[en] The ultimate goal of animal selection is to create a new generation of animals that are superior to the current population. Superior is interpreted broadly to include functionality of animals, cost reduction of production, consumer perception, quality of products, and reduced environmental impact. These factors contribute to overall sustainability and long-term economic profitability of animal production. An essential element of selection is a genetic evaluation system for the detection of superior animals to be used to produce future generations. Current genetic evaluations use phenotypic records and advanced statistical methods to separate genetic and environmental effects. These traditional methods are complemented by DNA-based technologies that provide genetic information at a molecular level. Genetic evaluation systems are highly complex and involve collection of data from thousands of farms, determination of milk characteristics in laboratories, processing and storage of data in regional computing centers, and application of advanced statistical procedures to estimate genetic merit. Genetic evaluations are widely distributed and are the primary determiner of the value of semen and embryos. Internationally, bull evaluations are combined across countries so that each country has a single national ranking of all bulls worldwide. Selection decisions on farms and by artificial insemination organizations are highly dependent on that genetic information. This article covers aspects of genetic selection that stretch from basic data collection (including identification systems), traits recorded and evaluated, and characteristics of current and future evaluation systems to new DNA-based technologies.
Disciplines :
Genetics & genetic processes Animal production & animal husbandry
Author, co-author :
Wiggans, George R.
Gengler, Nicolas ; Université de Liège - ULiège > Sciences agronomiques > Zootechnie
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Bibliography
Bourdon, R.M., 2000. Understanding Animal Breeding, second ed. Prentice Hall, Upper Saddle River, NJ.
Bulmer, M.G., 1985. The Mathematical Theory of Quantitative Genetics. Oxford University Press, New York.
Egger-Danner, C., Cole, J.B., Pryce, J.E., et al., 2015. Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits. Animal 9, 191-207.
Garrick, D.J., Ruvinsky, A., 2015. The Genetics of Cattle, second ed. CABI Wallingford, UK.
Gianola, D., Hammond, K., 1990. Advanced Series in Agricultural Sciences 18: Advances in Statistical Methods for Genetic Improvement of Livestock. Springer-Verlag, New York.
Grosu, H., Schaeffer, L., Oltenacu, P.A., et al., 2013. History of Genetic Evaluation Methods in Dairy Cattle. The Publishing House of the Romanian Academy Bucuresti, Romania.
Henderson, C.R., 1984. Applications of Linear Models in Animal Breeding. University of Guelph Guelph, ON.
Hill, W.G., Mackay, T.F.C., 1989. Evolution and Animal Breeding: Reviews on Molecular and Quantitative Approaches in Honour of Alan Robertson. CABI Wallingford, UK.
Lush, J.L., 1945. Animal Breeding Plans, third ed. The Collegiate Press, Ames, IA.
Lynch, M., Walsh, B., 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Sunderland, MA.
Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829.
Mrode, R.A., 2014. Linear Models for the Prediction of Animal Breeding Values, third ed. CABI Wallingford, UK.
van der Werf, Pryce, J.E., 2019. Advances in Breeding of Dairy Cattle. Burleigh Dodds Publishing, Cambridge, UK.
Van Vleck, L.D., 1993. Selection Index and Introduction to Mixed Model Methods. CRC Press, Boca Raton, FL.
Van Vleck, L.D., Pollak, E.J., Oltenacu, E.A.B., 1987. Genetics for the Animal Sciences. W.H. Freeman & Co., New York.
VanRaden, P.M., 2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414-4423.
VanRaden, P.M., Van Tassell, C.P., Wiggans, G.R., et al., 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92, 16-24.
Wiggans, G.R., VanRaden, P.M., Cooper, T.A., 2011. The genomic evaluation system in the United States: past, present. Future J. Dairy Sci. 94, 3202-3211.
Wiggans, G.R., Cole, J.B., Hubbard, S.M., et al., 2017. Genomic selection in dairy cattle: the USDA experience. Annu. Rev. Anim. Biosci. 5, 309-327.
https://www.cdcb.us - Council on Dairy Cattle Breeding.
http://www.interbull.org - International Bull Evaluation Service.
http://www.icar.org - International Committee for Animal Recording.
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