The effect of algorithms on copy number variant detection.
The effect of algorithms on copy number variant detection.
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DOI：
10.1371/journal.pone.0014456
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发表时间：
2010-12-30
期刊：
影响因子：
3.7
通讯作者：
Yu CE
中科院分区：
文献类型：
--
作者：
Tsuang DW;Millard SP;Ely B;Chi P;Wang K;Raskind WH;Kim S;Brkanac Z;Yu CE
拷贝数变异 (CNV) 的检测和 CNV 疾病关联研究的结果依赖于 CNV 的定义方式，并且由于基于阵列的技术只能推断 CNV，因此 CNV 调用算法可能会产生截然不同的结果。几位作者注意到 CNV 检测方法之间存在大规模差异，以及与这些方法相关的大量假阳性和假阴性率。在本研究中，我们使用四种常见 CNV 检测算法（PennCNV、QuantiSNP、HMMSeg 和 cnvPartition）的变体和两种重叠定义（任何重叠和至少 40% 的较小 CNV 的重叠）来说明不同算法和重叠定义对 CNV 发现的影响。我们使用富集 CNV 区域的 56 K Illumina 基因分型芯片来生成 48 名白人精神分裂症病例和 48 名年龄、种族和性别匹配的对照受试者的杂交强度和等位基因频率。没有算法发现两组之间的 CNV 负担存在差异。然而，不同算法调用的 CNV 总数在 102 到 3,765 之间。平均 CNV 大小范围为 46 kb 至 787 kb，每个受试者的平均 CNV 数量范围为 1 至 39。之前在正常受试者中未报道的新型 CNV 数量范围为 0 至 212。受多个公开可用的全基因组 SNP 阵列可用性的推动，研究人员正在进行大量分析，以鉴定复杂遗传性疾病中假定的其他 CNV。然而，基于阵列的研究中识别出的 CNV 数量，以及这些 CNV 是否新颖或有效，将取决于所使用的算法。因此，鉴于所使用的方法多种多样，将会存在许多误报和漏报。需要从高密度阵列推断的 CNV 识别指南和 CNV 验证金标准的建立。
The detection of copy number variants (CNVs) and the results of CNV-disease association studies rely on how CNVs are defined, and because array-based technologies can only infer CNVs, CNV-calling algorithms can produce vastly different findings. Several authors have noted the large-scale variability between CNV-detection methods, as well as the substantial false positive and false negative rates associated with those methods. In this study, we use variations of four common algorithms for CNV detection (PennCNV, QuantiSNP, HMMSeg, and cnvPartition) and two definitions of overlap (any overlap and an overlap of at least 40% of the smaller CNV) to illustrate the effects of varying algorithms and definitions of overlap on CNV discovery. We used a 56 K Illumina genotyping array enriched for CNV regions to generate hybridization intensities and allele frequencies for 48 Caucasian schizophrenia cases and 48 age-, ethnicity-, and gender-matched control subjects. No algorithm found a difference in CNV burden between the two groups. However, the total number of CNVs called ranged from 102 to 3,765 across algorithms. The mean CNV size ranged from 46 kb to 787 kb, and the average number of CNVs per subject ranged from 1 to 39. The number of novel CNVs not previously reported in normal subjects ranged from 0 to 212. Motivated by the availability of multiple publicly available genome-wide SNP arrays, investigators are conducting numerous analyses to identify putative additional CNVs in complex genetic disorders. However, the number of CNVs identified in array-based studies, and whether these CNVs are novel or valid, will depend on the algorithm(s) used. Thus, given the variety of methods used, there will be many false positives and false negatives. Both guidelines for the identification of CNVs inferred from high-density arrays and the establishment of a gold standard for validation of CNVs are needed.
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