ArticleFrontiers in genetics2014
SDS, a structural disruption score for assessment of missense variant deleteriousness.
Article in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Rationale and Roadmap for Developing Panels of Hotspot Cancer Driver Gene Mutations as Biomarkers of Cancer Risk.Environmental and molecular mutagenesis · 2020Review
- VIPdb, a genetic Variant Impact Predictor Database.Human mutation · 2019Article
- Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework.Genome medicine · 2017Review
- The challenge of comprehensive and consistent sequence variant interpretation between clinical laboratories.Genetics in medicine : official journal of the American College of Medical Genetics · 2016Article
- Insights into the genetic foundations of human communication.Neuropsychology review · 2015Review
- Associating rare genetic variants with human diseases.Frontiers in genetics · 2015Article
- Single nucleotide variations: biological impact and theoretical interpretation.Protein science : a publication of the Protein Society · 2014Review
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Authors and funding
2 authors at 1 institution in 1 country.
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Abstract
We have developed a novel structure-based evaluation for missense variants that explicitly models protein structure and amino acid properties to predict the likelihood that a variant disrupts protein function. A structural disruption score (SDS) is introduced as a measure to depict the likelihood that a case variant is functional. The score is constructed using characteristics that distinguish between causal and neutral variants within a group of proteins. The SDS score is correlated with standard sequence-based deleteriousness, but shows promise for improving discrimination between neutral and causal variants at less conserved sites. The prediction was performed on 3-dimentional structures of 57 gene products whose homozygous SNPs were identified as case-exclusive variants in an exome sequencing study of epilepsy disorders. We contrasted the candidate epilepsy variants with scores for likely benign variants found in the EVS database, and for positive control variants in the same genes that are suspected to promote a range of diseases. To derive a characteristic profile of damaging SNPs, we transformed continuous scores into categorical variables based on the score distribution of each measurement, collected from all possible SNPs in this protein set, where extreme measures were assumed to be deleterious. A second epilepsy dataset was used to replicate the findings. Causal variants tend to receive higher sequence-based deleterious scores, induce larger physico-chemical changes between amino acid pairs, locate in protein domains, buried sites or on conserved protein surface clusters, and cause protein destabilization, relative to negative controls. These measures were agglomerated for each variant. A list of nine high-priority putative functional variants for epilepsy was generated. Our newly developed SDS protocol facilitates SNP prioritization for experimental validation.
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