Improved exome prioritization of disease genes through cross-species phenotype comparison

By Peter N. Robinson, Sebastian Köhler, A. Oellrich, Kai Wang, C. Mungall, Suzanna Lewis, Nicole L. Washington, Sebastian Bauer, D. Seelow, P. Krawitz and 3 more. Genome Research, 2014

366 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b899291b5d41b1dacfd9c7256885b0949e42edb3

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Citations in the corpus

A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease. (2016, methodology): “56,66 To assess the performance of Genomiser on 22 published cases of compound heterozygosity in which one causal mutation is regulatory and the other is coding or splice site (Table S5), we spiked both mutations into a genome VCF ﬁle and ran our analysis as above.”
Settling the score: variant prioritization and Mendelian disease (2017, methodology): “Another tool, Phenotypic Interpretation of Variants in Exomes (PHIVE) 81 , uses a different approach: it is a variant filtering tool that uses a combination of variant frequency, predicted deleteriousness of the allele and a semantic similarity-based phenotypic relevance score that uses model…”
Human symptoms–disease network (2014, background): “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 .”
The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species (2016, methodology): “The Exomiser (12) and more recently, Genomiser tools (45) make use of the Monarch platform and phenotype matching algorithms to rank putative causative variants using a combined variant and phenotype score.”
The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery (2015, background): “…matching algorithms to identify cases with common phenotypes and disrupted genes [Washington et al., 2009; Gonzalez et al., 2012, Swaminathan et al., 2012, Gonzalez et al., 2013, Robinson et al., 2014; Zemo-jtel et al., 2014; Buske et al., 2015a; Lancaster et al., 2015; So-breira et al., 2015a].”
Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. (2015, background): “For example, if we would like to integrate information such as phenotypic information (38,39), pathway information (40) and protein interaction (41), we may consider those more complex machine learning algorithms.”
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources (2018, methodology): “This discovery was made possible because (i) the human diseases had been defined in terms of their component HPO phenotypes; (ii) the mouse phenotypes were mapped to the MP; and (iii) Monarch’s phenotype comparison algorithm (47) is designed to traverse HP and MP with ease.”
PERCH: a unified framework for disease gene prioritization (2017, background): “Besides association between genes, others utilize the similarity between phenotypes to infer the correlation of a gene with the disease [Robinson et al., 2014].”