STOPGAP: a database for systematic target opportunity assessment by genetic association predictions

Area of Science:

• Genetics
• Bioinformatics
• Genomic Medicine

Background:

• Human genetic association studies are crucial for understanding trait variation.
• Identifying specific effector genes from complex associations remains a challenge.

Purpose of the Study:

• To develop a comprehensive database (STOPGAP) for mapping genetic associations to effector gene candidates.
• To provide a tool for investigating genetic mechanisms and supporting drug discovery.

Main Methods:

• Integrated publicly available Genome-Wide Association Study (GWAS) data with linkage disequilibrium (LD) measures.
• Developed algorithms to partition associations into LD clusters and map variants to genes.
• Generated a variant-to-gene score to rank confidence in potential effector genes.

Main Results:

• Created the Systematic Target OPportunity assessment by Genetic Association Predictions (STOPGAP) database.
• STOPGAP contains extensive human genetic association data linked to candidate effector genes.
• The database utilizes functional genomic and variant annotation sources.

Conclusions:

• STOPGAP serves as a valuable resource for exploring genetic underpinnings of human traits.
• The database facilitates the identification of novel drug targets and mechanisms.
• STOPGAP supports diverse research applications in genetics and personalized medicine.