A versatile data repository for GWAS summary statistics-based downstream genomic analysis of human complex traits

Genome-wide association studies (GWAS) have identified thousands of loci associated with complex traits and diseases, yet the biological interpretation of these findings remains limited. We developed gact, an R package that integrates GWAS summary statistics with diverse genomic resources to facilitate the discovery of causal genes, pathways, and disease mechanisms. The package enables construction of a local database linking variants to genes, biological pathways, protein complexes, and drug-gene interactions, thereby supporting downstream analyses such as fine-mapping, polygenic scoring, and gene set enrichment. Applying gact to large-scale GWAS of coronary artery disease (CAD) and type 2 diabetes (T2D), we identified 142 and 577 significant genes, respectively, including canonical loci for T2D ( PATJ , DEAF1 ) and CAD ( OLIG1 ), as well as pleiotropic genes such as TCF7 and HNF1B. Bayesian gene set analyses revealed distinct biological signatures-lipid and vascular remodeling pathways in CAD versus beta-cell and islet biology in T2D-together with shared enrichment in extracellular matrix and immune signaling. Polygenic score (PGS) analyses demonstrated higher predictive accuracy for CAD than T2D, consistent with differences in common-variant heritability and GWAS power. Partitioned PGS further delineated T2D subgroups through archetypal clustering, separating individuals with predominantly inflammatory versus metabolic risk profiles. These results establish gact as a versatile platform for integrating genomic resources and advancing the biological interpretation of GWAS. By linking genetic associations to biological pathways and subtypes, gact enables a deeper understanding of disease heterogeneity and informs future precision medicine strategies.