iCPAGdb

iCPAGdb identifies shared genetic architecture across human diseases and molecular phenotypes by analyzing cross-phenotype associations from GWAS data.

Key Features:

• Cross-Phenotype Associations: Utilizes linkage disequilibrium (LD) information to capture shared single nucleotide polymorphisms (SNPs) by proxy and to calculate significance for enrichment across phenotypes.
• Multi-Scale Biological Data Integration: Incorporates clinical, cellular, and molecular GWAS catalogs to enable analysis across biological scales.
• Hypothesis Generation: Reveals known and novel phenotype associations to generate hypotheses about disease pathophysiology, biomarkers, and therapeutic strategies.

Scientific Applications:

• Severe COVID-19 GWAS: Identified unexpected genetic overlaps, including association between severe COVID-19 and idiopathic pulmonary fibrosis at the DPP9 locus.
• Transcriptomic Correlation: Showed that DPP9 expression is induced in SARS-CoV-2-infected patients compared to healthy controls or bacterial infections.
• ABO Colocalization: Revealed colocalization at the ABO locus suggesting that ABO-mediated glycosylation of CD209 (DC-SIGN) may influence COVID-19 severity.

Methodology:

Leverages cross-phenotype association analysis using linkage disequilibrium (LD) information to capture shared SNPs by proxy and assess enrichment significance across phenotypes.