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[[Single nucleotide polymorphism]] (SNP), a variation at a single site in DNA, is the most frequent variation in genome, about 5-10 million SNPs in human genome. As SNPs are highly conserved throughout evolution and within population, the map of SNPs serves as an excellent genotypic marker for research. SNP array is a type of [[microarray]] which is used to detect [[polymorphisms]] within a population. Examples of SNP arrays are the Affymetrix GeneChip Mapping array series 10K, 100K and 500K.
The basic principles of SNP-array is the same as microarray which is the convergence of DNA hybridization, fluorescence microscopy and solid surface DNA capture. The three mandatory components of the SNP arrays are (i) the array that contains immobilized nucleic acid sequences or target; (ii) one or more labeled probes; (iii) a detection system that records and interprets the hybridization signal. In order to achieve relative concentration independence and minimal cross-hybridization, raw sequences and SNPs of multiple databases are scanned to design the probes. Each SNP on the array is interrogated with different probes, approximately 40 in the case of Affymetrix GeneChip Mapping array. Depends on the purpose of experiments, the amount of SNPs present on an array is considered. For instance, Affymetrix GeneChip 10K microarray has a coverage of 10,000 of SNPs.
SNP array is a useful tool to study the whole genome. The most important application of SNP array is in determining disease susceptibility and consequently, in pharmacogenomics by measuring the efficacy of drug therapies specifically for the individual . As each individual has many single nucleotide polymorphisms that together create a unique DNA sequence, SNP-based linkage analysis could be performed to map disease loci, and hence determine disease susceptibility genes for an individual. The combination of SNP maps and high density SNP array allows the use of SNPs as the markers for Mendelian diseases with complex traits efficiently. For example, whole-genome genetic linkage analysis shows significant linkage for many diseases such as rheumatoid arthritis, a common chronic inflammatory disease, prostate cancer, neonatal diabetes. As a result, drugs can be personally designed to efficiently act on a group of individuals or even each individual.
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