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Journal article
Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies
Biometrics, v 76(3), pp 700-710
Sep 2020
PMID: 31733066
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies
- Creators
- Yanyi Song - University of Michigan–Ann ArborXiang Zhou - University of Michigan–Ann ArborMin Zhang - University of Michigan–Ann ArborWei Zhao - University of Michigan–Ann ArborYongmei Liu - Duke UniversitySharon L R Kardia - University of Michigan–Ann ArborAna V Diez Roux - Drexel UniversityBelinda L Needham - University of Michigan–Ann ArborJennifer A Smith - University of Michigan–Ann ArborBhramar Mukherjee - University of Michigan–Ann Arbor
- Publication Details
- Biometrics, v 76(3), pp 700-710
- Publisher
- Wiley
- Grant note
- N01HC95169 / NHLBI NIH HHS 1712933 / Division of Mathematical Sciences N01HC95161 / NHLBI NIH HHS UL1 TR001881 / NCATS NIH HHS N01HC95164 / NHLBI NIH HHS N01HC95167 / NHLBI NIH HHS R01 HL119443 / NHLBI NIH HHS N01HC95159 / NHLBI NIH HHS R01 MD011721 / NIMHD NIH HHS N01HC95163 / NHLBI NIH HHS N01HC95166 / NHLBI NIH HHS R01 HL141292 / NHLBI NIH HHS UL1 TR001079 / NCATS NIH HHS R01 HL101250 / NHLBI NIH HHS R01 HG009124 / NHGRI NIH HHS 141292 / NHLBI NIH HHS HHSN268201500003C / NHLBI NIH HHS N01HC95160 / NHLBI NIH HHS RF1 AG054474 / NIA NIH HHS 009124 / NHGRI NIH HHS 011724 / NIMHD NIH HHS R01 DK101921 / NIDDK NIH HHS HHSN268201500003I / NHLBI NIH HHS UL1 TR000040 / NCATS NIH HHS N01HC95168 / NHLBI NIH HHS N01HC95165 / NHLBI NIH HHS P30 DK063491 / NIDDK NIH HHS N01HC95162 / NHLBI NIH HHS UL1 TR001420 / NCATS NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative
- Web of Science ID
- WOS:000503365200001
- Scopus ID
- 2-s2.0-85077041040
- Other Identifier
- 991019169698304721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biology
- Mathematical & Computational Biology
- Statistics & Probability