Introducing single nucleotide polymorphisms

About single nucleotide polymorphisms

Genotyping is the technology that detects small genetic differences that can lead to major changes in phenotype, including both physical differences that make us unique and pathological changes underlying disease. It has a vast range of uses across basic scientific research, medicine, and agriculture.

Genotyping determines differences in genetic complement by comparing a DNA sequence to that of another sample or a reference sequence. It identifies small variations in genetic sequence within populations, such as single-nucleotide polymorphisms (SNPs).

SNPs (often pronounced “snips”) are single base-pair changes in DNA that occur at specific places in the genome. For example, most individuals carry the C nucleotide at a specific base position in the genome, but in a minority of individuals this is replaced by an A. This means there is a SNP at this specific position with two possible nucleotide variations: C or A.

There are over 660 million SNPs in the human genome, which makes them the most common type of genetic variation in humans. They can explain traits such as eye color and inherited diseases such as cystic fibrosis and sickle cell anemia, as well as act as markers indicating a risk of developing complex common diseases like diabetes and Alzheimer's disease.

SNP genotyping can accelerate the era of personalized medicine by predicting an individual's risk of developing certain diseases or designing targeted therapies specific to the genetic basis of the disease. As SNPs are also associated with individual therapeutic response, SNP-based assays could help select the best course of treatment.


SNP detection

There are many methods of detecting novel and known SNPs. These include DNA sequencing, mass spectrometry, molecular beacons, SNP microarrays, and PCR-based methods. 

SNP detection can be broken down into two sub-groups: SNP discovery and SNP screening.  SNP discovery includes SNPs that are not yet known. Researchers are looking for new SNPs in targeted areas and on a genome-wide scale.  SNP screening pertains to known SNPs and researchers are typically looking to genotype individuals or determine if a particular SNP is involved in producing a certain characteristic.  There are many methods for performing both discovery and screening. For a historical account, see the paper by Kwok and Chen (1).


Why perform SNP genotyping?

SNP genotyping has many applications including:

Disease association

Genome-wide association studies (GWAS) can identify connections between SNPs and common disease risk by comparing the polymorphisms across two different populations (one healthy and one diseased).

In addition to risk stratification, GWAS can begin to unravel the biological processes underlying disease states by identifying potential causal factors (2,3,4,5). 

Population genomics

SNPs also have implications for evolutionary biology, so GWAS can be useful in identifying forms of genetic variation that underlie phenotypic differences between healthy individuals. Understanding this normal genetic variation across different populations helps us to understand how different groups have evolved and diverged, and may have implications for protecting species against future environmental challenges.

Trait selection in agriculture

Understanding genetic variation has a particular benefit in the agricultural world, where trait selection in plants and livestock has been used for centuries to increase yield and quality.

While traditional selective breeding involved purely observational methods (selecting only plants or animals with superior phenotypic traits, such as size or strength, for breeding), modern selective breeding relies heavily on molecular biology techniques, including SNP genotyping.

Selective breeding pressures have generated breeds with more desirable phenotypes and changes to specific genomic regions associated with these phenotypes.
Detecting these functionally relevant genetic changes helps us to understand which particular genes and sequences are associated with particular phenotypic traits. This is useful for designing new and more intelligent breeding programs.


Single-celled organisms, such as bacteria, also have SNPs. SNP genotyping can discriminate between bacterial isolates and can also be used to characterize strains of antibiotic resistance (6,7). SNP-based strain detection is relevant in both clinical and agricultural research and has been used to study a range of infectious diseases in both humans and plants


Using real-time PCR to perform SNP genotyping

Real-time PCR enables you to screen known SNPs.  The benefits of real-time PCR are that it is easy, accurate, and can scale to high throughput. Another advantage of real-time PCR is that the bioinformatic analysis is less complex than for other technologies, such as sequencing and microarrays.

You can use TaqMan 5'-nuclease chemistry to determine whether a given SNP is present in your sample. TaqMan SNP genotyping assays include both a primer pair to amplify the target area and two allele-specific probes to detect your target SNP alleles and report the genotypes of your samples.

There are millions of predesigned TaqMan SNP genotyping assays available for SNP genotyping in both human and mouse subjects. You can also design custom TaqMan SNP genotyping assays, including assays for other species, using the online Custom Assay Design Tool.

TaqMan SNP genotyping assays can be applied not only to animal species, but also to plant species that carry at least two paired sets of chromosomes. The 30–80% of plant species that are polyploid can also be genotyped using TaqMan Assays. Each assay aligns uniquely with the genome to specifically detect either allele in the sequence of interest. They can be used on a range of real time PCR instrument types, providing unparalleled versatility.




  1. Kwok P-Y, Chen X (2003) Detection of single nucleotide polymorphisms. Curr Issues Mol Biol 5:43–60.
  2. Visscher PM, Wray NR, Zhang Q, et al. (2017) 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. Jul 6;101(1):5-22.
  3. Gaj P, Maryan N, Hennig EE et al. (2012) Pooled sample-based GWAS: A cost-effective alternative for identifying colorectal and prostate cancer risk variants in the Polish population. PLOS One 7(4):e35307.
  4. Figueroa JD, Ye Y, Siddiq A et al. (2014) Genome-wide association study identifies multiple loci associated with bladder cancer risk. Hum Mol Gen 23(5):1387–1398.
  5. Reddy MPL, Wang H, Liu S et al. (2011) Association between type 1 diabetes and GWAS SNPs in the southeast US Caucasian population. Genes Immun 12(3):208–212.
  6. Sengstake S, Bablishvili N, Schuitema A et al. (2014) Optimizing multiplex SNP-based data analysis for genotyping of Mycobacterium tuberculosis isolates, BMC Genomics, 15(1):572.
  7. Rathnayake I, Hargreaves M, Huygens F. (2011) SNP diversity of Enterococcus faecalis and Enterococcus faecium in a South East Queensland waterway, Australia, and associated antibiotic resistance gene profiles, BMC Microbiol, 11(1):201.

For Research Use Only. Not for use in diagnostic procedures.