{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:28:21Z","timestamp":1792787301708,"version":"4.5.0"},"reference-count":19,"publisher":"BMJ","issue":"2","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"unspecified","delay-in-days":91,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Johnson & Johnson"},{"name":"National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre","award":["NIHR203308"],"award-info":[{"award-number":["NIHR203308"]}],"id":[{"id":"https:\/\/ror.org\/0187kwz08","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Lupus Sci Med"],"accepted":{"date-parts":[[2026,8,24]]},"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Whole-exome sequencing (WES) is increasingly used to investigate patients with suspected monogenic forms of systemic lupus erythematosus (SLE) and other systemic autoimmune rheumatic diseases (SARDs). With the increasing use of artificial intelligence (AI)-assisted variant interpretation tools, there is a need to evaluate their real-world utility. In a SARD cohort, we compared the performance of AI-assisted and expert-guided WES analysis strategies in identifying disease-relevant variants within lupus-causing genes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analysed WES data from 120 SARD patients using the same platform, Emedgene. For the AI-assisted analysis, a fully automated phenotype-based variant analysis produced a list of prioritised potentially disease-causing variants for each case, labelled as \u2018most likely\u2019. For the expert-guided approach, expert-informed filtering criteria were applied. Both the \u2018most likely\u2019 AI-assisted and the expert-informed filtered variants then underwent further manual analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Before manual analysis, a total of 2906 variants potentially likely to be causative were identified with the Emedgene AI tool (mean 20.8 variants per case), approximately three times fewer than the 8391 variants identified by the expert-guided method (mean 59.9 per case). A comparable number of variants (78 vs 56) in lupus-causing genes were identified at the end of the manual analysis with both methods, including two monogenic cases\n                      <jats:italic>PEPD<\/jats:italic>\n                      (c.819-1G&gt;A protein change, homozygous) and\n                      <jats:italic>CYBB<\/jats:italic>\n                      (c.1314+1G&gt;A protein change, hemizygous).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI-assisted WES analysis substantially reduced the number of variants requiring manual review without missing the two monogenic cases. These findings support the integration of AI-assisted pipelines into genomic workflows for SLE and other SARDs which has the potential to improve efficiency and scalability of WES.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/lupus-2026-002049","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T11:15:23Z","timestamp":1790766923000},"page":"e002049","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Balancing automation and expertise: real-world evaluation of AI-assisted whole-exome sequencing in lupus-causing genes"],"prefix":"10.1136","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7382-9958","authenticated-orcid":false,"given":"Anastasia-Vasiliki","family":"Madenidou","sequence":"first","affiliation":[{"name":"Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK"},{"name":"Kellgren Centre for Rheumatology, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gillian I","family":"Rice","sequence":"additional","affiliation":[{"name":"Division of Evolution, Infection and Genomic Sciences, The University of Manchester, Manchester, UK"},{"name":"Manchester Centre for Genomic Medicine, Saint Mary\u2019s Hospital, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Terence","family":"Garner","sequence":"additional","affiliation":[{"name":"Division of Developmental Biology and Medicine, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6201-7391","authenticated-orcid":false,"given":"Sarah","family":"Dyball","sequence":"additional","affiliation":[{"name":"Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK"},{"name":"Kellgren Centre for Rheumatology, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Parker","sequence":"additional","affiliation":[{"name":"Kellgren Centre for Rheumatology, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice","family":"Chieng","sequence":"additional","affiliation":[{"name":"Department of Rheumatology, Royal Manchester Children\u2019s Hospital, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Stevens","sequence":"additional","affiliation":[{"name":"Division of Developmental Biology and Medicine, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3047-500X","authenticated-orcid":false,"given":"Ian N","family":"Bruce","sequence":"additional","affiliation":[{"name":"Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tracy A","family":"Briggs","sequence":"additional","affiliation":[{"name":"Division of Evolution, Infection and Genomic Sciences, The University of Manchester, Manchester, UK"},{"name":"Manchester Centre for Genomic Medicine, Saint Mary\u2019s Hospital, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"2026093004151853000_13.2.e002049.1","doi-asserted-by":"crossref","first-page":"e99","DOI":"10.1016\/S2665-9913(19)30142-0","article-title":"Contribution of rare and predicted pathogenic gene variants to childhood-onset lupus: a large, genetic panel analysis of British and French cohorts","volume":"2","author":"Belot","year":"2020","journal-title":"Lancet Rheumatol"},{"key":"2026093004151853000_13.2.e002049.2","doi-asserted-by":"crossref","DOI":"10.1136\/lupus-2024-001475","article-title":"Next generation sequencing analysis reveals complex genetic architecture of childhood-onset systemic lupus erythematosus","volume":"12","author":"Lewandowski","year":"2025","journal-title":"Lupus Sci Med"},{"key":"2026093004151853000_13.2.e002049.3","doi-asserted-by":"crossref","DOI":"10.1186\/s13073-021-00965-0","article-title":"Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases","volume":"13","author":"De La Vega","year":"2021","journal-title":"Genome Med"},{"key":"2026093004151853000_13.2.e002049.4","unstructured":"Emedgene. 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