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.

Human symptoms–disease network

“…Interestingly, the inclusion of disease phenotype similarities can substantially improve the performance of candidate gene prediction methods [21][22][23][24] . Resources like the Human Phenotype Ontology 25 (HPO) and the Mammalian Phenotype Ontology 26 provide a standardized vocabulary of phenotypic information that can also be used to transfer detailed knowledge of model organisms to interpret and predict associated phenomena in human 27,28 .…”

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

“…Clinical genetics labs and research sequencing centers have access to well-established variant analysis and annotation pipelines such as Exomiser/PHenotypic Interpretation of Variants in Exomes (PHIVE), 41 ANNOVAR, 42 and Codified Genomics (see Web Resources) that utilize existing tools to analyze entire sets of sequencing data. These require familiarity with bioinformatics data processing and access to these resources.…”

“…41,43 Although the similarities between human and model organism mutant phenotype can be informative, this approach may miss numerous opportunities in which the protein functions are part of conserved pathways among organisms when the orthologous phenotypes are not obviously analogous. 44 For example, a yeast model for angiogenesis 44 and a worm model for breast cancer 44 revealed molecular pathways that contribute to these disorders based on the ''phenology'' concept.…”

“…In conclusion, MARRVEL is a flexible web resource that provides a useful and accessible tool for efficiently matching an input against 18 million records of human variants and genes as well as model organism homologs. MARRVEL provides a step toward the overarching goal of integrating model organism databases with human gene-centric user interfaces 41 to improve the accessibility and evaluation of data typically used by experts fluent in specific data formats and software. Our future goals for MARRVEL include continuing to integrate additional human genomics and model organism resources as they become publicly available to ensure that MARRVEL remains a valuable and up-to-date analytical resource.…”

Variable expressivity and genetic heterogeneity involving DPT and SEMA3D genes in autosomal dominant familial Meniere’s disease

“…broadinstitute.org/). The analysis strategy to prioritize candidate variants scored them according to their effect on protein structure and phylogenetic conservation by using a seven-point scoring system (Pathogenic Variant or PAVAR 18 To improve the accuracy of the variant prioritization, we combined the previous results with other bioinformatics tools that include phenotype information such as Exomiser v.2, 21 and Variant Annotation Analysis and Search Tool (VAAST)+Phevor that prioritize the variants and the genes affected using a ranking system. 22,23 We used linkage information derived from the WES-common SNVs within each pedigree to reduce the list of candidate variants, according to the method described by Gazal et al 24 Validation by Sanger sequencing …”