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Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies
Author(s) -
Song Yanyi,
Zhou Xiang,
Zhang Min,
Zhao Wei,
Liu Yongmei,
Kardia Sharon L. R.,
Roux Ana V. Diez,
Needham Belinda L.,
Smith Jennifer A.,
Mukherjee Bhramar
Publication year - 2020
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13189
Subject(s) - causal inference , mediation , bayesian probability , shrinkage estimator , computer science , inference , structural equation modeling , causal structure , bayesian inference , data science , econometrics , bioinformatics , machine learning , biology , artificial intelligence , estimator , mathematics , statistics , physics , minimum variance unbiased estimator , quantum mechanics , political science , bias of an estimator , law
Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high‐throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of ‐omics data, joint analysis of molecular‐level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high‐dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high‐dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true nonnull contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi‐Ethnic Study of Atherosclerosis and identified DNA methylation regions that may actively mediate the effect of socioeconomic status on cardiometabolic outcomes.