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bioRxiv · 10.1101/192310

Whole-genome sequencing analysis of genomic copy number variation (CNV) using low-coverage and paired-end strategies is highly efficient and outperforms array based CNV analysis

Zhou, B.·Ho, S. S.·Zhang, X.·Pattni, R.·Haraksingh, R. R.·Urban, A. E.
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Abstract

BackgroundCNV analysis is an integral component to 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.\n\nMethodsWe compared the CNV detection capabilities of WGS strategies (short-insert, 3kb-, and 5kb-insert mate-pair) each at 1x, 3x, and 5x 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.\n\nResultsOverall, low-coverage WGS strategies detect drastically more GS CNVs compared to arrays and are accompanied with smaller percentages of CNV calls without validation. Furthermore, we show that WGS (at [≥]1x 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 1x coverage.\n\nConclusionsCNV analysis using low-coverage WGS is efficient and outperforms the array-based analysis that is currently used for clinical cytogenetics.

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Zhou, B., Ho, S. S., Zhang, X., Pattni, R., Haraksingh, R. R., Urban, A. E.. 2017-11-04. Whole-genome sequencing analysis of genomic copy number variation (CNV) using low-coverage and paired-end strategies is highly efficient and outperforms array based CNV analysis. https://doi.org/10.1101/192310

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