Genome wide selection in dairy cattle based on high-density genome-wide SNP analysis: from discovery to application

Raadsma, H.W., Zenger, K.R., Khatkar, M.S., Crump, R., Moser, G., Solkner, J., Cavanagh, J.A.L., Hawken, R.J., Hobbs, M., Barris, W., Nicholas, F.W., and Tier, B. (2007) Genome wide selection in dairy cattle based on high-density genome-wide SNP analysis: from discovery to application. In: Proceedings of the Association for the Advancement of Animal Breeding and Genetics (17) pp. 231-234. From: 17th AAABG Conference 2007, 23-26 September 2007, Armidale, NSW, Australia.

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A genome wide selection (GWS) platform was developed for prediction of genetic merit in dairy cattle. The critical components of the GWS platform included a genome wide SNP analysis assay representing 15,036 SNPs, 1546 progeny tested Holstein Friesian sires with EBV (ABV) for 42 lactation performance traits, and a series of complexity reduction methods with internal and external cross validation. Derived Molecular Breeding Values (MBV) using a fraction of the available SNP information, were shown to have high predictive value for genetic merit (r=0.65-0.87 with ABV) in bulls not used in the training data from which the SNP effects were derived. GWS can be used in the absence of SNP location and pedigree to make potentially highly accurate predictions of genetic merit at an early age from DNA analyses.

Item ID: 10163
Item Type: Conference Item (Research - E1)
ISBN: 978-1-921208-13-3
ISSN: 1328-3227
Keywords: quantitative genetics; dairy cattle; selective breeding; genetics; QTL; animal breeding
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Conference theme "Genetic Improvement - Making it Happen"

Date Deposited: 20 Jul 2010 04:12
FoR Codes: 06 BIOLOGICAL SCIENCES > 0604 Genetics > 060408 Genomics @ 50%
07 AGRICULTURAL AND VETERINARY SCIENCES > 0702 Animal Production > 070201 Animal Breeding @ 50%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970107 Expanding Knowledge in the Agricultural and Veterinary Sciences @ 50%
97 EXPANDING KNOWLEDGE > 970106 Expanding Knowledge in the Biological Sciences @ 50%
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