Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays.
Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays.
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DOI：
10.1093/nar/gkq073
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发表时间：
2010-06
影响因子：
14.9
通讯作者：
Demichelis F
中科院分区：
文献类型：
--
作者：
Oldridge DA;Banerjee S;Setlur SR;Sboner A;Demichelis F
通过基于阵列的平台检测拷贝数变异 (CNV) 为了解人类多样性提供了宝贵的见解。然而，次优的研究设计和数据处理会对 CNV 评估产生负面影响。我们通过评估 42 个 HapMap 样本进行 CNV 检测，定量评估应用短序列寡核苷酸阵列（Affymetrix 全基因组人类 SNP 阵列 6.0）时的影响。实施了多种处理和分割策略，并将结果与使用寡核苷酸阵列 CGH 平台获得的 CNV 评估进行比较，该平台旨在以高分辨率查询 CNV（安捷伦）。我们定量证明，用于检测 CNV 的不同参考模型（例如单一样本参考与合并样本参考）是平台间差异（高达 30%）的主要来源，并且位于分段重复区域（较高参考拷贝数）内的 CNV 显着难以检测（P < 0.0001）。在调整 Affymetrix 数据以模仿安捷伦实验设计（参考样本效应）后，我们应用了几种常见的分割方法并评估了 CNV 检测的差异敏感性和特异性，对于非片段重复区域分别为 39-77% 和 86-100%，对于片段重复区域分别为 18-55% 和 39-77%。我们的结果与任何基于芯片的 CNV 研究相关，并提供根据特定研究目标优化性能的指南。
The detection of copy number variants (CNV) by array-based platforms provides valuable insight into understanding human diversity. However, suboptimal study design and data processing negatively affect CNV assessment. We quantitatively evaluate their impact when short-sequence oligonucleotide arrays are applied (Affymetrix Genome-Wide Human SNP Array 6.0) by evaluating 42 HapMap samples for CNV detection. Several processing and segmentation strategies are implemented, and results are compared to CNV assessment obtained using an oligonucleotide array CGH platform designed to query CNVs at high resolution (Agilent). We quantitatively demonstrate that different reference models (e.g. single versus pooled sample reference) used to detect CNVs are a major source of inter-platform discrepancy (up to 30%) and that CNVs residing within segmental duplication regions (higher reference copy number) are significantly harder to detect (P < 0.0001). After adjusting Affymetrix data to mimic the Agilent experimental design (reference sample effect), we applied several common segmentation approaches and evaluated differential sensitivity and specificity for CNV detection, ranging 39–77% and 86–100% for non-segmental duplication regions, respectively, and 18–55% and 39–77% for segmental duplications. Our results are relevant to any array-based CNV study and provide guidelines to optimize performance based on study-specific objectives.
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