An Efficient Testing Procedure for High-Dimensional Mediators with FDR Control

Xueyan Bai, Yinan Zheng, Lifang Hou, Cheng Zheng, Lei Liu and Haixiang Zhang ()
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Xueyan Bai: Tianjin University
Yinan Zheng: Northwestern University
Lifang Hou: Northwestern University
Cheng Zheng: University of Nebraska Medical Center
Lei Liu: Washington University in St. Louis
Haixiang Zhang: Tianjin University

Statistics in Biosciences, 2025, vol. 17, issue 3, No 3, 615-629

Abstract: Abstract The field of mediation analysis commonly explores the pathways that connect environmental exposures with health outcomes. With the development of data collection techniques, greater efforts have been dedicated to addressing high-dimensional mediators. In this paper, we present an efficient approach to identify significant mediators while controlling the false discovery rate (FDR). We propose a three-step procedure that incorporates independent screening, variable selection together with refitted partial regression, and divide-aggregate composite-null test (DACT). The simulation includes a comparative analysis of our proposed method in comparison to eight competing approaches, demonstrating that our procedure has significant advantages over other methods. The proposed procedure is applied to investigate the mediation mechanisms of DNA methylation in the relationship between smoking and lung function. Three specific methylation sites (cg26331243, cg19862839, and cg12616487) are identified as potential epigenetic markers involved in mediating this relationship. Our proposed method is available with the R package HIMA at https://cran.r-project.org/web/packages/HIMA/ .

Keywords: High-dimentional mediation analysis; FDR control; Multiple testing; Variable selection (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12561-024-09447-4

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