使用SNP阵列数据分析拷贝数变化:调用CNV和关联测试的协议

Chiao-Feng Lin, Adam C. Naj, Li-San Wang
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引用次数: 25

摘要

高密度SNP基因分型技术为开展全基因组关联(GWA)研究提供了一种低成本、有效的工具。GWA研究的广泛采用确实导致了与疾病或性状相关的snp的发现,其中一些随后被证明是因果关系。然而,许多GWA研究几乎普遍存在的缺点——缺乏遗传力——促使人们对寻找其他类型的遗传变异,如拷贝数变异(CNV)产生了极大的兴趣。据报道,某些CNVs可改变疾病易感性。算法和工具已经开发出识别CNVs使用SNP阵列杂交强度数据。这种方法提供了一个额外的数据源,几乎没有额外的成本。在本单元中,我们演示了使用PennCNV从Illumina SNP阵列数据中调用cnv的步骤,并使用R和PLINK进行关联分析。咕咕叫。Protoc。嗡嗡声。79:1.27.1-1.27.15麝猫。©2013 by John Wiley &儿子,Inc。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analyzing Copy Number Variation Using SNP Array Data: Protocols for Calling CNV and Association Tests

Analyzing Copy Number Variation Using SNP Array Data: Protocols for Calling CNV and Association Tests

Analyzing Copy Number Variation Using SNP Array Data: Protocols for Calling CNV and Association Tests

High-density SNP genotyping technology provides a low-cost, effective tool for conducting Genome Wide Association (GWA) studies. The wide adoption of GWA studies has indeed led to discoveries of disease- or trait-associated SNPs, some of which were subsequently shown to be causal. However, the nearly universal shortcoming of many GWA studies—missing heritability—has prompted great interest in searching for other types of genetic variation, such as copy number variation (CNV). Certain CNVs have been reported to alter disease susceptibility. Algorithms and tools have been developed to identify CNVs using SNP array hybridization intensity data. Such an approach provides an additional source of data with almost no extra cost. In this unit, we demonstrate the steps for calling CNVs from Illumina SNP array data using PennCNV and performing association analysis using R and PLINK. Curr. Protoc. Hum. Genet. 79:1.27.1-1.27.15. © 2013 by John Wiley & Sons, Inc.

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Current Protocols in Human Genetics
Current Protocols in Human Genetics Biochemistry, Genetics and Molecular Biology-Genetics
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期刊介绍: Current Protocols in Human Genetics is the resource for designing and running successful research projects in all branches of human genetics.
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