Asian-Australasian Journal of Animal Sciences

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN+

  • Lopez, B.M. (Department of Animal Science and Technology, Sunchon National University) ;
  • Kang, H.S. (Department of Animal Science and Technology, Sunchon National University) ;
  • Kim, T.H. (Department of Animal Science and Technology, Sunchon National University) ;
  • Viterbo, V.S. (Department of Animal Science and Technology, Sunchon National University) ;
  • Kim, H.S. (Department of Animal Science and Technology, Sunchon National University) ;
  • Na, C.S. (Department of Animal Biotechnology, Chonbuk National University) ;
  • Seo, K.S. (Department of Animal Science and Technology, Sunchon National University)
  • Received : 2015.10.14
  • Accepted : 2016.01.07
  • Published : 2016.05.01
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Abstract

The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit.

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References

  1. Abell, C., J. Dekkers, M. Rothschild, J. Mabry, and K. Stalder. 2014. Total cost estimation for implementing genome-enabled selection in a multi-level swine production system. Genet. Sel. Evol. 46:32. https://doi.org/10.1186/1297-9686-46-32
  2. Akanno, E. C., F. S. Schenkel, R. M. Friendship, and J. A. B. Robinson. 2013. Relative economic returns from selection schemes for a nucleus swine breeding program. Livest. Res. Rural Dev. 25:Article #53.
  3. Cabling, M. M., H. S. Kang, B. M. Lopez, M. Jang, H. S. Kim, K. C. Nam, J. G. Choi, and K. S. Seo. 2015. Estimation of genetic associations between production and meat quality traits in Duroc pigs. Asian Australas. J. Anim. Sci. 28:1061-1065. https://doi.org/10.5713/ajas.14.0783
  4. Daetwyler, H. D., R. Pong-Wong, B. Villanueva, and J. A. Woolliams. 2010. The impact of genetic architecture on genome-wide evaluation methods. Genetics 185:1021-1031. https://doi.org/10.1534/genetics.110.116855
  5. Daetwyler, H. D., B. Villanueva, and J. A. Woolliams. 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3:e3395. https://doi.org/10.1371/journal.pone.0003395
  6. Dekkers, J. C. M. 2007. Prediction of response to marker-assisted and genomic selection using selection index theory. J. Anim. Breed. Genet. 124:331-341. https://doi.org/10.1111/j.1439-0388.2007.00701.x
  7. Erbe, M., F. Reinhardt, and H. Simianer. 2011. Empirical determination of the number of independent chromosome segments based on cross-validated data. In: 62nd Annual Meeting of the European Federation of Animal Science, Stavanger, Norway.
  8. Goddard, M. E., B. J. Hayes, and T. H. E. Meuwissen. 2011. Using the genomic relationship matrix to predict the accuracy of genomic selection. J. Anim. Breed. Genet. 128:409-421. https://doi.org/10.1111/j.1439-0388.2011.00964.x
  9. Haberland, A. M., H. Luther, A. Hofer, E. Tholen, H. Simianer, B. Lind, and C. Baes. 2014. Efficiency of different selection strategies against boar taint in pigs. Animal 8:11-19. https://doi.org/10.1017/S1751731113001857
  10. Haberland, A. M., E. C. G. Pimentel, F. Ytournel, M. Erbe, and H. Simianer. 2013. Interplay between heritability, genetic correlation and economic weighting in a selection index with and without genomic information. J. Anim. Breed. Genet. 130:456-467. https://doi.org/10.1111/jbg.12051
  11. Haberland, A. M., F. F. Ytournel, H. Luther, and H. Simianer. 2010. Evaluation of selection strategies including genomic breeding values. In Pigs 61st Annual Meeting of the European Association for Animal Production, Heraklion, Greece. 354 p.
  12. Hazel, L. N. and J. L. Lush. 1942. The efficiency of three methods of selection. J. Hered. 33:393-399. https://doi.org/10.1093/oxfordjournals.jhered.a105102
  13. Henryon, M., P. Berg, and A. C. Sorensen. 2014. Animal-breeding schemes using genomic information need breeding plans designed to maximise long-term genetic gains. Livest. Sci. 166:38-47. https://doi.org/10.1016/j.livsci.2014.06.016
  14. Hill, W. G. 1974. Prediction and evaluation of response to selection with overlapping generations. Anim. Sci. 18:117-139.
  15. Ibanez-Escriche, N. and O. Gonzalez-Recio. 2011. Review. Promises, pitfalls and challenges of genomic selection in breeding programs. Spanish J. Agric. Res. 9:404-413.
  16. Li, K. 2014. Modelling Genomic Selection Schemes in Bavarian Pig Breeding programs using ZPLAN+, PhD Thesis. Technische Universitat Munchen, Munchen, Germany.
  17. Lillehammer, M., T. H. Meuwissen, and A. K. Sonesson. 2013. Genomic selection for two traits in a maternal pig breeding scheme. J. Anim. Sci. 91:3079-3087. https://doi.org/10.2527/jas.2012-5113
  18. Meuwissen, T. 2007. Genomic selection: marker assisted selection on a genome wide scale. J. Anim. Breed. Genet. 124:321-322. https://doi.org/10.1111/j.1439-0388.2007.00708.x
  19. Miar, Y., G. Plastow, H. Bruce, R. Kemp, P. Charagu, C. Zhang, A. Huisman, and Z. Wang. 2014. Genomic selection of pork pH in purebred pigs for crossbred performance. In: 10th World Congress of Genetics Applied to Livestock Production. Vancouver, Canada.
  20. NSIF. 1987. Guidelines for Uniform Swine Improvement Programs. Knoxville, TN, USA.
  21. Schaeffer, L. R. 2006. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genetics 123:218-223. https://doi.org/10.1111/j.1439-0388.2006.00595.x
  22. Simianer, H. 2009. The potential of genomic selection to improve litter size in pig breeding programmes. In: 60th Annual Meeting of the European Association for Animal Production, Barcelona, Spain. 210 p.
  23. Tang, G., J. Liu, J. Xue, R. Yang, T. Liu, Z. Zeng, A. Jiang, Y. Jiang, M. Li, L. Zhu, L. Bai, S. Shuai, Z. Wang, and X. Li. 2014. Optimizing selection strategies of genomic selection in swine breeding program based on a dataset simulated. Livest. Sci. 166:111-120. https://doi.org/10.1016/j.livsci.2014.04.023
  24. Taubert, H., F. Reinhardt, and H. Simianer. 2010. ZPLAN+ A new software to evaluate and optimize animal breeding programs. In: Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany
  25. Taubert, H., S. Rensing, and F. Reinhardt. 2011. Comparing conventional and genomic breeding programs with ZPLAN+. Interbull Bull. pp. 162-168.
  26. Thomasen, J. R., C. Egger-Danner, A. Willam, B. Guldbrandtsen, M. S. Lund, and A. C. Sorensen. 2014. Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit. J. Dairy Sci. 97:458-470. https://doi.org/10.3168/jds.2013-6599
  27. Toghiani, S. 2012. Quantitative genetic application in the selection process for livestock production. Livestock Production, Dr. Khalid Javed edn. http://www.intechopen.com/books/livestock-production/quantitative-genetic-application-in-the-selectionprocess-for-livestock-production Accessed October 3, 2015.
  28. Van Grevenhof, I. E. M. and J. H. J. Van der Werf. 2015. Design of reference populations for genomic selection in crossbreeding programs. Genet. Sel. Evol. 47:14. https://doi.org/10.1186/s12711-015-0104-x

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