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 language | English (US) |
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Article number | btae055 |
Journal | Bioinformatics |
Volume | 40 |
Issue number | 2 |
DOIs | |
State | Published - 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