RT Journal Article
SR Electronic
T1 Profile-likelihood Bayesian model averaging for two-sample summary data Mendelian randomization in the presence of horizontal pleiotropy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.11.943712
DO 10.1101/2020.02.11.943712
A1 Chin Yang Shapland
A1 Qingyuan Zhao
A1 Jack Bowden
YR 2020
UL http://biorxiv.org/content/early/2020/02/11/2020.02.11.943712.abstract
AB Two-sample summary data Mendelian randomisation (MR) is a popular method for assessing causality in epidemiology, by using genetic variants as instrumental variables. If genes exert pleiotropic effects on the outcome not through the exposure of interest, this can lead to heterogeneous and (potentially) biased estimates of causal effect. We investigate the use of Bayesian model averaging (BMA) to preferentially search the space of models with the highest posterior likelihood. We develop a bespoke Metropolis-Hasting algorithm to perform the search using the recently developed Robust Adjusted Profile Likelihood (MR-RAPS) of Zhao et al as the basis for defining a posterior distribution that efficiently accounts for pleiotropic and weak instrument bias. We demonstrate how our general modelling approach can be extended from a standard one-parameter causal model to a two-parameter model, to allow a large proportion of SNPs to violate the Instrument Strength Independent of Direct Effect (InSIDE) assumption. We use Monte Carlo simulations to illustrate our methods and compare it to several related approaches. We finish by applying our approach in practice to investigate the changes in causal effect of their resulting high risk metabolite on the development age-related macular degeneration.