A Network-Driven Framework for Enhancing Gene-Disease Association Studies in Coronary Artery Disease.

Motivation: Over the last decade, genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with complex diseases. These associations have the potential to reveal the molecular mechanisms underlying complex diseases and lead to the identification of novel drug targets. Despite these advancements, the biological pathways and mechanisms linking genetic variants to complex diseases are still not fully understood. Most trait-associated variants reside in non-coding regions and are presumed to influence phenotypes through regulatory effects on gene expression. Yet, it is often unclear which genes they regulate and in which cell types this regulation occurs. Transcriptome-wide association studies (TWAS) aim to bridge this gap by detecting trait-associated tissue gene expression regulated by GWAS variants. However, traditional TWAS approaches frequently overlook the critical contributions of trans-regulatory effects and fail to integrate comprehensive regulatory networks. Here, we present a novel framework that leverages tissue-specific gene regulatory networks (GRNs) to integrate cis- and trans-genetic regulatory effects into the TWAS framework for complex diseases. Results: We validate our approach using coronary artery disease (CAD), utilizing data from the STARNET project, which provides multi-tissue gene expression and genetic data from around 600 living patients with cardiovascular disease. Preliminary results demonstrate the potential of our GRN-driven framework to uncover more genes and pathways that may underlie CAD. This framework extends traditional TWAS methodologies by utilizing tissue-specific regulatory insights and advancing the understanding of complex disease genetic architecture. Availability: https://github.com/guutama/GRN-TWAS.