Cooper Gregory M, Zerr Troy, Kidd Jeffrey M, Eichler Evan E, Nickerson Deborah A
Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
Nat Genet. 2008 Oct;40(10):1199-203. doi: 10.1038/ng.236. Epub 2008 Sep 7.
SNP genotyping has emerged as a technology to incorporate copy number variants (CNVs) into genetic analyses of human traits. However, the extent to which SNP platforms accurately capture CNVs remains unclear. Using independent, sequence-based CNV maps, we find that commonly used SNP platforms have limited or no probe coverage for a large fraction of CNVs. Despite this, in 9 samples we inferred 368 CNVs using Illumina SNP genotyping data and experimentally validated over two-thirds of these. We also developed a method (SNP-Conditional Mixture Modeling, SCIMM) to robustly genotype deletions using as few as two SNP probes. We find that HapMap SNPs are strongly correlated with 82% of common deletions, but the newest SNP platforms effectively tag about 50%. We conclude that currently available genome-wide SNP assays can capture CNVs accurately, but improvements in array designs, particularly in duplicated sequences, are necessary to facilitate more comprehensive analyses of genomic variation.
单核苷酸多态性(SNP)基因分型已成为一种将拷贝数变异(CNV)纳入人类性状遗传分析的技术。然而,SNP平台准确捕获CNV的程度仍不清楚。利用独立的、基于序列的CNV图谱,我们发现常用的SNP平台对很大一部分CNV的探针覆盖有限或没有覆盖。尽管如此,在9个样本中,我们利用Illumina SNP基因分型数据推断出368个CNV,并通过实验验证了其中三分之二以上。我们还开发了一种方法(SNP条件混合建模,SCIMM),使用少至两个SNP探针就能可靠地对缺失进行基因分型。我们发现,HapMap SNP与82%的常见缺失高度相关,但最新的SNP平台能有效标记约50%。我们得出结论,目前可用的全基因组SNP检测可以准确捕获CNV,但为了便于对基因组变异进行更全面的分析,阵列设计尤其是重复序列方面的改进是必要的。