Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST‐Indel)

By Christopher Douville, David L. Masica, Peter D. Stenson, David N. Cooper, Derek M. Gygax, Rick Kim, Michael Ryan, Rachel Karchin. 2015

138 citations recorded by the source corpus.

Insertion/deletion variants (indels) alter protein sequence and length, yet are highly prevalent in healthy populations, presenting a challenge to bioinformatics classifiers. Commonly used features—DNA and protein sequence conservation, indel length, and occurrence in repeat regions—are useful for inference of protein damage. However, these features can cause false positives when predicting the impact of indels on disease. Existing methods for indel classification suffer from low specificities, severely limiting clinical utility. Here, we further develop our variant effect scoring tool (VEST) to include the classification of in‐frame and frameshift indels (VEST‐indel) as pathogenic or benign. We apply 24 features, including a new “PubMed” feature, to estimate a gene's importance in human disease. When compared with four existing indel classifiers, our method achieves a drastically reduced false‐positive rate, improving specificity by as much as 90%. This approach of estimating gene importance might be generally applicable to missense and other bioinformatics pathogenicity predictors, which often fail to achieve high specificity. Finally, we tested all possible meta‐predictors that can be obtained from combining the four different indel classifiers using Boolean conjunctions and disjunctions, and derived a meta‐predictor with improved performance over any individual method.

Subfields: Molecular Biology, Plant Science

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Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, methodology): “Non-coding variants and more missense scores: AUROC performances of different scores on (A) non-coding variants and (B) the same missense variants from Figure 2B. Scores from VEST and REVEL are not included in the main comparison because they were trained on variants from these two test sets.”
Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, methodology): “…a machine learning method that predicts the functional significance of missense mutations observed through genome sequencing, allowingmutations to be prioritized in subsequent functional studies based on the probability that they impair protein activity (Carter et al., 2013; Douville et al., 2016).”
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): “…2013), FATHMM-MKL (Shihab et al. 2015), PinPor (Zhang et al. 2014), MutationTaster2 (Schwarz et al. 2014), Phen-Gen (Javed et al. 2014), VEST-indel (Douville et al. 2016), Gene Damage Index (Itan et al. 2015), DDIG-in (Folkman et al. 2015), RSVP (Peterson et al. 2016), ExonImpact (Li et al. 2017),…”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, methodology): “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.”