VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

Abstract

BackgroundNext generation sequencing (NGS) provides a key technology for deciphering the genetic underpinnings of human diseases. Typical NGS analyses of a patient depict tens of thousands non-reference coding variants, but only one or very few are expected to be significant for the relevant disorder. In a filtering stage, one employs family segregation, rarity in the population, predicted protein impact and evolutionary conservation as a means for shortening the variation list. However, narrowing down further towards culprit disease genes usually entails laborious seeking of gene-phenotype relationships, consulting numerous separate databases. Thus, a major challenge is to transition from the few hundred shortlisted genes to the most viable disease-causing candidates.ResultsWe describe a novel tool, VarElect (http://ve.genecards.org), a comprehensive phenotype-dependent variant/gene prioritizer, based on the widely-used GeneCards, which helps rapidly identify causal mutations with extensive evidence. The GeneCards suite offers an effective and speedy alternative, whereby >120 gene-centric automatically-mined data sources are jointly available for the task. VarElect cashes on this wealth of information, as well as on GeneCards’ powerful free-text Boolean search and scoring capabilities, proficiently matching variant-containing genes to submitted disease/symptom keywords. The tool also leverages the rich disease and pathway information of MalaCards, the human disease database, and PathCards, the unified pathway (SuperPaths) database, both within the GeneCards Suite. The VarElect algorithm infers direct as well as indirect links between genes and phenotypes, the latter benefitting from GeneCards’ diverse gene-to-gene data links in GenesLikeMe. Finally, our tool offers an extensive gene-phenotype evidence portrayal (“MiniCards”) and hyperlinks to the parent databases.ConclusionsWe demonstrate that VarElect compares favorably with several often-used NGS phenotyping tools, thus providing a robust facility for ranking genes, pointing out their likelihood to be related to a patient’s disease. VarElect’s capacity to automatically process numerous NGS cases, either in stand-alone format or in VCF-analyzer mode (TGex and VarAnnot), is indispensable for emerging clinical projects that involve thousands of whole exome/genome NGS analyses.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2722-2) contains supplementary material, which is available to authorized users.

GeneHancer: genome-wide integration of enhancers and target genes in GeneCards

“…Thus, a TF sequence variant becomes linked to user-entered disease phenotype keywords in a process known as ‘guilt by association’ (62, 65). Importantly, this line of analysis symmetrically applies to cases in which a variant occurs in a candidate target gene, whereas the phenotype is linked to a TF gene.…”

MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

“…This data enrichment is crucial for a comprehensive capacity to link genes with diseases and symptoms. Such a feature was then fully inherited by VarElect, the GeneCards-based NGS interpretation tool of the GeneCards Suite (34). …”

“…The applicability of MalaCards’ information to NGS interpretation has been demonstrated in additional projects (34). Currently, several hundred laboratories and clinical facilities worldwide utilize MalaCards-enriched VarElect to interpret sequenced genomes and identify culprit diseases.…”

“…Such ‘guilt by association’ logic prevails in NGS analyses, including in VarElect's indirect mode (34). In the inverse direction, perhaps there are numerous cases in which, having two genes in the same disease pathway attests to a yet undiscovered biological relationship between them.…”

Recurrent Copy Number Variants Associated with Syndromic Short Stature of Unknown Cause

“…The recurrent loci were selected, and the genomic positions were analyzed by DECIPHER [16]. The protein coding genes situated in CNVs loci were analyzed by VarElect NGS Phenotyper Program [17]. We used the following phenotype terms for prioritization of genes: “short stature” OR “growth impairment” OR height OR dwarfism OR dwarf OR “growth restriction” OR “growth retardation.” We selected the first 5 genes directly related to the phenotype.…”