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Single-nucleotide polymorphism

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Title: Single-nucleotide polymorphism  
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Subject: Molecular Inversion Probe, Haplogroup R1a, Snpstr, Tag SNP, Variations and drugs database
Collection: Biotechnology, Dna, Genetic Genealogy, Molecular Biology, Mutation, Population Genetics, Single-Nucleotide Polymorphisms
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Single-nucleotide polymorphism

DNA molecule 1 differs from DNA molecule 2 at a single base-pair location (a C/A polymorphism).

A single nucleotide polymorphism, also known as simple nucleotide polymorphism, (SNP, pronounced snip; plural snips) is a DNA sequence variation occurring commonly within a population (e.g. 1%) in which a single nucleotideA, T, C or G — in the genome (or other shared sequence) differs between members of a biological species or paired chromosomes. For example, two sequenced DNA fragments from different individuals, AAGCCTA to AAGCTTA, contain a difference in a single nucleotide. In this case we say that there are two alleles. Almost all common SNPs have only two alleles. The genomic distribution of SNPs is not homogenous; SNPs occur in non-coding regions more frequently than in coding regions or, in general, where natural selection is acting and 'fixing' the allele (eliminating other variants) of the SNP that constitutes the most favorable genetic adaptation.[1] Other factors, like genetic recombination and mutation rate, can also determine SNP density.[2]

SNP density can be predicted by the presence of microsatellites: AT microsatellites in particular are potent predictors of SNP density, with long (AT)(n) repeat tracts tending to be found in regions of significantly reduced SNP density and low GC content.[3]

Within a population, SNPs can be assigned a minor allele frequency — the lowest allele frequency at a locus that is observed in a particular population. This is simply the lesser of the two allele frequencies for single-nucleotide polymorphisms. There are variations between human populations, so a SNP allele that is common in one geographical or ethnic group may be much rarer in another.

These genetic variations between individuals (particularly in non-coding parts of the genome) are sometimes exploited in DNA fingerprinting, which is used in forensic science. Also, these genetic variations underlie differences in our susceptibility to disease. The severity of illness and the way our body responds to treatments are also manifestations of genetic variations. For example, a single base mutation in the APOE (apolipoprotein E) gene is associated with a higher risk for Alzheimer disease.[4]

Contents

  • Types 1
  • Importance 2
  • Examples 3
  • Databases 4
  • Nomenclature 5
  • SNP analysis 6
  • Programs for prediction of SNP effects 7
  • See also 8
  • Notes 9
  • References 10
  • External links 11

Types

Types of SNPs

Single-nucleotide polymorphisms may fall within coding sequences of genes, non-coding regions of genes, or in the intergenic regions (regions between genes). SNPs within a coding sequence do not necessarily change the amino acid sequence of the protein that is produced, due to degeneracy of the genetic code.

SNPs in the coding region are of two types, synonymous and nonsynonymous SNPs. Synonymous SNPs do not affect the protein sequence while nonsynonymous SNPs change the amino acid sequence of protein. The nonsynonymous SNPs are of two types: missense and nonsense.

SNPs that are not in protein-coding regions may still affect gene splicing, transcription factor binding, messenger RNA degradation, or the sequence of non-coding RNA. Gene expression affected by this type of SNP is referred to as an eSNP (expression SNP) and may be upstream or downstream from the gene.

Importance

Variations in the DNA sequences of humans can affect how humans develop diseases and respond to pathogens, chemicals, drugs, vaccines, and other agents. SNPs are also critical for personalized medicine.[5] However, their greatest importance in biomedical research is for comparing regions of the genome between cohorts (such as with matched cohorts with and without a disease) in genome-wide association studies.

The study of SNPs is also important in crop and livestock breeding programs. See SNP genotyping for details on the various methods used to identify SNPs.

SNPs are usually biallelic and thus easily assayed.[6] A single SNP may cause a Mendelian disease. For complex diseases, SNPs do not usually function individually, rather, they work in coordination with other SNPs to manifest a disease condition as has been seen in Osteoporosis.[7]

As of 8 June 2015, dbSNP listed 149,735,377 SNPs in humans.[8][9]

SNPs have been used in genome-wide association studies (GWAS), e.g. as high-resolution markers in gene mapping related to diseases or normal traits. The knowledge of SNPs will help in understanding pharmacokinetics (PK) or pharmacodynamics, i.e. how drugs act in individuals with different genetic variants. A wide range of human diseases, e.g. sickle-cell anemia, β-thalassemia and cystic fibrosis result from SNPs.[10][11][12] Diseases with different SNPs may become relevant pharmacogenomic targets for drug therapy.[13] Some SNPs are associated with the metabolism of different drugs.[14][15][16]

SNPs without an observable impact on the phenotype (so called silent mutations) are still useful as genetic markers in genome-wide association studies, because of their quantity and the stable inheritance over generations.[17]

On the other site, all types of SNPs can have observable phenotype or can result in disease:

Examples

Databases

As there are for genes, bioinformatics databases exist for SNPs. dbSNP is a SNP database from the National Center for Biotechnology Information (NCBI). Kaviar[27] is a compendium of SNPs from multiple data sources including dbSNP. SNPedia is a wiki-style database supporting personal genome annotation, interpretation and analysis.

The OMIM database describes the association between polymorphisms and diseases (e.g., gives diseases in text form), the Human Gene Mutation Database provides gene mutations causing or associated with human inherited diseases and functional SNPs, and GWAS Central allows users to visually interrogate the actual summary-level association data in one or more genome-wide association studies. The International SNP Map working group mapped the sequence flanking each SNP by alignment to the genomic sequence of large-insert clones in Genebank. These alignments were converted to chromosomal coordinates that is shown in Table 1.[28] Another database is the International HapMap Project, where researchers are identifying Tag SNP to be able to determine the collection of haplotypes present in each subject.
Chromosome Length(bp) All SNPs TSC SNPs
Total SNPs kb per SNP Total SNPs kb per SNP
1 214,066,000 129,931 1.65 75,166 2.85
2 222,889,000 103,664 2.15 76,985 2.90
3 186,938,000 93,140 2.01 63,669 2.94
4 169,035,000 84,426 2.00 65,719 2.57
5 170,954,000 117,882 1.45 63,545 2.69
6 165,022,000 96,317 1.71 53,797 3.07
7 149,414,000 71,752 2.08 42,327 3.53
8 125,148,000 57,834 2.16 42,653 2.93
9 107,440,000 62,013 1.73 43,020 2.50
10 127,894,000 61,298 2.09 42,466 3.01
11 129,193,000 84,663 1.53 47,621 2.71
12 125,198,000 59,245 2.11 38,136 3.28
13 93,711,000 53,093 1.77 35,745 2.62
14 89,344,000 44,112 2.03 29,746 3.00
15 73,467,000 37,814 1.94 26,524 2.77
16 74,037,000 38,735 1.91 23,328 3.17
17 73,367,000 34,621 2.12 19,396 3.78
18 73,078,000 45,135 1.62 27,028 2.70
19 56,044,000 25,676 2.18 11,185 5.01
20 63,317,000 29,478 2.15 17,051 3.71
21 33,824,000 20,916 1.62 9,103 3.72
22 33,786,000 28,410 1.19 11,056 3.06
X 131,245,000 34,842 3.77 20,400 6.43
Y 21,753,000 4,193 5.19 1,784 12.19
RefSeq 15,696,674 14,534 1.08
Totals 2,710,164,000 1,419,190 1.91 887,450 3.05

Nomenclature

The nomenclature for SNPs can be confusing: several variations can exist for an individual SNP and consensus has not yet been achieved. One approach is to write SNPs with a prefix, period and "greater than" sign showing the wild-type and altered nucleotide or amino acid; for example, c.76A>T.[29][30][31] SNPs are frequently referred to by their dbSNP rs number, as in the examples above.

SNP analysis

Analytical methods to discover novel SNPs and detect known SNPs include:

Programs for prediction of SNP effects

An important group of SNPs are those that corresponds to missense mutations causing amino acid change on protein level. Point mutation of particular residue can have different effect on protein function (from no effect to complete disruption its function). Usually, change in amino acids with similar size and physico-chemical properties (e.g. substitution from leucine to valine) has mild effect, and opposite. Similarly, if SNP disrupts secondary structure elements (e.g. substitution to proline in alpha helix region) such mutation usually may affect whole protein structure and function. Using those simple and many other machine learning derived rules a group of programs for the prediction of SNP effect was developed:

  • SIFT
  • SNAP2
  • SuSPect
  • PolyPhen-2
  • PredictSNP
  • MutationTaster

See also

Notes

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  8. ^ National Center for Biotechnology Information, United States National Library of Medicine. 2014. NCBI dbSNP build 142 for human. http://www.ncbi.nlm.nih.gov/mailman/pipermail/dbsnp-announce/2014q4/000147.html
  9. ^ National Center for Biotechnology Information, United States National Library of Medicine. 2015. NCBI dbSNP build 144 for human. Summary Page. http://www.ncbi.nlm.nih.gov/projects/SNP/snp_summary.cgi?view+summary=view+summary&build_id=144
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References

  • Nature Reviews Glossary
  • Human Genome Project Information — SNP Fact Sheet
  • Relation of SNP's with Cancer

External links

  • NCBI resources — Introduction to SNPs from NCBI
  • The SNP Consortium LTD — SNP search
  • NCBI dbSNP database — "a central repository for both single base nucleotide substitutions and short deletion and insertion polymorphisms"
  • HGMD — the Human Gene Mutation Database, includes rare mutations and functional SNPs
  • SNPedia - a wiki devoted to the medical consequences of DNA variations, including software to analyze personal genomes
  • International HapMap Project — "a public resource that will help researchers find genes associated with human disease and response to pharmaceuticals"
  • GWAS Central — a central database of summary-level genetic association findings
  • 1000 Genomes Project — A Deep Catalog of Human Genetic Variation
  • WatCut — an online tool for the design of SNP-RFLP assays
  • SNPStats — SNPStats, a web tool for analysis of genetic association studies
  • Restriction HomePage — a set of tools for DNA restriction and SNP detection, including design of mutagenic primers
  • American Association for Cancer Research Cancer Concepts Factsheet on SNPs
  • PharmGKB — The Pharmacogenetics and Pharmacogenomics Knowledge Base, a resource for SNPs associated with drug response and disease outcomes.
  • GEN-SNiP — Online tool that identifies polymorphisms in test DNA sequences.
  • Rules for Nomenclature of Genes, Genetic Markers, Alleles, and Mutations in Mouse and Rat
  • HGNC Guidelines for Human Gene Nomenclature
  • SNP effect predictor with galaxy integration
  • Human Gene Mutation Database
  • GWAS Central
  • Open SNP — a portal for sharing own SNP test results
  • The HapMap Project
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