Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework

Abstract

In Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying alternative pathways to the traits under investigation. Here, we develop MR-TRYX, a framework that exploits horizontal pleiotropy to discover putative risk factors for disease. We begin by detecting outliers in a single exposure–outcome MR analysis, hypothesising they are due to horizontal pleiotropy. We search across hundreds of complete GWAS summary datasets to systematically identify other (candidate) traits that associate with the outliers. We develop a multi-trait pleiotropy model of the heterogeneity in the exposure–outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways are uncovered with already established causal effects, validating the approach, but also alternative putative causal pathways. Adjustment for pleiotropic pathways reduces the heterogeneity across the analyses.

In Mendelian randomization (MR) studies, one typically selects SNPs as instrumental variables that do not directly affect the outcome to avoid violation of MR assumptions. Here, Cho et al. present a framework, MR-TRYX, that leverages knowledge of such outliers of horizontal pleiotropy to identify putative causal relationships between exposure and outcome.