RT Journal
A1 Komura, Daisuke
A1 Shen, Fan
A1 Ishikawa, Shumpei
A1 Fitch, Karen R.
A1 Chen, Wenwei
A1 Zhang, Jane
A1 Liu, Guoying
A1 Ihara, Sigeo
A1 Nakamura, Hiroshi
A1 Hurles, Matthew E.
A1 Lee, Charles
A1 Scherer, Stephen W.
A1 Jones, Keith W.
A1 Shapero, Michael H.
A1 Huang, Jing
A1 Aburatani, Hiroyuki
T1 Genome-wide detection of human copy number variations using high-density DNA oligonucleotide arrays
JF Genome Research 
JO Genome Research 
YR 2006 
FD December 01 
VO 16 
IS 12 
SP 1575 
OP 1584 
DO 10.1101/gr.5629106 
UL http://genome.cshlp.org/content/16/12/1575.abstract 
AB Recent reports indicate that copy number variations (CNVs) within the human genome contribute to nucleotide diversity to a larger extent than single nucleotide polymorphisms (SNPs). In addition, the contribution of CNVs to human disease susceptibility may be greater than previously expected, although a complete understanding of the phenotypic consequences of CNVs is incomplete. We have recently reported a comprehensive view of CNVs among 270 HapMap samples using high-density SNP genotyping arrays and BAC array CGH. In this report, we describe a novel algorithm using Affymetrix GeneChip Human Mapping 500K Early Access (500K EA) arrays that identified 1203 CNVs ranging in size from 960 bp to 3.4 Mb. The algorithm consists of three steps: (1) Intensity pre-processing to improve the resolution between pairwise comparisons by directly estimating the allele-specific affinity as well as to reduce signal noise by incorporating probe and target sequence characteristics via an improved version of the Genomic Imbalance Map (GIM) algorithm; (2) CNV extraction using an adapted SW-ARRAY procedure to automatically and robustly detect candidate CNV regions; and (3) copy number inference in which all pairwise comparisons are summarized to more precisely define CNV boundaries and accurately estimate CNV copy number. Independent testing of a subset of CNVs by quantitative PCR and mass spectrometry demonstrated a >90% verification rate. The use of high-resolution oligonucleotide arrays relative to other methods may allow more precise boundary information to be extracted, thereby enabling a more accurate analysis of the relationship between CNVs and other genomic features.