Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis

• Published in: Journal of medical genetics Vol. 55; no. 11; pp. 735 - 743
• Main Authors: Zhou, Bo, Ho, Steve S, Zhang, Xianglong, Pattni, Reenal, Haraksingh, Rajini R, Urban, Alexander E
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group Ltd 01.11.2018; BMJ Publishing Group LTD
• Subjects: array Cgh (acgh); Comparative analysis; Comparative Genomic Hybridization - methods; Comparative Genomic Hybridization - standards; Consortia; Copy number; Copy-number variation; copy-number variation (cnv); Cytogenetics; Deoxyribonucleic acid; discordant read-pair analysis; DNA; DNA Copy Number Variations; DNA microarrays; Encyclopedias; Genetic Association Studies - methods; Genetic Association Studies - standards; Genetic Predisposition to Disease; Genetic Testing; Genome, Human; Genomes; Genomics; Genomics - methods; Genomics - standards; Humans; mate-pair sequencing; Next-generation sequencing; Oligonucleotides; read-depth analysis; Reference Standards; Reproducibility of Results; Sensitivity and Specificity; Whole Genome Sequencing; read-depth analysis; array Cgh (acgh); copy-number variation (cnv); discordant read-pair analysis; mate-pair sequencing
• ISSN: 0022-2593; 1468-6244; 1468-6244
• DOI: 10.1136/jmedgenet-2018-105272

BackgroundCopy number variation (CNV) analysis is an integral component of the study of human genomes in both research and clinical settings. Array-based CNV analysis is the current first-tier approach in clinical cytogenetics. Decreasing costs in high-throughput sequencing and cloud computing have opened doors for the development of sequencing-based CNV analysis pipelines with fast turnaround times. We carry out a systematic and quantitative comparative analysis for several low-coverage whole-genome sequencing (WGS) strategies to detect CNV in the human genome.MethodsWe compared the CNV detection capabilities of WGS strategies (short insert, 3 kb insert mate pair and 5 kb insert mate pair) each at 1×, 3× and 5× coverages relative to each other and to 17 currently used high-density oligonucleotide arrays. For benchmarking, we used a set of gold standard (GS) CNVs generated for the 1000 Genomes Project CEU subject NA12878.ResultsOverall, low-coverage WGS strategies detect drastically more GS CNVs compared with arrays and are accompanied with smaller percentages of CNV calls without validation. Furthermore, we show that WGS (at ≥1× coverage) is able to detect all seven GS deletion CNVs >100 kb in NA12878, whereas only one is detected by most arrays. Lastly, we show that the much larger 15 Mbp Cri du chat deletion can be readily detected with short-insert paired-end WGS at even just 1× coverage.ConclusionsCNV analysis using low-coverage WGS is efficient and outperforms the array-based analysis that is currently used for clinical cytogenetics.