Your browser doesn't support javascript.
loading
Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease.
Jacobsen, Julius O B; Kelly, Catherine; Cipriani, Valentina; Research Consortium, Genomics England; Mungall, Christopher J; Reese, Justin; Danis, Daniel; Robinson, Peter N; Smedley, Damian.
  • Jacobsen JOB; William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK.
  • Kelly C; William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK.
  • Cipriani V; William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK.
  • Research Consortium GE; Genomics England, London, UK.
  • Mungall CJ; Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
  • Reese J; Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
  • Danis D; The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA.
  • Robinson PN; The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA.
  • Smedley D; William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK.
Hum Mutat ; 43(8): 1071-1081, 2022 08.
Article en En | MEDLINE | ID: mdl-35391505
ABSTRACT
Rare disease diagnostics and disease gene discovery have been revolutionized by whole-exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype-phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be overcome if we are going to improve detection rates and help the majority of patients that still remain without a molecular diagnosis after state-of-the-art genomic interpretation.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Enfermedades Raras / Exoma Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Enfermedades Raras / Exoma Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article