Genotyping is often used to detect known variations in specific genes, or identify new ones that may be associated with a specific phenotype. While this can include copy number variations and insertions and deletions (indels), most of the time, single base pair changes, referred to as single nucleotide polymorphisms (SNPs), are of primary interest.
These single changes can result in changes in gene expression or function. While this sounds like a mutation, SNPs are present in at least 1% of the population. So instead of it being a mutation causing a disease, it is a genetic variant that either causes disease or increases the risk of disease in some way².
A variety of inherited diseases have been associated with specific SNPs, such as many cancers and non-inherited diseases. Additionally, SNPs can predict disease risk, drug response, and more. This information can be used to create personalized treatment plans and identify new causes or markers of disease. With over 3 billion base pairs in the human genome, identifying key SNPs is not always easy. Therefore, genotyping methods used to detect these key SNPs involved in various disease processes is an important area of research.
There are two main approaches to identifying SNPs: genomic and functional.
In the genomic approach, scientists will sequence the entire genome of various samples and compare them while looking for SNPs. This requires extensive collaborations, computer analyses, and resources. In the end, the findings are publicly available, which allows other researchers and scientists to the ability to reference them. The genomic approach is mainly used in identifying unknown SNPs that may be associated with specific diseases and allows for evaluation of many samples at once.
The common SNPs identified from these studies can then be used to create an array containing probes for about a million SNPs at a time. These arrays can be used in genome-wide association studies (GWAS) of patients with specific diseases to evaluate if specific SNPs have an association with that disease¹.
More commonly used is the functional approach. This is where scientists are interested in a specific gene or disease process and will look for SNPs specifically associated with it. They will compare the genetic sequences of patients with the disease to healthy individuals to look for the presence of new or key SNPs. Not only can this allow for personalized treatment options, but discovering new SNPs can provide insight into the genes and proteins involved in a disease process. This can open new avenues for treatment.
When a disease-associated SNP is known, real-time PCR (qPCR) can be used to detect specific SNPs in each sample. In traditional qPCR, DNA is amplified over multiple cycles, allowing for detection of specific DNA sequences. TaqMan polymerase makes use of a gene-specific probe that contains a fluorescent dye that is quenched unless it binds to the specific sequence it is designed for. Therefore, the amount of fluorescence detected is a direct correlation to the relative amount of that specific sequence in the sample.
Genotyping analysis for SNPs takes this a step further by using two different probes: one specific for the wild-type variant and the other specific for the known SNP. These probes allow for the detection of homozygous or heterozygous SNPs by evaluating the ratio of the two fluorescent probes used. If SNPs cannot be found, the fluorescence used on that probe will not be detected.
If the SNP is one allele (heterozygous), you will see a 1:1 ration of the two reporting dyes. If it is on both alleles (homozygous), you will see more of the second color which was specific for the SNP.
If there is the potential for a SNP but it is has not been identified yet, TaqMan can be used in a slightly different way. This method looks at the intensity of the same fluorescent probe used between samples.
When there is a SNP present, it will prevent Taq polymerase from binding to the site and activating the fluorescent probe as it would in the wild-type gene. In this case, you can look at the fluorescence intensity and compare it to a known wild-type sample to get an indication of the SNP presence. Even though this method is more useful, easier to do, and more economical than using a microarray to identify SNPs, this method does not yield information other than the presence of the SNP. Further studies will be needed.
The Azure Cielo is a qPCR system designed to provide high-quality data through advanced and high-performance optical technology.
With up to 6 high-powered channel-specific excitation and emission LEDs, Azure Cielo can detect up to 6 different fluorophores per well. This allows for the possibility to look for multiple SNPs per sample, allowing you to conserve sample, time, and money. In addition, the Cielo has increased sensitivity compared to other brands and is able to detect genetic material at very low amounts, making it well-suited for amplifying the cDNA or DNA of difficult targets, including low copy number variants in the genome.
Aaron K. Wong, R. S. (2021). Decoding disease: from genomes to networks to phenotypes. Nature Reviews Genetics, 774–790.
University of Utah Genetics Science Learning Center. (2022, March 10). Learn. Genetics. From https://learn.genetics.utah.edu/content/precision/snips
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