Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization

Abstract

Inference of directed biological networks from observational genomics datasets is a crucial but notoriously difficult challenge. Modern population-scale biobanks, containing simultaneous measurements of traits, biomarkers, and genetic variation, offer an unprecedented opportunity to study biological networks. Mendelian randomization (MR) has received attention as a class of methods for inferring causal effects in observational data that uses genetic variants as instrumental variables, but MR methods rely on assumptions that limit their application to complex traits at the biobank-scale. Moreover, MR estimates the total effect of one trait on another, which may be mediated by other factors. Biobanks include measurements of many potential mediators, in principle enabling the conversion of MR estimates into direct effects representing a causal network. Here, we show that this can be accomplished by a flexible two stage procedure we call bidirectional mediated Mendelian randomization (bimmer). First, bimmer estimates the effect of every trait on every other. Next, bimmer finds a parsimonious network that explains these effects using direct and mediated causal paths. We introduce novel methods for both steps and show via extensive simulations that bimmer is able to learn causal network structures even in the presence of non-causal genetic correlation. We apply bimmer to 405 phenotypes from the UK biobank and demonstrate that learning the network structure is invaluable for interpreting the results of phenome-wide MR, while lending causal support to several recent observational studies.