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Title:
Critical assessment of missense variant effect predictors on disease-relevant variant data | Human Genetics
Description:
Regular, systematic, and independent assessments of computational tools that are used to predict the pathogenicity of missense variants are necessary to evaluate their clinical and research utility and guide future improvements. The Critical Assessment of Genome Interpretation (CAGI) conducts the ongoing Annotate-All-Missense (Missense Marathon) challenge, in which missense variant effect predictors (also called variant impact predictors) are evaluated on missense variants added to disease-relevant databases following the prediction submission deadline. Here we assess predictors submitted to the CAGI 6 Annotate-All-Missense challenge, predictors commonly used in clinical genetics, and recently developed deep learning methods. We examine performance across a range of settings relevant for clinical and research applications, focusing on different subsets of the evaluation data as well as high-specificity and high-sensitivity regimes. Our evaluations reveal notable advances in current methods relative to older, well-cited tools in the field. While meta-predictors tend to outperform their constituent individual predictors, several newer individual predictors perform comparably to commonly used meta-predictors. Predictor performance varies between high-specificity and high-sensitivity regimes, highlighting that different methods may be optimal for different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors trained on pathogenicity labels from curated variant databases often inherit gene-level label imbalances. Our findings help illuminate the clinical and research utility of modern missense variant effect predictors and identify potential areas for future development.
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Keywords {🔍}
variants, predictors, variant, article, pubmed, performance, google, scholar, dataset, evaluation, missense, allele, pathogenic, frequency, clinical, benign, human, effect, pathogenicity, challenge, cas, genetics, methods, research, central, data, databases, fig, cagi, clinvar, tools, genome, predictions, gene, hgmd, full, computational, prediction, interpretation, metapredictors, assessment, score, individual, rare, classification, evidence, scores, nature, highsensitivity, predictive,
Topics {✒️}
apply acmg/amp guidelines article download pdf learn gene-level properties dong-wook kim human disease-related mutations disease-relevant variant data label-balanced dataset consists positive-unlabeled classification setting genome-wide prediction compiles disease-relevant variants previously published methods—3cnet gene label-balanced dataset control cohort exomes high-confidence disease-causing gene label-balanced setting benign allele-frequency distributions université de montpellier gene label-balanced subset higher impact annotation related subjects high-confidence missense variants transcript-specific functional predictions shihab ha privacy choices/manage cookies high-confidence pathogenicity classifications protein structural properties samocha ke single nucleotide polymorphisms protein structural stability hgmd disease-causing variants true positive rates disease variant prediction variant-level properties disease variant effects identify potential areas pp3/bp4 criteria complementary information related sixth team submission exome sequencing studies predictive feature reach org/download/bulk additional information publisher' missense variant interpretation single-nucleotide variants deleteriousness prediction methods coding sequence variation de novo variants changwon keum determining genetic susceptibility integrating numerous lines
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headline:Critical assessment of missense variant effect predictors on disease-relevant variant data
description:Regular, systematic, and independent assessments of computational tools that are used to predict the pathogenicity of missense variants are necessary to evaluate their clinical and research utility and guide future improvements. The Critical Assessment of Genome Interpretation (CAGI) conducts the ongoing Annotate-All-Missense (Missense Marathon) challenge, in which missense variant effect predictors (also called variant impact predictors) are evaluated on missense variants added to disease-relevant databases following the prediction submission deadline. Here we assess predictors submitted to the CAGI 6 Annotate-All-Missense challenge, predictors commonly used in clinical genetics, and recently developed deep learning methods. We examine performance across a range of settings relevant for clinical and research applications, focusing on different subsets of the evaluation data as well as high-specificity and high-sensitivity regimes. Our evaluations reveal notable advances in current methods relative to older, well-cited tools in the field. While meta-predictors tend to outperform their constituent individual predictors, several newer individual predictors perform comparably to commonly used meta-predictors. Predictor performance varies between high-specificity and high-sensitivity regimes, highlighting that different methods may be optimal for different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors trained on pathogenicity labels from curated variant databases often inherit gene-level label imbalances. Our findings help illuminate the clinical and research utility of modern missense variant effect predictors and identify potential areas for future development.
datePublished:2025-03-21T00:00:00Z
dateModified:2025-03-21T00:00:00Z
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Human Genetics
Molecular Medicine
Gene Function
Metabolic Diseases
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headline:Critical assessment of missense variant effect predictors on disease-relevant variant data
description:Regular, systematic, and independent assessments of computational tools that are used to predict the pathogenicity of missense variants are necessary to evaluate their clinical and research utility and guide future improvements. The Critical Assessment of Genome Interpretation (CAGI) conducts the ongoing Annotate-All-Missense (Missense Marathon) challenge, in which missense variant effect predictors (also called variant impact predictors) are evaluated on missense variants added to disease-relevant databases following the prediction submission deadline. Here we assess predictors submitted to the CAGI 6 Annotate-All-Missense challenge, predictors commonly used in clinical genetics, and recently developed deep learning methods. We examine performance across a range of settings relevant for clinical and research applications, focusing on different subsets of the evaluation data as well as high-specificity and high-sensitivity regimes. Our evaluations reveal notable advances in current methods relative to older, well-cited tools in the field. While meta-predictors tend to outperform their constituent individual predictors, several newer individual predictors perform comparably to commonly used meta-predictors. Predictor performance varies between high-specificity and high-sensitivity regimes, highlighting that different methods may be optimal for different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors trained on pathogenicity labels from curated variant databases often inherit gene-level label imbalances. Our findings help illuminate the clinical and research utility of modern missense variant effect predictors and identify potential areas for future development.
datePublished:2025-03-21T00:00:00Z
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Human Genetics
Molecular Medicine
Gene Function
Metabolic Diseases
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