Estimation and inference for the indirect effect in high-dimensional linear mediation models

• 成果类型: Article
• 署名作者: Zhou, Ruixuan Rachel; Wang, Liewei; Zhao, Sihai Dave
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; Mayo Clinic
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaa016
• 发表日期: 2020
• 页码: 573589
• 关键词: confidence-intervals; genetic association; complement; selection; Lasso

摘要

Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect effect estimators. Under complete mediation, where the indirect effect is equivalent to the total effect, we further prove that our approach gives a more powerful test compared to directly testing for the total effect. We confirm our theoretical results in simulations, as well as in an integrative analysis of gene expression and genotype data from a pharmacogenomic study of drug response. We present a novel analysis of gene sets to understand the molecular mechanisms of drug response, and also identify a genome-wide significant noncoding genetic variant that cannot be detected using standard analysis methods.