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Title: Analysis of copy number variation in the bovine genome

Author
item Liu, Ge - George
item Van Tassell, Curtis - Curt
item Sonstegard, Tad
item Li, Robert
item Alexander, Leeson
item Keele, John
item MATUKUMALLI, LAKSHMI - GEORGE MASON UNIVERSITY
item BROWN, TWAIN - 1265-90-00
item Smith, Timothy - Tim
item Gasbarre, Louis

Submitted to: Plant and Animal Genome VX Conference Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/26/2007
Publication Date: 1/12/2008
Citation: Liu, G., Van Tassell, C.P., Sonstegard, T.S., Li, R.W., Alexander, L.J., Keele, J.W., Matukumalli, L.K., Brown, T., Smith, T.P., Gasbarre, L.C. 2008. Analysis of copy number variation in the bovine genome. Plant and Animal Genome (PAG) XVI Conference. San Diego, CA. p517.

Interpretive Summary:

Technical Abstract: We initiated a systematic study of the copy number variation (CNV) within the Bovine HapMap cattle population using array comparative genomic hybridization (array CGH). Oligonucleotide CGH arrays were designed and fabricated to provide a genome-wide coverage with an average interval of 6 kb using the newest version of bovine genome assembly. In the initial screening, three Holstein bulls were selected to represent major branches of the Holstein breed with some maternal linkages between branches. Dual-label hybridizations were performed using either Hereford L1 Dominette 01449 or L1 Domino 99375 as reference. The CNVs were represented by gains and losses of normalized fluorescence intensities relative to the reference. Our data, for the first time, demonstrated that significant amounts of CNV exist in cattle; many CNVs are common both across diverse cattle breeds and among individuals within a breed; and array CGH is an effective way to detect these bovine CNVs. Selected CNVs were further successfully confirmed by independent methods using Q-PCR. We are also investigating the frequency, pattern and impact of such CNVs in cattle to probe their utility in improving selection for health, well-being and productive efficiency of cattle. Our strategy based on genome higher-order architecture variation is a powerful approach to identify novel genomic variation and candidate genes for important economic traits.