Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies

Zhongshang Yuan, Huanhuan Zhu, Ping Zeng, Sheng Yang, Shiquan Sun, Can Yang, Jin Liu and Xiang Zhou ()
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Zhongshang Yuan: Shandong University
Huanhuan Zhu: University of Michigan
Ping Zeng: Xuzhou Medical University
Sheng Yang: University of Michigan
Shiquan Sun: University of Michigan
Can Yang: Hong Kong University of Science and Technology
Jin Liu: Duke-NUS Medical School
Xiang Zhou: University of Michigan

Nature Communications, 2020, vol. 11, issue 1, 1-14

Abstract: Abstract Integrating results from genome-wide association studies (GWASs) and gene expression studies through transcriptome-wide association study (TWAS) has the potential to shed light on the causal molecular mechanisms underlying disease etiology. Here, we present a probabilistic Mendelian randomization (MR) method, PMR-Egger, for TWAS applications. PMR-Egger relies on a MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and is scalable to hundreds of thousands of individuals. In simulations, PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust under various types of model misspecifications, is more powerful than existing TWAS/MR approaches, and can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank.

Date: 2020
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DOI: 10.1038/s41467-020-17668-6

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