Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses

Abstract

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.