Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

ABSTRACT: Mendelian randomization (MR) is a valuable tool for detecting causal effects by using genetic variant associations. Opportunities to apply MR are growing rapidly with the increasing number of genome-wide association studies (GWAS). However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Correlated horizontal pleiotropy, which arises when variants affect both traits through a heritable shared factor, remains a particularly challenging problem. We propose a new MR method, Causal Analysis Using Summary Effect estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate, in simulations, that CAUSE avoids more false positives induced by correlated horizontal pleiotropy than other methods. App

SUBMITTER: Morrison J 

PROVIDER: S-EPMC7343608 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature