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Genomic selection: the option for new robustness traits?

Published online by Cambridge University Press:  30 July 2013

M. P. L. Calus*
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
D. P. Berry
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
G. Banos
Affiliation:
Animal & Veterinary Sciences Group, SRUC, Roslin Institute Building, Easter Bush, Penicuik, EH25 9RG Scotland, UK
Y. de Haas
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
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 is rapidly becoming the state-of-the-art genetic selection methodology in dairy cattle breeding schemes around the world. The objective of this paper was to explore possibilities to apply genomic selection for traits related to dairy cow robustness. Deterministic simulations indicate that replacing progeny testing with genomic selection may favour genetic response for production traits at the expense of robustness traits, owing to a disproportional change in accuracies obtained across trait groups. Nevertheless, several options are available to improve the accuracy of genomic selection for robustness traits. Moreover, genomic selection opens up the opportunity to begin selection for new traits using specialised reference populations of limited size where phenotyping of large populations of animals is currently prohibitive. Reference populations for such traits may be nucleus-type herds, research herds or pooled data from (international) research experiments or research herds. The RobustMilk project has set an example for the latter approach, by collating international data for progesterone-based traits, feed intake and energy balance-related traits. Reference population design, both in terms of relatedness of the animals and variability in phenotypic performance, is important to optimise the accuracy of genomic selection. Use of indicator traits, combined with multi-trait genomic prediction models, can further contribute to improved accuracy of genomic prediction for robustness traits. Experience to date indicates that for newly recorded robustness traits that are negatively correlated with the main breeding goal, cow reference populations of ⩾10 000 are required when genotyping is based on medium- or high-density single-nucleotide polymorphism arrays. Further genotyping advances (e.g. sequencing) combined with post-genomics technologies will enhance the opportunities for (genomic) selection to improve cow robustness.

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Full Paper
Copyright
Copyright © The Animal Consortium 2013 

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References

Aguilar, I, Misztal, I, Tsuruta, S, Wiggans, GR, Lawlor, TJ 2011. Multiple trait genomic evaluation of conception rate in Holsteins. J Dairy Sci 94, 26212624.Google Scholar
Aguilar, I, Misztal, I, Johnson, D, Legarra, A, Tsuruta, S, Lawlor, T 2010. Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci 93, 743752.Google Scholar
Amer, PR 2012. Turning science on robust cattle into improved genetic selection decisions. Animal 6, 551556.Google Scholar
Amer, PR, Banos, G 2010. Implications of avoiding overlap between training and testing data sets when evaluating genomic predictions of genetic merit. J Dairy Sci 93, 33203330.Google Scholar
Banos, G, Coffey, MP, Veerkamp, RF, Berry, DP, Wall, E 2012. Merging and characterising phenotypic data on conventional and rare traits from dairy cattle experimental resources in three countries. Animal 6, 10401048.CrossRefGoogle ScholarPubMed
Bastin, C, Gengler, N, Soyeurt, H, McParland, S, Wall, E, Calus, MPL 2012. Genome-wide association study for milk fatty acid composition using cow versus bull data. In Proceedings of the 63rd Annual Meeting of the EAAP, Bratislava, Slovakia, p. 93.Google Scholar
Berry, DP, McParland, S, Bastin, C, Wall, E, Gengler, N, Soyeurt, H 2013. Phenotyping of robustness and milk quality. Advances in Animal Biosciences 4, 600605.CrossRefGoogle Scholar
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
Buch, LH, Kargo, M, Berg, P, Lassen, J, Sorensen, AC 2012. The value of cows in reference populations for genomic selection of new functional traits. Animal 6, 880886.Google 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.CrossRefGoogle ScholarPubMed
Calus, MPL, De Haas, Y, Veerkamp, RF , 2012. Increasing accuracy of genomic prediction combining cow and bull reference populations. In Proceedings of the 4th International Conference on Quantitative Genetics, Book of Abstracts, Edinburgh, p. 136.Google Scholar
Calus, MPL, de Haas, Y, Pszczola, M, Veerkamp, RF 2013. Predicted accuracy of and response to genomic selection for new traits in dairy cattle. Animal 7, 183191.Google Scholar
Daetwyler, HD 2009. Genome-wide evaluation of populations. PhD thesis, Wageningen University, Wageningen.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. J Dairy Sci 94, 61226134.Google Scholar
de Haas, Y, Calus, MPL, Veerkamp, RF, Wall, E, Coffey, MP, Daetwyler, HD, Hayes, BJ, Pryce, JE 2012. Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. J Dairy Sci 95, 61036112.Google Scholar
Egger-Danner, C, Willam, A, Fuerst, C, Schwarzenbacher, H, Fuerst-Waltl, B 2012. Hot topic: effect of breeding strategies using genomic information on fitness and health. J Dairy Sci 95, 46004609.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
Hayes, BJ, Bowman, PJ, Chamberlain, AJ, Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92, 433443.Google Scholar
Hickey, J, Kinghorn, B, Tier, B, van der Werf, J, Cleveland, M 2012. A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation. Genetics Selection Evolution 44, 9.CrossRefGoogle Scholar
Jia, Y, Jannink, J-L 2012. Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192, 15131522.Google Scholar
Jiménez-Montero, JA, Gonzalez-Recio, O, Alenda, R 2012. Genotyping strategies for genomic selection in dairy cattle. Animal 6, 12161224.Google Scholar
Lund, M, de Roos, 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
McParland, S, Kearney, JF, Rath, M, Berry, DP 2007. Inbreeding effects on milk production, calving performance, fertility, and conformation in Irish Holstein–Friesians. J Dairy Sci 90, 44114419.Google Scholar
McParland, S, Banos, G, O'Donovan, M, Coffey, M, MCCarthy, B, O'Neill, B, Wall, E, Berry, D 2011. Genetic evaluations for energy balance a real possibility? In Proceedings of the Interbull Open Meeting, Interbull, Stavanger, Norway, pp. 255–259.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
Miglior, F, Muir, BL, Van Doormaal, BJ 2005. Selection indices in Holstein cattle of various countries. J Dairy Sci 88, 12551263.Google Scholar
Pryce, JE, Arias, J, Bowman, PJ, Davis, SR, Macdonald, KA, Waghorn, GC, Wales, WJ, Williams, YJ, Spelman, RJ, Hayes, BJ 2012. Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625,000 single nucleotide polymorphism markers. J Dairy Sci 95, 21082119.Google Scholar
Pszczola, M, Strabel, T, Mulder, HA, Calus, MPL 2012a. Reliability of direct genomic values for animals with different relationships within and to the reference population. J Dairy Sci 95, 389400.Google Scholar
Pszczola, M, Veerkamp, RF, De Haas, Y, Strabel, T, Calus, MPL , 2012b. Predictor traits improve accuracy of genomic breeding values for scarcely recorded traits. In Proceedings of the 4th International Conference on Quantitative Genetics, Book of Abstracts, Edinburgh, p. 151.Google Scholar
Shook, GE 2006. Major advances in determining appropriate selection goals. J Dairy Sci 89, 13491361.Google Scholar
Ten Napel, J, Calus, MPL, Mulder, HA, Veerkamp, RF 2009. Genetic concepts to improve robustness of dairy cows. In Breeding for robustness in cattle (eds. M Klopčič, R Reents, J Philipsson and A Kuipers), EAAP publication no. 126, pp. 3544. Wageningen Academic Publisher, Wageningen, The Netherlands.CrossRefGoogle Scholar
Toro, MA, Varona, L 2010. A note on mate allocation for dominance handling in genomic selection. Genetics Selection Evolution 42.Google Scholar
Tsuruta, S, Misztal, I, Aguilar, I, Lawlor, TJ 2011. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J Dairy Sci 94, 41984204.Google Scholar
Van Grevenhof, E, Van Arendonk, J, Bijma, P 2012. Response to genomic selection: the Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting. Genetics Selection Evolution 44, 26.Google Scholar
Veerkamp, RF, Calus, MPL, De Haas, Y 2012a. Selection for feed intake in dairy cattle using genomic selection. In Proceedings of the ICAR 2012 Conference in Cork, Ireland, pp. 34–35.Google Scholar
Veerkamp, RF, Mulder, HA, Thompson, R, Calus, MPL 2011. Genomic and pedigree-based genetic parameters for scarcely recorded traits when some animals are genotyped. J Dairy Sci 94, 41894197.Google Scholar
Veerkamp, RF, Coffey, MP, Berry, DP, de Haas, Y, Strandberg, E, Bovenhuis, H, Calus, MPL, Wall, E 2012b. Genome-wide associations for feed utilisation complex in primiparous Holstein–Friesian dairy cows from experimental research herds in four European countries. Animal 6, 17381749.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. J Dairy Sci 93, 27572764.Google Scholar
Wall, E, Coffey, M, Veerkamp, RF, McParland, Sand Banos, G , 2011. Lessons learned in pooling data for reference populations. In Proceedings of the Interbull Open Meeting, Interbull, Stavanger, Norway, pp. 12–18.Google Scholar
Wientjes, YCJ, Veerkamp, RF, Calus, MPL 2013. The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction. Genetics 193, 621631.Google Scholar
Wijga, S, Bastiaansen, JWM, Wall, E, Strandberg, E, de Haas, Y, Giblin, L, Bovenhuis, H 2012. Genomic associations with somatic cell score in first-lactation Holstein cows. J Dairy Sci 95, 899908.CrossRefGoogle ScholarPubMed