webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Transcriptome-wide association studies (TWAS) are crucial for identifying causal genes in diseases.
• Existing resources lack comprehensive TWAS-derived gene-disease associations from GWAS summary statistics.
• Conducting TWAS is challenging due to complex software pipelines.

Purpose of the Study:

• To introduce webTWAS, a novel resource consolidating GWAS datasets and TWAS findings.
• To provide a comprehensive database of gene-disease associations discovered via TWAS.
• To simplify the process of identifying potential causal genes for human diseases.

Main Methods:

• Integrated 1298 high-quality European GWAS summary statistics with multiple TWAS software packages.
• Calculated 235,064 gene-disease associations using seven statistical models and three TWAS software packages.
• Implemented tissue-specific enrichment analysis and a user-friendly web server for custom analyses.

Main Results:

• Compiled a database of 235,064 gene-disease associations across a wide range of human diseases.
• Prioritized associations from extensive GWAS summary statistics, enhancing credibility.
• Identified significant tissues for disease associations through enrichment analysis.

Conclusions:

• webTWAS offers a valuable, comprehensive resource for exploring TWAS-identified gene-disease associations.
• The platform simplifies gene-disease association discovery and facilitates custom TWAS analyses.
• webTWAS enhances research into the genetic underpinnings of human diseases.