Copyright © 2008 The American Society of Human Genetics. All rights reserved.
The American Journal of Human Genetics, Volume 82, Issue 3, 685-695, 24 January 2008
doi:10.1016/j.ajhg.2007.12.010
Article
The Fine-Scale and Complex Architecture of Human Copy-Number Variation
George H. Perry1, 2, Amir Ben-Dor3, Anya Tsalenko3, Nick Sampas3, Laia Rodriguez-Revenga1, Charles W. Tran1, Alicia Scheffer3, Israel Steinfeld3, Peter Tsang3, N. Alice Yamada3, Han Soo Park4, Jong-Il Kim4, Jeong-Sun Seo4, Zohar Yakhini3, Stephen Laderman3, Laurakay Bruhn3 and Charles Lee1, 5, 
, 
1 Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA
2 School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287, USA
3 Agilent Technologies, Santa Clara, CA 95051 USA
4 Department of Biochemistry, College of Medicine, Seoul National University, Seoul, South Korea
5 Harvard Medical School, Boston, MA 02115, USA
Corresponding authorAbstract
Despite considerable excitement over the potential functional significance of copy-number variants (CNVs), we still lack knowledge of the fine-scale architecture of the large majority of CNV regions in the human genome. In this study, we used a high-resolution array-based comparative genomic hybridization (aCGH) platform that targeted known CNV regions of the human genome at approximately 1 kb resolution to interrogate the genomic DNAs of 30 individuals from four HapMap populations. Our results revealed that 1020 of 1153 CNV loci (88%) were actually smaller in size than what is recorded in the Database of Genomic Variants based on previously published studies. A reduction in size of more than 50% was observed for 876 CNV regions (76%). We conclude that the total genomic content of currently known common human CNVs is likely smaller than previously thought. In addition, approximately 8% of the CNV regions observed in multiple individuals exhibited genomic architectural complexity in the form of smaller CNVs within larger ones and CNVs with interindividual variation in breakpoints. Future association studies that aim to capture the potential influences of CNVs on disease phenotypes will need to consider how to best ascertain this previously uncharacterized complexity.
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