Using multi-scale genomics to associate poorly annotated genes with rare diseases
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Le résumé fourni par la source
BACKGROUND: Next-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic disorders. However, a substantial portion of sequenced patients remains undiagnosed. This may be attributed not only to the challenges posed by harder-to-detect variants, such as non-coding and structural variations but also to the existence of variants in genes not previously associated with the patient's clinical phenotype. This study introduces EvORanker, an algorithm that integrates unbiased data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes. METHODS: EvORanker utilizes clinical data, multi-scale phylogenetic profiling, and other omics data to prioritize disease-associated genes. It was evaluated on solved exomes and simulated genomes, compared with existing methods, and applied to 6260 knockout genes with mouse phenotypes lacking human associations. Additionally, EvORanker was made accessible as a user-friendly web tool. RESULTS: In the analyzed exomic cohort, EvORanker accurately identified the "true" disease gene as the top candidate in 69% of cases and within the top 5 candidates in 95% of cases, consistent with results from the simulated dataset. Notably, EvORanker outperformed existing methods, particularly for poorly annotated genes. In the case of the 6260 knockout genes with mouse phenotypes, EvORanker linked 41% of these genes to observed human disease phenotypes. Furthermore, in two unsolved cases, EvORanker successfully identified DLGAP2 and LPCAT3 as disease candidates for previously uncharacterized genetic syndromes. CONCLUSIONS: We highlight clade-based phylogenetic profiling as a powerful systematic approach for prioritizing potential disease genes. Our study showcases the efficacy of EvORanker in associating poorly annotated genes to disease phenotypes observed in patients. The EvORanker server is freely available at https://ccanavati.shinyapps.io/EvORanker/ .
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Using multi-scale genomics to associate poorly annotated genes with rare diseases
- Date Crossref
- 04/01/2024
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Hebrew University of Jerusalem Department of Developmental Biology and Cancer Research pays non établi dans la noticeUniversité ou école supérieure
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Rafik Hariri University Hospital pays non établi dans la noticeÉtablissement de santé
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Tel Aviv University pays non établi dans la noticeUniversité ou école supérieure
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Shaare Zedek Medical Center pays non établi dans la noticeÉtablissement de santé
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Bethlehem University Hereditary Research Laboratory and Department of Life Sciences pays non établi dans la noticeUniversité ou école supérieure
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Molecular Genetics Lab pays non établi dans la noticeStructure de recherche
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Faculty of Medicine and Sagol School of Neuroscience Department of Human Molecular Genetics and Biochemistry pays non établi dans la noticeUniversité ou école supérieure
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Medical Genetics Institute pays non établi dans la noticeStructure de recherche
Department of Developmental Biology and Cancer Research — Hebrew University of Jerusalem, Rafik Hariri University Hospital et Tel Aviv University, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.