A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation

Area of Science:

• Genetics
• Pharmacology
• Computational Biology

Background:

• Genome-wide association studies (GWAS) reveal pleiotropic loci, often in non-coding DNA, influencing multiple diseases.
• Significant challenges exist in determining if, how, and for which diseases pleiotropy can guide clinical therapeutics.
• There is a need for integrative tools to leverage cross-disease GWAS data for therapeutic target identification.

Purpose of the Study:

• To introduce and validate a pleiotropy-driven computational approach for prioritizing and evaluating therapeutic targets from cross-disease GWAS summary data.
• To demonstrate the approach's ability to identify specific diseases where pleiotropy informs therapeutic strategies.
• To showcase the tool's versatility in advanced analyses like pathway crosstalk identification.

Main Methods:

• Development of a novel pleiotropy-driven computational framework.
• Application of the framework to cross-disease GWAS summary statistics from neuropsychiatric and inflammatory disease systems.
• Evaluation of the approach's performance in recovering known clinical proof-of-concept therapeutic targets.

Main Results:

• The pleiotropy-driven approach successfully prioritized therapeutic targets across different disease systems.
• The method demonstrated improved performance in identifying clinical proof-of-concept targets compared to existing methods.
• Specific diseases were identified where pleiotropic genetic effects have direct implications for clinical therapeutics.
• The approach facilitated pathway crosstalk identification and downstream analyses.

Conclusions:

• The developed integrated solution effectively bridges the gap between understanding genetic pleiotropy and discovering clinical therapeutics.
• This pleiotropy-driven approach offers a powerful tool for therapeutic target prioritization and evaluation using cross-disease GWAS data.
• The findings highlight the potential of leveraging pleiotropic genetic associations for advancing precision medicine and drug discovery.