2024/12/18 by Letizia Vestito, Julius O.B. Jacobsen, Susan Walker +6 · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · #Biomedical Text Mining and Ontologies #Genomics and Phylogenetic Studies #Genomics and Rare Diseases
paper · pdf · doi:10.1038/s41525-024-00456-2
openalex publication_date 2024/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
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.