Multi-tissue integrated Mendelian randomization method identifies disease risk genes.

Abstract

Mendelian randomization (MR) leverages genetic variants as instrumental variables to infer causal relationships between molecular traits and diseases, however, identifying the specific causal genes underlying disease risk remains challenging. Here, we present MULTI (Multi-tissue Unified Likelihood-based Transcriptomic Integration), a Bayesian MR framework that integrates genetic information across tissues to improve the accuracy of causal inference. MULTI yields reliable estimates even when the number of instruments in a single tissue is limited, and further increases statistical power by adaptively integrating information from similar tissues without inflating type I error rates. Extensive simulations confirm its robustness under diverse genetic architectures, and applications to real datasets demonstrate its capacity to reveal tissue-specific causal mechanisms and coordinated cross-tissue regulation. MULTI offers a practical and extensible framework for elucidating the molecular architecture of complex human diseases.