Performance of mutation pathogenicity prediction methods on missense variants
Open accessAbstract
Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans. The number of SNPs identified in the human genome is growing rapidly, but attaining experimental knowledge about the possible disease association of variants is laborious and time-consuming. Several computational methods have been developed for the classification of SNPs according to their predicted pathogenicity. In this study, we have evaluated the performance of nine widely used pathogenicity prediction methods available on the Internet. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO. The methods were tested with a set of over 40,000 pathogenic and neutral variants. We also assessed whether the type of original or substituting amino acid residue, the structural class of the protein, or the structural environment of the amino acid substitution, had an effect on the prediction performance. The performances of the programs ranged from poor (MCC 0.19) to reasonably good (MCC 0.65), and the results from the programs correlated poorly. The overall best performing methods in this study were SNPs&GO and MutPred, with accuracies reaching 0.82 and 0.81, respectively.
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Most algorithms for missense variant prediction are 65-80% accurate when examining known disease variants.
“In general, most algorithms for missense variant prediction are 65-80% accurate when examining known disease variants. 16”
From Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (2015) · cites this paper as [16]
The Discussion reports accuracies of 0.60–0.82 across the evaluated methods, broadly consistent with the claimed range but not establishing its exact 65–80% bounds. The Datasets section shows that accuracy was evaluated using both known pathogenic and neutral variants, not known disease variants alone. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSeveral efforts have evaluated the performance of available prediction software, to compare them to each other and to assess their ability to predict known disease-causing variants.
“Several efforts have evaluated the performance of available prediction software, to compare them to each other and to assess their ability to predict 'known' disease-causing variants. 14-17”
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References 66 · Cited by 537
Research works printed but not linked: 2
- Daily KM, Radivojac P., Dunker AK. 2005. Intrinsic disorder and protein modifications: building an SVM predictor for methylation. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB: 475-481.
- Saunders CT, Baker D. 2002. Evaluation of structural and evolutionary contributions to deleterious mutation prediction. J Mol Biol 322:891-901.
- Authors
- Thusberg, J., Olatubosun, A., & Vihinen, M.
- Affiliations
- Buck Institute for Research on Aging (US)
- Tampere University (FI)
- Corresponding author
- Vihinen, Mauno
- Venue
- Human Mutation
- Publisher
- Wiley
- Published
- 2011-02-22
- Volume
- 32
- Issue
- 4
- Pages
- 358-368
- ISSN
- 1059-7794 (ISSN-L)
- 1098-1004
- OpenAlex
- W2063714349
- Cited by
- 537
- References
- 66
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 20 · 2023: 22 · 2022: 24 · 2021: 30 · 2020: 29 · 2019: 43 · 2018: 29 · 2017: 49 · 2016: 42 · 2015: 50 · 2014: 69 · 2013: 46 · 2012: 45
- FWCI
- 33.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Human Mutation (publishedVersion)
- PubMed (publishedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- The Tampere Graduate School in Biomedicine and Biotechnology
- The Sigrid Jusélius Foundation
- The Academy of Finland
- The Medical Research Fund of Tampere University Hospital
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- missense mutations, single nucleotide polymorphisms, pathogenicity prediction, variant interpretation, computational prediction methods, method performance comparison, amino acid substitutions, protein structure, PolyPhen, SIFT, panther, SNAP
- MeSH terms
- Humans, Phenotype, Genetic Variation, Genome, Human, Computational Biology, Mutation, Missense, Polymorphism, Single Nucleotide
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