Format

Send to

Choose Destination
PLoS One. 2014 Jan 21;9(1):e85096. doi: 10.1371/journal.pone.0085096. eCollection 2014.

PSCC: sensitive and reliable population-scale copy number variation detection method based on low coverage sequencing.

Author information

1
BGI-Shenzhen, Shenzhen, China.
2
BGI-Shenzhen, Shenzhen, China ; State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
3
Department of Clinical Genetics, Aarhus University Hospital, Aarhus, Denmark.
4
Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.
5
Guangzhou Children's Social Welfare Home, Guangzhou, China.
6
BGI-Shenzhen, Shenzhen, China ; Clinical laboratory of BGI Health, Shenzhen, China.
7
BGI-Shenzhen, Shenzhen, China ; The Guangdong Enterprise Key Laboratory of Human Disease Genomics, BGI-Shenzhen, Shenzhen, China.

Abstract

BACKGROUND:

Copy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method.

METHODOLOGY/PRINCIPAL FINDINGS:

In our novel method, two-step correction was used to remove biases caused by local GC content and complex genomic characteristics. We chose a binary segmentation method to locate CNV segments and designed combined statistics tests to ensure the stable performance of the false positive control. The simulation data showed that our PSCC method could achieve 99.7%/100% and 98.6%/100% sensitivity and specificity for over 300 kb CNV calling in the condition of LCS (∼2×) and ultra LCS (∼0.2×), respectively. Finally, we applied this novel method to analyze 34 clinical samples with an average of 2× LCS. In the final results, all the 31 pathogenic CNVs identified by aCGH were successfully detected. In addition, the performance comparison revealed that our method had significant advantages over existing methods using ultra LCS.

CONCLUSIONS/SIGNIFICANCE:

Our study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.

PMID:
24465483
PMCID:
PMC3897425
DOI:
10.1371/journal.pone.0085096
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center