» Articles » PMID: 32632436

Testing Cell-type-specific Mediation Effects in Genome-wide Epigenetic Studies

Overview
Journal Brief Bioinform
Specialty Biology
Date 2020 Jul 8
PMID 32632436
Citations 3
Authors
Affiliations
Soon will be listed here.
Abstract

Epigenome-wide mediation analysis aims to identify DNA methylation CpG sites that mediate the causal effects of genetic/environmental exposures on health outcomes. However, DNA methylations in the peripheral blood tissues are usually measured at the bulk level based on a heterogeneous population of white blood cells. Using the bulk level DNA methylation data in mediation analysis might cause confounding bias and reduce study power. Therefore, it is crucial to get fine-grained results by detecting mediation CpG sites in a cell-type-specific way. However, there is a lack of methods and software to achieve this goal. We propose a novel method (Mediation In a Cell-type-Specific fashion, MICS) to identify cell-type-specific mediation effects in genome-wide epigenetic studies using only the bulk-level DNA methylation data. MICS follows the standard mediation analysis paradigm and consists of three key steps. In step1, we assess the exposure-mediator association for each cell type; in step 2, we assess the mediator-outcome association for each cell type; in step 3, we combine the cell-type-specific exposure-mediator and mediator-outcome associations using a multiple testing procedure named MultiMed [Sampson JN, Boca SM, Moore SC, et al. FWER and FDR control when testing multiple mediators. Bioinformatics 2018;34:2418-24] to identify significant CpGs with cell-type-specific mediation effects. We conduct simulation studies to demonstrate that our method has correct FDR control. We also apply the MICS procedure to the Normative Aging Study and identify nine DNA methylation CpG sites in the lymphocytes that might mediate the effect of cigarette smoking on the lung function.

Citing Articles

IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA.

Shao Z, Wang T, Zhang M, Jiang Z, Huang S, Zeng P PLoS Comput Biol. 2021; 17(8):e1009250.

PMID: 34464378 PMC: 8437300. DOI: 10.1371/journal.pcbi.1009250.


Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges.

Zeng P, Shao Z, Zhou X Comput Struct Biotechnol J. 2021; 19:3209-3224.

PMID: 34141140 PMC: 8187160. DOI: 10.1016/j.csbj.2021.05.042.


Bayesian estimation of cell type-specific gene expression with prior derived from single-cell data.

Wang J, Roeder K, Devlin B Genome Res. 2021; 31(10):1807-1818.

PMID: 33837133 PMC: 8494232. DOI: 10.1101/gr.268722.120.

References
1.
Jia J, Conlon T, Sarker R, Tasdemir D, Smirnova N, Srivastava B . Cholesterol metabolism promotes B-cell positioning during immune pathogenesis of chronic obstructive pulmonary disease. EMBO Mol Med. 2018; 10(5). PMC: 5938615. DOI: 10.15252/emmm.201708349. View

2.
Yan T, Li Q, Li Y, Li Z, Zheng G . Genetic association with multiple traits in the presence of population stratification. Genet Epidemiol. 2013; 37(6):571-80. DOI: 10.1002/gepi.21738. View

3.
Zheng S, Breeze C, Beck S, Teschendorff A . Identification of differentially methylated cell types in epigenome-wide association studies. Nat Methods. 2018; 15(12):1059-1066. PMC: 6277016. DOI: 10.1038/s41592-018-0213-x. View

4.
Panni T, Mehta A, Schwartz J, Baccarelli A, Just A, Wolf K . Genome-Wide Analysis of DNA Methylation and Fine Particulate Matter Air Pollution in Three Study Populations: KORA F3, KORA F4, and the Normative Aging Study. Environ Health Perspect. 2016; 124(7):983-90. PMC: 4937859. DOI: 10.1289/ehp.1509966. View

5.
Baron R, Kenny D . The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986; 51(6):1173-82. DOI: 10.1037//0022-3514.51.6.1173. View