Linear high-dimensional mediation models adjusting for confounders using propensity score method

Linghao Luo; Yuting Yan; Yidan Cui; Xin Yuan; Zhangsheng Yu

doi:10.3389/fgene.2022.961148

Linear high-dimensional mediation models adjusting for confounders using propensity score method

Front Genet. 2022 Oct 10:13:961148. doi: 10.3389/fgene.2022.961148. eCollection 2022.

Authors

Linghao Luo^{1

2}, Yuting Yan³, Yidan Cui^{1

2}, Xin Yuan^{1

2}, Zhangsheng Yu^{1

2

4}

Affiliations

¹ Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
² SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.
³ Jinmai Community Service Center, Guiyang, China.
⁴ Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

High-dimensional mediation analysis has been developed to study whether epigenetic phenotype in a high-dimensional data form would mediate the causal pathway of exposure to disease. However, most existing models are designed based on the assumption that there are no confounders between the exposure, the mediators, and the outcome. In practice, this assumption may not be feasible since high-dimensional mediation analysis (HIMA) tends to be observational where a randomized controlled trial (RCT) cannot be conducted for some economic or ethical reasons. Thus, to deal with the confounders in HIMA cases, we proposed three propensity score-related approaches named PSR (propensity score regression), PSW (propensity score weighting), and PSU (propensity score union) to adjust for the confounder bias in HIMA, and compared them with the traditional covariate regression method. The procedures mainly include four parts: calculating the propensity score, sure independence screening, MCP (minimax concave penalty) variable selection, and joint-significance testing. Simulation results show that the PSU model is the most recommended. Applying our models to the TCGA lung cancer dataset, we find that smoking may lead to lung disease through the mediation effect of some specific DNA-methylation sites, including site Cg24480765 in gene RP11-347H15.2 and site Cg22051776 in gene KLF3.

Keywords: MCP; SIS; confounders; high-dimensional mediation model; inverse probability weighting; joint-significance test; propensity score.