{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T01:11:29Z","timestamp":1768698689766,"version":"3.49.0"},"posted":{"date-parts":[[2025,5,6]]},"group-title":"Genetic and Genomic Medicine","reference-count":82,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,5,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  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\u2014Pleiotropic and Epigenomic Prediction Accuracy (PEPA)\u2014that 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\n                  <jats:italic>N<\/jats:italic>\n                  =290K), we 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\u2019s tendency to upweight correlated structure\u2014signals present in both auxiliary trait and tissue data\u2014thereby emphasizing shared components. For type 2 diabetes (T2D), J-PEP identified clusters refining canonical pathological processes while revealing underexplored immune and developmental signals. For hypertension (HTN), J-PEP identified stromal and adrenal-endocrine processes that were not identified in prior analyses. For neutrophil count, J-PEP identified hematopoietic, hepatic-inflammatory, and neuroimmune processes, expanding biological interpretation beyond classical immune regulation. Notably, integrating single-cell chromatin accessibility data refined bulk-based clusters, enhancing cell-type resolution and specificity. For T2D, single-cell data refined a bulk endocrine cluster to pancreatic islet \u03b2-cells, consistent with established \u03b2-cell dysfunction in insulin deficiency; for HTN, single-cell data refined a bulk endocrine cluster to adrenal cortex cells, consistent with a GO enrichment for neutrophil-mediated inflammation that implicates feedback between aldosterone production in the adrenal gland and local immune signaling. In conclusion, J-PEP provides a principled framework for partitioning GWAS loci into interpretable, tissue-informed clusters that provide biological insights on complex disease.\n                <\/jats:p>","DOI":"10.1101\/2025.05.05.25327017","type":"posted-content","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T20:50:19Z","timestamp":1746564619000},"source":"Crossref","is-referenced-by-count":0,"title":["Mapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0146-9428","authenticated-orcid":false,"given":"Gaspard","family":"Kerner","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0614-0189","authenticated-orcid":false,"given":"Nolan","family":"Kamitaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2969-2808","authenticated-orcid":false,"given":"Benjamin","family":"Strober","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2971-7975","authenticated-orcid":false,"given":"Alkes L.","family":"Price","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025052523450478000_2025.05.05.25327017v2.1","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-019-0127-1"},{"key":"2025052523450478000_2025.05.05.25327017v2.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2019.02.003"},{"key":"2025052523450478000_2025.05.05.25327017v2.3","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1879-7"},{"key":"2025052523450478000_2025.05.05.25327017v2.4","doi-asserted-by":"publisher","DOI":"10.1016\/J.AJHG.2022.12.011"},{"key":"2025052523450478000_2025.05.05.25327017v2.5","doi-asserted-by":"publisher","DOI":"10.1093\/NAR\/GKAC1010"},{"key":"2025052523450478000_2025.05.05.25327017v2.6","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-025-01128-6"},{"key":"2025052523450478000_2025.05.05.25327017v2.7","doi-asserted-by":"publisher","DOI":"10.1038\/nature09266"},{"key":"2025052523450478000_2025.05.05.25327017v2.8","doi-asserted-by":"publisher","DOI":"10.1126\/science.1242088"},{"key":"2025052523450478000_2025.05.05.25327017v2.9","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1502214"},{"key":"2025052523450478000_2025.05.05.25327017v2.10","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-021-00955-3"},{"key":"2025052523450478000_2025.05.05.25327017v2.11","doi-asserted-by":"publisher","DOI":"10.1126\/science.adh7699"},{"key":"2025052523450478000_2025.05.05.25327017v2.12","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-022-01174-0"},{"key":"2025052523450478000_2025.05.05.25327017v2.13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1002254"},{"key":"2025052523450478000_2025.05.05.25327017v2.14","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3461"},{"key":"2025052523450478000_2025.05.05.25327017v2.15","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3406"},{"key":"2025052523450478000_2025.05.05.25327017v2.16","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3570"},{"key":"2025052523450478000_2025.05.05.25327017v2.17","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-11953-9"},{"key":"2025052523450478000_2025.05.05.25327017v2.18","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1038\/s41562-019-0566-x","article-title":"Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits","volume":"3","year":"2019","journal-title":"Nat. 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