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

TL;DR

This paper introduces new statistical inference methods for estimating the indirect effect in high-dimensional linear mediation models, applicable to incomplete and complete mediation scenarios, with proven consistency and improved power.

Contribution

It develops novel inference procedures for high-dimensional mediators, including tests with higher power under complete mediation, and demonstrates their effectiveness through simulations and real data analysis.

Findings

Proposed consistent and asymptotically normal estimators.

Enhanced testing power in complete mediation scenarios.

Identification of significant genetic variants in pharmacogenomics.

Abstract

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, as well as 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.