A network-driven framework for enhancing gene-disease association studies in coronary artery disease

Abstract

Transcriptome-wide association studies (TWAS) link genetic variation to complex traits by leveraging expression quantitative trait loci (eQTL) data. However, most implementations are typically limited to local (cis-acting) effects and fail to account for long-range (trans) regulatory influences mediated through gene networks. We introduce GRN-TWAS, a framework that reconstructs gene regulatory networks (GRNs) and integrates their topology into gene expression prediction models, thereby propagating distal (trans) regulatory effects through tissue-specific gene networks to trait- or disease-associated phenotypes. By incorporating network-derived trans-eQTLs, GRN-TWAS generates gene expression imputation models that capture both local and distal genetic components, enabling a more complete, systems-level view of genetic regulation consistent with the omnigenic model hypothesis. Using genotype and multi-tissue expression data from 600 coronary artery disease (CAD) cases in the STARNET study together with GWAS summary statistics, we show that GRN-TWAS improves gene-expression prediction and sharpens discovery of CAD-associated genes. Across seven tissues, the framework identified 5,779 transcriptome-wide significant genes, more than 50\% of which appear to be previously unreported in the CAD literature. A knowledge-based gene-ranking engine then prioritized 882 genes as highly CAD-relevant, including 237 regulated exclusively through trans effects. Key-driver analysis highlighted 18 putative trans mediators with high network centrality and disease relevance, offering mechanistic hypotheses that complement association signals. Collectively, these results demonstrate that embedding network topology into TWAS improves discovery and interpretability by exposing tissue-specific regulatory routes from genotype to phenotype and expanding the landscape of gene-disease associations.