postGWAS: A web server for deciphering the causality post the genome-wide association studies

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) identify numerous disease susceptibility variants, often in non-coding regions with high linkage disequilibrium (LD).
• Translating these GWAS signals into actionable clinical drug targets requires identifying the specific causal variants and genes.
• Existing post-GWAS analytical methods are diverse and often require significant computational expertise, lacking integrated platforms.

Purpose of the Study:

• To develop and present a user-friendly web server that integrates multiple post-GWAS analysis methodologies.
• To provide researchers with a seamless platform for identifying causal variants and genes from complex GWAS data.
• To facilitate the interpretation and understanding of GWAS results without requiring advanced computational skills.

Main Methods:

• Integration of 9 distinct post-GWAS methods and 12 models.
• Categorization of methods into fine-mapping, colocalization, and transcriptome-wide association study (TWAS) approaches.
• Development of a web server interface for accessible analysis and result visualization.

Main Results:

• A comprehensive web server for post-GWAS analysis is now available.
• The server integrates diverse methods for deciphering causality in complex GWAS signals.
• It simplifies the identification of causal variants and genes, aiding in drug target discovery.

Conclusions:

• The developed post-GWAS server offers a convenient and integrated solution for genetic researchers.
• It lowers the barrier to entry for complex genetic data analysis, promoting wider adoption.
• Facilitates the translation of GWAS findings into potential clinical applications and drug targets.