Efficient reinterpretation of rare disease cases using Exomiser.

Autor: Vestito L; William Harvey Research Institute, Clinical Pharmacology and Precision Medicine, Queen Mary University of London, London, UK., Jacobsen JOB; William Harvey Research Institute, Clinical Pharmacology and Precision Medicine, Queen Mary University of London, London, UK., Walker S; Genomics England, United Kingdom Department of Health and Social Care, London, UK., Cipriani V; William Harvey Research Institute, Clinical Pharmacology and Precision Medicine, Queen Mary University of London, London, UK., Harris NL; Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA., Haendel MA; Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA., Mungall CJ; Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA., Robinson P; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany., Smedley D; William Harvey Research Institute, Clinical Pharmacology and Precision Medicine, Queen Mary University of London, London, UK. d.smedley@qmul.ac.uk.
Jazyk: angličtina
Zdroj: NPJ genomic medicine [NPJ Genom Med] 2024 Dec 18; Vol. 9 (1), pp. 65. Date of Electronic Publication: 2024 Dec 18.
DOI: 10.1038/s41525-024-00456-2
Abstrakt: Whole genome sequencing has transformed rare disease research; however, 50-80% of rare disease patients remain undiagnosed after such testing. Regular reanalysis can identify new diagnoses, especially in newly discovered disease-gene associations, but efficient tools are required to support clinical interpretation. Exomiser, a phenotype-driven variant prioritisation tool, fulfils this role; within the 100,000 Genomes Project (100kGP), diagnoses were identified after reanalysis in 463 (2%) of 24,015 unsolved patients after previous analysis for variants in known disease genes. However, extensive manual interpretation was required. This led us to develop a reanalysis strategy to efficiently reveal candidates from recent disease gene discoveries or newly designated pathogenic/likely pathogenic variants. Optimal settings to highlight new candidates from Exomiser reanalysis were identified with high recall (82%) and precision (88%) when including Exomiser's automated ACMG/AMP classifier, which correctly converted 92% of variants from unknown significance to pathogenic/likely pathogenic. In conclusion, Exomiser efficiently reinterprets previously unsolved cases.
Competing Interests: Competing interests: The authors declare no competing interests
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz: