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Title: A high-resolution map of copy number variation in the cattle genome

item Liu, Ge - George
item Keele, John
item Van Tassell, Curtis - Curt
item Sonstegard, Tad
item Alexander, Leeson
item Li, Robert
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: 1/11/2008
Citation: Liu, G., Keele, J.W., Van Tassell, C.P., Sonstegard, T.S., Alexander, L.J., Li, R.W., Matukumalli, L.K., Smith, T.P., Gasbarre, L.C. 2008. A high-resolution map of copy number variation in the cattle genome. Plant and Animal Genome VX Conference Abstracts. The NRI Animal Genome PD Meeting, San Diego, CA. p84.

Interpretive Summary:

Technical Abstract: We conducted a systematic study of the cattle copy number variation (CNV) 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 Bta3.1 genome assembly. Dual-label hybridizations were performed using either Hereford L1 Dominette 01449 or L1 Domino 99375 as reference. Multiple bulls from both dairy and beef breeds were selected to represent the cattle population. The CNVs were represented by gains and losses of normalized fluorescence intensities relative to the reference. Up to now (November 2007), over 40 hybridizations were performed and more than 200 CNV regions were detected using stringent criteria. This dataset provided us a preliminary version of the first genome-wide cattle CNV map. It 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 cattle 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.