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

Ruixuan Rachel Zhou, Liewei Wang, Sihai Dave Zhao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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, 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.

Original languageEnglish (US)
Pages (from-to)573-589
Number of pages17
JournalBiometrika
Volume107
Issue number3
DOIs
StatePublished - Sep 1 2020

Keywords

  • High-dimensional inference
  • Integrative genomics
  • Mediation analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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