SNP array

In molecular biology and bioinformatics, a SNP array is a type of DNA microarray which is used to detect polymorphisms within a population. A single nucleotide polymorphism (SNP), a variation at a single site in DNA, is the most frequent type of variation in the genome. For example, there are around 10 million SNPs that have been identified in the human genome [ Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308–311. doi: 10.1093/nar/29.1.308. [PubMed] Link: http://www.ncbi.nlm.nih.gov/pubmed/11125122 ] . As SNPs are highly conserved throughout evolution and within a population, the map of SNPs serves as an excellent genotypic marker for research.

Principles

The basic principles of SNP array are the same as the DNA microarray. These are the convergence of DNA hybridization, fluorescence microscopy, and solid surface DNA capture. The three mandatory components of the SNP arrays are:
# The array that contains immobilized nucleic acid sequences or target;
# One or more labeled Allele specific oligonucleotide (ASO) probes;
# A detection system that records and interprets the hybridization signal.

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. Depending on the purpose of experiments, the amount of SNPs present on an array is considered.

Applications

An 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 genetic 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, prostate cancer, and neonatal diabetes. As a result, drugs can be personally designed to efficiently act on a group of individuals who share a common allele - or even a single individual.

In addition, SNP array can be used for studying the Loss of heterozygosity (LOH). LOH is a form of allelic imbalance that can result from the complete loss of an allele or from an increase in copy number of one allele relative to the other. While other chip-based methods (e.g. Comparative genomic hybridization) can detect only genomic gains or deletions, SNP array has the additional advantage of detecting copy number neutral LOH due to uniparental disomy (UPD). In UPD, one allele or whole chromosome from one parent are missing leading to reduplication of the other parental allele (uni-parental = from one parent, disomy = duplicated). In a disease setting this occurrence may be pathologic when the wildtype allelle (e.g. from the mother) is missing and instead two copies of the homozygous allelle (e.g. from the father) are present.Using high density SNP array to detect LOH allows identification of pattern of allelic imbalance with potential prognostic and diagnostic utilities. This usage of SNP array has a huge potential in cancer diagnostics as LOH is a prominent characteristic of most human cancers. Recent studies based on the SNP array technology have shown that not only solid tumors (e.g. gastric cancer, liver cancer etc) but also hematologic malignancies (ALL, MDS, CML etc) have a high rate of LOH due to genomic deletions or UPD and genomic gains. The results of these studies may help to gain insights into mechanisms of these diseases and to create targeted drugs.

References

Further reading

* Affymetrix, 2006. Technology, [online] , Address: http://www.affymetrix.com/technology/design/index.affx.
* Barnes, M.R. (2003) Chapter 3: Human Genetic Variation: Databases and Concepts, "Bioinformatics for geneticists", edited by Barnes, M.R. and Gray, I.C., John Wiley and Sons, Ltd.
* Hehir-Kwa, J., Egmont-Petersen, M., Janssen,I., Smeets, D., Geurts van Kessel, A., Veltman, J. (2007) "Genome-wide copy number profiling on high-density BAC, SNP and oligonucleotide microarrays: a platform comparison based on statistical power analysis" "DNA Research". 14:1-11. Link: http://dnaresearch.oxfordjournals.org/cgi/content/full/14/1/1
* John, S., Shephard, N., Liu, G., Zeggini, E., Cao, M., Chen, W., Vasavda, N., Mills, T., Barton, A., Hinks, A., Eyre, S., Johes, K.W., Ollier, W., Silman, A., Gibson, N., Worthington, J., and Kennedy, G.C. (2004) "Whole-Genome scan, in a complex disease, using 11,245 single-nucleotide polymorphism: comparison with microsatellites." "American Journal of Human Genetics". 75(1):54-64. PMID 15154113
* Mei, R., Galipeau, P.C., Prass, C., Berno, A., Ghandour, G., Patil, N., Wolff, R.K., Chee, M.S., Reid, B.J., and Lockhart, D.J. (2000) "Genome-wide detection of allelic imbalance using human SNPs and high-density DNA arrays." "Genome Research". 10:1126-1137. PMID 10958631
* Schaid, D.J., Guenther, J.C., Christensen, G.B., Hebbring, S., Rosenow, C., Hilker, C.A., McDonnell, S.K., Cunningham, J.M., Slager, S.L., Blute, M.L., and Thibodeau, S.N. (2004) "Comparison of Microsatellites Versus Single Nucleotide Polymorphisms by a Genome Linkage Screen for Prostate Cancer Susceptibility Loci", "American Journal of Human Genetics". 75 (6): 948-65. PMID 15514889
* Sellick GS, Longman C, Tolmie J, Newbury-Ecob R, Geenhalgh L, Hughes S, Whiteford M, Garrett C, Houlston RS., "Genome-wide linkage searches for Mendelian disease loci can be efficiently conducted using high-density SNP genotyping arrays." "Nucleic Acids Research". 32(20):e164. PMID 15561999
* Sheils, O., Finn, S. and O'Leary J. (2003) "Nucleic acid microarray: an overview." "Current Diagnostic Pathology". 9:155-158.


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