Comprehensive Survey of SNPs in the Affymetrix Exon Array Using the 1000 Genomes Dataset
Comprehensive Survey of SNPs in the Affymetrix Exon Array Using the 1000 Genomes Dataset
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DOI：
10.1371/journal.pone.0009366
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发表时间：
2010-02-23
期刊：
影响因子：
3.7
通讯作者：
Cox, Nancy J.
中科院分区：
文献类型：
--
作者：
Gamazon, Eric R.;Zhang, Wei;Cox, Nancy J.
微阵列基因表达数据已被用于全基因组关联研究，使研究人员能够研究基因调控以及其他复杂的表型，包括疾病风险和药物反应。然而，为了从这些研究中得出科学合理的结论，有必要对基因表达强度进行可靠的总结。在可能影响使用微阵列平台的表达谱分析的各种因素中，靶mRNA中的单核苷酸多态性（SNP）可能导致信号强度测量降低并导致假结果。最近发布的1000个基因组计划数据集提供了一个机会，以评估国际HapMap项目淋巴母细胞样细胞系（LCL）中已知和新的SNP的分布。我们将1000个基因组计划的基因型数据映射到Affyssin GeneChip Human Exon 1.0ST阵列（外显子阵列），该阵列已用于我们以前的研究，并且基因表达数据已公开。我们还评估了这些SNP对我们先前确定的差异剪接探针集的潜在影响。尽管千人基因组计划的数据允许对这个特定阵列中的SNP进行全面调查，但同样的方法肯定可以应用于其他微阵列平台。此外，我们提出了一个详细的目录SNP含有探针集（外显子水平）和转录簇（基因水平），这可以考虑在评估结果使用外显子阵列，以及有利于后续实验的设计和数据重新分析。
Microarray gene expression data has been used in genome-wide association studies to allow researchers to study gene regulation as well as other complex phenotypes including disease risks and drug response. To reach scientifically sound conclusions from these studies, however, it is necessary to get reliable summarization of gene expression intensities. Among various factors that could affect expression profiling using a microarray platform, single nucleotide polymorphisms ( SNPs) in target mRNA may lead to reduced signal intensity measurements and result in spurious results. The recently released 1000 Genomes Project dataset provides an opportunity to evaluate the distribution of both known and novel SNPs in the International HapMap Project lymphoblastoid cell lines (LCLs). We mapped the 1000 Genomes Project genotypic data to the Affymetrix GeneChip Human Exon 1.0ST array (exon array), which had been used in our previous studies and for which gene expression data had been made publicly available. We also evaluated the potential impact of these SNPs on the differentially spliced probesets we had identified previously. Though the 1000 Genomes Project data allowed a comprehensive survey of the SNPs in this particular array, the same approach can certainly be applied to other microarray platforms. Furthermore, we present a detailed catalogue of SNP-containing probesets (exon-level) and transcript clusters (gene-level), which can be considered in evaluating findings using the exon array as well as benefit the design of follow-up experiments and data re-analysis.