Bayesian Mendelian randomization with study heterogeneity and data partitioning for large studies

TL;DR

This paper introduces a Bayesian Mendelian randomization model that accounts for study heterogeneity and employs data partitioning to improve computational efficiency in large-scale studies, maintaining accuracy despite data missingness.

Contribution

It advances Bayesian MR by incorporating random effects for heterogeneity and using subset posterior aggregation to handle large datasets efficiently.

Findings

Random effect Bayesian MR outperforms inverse-variance weighted estimation.

Data partitioning minimally affects causal effect variation but impacts unbiasedness under certain conditions.

Partitioning improves computational efficiency with minimal bias increase.

Abstract

Background: Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies required in MR is often overlooked. When dealing with large studies, recently developed Bayesian MR is limited by its computational expensiveness. Methods: We addressed study heterogeneity by proposing a random effect Bayesian MR model with multiple exposures and outcomes. For large studies, we adopted a subset posterior aggregation method to tackle the problem of computation. In particular, we divided data into subsets and combine estimated subset causal effects obtained from the subsets". The performance of our method was evaluated by a number of simulations, in which part of exposure data was missing. Results: Random effect Bayesian MR outperformed conventional inverse-variance weighted estimation, whether the true causal effects are zero or non-zero. Data partitioning of large studies had little impact on variations of the estimated causal effects, whereas it notably affected unbiasedness of the estimates with weak instruments and high missing rate of data. Our simulation results indicate that data partitioning is a good way of improving computational efficiency, for little cost of decrease in unbiasedness of the estimates, as long as the sample size of subsets is reasonably large. Conclusions: We have further advanced Bayesian MR by including random effects to explicitly account for study heterogeneity. We also adopted a subset posterior aggregation method to address the issue of computational expensiveness of MCMC, which is important especially when dealing with large studies. Our proposed work is likely to pave the way for more general model settings, as Bayesian approach itself renders great flexibility in model constructions.