Asian-Australasian Journal of Animal Sciences

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Application of random regression models for genetic analysis of 305-d milk yield over different lactations of Iranian Holsteins

  • Received : 2016.11.17
  • Accepted : 2017.04.11
  • Published : 2017.10.01
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Abstract

Objective: During the last decade, genetic evaluation of dairy cows using longitudinal data (test day milk yield or 305-day milk yield) using random regression method has been officially adopted in several countries. The objectives of this study were to estimate covariance functions for genetic and permanent environmental effects and to obtain genetic parameters of 305-day milk yield over seven parities. Methods: Data including 60,279 total 305-day milk yield of 17,309 Iranian Holstein dairy cows in 7 parities calved between 20 to 140 months between 2004 and 2011. Residual variances were modeled by homogeneous and step functions with 7 and 10 classes. Results: The results showed that a third order polynomial for additive genetic and permanent environmental effects plus a step function with 10 classes for the residual variance was the most adequate and parsimonious model to describe the covariance structure of the data. Heritability estimates obtained by this model varied from 0.17 to 0.28. The performance of this model was better than repeatability model. Moreover, 10 classes of residual variance produce the more accurate result than 7 classes or homogeneous residual effect. Conclusion: A quadratic Legendre polynomial for additive genetic and permanent environmental effects with 10 step function residual classes are sufficient to produce a parsimonious model that explained the change in 305-day milk yield over consecutive parities of Iranian Holstein cows.

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References

  1. Bignardi AB, El Faro L, Torres Junior RAA, et al. Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows. Gene Mol Res 2011;10:3565-75. https://doi.org/10.4238/2011.October.31.4
  2. Sesana RC, Baldi F, Bignardi RRA, et al. Estimates of genetic parameters for total milk yield over multiple ages in Brazilian Murrah buffaloes using different models. Gene Mol Res 2014;13:2784-95. https://doi.org/10.4238/2014.April.14.7
  3. Gue Z, Lund MS, Madsen P, Korsgaard L, Jensen J. Genetic parameter estimation for milk yield over multiple parities and various length of lactation in Danish jerseys by random regression models. J Dairy Sci 2002;85:1596-606. https://doi.org/10.3168/jds.S0022-0302(02)74230-6
  4. Hammami H, Rekik B, Soyeurt H, Ben Gara A, Gengler N. Genetic parameters for Tunisian Holstein using a test- day random regression model. J Dairy Sci 2008;91:2118-26. https://doi.org/10.3168/jds.2007-0382
  5. Meyer K. WOMBAT - A tool for mixed model analysis in quantitative genetics by REML. J Zhejiang Univ Sci B 2007;8:815-21. https://doi.org/10.1631/jzus.2007.B0815
  6. Pool MH, Meuwissen T. Reduction of the number of parameters needed for a polynomial random regression test day model. Livest Prod Sci 2000;64:133-45. https://doi.org/10.1016/S0301-6226(99)00166-9
  7. Strabel T, Misztal I. Genetic parameters for first and second lactation milk yields of Polish Black and White cattle with random regression test-day models. J Dairy Sci 1999;82:2805-10. https://doi.org/10.3168/jds.S0022-0302(99)75538-4
  8. Cobuci JA, Euclydes RF, Lopes OS, et al. Estimation of genetic parameters for test-day milk yield in Holstein cows using a random regression model. Gene Mol Biol 2005;28:75-83. https://doi.org/10.1590/S1415-47572005000100013
  9. Misztal I, Strabel T, Jamrozik J, Mantysaari EA, Meuwissen THE. Strategies for estimating the parameters needed for different test-day models. J Dairy Sci 2000;83:1125-34. https://doi.org/10.3168/jds.S0022-0302(00)74978-2
  10. Jensen J. Genetic evaluation of dairy cattle using test day models. J Dairy Sci 2001;84:2803-12. https://doi.org/10.3168/jds.S0022-0302(01)74736-4
  11. Bignardi A, Faro L, Cardoso V, Machado P, Albuquerque L. Random regression models to estimate test day milk yield genetic parameters of Holstein cows in southeastern Brazil. Livest Prod Sci 2008;123:1-7.
  12. Sesana RC, Bignardi AB, ELfaro L, et al. Random regression models to describe the genetic variation of milk yield over multiple parities in Buffaloes. Ital J Anim Sci 2007;6(Suppl 2);364-7. https://doi.org/10.4081/ijas.2007.s2.364
  13. Muir BL, Kistemaker G, Jamrozik J, Canavesis F. Genetic parameters for multiple trait multiple lactation random regression test day model in Italian Holsteins. J Dairy Sci 2007;90:1564-74. https://doi.org/10.3168/jds.S0022-0302(07)71642-9
  14. Yang RQ, Ren HY, Schaeffer LR, Xu SZ. Estimation of genetic parameters for locational milk yields two-dimensional random regressions on parities and days in milk in Chinese Simmental cattle. J Anim Breed Genet 2005;122:49-55. https://doi.org/10.1111/j.1439-0388.2004.00480.x
  15. Zavadilova L, Jamrozik J, Schaeffer LR. Genetic parameters for test-day model with random regressions for production traits of Czech Holstein cattle. Czech J Anim Sci 2005;50:142-54.
  16. Kirkpatrick M, Lofsvold D, Bulmer M. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 1990;124: 979-93.
  17. Togashi K, Lin CY, Atagi Y, Hagiya K, Nakanishi T. Genetic characteristics of Japanese Holstein cows based on multiple lactation random regression test day animal model. J Dairy Sci 2008;114:194-201.
  18. Reents R, Dopp L, Schmutz M, Reinhardt F. Impact of application of a test-day model to dairy production traits on genetic evaluations of cows. Interbull Meeting, Rotorua, New Zealand. 1998;18-19:49-54.