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The past decade has seen a rapid growth in omics technologies. Genome-wideassociation studies (GWAS) have uncovered susceptibility variants for a varietyof complex traits. However, the functional significance of most discoveredvariants are still not fully understood. On the other hand, there is increasinginterest in exploring the role of epigenetic variations such as DNA methylationin disease pathogenesis. In this work, we present a general framework forepigenome-wide association study and integrative analysis with thetranscriptome based on GWAS summary statistics and data from methylation andexpression quantitative trait loci (QTL) studies. The framework is based onMendelian randomization, which is much less vulnerable to confounding andreverse causation compared to conventional studies. The framework was appliedto five complex diseases. We first identified loci that are differentiallymethylated due to genetic variations, and then developed several approaches forjoint testing with the GWAS-imputed transcriptome. We discovered a number ofnovel candidate genes that are not implicated in the original GWAS studies. Wealso observed strong evidence (lowest p = 2.01e-184) for differentialexpression among the top genes mapped to methylation loci. The frameworkproposed here opens a new way of analyzing GWAS summary data and will be usefulfor gaining deeper insight into disease mechanisms.

Epigenome-wide association study and integrative analysis with the transcriptome based on GWAS summary statistics