A comparison of the genes and genesets identified by EWAS and GWAS of fourteen complex traits

Abstract

AbstractIdentifying the genes, properties of these genes and pathways to understand the underlying biology of complex traits responsible for differential health states in the population is a common goal of epigenome-wide and genome-wide association studies (EWAS and GWAS). GWAS identify genetic variants that effect the trait of interest or variants that are in linkage disequilibrium with the true causal variants. EWAS identify variation in DNA methylation, a complex molecular phenotype, associated with the trait of interest. Therefore, while GWAS in principle will only detect variants within or near causal genes, EWAS can also detect genes that confound the association between a trait and a DNA methylation site, or are reverse causal. Here we systematically compare association EWAS and GWAS results of 14 complex traits (N > 4500). A small fraction of detected genomic regions were shared by both EWAS and GWAS (0-9%). We evaluated if the genes or gene ontology terms flagged by GWAS and EWAS overlapped, and after a multiple testing correction, found substantial overlap for diastolic blood pressure (gene overlap P = 5.2×10−6, term overlap P = 0.001). We superimposed our empirical findings against simulated models of varying genetic and epigenetic architectures and observed that in a majority of cases EWAS and GWAS are likely capturing distinct genesets, implying that genes identified by EWAS are not generally causally upstream of the trait. Overall our results indicate that EWAS and GWAS are capturing different aspects of the biology of complex traits.