Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T10:54:03.443Z Has data issue: false hasContentIssue false
  • English
  • Français

Integrating genomic selection into dairy cattle breeding programmes: a review

Published online by Cambridge University Press:  03 December 2012

A. Bouquet  and
J. Juga
A. Bouquet*
Affiliation:
Department of Agricultural Sciences, University of Helsinki, PO Box 27, FI-00014, Helsinki, Finland
J. Juga
Affiliation:
Department of Agricultural Sciences, University of Helsinki, PO Box 27, FI-00014, Helsinki, Finland
*
†E-mail: alban.bouquet@gmail.com
Get access

Abstract

Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However, recent simulation studies have shown that putting constraints on genomic inbreeding rates for defining optimal contributions of breeding animals could significantly reduce achievable genetic gain. Finally, the article summarizes the potential of genomic selection to include new traits in the breeding goal to meet societal demands regarding animal health and environmental efficiency in animal production.

Keywords

genomic selectionbreeding programmedairy cattlegenetic gaininbreeding rates
Type
Breeding and genetics
Information
animal , Volume 7 , Issue 5 , May 2013 , pp. 705 - 713
Copyright
Copyright © The Animal Consortium 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdel-Azim, G, Schnell, S 2007. Genetic impacts of using female-sorted semen in commercial herds. Journal of Dairy Science 90, 1554–1563.Google Scholar
Agerholm, JS, Bendixen, C, Andersen, O, Arnbjerg, J 2001. Complex vertebral malformation in Holstein calves. Journal of Veterinary Diagnostic Investigation 13, 283–289.CrossRefGoogle ScholarPubMed
Bastiaansen, JWM, Costar, A, Calus, MPL, van Arendonk, JAM, Bovenhuis, H 2012. Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures. Genetics Selection Evolution 44, 3.Google Scholar
Boichard, D, Brochard, M 2012. New phenotypes for new breeding goals in dairy cattle. Animal 6, 544–550.Google Scholar
Boichard, D, Ducrocq, V, Fritz, S, Colleau, JJ 2010. Where is dairy cattle breeding going? A vision of the future. Interbull Bulletin 41, 63–68.Google Scholar
Brøndum, RF, Rius-Vilarrasa, E, Strandén, I, Su, G, Gulbrandtsen, B, Fikse, WF, Lund, MS 2011. Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations. Journal of Dairy Science 94, 4700–4707.Google Scholar
Buch, LH, Sørensen, MK, Berg, P, Pedersen, LD, Sørensen, AC 2012a. Genomic selection strategies in dairy cattle: strong positive interaction between use of genotypic information and intensive use of bulls on genetic gain. Journal of Animal Breeding and Genetics 129, 138–151.CrossRefGoogle ScholarPubMed
Buch, LH, Kargo, M, Berg, P, Lassen, J, Sørensen, AC 2012b. The value of cows in reference populations for genomic selection of new functional traits. Animal 6, 880–886.Google Scholar
Bulmer, MG 1980. The mathematical theory of quantitative genetics. Oxford University Press, Oxford, UK. 254pp.Google Scholar
Calus, MPL, Veerkamp, RF 2011. Accuracy of multi-trait genomic selection using different methods. Genetics Selection Evolution 43, 26.Google Scholar
Calus, MPL, de Haas, Y, Pszczola, M, Veerkamp, RF 2011. Predicted response of genomic selection for new traits using combined cow and bull reference populations. Interbull Bulletin 44, 231–234.Google Scholar
Colleau, JJ, Fritz, S, Guillaume, F, Baur, A, Dupassieux, D, Boscher, MY, Journaux, L, Eggen, A, Boichard, D 2009. Simulating the potential of genomic selection in dairy cattle breeding. Proceedings of the 16th Rencontres Recherches Ruminants, Institut de l′Elevage, Paris, France, 419pp.Google Scholar
Colleau, JJ, Fritz, S, Ponsart, C, Le Bourhis, D, Lacaze, S, Tissier, M, Mervant, G, Amigues, Y, Druet, T, Malafosse, A, Boichard, D, Humblot, P 2008. The value of embryo typing in a marker assisted selection program for dairy cattle. Proceedings of the 15th Rencontres Recherches Ruminants, Institut de l′Elevage, Paris, France, pp. 427–430.Google Scholar
Daetwyler, HD, Villanueva, B, Bijma, P, Woolliams, JA 2007. Inbreeding in genome-wide selection. Journal of Animal Breeding and Genetics 124, 369–376.Google 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
Dassonneville, R, Brøndum, RF, Druet, T, Fritz, S, Guillaume, F, Guldbrandtsen, B, Lund, MS, Ducrocq, V, Su, G 2011. Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations. Journal of Dairy Science 94, 3679–3686.Google Scholar
de Cara, MAR, Fernandez, J, Toro, MA, Villanueva, B 2011. Using genome-wide information to minimize the loss of diversity in conservation programmes. Journal of animal Breeding and Genetics 128, 456–464.Google 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.Google Scholar
Dekkers, JCM, Gibson, JP, Bijma, P, Van Arendonk, JAM 2004. Design and optimization of animal breeding programmes. Lecture notes for the ‘Animal Breeding Strategies’ Course, Iowa State University. Retrieved December 19, 2011, from http://www.anslab.iastate.edu/Class/AnS652X/Chapter1,2.pdfGoogle 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, 1559–1567.Google Scholar
Engelsma, KA, Veerkamp, RF, Calus, MPL, Windig, JJ 2011. Consequences when prioritizing animals for conservation with pedigree or genomic information. Journal of Animal Breeding and Genetics 128, 473–481.Google Scholar
Fernández, J, Meuwissen, THE, Toro, MA, Mäki-Tanila, A 2011. Management of genetic diversity in small farm animal populations. Animal 5, 1684–1698.Google Scholar
Food and Agriculture Organization (FAO) 1998. Secondary guidelines for development of national farm animal genetic resources management plans: management of small populations at risk, FAO, Roma, Italy, 219 pp.Google Scholar
Fritz, S, Guillaume, F, Croiseau, P, Baur, A, Hozé, C, Dassonneville, R, Boscher, MY, Journaux, L, Boichard, D, Ducrocq, V 2010. Implementing genomic selection in the three main French dairy cattle breeds. Proceedings of the 17th Rencontres Recherches Ruminants, Institut de l′Elevage, Paris, France, pp. 455–458.Google Scholar
Georges, M, Massey, JM 1991. Velogenetics, or the synergistic use of marker-assisted selection and germ-line manipulation. Theriogenology 35, 151–159.Google Scholar
Goddard, ME 2009. Genomic selection: prediction of accuracy and maximization of long term response. Genetica 136, 245–257.Google Scholar
Goddard, ME, Hayes, BJ, Meuwissen, THE 2011. Using the genomic relationship matrix to predict the accuracy of genomic selection. Journal of Animal Breeding and Genetics 128, 409–421.Google Scholar
Habier, D, Fernando, RL, Dekkers, JCM 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397.Google Scholar
Harris, BL, Johnson, DL 2010. Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation. Journal of Dairy Science 93, 1243–1252.Google Scholar
Harris, BL, Creagh, FE, Winkelman, AM, Johnson, DL 2011. Experiences with the Illumina high-density Bovine BeadChip. Interbull Bulletin 44, 3–7.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ, Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.Google Scholar
Hu, XS, Li, B 2006. Additive genetic variation and the distribution of QTN effects among sites. Journal of Theoretical Biology 243, 76–85.CrossRefGoogle ScholarPubMed
Humblot, P, Le Bourhis, D, Fritz, S, Colleau, JJ, Gonzalez, C, Guyader-Joly, C, Malafosse, A, Heyman, Y, Amigues, Y, Tissier, M, Ponsart, C 2010. Reproductive technologies and genomic selection in cattle. Veterinary Medicine International 2010, e192787.Google Scholar
Ibanez-Escriche, N, Gonzalez-Recio, O 2011. Promises, pitfalls and challenges in genomic selection in breeding programs. Spanish Journal of Agricultural research 9, 404–413.Google Scholar
Jorjani, H, Zumbach, B, Dürr, J, Santus, E 2011. Joint genomic evaluation in Brown Swiss populations. Interbull Bulletin 41, 8–14.Google Scholar
König, S, Swalve, HH 2009. Application of selection index calculations to determine selection strategies in genomic breeding programs. Journal of Dairy Science 92, 5292–5303.Google Scholar
König, S, Simianer, H, Willam, A 2009. Economic evaluation of genomic breeding programmes. Journal of Dairy Science 92, 382–391.Google Scholar
Korpiaho, PI, Strandén, I, Mäntysaari, EA 2003. Bull selection across age classes and variable female reproductive rates in an open nucleus breeding scheme of dairy cattle. Acta Agriculturae Scandinavia A: Animal Science 53, 74–82.Google Scholar
Lande, R, Thompson, R 1990. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124, 743–756.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, 493–500.CrossRefGoogle ScholarPubMed
Lund, MS, de Roos, APW, de Vries, AG, Druet, T, Ducrocq, V, Fritz, S, Guillaume, G, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, M, Su, G 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genetics Selection Evolution 43, 43.Google Scholar
Mackay, TFC, Stone, EA, Ayroles, JF 2009. The genetics of quantitative traits: challenges and prospects. Nature Reviews Genetics 10, 565–577.Google Scholar
McHugh, N, Meuwissen, THE, Cromie, AR, Sonesson, AK 2011. Use of female information in dairy cattle genomic breeding programs. Journal of Dairy Science 94, 4109–4118.Google Scholar
Meuwissen, THE 1997. Maximizing the response to selection with a predefined rate of inbreeding. Journal of Animal Science 75, 934–940.CrossRefGoogle ScholarPubMed
Meuwissen, THE 2009. Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genetics Selection Evolution 41, 35.Google Scholar
Meuwissen, THE, Woolliams, JA 1994. Effective sizes of livestock populations to prevent a decline in fitness. Theoretical Applied Genetics 89, 1019–1026.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
Muir, WM 2007. Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. Journal of Animal Breeding and Genetics 124, 342–355.Google Scholar
Patry, C, Jorjani, H, Ducrocq, V 2011. Implementation of genomic selection at national level: impact of pre-selection and biased national BLUP evaluations on international genetic evaluations. Interbull Bulletin 44, 67–73.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, 152–163.Google Scholar
Pryce, JE, Daetwyler, HD 2012. Designing dairy cattle breeding schemes under genomic selection: a review of internation research. Animal Production Science 52, 107–114.Google 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, 5455–5466.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, 389–400.Google Scholar
Schaeffer, LR 2006. Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218–223.Google Scholar
Schefers, JM, Weigel, KA 2012. Genomic selection in dairy cattle: integration of DNA testing into breeding programs. Animal Frontiers 2, 4–9.Google Scholar
Smith, C 1967. Improvement of metric traits through specific genetic loci. Animal Production 9, 349–358.Google Scholar
Soller, M 1978. The use of loci associated with quantitative effects in dairy cattle improvement. Animal Production 27, 133–139.Google Scholar
Sonesson, AK, Woolliams, JA, Meuwissen, THE 2010. Maximising genetic gain whilst controlling rates of genomic inbreeding using genomic optimum contribution selection. Proceedings of the 9th WCGALP, Leipzig, Germany, 150 p.Google Scholar
Sørensen, AC, Sørensen, MK 2009. Genotyping both males and females is favourable in genomic dairy cattle breeding schemes. Interbull Bulletin 40, 94–97.Google Scholar
Strauss, S 2010. Biotech breeding goes bovine. Nature Biotechnology 6, 540–543.Google Scholar
Su, G, Guldbrandtsen, B, Gregersen, BR, Lund, MS 2010. Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. Journal of Dairy Science 93, 1175–1183.Google Scholar
Su, G, Brondum, RF, Ma, P, Guldbrandtsen, B, Aamand, GP, Lund, MS 2011. Genomic prediction using high-density SNP markers in Nordic Holstein and Red. Interbull Bulletin 44, 157–161.Google Scholar
VanRaden, PM, Wiggans, GR, van Tassel, CP, Sonstegaard, TS, Schenkel, F 2009. Benefits from cooperation in genomics. Interbull Bulletin 39, 67–72.Google Scholar
Veerkamp, RF, Berry, DP, Wall, E, de Haas, Y, McParland, S, Coffey, M, Calus, MPL 2011. Use of phenotypes from research herds to develop genomic selection for scarcely recorded traits like feed efficiency. Interbull Bulletin 44, 249–254.Google Scholar
Villanueva, B, Pong-Wong, R, Fernandez, J, Toro, MA 2005. Benefits from marker-assisted selection under an additive polygenic genetic model. Journal of Animal Science 83, 1747–1752.Google Scholar
Weigel, KA, de Los Campos, G, Vazquez, AI, Rosa, GJ, Gianola, D, Van Tassel, CP 2010. Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle. Journal of Dairy Science 93, 5423–5435.Google Scholar
Wiggans, GR, VanRaden, PM, Cooper, TA 2011. The genomic evaluation system in the United States: past, present, future. Journal of Dairy Science 94, 3202–3211.Google Scholar
Williams, JL 2005. The use of marker-assisted selection in animal breeding and biotechnology. Scientific and Technical Review 24, 379–391.Google Scholar
Ytournel, F, Willam, A, Simianer, H 2011. Impact of genomic selection on functional traits in a dual-purpose cattle breeding program. Proceedings of the 62nd EAAP meeting, Stavanger, Norway, Wageningen Academic Publishers, The Netherlands, p. 213.Google Scholar