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.
Highlights
Rare genetic diseases are classical models for studying gene function and linking genotypes to disease phenotypes
Results eDiVA: a platform for pathogenicity estimation and causal variant prioritization eDiVA is a disease variant prioritization tool optimized for Next Generation Sequencing (NGS) based genetic disease diagnostics in families and parent child trios
It is composed of four components: eDiVA-Predict handles read alignment and variant prediction, eDiVA-Annotate performs functional annotation of variants, eDiVA-Score estimates the probability of variants to be pathogenic, and eDiVA-Prioritize filters and ranks variants according to various quality criteria, proper segregation, and likelihood to cause phenotypic changes. eDiVA is available as standalone software at https://github.com/mbosio85/ediva, and as a web-service providing access to functional annotation, pathogenicity classification and causal variant prioritization modules
Summary
Rare genetic diseases are classical models for studying gene function and linking genotypes to disease phenotypes. Other methods, such as eXtasy (Sifrim et al, 2013), PhenoDB (Sobreira et al, 2015), Phen-Gen (Javed et al, 2014), VarSifer (Teer et al, 2012), KGGseq (Li et al, 2012), and SPRING (Wu et al, 2014), focus on prioritization of potentially causal variants using both functional annotation and clinical information These tools systematically filter, evaluate, and prioritize thousands of variants, taking into account knowledge found in genome annotation databases (Rhead et al, 2010) , disease gene repositories (OMIM - Online Mendelian Inheritance in Man; Landrum et al, 2014) and patient pedigree information, as well as phenotype descriptions and disease definitions provided e.g. as Human Phenotype Ontology (HPO) terms (Köhler et al, 2014). Methods such as Endeavour (Tranchevent et al, 2008) and GeneDistiller (Seelow et al, 2008) prioritize disease genes, not individual variants, by integrating diverse genomic data sources
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