{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T20:06:01Z","timestamp":1790280361767,"version":"4.1.0"},"reference-count":39,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000341","name":"Arthritis Research UK","doi-asserted-by":"publisher","award":["20385"],"award-info":[{"award-number":["20385"]}],"id":[{"id":"10.13039\/501100000341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010767","name":"Innovative Medicines Initiative","doi-asserted-by":"publisher","award":["101007757"],"award-info":[{"award-number":["101007757"]}],"id":[{"id":"10.13039\/501100010767","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["PASMP3_134380"],"award-info":[{"award-number":["PASMP3_134380"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthritis &amp; Rheumatology"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>There is growing evidence that genetic data are of benefit in the rheumatology outpatient setting by aiding early diagnosis. A genetic probability tool (G\u2010PROB) has been developed to aid diagnosis has not yet been tested in a real\u2010world setting. Our aim was to assess whether G\u2010PROB could aid diagnosis in the rheumatology outpatient setting using data from the Norfolk Arthritis Register (NOAR), a prospective observational cohort of patients presenting with early inflammatory arthritis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Genotypes and clinician diagnoses were obtained from patients from NOAR. Six G\u2010probabilities (0%\u2013100%) were created for each patient based on known disease\u2010associated odds ratios of published genetic risk variants, each corresponding to one disease of rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis, spondyloarthropathy, gout, or \u201cother diseases.\u201d Performance of the G\u2010probabilities compared with clinician diagnosis was assessed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We tested G\u2010PROB on 1,047 patients. Calibration of G\u2010probabilities with clinician diagnosis was high, with regression coefficients of 1.047, where 1.00 is ideal. G\u2010probabilities discriminated clinician diagnosis with pooled areas under the curve (95% confidence interval) of 0.85 (0.84\u20130.86). G\u2010probabilities &lt;5% corresponded to a negative predictive value of 96.0%, for which it was possible to suggest &gt;2 unlikely diseases for 94% of patients and &gt;3 for 53.7% of patients. G\u2010probabilities &gt;50% corresponded to a positive predictive value of 70.4%. In 55.7% of patients, the disease with the highest G\u2010probability corresponded to clinician diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>G\u2010PROB converts complex genetic information into meaningful and interpretable conditional probabilities, which may be especially helpful at eliminating unlikely diagnoses in the rheumatology outpatient setting.<\/jats:p><jats:p><jats:boxed-text content-type=\"graphic\" position=\"anchor\"><jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/art42760-toc-0001-m.png\"><jats:alt-text>image<\/jats:alt-text><\/jats:graphic><\/jats:boxed-text><\/jats:p><\/jats:sec>","DOI":"10.1002\/art.42760","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T19:48:42Z","timestamp":1701114522000},"page":"696-703","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Using Polygenic Risk Scores to Aid Diagnosis of Patients With Early Inflammatory Arthritis: Results From the Norfolk Arthritis Register"],"prefix":"10.1002","volume":"76","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0801-6974","authenticated-orcid":false,"given":"Ryan M.","family":"Hum","sequence":"first","affiliation":[{"name":"Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, Lydia Becker Institute of Immunology and Inflammation The University of Manchester  Manchester 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Fu","family":"Yap","sequence":"additional","affiliation":[{"name":"Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, Lydia Becker Institute of Immunology and Inflammation The University of Manchester  Manchester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7228-1727","authenticated-orcid":false,"given":"Mehreen","family":"Soomro","sequence":"additional","affiliation":[{"name":"Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, Lydia Becker Institute of Immunology and Inflammation The University of Manchester  Manchester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-9344","authenticated-orcid":false,"given":"Darren","family":"Plant","sequence":"additional","affiliation":[{"name":"Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, Lydia Becker Institute of Immunology and Inflammation The 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