Jia Zhao1,2, Jingsi Ming1, Xianghong Hu3,4, Gang Chen5, Jin Liu6, Can Yang1. 1. Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR 999077. 2. School of Mathematical Sciences, Beijing Normal University, Beijing 100875. 3. Department of Mathematics, Hong Kong Baptist University, Hong Kong SAR 999077. 4. Department of Mathematics, Southern University of Science and Technology, Shenzhen 518055. 5. The WeGene Company, Shenzhen 518042, China. 6. Centre for Quantitative Medicine, Duke-NUS Medical School, 169857 Singapore.
Abstract
MOTIVATION: The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on another (outcome). Mendelian randomization (MR), an instrumental variable (IV) method, has been introduced for causal inference using GWAS data. Due to the polygenic architecture of complex traits/diseases and the ubiquity of pleiotropy, however, MR has many unique challenges compared to conventional IV methods. RESULTS: We propose a Bayesian weighted Mendelian randomization (BWMR) for causal inference to address these challenges. In our BWMR model, the uncertainty of weak effects owing to polygenicity has been taken into account and the violation of IV assumption due to pleiotropy has been addressed through outlier detection by Bayesian weighting. To make the causal inference based on BWMR computationally stable and efficient, we developed a variational expectation-maximization (VEM) algorithm. Moreover, we have also derived an exact closed-form formula to correct the posterior covariance which is often underestimated in variational inference. Through comprehensive simulation studies, we evaluated the performance of BWMR, demonstrating the advantage of BWMR over its competitors. Then we applied BWMR to make causal inference between 130 metabolites and 93 complex human traits, uncovering novel causal relationship between exposure and outcome traits. AVAILABILITY AND IMPLEMENTATION: The BWMR software is available at https://github.com/jiazhao97/BWMR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on another (outcome). Mendelian randomization (MR), an instrumental variable (IV) method, has been introduced for causal inference using GWAS data. Due to the polygenic architecture of complex traits/diseases and the ubiquity of pleiotropy, however, MR has many unique challenges compared to conventional IV methods. RESULTS: We propose a Bayesian weighted Mendelian randomization (BWMR) for causal inference to address these challenges. In our BWMR model, the uncertainty of weak effects owing to polygenicity has been taken into account and the violation of IV assumption due to pleiotropy has been addressed through outlier detection by Bayesian weighting. To make the causal inference based on BWMR computationally stable and efficient, we developed a variational expectation-maximization (VEM) algorithm. Moreover, we have also derived an exact closed-form formula to correct the posterior covariance which is often underestimated in variational inference. Through comprehensive simulation studies, we evaluated the performance of BWMR, demonstrating the advantage of BWMR over its competitors. Then we applied BWMR to make causal inference between 130 metabolites and 93 complex human traits, uncovering novel causal relationship between exposure and outcome traits. AVAILABILITY AND IMPLEMENTATION: The BWMR software is available at https://github.com/jiazhao97/BWMR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Kai Wang; Xian Shi; Ziwei Zhu; Xingjie Hao; Liangkai Chen; Shanshan Cheng; Roger S Y Foo; Chaolong Wang Journal: Genome Med Date: 2022-06-14 Impact factor: 15.266