ArticleMolecular genetics & genomic medicine2015
Variability in pathogenicity prediction programs: impact on clinical diagnostics.
Article in Molecular genetics & genomic medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.BMC bioinformatics · 2025Article
- Artificial Intelligence Advancements in Cardiomyopathies: Implications for Diagnosis and Management of Arrhythmogenic Cardiomyopathy.Current heart failure reports · 2024Review
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- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data.Journal of neurodevelopmental disorders · 2022Article
- Interpreting protein variant effects with computational predictors and deep mutational scanning.Disease models & mechanisms · 2022Review
- Findings from precision oncology in the clinic: rare, novel variants are a significant contributor to scaling molecular diagnostics.BMC medical genomics · 2022Article
- Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants.Frontiers in cardiovascular medicine · 2022Article
- Article
- VPMBench: a test bench for variant prioritization methods.BMC bioinformatics · 2021Article
- In silico analysis of BRCA1 and BRCA2 missense variants and the relevance in molecular genetic testing.Scientific reports · 2021Article
- Functional analysis of epilepsy-associated variants in STXBP1/Munc18-1 using humanized Caenorhabditis elegans.Epilepsia · 2020Article
- A decision tree to improve identification of pathogenic mutations in clinical practice.BMC medical informatics and decision making · 2020Article
- Potential scrapie-associated polymorphisms of the prion protein gene (PRNP) in Korean native black goats.Scientific reports · 2019Article
- Inherited glycophosphatidylinositol deficiency variant database and analysis of pathogenic variants.Molecular genetics & genomic medicine · 2019Article
- Comparison of PredictiveTheScientificWorldJournal · 2019Article
- Gene variants identified by whole-exome sequencing in 33 French women with premature ovarian insufficiency.Journal of assisted reproduction and genetics · 2019Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Current practice by clinical diagnostic laboratories is to utilize online prediction programs to help determine the significance of novel variants in a given gene sequence. However, these programs vary widely in their methods and ability to correctly predict the pathogenicity of a given sequence change. The performance of 17 publicly available pathogenicity prediction programs was assayed using a dataset consisting of 122 credibly pathogenic and benign variants in genes associated with the RASopathy family of disorders and limb-girdle muscular dystrophy. Performance metrics were compared between the programs to determine the most accurate program for loss-of-function and gain-of-function mechanisms. No one program correctly predicted the pathogenicity of all variants analyzed. A major hindrance to the analysis was the lack of output from a significant portion of the programs. The best performer was MutPred, which had a weighted accuracy of 82.6% in the full dataset. Surprisingly, combining the results of the top three programs did not increase the ability to predict pathogenicity over the top performer alone. As the increasing number of sequence changes in larger datasets will require interpretation, the current study demonstrates that extreme caution must be taken when reporting pathogenicity based on statistical online protein prediction programs in the absence of functional studies.
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