Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances.
Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances.
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DOI：
10.1093/nar/gkp552
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发表时间：
2009-07
影响因子：
14.9
通讯作者：
LaFramboise T
中科院分区：
文献类型：
--
作者：
LaFramboise T
阵列制造商最初设计单核苷酸多态性（SNP）阵列，以同时对基因组中数千个SNP的人类DNA进行基因分型。在最初开发的十年中，该平台的应用已经扩展到包括检测和表征拷贝数变异（无论是体细胞的、遗传的还是新生的）以及癌细胞中的杂合性丢失。该技术对人口和分子遗传学的深刻见解做出了令人印象深刻的贡献，这得益于计算方法的进步，事实上，这些见解和方法促进了阵列本身的发展。本文介绍了最常用的SNP阵列平台，调查用于将原始数据转换为DNA水平上的推断的计算方法，并详细介绍了广泛的应用。尽管SNP阵列的长期未来尚不清楚，但成本考虑确保了它们至少在未来几年内的相关性。即使新兴技术似乎准备接管至少一些应用程序，研究人员与这些新的数据源正在采用最初为SNP阵列开发的计算方法。
Array manufacturers originally designed single nucleotide polymorphism (SNP) arrays to genotype human DNA at thousands of SNPs across the genome simultaneously. In the decade since their initial development, the platform's applications have expanded to include the detection and characterization of copy number variation—whether somatic, inherited, or de novo—as well as loss-of-heterozygosity in cancer cells. The technology's impressive contributions to insights in population and molecular genetics have been fueled by advances in computational methodology, and indeed these insights and methodologies have spurred developments in the arrays themselves. This review describes the most commonly used SNP array platforms, surveys the computational methodologies used to convert the raw data into inferences at the DNA level, and details the broad range of applications. Although the long-term future of SNP arrays is unclear, cost considerations ensure their relevance for at least the next several years. Even as emerging technologies seem poised to take over for at least some applications, researchers working with these new sources of data are adopting the computational approaches originally developed for SNP arrays.
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