Literature DB >> 32632436

Testing cell-type-specific mediation effects in genome-wide epigenetic studies.

Xiangyu Luo1, Joel Schwartz2, Andrea Baccarelli3, Zhonghua Liu4.   

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.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Keywords:  DNA methylation; cell-type specific; inverse regression; mediation analysis; multiple testing

Year:  2021        PMID: 32632436     DOI: 10.1093/bib/bbaa131

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  2 in total

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

Authors:  Jiebiao Wang; Kathryn Roeder; Bernie Devlin
Journal:  Genome Res       Date:  2021-04-09       Impact factor: 9.043

Review 2.  Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges.

Authors:  Ping Zeng; Zhonghe Shao; Xiang Zhou
Journal:  Comput Struct Biotechnol J       Date:  2021-05-26       Impact factor: 7.271

  2 in total

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