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Hum Mutat. 2019 Jul;40(7):865-878. doi: 10.1002/humu.23772. Epub 2019 May 21.

eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics.

Author information

1
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.
2
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
3
Barcelona Supercomputing Center (BSC), Barcelona, Spain.
4
Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany.
5
NKI Netherlands Cancer Institute, The Netherlands.
6
Institut de Recerca Sant Joan de Déu, University of Barcelona, Barcelona, Spain.
7
Department of Neurology, University Hospital Zurich, Zurich, Switzerland.
8
L'Institut du Thorax, INSERM, CNRS, Univ Nantes, Nantes, France.
9
Service de Cardiologie, L'institut du thorax, CHU Nantes, Nantes, France.
10
Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain.
11
Sidra Medicine, Doha, Qatar.
12
Women's Health Dexeus, Barcelona, Spain.
13
Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.

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.

KEYWORDS:

NGS diagnostics; disease variant prioritization; machine learning; rare genetic disease; whole-exome sequencing

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