Single-nucleotide polymorphism


A single-nucleotide polymorphism is a substitution of a single nucleotide at a specific position in the genome, that is present in a sufficiently large fraction of the population.
For example, at a specific base position in the human genome, the C nucleotide may appear in most individuals, but in a minority of individuals, the position is occupied by an A. This means that there is a SNP at this specific position, and the two possible nucleotide variations – C or A – are said to be the alleles for this specific position.
SNPs pinpoint differences in our susceptibility to a wide range of diseases. The severity of illness and the way the body responds to treatments are also manifestations of genetic variations. For example, a single-base mutation in the APOE gene is associated with a lower risk for Alzheimer's disease.
A single-nucleotide variant is a variation in a single nucleotide without any limitations of frequency and may arise in somatic cells. A somatic single-nucleotide variation may also be called a single-nucleotide alteration.

Types

Single-nucleotide polymorphisms may fall within coding sequences of genes, non-coding regions of genes, or in the intergenic regions. 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 noncoding RNA. Gene expression affected by this type of SNP is referred to as an eSNP and may be upstream or downstream from the gene.

Applications

More than 335 million SNPs have been found across humans from multiple populations. A typical genome differs from the reference human genome at 4 to 5 million sites, most of which consist of SNPs and short indels.

Within a genome

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 of the SNP that constitutes the most favorable genetic adaptation. Other factors, like genetic recombination and mutation rate, can also determine SNP density.
SNP density can be predicted by the presence of microsatellites: AT microsatellites in particular are potent predictors of SNP density, with long repeat tracts tending to be found in regions of significantly reduced SNP density and low GC content.

Within a population

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. 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.

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. Examples include biomedical research, forensics, pharmacogenetics, and disease causation, as outlined below.

Clinical research

SNPs' greatest importance in clinical research is for comparing regions of the genome between cohorts in genome-wide association studies. SNPs have been used in genome-wide association studies as high-resolution markers in gene mapping related to diseases or normal traits. SNPs without an observable impact on the phenotype are still useful as genetic markers in genome-wide association studies, because of their quantity and the stable inheritance over generations.

Forensics

SNPs were used initially for matching a forensic DNA sample to a suspect but it has been phased out with development of STR-based DNA fingerprinting techniques. Current next-generation-sequencing techniques may allow for better use of SNP genotyping in a forensic application so long as problematic loci are avoided. In the future SNPs may be used in forensics for some phenotypic clues like eye color, hair color, ethnicity, etc. Kidd et al. have demonstrated that a panel of 19 SNPs can identify the ethnic group with good probability of match in 40 population groups studied. One example of how this might potentially be useful is in the area of artistic reconstruction of possible premortem appearances of skeletal remains of unknown individuals. Although a facial reconstruction can be fairly accurate based strictly upon anthropological features, other data that might allow a more accurate representation include eye color, skin color, hair color, etc.
In a situation with a low amount of forensic sample or a degraded sample, SNP methods can be a good alternative to STR methods due to the abundance of potential markers, amenability to automation, and potential reduction of required fragment length to only 60–80 bp. In the absence of a STR match in DNA profile database; different SNPs can be used to get clues regarding ethnicity, phenotype, lineage, and even identity.

Pharmacogenetics

Some SNPs are associated with the metabolism of different drugs. SNP's can be mutations, such as deletions, which can inhibit or promote enzymatic activity; such change in enzymatic activity can lead to decreased rates of drug metabolism. The association of a wide range of human diseases like cancer, infectious diseases autoimmune, neuropsychiatric and many other diseases with different SNPs can be made as relevant pharmacogenomic targets for drug therapy.

Disease

A single SNP may cause a Mendelian disease, though 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. One of the earliest successes in this field was finding a single base mutation in the non-coding region of the APOC3 that associated with higher risks of hypertriglyceridemia and atherosclerosis.
All types of SNPs can have an observable phenotype or can result in disease:
As there are for genes, bioinformatics databases exist for SNPs.
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. This list has greatly increased since, with, for instance, the Kaviar database now listing 162 million single nucleotide variants.
ChromosomeLengthAll SNPsTSC SNPs
Total SNPskb per SNPTotal SNPskb per SNP
1214,066,000129,9311.6575,1662.85
2222,889,000103,6642.1576,9852.90
3186,938,00093,1402.0163,6692.94
4169,035,00084,4262.0065,7192.57
5170,954,000117,8821.4563,5452.69
6165,022,00096,3171.7153,7973.07
7149,414,00071,7522.0842,3273.53
8125,148,00057,8342.1642,6532.93
9107,440,00062,0131.7343,0202.50
10127,894,00061,2982.0942,4663.01
11129,193,00084,6631.5347,6212.71
12125,198,00059,2452.1138,1363.28
1393,711,00053,0931.7735,7452.62
1489,344,00044,1122.0329,7463.00
1573,467,00037,8141.9426,5242.77
1674,037,00038,7351.9123,3283.17
1773,367,00034,6212.1219,3963.78
1873,078,00045,1351.6227,0282.70
1956,044,00025,6762.1811,1855.01
2063,317,00029,4782.1517,0513.71
2133,824,00020,9161.629,1033.72
2233,786,00028,4101.1911,0563.06
X131,245,00034,8423.7720,4006.43
Y21,753,0004,1935.191,78412.19
RefSeq15,696,67414,5341.08--
Totals2,710,164,0001,419,1901.91887,4503.05

Nomenclature

The nomenclature for SNPs can be confusing: several variations can exist for an individual SNP, and consensus has not yet been achieved.
The rs### standard is that which has been adopted by dbSNP and uses the prefix "rs", for "reference SNP", followed by a unique and arbitrary number. SNPs are frequently referred to by their dbSNP rs number, as in the examples above.
The Human Genome Variation Society uses a standard which conveys more information about the SNP. Examples are:
SNPs are usually biallelic and thus easily assayed. Analytical methods to discover novel SNPs and detect known SNPs include:
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. Usually, change in amino acids with similar size and physico-chemical properties has mild effect, and opposite. Similarly, if SNP disrupts secondary structure elements 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:
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