High-dimensional quantile mediation analysis with application to a birth cohort study of mother–newborn pairs

Haixiang Zhang, Xiumei Hong, Yinan Zheng, Lifang Hou, Cheng Zheng, Xiaobin Wang, Lei Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Motivation: There has been substantial recent interest in developing methodology for high-dimensional mediation analysis. Yet, the majority of mediation statistical methods lean heavily on mean regression, which limits their ability to fully capture the complex mediating effects across the outcome distribution. To bridge this gap, we propose a novel approach for selecting and testing mediators throughout the full range of the outcome distribution spectrum. Results: The proposed high-dimensional quantile mediation model provides a comprehensive insight into how potential mediators impact outcomes via their mediation pathways. This method’s efficacy is demonstrated through extensive simulations. The study presents a real-world data application examining the mediating effects of DNA methylation on the relationship between maternal smoking and offspring birthweight.

Original languageEnglish (US)
Article numberbtae055
JournalBioinformatics
Volume40
Issue number2
DOIs
StatePublished - Feb 1 2024

Funding

This work was partly supported by NIH [R21 AG063370, R21 AG068955, R01 AG081244, and UL1 TR002345].

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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