eDiVA—Classification and prioritization of pathogenic variants for clinical diagnostics

Mattia Bosio, Oliver Drechsel, Rubayte Rahman, Francesc Muyas, Raquel Rabionet, Daniela Bezdan, Laura Domenech Salgado, Hyun Hor, Jean Jacques Schott, Francina Munell, Roger Colobran, Alfons Macaya, Xavier P. Estivill, Stephan Ossowski

Research output: Contribution to journalArticle

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)865-878
Number of pages14
JournalHuman mutation
Issue number7
Publication statusPublished - 1 Jul 2019



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

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

Bosio, M., Drechsel, O., Rahman, R., Muyas, F., Rabionet, R., Bezdan, D., Domenech Salgado, L., Hor, H., Schott, J. J., Munell, F., Colobran, R., Macaya, A., Estivill, X. P., & Ossowski, S. (2019). eDiVA—Classification and prioritization of pathogenic variants for clinical diagnostics. Human mutation, 40(7), 865-878. https://doi.org/10.1002/humu.23772