ArticleAmerican journal of human genetics2022
Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity.
Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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58 citing papers in PubMed, 84 citations in OpenAlex.
- Probabilistic mapping of sub-genic intolerance reveals functional and disease-critical protein regions.bioRxiv : the preprint server for biology · 2026Article
- Biallelic RDH11 variants cause syndromic retinitis pigmentosa with early-onset cataracts and neurodevelopmental delay: a multicenter case series.European journal of human genetics : EJHG · 2026Article
- Validation structures for sequence variants of uncertain significance in hereditary cancer.European journal of human genetics : EJHG · 2026Review
- Missense variants in KATNA1 alter microtubule dynamics and underlie dominant macular dystrophy.Research square · 2026Article
- A CLN8 biallelic missense variant causes epilepsy with severe treatment-resistant psychosis.Molecular genetics and genomics : MGG · 2026Article
- Defining the molecular tolerance-to-damage landscape of SMARCA4 helicase genetic alterations.bioRxiv : the preprint server for biology · 2026Article
- Association of Autosomal Dominant Snowflake Vitreoretinal Degeneration with Retinoschisis.Ophthalmology science · 2026Article
- Loss of RPGR disrupts motile cilia and causes primary ciliary dyskinesia by affecting F-actin dynamics.The Journal of clinical investigation · 2026Article
- Bi-allelic variants in FSD1L cause retinitis pigmentosa with or without neurological involvement.American journal of human genetics · 2026Article
- Loss-of-function variants in SAXO6, encoding a microtubule inner protein of photoreceptor cilia, cause a late-onset retinal dystrophy.American journal of human genetics · 2026Article
- Disease- and gene-specific deep learning for pathogenicity prediction of rare missense variants in cancer predisposition genes.BioData mining · 2026Article
- DBP-CanPred: a machine learning model for predicting cancer-causing mutations in DNA-binding proteins.Frontiers in bioinformatics · 2026Article
- Comprehensive in Silico Reclassification of MECP2 Variants of Uncertain Significance in Rett Syndrome: Performance Evaluation and Structural Analysis.Journal of molecular neuroscience : MN · 2025Article
- RPE65 Variant p.(E519K) Causes a Novel Dominant Adult-Onset Maculopathy in 83 Affected Individuals.Investigative ophthalmology & visual science · 2025Article
- Conserved missense variant pathogenicity and correlated phenotypes across paralogous genes.Genome biology · 2025Article
- Making Sense of Missense: Benchmarking MutScore for Variant Interpretation in Inherited Cardiac Diseases.Molecular diagnosis & therapy · 2025Article
- Defective IFT57 underlies a novel cause of Bardet-Biedl syndrome.Human molecular genetics · 2025Article
- Missense variants pathogenicity annotation from homologous proteins.Bioinformatics (Oxford, England) · 2025Article
- A series of reviews in familial cancer: genetic cancer risk in context variants of uncertain significance in MMR genes: which procedures should be followed?Familial cancer · 2025Review
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
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Abstract
We used a machine learning approach to analyze the within-gene distribution of missense variants observed in hereditary conditions and cancer. When applied to 840 genes from the ClinVar database, this approach detected a significant non-random distribution of pathogenic and benign variants in 387 (46%) and 172 (20%) genes, respectively, revealing that variant clustering is widespread across the human exome. This clustering likely occurs as a consequence of mechanisms shaping pathogenicity at the protein level, as illustrated by the overlap of some clusters with known functional domains. We then took advantage of these findings to develop a pathogenicity predictor, MutScore, that integrates qualitative features of DNA substitutions with the new additional information derived from this positional clustering. Using a random forest approach, MutScore was able to identify pathogenic missense mutations with very high accuracy, outperforming existing predictive tools, especially for variants associated with autosomal-dominant disease and cancer. Thus, the within-gene clustering of pathogenic and benign DNA changes is an important and previously underappreciated feature of the human exome, which can be harnessed to improve the prediction of pathogenicity and disambiguation of DNA variants of uncertain significance.
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