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High-Dimensional Mediation Analysis with Applications to Causal Gene Identification

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  • Qi Zhang

    (University of New Hampshire)

Abstract

Mediation analysis has been a popular framework for elucidating the mediating mechanism of the exposure effect on the outcome in many disciplines including genetic studies. Previous literature in causal mediation primarily focused on the classical settings with univariate exposure and univariate mediator, with recent growing interests in high-dimensional mediator. In this paper, we study the mediation model with high-dimensional exposure, high-dimensional continuous mediator, and a continuous outcome. We introduce two procedures for mediator selection, MedFix and MedMix, and develop the corresponding causal effect tests. Our study is motivated by the causal gene identification problem in biomedical studies, where causal genes are defined as the genes that mediate the genetic effect. For this problem, the genetic variants are the high-dimensional exposure, the gene expressions the high-dimensional mediator, and the phenotype of interest the outcome. We evaluate the proposed methods using a mouse f2 dataset for diabetes study, and extensive real data-driven simulations. We show that the mixed model-based approach (MedMix) leads to higher accuracy in mediator selection with reasonable reproducibility across independent measurements of the response and is more robust against model misspecification. The R code and additional materials are available on Github ( https://github.com/QiZhangStat/highMed ).

Suggested Citation

  • Qi Zhang, 2022. "High-Dimensional Mediation Analysis with Applications to Causal Gene Identification," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 432-451, December.
    • Handle: RePEc:spr:stabio:v:14:y:2022:i:3:d:10.1007_s12561-021-09328-0
    DOI: 10.1007/s12561-021-09328-0
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