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使用 Illumina Infinium 1M SNP 芯片评估拷贝数变异:西班牙膀胱癌/EPICURO 研究中方法学方法的比较。

Assessment of copy number variation using the Illumina Infinium 1M SNP-array: a comparison of methodological approaches in the Spanish Bladder Cancer/EPICURO study.

机构信息

Centro Nacional de Investigaciones Oncológicas (CNIO) Madrid, Spain.

出版信息

Hum Mutat. 2011 Feb;32(2):240-8. doi: 10.1002/humu.21398. Epub 2011 Jan 25.

DOI:10.1002/humu.21398
PMID:21089066
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3230937/
Abstract

High-throughput single nucleotide polymorphism (SNP)-array technologies allow to investigate copy number variants (CNVs) in genome-wide scans and specific calling algorithms have been developed to determine CNV location and copy number. We report the results of a reliability analysis comparing data from 96 pairs of samples processed with CNVpartition, PennCNV, and QuantiSNP for Infinium Illumina Human 1Million probe chip data. We also performed a validity assessment with multiplex ligation-dependent probe amplification (MLPA) as a reference standard. The number of CNVs per individual varied according to the calling algorithm. Higher numbers of CNVs were detected in saliva than in blood DNA samples regardless of the algorithm used. All algorithms presented low agreement with mean Kappa Index (KI) <66. PennCNV was the most reliable algorithm (KI(w=) 98.96) when assessing the number of copies. The agreement observed in detecting CNV was higher in blood than in saliva samples. When comparing to MLPA, all algorithms identified poorly known copy aberrations (sensitivity = 0.19-0.28). In contrast, specificity was very high (0.97-0.99). Once a CNV was detected, the number of copies was truly assessed (sensitivity >0.62). Our results indicate that the current calling algorithms should be improved for high performance CNV analysis in genome-wide scans. Further refinement is required to assess CNVs as risk factors in complex diseases.

摘要

高通量单核苷酸多态性(SNP)- 阵列技术可用于全基因组扫描中的拷贝数变异(CNV)研究,并且已经开发出特定的调用算法来确定 CNV 的位置和拷贝数。我们报告了使用 CNVpartition、PennCNV 和 QuantiSNP 对 Infinium Illumina Human 1Million 探针芯片数据进行 96 对样本处理的可靠性分析结果。我们还使用多重连接依赖性探针扩增(MLPA)作为参考标准进行了有效性评估。每个个体的 CNV 数量因调用算法而异。无论使用哪种算法,唾液中的 CNV 数量都高于血液 DNA 样本。所有算法的平均 Kappa 指数(KI)均<66,一致性均较低。在评估拷贝数时,PennCNV 是最可靠的算法(KI(w=)98.96)。在检测 CNV 时,血液样本中的一致性高于唾液样本。与 MLPA 相比,所有算法都识别出了拷贝数未知的畸变(敏感性=0.19-0.28)。相比之下,特异性非常高(0.97-0.99)。一旦检测到 CNV,就会真正评估拷贝数(敏感性>0.62)。我们的结果表明,当前的调用算法应在全基因组扫描中进行高性能的 CNV 分析中进行改进。需要进一步改进以评估 CNV 作为复杂疾病的风险因素。

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