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eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics

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dc.contributor.author Bosio, Mattia
dc.contributor.author Drechsel, Oliver
dc.contributor.author Rahman, Rubayte
dc.contributor.author Muyas, Francesc
dc.contributor.author Rabionet, Raquel
dc.contributor.author Bezdan, Daniela
dc.contributor.author Domènech Salgado, Laura, 1989-
dc.contributor.author Hor, Hyun
dc.contributor.author Schott, Jean-Jacques
dc.contributor.author Munell, Francina
dc.contributor.author Colobran, Roger
dc.contributor.author Macaya, Alfons
dc.contributor.author Estivill, Xavier, 1955-
dc.contributor.author Ossowski, Stephan
dc.date.accessioned 2019-10-02T10:00:31Z
dc.date.available 2019-10-02T10:00:31Z
dc.date.issued 2019
dc.identifier.citation Bosio M, Drechsel O, Rahman R, Muyas F, Rabionet R, Bezdan D et al. eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics. Hum Mutat. 2019;40(7):865-78. DOI: 10.1002/humu.23772
dc.identifier.issn 1059-7794
dc.identifier.uri http://hdl.handle.net/10230/42370
dc.description.abstract Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole-exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20-30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single-nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent-child trios. eDiVA combines next-generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning-based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state-of-the-art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.
dc.description.sponsorship This project has received funding from the “la Caixa” Foundation, the CRG emergent translational research award and the European Union's H2020 Research and Innovation Programme under the grant agreement No 635290 (PanCanRisk).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Wiley
dc.relation.ispartof Human Mutation. 2019;40(7):865-78
dc.rights © 2019 The Authors Human Mutation Published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1002/humu.23772
dc.subject.keyword Disease variant prioritization
dc.subject.keyword Machine learning
dc.subject.keyword NGS diagnostics
dc.subject.keyword Rare genetic disease
dc.subject.keyword Whole-exome sequencing
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/635290
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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