Design and role of an information nucleus in sheep breeding programs
J. H. J. van der Werf A B D , B. P. Kinghorn A B and R. G. Banks A CA Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW 2351, Australia.
B School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
C Meat & Livestock Australia c/o Animal Science, University of New England, Armidale, NSW 2351, Australia.
D Corresponding author. Email: jvanderw@une.edu.au
Animal Production Science 50(12) 998-1003 https://doi.org/10.1071/AN10151
Submitted: 13 August 2010 Accepted: 20 October 2010 Published: 23 November 2010
Abstract
The Australian sheep Cooperative Research Centre has initiated an information nucleus with the aim to estimate genetic parameters for new traits, to undertake a large-scale whole-genome association study and to enhance the breeding values of breeding animals in commercial studs. This paper presents the rationale behind the current design factors to meet the main objectives. It then discusses the potential design of an information nucleus if it were a sustainable part of commercial sheep-breeding programs in the long term. Advantages of such an information nucleus are summarised and quantified where possible.
References
Cameron ND, Thompson R (1986) Design of multivariate selection experiments to estimate genetic parameters. Theoretical and Applied Genetics 72, 466–476.| Design of multivariate selection experiments to estimate genetic parameters.Crossref | GoogleScholarGoogle Scholar |
Daetwyler HD, Hickey JM, Henshall J, Dominik S, Gredler B, van der Werf JHJ, Hayes JHJ (2010) Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population. Animal Production Science 50, 1004–1010.
| Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population.Crossref | GoogleScholarGoogle Scholar |
Deepani MLNAR, Kinghorn BP (2009) Use of genotype probabilities and selective genotyping for estimation of marker effects. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 18, 68–71.
Goddard ME (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257.
| Genomic selection: prediction of accuracy and maximisation of long term response.Crossref | GoogleScholarGoogle Scholar | 18704696PubMed |
Goodswen SJ, Gondro C, Kadarmideen HN, van der Werf JHJ (2010) Evaluating haplotype diversity within and between Australian sheep breeds. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production. Communication PP3-84.
Kinghorn BP, Meszaros SA, Vagg RD (2002) Dynamic and tactical decision systems for animal breeding. Proceedings of the 79th Conference on Genetics Applied to Livestock Production. Leipzig, Montpellier, France. Communication No. 23–07.
Kinghorn BP, Bastiaansen JWM, van der Steen HAM, Deeb N, Yu N, Mileham AJ (2006) Proceedings of the 8th World Congress on Genetics Applied to Livestock Production. Paper 20-05.
Meuwissen THE (1997) Maximizing the response of selection with a predefined rate of inbreeding. Journal of Animal Science 75, 934–940.
Robertson A (1959a) Experimental design in the evaluation of genetic parameters. Biometrics 15, 219–226.
| Experimental design in the evaluation of genetic parameters.Crossref | GoogleScholarGoogle Scholar |
Robertson A (1959b) The sampling variance of the genetic correlation coefficient. Biometrics 15, 469–485.
| The sampling variance of the genetic correlation coefficient.Crossref | GoogleScholarGoogle Scholar |
van der Werf JHJ (2009) Potential benefit of genomic selection in sheep. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 18, 38–41.
van Dijk G, van Boekel P (2001) Governance of innovation in animal production: new roles for science, business and the public sector. Livestock Production Science 72, 9–23.
| Governance of innovation in animal production: new roles for science, business and the public sector.Crossref | GoogleScholarGoogle Scholar |
