Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight

Abstract

Mendelian randomisation (MR) is an approach to causal inference that uses genetic variants to infer whether or not a causal effect exists, unbiased by unobserved confounding. MR estimation usually considers the effect of a single exposure on an outcome; it has recently been extended to explore potential effects of multiple exposures using multivariable MR (MVMR). Existing MVMR models are restricted to a few exposure traits in a single estimation, particularly if those traits are highly correlated. However, for many relationships of interest there are many highly correlated exposures which may have a causal effect on the outcome. MVMR Bayesian model averaging (MVMR-BMA) provides a hypothesis-free exposure selection approach with many correlated exposures. Although potentially very powerful, BMA approaches to estimation are not commonly applied in epidemiological studies. Here we describe the application of MVMR-BMA to the selection of maternal metabolites that are causal for offspring birthweight. We describe the inputs and outputs of the model in detail and discuss the appropriate sensitivity analyses, illustrating these with our application. Through this, we hope to provide a guide to help other researchers, who are potentially unfamiliar with the terminology of Bayesian analysis, but would like to apply the method to their data.