eDiVA - Classification and prioritization of pathogenic variants for clinical diagnostics

Abstract

Mendelian diseases have shown to be an 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 eDiVA (http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing SNVs and InDels) for rare diseases using WES of families or parent-child trios. eDiVA combines NGS 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 good or better than existing approaches in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.