Assessing the genetic effect mediated through gene expression from summary eQTL and GWAS data

Integrating genome-wide association (GWAS) and expression quantitative trait locus (eQTL) data can boost power to detect novel disease loci or pinpoint the susceptibility gene at a known disease locus. However, it is often the case that multiple eQTL genes co-localize at disease loci (an effect of linkage disequilibrium, LD), making the identification of the true susceptibility gene challenging. To distinguish between true susceptibility genes (i.e. when the genetic effect on phenotype is mediated through expression) and spurious co-localizations, we developed an approach to quantify the genetic effect mediated through expression. Our approach can be viewed as an extension of the standard Mendelian randomization Egger technique to incorporate LD among variants while only requiring summary association data (both GWAS and eQTL) along with LD from reference panels. Through simulations we show that when eQTLs have pleiotropic or LD-confounded effects on disease, our approach provides adequate control of Type I error, more power, and less bias than previously-proposed methods. When there is no effect of gene expression on disease, our method has the desired Type I Error, while LD-aware Mendelian randomization, which assumes no pleiotropy, can have inflated Type I Error. In the presence of direct effect of genetic variants on traits, our approach attained up to 3x greater power than the standard approaches while properly controlling Type I error. To illustrate our method, we analyzed recent large scale breast cancer GWAS with gene expression in breast tissue from GTEx.