Bayesian Network-based Mendelian Randomization for Variant Prioritization and Phenotypic Causal Inference

Abstract

Abstract Mendelian randomization is a powerful method for for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian Network-based Mendelian Randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, a series of Bayesian network structural learning processes, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlight the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.