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Phenotype-aware prioritisation of rare Mendelian disease variants.
Kelly, Catherine; Szabo, Anita; Pontikos, Nikolas; Arno, Gavin; Robinson, Peter N; Jacobsen, Jules O B; Smedley, Damian; Cipriani, Valentina.
Afiliação
  • Kelly C; William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK.
  • Szabo A; UCL Institute of Ophthalmology, University College London, London, EC1V 9EL, UK.
  • Pontikos N; UCL Institute of Ophthalmology, University College London, London, EC1V 9EL, UK.
  • Arno G; UCL Institute of Ophthalmology, University College London, London, EC1V 9EL, UK.
  • Robinson PN; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
  • Jacobsen JOB; William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK.
  • Smedley D; William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK. Electronic address: d.smedley@qmul.ac.uk.
  • Cipriani V; William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK; UCL Institute of Ophthalmology, University College London, London, EC1V 9EL, UK; Moorfields Eye Hospital NHS Foundation Trust, London, EC1V 2PD, UK; UCL Genetics Institute, University College London, London, WC
Trends Genet ; 38(12): 1271-1283, 2022 12.
Article em En | MEDLINE | ID: mdl-35934592
A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Doenças Raras / Exoma Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Trends Genet Assunto da revista: GENETICA Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Doenças Raras / Exoma Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Trends Genet Assunto da revista: GENETICA Ano de publicação: 2022 Tipo de documento: Article