as oxidative stress on complex traits and disease may be partly mediated through changes in epigenetic marks (e.g. DNA methylation). Studies relating differential methylation with intermediate phenotypes and disease endpoints may be useful in identifying additional candidate genes and mechanisms involved in disease.To investigate the role of epigenetic variation in oxidative stress marker levels and subsequent development of CVD and T2D, we performed analyses of genome-wide DNA methylation in blood, ten markers of oxidative stress (total glutathione [TGSH], reduced glutathione [GSH], oxidised glutathione [GSSG], GSSG to GSH ratio, homocysteine [HCY], oxidised low-density lipoprotein (oxLDL), antibodies against oxLDL [OLAB], conjugated dienes [CD], baseline conjugated dienes [BCD]-LDL and total antioxidant capacity [TAOC]) and incident disease in up to 966 age-matched individuals.In total, we found 66 cytosine-guanine (CpG) sites associated with one or more oxidative stress markers (false discovery rate [FDR] <0.05). These sites were enriched in regulatory regions of the genome. Genes annotated to CpG sites showed enrichment in annotation clusters relating to phospho-metabolism and proteins with pleckstrin domains. We investigated the contribution of oxidative stress-associated CpGs to development of cardiometabolic disease. Methylation variation at CpGs in the 3'-UTR of HIST1H4D (cg08170869; histone cluster 1, H4d) and in the body of DVL1 (cg03465880; dishevelled-1) were associated with incident T2D events during 10 years of follow-up (all permutation p-values <0.01), indicating a role of epigenetic regulation in oxidative stress processes leading to development or progression of diabetes. Methylation QTL (meQTL) analysis showed significant associations with genetic sequence variants in cis at 28 (42%) of oxidative stress phenotype-associated sites (FDR?<?0.05). Integrating cis-meQTLs with genotype-phenotype associations indicated that genetic effects on oxidative stress phenotype at one locus (cg07547695; BCL2L11) may be mediated through DNA methylation.In conclusion, we report novel associations of DNA methylation with oxidative stress, some of which also show evidence of a relation with T2D incidence.
Project description:DNA methylation is an important molecular-level phenotype that links genotypes and complex disease traits. Previous studies have found local correlation between genetic variants and DNA methylation levels (cis-meQTLs). However, general mechanisms underlying cis-meQTLs are unclear. We conducted a cis-meQTL analysis of the Genetics of Lipid Lowering Drugs and Diet Network data (n = 593). We found that over 80% of genetic variants at CpG sites (meSNPs) are meQTL loci (P-value<10(-9)), and meSNPs account for over two thirds of the strongest meQTL signals (P-value<10(-200)). Beyond direct effects on the methylation of the meSNP site, the CpG-disrupting allele of meSNPs were associated with lowered methylation of CpG sites located within 45 bp. The effect of meSNPs extends to as far as 10 kb and can contribute to the observed meQTL signals in the surrounding region, likely through correlated methylation patterns and linkage disequilibrium. Therefore, meSNPs are behind a large portion of observed meQTL signals and play a crucial role in the biological process linking genetic variation to epigenetic changes.
Project description:The genetic regulation of the human epigenome is not fully appreciated. Here we describe the effects of genetic variants on the DNA methylome in human lung based on methylation-quantitative trait loci (meQTL) analyses. We report 34,304 cis- and 585 trans-meQTLs, a genetic-epigenetic interaction of surprising magnitude, including a regulatory hotspot. These findings are replicated in both breast and kidney tissues and show distinct patterns: cis-meQTLs mostly localize to CpG sites outside of genes, promoters and CpG islands (CGIs), while trans-meQTLs are over-represented in promoter CGIs. meQTL SNPs are enriched in CTCF-binding sites, DNaseI hypersensitivity regions and histone marks. Importantly, four of the five established lung cancer risk loci in European ancestry are cis-meQTLs and, in aggregate, cis-meQTLs are enriched for lung cancer risk in a genome-wide analysis of 11,587 subjects. Thus, inherited genetic variation may affect lung carcinogenesis by regulating the human methylome.
Project description:DNA methylation may contribute to the etiology of complex genetic disorders through its impact on genome integrity and gene expression; it is modulated by DNA-sequence variants, named methylation quantitative trait loci (meQTLs). Most meQTLs influence methylation of a few CpG dinucleotides within short genomic regions (<3 kb). Here, we identified a layered genetic control of DNA methylation at numerous CpGs across a long 300 kb genomic region. This control involved a single long-range meQTL and multiple local meQTLs. The long-range meQTL explained up to 75% of variance in methylation of CpGs located over extended areas of the 300 kb region. The meQTL was identified in four samples (P = 2.8 × 10(-17), 3.1 × 10(-31), 4.0 × 10(-71) and 5.2 × 10(-199)), comprising a total of 2796 individuals. The long-range meQTL was strongly associated not only with DNA methylation but also with mRNA expression of several genes within the 300 kb region (P = 7.1 × 10(-18)-1.0 × 10(-123)). The associations of the meQTL with gene expression became attenuated when adjusted for DNA methylation (causal inference test: P = 2.4 × 10(-13)-7.1 × 10(-20)), indicating coordinated regulation of DNA methylation and gene expression. Further, the long-range meQTL was found to be in linkage disequilibrium with the most replicated locus of multiple sclerosis, a disease affecting primarily the brain white matter. In middle-aged adults free of the disease, we observed that the risk allele was associated with subtle structural properties of the brain white matter found in multiple sclerosis (P = 0.02). In summary, we identified a long-range meQTL that controls methylation and expression of several genes and may be involved in increasing brain vulnerability to multiple sclerosis.
Project description:Inherited genetic variation affects local gene expression and DNA methylation in humans. Most expression quantitative trait loci (cis-eQTLs) occur at the same genomic location as a methylation QTL (cis-meQTL), suggesting a common causal variant and shared mechanism. Using DNA and RNA from peripheral blood of Bangladeshi individuals, here we use co-localization methods to identify eQTL-meQTL pairs likely to share a causal variant. We use partial correlation and mediation analyses to identify >400 of these pairs showing evidence of a causal relationship between expression and methylation (i.e., shared mechanism) with many additional pairs we are underpowered to detect. These co-localized pairs are enriched for SNPs showing opposite associations with expression and methylation, although many SNPs affect multiple CpGs in opposite directions. This work demonstrates the pervasiveness of co-regulated expression and methylation in the human genome. Applying this approach to other types of molecular QTLs can enhance our understanding of regulatory mechanisms.
Project description:To explore the roles of cell-type specific methylation QTL (meQTL) for melanoma GWAS annotation, we generated DNA methylation data from the same sample set as our previouse eQTL study including 106 primary melanocyte cultures. The Illumina Infinium 450k Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 480,000 CpGs in 106 primary melanocyte cultures. To be able to identify the DNA methylation probes whose methylation levels are correlated with germline genetic variants including the ones associated with melanoma, meQTL analysis was performed in these 106 primary melanocyte cultures using the same method as we did for eQTL study. Overall design: Bisulphite converted DNA from the 106 samples were hybridized to the Illumina Infinium 450k Human Methylation Beadchip v1.2
Project description:Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression-methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer. To this end, we constructed a user-friendly database named DNA Methylation Interactive Visualization Database (DNMIVD), which comprehensively provides the following important resources: (i) diagnostic and prognostic models based on DNA methylation for multiple cancer types of The Cancer Genome Atlas (TCGA); (ii) meQTL, emQTL and pathway-meQTL for diverse cancers; (iii) Functional Epigenetic Modules (FEM) constructed from Protein-Protein Interactions (PPI) and Co-Occurrence and Mutual Exclusive (COME) network by integrating DNA methylation and gene expression data of TCGA cancers; (iv) differentially variable and differentially methylated CpGs and differentially methylated genes as well as related enhancer information; (v) correlations between methylation of gene promoter and corresponding gene expression and (vi) patient survival-associated CpGs and genes with different endpoints. DNMIVD is freely available at http://www.unimd.org/dnmivd/. We believe that DNMIVD can facilitate research of diverse cancers.
Project description:DNA methylation is an important epigenetic mechanism for regulating gene expression. Aberrant DNA methylation has been observed in various human diseases, including cancer. Single-nucleotide polymorphisms can contribute to tumor initiation, progression and prognosis by influencing DNA methylation, and DNA methylation quantitative trait loci (meQTL) have been identified in physiological and pathological contexts. However, no database has been developed to systematically analyze meQTLs across multiple cancer types. Here, we present Pancan-meQTL, a database to comprehensively provide meQTLs across 23 cancer types from The Cancer Genome Atlas by integrating genome-wide genotype and DNA methylation data. In total, we identified 8 028 964 cis-meQTLs and 965 050 trans-meQTLs. Among these, 23 432 meQTLs are associated with patient overall survival times. Furthermore, we identified 2 214 458 meQTLs that overlap with known loci identified through genome-wide association studies. Pancan-meQTL provides a user-friendly web interface (http://bioinfo.life.hust.edu.cn/Pancan-meQTL/) that is convenient for browsing, searching and downloading data of interest. This database is a valuable resource for investigating the roles of genetics and epigenetics in cancer.