Integrative Multi-Omics Approach for Improving Causal Gene Identification

Authors

Austin King
Chong Wu

Language

English

Publication Date

1-1-2025

Journal

Genetic Epidemiology

DOI

10.1002/gepi.22601

PMID

39444114

PMCID

PMC11659073

PubMedCentral® Posted Date

1-1-2026

PubMedCentral® Full Text Version

Author MSS

Abstract

Transcriptome-wide association studies (TWAS) have been widely used to identify thousands of likely causal genes for diseases and complex traits using predicted expression models. However, most existing TWAS methods rely on gene expression alone and overlook other regulatory mechanisms of gene expression, including DNA methylation and splicing, that contribute to the genetic basis of these complex traits and diseases. Here we introduce a multi-omics method that integrates gene expression, DNA methylation, and splicing data to improve the identification of associated genes with our traits of interest. Through simulations and by analyzing genome-wide association study (GWAS) summary statistics for 24 complex traits, we show that our integrated method, which leverages these complementary omics biomarkers, achieves higher statistical power, and improves the accuracy of likely causal gene identification in blood tissues over individual omics methods. Finally, we apply our integrated model to a lung cancer GWAS data set, demonstrating the integrated models improved identification of prioritized genes for lung cancer risk.

Keywords

Humans, Genome-Wide Association Study, DNA Methylation, Lung Neoplasms, Polymorphism, Single Nucleotide, Transcriptome, Genomics, Models, Genetic, Genetic Predisposition to Disease, Multifactorial Inheritance, Computer Simulation, Quantitative Trait Loci, Multiomics

Published Open-Access

yes

Recommended Citation

Austin King and Chong Wu, "Integrative Multi-Omics Approach for Improving Causal Gene Identification" (2025). Faculty, Staff and Student Publications. 6805.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6805