文献_2012-Copy Number Variation detection from 1000 Genomes project exon capture sequencing data.pdfVIP

Wu et al. BMC Bioinformatics 2012, 13:305 /1471-2105/13/305 METHODOLOGY ARTICLE Open Access Copy Number Variation detection from 1000 Genomes project exon capture sequencing data 1† 1† 1 2 2 2 Jiantao Wu , Krzysztof R Grzeda , Chip Stewart , Fabian Grubert , Alexander E Urban , Michael P Snyder and Gabor T Marth1* 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