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Genome-wide association studies have identified thousands of disease-associated loci, yet their biological interpretation remains limited. We propose joint pleiotropic and epigenomic partitioning (J-PEP), a clustering framework that integrates pleiotropic SNP effects on auxiliary traits and tissue-specific epigenomic data to partition disease-associated loci into biologically distinct clusters. We introduce a metric-pleiotropic and epigenomic prediction accuracy (PEPA)-that evaluates how well the clusters predict SNP-to-trait and SNP-to-tissue associations in off-chromosome data. Analyzing summary statistics for 165 diseases/traits (average N = 290,000), J-PEP attained 16%-30% higher PEPA than pleiotropic or epigenomic partitioning approaches, with larger improvements for well-powered traits, consistent with simulations; these gains arise from J-PEP's tendency to upweight signals present in both auxiliary trait and tissue data, emphasizing shared components. Notably, integrating single-cell chromatin accessibility data refined bulk-based clusters, enhancing cell-type resolution and specificity. For type 2 diabetes, hypertension, and other diseases/traits, J-PEP clusters recapitulated known pathways while revealing underexplored biological processes.