{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T18:40:57Z","timestamp":1766083257514,"version":"3.48.0"},"reference-count":53,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000071","name":"National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["R03 HD100883"],"award-info":[{"award-number":["R03 HD100883"]}],"id":[{"id":"10.13039\/100000071","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01 GM134005"],"award-info":[{"award-number":["R01 GM134005"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosgenetics.org"],"crossmark-restriction":false},"short-container-title":["PLoS Genet"],"abstract":"<jats:p>\n                    Identifying genes associated with rare diseases remains challenging due to the scarcity of patients and the limited statistical power of traditional association methods. Here, we introduce PERADIGM (\n                    <jats:bold>P<\/jats:bold>\n                    henotype\n                    <jats:bold>E<\/jats:bold>\n                    mbedding similarity-based\n                    <jats:bold>RA<\/jats:bold>\n                    re\n                    <jats:bold>DI<\/jats:bold>\n                    sease\n                    <jats:bold>G<\/jats:bold>\n                    ene\n                    <jats:bold>M<\/jats:bold>\n                    apping), a novel framework that leverages natural language processing techniques to integrate comprehensive phenotype information from electronic health records for rare disease gene discovery. PERADIGM employs an embedding model to capture relationships between ICD-10 codes, providing a nuanced representation of individual phenotypes. By utilizing patient similarity scores, it enhances the identification of candidate genes associated with disease-specific phenotypes, surpassing conventional methods that rely on binary disease status. We applied PERADIGM to the UK Biobank dataset for three rare diseases: autosomal dominant polycystic kidney disease (ADPKD), Marfan syndrome, and neurofibromatosis type 1 (NF1). PERADIGM identified additional candidate genes associated with ADPKD-related and Marfan syndrome-related phenotypes, some of which are supported by existing literature, and demonstrated enhanced signal detection for NF1-specific phenotypes beyond traditional methods. Our findings demonstrate the potential of PERADIGM to identify genes associated with rare diseases and related phenotypes by incorporating phenotype embeddings and patient similarity, providing a powerful tool for precision medicine and a deeper understanding of rare disease genetics and clinical manifestations.\n                  <\/jats:p>","DOI":"10.1371\/journal.pgen.1011976","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T18:32:06Z","timestamp":1766082726000},"page":"e1011976","update-policy":"https:\/\/doi.org\/10.1371\/journal.pgen.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["PERADIGM: Phenotype embedding similarity-based rare disease gene mapping"],"prefix":"10.1371","volume":"21","author":[{"given":"Wangjie","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1738-0885","authenticated-orcid":true,"given":"Yuhan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianlei","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Somlo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8283-1507","authenticated-orcid":true,"given":"Whitney","family":"Besse","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1195-9607","authenticated-orcid":true,"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"issue":"7920","key":"pgen.1011976.ref001","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1038\/s41586-022-04965-x","article-title":"The sequences of 150,119 genomes in the UK Biobank","volume":"607","author":"BV Halldorsson","year":"2022","journal-title":"Nature."},{"issue":"7877","key":"pgen.1011976.ref002","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1038\/s41586-021-03855-y","article-title":"Rare variant contribution to human disease in 281,104 UK Biobank exomes","volume":"597","author":"Q Wang","year":"2021","journal-title":"Nature."},{"issue":"1","key":"pgen.1011976.ref003","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1186\/s13073-022-01094-y","article-title":"Enhanced rare disease mapping for phenome-wide genetic association in the UK Biobank","volume":"14","author":"MT Patrick","year":"2022","journal-title":"Genome Med."},{"issue":"7726","key":"pgen.1011976.ref004","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK Biobank resource with deep phenotyping and genomic data","volume":"562","author":"C Bycroft","year":"2018","journal-title":"Nature."},{"issue":"1","key":"pgen.1011976.ref005","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ajhg.2014.06.009","article-title":"Rare-variant association analysis: study designs and statistical tests","volume":"95","author":"S Lee","year":"2014","journal-title":"Am J Hum Genet."},{"issue":"1","key":"pgen.1011976.ref006","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.ajhg.2011.05.029","article-title":"Rare-variant association testing for sequencing data with the sequence kernel association test","volume":"89","author":"MC Wu","year":"2011","journal-title":"Am J Hum Genet."},{"issue":"4","key":"pgen.1011976.ref007","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1093\/biostatistics\/kxs014","article-title":"Optimal tests for rare variant effects in sequencing association studies","volume":"13","author":"S Lee","year":"2012","journal-title":"Biostatistics."},{"issue":"9147","key":"pgen.1011976.ref008","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/S0140-6736(98)03495-3","article-title":"Comparison of phenotypes of polycystic kidney disease types 1 and 2. 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