Industry benefits from using genomic information in two- and three-tier sheep breeding systems
B. J. Horton A B F , R. G. Banks C D and J. H. J. van der Werf A EA Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW 2351, Australia.
B Tasmanian Institute of Agriculture, University of Tasmania, PO Box 46, Kings Meadows, Tas. 7249, Australia.
C Meat and Livestock Australia, c/o Animal Science, University of New England, Armidale, NSW 2351, Australia.
D Present address: Animal Genetics and Breeding Unit, University of New England, Armidale, NSW 2351, Australia.
E School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
F Corresponding author. Email: brian.horton@utas.edu.au
Animal Production Science 55(4) 437-446 https://doi.org/10.1071/AN13265
Submitted: 24 June 2013 Accepted: 23 December 2013 Published: 18 February 2014
Abstract
A model of the sheep breeding industry with nucleus flocks, multiplier flocks and commercial sheep flocks was used to examine the value of genomic selection. The model reflected a dual-purpose Merino breeding objective, with genomic information improving selection accuracy by 39% for rams at 6 months of age and by 17% at 18 months. The current level of net dollar benefit to the sheep industry from selection, but without genomic testing, can be improved by 10–14% for a closed three-tiered breeding structure with rams used at 18 months. If the rams are first used at 6–7 months then the dollar gains can be improved by 15–17%, since genomic information can provide proportionately greater gains for young animals that have limited phenotypic information. In a two-tiered breeding system, with nucleus flocks selling rams direct to commercial producers, rather than through multiplier flocks, the dollar gains to industry from genomic testing increased to ~12–13% for rams bred at 18 months, and 20–22% if nucleus rams are used at 6–7 months. The optimal structure requires two-stage selection, with an initial selection based on information available without genomic testing, to limit the cost of testing to only the superior rams. However, the optimum proportion of rams tested depends on the system and the cost of testing. In order to recover the cost of genomic testing, the nucleus flocks must recover up to 5% of the extra genetic gain as extra profit from sale of rams to commercial sheep producers.
References
Banks RG (1987) The breeding structure of the Merino industry and its influence on genetic progress. In ‘Merino improvement programs in Australia, Proceedings of the national symposium. Leura, NSW’. (Ed. BJ McGuirk) pp. 125–135. (Australian Wool Corporation: Melbourne)Bird PJWN, Mitchell G (1980) The choice of discount rate in animal breeding investment appraisal. Animal Breeding Abstracts 48, 499–505.
Daetwyler HD, Hickey JM, Henshall JM, Dominik S, Gredier B, van der Werf JHJ, Hayes BJ (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 |
Dekkers JCM (2007) Prediction of response to marker assisted and genomic selection using selection index theory. Journal of Animal Breeding and Genetics 124, 331–341.
| Prediction of response to marker assisted and genomic selection using selection index theory.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD2sjjvFyiuw%3D%3D&md5=058598d0d39669ca1207a16f06fae460CAS |
Dominik S, van der Werf JHJ, Banks RG (2011) The value of genomic selection for stud and commercial Merino rams. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 19, 323–326.
Hill WG (1974) Prediction and evaluation of response to selection with overlapping generations. Animal Production 18, 117–139.
| Prediction and evaluation of response to selection with overlapping generations.Crossref | GoogleScholarGoogle Scholar |
Horton BJ (1996) A method of using a genetic algorithm to examine the optimum structure of the Australian sheep breeding industry: open-nucleus breeding systems, MOET and AI. Australian Journal of Experimental Agriculture 36, 249–258.
| A method of using a genetic algorithm to examine the optimum structure of the Australian sheep breeding industry: open-nucleus breeding systems, MOET and AI.Crossref | GoogleScholarGoogle Scholar |
Horton B (2013) A method for estimating the economic value of changes in the risk of breech strike. Animal Production Science 53, 1126–1133.
| A method for estimating the economic value of changes in the risk of breech strike.Crossref | GoogleScholarGoogle Scholar |
James JW (1977) Open nucleus breeding schemes. Animal Production 24, 287–305.
| Open nucleus breeding schemes.Crossref | GoogleScholarGoogle Scholar |
James JW (1978) Effective population size in open nucleus breeding schemes. Acta Agriculturae Scandinavica 28, 387–392.
| Effective population size in open nucleus breeding schemes.Crossref | GoogleScholarGoogle Scholar |
James JW (1987) Determination of optimal selection polices. Journal of Animal Breeding and Genetics 104, 23–27.
| Determination of optimal selection polices.Crossref | GoogleScholarGoogle Scholar |
Jantaravareerat M, Thomopoulos NT (1998) Some new tables for the standard bivariate normal distribution. In ‘Proceedings of the 30th symposium on the interface of computing science and statistics, May 1998. Minneapolis, Minnesota’. pp. 179–184. (Interface Foundation of North America: Fairfax Station, VA)
Jopson NB, Amer PR, McEwan JC (2004) Comparison of two-stage selection breeding programmes for terminal sire sheep. Proceedings of the New Zealand Society of Animal Production 64, 212–216.
Keller DS, Gearheart WW, Smith C (1990) A comparison of factors reducing selection response in closed nucleus breeding schemes. Journal of Animal Science 68, 1553–1561.
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
Pollak EJ, Bennett GL, Snelling WM, Thallman RM, Kuehn LA (2012) Genomics and the global cattle industry. Animal Production Science 52, 92–99.
| Genomics and the global cattle industry.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XjtlSjtbk%3D&md5=ec79edaf99cf665c61a1912b9ffe5d4cCAS |
Ponzoni RW (1988) The derivation of economic values combining income and expenses in different ways: an example with the Australian Merino sheep. Journal of Animal Breeding and Genetics 105, 143–153.
| The derivation of economic values combining income and expenses in different ways: an example with the Australian Merino sheep.Crossref | GoogleScholarGoogle Scholar |
Sise JA, Amer PR (2009) SNP predictors to accelerate the rate of genetic progress in sheep. Proceedings 18th Meeting of the Association for the Advancement of Animal Breeding and Genetics 18, 220–223.
Sise JA, Linscott EM, Amer PR (2011) Predicting the benefits of two stage genomic selection in young breeding rams. Proceedings of the New Zealand Society of Animal Production 71, 266–269.
Spelman RJ, Hayes BJ, Berry DP (2013) Use of molecular technologies for the advancement of animal breeding: genomic selection in dairy cattle populations in Australia, Ireland and New Zealand. Animal Production Science 53, 869–875.
Swan AA, Johnston DJ, Brown DJ, Tier B, Graser H-U (2012) Integration of genomic information into beef cattle and sheep genetic evaluations in Australia. Animal Production Science 52, 126–132.
| Integration of genomic information into beef cattle and sheep genetic evaluations in Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XjtlSjtbY%3D&md5=5a5c2ba7c7d0a31cb98567d5f3ac8018CAS |
Turner HN, Young SSY (1969) ‘Quantitative genetics in sheep breeding.’ (Macmillan: Melbourne)
van der Werf JHJ (2009) Potential benefits of genomic selection in sheep. Proceedings of the 18th Meeting of the Association for the Advancement of Animal Breeding and Genetics 18, 38–41.
van der Werf JHJ, Horton BJ, Banks RG (2011) Optimising sheep breeding programs with genomic selection. Proceedings 19th Meeting of the Association for the Advancement of Animal Breeding and Genetics 19, 315–322.
While L, Barone L, Hingston P, Huband S, Tuppurainen D, Bearman R (2004) A multi-objective evolutionary algorithm approach for crusher optimisation and flowsheet design. Minerals Engineering 17, 1063–1074.
Wickham BW, Am PR, Berry DP, Burke M, Coughlan S, Cromie A, Kearney JF, McHugh N, McParland S, O’Conell K (2012) Industrial perspective: capturing the benefits of genomics to Irish cattle breeding. Animal Production Science 52, 172–179.
| Industrial perspective: capturing the benefits of genomics to Irish cattle breeding.Crossref | GoogleScholarGoogle Scholar |
Wray NR, Woolliams JA, Thompson R (1994) Prediction of rates of inbreeding in populations undergoing index selection. Theoretical and Applied Genetics 87, 878–892.
| Prediction of rates of inbreeding in populations undergoing index selection.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2c7jtVCmsg%3D%3D&md5=2c4f48177e256087f4239883def9a454CAS | 24190476PubMed |
