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

Linghao Luo1,2, Yuting Yan3, Yidan Cui1,2

  • 1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Frontiers in Genetics
|October 27, 2022
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Summary
This summary is machine-generated.

This study introduces three propensity score methods (propensity score regression, weighting, and union) to address confounders in high-dimensional mediation analysis. The propensity score union model is recommended for analyzing exposure-disease pathways, like smoking and lung cancer via DNA methylation.

Keywords:
MCPSISconfoundershigh-dimensional mediation modelinverse probability weightingjoint-significance testpropensity score

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Area of Science:

  • Genetics and Bioinformatics
  • Epidemiology
  • Statistical Genetics

Background:

  • High-dimensional mediation analysis (HIMA) investigates epigenetic mediation of exposure-disease pathways.
  • Existing HIMA models often assume no confounders, which is unrealistic in observational studies.
  • Confounder bias is a significant challenge in applying HIMA to real-world data.

Purpose of the Study:

  • To develop and evaluate methods for adjusting confounder bias in high-dimensional mediation analysis.
  • To compare propensity score-based approaches with traditional covariate regression.
  • To identify epigenetic mediators in the smoking-lung cancer pathway.

Main Methods:

  • Proposed three propensity score-related methods: propensity score regression (PSR), propensity score weighting (PSW), and propensity score union (PSU).
  • Integrated propensity score calculation with sure independence screening, minimax concave penalty (MCP) variable selection, and joint-significance testing.
  • Compared the performance of PSR, PSW, and PSU against traditional covariate regression using simulations.

Main Results:

  • Simulation results indicated that the propensity score union (PSU) model performed best in adjusting for confounder bias.
  • Application to the TCGA lung cancer dataset revealed specific DNA methylation sites mediating the effect of smoking on lung disease.
  • Identified key mediating sites, including Cg24480765 (gene RP11-347H15.2) and Cg22051776 (gene KLF3).

Conclusions:

  • Propensity score methods, particularly PSU, effectively adjust for confounder bias in high-dimensional mediation analysis.
  • The findings highlight the role of specific DNA methylation sites as mediators in the causal pathway from smoking to lung cancer.
  • This approach enhances the reliability of HIMA in observational epidemiological research.