Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

Copy Number Variation detection from 1000 Genomes project exon capture sequencing data

Jiantao Wu1, Krzysztof R Grzeda1, Chip Stewart1, Fabian Grubert2, Alexander E Urban2, Michael P Snyder2 and Gabor T Marth1*

  • * Corresponding author: Gabor T Marth marth@bc.edu

  • † Equal contributors

Author Affiliations

1 Boston College, Boston, Chestnut Hill, MA, USA

2 Stanford University School of Medicine, Stanford, CA, USA

For all author emails, please log on.

BMC Bioinformatics 2012, 13:305  doi:10.1186/1471-2105-13-305

Published: 17 November 2012

Abstract

Background

DNA capture technologies combined with high-throughput sequencing now enable cost-effective, deep-coverage, targeted sequencing of complete exomes. This is well suited for SNP discovery and genotyping. However there has been little attention devoted to Copy Number Variation (CNV) detection from exome capture datasets despite the potentially high impact of CNVs in exonic regions on protein function.

Results

As members of the 1000 Genomes Project analysis effort, we investigated 697 samples in which 931 genes were targeted and sampled with 454 or Illumina paired-end sequencing. We developed a rigorous Bayesian method to detect CNVs in the genes, based on read depth within target regions. Despite substantial variability in read coverage across samples and targeted exons, we were able to identify 107 heterozygous deletions in the dataset. The experimentally determined false discovery rate (FDR) of the cleanest dataset from the Wellcome Trust Sanger Institute is 12.5%. We were able to substantially improve the FDR in a subset of gene deletion candidates that were adjacent to another gene deletion call (17 calls). The estimated sensitivity of our call-set was 45%.

Conclusions

This study demonstrates that exonic sequencing datasets, collected both in population based and medical sequencing projects, will be a useful substrate for detecting genic CNV events, particularly deletions. Based on the number of events we found and the sensitivity of the methods in the present dataset, we estimate on average 16 genic heterozygous deletions per individual genome. Our power analysis informs ongoing and future projects about sequencing depth and uniformity of read coverage required for efficient detection.