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Title: Initial analysis of copy number variation in the cow 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 Smith, Timothy - Tim
item Gasbarre, Louis

Submitted to: Gordon Research Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 6/27/2007
Publication Date: 7/22/2007
Citation: Liu, G., Van Tassell, C.P., Sonstegard, T.S., Li, R.W., Alexander, L.J., Keele, J.W., Matukumalli, L.K., Smith, T.P., Gasbarre, L.C. 2007. Initial analysis of copy number variation in the cow genome. [abstract}. Gordon Research Conference Proceedings.

Interpretive Summary:

Technical Abstract: As a complement to the Bovine HapMap Consortium 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 Hereford 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 cattle; many CNV are common both across diverse cattle breeds and among individuals within a breed; and array CGH is an effective way to detect these bovine CNV. Selected CNVs were further successfully confirmed by independent methods using Q-PCR. We are also investigating the frequency, pattern and impact of such CNV 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.