title: Improved exome prioritization of disease genes through cross-species phenotype comparison.
abstract: Numerous new disease-gene associations have been identified by whole-exome sequencing studies in the last few years. However, many cases remain unsolved due to the sheer number of candidate variants remaining after common filtering strategies such as removing low quality and common variants and those deemed unlikely to be pathogenic. The observation that each of our genomes contains about 100 genuine loss-of-function variants makes identification of the causative mutation problematic when using these strategies alone. We propose using the wealth of genotype to phenotype data that already exists from model organism studies to assess the potential impact of these exome variants. Here, we introduce PHenotypic Interpretation of Variants in Exomes (PHIVE), an algorithm that integrates the calculation of phenotype similarity between human diseases and genetically modified mouse models with evaluation of the variants according to allele frequency, pathogenicity, and mode of inheritance approaches in our Exomiser tool. Large-scale validation of PHIVE analysis using 100,000 exomes containing known mutations demonstrated a substantial improvement (up to 54.1-fold) over purely variant-based (frequency and pathogenicity) methods with the correct gene recalled as the top hit in up to 83% of samples, corresponding to an area under the ROC curve of >95%. We conclude that incorporation of phenotype data can play a vital role in translational bioinformatics and propose that exome sequencing projects should systematically capture clinical phenotypes to take advantage of the strategy presented here.
authors.affiliation: Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany;
authors.forename: Peter N
authors.initials: PN
authors.lastname: Robinson
authors.forename: Sebastian
authors.initials: S
authors.lastname: Köhler
authors.forename: Anika
authors.initials: A
authors.lastname: Oellrich
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Mungall
authors.forename: Suzanna E
authors.initials: SE
authors.lastname: Lewis
authors.forename: Nicole
authors.initials: N
authors.lastname: Washington
authors.forename: Sebastian
authors.initials: S
authors.lastname: Bauer
authors.forename: Dominik
authors.initials: D
authors.lastname: Seelow
authors.forename: Peter
authors.initials: P
authors.lastname: Krawitz
authors.forename: Christian
authors.initials: C
authors.lastname: Gilissen
authors.forename: Melissa
authors.initials: M
authors.lastname: Haendel
authors.forename: Damian
authors.initials: D
authors.lastname: Smedley
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D030541:Databases, Genetic; D059472:Exome / Q000235:genetics*; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D051379:Mice; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D017422:Sequence Analysis, DNA; D012984:Software