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

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,...

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Autores principales: Luo, Linghao, Yan, Yuting, Cui, Yidan, Yuan, Xin, Yu, Zhangsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9589256/
https://www.ncbi.nlm.nih.gov/pubmed/36299590
http://dx.doi.org/10.3389/fgene.2022.961148
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author Luo, Linghao
Yan, Yuting
Cui, Yidan
Yuan, Xin
Yu, Zhangsheng
author_facet Luo, Linghao
Yan, Yuting
Cui, Yidan
Yuan, Xin
Yu, Zhangsheng
author_sort Luo, Linghao
collection PubMed
description 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.
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spelling pubmed-95892562022-10-25 Linear high-dimensional mediation models adjusting for confounders using propensity score method Luo, Linghao Yan, Yuting Cui, Yidan Yuan, Xin Yu, Zhangsheng Front Genet Genetics 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. Frontiers Media S.A. 2022-10-10 /pmc/articles/PMC9589256/ /pubmed/36299590 http://dx.doi.org/10.3389/fgene.2022.961148 Text en Copyright © 2022 Luo, Yan, Cui, Yuan and Yu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Luo, Linghao
Yan, Yuting
Cui, Yidan
Yuan, Xin
Yu, Zhangsheng
Linear high-dimensional mediation models adjusting for confounders using propensity score method
title Linear high-dimensional mediation models adjusting for confounders using propensity score method
title_full Linear high-dimensional mediation models adjusting for confounders using propensity score method
title_fullStr Linear high-dimensional mediation models adjusting for confounders using propensity score method
title_full_unstemmed Linear high-dimensional mediation models adjusting for confounders using propensity score method
title_short Linear high-dimensional mediation models adjusting for confounders using propensity score method
title_sort linear high-dimensional mediation models adjusting for confounders using propensity score method
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9589256/
https://www.ncbi.nlm.nih.gov/pubmed/36299590
http://dx.doi.org/10.3389/fgene.2022.961148
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