Estimation of Mediation Effect for High-Dimensional Omics Mediators with Application to the Framingham Heart Study

• Activity Number: 27 - SPEED: Causal Inference and Related Methodology Part 1
• Type: Contributed
• Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Tianzhong Yang* and Jingbo Niu and Han Chen and Peng Wei
• Companies: The University of Minnesota Twin Cities and Baylor College of Medicine and the University of Texas Health Science Center at Houston and The University of Texas MD Anderson Cancer Center
• Keywords: R-squared measure; mediation analysis; high-dimensional mediators; iterative sure independence screening; gene expression; total effect size measure

Abstract

Environmental exposures can regulate intermediate molecular phenotypes, such as gene expression, by different mechanisms and thereby lead to different health outcomes. It is of significant scientific interest to unravel the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposure and traits. Mediation analysis is an important tool for investigating such relationships. However, it has mainly focused on low-dimensional settings and there is a lack of a good measure of the total mediation effect. Here, we extend an R-squared (Rsq) effect size measure, originally proposed in the single-mediator setting, to the moderate- and high-dimensional mediator settings in the mixed model framework. We show good statistical properties of this measure and evaluate methods of excluding non-mediators through extensive simulation settings. We apply this measure to quantify the amount of aging-related variation in blood pressure and lung function explained by gene expression in the Framingham Heart Study.