ArticlePLoS computational biology2019
How good are pathogenicity predictors in detecting benign variants?
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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Who cites it
72 citing papers in PubMed.
- Relevance of pathogenicity prediction tools in human RYR1 variants of unknown significance.Scientific reports · 2021Trial
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- A novel phenotype-guided genome analysis pipeline for variant discovery.NPJ genomic medicine · 2026Article
- An Integrative Variant Scoring Function for Finding Novel Genes Associated with Ovarian and Thyroid Cancer.International journal of environmental research and public health · 2026Article
- Novel pathogenic variant inFrontiers in molecular biosciences · 2026Article
- Expanding the genetic landscape ofFrontiers in genetics · 2026Article
- Genetic Characterization of Joubert Syndrome with COACH Features and in Silico Prediction ofMedical journal of the Islamic Republic of Iran · 2026Article
- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- Population frequency of Predicted pathogenic MisMatch Repair (MMR) gene variants in Lynch syndrome from bioinformatic analyses of the general population.Scientific reports · 2025Article
- Genetic variants in the CD59 gene: An exploratory study of large genome databases.Transfusion · 2025Article
- Patients with anorexia nervosa have an increased burden of rare, damaging mutations in the BBOX1 gene.Journal of eating disorders · 2025Article
- Assessing the performance of 28 pathogenicity prediction methods on rare single nucleotide variants in coding regions.BMC genomics · 2025Article
- PRP: pathogenic risk prediction for rare nonsynonymous single nucleotide variants.Human genetics · 2025Article
- Evaluating variant pathogenicity prediction tools to establish African inclusive guidelines for germline genetic testing.Communications medicine · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- Deep learning tools predict variants in disordered regions with lower sensitivity.BMC genomics · 2025Article
- Whole Exome Sequencing in Drug-Induced Angioedema Caused by Angiotensin-Converting Enzyme Inhibitors: A Pilot Study in Five Patients.Journal of clinical medicine · 2025Article
- Ultrarare Variants in DNA Damage Repair Genes in Pediatric Acute-Onset Neuropsychiatric Syndrome or Acute Behavioral Regression in Neurodevelopmental Disorders.Developmental neuroscience · 2025Article
- Benchmarking of variant pathogenicity prediction methods using a population genetics approach.Bioinformatics advances · 2025Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational tools are widely used for interpreting variants detected in sequencing projects. The choice of these tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment. The performance of the methods varies widely in different performance assessments, for example due to the contents and sizes of test datasets. To address this issue, we obtained 63,160 common amino acid substitutions (allele frequency ≥1% and <25%) from the Exome Aggregation Consortium (ExAC) database, which contains variants from 60,706 genomes or exomes. We evaluated the specificity, the capability to detect benign variants, for 10 variant interpretation tools. In addition to overall specificity of the tools, we tested their performance for variants in six geographical populations. PON-P2 had the best performance (95.5%) followed by FATHMM (86.4%) and VEST (83.5%). While these tools had excellent performance, the poorest method predicted more than one third of the benign variants to be disease-causing. The results allow choosing reliable methods for benign variant interpretation, for both research and clinical purposes, as well as provide a benchmark for method developers.
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