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Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle

  • Naserkheil, Masoumeh (Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran) ;
  • Miraie-Ashtiani, Seyed Reza (Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran) ;
  • Nejati-Javaremi, Ardeshir (Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran) ;
  • Son, Jihyun (Department of Animal Life and Resources, Hankyong National University) ;
  • Lee, Deukhwan (Department of Animal Life and Resources, Hankyong National University)
  • Received : 2015.09.11
  • Accepted : 2016.02.29
  • Published : 2016.12.01

Abstract

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage ($0.213{\pm}0.007$). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran.

Keywords

Holstein Dairy Cattle;Milk Protein Yields;Random Regression Model;Test-day Records

References

  1. Albuquerque, L. G. and K. Meyer. 2001. Estimates of covariance functions for growth from birth to 630 days of age in Nelore cattle. J. Anim. Sci. 79:2776-2789. https://doi.org/10.2527/2001.79112776x
  2. Araujo, C. V., R. A. Torres, C. N. Costa, R. A. Torres Filho, S. I. Araujo, P. S. Lopes, A. J. Regazzi, C. S. Pereira, and J. L. R. Sarmento. 2006. Random regressions models to describe the genetic variation of milk yield in Holstein breed. R. Bras. Zootec. 35:975-981. https://doi.org/10.1590/S1516-35982006000400006
  3. Bormann, J., G. R. Wiggans, T. Druet, and N. Gengler. 2003. Within-herd effects of age at test-day and lactation stage on test-day yields. J. Dairy Sci. 86:3765-3774. https://doi.org/10.3168/jds.S0022-0302(03)73983-6
  4. Brotherstone, S., I. M. S. White, and K. Meyer. 2000. Genetic modelling of daily milk yield using orthogonal polynomials and parametric curves. J. Anim. Sci. 70:407-415. https://doi.org/10.1017/S1357729800051754
  5. Cobuci, J. A., R. F. Euclydes, P. S. Lopes, C. N. Costa, R. A. Torres, and C. S. Pereira. 2005. Estimation of genetic parameters for test-day milk in Holstein cows using a random regression model. Genet. Mol. Biol. 28:75-83. https://doi.org/10.1590/S1415-47572005000100013
  6. Druet, T., F. Jaffrezic, D. Boichard, and V. Ducrocv. 2003. Modelling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows. J. Dairy Sci. 86:2480-2490. https://doi.org/10.3168/jds.S0022-0302(03)73842-9
  7. Druet, T., F. Jaffrezic, and V. Ducrocv. 2005. Estimation of genetic parameters for test-day records of dairy traits in the first three lactations. Genet. Sel. Evol. 37:257-271. https://doi.org/10.1186/1297-9686-37-4-257
  8. El Faro, L., L. G. Albuquerque, and V. L. Cardoso. 2008. Variance component estimates for test-day milk yield applying random regression models. Genet. Mol. Biol. 33:665-673.
  9. Gengler, N., A. Tijani, G. R. Wiggans, and I. Misztal. 1999. Estimation of (co)variance function coefficients for test day yield with a expectation-maximization restricted maximum likelihood algorithm. J. Dairy Sci. 82:1849.e1-1849.e23.
  10. Henderson Jr., C. R. 1982. Analysis of covariance in the mixed model: higher level, nonhomogeneous, and random regressions. Biometrics 38:623-640. https://doi.org/10.2307/2530044
  11. Jamrozik, J. and L. R. Schaeffer. 2002. Bayesian comparison of random regression models for test-day yields in dairy cattle. In: Proceedings of the 7th World Congress on Genetics Applied to Livestock Production. Montpellier, France. Communication no. 01-03.
  12. Jamrozik, J. and L. R. Schaeffer. 1997. Estimates of genetic parameters for a test-day model with random regressions for yield traits of first lactation Holsteins. J. Dairy Sci. 80:762-770. https://doi.org/10.3168/jds.S0022-0302(97)75996-4
  13. Jamrozik, J., G. J. Kistemaker, J. C. M. Dekkers, and L. R. Schaeffer. 1997. Comparison of possible covariates for use in random regression model for analyses of test-day yields. J. Dairy Sci. 80:2550-2556. https://doi.org/10.3168/jds.S0022-0302(97)76210-6
  14. Kettunen, A., E. A. Mantysaari, and L. Poso. 2000. Estimation of genetic parameters for daily milk yield of primiparous Ayrshire cows by random regression test-day models. Livest. Prod. Sci. 66:251-261. https://doi.org/10.1016/S0301-6226(00)00166-4
  15. Kirkpatrick, M. and N. Heckman. 1989. A quantitative genetic model for growth, shape, reaction norms, and other infinitedimensional characters. J. Math. Biol. 27:429-450. https://doi.org/10.1007/BF00290638
  16. Kirkpatrick, M., D. Lofsvold, and M. Bulmer. 1990. Analysis of the inheritance, selection, and evolution of growth trajectories. Genetics 124:979-993.
  17. Laird, N. M. and J. H. Ware. 1982. Random effects models for longitudinal data. Biometrics 38:963-974. https://doi.org/10.2307/2529876
  18. Meyer, K. 1998. Estimating covariance functions for longitudinal data using a random regression model. Genet. Sel. Evol. 30:221-240. https://doi.org/10.1186/1297-9686-30-3-221
  19. Meyer, K. 1999. Random regression models to describe phenotypic variation in weights of beef cows when age and season effects are confounded. In: Proceedings of the 50th Annual Meeting of the European Association for Animal Pproduction. Zurich, Switzerland.
  20. Meyer, K. 2007. WOMBAT - A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). J. Zhejiang Univ. Sci. B 8:815-821. https://doi.org/10.1631/jzus.2007.B0815
  21. Olori, V. E., W. G. Hill, B. J. McGuirk, and S. Brotherstone. 1999. Estimating variance components for test-day milk records by restricted maximum likelihood with a random regression animal model. Livest. Prod. Sci. 61:53-63. https://doi.org/10.1016/S0301-6226(99)00052-4
  22. Pereira, R. J., A. B. Bignardi, L. EL Faro, R. S. Verneque, A. E. Vercesi Filho, and L. G. Aalbuquerque. 2013. Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle. J. Dairy Sci. 96:565-574. https://doi.org/10.3168/jds.2011-5051
  23. Pletcher, S. D. and C. J. Geyer. 1999. The genetic analysis of age dependent traits: modelling the character process. Genetics 153:825-835.
  24. Pool, M. H., L. L. G. Janss, and T. H. E. Meuwissen. 2000. Genetic parameters of Legendre polynomials for first parity lactation curves. J. Dairy Sci. 83:2640-2649. https://doi.org/10.3168/jds.S0022-0302(00)75157-5
  25. Rekaya, R., M. J. Carabano, and M. A. Toro. 1999. Use of test-day yields for the genetic evaluation of production traits in Holstein-Friesian cattle. Livest. Prod. Sci. 57:203-217. https://doi.org/10.1016/S0301-6226(98)00181-X
  26. Rekaya, R., M. J. Carabano, and M. A. Toro. 2000. Bayesian analysis of lactation curves of Holstein-Friesian cattle using a nonlinear model. J. Dairy Sci. 83:2691-2701. https://doi.org/10.3168/jds.S0022-0302(00)75163-0
  27. SAS (Statistical Analysis System) Institute Inc. 2009. SAS/STAT User's Guide: Version 9.2. 2nd edition. SAS Institute Inc., Cary, NC, USA.
  28. Schaeffer, L. R. and J. C. M. Dekkers. 1994. Random regressions in animal models for test-day production in dairy cattle. In: Proceedings of the 5th World Congress on Genetics Applied to Livestock Production. Guelph, Canada. pp. 443-446.
  29. Schaeffer, L. R. 2004. Application of random regression models in animal breeding. Livest. Prod. Sci. 86:35-45. https://doi.org/10.1016/S0301-6226(03)00151-9
  30. Strabel, T. and J. Jamrozik. 2006. Genetic analysis of milk production traits of Polish black and white cattle using largescale random regression test-day models. J. Dairy Sci. 89:3152-3163. https://doi.org/10.3168/jds.S0022-0302(06)72589-9
  31. Strabel, T., E. Ptak, J. Szyda, and J. Jamrozik. 2004. Multiplelactation random regression test-day model for Polish Black and White cattle. Interbull Bull. 32:133-136.
  32. Strabel, T. and I. Misztal. 1999. Genetic parameters for first and second lactation milk yield of Polish black and white cattle with random regression test-day models. J. Dairy Sci. 82:2805-2810. https://doi.org/10.3168/jds.S0022-0302(99)75538-4
  33. Van Der Werf, J. H. J., M. E. Goddard, and K. Meyer. 1998. The use of covariance functions and random regressions for genetic evaluation of milk production based on test-day records. J. Dairy Sci. 81:3300-3308. https://doi.org/10.3168/jds.S0022-0302(98)75895-3
  34. Wilmink, J. B. M. 1987. Adjustment of test day milk, fat and protein yield for age, season and stage of lactation. Livest. Prod. Sci. 16:335-348. https://doi.org/10.1016/0301-6226(87)90003-0
  35. Zavadilova, L., J. Jamrozik, and L. R. Schaeffer. 2005. Genetic parameters for test-day model with random regressions for production traits of Czech Holstein cattle. Czech J. Anim. Sci. 50:142-154.

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