A Bayesian approach to Mendelian randomization with multiple pleiotropic variants. (1st August 2018)

• Record Type: Journal Article
• Authors: Berzuini, Carlo; Guo, Hui; Burgess, Stephen; Bernardinelli, Luisa
• Is Part Of: Biostatistics. Volume 21:Number 1(2020)
• Page Start: 86
• Page End: 101
• Publication Date: 2018-08-01
• Subjects: Correlated instruments -- Egger regression -- Instrumental variable -- Mediation -- Median estimator -- Metabolomics -- Shrinkage -- Sparsity prior; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxy027
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: We propose a Bayesian approach to Mendelian randomization (MR), where instruments are allowed to exert pleiotropic (i.e. not mediated by the exposure) effects on the outcome. By having these effects represented in the model by unknown parameters, and by imposing a shrinkage prior distribution that assumes an unspecified subset of the effects to be zero, we obtain a proper posterior distribution for the causal effect of interest. This posterior can be sampled via Markov chain Monte Carlo methods of inference to obtain point and interval estimates. The model priors require a minimal input from the user. We explore the performance of our method by means of a simulation experiment. Our results show that the method is reasonably robust to the presence of directional pleiotropy and moderate correlation between the instruments. One section of the article elaborates the model to deal with two exposures, and illustrates the possibility of using MR to estimate direct and indirect effects in this situation. A main objective of the article is to create a basis for developments in MR that exploit the potential offered by a Bayesian approach to the problem, in relation with the possibility of incorporating external information in the prior, handling multiple sources of uncertainty, and flexibly elaborating the basic model.