PT  - JOURNAL ARTICLE
AU  - Song, Yanyi
AU  - Zhou, Xiang
AU  - Zhang, Min
AU  - Zhao, Wei
AU  - Liu, Yongmei
AU  - Kardia, Sharon L. R.
AU  - Diez Roux, Ana V.
AU  - Needham, Belinda L.
AU  - Smith, Jennifer A.
AU  - Mukherjee, Bhramar
TI  - Bayesian Shrinkage Estimation of High Dimensional Causal Mediation Effects in Omics Studies
AID  - 10.1101/467399
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 467399
4099  - http://biorxiv.org/content/early/2018/11/14/467399.short
4100  - http://biorxiv.org/content/early/2018/11/14/467399.full
AB  - 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.