ReviewGenetics2016
Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation.
Review in Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
54 citing papers in PubMed.
- Identification of candidate nsSNPs of the human FNDC5 gene and their structural and functional consequences using in silico analysis.Scientific reports · 2025Article
- Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance.British journal of clinical pharmacology · 2025Review
- A structural genomics approach to investigate Dystrophin mutations and their impact on the molecular pathways of Duchenne muscular dystrophy.Frontiers in genetics · 2025Article
- Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.Human genomics · 2024Article
- Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis.Communications biology · 2024Article
- Accuracy of renovo predictions on variants reclassified over time.Journal of translational medicine · 2024Article
- Functional analysis ofbioRxiv : the preprint server for biology · 2024Article
- Article
- The functional impact of 1,570 individual amino acid substitutions in human OTC.American journal of human genetics · 2023Article
- Variant curation and interpretation in hereditary cancer genes: An institutional experience in Latin America.Molecular genetics & genomic medicine · 2023Article
- Personalized structural biology reveals the molecular mechanisms underlying heterogeneous epileptic phenotypes caused byHGG advances · 2022Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Intragenic compensation through the lens of deep mutational scanning.Biophysical reviews · 2022Review
- Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.Nature communications · 2022Article
- Assessing the clinical utility of protein structural analysis in genomic variant classification: experiences from a diagnostic laboratory.Genome medicine · 2022Article
- A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.American journal of human genetics · 2022Article
- How Functional Genomics Can Keep Pace With VUS Identification.Frontiers in cardiovascular medicine · 2022Review
- Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays.Scientific reports · 2021Article
- Classifying molecular phenotypes ofJIMD reports · 2021Article
- Molecular Genetic Basis of Hypertrophic Cardiomyopathy.Circulation research · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As personal genome sequencing becomes a reality, understanding the effects of genetic variants on phenotype-particularly the impact of germline variants on disease risk and the impact of somatic variants on cancer development and treatment-continues to increase in importance. Because of their clear potential for affecting phenotype, nonsynonymous genetic variants (variants that cause a change in the amino acid sequence of a protein encoded by a gene) have long been the target of efforts to predict the effects of genetic variation. Whole-genome sequencing is identifying large numbers of nonsynonymous variants in each genome, intensifying the need for computational methods that accurately predict which of these are likely to impact disease phenotypes. This review focuses on nonsynonymous variant prediction with two aims in mind: (1) to review the prioritization methods that have been developed to date and the principles on which they are based and (2) to discuss the challenges to further improving these methods.
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