Advertisement

Possibility of modifying the growth trajectory in Raeini Cashmere goat

  • Heydar Ghiasi
  • M. S. Mokhtari
Regular Articles

Abstract

The objective of this study was to investigate the possibility of modifying the growth trajectory in Raeini Cashmere goat breed. In total, 13,193 records on live body weight collected from 4788 Raeini Cashmere goats were used. According to Akanke’s information criterion (AIC), the sing-trait random regression model included fourth-order Legendre polynomial for direct and maternal genetic effect; maternal and individual permanent environmental effect was the best model for estimating (co)variance components. The matrices of eigenvectors for (co)variances between random regression coefficients of direct additive genetic were used to calculate eigenfunctions, and different eigenvector indices were also constructed. The obtained results showed that the first eigenvalue explained 79.90% of total genetic variance. Therefore, changing the body weights applying the first eigenfunction will be obtained rapidly. Selection based on the first eigenvector will cause favorable positive genetic gains for all body weight considered from birth to 12 months of age. For modifying the growth trajectory in Raeini Cashmere goat, the selection should be based on the second eigenfunction. The second eigenvalue accounted for 14.41% of total genetic variance for body weights that is low in comparison with genetic variance explained by the first eigenvalue. The complex patterns of genetic change in growth trajectory observed under the third and fourth eigenfunction and low amount of genetic variance explained by the third and fourth eigenvalues.

Keywords

Body weight Eigenfunction Random regression Goat 

Notes

Acknowledgements

The authors wish to thank all breeding station staff of Raeini Cashmere goat which were involved in data collection and maintaining the flock.

Compliance with ethical standards

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Albuquerque, L.D. and Meyer, K., 2001. Estimates of covariance functions for growth from birth to 630 days of age in Nelore cattle, Journal of Animal Science, 79(11), 2776–2789.CrossRefPubMedGoogle Scholar
  2. Barazandeh, A., Moghbeli, S.M., Hossein-Zadeh, N.G. and Vatankhah, M., 2012. Genetic evaluation of growth in Raeini goat using random regression models, Livestock Science, 145(1), 1–6.CrossRefGoogle Scholar
  3. Bhattacharyya, H.K., Bhat, F.A. and Buchoo, B.A., 2015. Prevalence of dystocia in sheep and goats: a study of 70 cases (2004–2011). Journal of Advanced Veterinary Research, 5, 14–20.Google Scholar
  4. Boligon, A.A., Mercadante, M.E.Z., Forni, S., Lobo, R.B. and Albuquerque, L.G.D., 2010. Covariance functions for body weight from birth to maturity in Nellore cows, Journal of Animal Science, 88(3), 849–859.CrossRefPubMedGoogle Scholar
  5. Daskiran, I., Koncagul, S. and Bingol, M., 2010. Growth characteristics of indigenous Norduz female and male lambs, Journal of Agricultural Sciences, 16, 62–69.Google Scholar
  6. Fischer, T.M., Van der Werf, J.H.J., Banks, R.G. and Ball, A.J., 2004. Description of lamb growth using random regression on field data, Livestock Production Science, 89(2), 175–185.CrossRefGoogle Scholar
  7. Fitzhugh, H.A., 1976. Analysis of growth curves and strategies for altering their shape, Journal of Animal Science, 42(4), 1036–1051.CrossRefPubMedGoogle Scholar
  8. Fitzhugh, H.A., Taylor, S., 1971. Genetic analysis of degree of maturity, Journal of Animal Science, 33(4), 717–725.CrossRefPubMedGoogle Scholar
  9. Ghafouri-Kesbi, F. and Gholizadeh, M., 2017. Genetic and phenotypic aspects of growth rate and efficiency-related traits in sheep, Small Ruminant Research, 149, 181–187.CrossRefGoogle Scholar
  10. Ibanez-Escriche, N. and Blasco, A., 2011. Modifying growth curve parameters by multitrait genomic selection, Journal of Animal Science, 89 (3), 661–668.CrossRefPubMedGoogle Scholar
  11. Keskin, I., Dag, B., Sariyel, V. and Gokmen, M., 2010. Estimation of growth curve parameters in Konya Merino sheep, South African Journal of Animal Science, 39 (2), 163–168.Google Scholar
  12. Kheirabadi, K. and Rashidi, A., 2016. Genetic description of growth traits in Markhoz goat using random regression models, Small Ruminant Research, 144, 305–312.CrossRefGoogle Scholar
  13. Kirkpatrick, M. and Heckman, N., 1989. A quantitative genetic model for growth, shape, reaction norms, and other infinite-dimensional characters, Journal of Mathematical Biology, 27(4), 429–450.CrossRefPubMedGoogle Scholar
  14. Kirkpatrick, M., Lofsvold, D. and Bulmer, M., 1990. Analysis of the inheritance, selection and evolution of growth trajectories, Genetics, 124(4), 979–993.PubMedPubMedCentralGoogle Scholar
  15. Maghsoudi, A., Vaez Torshizi, R. and Safi Jahanshahi, A., 2009. Estimates of (co)variance components for productive and composite reproductive traits in Iranian Cashmere goats, Livestock Science, 126, 162–167.CrossRefGoogle Scholar
  16. Medeiros, A.N., Resende, K.T., Teixeira, I.A.M.A., Araújo, M.J., Yanez, E.A. and Ferreira, A.C.D., 2014. Energy requirements for maintenance and growth of male Saanen goat kids, Asian-Australasian Journal of Animal Sciences, 27(9), 1293–1302.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Meyer, K., 1998. Estimating covariance functions for longitudinal data using a random regression model, Genetics Selection Evolution, 30(3), 221.CrossRefGoogle Scholar
  18. Meyer, K., 2007. WOMBAT-A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML), Journal of Zhejiang University-Science B, 8(11), 815–821.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Mokhtari, M.S., Moghbeli Damaneh, M. and Gutierrez, J.P., 2017. Genetic variability and population structure of Raeini Cashmere goats determined by pedigree analysis, Journal of Livestock Science and Technologies, 5 (1), 43–50.Google Scholar
  20. Pena, F., Perea, J., García, A. and Acero, R., 2007. Effects of weight at slaughter and sex on the carcass characteristics of Florida suckling kids, Meat Science, 75(3), 543–550.CrossRefPubMedGoogle Scholar
  21. R Core Team, 2015. R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna.Google Scholar
  22. Sousa, J.E.R.D., Sarmento, J.L.R., Sousa, W.H.D., Souza, M.D.S.M.D. and Ferreira, I.C., 2011. Estimates of covariance functions for growth of Anglo-Nubian goats, Revista Brasileira de Zootecnia, 40(1), 106–114.CrossRefGoogle Scholar
  23. Taylor, S. and Fitzhugh, H.A., 1971. Genetic relationships between mature weight and time taken to mature within a breed, Journal of Animal Science, 33(4), 726–731.CrossRefPubMedGoogle Scholar
  24. Togashi, K. and Lin, C.Y., 2006. Selection for milk production and persistency using eigenvectors of the random regression coefficient matrix, Journal of Dairy Science, 89, 4866–4873.CrossRefPubMedGoogle Scholar
  25. Toplu, H.D.O., Goksoy, E.O. and Nazligul, A., 2013. Effects of slaughter age and gender on carcass characteristics of Turkish indigenous hair goat kids reared under an extensive production system, Archives Animal Breeding, 56(1), 75–88.Google Scholar
  26. Ulutas, Z., Sezer, M., Aksoy, Y., Sirin, E., Sen, U., Kuran, M. and Akbas, Y., 2010. The effect of birth types on growth curve parameters of Karayaka lambs, Journal of Animal and Veterinary Advances, 9(9), 1384–1388.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Animal Science, Faculty of Agricultural SciencePayame Noor UniversityTehranIran
  2. 2.Department of Animal Science, Faculty of AgricultureUniversity of JiroftJiroftIran

Personalised recommendations