Exploring and accounting for genetically driven effect heterogeneity in Mendelian Randomization

Abstract

Mendelian randomization uses genetic variants as instrumental variables to estimate the causal effect of a modifiable health exposure or drug target on a downstream outcome. A crucial assumption for accurate estimation of the average causal effect in MR, termed Homogeneity, is that the causal effect does not vary across levels of any instrument used in the analysis. In contrast, the science of pharmacogenetics seeks to actively uncover and exploit genetically driven effect heterogeneity for precision medicine. In this paper we consider a recently proposed method for performing pharmacogenetic analysis on observational data (The Triangulation WIthin a STudy (TWIST) framework), and explore how it can be combined with traditional MR approaches to properly characterise average and causal effects and genetically driven effect heterogeneity. We propose two new methods which not only estimate the genetically driven effect heterogeneity but also enable the estimation of a causal effect in the genetic group with and without the risk allele separately. Both methods utilise homogeneity-respecting and homogeneity-violating genetic variants and rely on a different set of assumptions. Using data from the ALSPAC study, we apply our new method to estimate the causal effect of smoking before and during pregnancy on offspring birth weight in mothers whose genetics mean they find it (relatively) easier or harder to quit.