High-Resolution ROMA Analysis of Genome Copy Number Variation in the HapMap

描述(申请人提供)：根据我们小组和其他人最近发表的研究，我们发现在细胞遗传学水平上看不到的大规模DNA拷贝数变体(CNV)是人类基因组的一个普遍特征。我们的发现表明，平均而言，两个个体相差十几个CNV，涉及3Mb或大约0.1%的基因组。这与单核苷酸多态(SNPs)造成的0.1%的遗传差异相当。然而，与SNPs等核苷酸序列变体相比，基因组中的结构变异还没有得到很好的表征。关于这些结构变异的基因组位置、频率和稳定性以及它们在人类进化和遗传病中的重要性，还有很多需要了解。为了能够在这方面进行进一步的研究，有必要通过对大量个体样本的表征和构建有效CNV的数据库来扩展目前关于拷贝数变异的知识。一个全面的CNV目录将促进对(1)CNV与疾病风险的关联，(2)CNV对药物治疗反应的影响，以及(3)结构变化在人类进化中的作用的大规模研究。我们建议使用一种强大的高分辨率CNV发现方法-代表性寡核苷酸微阵列分析(ROMA)来收集来自国际HapMap项目的270个个体的基因组拷贝数变异的数据资源。我们将使用提供8kb分辨率的380,000个探头阵列来执行ROMA扫描。此外，我们将把我们的数据与使用其他CNV发现方法获得的CNV信息进行集成。我们将选择一组600个常见的CNV(次要等位基因频率=1%)进行精细表征，并使用分辨率为每5BP一个探针的平铺路径寡核苷酸阵列以更高的分辨率定义常见CNV的边界。对于进一步的缺失和复制子集，我们将在序列水平上表征CNV连接。最后，为了将CNV整合到基于SNP的HapMap中，我们将识别与CNV连锁不平衡的SNP标记。关于拷贝数变化的所有信息将通过数据库SNP提供，原始微阵列数据将从www.hapmap.org获得。

DESCRIPTION (provided by applicant): Based on recent studies published by our group and others, it was discovered that large-scale DMA copy number variants invisible at the cytogenetic level (CNVs), are a ubiquitous characteristic of the human genome. Our findings indicated that, on average, two individuals differ by a dozen CNVs involving 3 Mb or approximately 0.1 % of the genome. This is comparable to the 0.1 % of genetic difference that is due to single nucleotide polymorphisms (SNPs). However, in contrast to nucleotide sequence variants such as SNPs, structural variation in the genome has not been well characterized. Much remains to be learned about the genomic locations, frequency, and stability of these structural variants and their importance in human evolution and genetic disease. To enable further research in this are it is necessary to expand the current knowledge of copy number variation by characterizing a large sample of individuals and constructing a database of validated CNVs. A comprehensive catalog of CNVs will facilitate large-scale studies of (1) the association of CNVs with disease risk (2) the effects of CNVs on response to drug treatment, and (3) the role of structural variation in human evolution. We propose to collect a data resource on genome copy number variation on 270 individuals from the international HapMap project using a powerful high-resolution CNV discovery method, Representational Oligonucleotide Microarray Analysis (ROMA). We will perform ROMA scans using a 380,000 probe array that provides a resolution of 8 kb. In addtion, we will integrate our data with CNV information obtain using other CNV discovery methods. We will select a set of 600 common CNVs (minor allele frequency >= 1%) for fine-scale characterization, and the boundaries of common CNVs will be defined at higher resolution using a tiling path Oligonucleotide array with a resolution of one probe every 5 bp. For a further subset of deletions and duplications, we will characterize the CNV junctions at the sequence level. Lastly, in order to integrate CNVs into the context the SNP-based HapMap, we will identify SNP markers that are in linkage disequilibrium with CNVs. All information on copy number variation will be made available through dbSNP and raw microarray data will be made available from www.hapmap.org .