|Liu, Ge - George|
|Van Tassell, Curtis - Curt|
|Smith, Timothy - Tim|
Submitted to: International Symposium on Animal Genomics for Animal Health
Publication Type: Abstract Only
Publication Acceptance Date: 9/20/2007
Publication Date: 10/23/2007
Citation: Liu, G., Van Tassell, C.P., Sonstegard, T.S., Alexander, L.J., Keele, J.W., Matukumalli, L.K., Smith, T.P., Gasbarre, L.C. 2007. Bovine copy number variation and its implication in animal health. [ abstract]. International Symposium on Animal Genomics for Animal Health. pp. 32.
Technical Abstract: Recently it has become apparent that previously unappreciated genomic structural variation, including copy number variations (CNV), contributes significantly to individual health and disease in humans and rodents. As a complement to the bovine HapMap project, we initiated a systematic study of the CNV within the same cattle population using array comparative genomic hybridization (array CGH). Oligonucleotide CGH arrays were designed and fabricated to cover all chromosomes with an average interval of 6 kb using the version 3 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 L1 Dominette 01449 or L1 Domino 99375 as reference. The CNV 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 the cattle; many CNV are common among diverse cattle individuals; and array CGH is an effective way to detect bovine CNV even within a breed. Selected CNV are being confirmed by independent methods using Q-PCR and/or FISH. We are also investigating the frequency, pattern and impact of such CNV in cattle to investigate 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 generate resources for the identification of novel genomic variation and candidate genes for important economic traits.