Bayesian weighted Mendelian randomization for causal inference based on summary statistics

Zhao，Jia1,2; Ming，Jingsi1; Hu，Xianghong3,4; Chen，Gang5; Liu，Jin6; Yang，Can1; Schwartz，Russell7

2020-03-01

10.1093/bioinformatics/btz749

BIOINFORMATICS   影响因子和分区

1367-4803

1460-2059

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.

SCI

英语

其他

National Science Funding of China[61501389] ; Hong Kong Research Grant Council[12316116][12301417][16307818] ; Hong Kong University of Science and Technology[R9405][IGN17SC02] ; Duke-NUS Medical School 546 WBS[R-913-200-098-263] ; Ministry of Education, Singapore. AcRF Tier 2[MOE2016-T2-2-547 029][MOE2018-T2-1-046][MOE2018-T2-2-006]

Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics

Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability

WOS:000535656600024

OXFORD UNIV PRESS

BIOLOGY & BIOCHEMISTRY

2-s2.0-85081737169

Scopus

1.Department of Mathematics,Hong Kong University of Science and Technology,Hong Kong SAR,999077,Hong Kong
2.School of Mathematical Sciences,Beijing Normal University,Beijing,100875,China
3.Department of Mathematics,Hong Kong Baptist University,Hong Kong SAR,999077,Hong Kong
4.Department of Mathematics,Southern University of Science and Technology,Shenzhen,518055,China
5.WeGene Company,Shenzhen,518042,China
6.Centre for Quantitative Medicine,Duke-NUS Medical School,Singapore,169857,Singapore
7.

Zhao，Jia,Ming，Jingsi,Hu，Xianghong,et al. Bayesian weighted Mendelian randomization for causal inference based on summary statistics[J]. BIOINFORMATICS,2020,36(5):1501-1508.

Zhao，Jia.,Ming，Jingsi.,Hu，Xianghong.,Chen，Gang.,Liu，Jin.,...&Schwartz，Russell.(2020).Bayesian weighted Mendelian randomization for causal inference based on summary statistics.BIOINFORMATICS,36(5),1501-1508.

Zhao，Jia,et al."Bayesian weighted Mendelian randomization for causal inference based on summary statistics".BIOINFORMATICS 36.5(2020):1501-1508.