HIMA2: high-dimensional mediation analysis and its application in epigenome-wide DNA methylation data

Chamila Perera, Haixiang Zhang, Yinan Zheng, Lifang Hou, Annie Qu, Cheng Zheng, Ke Xie, Lei Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Mediation analysis plays a major role in identifying significant mediators in the pathway between environmental exposures and health outcomes. With advanced data collection technology for large-scale studies, there has been growing research interest in developing methodology for high-dimensional mediation analysis. In this paper we present HIMA2, an extension of the HIMA method (Zhang in Bioinformatics 32:3150–3154, 2016). First, the proposed HIMA2 reduces the dimension of mediators to a manageable level based on the sure independence screening (SIS) method (Fan in J R Stat Soc Ser B 70:849–911, 2008). Second, a de-biased Lasso procedure is implemented for estimating regression parameters. Third, we use a multiple-testing procedure to accurately control the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. We demonstrate its practical performance using Monte Carlo simulation studies and apply our method to identify DNA methylation markers which mediate the pathway from smoking to reduced lung function in the Coronary Artery Risk Development in Young Adults (CARDIA) Study.

Original languageEnglish
Article number296
JournalBMC bioinformatics
Volume23
Issue number1
DOIs
StatePublished - Dec 2022

Keywords

  • Causality
  • Epigenetics
  • Joint significant test
  • Variable selection

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