biostudies-literatureUnknownKerner GNIMH NIH HHSNHGRI NIH HHSGenome-wide association studies (GWAS) have identified thousands of disease-associated loci, yet their interpretation remains limited by the heterogeneity of underlying biological processes. 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. To benchmark J-PEP against existing methods, 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 using off-chromosome data, avoiding overfitting. Applying J-PEP to GWAS summary statistics for 165 diseases/traits (average <i>N</i>=290K), medRxiv : the preprint server for health sciences2025.05.05.25327017https://www.ebi.ac.uk/biostudies/studies/S-EPMC12083580biostudies-literatureMapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning.PMC12083580R01 MH101244R01 HG013083R01 HG006399U01 HG012009R37 MH107649T32 HG002295Kamitaki NL Price AKerner GStrober BfalseMapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning.Genome-wide association studies (GWAS) have identified thousands of disease-associated loci, yet their interpretation remains limited by the heterogeneity of underlying biological processes. 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. To benchmark J-PEP against existing methods, 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 using off-chromosome data, avoiding overfitting. Applying J-PEP to GWAS summary statistics for 165 diseases/traits (average <i>N</i>=290K), 2025-01-01T00:00:00Z2025 May2026-06-19T03:15:31.903Z2026-06-19T03:07:34.727ZS-EPMC120835804038542510.1101/2025.05.05.25327017