  The past decade has seen a rapid growth in omics technologies. Genome-wide
association studies (GWAS) have uncovered susceptibility variants for a variety
of complex traits. However, the functional significance of most discovered
variants are still not fully understood. On the other hand, there is increasing
interest in exploring the role of epigenetic variations such as DNA methylation
in disease pathogenesis. In this work, we present a general framework for
epigenome-wide association study and integrative analysis with the
transcriptome based on GWAS summary statistics and data from methylation and
expression quantitative trait loci (QTL) studies. The framework is based on
Mendelian randomization, which is much less vulnerable to confounding and
reverse causation compared to conventional studies. The framework was applied
to five complex diseases. We first identified loci that are differentially
methylated due to genetic variations, and then developed several approaches for
joint testing with the GWAS-imputed transcriptome. We discovered a number of
novel candidate genes that are not implicated in the original GWAS studies. We
also observed strong evidence (lowest p = 2.01e-184) for differential
expression among the top genes mapped to methylation loci. The framework
proposed here opens a new way of analyzing GWAS summary data and will be useful
for gaining deeper insight into disease mechanisms.
