Abstract
The identification of genes and regulatory elements underlying the associations discovered by GWAS is essential to understanding the aetiology of complex traits (including diseases). Here, we demonstrate an analytical paradigm of prioritizing genes and regulatory elements at GWAS loci for follow-up functional studies. We perform an integrative analysis that uses summary-level SNP data from multi-omics studies to detect DNA methylation (DNAm) sites associated with gene expression and phenotype through shared genetic effects (i.e., pleiotropy). We identify pleiotropic associations between 7858 DNAm sites and 2733 genes. These DNAm sites are enriched in enhancers and promoters, and >40% of them are mapped to distal genes. Further pleiotropic association analyses, which link both the methylome and transcriptome to 12 complex traits, identify 149 DNAm sites and 66 genes, indicating a plausible mechanism whereby the effect of a genetic variant on phenotype is mediated by genetic regulation of transcription through DNAm.
Highlights
The identification of genes and regulatory elements underlying the associations discovered by Genome-wide association studies (GWAS) is essential to understanding the aetiology of complex traits
If the association signals are significant in all three steps, we predict with strong confidence that the identified DNA methylation (DNAm) sites and target genes are functionally relevant to the trait through the genetic regulation of gene expression at the DNAm sites
We introduced an integrative analysis based on the SMR & HEIDI method to map DNAm sites to putative target genes and further map both to a complex trait
Summary
The identification of genes and regulatory elements underlying the associations discovered by GWAS is essential to understanding the aetiology of complex traits (including diseases). We perform an integrative analysis that uses summary-level SNP data from multi-omics studies to detect DNA methylation (DNAm) sites associated with gene expression and phenotype through shared genetic effects (i.e., pleiotropy). The SMR & HEIDI approach uses summarylevel data from GWAS and expression quantitative trait locus (eQTL) studies to test if a transcript and phenotype are associated because of a shared causal variant (i.e., pleiotropy). Data from recent eQTL and methylation quantitative trait locus (mQTL) studies[25,26] provide an opportunity to incorporate mQTL data into the SMR analysis to map DNAm to transcripts through a shared genetic factor and to further detect pleiotropic associations of DNAm and transcripts with phenotype. We mapped 7858 DNAm sites to 2733 putative target genes in cis-regions and linked them to 14 complex traits
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