SNP array

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In molecular biology, 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. Around 335 million SNPs have been identified in the human genome, [1] 15 million of which are present at frequencies of 1% or higher across different populations worldwide. [2]

Contents

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: [3]

  1. An array containing immobilized allele-specific oligonucleotide (ASO) probes.
  2. Fragmented nucleic acid sequences of target, labelled with fluorescent dyes.
  3. A detection system that records and interprets the hybridization signal.

The ASO probes are often chosen based on sequencing of a representative panel of individuals: positions found to vary in the panel at a specified frequency are used as the basis for probes. SNP chips are generally described by the number of SNP positions they assay. Two probes must be used for each SNP position to detect both alleles; if only one probe were used, experimental failure would be indistinguishable from homozygosity of the non-probed allele. [4]

Applications

DNA copy number profile for the T47D breast cancer cell line (Affymetrix SNP Array) LRR and BAF profiles for the T47D breast cancer cell line top.svg
DNA copy number profile for the T47D breast cancer cell line (Affymetrix SNP Array)
LOH profile for the T47D breast cancer cell line (Affymetrix SNP Array) LRR and BAF profiles for the T47D breast cancer cell line bottom.svg
LOH profile for the T47D breast cancer cell line (Affymetrix SNP Array)

An SNP array is a useful tool for studying slight variations between whole genomes. The most important clinical applications of SNP arrays are for determining disease susceptibility [5] and for measuring the efficacy of drug therapies designed specifically for individuals. [6] In research, SNP arrays are most frequently used for genome-wide association studies. [7] Each individual has many SNPs. SNP-based genetic linkage analysis can be used to map disease loci, and determine disease susceptibility genes in individuals. The combination of SNP maps and high density SNP arrays allows SNPs to be used as markers for genetic diseases that have complex traits. For example, genome-wide association studies have identified SNPs associated with diseases such as rheumatoid arthritis [8] and prostate cancer. [9] A SNP array can also be used to generate a virtual karyotype using software to determine the copy number of each SNP on the array and then align the SNPs in chromosomal order. [10]

SNPs can also be used to study genetic abnormalities in cancer. For example, SNP arrays can be used to study loss of heterozygosity (LOH). LOH occurs when one allele of a gene is mutated in a deleterious way and the normally-functioning allele is lost. LOH occurs commonly in oncogenesis. For example, tumor suppressor genes help keep cancer from developing. If a person has one mutated and dysfunctional copy of a tumor suppressor gene and his second, functional copy of the gene gets damaged, they may become more likely to develop cancer. [11]

Other chip-based methods such as comparative genomic hybridization can detect genomic gains or deletions leading to LOH. SNP arrays, however, have an additional advantage of being able to detect copy-neutral LOH (also called uniparental disomy or gene conversion). Copy-neutral LOH is a form of allelic imbalance. In copy-neutral LOH, one allele or whole chromosome from a parent is missing. This problem leads to duplication of the other parental allele. Copy-neutral LOH may be pathological. For example, say that the mother's allele is wild-type and fully functional, and the father's allele is mutated. If the mother's allele is missing and the child has two copies of the father's mutant allele, disease can occur.

High density SNP arrays help scientists identify patterns of allelic imbalance. These studies have potential prognostic and diagnostic uses. Because LOH is so common in many human cancers, SNP arrays have great potential in cancer diagnostics. For example, recent SNP array studies have shown that solid tumors such as gastric cancer and liver cancer show LOH, as do non-solid malignancies such as hematologic malignancies, ALL, MDS, CML and others. These studies may provide insights into how these diseases develop, as well as information about how to create therapies for them. [12]

Breeding in a number of animal and plant species has been revolutionized by the emergence of SNP arrays. The method is based on the prediction of genetic merit by incorporating relationships among individuals based on SNP array data. [13] This process is known as genomic selection. Crop-specific arrays find use in agriculture. [14] [15]

References

  1. "dbSNP Summary". www.ncbi.nlm.nih.gov. Archived from the original on December 14, 2012. Retrieved 4 October 2017.
  2. The 1000 Genomes Project Consortium (2010). "A map of human genome variation from population-scale sequencing". Nature. 467 (7319): 1061–1073. Bibcode:2010Natur.467.1061T. doi:10.1038/nature09534. ISSN   0028-0836. PMC   3042601 . PMID   20981092.{{cite journal}}: CS1 maint: numeric names: authors list (link)
  3. LaFramboise, T. (1 July 2009). "Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances". Nucleic Acids Research. 37 (13): 4181–4193. doi:10.1093/nar/gkp552. PMC   2715261 . PMID   19570852.
  4. Rapley, Ralph; Harbron, Stuart (2004). Molecular analysis and genome discovery. Chichester [u.a.]: Wiley. ISBN   978-0-471-49919-0.
  5. Schaaf, Christian P.; Wiszniewska, Joanna; Beaudet, Arthur L. (22 September 2011). "Copy Number and SNP Arrays in Clinical Diagnostics". Annual Review of Genomics and Human Genetics. 12 (1): 25–51. doi:10.1146/annurev-genom-092010-110715. PMID   21801020.
  6. Alwi, Zilfalil Bin (2005). "The Use of SNPs in Pharmacogenomics Studies". The Malaysian Journal of Medical Sciences. 12 (2): 4–12. ISSN   1394-195X. PMC   3349395 . PMID   22605952.
  7. The International HapMap Consortium (2003). "The International HapMap Project" (PDF). Nature. 426 (6968): 789–796. Bibcode:2003Natur.426..789G. doi:10.1038/nature02168. hdl: 2027.42/62838 . ISSN   0028-0836. PMID   14685227. S2CID   4387110.
  8. Walsh, Alice M.; Whitaker, John W.; Huang, C. Chris; Cherkas, Yauheniya; Lamberth, Sarah L.; Brodmerkel, Carrie; Curran, Mark E.; Dobrin, Radu (30 April 2016). "Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations". Genome Biology. 17 (1): 79. doi: 10.1186/s13059-016-0948-6 . PMC   4853861 . PMID   27140173.
  9. Amin Al Olama, A.; et al. (November 2010). "The genetics of type 2 diabetes: what have we learned from GWAS?". Annals of the New York Academy of Sciences. 1212 (1): 59–77. Bibcode:2010NYASA1212...59B. doi:10.1111/j.1749-6632.2010.05838.x. PMC   3057517 . PMID   21091714.
  10. Sato-Otsubo, Aiko; Sanada, Masashi; Ogawa, Seishi (February 2012). "Single-Nucleotide Polymorphism Array Karyotyping in Clinical Practice: Where, When, and How?". Seminars in Oncology. 39 (1): 13–25. doi:10.1053/j.seminoncol.2011.11.010. PMID   22289488.
  11. Zheng, Hai-Tao (2005). "Loss of heterozygosity analyzed by single nucleotide polymorphism array in cancer". World Journal of Gastroenterology. 11 (43): 6740–4. doi: 10.3748/wjg.v11.i43.6740 . PMC   4725022 . PMID   16425377.
  12. Mao, Xueying; Young, Bryan D; Lu, Yong-Jie (2007). "The Application of Single Nucleotide Polymorphism Microarrays in Cancer Research". Current Genomics. 8 (4): 219–228. doi:10.2174/138920207781386924. ISSN   1389-2029. PMC   2430687 . PMID   18645599.
  13. Meuwissen TH, Hayes BJ, Goddard ME (2001). "Prediction of total genetic value using genome-wide dense marker maps". Genetics. 157 (4): 1819–29. doi:10.1093/genetics/157.4.1819. PMC   1461589 . PMID   11290733.
  14. Hulse-Kemp, Amanda M; Lemm, Jana; Plieske, Joerg; Ashrafi, Hamid; Buyyarapu, Ramesh; Fang, David D; Frelichowski, James; Giband, Marc; Hague, Steve; Hinze, Lori L; Kochan, Kelli J; Riggs, Penny K; Scheffler, Jodi A; Udall, Joshua A; Ulloa, Mauricio; Wang, Shirley S; Zhu, Qian-Hao; Bag, Sumit K; Bhardwaj, Archana; Burke, John J; Byers, Robert L; Claverie, Michel; Gore, Michael A; Harker, David B; Islam, Mohammad Sariful; Jenkins, Johnie N; Jones, Don C; Lacape, Jean-Marc; Llewellyn, Danny J; Percy, Richard G; Pepper, Alan E; Poland, Jesse A; Mohan Rai, Krishan; Sawant, Samir V; Singh, Sunil Kumar; Spriggs, Andrew; Taylor, Jen M; Wang, Fei; Yourstone, Scott M; Zheng, Xiuting; Lawley, Cindy T; Ganal, Martin W; Van Deynze, Allen; Wilson, Iain W; Stelly, David M (2015-06-01). "Development of a 63K SNP Array for Cotton and High-Density Mapping of Intraspecific and Interspecific Populations of Gossypium spp". G3: Genes, Genomes, Genetics . 5 (6). Genetics Society of America (OUP): 1187–1209. doi:10.1534/g3.115.018416. ISSN   2160-1836. PMC   4478548 . PMID   25908569. S2CID   11590488.
  15. Rasheed, Awais; Hao, Yuanfeng; Xia, Xianchun; Khan, Awais; Xu, Yunbi; Varshney, Rajeev K.; He, Zhonghu (2017). "Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives". Molecular Plant . 10 (8). Chin Acad Sci+Chin Soc Plant Bio+Shanghai Inst Bio Sci (Elsevier): 1047–1064. Bibcode:2017MPlan..10.1047R. doi: 10.1016/j.molp.2017.06.008 . ISSN   1674-2052. PMID   28669791. S2CID   33780984.

Further reading