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1、Executive Summary The study of single nucleotide polymorphisms (SNPs) appears to have reached a tipping point in recent years, in terms of the product performance, the revenue growth, and the scientific acceptance across numerous applications. The prices and the amount of content provided in product
2、s have continued to improve, with the cost per genotype dropping on the order of ten-fold every two years since 2000. The analysis of SNPs has become an appealing method for several reasons -they are numerous, they account for 80% to 90% of genetic variation, they are stable, and they are easy to sc
3、ore. As the scientific and ethical issues become sorted out, this is expected to become an important tool. One recent indication that these technologies will soon be more mainstream is the passing in the senate of the Genetic Information Nondiscrimination Act (GINA). WHAT IS A SNP? A SNP is a single
4、 base pair mutation that occurs at a specific site in the DNA sequence. There are usually two variations, or alleles, with one occurring only in between 1% to 5% of the population. On average, two humans DNA sequences differ in more than three million positions. In some cases, this variation results
5、 in the one minor allele causing or predisposing the individual to have a certain disease or trait. SNPs are the most common type of genetic variation. 2The fields relating to genomics and post-genomics have been experiencing major advancements in the last decade. With the completion of the human ge
6、nome, there was now a reference to use to look at the genetic variation between individuals. While it was known that a relatively small percentage, 0.1%, of any two humans DNA sequence was all that differed, this still amounts to millions of bases. The next step was to produce large amounts of data
7、concerning the frequencies of the variations in different populations. This process is still occurring to this day, but the endeavor has already succeeding in depositing over ten million useful SNPs in the public databases. SNPs are responsible for over 80% of the variation between two individuals i
8、deal for the task of hunting for correlations between genotype and phenotype. SNPs are useful for several reasons. One of the key factors is that SNPs they are responsible for over 80% of the variation between two individuals. Another is that they are fairly stable through the generations. Furthermo
9、re, the remaining 20% of variation is comprised of a range of variations that are rare in the population. The density of SNPs in the human genome (between two people) is approximately one per every 1000 base pairs; thus, they are present throughout most genes and other important sequences. It is est
10、imated that out of the whole population, one out of 300 bases differs such that the minor allele occurs in over 1% of the people. This abundance of SNPs makes them ideal for the task of hunting for correlations between genotype and phenotype, the observable differences in behavior or appearance betw
11、een organisms. The analysis of SNPs is thus useful in the life sciences for many application areas, including pharmacogenomics / pharmacogenetics, linkage analysis, genetic association studies, and identity testing. APPLICATIONS The use of SNP analysis has spread into numerous areas in recent years.
12、 In some ways, it has been adopted in most of the same places as sequencing and PCR. However, there is a slightly different focus in this market, probably due to the higher costs typically involved, as well as the different advantages. The emphasis of most research has been on finding near-term uses
13、 for SNPs in diagnostics, biotechnology and drug development, as opposed to pursuing basic research questions. Nonetheless, SNP analysis is used across the whole of life sciences, including the following applications: agriculture, breeding consumer genetics disease research food testing genetic anal
14、ysis identity testing molecular diagnostics pathogen identification pharmaceutical drug discovery and development pharmacogenomics THE MARKET High growth will be driven largely in the near term by rapid adoption of whole-genome SNP analysis products. Longer term, lower multiplexing products will pic
15、k up the growth. Demand will be strong throughout the forecast period, due to the many applications with proven value in life sciences. Longer term, there are likely to be new products introduced which maintain the momentum. Beyond the forecast period, diagnostic applications are likely to grow strongly. Figure 1-1 details the SNP Analysis Market, including instruments, software and consumables. SNP Analysis revenues are expected to exceed $625 million in 2007 and grow to over 2.2 billion by 2012.
