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Front. Genet. | doi: 10.3389/fgene.2019.01195

Testing mediation effects in high-dimensional epigenetic studies

Yuzhao Gao1,  Haitao Yang2,  Ruiling Fang1, Yanbo Zhang1,  Ellen L. Goode3 and  Yuehua Cui4*
  • 1Shanxi Medical University, China
  • 2Hebei Medical University, China
  • 3Mayo Clinic, United States
  • 4Department of Statistics and Probability, Michigan State University, United States

Mediation analysis has been a powerful tool to identify factors mediating the association between exposure variables and outcomes. It has been applied to various genomic applications with the hope to gain novel insights into the underlying mechanism of various diseases. Given the high-dimensional nature of epigenetic data, recent effort on epigenetic mediation analysis is to first reduce the data dimension by applying high-dimensional variable selection techniques, then conducting testing in a low dimensional setup. In this paper, we propose to assess the mediation effect by adopting a high-dimensional testing procedure which can produce unbiased estimates of the regression coefficients and can properly handle correlations between variables. When the data dimension is ultra-high, we first reduce the data dimension from ultra-high to high by adopting a sure independent screening (SIS) method. We apply the method to two high-dimensional epigenetic studies: one is to assess how DNA methylations mediate the association between alcohol consumption and epithelial ovarian cancer (EOC) status; the other one is to assess how methylation signatures mediate the association between childhood maltreatment and post-traumatic stress disorder (PTSD) in adulthood. We compare the performance of the method with its counterpart via simulation studies. Our method can be applied to other high-dimensional mediation studies where high-dimensional mediation variables are collected.

Keywords: De-sparsify, DNA Methylation, High-dimensional testing, High-dimensional mediation, Mediation analysis

Received: 10 Apr 2019; Accepted: 29 Oct 2019.

Copyright: © 2019 Gao, Yang, Fang, Zhang, Goode and Cui. 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.

* Correspondence: Dr. Yuehua Cui, Michigan State University, Department of Statistics and Probability, East Lansing, 48824, MI, United States, cuiy@msu.edu