Faculty, Staff and Student Publications

Publication Date

11-1-2024

Journal

PLoS Genetics

Abstract

Meta-analysis is used to aggregate the effects of interest across multiple studies, while its methodology is largely underexplored in mediation analysis, particularly in estimating the total mediation effect of high-dimensional omics mediators. Large-scale genomic consortia, such as the Trans-Omics for Precision Medicine (TOPMed) program, comprise multiple cohorts with diverse technologies to elucidate the genetic architecture and biological mechanisms underlying complex human traits and diseases. Leveraging the recent established asymptotic standard error of the R-squared (R2)-based mediation effect estimation for high-dimensional omics mediators, we have developed a novel meta-analysis framework requiring only summary statistics and allowing inter-study heterogeneity. Whereas the proposed meta-analysis can uniquely evaluate and account for potential effect heterogeneity across studies due to, for example, varying genomic profiling platforms, our extensive simulations showed that the developed method was more computationally efficient and yielded satisfactory operating characteristics comparable to analysis of the pooled individual-level data when there was no inter-study heterogeneity. We applied the developed method to 5 TOPMed studies with over 5800 participants to estimate the mediation effects of gene expression on age-related variation in systolic blood pressure and sex-related variation in high-density lipoprotein (HDL) cholesterol. The proposed method is available in R package MetaR2M on GitHub.

Keywords

Humans, Genomics, Precision Medicine, Cholesterol, HDL, Female, Male, Blood Pressure, Genome-Wide Association Study, Meta-Analysis as Topic, Computer Simulation

DOI

10.1371/journal.pgen.1011483

PMID

39561194

PMCID

PMC11614268

PubMedCentral® Posted Date

11-19-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

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