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Activity Number: 530 - Integrative Genomics: EQTL and GWAS
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #328515 Presentation
Title: Estimation and Inference for the Indirect Effect in High-Dimensional Linear Mediation Models
Author(s): Ruixuan Zhou* and Liewei Wang and Dave Zhao
Companies: University of Illinois at Urbana-Champaign and Mayo Clinic and University of Illinois at Urbana-Champaign
Keywords: High-dimensional Inference; Integrative Genomics; Mediation Analysis
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, and prove consistency and asymptotic normality of our indirect effect estimators. Under complete mediation, where the indirect effect is equivalent to the total effect, we 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 Ontology 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 genome-wide association study analysis methods.


Authors who are presenting talks have a * after their name.

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