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Predicted accuracy of and response to genomic selection for new traits in dairy cattle

Published online by Cambridge University Press:  06 July 2012

M. P. L. Calus*
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
Y. de Haas
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
M. Pszczola
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands Animal Breeding and Genomics Centre, Wageningen University, 6700 AH Wageningen, The Netherlands Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska 33, 60-637 Poznan, Poland
R. F. Veerkamp
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
*
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Abstract

Genomic selection relaxes the requirement of traditional selection tools to have phenotypic measurements on close relatives of all selection candidates. This opens up possibilities to select for traits that are difficult or expensive to measure. The objectives of this paper were to predict accuracy of and response to genomic selection for a new trait, considering that only a cow reference population of moderate size was available for the new trait, and that selection simultaneously targeted an index and this new trait. Accuracy for and response to selection were deterministically evaluated for three different breeding goals. Single trait selection for the new trait based only on a limited cow reference population of up to 10 000 cows, showed that maximum genetic responses of 0.20 and 0.28 genetic standard deviation (s.d.) per year can be achieved for traits with a heritability of 0.05 and 0.30, respectively. Adding information from the index based on a reference population of 5000 bulls, and assuming a genetic correlation of 0.5, increased genetic response for both heritability levels by up to 0.14 genetic s.d. per year. The scenario with simultaneous selection for the new trait and the index, yielded a substantially lower response for the new trait, especially when the genetic correlation with the index was negative. Despite the lower response for the index, whenever the new trait had considerable economic value, including the cow reference population considerably improved the genetic response for the new trait. For scenarios with a zero or negative genetic correlation with the index and equal economic value for the index and the new trait, a reference population of 2000 cows increased genetic response for the new trait with at least 0.10 and 0.20 genetic s.d. per year, for heritability levels of 0.05 and 0.30, respectively. We conclude that for new traits with a very small or positive genetic correlation with the index, and a high positive economic value, considerable genetic response can already be achieved based on a cow reference population with only 2000 records, even when the reliability of individual genomic breeding values is much lower than currently accepted in dairy cattle breeding programs. New traits may generally have a negative genetic correlation with the index and a small positive economic value. For such new traits, cow reference populations of at least 10 000 cows may be required to achieve acceptable levels of genetic response for the new trait and for the whole breeding goal.

Type
Breeding and genetics
Copyright
Copyright © The Animal Consortium 2012

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References

Berry, DP, Bastiaansen, JWM, Veerkamp, RF, Wijga, S, Wall, E, Berglund, B, Calus, MPL 2012. Genome-wide associations for fertility traits in Holstein–Friesian dairy cows using data from experimental research herds in four European countries. Animal 6, 12061215.Google Scholar
Bijma, P. Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations. Journal of Animal Breeding and Genetics, doi:10.1111/j.1439-0388.2012.00991.x, Published online by Blackwell Verlag GmbH 22 February 2012.Google Scholar
Buch, LH, Sørensen, MK, Berg, P, Pedersen, LD, Sørensen, AC 2012. Genomic selection strategies in dairy cattle: strong positive interaction between use of genotypic information and intensive use of young bulls on genetic gain. Journal of Animal Breeding and Genetics 129, 138151.Google Scholar
Bulmer, M 1971. The effect of selection on genetic variability. The American Naturalist 105, 201211.CrossRefGoogle Scholar
Calus, MPL 2010. Genomic breeding value prediction: methods and procedures. Animal 4, 157164.Google Scholar
Calus, MPL, Veerkamp, RF 2011. Accuracy of multi-trait genomic selection using different methods. Genetics Selection Evolution 43, 26.Google Scholar
Daetwyler, HD 2009. Genome-wide evaluation of populations. PhD thesis. Wageningen University.Google Scholar
Daetwyler, HD, Villanueva, B, Woolliams, JA 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3, e3395.Google Scholar
de Haas, Y, Windig, JJ, Calus, MPL, Dijkstra, J, de Haan, M, Bannink, A, Veerkamp, RF 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. Journal of Dairy Science 94, 61226134.Google Scholar
de Roos, APW, Hayes, BJ, Goddard, ME 2009. Reliability of genomic predictions across multiple populations. Genetics 183, 15451553.Google Scholar
de Roos, APW, Schrooten, C, Veerkamp, RF, van Arendonk, JAM 2011. Effects of genomic selection on genetic improvement, inbreeding, and merit of young versus proven bulls. Journal of Dairy Science 94, 15591567.Google Scholar
Dekkers, J 2007. Prediction of response to marker-assisted and genomic selection using selection index theory. Journal of Animal Breeding and Genetics 124, 331341.Google Scholar
Goddard, M 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245257.Google Scholar
Habier, D, Fernando, R, Dekkers, J 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 23892397.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 42, 5.Google Scholar
Hayes, B, Bowman, P, Chamberlain, A, Goddard, M 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433443.Google Scholar
Kirkpatrick, BW, Shi, X, Shook, GE, Collins, MT 2011. Whole-genome association analysis of susceptibility to paratuberculosis in Holstein cattle. Animal Genetics 42, 149160.Google Scholar
Lillehammer, M, Meuwissen, THE, Sonesson, AK 2011. A comparison of dairy cattle breeding designs that use genomic selection. Journal of Dairy Science 94, 493500.Google Scholar
Lund, M, de Ross, S, de Vries, A, Druet, T, Ducrocq, V, Fritz, S, Guillaume, F, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, F, Su, G 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genetics Selection Evolution 43, 43.Google Scholar
Meuwissen, THE, Hayes, BJ, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 18191829.Google Scholar
Mrode, R 2005. Linear models for the prediction of animal breeding values. CABI Publishing, Wallingford.Google Scholar
Pedersen, LD, Kargo, M, Berg, P, Voergaard, J, Buch, LH, Sørensen, AC 2012. Genomic selection strategies in dairy cattle breeding programmes: sexed semen cannot replace multiple ovulation and embryo transfer as superior reproductive technology. Journal of Animal Breeding and Genetics 129, 152163.Google Scholar
Pérez-Cabal, MA, Vazquez, AI, Gianola, D, Rosa, GJM, Weigel, KA 2012. Accuracy of genome enabled prediction in a dairy cattle population using different cross-validation layouts. Frontiers in Genetics 3, 27.CrossRefGoogle Scholar
Pryce, JE, Goddard, ME, Raadsma, HW, Hayes, BJ 2010. Deterministic models of breeding scheme designs that incorporate genomic selection. Journal of Dairy Science 93, 54555466.Google Scholar
Pszczola, M, Strabel, T, Mulder, HA, Calus, MPL 2012. Reliability of direct genomic values for animals with different relationships within and to the reference population. Journal of Dairy Science 95, 389400.Google Scholar
Rutten, MJM, Bijma, P, Woolliams, JA, van Arendonk, JAM 2002. SelAction: software to predict selection response and rate of inbreeding in livestock breeding programs. Journal of Heredity 93, 456458.Google Scholar
Schrooten, C, Bovenhuis, H, van Arendonk, JAM, Bijma, P 2005. Genetic progress in multistage dairy cattle breeding schemes using genetic markers. Journal of Dairy Science 88, 15691581.Google Scholar
Sigurdsson, A, Banos, G 1995. Dependent-variables in international sire evaluations. Acta Agriculturae Scandinavica Section A – Animal Science 45, 209217.Google Scholar
Van Grevenhof, EM 2011. Breeding against osteochondrosis. PhD thesis. Wageningen University.Google Scholar
VanRaden, PM, Wiggans, GR 1991. Derivation, calculation, and use of national animal-model information. Journal of Dairy Science 74, 27372746.Google Scholar
Veerkamp, RF, Oldenbroek, JK, Van Der Gaast, HJ, Van Der Werf, JHJ 2000. Genetic correlation between days until start of luteal activity and milk yield, energy balance, and live weights. Journal of Dairy Science 83, 577583.Google Scholar
Verbyla, KL, Calus, MPL, Mulder, HA, de Haas, Y, Veerkamp, RF 2010. Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information. Journal of Dairy Science 93, 27572764.Google Scholar
Wall, E, Simm, G, Moran, D 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4, 366376.Google Scholar
Zhong, S, Dekkers, JCM, Fernando, RL, Jannink, J-L 2009. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182, 355364.CrossRefGoogle Scholar