Focus: SNP Genotyping and the READIT™ System

"SNP"ing Away the Mysteries of the Human Genome: The READIT™ Assay

By Kelly Grooms, B.S.
Promega Corporation

What's in a SNP?

Humans are 99.9% genetically identical (1). The most common type of genetic variability found in humans occurs in the form of Single Nucleotide Polymorphisms (SNPs). These SNPs (pronounced "snips") occur when there are two or more possible nucleotides at a specific mapped location in the genome; they occur in the human genome with an estimated frequency of 1 for every 1,200–1,500bp (1). At the time of its publication, scientists had identified over a million SNPs using the rough draft of the human genome (1,2). The identification of these sources of genetic variability holds great promise for the treatment, prevention and understanding of disease.

Spawned by the completion of the first phase of the Human Genome Project, the field of pharmacogenomics has emerged to identify SNPs that will aid in understanding how genetic differences influence an individual's susceptibility to disease and response to therapeutics. Groups such as the SNP Consortium, a non-profit foundation supported by the Welcome Trust and eleven pharmaceutical and technology companies, are working to create databases filled with single base changes.

Not all SNPs cause disease; they can also help determine how a person responds to therapeutic treatments or act as a marker for populations at risk for developing a disease. Examples of the variety of consequences of SNPs include the missense mutation (A®U) that causes Sickle Cell Anemia, the APOE*E4 allele implicated in susceptibility to late-onset Alzheimer's disease (3), the Factor V 1691G®A allele (FV Leiden) involved in hereditary deep-vein thrombosis (4) and several forms of the cytochrome P450 (CYP) gene that affect drug metabolism (5,6).

With the potential number of SNPs stretching into the millions, researchers face the daunting task of finding the ones that are informative. One way to do this is to screen a piece of DNA for sequence changes. Screening technologies identify sequence changes by comparing the results from an experimental sample to those of a sample with a known sequence. Screening samples for sequence changes is more time efficient than sequencing and helps to narrow the field to those samples with the most promise of reveling an interesting SNP.

Can't See the Function for the SNPs?

Identifying a polymorphic sequence doesn't necessarily mean the discovery of a genetic variation that has a clinically important effect. There are many reasons for this. We may have sequenced the human genome, but we are not anywhere close to knowing what most of that sequence does. Simply put, knowing a sequence doesn't identify its function. In fact, many of the identified SNPs will have no characterized phenotype. Some occur in the untranslated, or noncoding, regions of the genome that do not produce proteins. Others will be synonymous, or silent, changes that do not change the original amino acid sequence and, thus, don't change the protein expressed. Detailed analysis of the SNP content of almost 300 genes illustrates the challenges facing researchers. Generally, changes in noncoding sequences and silent changes in coding sequences are more common. As an added twist, the frequency of polymorphism varies greatly between genes (7).

A useful SNP might have any number of phenotypic results. However, in addition to producing discernable changes, SNPs can also be used as landmarks on a genetic road map created to point scientists to an important gene. We know that a SNP located close to the sequence that codes for a gene will most likely be inherited along with that gene. Using this knowledge, researchers can compare an individual's "map" of known SNPs to a map of the same SNPs from a control group. If the pattern from the affected person varies, those differences could point to a genetic cause. Estimates of the number of SNPs required to create a useful map have ranged from 100,000 (one SNP per every 30kb of DNA) to 1 million (one SNP per every 3kb or less). In general, the more SNPs on the map the better, and the ideal number is probably between 600,000 and 1 million. However, the number of SNPs on the map must be balanced against the cost of identifying them, which is hovering around $100 per SNP (8). Costs should drop with the completion of the Human Genome Sequence and useful maps can still be made using a smaller number of markers. For example, maps with SNPs spaced 30kb apart have been successfully used to identify genes involved in diseases such as psoriasis, migraine, Alzheimer's and diabetes (8).

The Genetic Road Map: Stopping to Read the SNPs Along the Way

Once a useful SNP, whether it is a marker or a coding sequence change, has been identified, the next hurdle is identifying which samples contain that SNP. The relatively new world of  SNP genotyping is becoming increasingly more competitive. A useful detection method must meet several important criteria: i) accuracy—new methods must be validated against existing "gold standard" methods, and an accuracy of >99% is essential; ii) reliability—repeating tests because of assay failure increases costs, delays results and wastes precious samples; iii) convenience—minimizing complexity and hands-on time reduces costs and decreases the possibility of operator errors; and iv) affordability—reagent costs, technician time, equipment requirements and royalty payments all contribute to the overall cost of a method (9). The READIT™ SNP Genotyping System^(a,b) now available from Promega is a versatile and cost-effective assay that offers a new approach to genotyping and SNP scoring (9,10).

The READIT™ Assay is a three-step system that interrogates samples for a specific sequence and produces a light signal if the target sequence is present. The assay uses hybridization specificity and a coupled reaction using two thermostable enzymes, READase™ Polymerase and READase™ Kinase, to generate high-energy adenosine triphosphate (ATP). When luciferase is added, it uses the ATP to produce light detectable with a luminometer. The procedure is compatible with several automated systems and is simple to perform. The companion READIT™ Calculator software (Cat.# MD1240) processes the results downloaded from the luminometer and assigns a genotype to each sample. The software can perform statistical analysis on a defined group of samples and contains error-checking algorithms to identify problematic samples.

To detect a SNP, the assay requires two unmodified DNA probes (interrogation probes) that differ at the 3´ end, each forming perfect hybrids to one of the two possible sequences. If the probe matches the target sequence, the READase™ polymerase catalyzes depolymerization, or pyrophosphorylation, of the DNA target, shortening the 3´ end of the target and releasing a high-energy deoxynucleotide triphosphate (dNTP). In a coupled reaction, READase™ Kinase transfers the terminal phosphate from the liberated dNTPs to adenosine diphosphate, producing ATP. ATP catalyzes a reaction with luciferase to produce light, and the light is used to monitor ATP production (Figure 1). The light signal produced is proportional to the ATP present in the reaction. Aside from SNPs, the READIT™ Assay can also analyze samples for sequence variations such as deletions, insertions and chromosomal translocations. For more detailed information about the READIT™ Assay, refer to the READIT™ SNP Genotyping System Technical Manual (#TM053) (11).

In a study analyzing over 500 DNA samples, perfect concordance was observed between the READIT™ Assay and the genotyping assignments made in independent testing laboratories (10). A larger study of over 2,000 samples confirmed the accuracy of the READIT™ System (unpublished data). These studies highlight the flexibility of the READIT™ System. Figure 2 shows sample results from the READIT™ Assay. Existing standard PCR parameters can be used to generate the template DNA, and interrogation probes can be designed for any sequence. This allows researchers to develop their own assays. Additional information about designing a READIT™ Assay can be found in the READIT™ SNP Genotyping System Technical Manual (#TM053) as well as online at www.promega.com/readit.

The READIT™ SNP Genotyping System is designed as a cost-effective solution for the SNP scoring and genotyping market. In addition to SNP detection, the READIT™ System can be used in many other applications including detecting insertions, deletions and translocations, detecting pathogens, and determining zygosity in amplified samples. Identifying genetic variability is important in many areas of biology including plant and animal breeding. As the genomes of more organisms are sequenced (e.g., Arabidopsis thaliana [12]) the demand for versatile genetic analysis methods such as the READIT™ Assay will increase.

Conclusions

As the scientific and medical communities begin to decipher the secrets locked in the human genetic code, the way we approach the diagnosis, treatment and prevention of disease will change dramatically. With only a fraction of the genome decoded, scientist have already created large databases filled with thousands of single nucleotide sequence changes. Some of these SNPs could define the genetic basis of what keeps us healthy or makes us sick. The development of detection technologies such as the READIT™ Assay will benefit researchers by allowing them to develop their own assays while maintaining a high level of accuracy, and, thus, help realize the full potential of the Human Genome Project and the upcoming SNP revolution.

References

1. Venter, J.C. et al. (2001) The sequence of the human genome. Science 291, 1304–51.
2. Lander, E.S. et al. (2001) Initial sequencing and analysis of the human genome. Nature 409, 860–921.
3. Saunders, A.M. et al. (1993) Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer's disease. NeuroBiol. 43, 1467–1472.
4. Corder, E.H. et al. (1994) Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat. Genet. 7, 180–184.
5. van der Weide, J. and Steijn, L.S. (1999) Cytochrome P450 enzyme system: Genetic polymorphisms and impact on clinical pharmacology. Ann. Clin. Biochem. 36, 722–729.
6. Tanaka, E. (1999) Update: Genetic polymorphism of drug metabolizing enzymes in humans. J. Clin. Pharm. Ther. 24, 323–329.
7. Gray, I.C., Campbell, D.A. and Spurr, N.K. (2000) Single nucleotide polymorphisms as tools in human genetics. Human Mol. Gen. 9, 2403–2410.
8. Roberts, L. (2000) Human genome research. SNP mappers confront reality and find it daunting. Science 287, 1898–1899
9. Ryan, A. et al. (2000) Technologies for mutation detection. IVD Tech. July/August.
10. Rhodes, R.B. et al. (2001) Analysis of the Factor V Leiden mutation using the READIT™ assay. Mol. Diagnosis 6, 55–61.
11. The READIT™ SNP Genotyping System Technical Manual #TM053, Promega Corporation.
12. The Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796–811.

^(a)U.S. Pat. No. 6,159,693 has been issued to Promega Corporation for nucleic acid detection using depolymerization. Other patents pending.

^(b)For Research Use Only. Not for use in diagnostic procedures.

READase and READIT are trademarks of Promega Corporation.

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