ArticleEuropean journal of human genetics : EJHG2022
Variant pathogenic prediction by locus variability: the importance of the current picture of evolution.
Article in European journal of human genetics : EJHG, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Benchmarking of variant pathogenicity prediction methods using a population genetics approach.Bioinformatics advances · 2025Article
- DrivR-Base: a feature extraction toolkit for variant effect prediction model construction.Bioinformatics (Oxford, England) · 2024Article
- No gene to predict the future?European journal of human genetics : EJHG · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate detection of pathogenic single nucleotide variants (SNVs) is a key challenge in whole exome and whole genome sequencing studies. To date, several in silico tools have been developed to predict deleterious variants from this type of data. However, these tools have limited power to detect new pathogenic variants, especially in non-coding regions. In this study, we evaluate the use of a new metric, the Shannon Entropy of Locus Variability (SELV), calculated as the Shannon entropy of the variant frequencies reported in genome-wide population studies at a given locus, as a new predictor of potentially pathogenic variants in non-coding nuclear and mitochondrial DNA and also in coding regions with a selective pressure other than that imposed by the genetic code, e.g splice-sites. For benchmarking, SELV was compared to predictors of pathogenicity in different genomic contexts. In nuclear non-coding DNA, SELV outperformed CDTS (AUC
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