ArticleNature genetics2015
A method for calculating probabilities of fitness consequences for point mutations across the human genome.
Article in Nature genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 183 papers, 2 of them syntheses that pooled it.
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Who cites it
183 citing papers in PubMed, 2 syntheses or guidelines pooled it, 302 citations in OpenAlex.
- Pooled it
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- A predictive framework for stop-loss variants with C-terminal extensions.Nucleic acids research · 2026Article
- Tracing the evolutionary histories of ultra-rare variants using variational dating of large ancestral recombination graphs.bioRxiv : the preprint server for biology · 2026Article
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- Stratifying variant deleteriousness and trait-modulating effect under human recent adaptation using the FIND model.Genome biology · 2025Article
- DRP-PSM: Multi-Level Feature Integration Reveals Hierarchical Contributions to Pathogenic Synonymous Mutation Prediction.ACS omega · 2025Article
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- The quantitative impact of 3'UTRs on gene expression.Nucleic acids research · 2025Article
- The landscape of fitness effects of putatively functional noncoding mutations in humans.bioRxiv : the preprint server for biology · 2025Article
- Novel WAC gene variant identified in the first documented case of DeSanto-Shinawi Syndrome in India.Molecular and cellular pediatrics · 2025Article
- Evaluating variant pathogenicity prediction tools to establish African inclusive guidelines for germline genetic testing.Communications medicine · 2025Article
- Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.Human genetics · 2025Article
123 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
4 authors at 2 institutions in 2 countries.
Funding
Abstract
We describe a new computational method for estimating the probability that a point mutation at each position in a genome will influence fitness. These 'fitness consequence' (fitCons) scores serve as evolution-based measures of potential genomic function. Our approach is to cluster genomic positions into groups exhibiting distinct 'fingerprints' on the basis of high-throughput functional genomic data, then to estimate a probability of fitness consequences for each group from associated patterns of genetic polymorphism and divergence. We have generated fitCons scores for three human cell types on the basis of public data from ENCODE. In comparison with conventional conservation scores, fitCons scores show considerably improved prediction power for cis regulatory elements. In addition, fitCons scores indicate that 4.2-7.5% of nucleotides in the human genome have influenced fitness since the human-chimpanzee divergence, and they suggest that recent evolutionary turnover has had limited impact on the functional content of the genome.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.