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EyeG2P: an automated variant filtering approach improves efficiency of diagnostic genomic testing for inherited ophthalmic disorders.
Lenassi, Eva; Carvalho, Ana; Thormann, Anja; Abrahams, Liam; Arno, Gavin; Fletcher, Tracy; Hardcastle, Claire; Lopez, Javier; Hunt, Sarah E; Short, Patrick; Sergouniotis, Panagiotis I; Michaelides, Michel; Webster, Andrew; Cunningham, Fiona; Ramsden, Simon C; Kasperaviciute, Dalia; Fitzpatrick, David R; Black, Graeme C; Ellingford, Jamie M.
Afiliación
  • Lenassi E; Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
  • Carvalho A; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
  • Thormann A; Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
  • Abrahams L; MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
  • Arno G; Medical Genetic Unit, Pediatric Hospital, Coimbra Hospital and Universitary Centre (CHUC), Coimbra, Portugal.
  • Fletcher T; European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
  • Hardcastle C; Genomics England Ltd, London, UK.
  • Lopez J; UCL Institute of Ophthalmology, University College London, London, UK.
  • Hunt SE; Department of Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, UK.
  • Short P; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
  • Sergouniotis PI; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
  • Michaelides M; Genomics England Ltd, London, UK.
  • Webster A; European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
  • Cunningham F; Sano Genetics Ltd, Cambridge, UK.
  • Ramsden SC; Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
  • Kasperaviciute D; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
  • Fitzpatrick DR; Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
  • Black GC; Department of Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, UK.
  • Ellingford JM; UCL Institute of Ophthalmology, University College London, London, UK.
J Med Genet ; 60(8): 810-818, 2023 08.
Article en En | MEDLINE | ID: mdl-36669873
BACKGROUND: Genomic variant prioritisation is one of the most significant bottlenecks to mainstream genomic testing in healthcare. Tools to improve precision while ensuring high recall are critical to successful mainstream clinical genomic testing, in particular for whole genome sequencing where millions of variants must be considered for each patient. METHODS: We developed EyeG2P, a publicly available database and web application using the Ensembl Variant Effect Predictor. EyeG2P is tailored for efficient variant prioritisation for individuals with inherited ophthalmic conditions. We assessed the sensitivity of EyeG2P in 1234 individuals with a broad range of eye conditions who had previously received a confirmed molecular diagnosis through routine genomic diagnostic approaches. For a prospective cohort of 83 individuals, we assessed the precision of EyeG2P in comparison with routine diagnostic approaches. For 10 additional individuals, we assessed the utility of EyeG2P for whole genome analysis. RESULTS: EyeG2P had 99.5% sensitivity for genomic variants previously identified as clinically relevant through routine diagnostic analysis (n=1234 individuals). Prospectively, EyeG2P enabled a significant increase in precision (35% on average) in comparison with routine testing strategies (p<0.001). We demonstrate that incorporation of EyeG2P into whole genome sequencing analysis strategies can reduce the number of variants for analysis to six variants, on average, while maintaining high diagnostic yield. CONCLUSION: Automated filtering of genomic variants through EyeG2P can increase the efficiency of diagnostic testing for individuals with a broad range of inherited ophthalmic disorders.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Bases de Datos Genéticas / Oftalmopatías Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: J Med Genet Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Bases de Datos Genéticas / Oftalmopatías Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: J Med Genet Año: 2023 Tipo del documento: Article