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Original article
Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis
  1. Bo Zhou1,2,
  2. Steve S Ho1,2,
  3. Xianglong Zhang1,2,
  4. Reenal Pattni1,2,
  5. Rajini R Haraksingh3,
  6. Alexander E Urban1,2,4
  1. 1 Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
  2. 2 Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
  3. 3 Department of Life Sciences, The University of the West Indies, Saint Augustine, Trinidad and Tobago
  4. 4 Program on Genetics of Brain Function, Stanford Center for Genomics and Personalized Medicine, Tasha and John Morgridge Faculty Scholar, Stanford Child Health Research Institute, Stanford University, Stanford, California, USA
  1. Correspondence to Dr Alexander E Urban, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA; aeurban{at}stanford.edu

Abstract

Background Copy 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.

Methods We 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.

Results Overall, 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.

Conclusions CNV analysis using low-coverage WGS is efficient and outperforms the array-based analysis that is currently used for clinical cytogenetics.

  • read-depth analysis
  • discordant read-pair analysis
  • mate-pair sequencing
  • array Cgh (acgh)
  • copy-number variation (cnv)

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Footnotes

  • Contributors BZ and AEU conceived and designed the study. BZ and RP performed the experiments. BZ designed the analysis pipeline. BZ, SSH and XZ performed the analysis. RRH contributed code. BZ, SSH and AEU wrote the manuscript.

  • Funding This work was supported by the Stanford Medicine Faculty Innovation Program and from the National Institutes of Health NHGRI grant P50 HG007735. BZ was additionally funded by NIH Grant T32 HL110952.

  • Competing interests None declared.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Correction notice This article has been corrected since it was published Online First. RRH’s affiliation has been corrected.

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