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Turning science on robust cattle into improved genetic selection decisions

Published online by Cambridge University Press:  19 December 2011

P. R. Amer
P. R. Amer*
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
*
†E-mail: [email protected]
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Abstract

More robust cattle have the potential to increase farm profitability, improve animal welfare, reduce the contribution of ruminant livestock to greenhouse gas emissions and decrease the risk of food shortages in the face of increased variability in the farm environment. Breeding is a powerful tool for changing the robustness of cattle; however, insufficient recording of breeding goal traits and selection of animals at younger ages tend to favour genetic change in productivity traits relative to robustness traits. This paper has extended a previously proposed theory of artificial evolution to demonstrate, using deterministic simulation, how choice of breeding scheme design can be used as a tool to manipulate the direction of genetic progress, whereas the breeding goal remains focussed on the factors motivating individual farm decision makers. Particular focus was placed on the transition from progeny testing or mass selection to genomic selection breeding strategies. Transition to genomic selection from a breeding strategy where candidates are selected before records from progeny being available was shown to be highly likely to favour genetic progress in robustness traits relative to productivity traits. This was shown even with modest numbers of animals available for training and when heritability for robustness traits was only slightly lower than that for productivity traits. When transitioning from progeny testing to a genomic selection strategy without progeny testing, it was shown that there is a significant risk that robustness traits could become less influential in selection relative to productivity traits. Augmentations of training populations using genotyped cows and support for industry-wide improvements in phenotypic recording of robustness traits were put forward as investment opportunities for stakeholders wishing to facilitate the application of science on robust cattle into improved genetic selection schemes.

Keywords

selection indexartificial evolutiongenomic selection
Type
Full Paper
Information
animal , Volume 6 , Issue 4 , April 2012 , pp. 551 - 556
Copyright
Copyright © The Animal Consortium 2011

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References

Amer, PR 1994. Economic theory and breeding objectives. Proceedings of the 5th World Congress on Genetics Applied to Livestock Production, Guelph, Ontario, Canada, 18, 197–204.Google Scholar
Amer, PR, Banos, G 2010. Implications of avoiding overlap between training and testing data sets when evaluating genomic predictions of genetic merit. Journal of Dairy Science 93, 3320–3330.CrossRefGoogle ScholarPubMed
Berry, DP, Buckley, F, Dillon, P, Evans, RD, Rath, M, Veerkamp, RF 2003. Estimation of genotype × environment interactions, in a grass-based system, for milk yield, body condition score, and body weight using random regression models. Livestock Production Science 83, 191–203.CrossRefGoogle Scholar
Daetwyler, HD, Pong-Wong, R, Villanueva, B, Woolliams, JA 2010. The impact of genetic architecture on genome-wide evaluation methods. Genetics 185, 1021–1031.Google Scholar
Fulkerson, WJ, Davison, TM, Garcia, SC, Hough, G, Goddard, ME, Dobos, R, Blockey, M 2008. Holstein-Friesian dairy cows under a predominantly grazing system: Interaction between genotype and environment. Journal of Dairy Science 91, 826–839.Google Scholar
Gibson, JP 1989. Selection strategies and artificial evolution. Theoretical and Applied Genetics 78, 87–92.Google Scholar
Goddard, ME 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–252.Google Scholar
Habier, D, Tetens, J, Seefried, FR, Lichtner, P, Thaller, G 2010. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics Selection Evolution 2010, 42:5, doi:10.1186/1297-9686-42-5.CrossRefGoogle ScholarPubMed
Harris, BL, Winkelman, AM 2000. Influence of North American Holstein genetics on dairy cattle performance in New Zealand. Proceedings of the Australian Large Herds Conference 6, 122–136.Google Scholar
Lopez-Villalobos, N 2010. Past, present and future breeding objectives in dairy cattle. Updates on ruminant production and medicine. XXVI World Buiatrics Congress, 14–18 November 2010, Santiago, Chile, pp. 114–125.Google Scholar
Meuwissen, THE, Hayes, BJ, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.Google Scholar
Miglior, F, Muir, BL, Van Doormaal, BJ 2005. Selection indices in Holstein cattle of various countries. Journal of Dairy Science 88, 1255–1263.CrossRefGoogle ScholarPubMed
Morris, CA, Baker, RL, Hickey, SM, Johnson, DL, Cullen, NG, Wilson, JA 1993. Evidence of genotype by environment interaction for reproductive and maternal traits in beef cattle. Animal Production 56, 69–83.Google Scholar
Nielsen, HM, Olesen, I, Navrud, S, Kolstad, K, Amer, PR 2011. How to consider the value of farm animals in breeding goals. A review of current status and future challenges. Journal of Agricultural and Environmental Ethics. 24, 309–330.Google Scholar
Olesen, I, Groen, AF, Gjerde, B 2000. Definition of animal breeding goals for sustainable production systems. Journal of Animal Science 78, 570–582.Google Scholar
Orskov, ER 1993. Reality in rural development aid with emphasis on livestock. Rowett Research Services Ltd, Aberdeen, United Kingdom.Google Scholar
Rauw, WM, Kanis, E, Noordhuizen-Stassen, EN, Grommers, FJ 1998. Undesirable side effects of selection for high production efficiency in farm animals: a review. Livestock Production Science 56, 15–33.CrossRefGoogle Scholar
Roughsedge, T, Amer, PR, Thompson, R, Simm, G 2005. Development of a maternal breeding goal and tools to select for this goal in UK beef production. Animal Science 81, 221–232.CrossRefGoogle Scholar
VanRaden, PM, Sanders, AH, Tooker, ME, Miller, RH, Norman, HD, Kuhn, MT, Wiggans, GR 2004. Development of a national genetic evaluation for cow fertility. Journal of Dairy Science 87, 2285–2292.CrossRefGoogle ScholarPubMed
Veerkamp, RF, Simm, G, Oldham, JD 1994. Effects of interaction between genotype and feeding system on milk production, feed intake, efficiency and body tissue mobilization in dairy cows. Livestock Production Science 39, 229–241.CrossRefGoogle Scholar
Wall, E 2010. Dairy breeding goals in a changing world. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Liepzig, Germany. Paper 41.Google Scholar
Woolliams, JA, VanRaden, PM, Daetwyler, HD 2010. How much genetic variation is explained by dense SNP chips? Book of Abstracts of the 61st Annual Meeting of the European Association for Animal Production, Heraklion, Greece, 175pp.Google Scholar