SNP genotyping is mainly being carried out in core lab-type environments. As with similar applications such as gene-expression analysis, this has resulted from a number of factors, typically including the requirements for expensive equipment, experienced personnel, and significant data-management and analysis capabilities. As a result, labs are generally expecting to use SNP analysis in multiple experiments over long periods of time.
Expert staff are important due to their ability to most effectively run the instruments and also assist with the significant challenge of data analysis. Regardless of the data-analysis issues, there are large amounts of data produced, and this creates requirements for continuous database management. Although the instrument manufacturers generally provide software for this purpose, core labs often create their own systems in order to provide enhanced analytical and data-management capabilities.
Technology platform companies have been increasing the multiplexing and density of probes, features, and labels. This has occurred with several different technologies such as PCR, microarrays, and microfluidic chips. This will remain an important driver of the market’s success and growth into various end-user segments. It makes the experiments more affordable, efficient, and flexible and expands laboratories’ capabilities using existing equipment.
As the number of samples multiplexed increases, the products will become more appealing in diagnostics, pharmacogenetics, and other applications where large numbers of patients need to be screened. It will also enable more basic research, where the SNP-validation phase requires looking at higher numbers of samples.
Going in the other direction, as the number of SNPs analyzed increases, it increases the power of the information produced by the research experiments. It appears that some whole-genome SNP products are reaching the number of SNPs where the end-users’ needs will be mostly satisfied; the multiplexing of samples, however, is still a significant area of opportunity for all of the suppliers.
Fragmentation has existed in the market for several years, relating to the number of SNPs multiplexed by the different products. To a large extent, this is still occurring; new types of products are being introduced continuously, offering varying levels of parallelization. Different experiments will continue to have varying needs. In recent years, the prices for SNP analysis technologies have dropped significantly, creating an overall trend toward whole-genome analysis products. Furthermore, the higher number of SNPs offered on a single chip has given the whole-genome SNP products significantly more power than previous generations.
This has also occurred because the price for the SNP analysis step has decreased enough relative to the previous steps in a typical experiment, making it a lost opportunity to look at fewer SNPs than is possible. In many research applications, more data is preferred over less data, all things being equal. Nonetheless, it is expected that a certain proportion of the market will continue to use focused SNP analysis tools, and this may increase to become the majority in the longer term. This shift would be expected to occur as many of the SNPs become validated and move into routine testing.
Despite the promising growth scenario, there are some challenges ahead for SNP-analysis technology. One of the obstacles is the healthcare professionals’ aversion toward risks, changes, and new approaches. Applications will need to be rigorously proven before tests achieve widespread acceptance. Another obstacle is the established paradigm of running one test at a time on a sample. At hospitals where large, centralized testing labs are geared toward simple, cheap, standardized tests, these novel and relatively expensive technologies will take time to penetrate.
Uniformity of SNP-analysis methods presents a challenge. There is still some uncertainty regarding how to design experiments using SNPs as well as how to interpret the data coming from such experiments; this is especially true for complex diseases involving multiple genes such as cancer and diabetes.
Improper sample handling and preperation can affect the reputation of any technology including SNP-genotyping tools when poor data results from these lab errors. The use of multiplexed or large-scale SNP genotyping technologies typically involves small amounts of samples and reagents, making them particularly vulnerable to contamination and other variations in handling. DNA samples are usually amplified in some way prior to analysis, and this step can further multiply any error introduced in previous processes.
Finally, privacy is a concern with any genetic-testing technology. There is still some question about how the public will react once this type of technology reaches a tipping point in terms of mainstream healthcare usage. The SNP market’s growth could be slowed considerably if the public’s fear is encouraged by the media or some type of event. The confidentiality of DNA sequence data is likely to become an issue, due to concerns regarding how insurance companies, employers, the government, or others might use such information. The recent signing of the Genetic GINA should do some to allay these fears.