Sensitive and accurate detection of copy number variants using read depth of coverage
Sensitive and accurate detection of copy number variants using read depth of coverage
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DOI：
10.1101/gr.092981.109
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发表时间：
2009-09-01
期刊：
影响因子：
7
通讯作者：
Sebat, Jonathan
中科院分区：
文献类型：
--
作者：
Yoon, Seungtai;Xuan, Zhenyu;Sebat, Jonathan
直接检测全基因组拷贝数变异（CNV）的方法已成为鉴定疾病遗传风险因素的有效工具。下一代测序平台在遗传学研究中的应用有望提高检测CNV以及倒位、插入缺失和SNP的灵敏度。需要新的计算方法从基因组序列数据中系统地检测这些变体。现有的基于序列的CNV检测方法主要基于Tuzun et al.和Korbel et al.先前报道的配对末端读段作图（PEM）。由于PEM方法的局限性，某些类别的CNV难以确定，包括位于复杂基因组区域内的大插入和变体。为了克服这些限制，我们开发了一种使用读取覆盖深度的CNV检测方法。逐事件检验（Event-wise Testing，EWT）是一种基于显著性检验的方法。与通常通过对基因组中的每个点进行似然评估来操作的标准分割算法相比，EWT在数据点的间隔上工作，快速搜索特定类别的事件。通过检验每个可能事件的显著性并调整多重检验来控制总体假阳性率。在多个基因组中检查通过EWT在个体基因组中检测到的缺失和重复，以鉴定个体之间的多态性。我们使用基于真实的数据的模拟来估计错误率，并且我们将EWT应用于来自五个个体的配对端鸟枪序列数据（30 x）的1号染色体的分析。我们的研究结果表明，读取深度分析是检测CNVs的有效方法，并且它捕获了对已建立的基于PEM的方法难治的结构变体。
Methods for the direct detection of copy number variation (CNV) genome-wide have become effective instruments for identifying genetic risk factors for disease. The application of next-generation sequencing platforms to genetic studies promises to improve sensitivity to detect CNVs as well as inversions, indels, and SNPs. New computational approaches are needed to systematically detect these variants from genome sequence data. Existing sequence-based approaches for CNV detection are primarily based on paired-end read mapping (PEM) as reported previously by Tuzun et al. and Korbel et al. Due to limitations of the PEM approach, some classes of CNVs are difficult to ascertain, including large insertions and variants located within complex genomic regions. To overcome these limitations, we developed a method for CNV detection using read depth of coverage. Event-wise testing (EWT) is a method based on significance testing. In contrast to standard segmentation algorithms that typically operate by performing likelihood evaluation for every point in the genome, EWT works on intervals of data points, rapidly searching for specific classes of events. Overall false-positive rate is controlled by testing the significance of each possible event and adjusting for multiple testing. Deletions and duplications detected in an individual genome by EWT are examined across multiple genomes to identify polymorphism between individuals. We estimated error rates using simulations based on real data, and we applied EWT to the analysis of chromosome 1 from paired-end shotgun sequence data (30x) on five individuals. Our results suggest that analysis of read depth is an effective approach for the detection of CNVs, and it captures structural variants that are refractory to established PEM-based methods.