Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease
Julius O. B. Jacobsen
William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK
Search for more papers by this authorCatherine Kelly
William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK
Search for more papers by this authorValentina Cipriani
William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK
Search for more papers by this authorGenomics England Research Consortium
Genomics England, London, UK
Search for more papers by this authorChristopher J. Mungall
Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA
Search for more papers by this authorJustin Reese
Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA
Search for more papers by this authorDaniel Danis
The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
Search for more papers by this authorPeter N. Robinson
The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
Search for more papers by this authorCorresponding Author
Damian Smedley
William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK
Correspondence Damian Smedley, William Harvey Research Institute, Charterhouse Sq, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London EC1M 6BQ, UK.
Email: [email protected]
Search for more papers by this authorJulius O. B. Jacobsen
William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK
Search for more papers by this authorCatherine Kelly
William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK
Search for more papers by this authorValentina Cipriani
William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK
Search for more papers by this authorGenomics England Research Consortium
Genomics England, London, UK
Search for more papers by this authorChristopher J. Mungall
Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA
Search for more papers by this authorJustin Reese
Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA
Search for more papers by this authorDaniel Danis
The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
Search for more papers by this authorPeter N. Robinson
The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
Search for more papers by this authorCorresponding Author
Damian Smedley
William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London, UK
Correspondence Damian Smedley, William Harvey Research Institute, Charterhouse Sq, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, London EC1M 6BQ, UK.
Email: [email protected]
Search for more papers by this authorAbstract
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.
CONFLICTS OF INTEREST
Julius Jacobsen and Damian Smedley declare they previously acted as part-time consultants for Congenica Ltd. The other authors declare no other potential conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
All data described in the paper are already provided in the paper except for access to the 100,000 Genomes Project samples which is by application to Genomics England.
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