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.

Guidelines for performing Mendelian randomization investigations

“…This is particularly true if many genetic variants are judged to be heterogeneous: the removal of too many variants from the analysis could provide a false impression of agreement amongst the remaining variants, and over-precision in the causal estimate. Removing a variant from the analysis is more justified when a pleiotropic association of the variant has been identified 75 .…”

MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates

“…Our method has a very different objective, which is to find clusters of variants having similar causal effects, rather than to label some variants as valid and others as invalid. Another related method is the MR-TRYX method ( Cho et al , 2020 ). This method also considers whether different genetic variants have similar causal estimates, but instead of focusing on subgroups of variants with similar estimates (as we do here), it instead focuses on individual outliers, and tries to find associations of those variants that may explain why they are outliers.…”

Understanding the Assumptions Underlying Mendelian Randomization

“…Multivariable MR-Egger 38 MR-Egger approach that includes additional in the model MR-TRYX 37 Large-scale evaluation of potential confounding using GWAS summary statistics database Negative control population MR methods PRMR 45 Estimates the total component of not mediated by using a negative control population…”