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Bayesian Shrinkage Estimation of High Dimensional Causal Mediation Effects in Omics Studies

By Yanyi Song, Xiang Zhou, Min Zhang, Wei Zhao, Yongmei Liu, Sharon L. R. Kardia, Ana V. Diez Roux, Belinda L. Needham, Jennifer A. Smith, Bhramar Mukherjee

Posted 14 Nov 2018
bioRxiv DOI: 10.1101/467399 (published DOI: 10.1111/biom.13189)

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 non-null mediators. We also construct tests for natural indirect effects using a permutation procedure. The Bayesian method helps us to understand the structure of the composite null hypotheses. We applied our method to Multi-Ethnic Study of Atherosclerosis (MESA) and identified DNA methylation regions that may actively mediate the effect of socioeconomic status (SES) on cardiometabolic outcome.

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