Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into...

Area of Science:

• Genetics
• Bioinformatics
• Systems Biology

Background:

• Transcriptome-wide association studies (TWAS) integrate gene expression with genome-wide association studies (GWAS) to identify disease genes.
• Current TWAS methods primarily utilize cis-variants, leaving trans-variants for gene expression prediction largely unexplored.
• Transcription factors (TFs) play a crucial role in gene regulation, making TF-linked variants important for understanding gene expression.

Purpose of the Study:

• To introduce transTF-TWAS, a novel method that incorporates TF-linked trans-variants into TWAS.
• To enhance the prediction of gene expression and alternative splicing for TF downstream target genes.
• To improve the identification of disease susceptibility genes by leveraging trans-acting variants.

Main Methods:

• Developed transTF-TWAS by integrating TF-linked trans-variants into gene expression prediction models.
• Utilized Genotype-Tissue Expression (GTEx) project data for model training.
• Applied the developed models to large GWAS datasets for various diseases, including breast, prostate, and lung cancers.

Main Results:

• transTF-TWAS demonstrated superior performance in constructing gene expression prediction models compared to existing TWAS approaches.
• The method significantly outperformed other TWAS methods in identifying disease-associated genes.
• Simulations and real-data analyses confirmed the efficacy of transTF-TWAS.

Conclusions:

• transTF-TWAS offers a significant advancement in the discovery of disease risk genes.
• The study highlights the importance of TF-linked trans-variants in understanding genetic susceptibility.
• Findings provide new insights into genetically driven TF regulators and their regulatory networks in disease.