eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics
Bosio, Mattia (Barcelona Supercomputing Center)
Drechsel, Oliver (Robert Koch Institute)
Rahman, Rubayte (The Netherlands Cancer Institute (Amsterdam, Països Baixos))
Muyas, Francesc (Universitat Pompeu Fabra)
Rabionet, Raquel (Institut de Recerca Sant Joan de Déu)
Bezdan, Daniela (Universitat Pompeu Fabra)
Domenech Salgado, Laura (Universitat Pompeu Fabra)
Hor, Hyun (University Hospital Zurich (Suïssa))
Schott, Jean-Jacques (Service de Cardiologie. L'institut du thorax. CHU Nantes)
Munell Casadesus, Francina (Hospital Universitari Vall d'Hebron)
Colobrán Oriol, Roger (Hospital Universitari Vall d'Hebron)
Macaya Ruiz, Alfons (Hospital Universitari Vall d'Hebron)
Estivill, Xavier (Women's Health Dexeus)
Ossowski, Stephan (Institute of Medical Genetics and Applied Genomics. University of Tübingen)
Universitat Autònoma de Barcelona

Fecha:	2019
Resumen:	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.
Ayudas:	European Commission 635290
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Disease variant prioritization ; Machine learning ; NGS diagnostics ; Rare genetic disease ; Whole-exome sequencing
Publicado en:	Human mutation, Vol. 40 Núm. 7 (july 2019) , p. 865-878, ISSN 1098-1004

DOI: 10.1002/humu.23772
PMID: 31026367

14 p, 2.3 MB

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