Identifying Mendelian disease genes with the Variant Effect Scoring Tool

By Hannah Carter, Christopher Douville, Peter D. Stenson, David N. Cooper, Rachel Karchin. 2013

530 citations recorded by the source corpus.

Whole exome sequencing studies identify hundreds to thousands of rare protein coding variants of ambiguous significance for human health. Computational tools are needed to accelerate the identification of specific variants and genes that contribute to human disease. We have developed the Variant Effect Scoring Tool (VEST), a supervised machine learning-based classifier, to prioritize rare missense variants with likely involvement in human disease. The VEST classifier training set comprised ~ 45,000 disease mutations from the latest Human Gene Mutation Database release and another ~45,000 high frequency (allele frequency > 1%) putatively neutral missense variants from the Exome Sequencing Project. VEST outperforms some of the most popular methods for prioritizing missense variants in carefully designed holdout benchmarking experiments (VEST ROC AUC = 0.91, PolyPhen2 ROC AUC = 0.86, SIFT4.0 ROC AUC = 0.84). VEST estimates variant score p-values against a null distribution of VEST scores for neutral variants not included in the VEST training set. These p-values can be aggregated at the gene level across multiple disease exomes to rank genes for probable disease involvement. We tested the ability of an aggregate VEST gene score to identify candidate Mendelian disease genes, based on whole-exome sequencing of a small number of disease cases. We used whole-exome data for two Mendelian disorders for which the causal gene is known. Considering only genes that contained variants in all cases, the VEST gene score ranked dihydroorotate dehydrogenase (DHODH) number 2 of 2253 genes in four cases of Miller syndrome, and myosin-3 (MYH3) number 2 of 2313 genes in three cases of Freeman Sheldon syndrome. Our results demonstrate the potential power gain of aggregating bioinformatics variant scores into gene-level scores and the general utility of bioinformatics in assisting the search for disease genes in large-scale exome sequencing studies. VEST is available as a stand-alone software package at http://wiki.chasmsoftware.org and is hosted by the CRAVAT web server at http://www.cravat.us

Subfields: Genetics, Molecular Biology

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Citations in the corpus

dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “…of rank scores (upper triangle) and agreement ratio of binary predictions (lower triangle) between pairs of deleteriousness prediction scores or conservation scores LIST-S2, MPC, MutationAssessor [45], MVP, Polyphen2 HDIV and Polyphen2 HVAR [46], PROVEAN [47], SIFT [48], SIFT4G, VEST4 [49]).”
dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Non-synonymous and Splice Site SNVs (2016, background): “0, the new version added 11 new prediction scores: MetaSVM and MetaLR [Dong et al., 2015], CADD [Kircher et al., 2014], VEST3 [Carter et al., 2013], PROVEAN [Choi et al., 2012], 4× fitCons scores [Gulko et al., 2015], fathmm-MKL [Shihab et al., 2015], and DANN [Quang et al., 2015]; two conservation…”
Network-based stratification of tumor mutations (2013, methodology): “Missense mutations were scored using three methods: CHASM 13 , VEST 44 and MutationAssessor 43 .”
The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine (2013, unknown): “2013), NEST (Carter et al. 2013) and FATHMM (Shihab et al.”
Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST‐Indel) (2015, methodology): “For each In-frame, frameshift, or missense variant, a VEST score was computed using homology-restricted 10-fold cross-validation with the appropriate Random Forest (Performance Assessment).”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, methodology): “Many other methods such as PolyPhen-2 [45], FATHMM-XF [46], CADD [47],PrimateAI [48], and VEST [49], combine various sources of information in addition to sequence conservation, such as predicted or real structural properties, functional and epigenetic information from experiments, and genomic conte”
Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, methodology): “The collection was comprised of 8 mutation-level algorithms (SIFT [Ng and Henikoff, 2002], PolyPhen2 [Adzhubei et al., 2013], MutationAssessor [Reva et al., 2011], transFIC [Gonzalez-Perez et al., 2012], fathmm [Shihab et al., 2013], CHASM [Carter et al., 2009], CanDrA [Mao et al., 2013] and VEST [C”
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies (2017, unknown): “HGMD has also been used by a number of different groups to aid the development of a wide variety of post-NGS variant interpretation and exome prioritisation algorithms including MutPred (Li et al. 2009), MutPred Splice (Mort et al. 2014), PROVEAN (Choi et al. 2012), CAROL (Lopes et al. 2012), regSNP”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, background): “CADD [Kircher et al., 2014], MutationTaster2 [Schwarz et al., 2014], PROVEAN [Choi et al., 2012], and VEST [Carter et al., 2013; Douville et al., 2016] can predict impacts of insertions, deletions, as well as amino acid substitutions.”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, methodology): “For example, for benign variants the best performing combinations of three algorithms consisted of VEST3 [18], REVEL [19], and MetaSVM [20] with a true concordance rate of 81.3% and a false concordance rate of 2.8%, whereas for pathogenic variants the same combination resulted in a 70% true…”
Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022, unknown): “For meta-predictors such as BayesDel and REVEL that incorporate prediction scores from other tools, we also removedtrainingvariantsfortheirconstituenttoolswhenavailable; e.g., FATHMM, MutPred, 29 VEST3, 25 and MutationTaster.”