Integrated study of copy number states and genotype calls using high-density SNP arrays.
Integrated study of copy number states and genotype calls using high-density SNP arrays.
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DOI：
10.1093/nar/gkp493
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发表时间：
2009-09
影响因子：
14.9
通讯作者：
Perou CM
中科院分区：
文献类型：
--
作者：
Sun W;Wright FA;Tang Z;Nordgard SH;Van Loo P;Yu T;Kristensen VN;Perou CM
我们提出了一个统计框架，命名为genoCN，同时解剖拷贝数状态和基因型使用高密度SNP（单核苷酸多态性）阵列。基因组DNA拷贝数差异至少有两种类型：拷贝数变异（CNVs）和拷贝数畸变（CNA）。虽然CNV是天然存在的和可遗传的，但CNA是仅在肿瘤组织中最常观察到的获得性体细胞改变。与CNA相比，CNV往往较短，并且在基因组中更稀疏地定位。GenoCN由两个组件组成，即genoCNV和genoCNA，分别用于CNV和CNA研究。与大多数现有方法相比，genoCN更灵活，因为模型参数是从数据中估计的，而不是先验决定的。GenoCNA还纳入了CNA研究的两个重要策略。首先，组织污染的影响明确建模。其次，如果对一个个体的肿瘤和正常组织进行SNP阵列，则来自正常组织的基因型调用用于研究肿瘤组织中的CNA。我们通过应用于162个HapMap个体和一个脑肿瘤（胶质母细胞瘤）数据集来评估genoCN，并表明我们的方法可以成功地识别两种类型的拷贝数差异并产生高质量的基因型调用。
We propose a statistical framework, named genoCN, to simultaneously dissect copy number states and genotypes using high-density SNP (single nucleotide polymorphism) arrays. There are at least two types of genomic DNA copy number differences: copy number variations (CNVs) and copy number aberrations (CNAs). While CNVs are naturally occurring and inheritable, CNAs are acquired somatic alterations most often observed in tumor tissues only. CNVs tend to be short and more sparsely located in the genome compared with CNAs. GenoCN consists of two components, genoCNV and genoCNA, designed for CNV and CNA studies, respectively. In contrast to most existing methods, genoCN is more flexible in that the model parameters are estimated from the data instead of being decided a priori. GenoCNA also incorporates two important strategies for CNA studies. First, the effects of tissue contamination are explicitly modeled. Second, if SNP arrays are performed for both tumor and normal tissues of one individual, the genotype calls from normal tissue are used to study CNAs in tumor tissue. We evaluated genoCN by applications to 162 HapMap individuals and a brain tumor (glioblastoma) dataset and showed that our method can successfully identify both types of copy number differences and produce high-quality genotype calls.
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通讯作者：
Thomson, Elizabeth