Accounting for human polymorphisms predicted to affect protein function.

By Pauline Crystal Ng, Steven Henikoff. Genome Research, 2002

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A major interest in human genetics is to determine whether a nonsynonymous single-base nucleotide polymorphism (nsSNP) in a gene affects its protein product and, consequently, impacts the carrier's health. We used the SIFT (Sorting Intolerant From Tolerant) program to predict that 25% of 3084 nsSNPs from dbSNP, a public SNP database, would affect protein function. Some of the nsSNPs predicted to affect function were variants known to be associated with disease. Others were artifacts of SNP discovery. Two reports have indicated that there are thousands of damaging nsSNPs in an individual's human genome; we find the number is likely to be much lower.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/99d1b819071b9d7bfe87d584d675f9b183c1231f

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Citations in the corpus

Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm (2009, methodology): “SIFT is a multi-step algorithm that uses a sequence homology-based approach to classify amino acid substitutions (AASs”
Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods (2006, background): “The ﬁrst is SIFT which calculates normalized probabilities that speciﬁc substitutions would be tolerated at a given position, and assigns the mutation effect from a speciﬁc probabilities cutoff value (13,14).”
RAD51 and Breast Cancer Susceptibility: No Evidence for Rare Variant Association in the Breast Cancer Family Registry Study (2012, methodology): “Missense substitutions observed during our mutation screening of RAD51 were scored using the Align-GVGD and SIFT software programs with our curated alignments and with PolyPhen2.”
SIFT: predicting amino acid changes that affect protein function (2003, methodology): “The SIFT algorithm and software have been described previously (2,3).”
The impact of next-generation sequencing on genomics (2011, methodology): “SIFT (Sorting Intolerant From Tolerant) is used to predict whether an amino acid substitution affects protein function based on sequence homology and the physical properties of the amino acid can be applied to find non-synonymous polymorphisms within NGS data (Ng and Henikoff, 2001,2002).”
SIFT missense predictions for genomes (2015, methodology): “The most significant hits are added first until the median sequence conservation is reached (the default is 2.75, as set by Ng and Henikoff 11 ).”
PMUT: a web-based tool for the annotation of pathological mutations on proteins (2005, background): “Others are centered on the study of non-synonymous mutations mapped on proteins; for discussions see (Chasman and Adams, 2001; Ferrer-Costa et al., 2002, 2004, 2005; Ng and Henikoff, 2002; Saunders and Baker, 2002; Sunyaev et al., 2001; Wang and Moult, 2001).”
Candidate-gene approaches for studying complex genetic traits: practical considerations (2002, methodology): “This approach has been used by some groups already, either by selecting only coding single-nucleotide polymorphisms (SNPs) to genotype, or by using more computational approaches to prioritizing SNPs on the basis of their predicted functional effect”
3DCoffee: combining protein sequences and structures within multiple sequence alignments. (2004, background): “The incentive for doing so is very strong, considering the critical role MSAs play in so many sequence analysis applications,(5) like phylogenetic reconstruction, structure prediction, functional characterization, database searches and nonsynonymous single nucleotide polymorphism characterization.(6”
SIFT web server: predicting effects of amino acid substitutions on proteins (2012, methodology): “SIFT was originally trained and tested on LacI, lysozyme and HIV protease substitutions (21,22).”
Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information (2006, background): “Finally SNPs can also be responsible of genetic diseases (Ng and Henikoff, 2002; Bell, 2004).”
The amino-acid mutational spectrum of human genetic disease (2003, background): “Several recent studies have focused on developing evolutionary and structural approaches to predict potentially deleterious human mutations [1-4,18,19].”
Improved exome prioritization of disease genes through cross-species phenotype comparison (2014, methodology): “Information concerning predicted pathogenicity from SIFT (Ng and Henikoff 2002), PolyPhen-2 (Adzhubei et al. 2010), and MutationTaster (Schwarz et al. 2010) were extracted from dbNSFP (Liu et al. 2011).”