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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 53 issue 3 (march 2019) : 294-298,   Doi: 10.18805/ijar.B-848

Efficiency of genomic selection to improve meat quality in pigs using ZPLAN+

B
B. I. Lopez
C
C. W. Song
K
K.S. Seo
1Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, South Korea
Cite article:- Lopez I. B., Song W. C., Seo K.S. (2018). Efficiency of genomic selection to improve meat quality in pigs using ZPLAN+. Indian Journal of Animal Research. 53(3): 294-298. doi: 10.18805/ijar.B-848.

ABSTRACT

The phenotype or genomic enhanced breeding value (GEBV) of ultrasound intramuscular fat (UIMF) was used as the target trait to improve meat quality. The ZPLAN+ software was employed to calculate and compare the genetic gain and accuracy of each selection scenario. The first scenario reflected the current conventional selection program in which the selection index is composed of average daily gain (ADG), feed conversion ratio (FCR) and ultrasound backfat (UBF). In the second scenario, UIMF was added into the basic selection index as an indicator trait for meat quality. In scenario 3, UIMF was also incorporated into index; however, the GEBV was used instead of phenotype. In scenario 4 and 5, selection was based strictly on the GEBV, and UIMF was included in scenario 5. The results showed that the accuracies of scenario 3, 4 and 5, in which GEBV information was used, increased with increasing accuracy of the GEBV. Moreover, the trends of scenario 4 and 5 changed more rapidly relative to scenario 3. The addition of UIMF to the selection index had a positive effect on the genetic gain of ADG and FCR, but a negative effect on UBF. The addition of UIMF to the selection index led to improvement of other traits and to the overall meat quality, especially when genomic selection was applied.

REFERENCES

  1. Cabling, M.M., Kang, H.S., Lopez, B.M., Jang, M., Kim, H.S., Nam, K.C., and Seo, K.S. (2015). Estimation of genetic associations between production and meat quality traits in Duroc pigs. Asian Austral J Anim, 28: 1061-1065.
  2. Daetwyler, H.D., Pong-Wong, R., Villanueva, B. and Woolliams, J.A. (2010). The impact of genetic architecture on genome-wide evaluation methods. Genetics, 185: 1021-1031.
  3. Daetwyler, H.D., Villanueva, B. and Woolliams, J.A. (2008). Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE, 3: e3395.
  4. 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.
  5. Erbe, M., Reinhardt, F. and Simianer, H. (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.
  6. Goddard, M.E., Hayes, B.J. and Meuwissen, T.H.E. (2011). Using the genomic relationship matrix to predict the accuracy of genomic selection. J Anim Breed Genet, 128: 409-421.
  7. Haberland, A.M., Pimentel, E.C.G., Ytournel, F., Erbe, M. and Simianer, H. (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.
  8. Li, K. (2014). Modelling genomic selection schemes in Bavarian pig breeding programs using ZPLAN+, PhD Diss. Technische Universität München, Germany.
  9. Meuwissen, T.H.E., Hayes, B.J. and Goddard, M.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157: 1819-1829.
  10. Miar, Y., Plastow, G., Bruce, H., Moore, S., Manafiazar, G., Kemp, R., Charagu, P., Huisman, A., van Haandel, B., Zhang, C. and McKay, R. (2014). Genetic and phenotypic correlations between performance traits with meat quality and carcass characteristics in commercial crossbred pigs. PLoS ONE, 9: e110105.
  11. Pimentel, E.C., and Konig, S. (2012). Genomic selection for the improvement of meat quality in beef. J Anim Sci, 90: 3418-3426.
  12. Schaeffer, L.R. (2006). Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet, 123: 218-223.
  13. Seo, S.H., Kim, E.M. and Kim, Y.B. (2012). Preferences and consumption patterns of consumer to develop processed pork products for export. Korean J Food Sci An, 32: 18-32.
  14. Simianer, H. (2009). The potential of genomic selection to improve litter size in pig breeding programmes. 60th Annual Meeting of the European Association for Animal Production. p 210, Barcelona.
  15. Sitzenstock, F., Ytournel, F., Sharifi, A.R., Cavero, D., Täubert, H., Preisinger, R. and Simianer, H. (2013). Efficiency of genomic selection in an established commercial layer breeding program. Genet Sel Evol, 45: 29.
  16. Täubert, H., Reinhardt F. and Simianer, H. (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.
  17. Täubert, H., Rensing S. and Reinhardt, F. (2011). Comparing conventional and genomic breeding programs with ZPLAN+. In: Interbull Bulletin, p 162-168.
  18. Van Grevenhof, I.E.M. and Van der Werf, J.H.J. (2015). Design of reference populations for genomic selection in crossbreeding programs. Genet Sel Evol, 47: 14.
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Indian Journal of Animal Research
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    Research Article

    volume 53 issue 3 (march 2019) : 294-298,   Doi: 10.18805/ijar.B-848

    Efficiency of genomic selection to improve meat quality in pigs using ZPLAN+

    B
    B. I. Lopez
    C
    C. W. Song
    K
    K.S. Seo
    1Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, South Korea
    Cite article:- Lopez I. B., Song W. C., Seo K.S. (2018). Efficiency of genomic selection to improve meat quality in pigs using ZPLAN+. Indian Journal of Animal Research. 53(3): 294-298. doi: 10.18805/ijar.B-848.

    ABSTRACT

    The phenotype or genomic enhanced breeding value (GEBV) of ultrasound intramuscular fat (UIMF) was used as the target trait to improve meat quality. The ZPLAN+ software was employed to calculate and compare the genetic gain and accuracy of each selection scenario. The first scenario reflected the current conventional selection program in which the selection index is composed of average daily gain (ADG), feed conversion ratio (FCR) and ultrasound backfat (UBF). In the second scenario, UIMF was added into the basic selection index as an indicator trait for meat quality. In scenario 3, UIMF was also incorporated into index; however, the GEBV was used instead of phenotype. In scenario 4 and 5, selection was based strictly on the GEBV, and UIMF was included in scenario 5. The results showed that the accuracies of scenario 3, 4 and 5, in which GEBV information was used, increased with increasing accuracy of the GEBV. Moreover, the trends of scenario 4 and 5 changed more rapidly relative to scenario 3. The addition of UIMF to the selection index had a positive effect on the genetic gain of ADG and FCR, but a negative effect on UBF. The addition of UIMF to the selection index led to improvement of other traits and to the overall meat quality, especially when genomic selection was applied.

    REFERENCES

    1. Cabling, M.M., Kang, H.S., Lopez, B.M., Jang, M., Kim, H.S., Nam, K.C., and Seo, K.S. (2015). Estimation of genetic associations between production and meat quality traits in Duroc pigs. Asian Austral J Anim, 28: 1061-1065.
    2. Daetwyler, H.D., Pong-Wong, R., Villanueva, B. and Woolliams, J.A. (2010). The impact of genetic architecture on genome-wide evaluation methods. Genetics, 185: 1021-1031.
    3. Daetwyler, H.D., Villanueva, B. and Woolliams, J.A. (2008). Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE, 3: e3395.
    4. 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.
    5. Erbe, M., Reinhardt, F. and Simianer, H. (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.
    6. Goddard, M.E., Hayes, B.J. and Meuwissen, T.H.E. (2011). Using the genomic relationship matrix to predict the accuracy of genomic selection. J Anim Breed Genet, 128: 409-421.
    7. Haberland, A.M., Pimentel, E.C.G., Ytournel, F., Erbe, M. and Simianer, H. (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.
    8. Li, K. (2014). Modelling genomic selection schemes in Bavarian pig breeding programs using ZPLAN+, PhD Diss. Technische Universität München, Germany.
    9. Meuwissen, T.H.E., Hayes, B.J. and Goddard, M.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157: 1819-1829.
    10. Miar, Y., Plastow, G., Bruce, H., Moore, S., Manafiazar, G., Kemp, R., Charagu, P., Huisman, A., van Haandel, B., Zhang, C. and McKay, R. (2014). Genetic and phenotypic correlations between performance traits with meat quality and carcass characteristics in commercial crossbred pigs. PLoS ONE, 9: e110105.
    11. Pimentel, E.C., and Konig, S. (2012). Genomic selection for the improvement of meat quality in beef. J Anim Sci, 90: 3418-3426.
    12. Schaeffer, L.R. (2006). Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet, 123: 218-223.
    13. Seo, S.H., Kim, E.M. and Kim, Y.B. (2012). Preferences and consumption patterns of consumer to develop processed pork products for export. Korean J Food Sci An, 32: 18-32.
    14. Simianer, H. (2009). The potential of genomic selection to improve litter size in pig breeding programmes. 60th Annual Meeting of the European Association for Animal Production. p 210, Barcelona.
    15. Sitzenstock, F., Ytournel, F., Sharifi, A.R., Cavero, D., Täubert, H., Preisinger, R. and Simianer, H. (2013). Efficiency of genomic selection in an established commercial layer breeding program. Genet Sel Evol, 45: 29.
    16. Täubert, H., Reinhardt F. and Simianer, H. (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.
    17. Täubert, H., Rensing S. and Reinhardt, F. (2011). Comparing conventional and genomic breeding programs with ZPLAN+. In: Interbull Bulletin, p 162-168.
    18. Van Grevenhof, I.E.M. and Van der Werf, J.H.J. (2015). Design of reference populations for genomic selection in crossbreeding programs. Genet Sel Evol, 47: 14.
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