Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
PLoS One. 2011;6(11):e27859. doi: 10.1371/journal.pone.0027859. Epub 2011 Nov 30.
Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications.
准确高效地检测基因组范围内的拷贝数变异(CNVs)对于理解人类基因组变异、全基因组 CNV 关联类型研究、细胞遗传学研究和诊断以及从测序技术中鉴定的 CNVs 的独立验证至关重要。存在许多基于阵列的 CNV 检测平台,利用阵列比较基因组杂交(aCGH)、单核苷酸多态性(SNP)基因分型或两者兼用。我们定量评估了 12 种领先的全基因组 CNV 检测平台在准确检测 HapMap CEU 样本 NA12878 基因组中 Gold Standard CNV 集方面的能力,发现性能存在显著差异。分析的技术包括 NimbleGen 4.2 M、2.1 M 和 3×720 K 全基因组和 CNV 重点阵列、Agilent 1×1 M CGH 和高分辨率和 2×400 K CNV 和 SNP+CGH 阵列、Illumina Human Omni1Quad 阵列和 Affymetrix SNP 6.0 阵列。使用的 Gold Standard 是来自 1000 基因组计划测序的 3997 个经过验证的 CNV 和基于超高分辨率 aCGH 的 756 个经过验证的 CNV。我们发现,CNV 重点阵列的 CNV 调用的灵敏度、总数、大小范围和断点分辨率最高。我们的结果对于基本和临床应用的成本效益型 CNV 检测和验证都非常重要。