{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Kerner G"],"funding":["NIMH NIH HHS","NHGRI NIH HHS"],"pubmed_abstract":["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), "],"journal":["medRxiv : the preprint server for health sciences"],"pagination":["2025.05.05.25327017"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12083580"],"repository":["biostudies-literature"],"pubmed_title":["Mapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning."],"pmcid":["PMC12083580"],"funding_grant_id":["R01 MH101244","R01 HG013083","R01 HG006399","U01 HG012009","R37 MH107649","T32 HG002295"],"pubmed_authors":["Kamitaki N","L Price A","Kerner G","Strober B"],"additional_accession":[]},"is_claimable":false,"name":"Mapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning.","description":"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), ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 May","modification":"2026-06-19T03:15:31.903Z","creation":"2026-06-19T03:07:34.727Z"},"accession":"S-EPMC12083580","cross_references":{"pubmed":["40385425"],"doi":["10.1101/2025.05.05.25327017"]}}