2026-04-14T05:32:00Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/423702025-12-12T02:43:49Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Bosio, Mattia author Drechsel, Oliver author Rahman, Rubayte author Muyas Remolar, Francesc, 1992- author Rabionet Janssen, Raquel author Bezdan, Daniela author Domènech Salgado, Laura, 1989- author Hor, Hyun author Schott, Jean-Jacques author Munell, Francina author Colobran, Roger author Macaya, Alfons author Estivill, Xavier, 1955- author Ossowski, Stephan author 2019-10-02T10:00:31Z 2019-10-02T10:00:31Z 2019 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. 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). Disease variant prioritization Machine learning NGS diagnostics Rare genetic disease Whole-exome sequencing eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics