Heterogeneity of variance copmonents for milk yield test-day records and their effects on genetic parameters and breeding values of Iranian Holstein cows

Authors

1 Department of Agriculture, Minab Higher Education Center, University of Hormozgan, Bandar Abbas, Iran

2 Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

Abstract

Background and objectives: One major component in designing breeding programs is accurate assessment of breeding values. In most countries, selection decisions in animal breeding are usually based on predicted genetic values obtained using best linear unbiased predictors (BLUP). The first applications of BLUP and mixed linear models are associated with assuming homogeneous variance components. However, numerous studies have showed that there exists heterogeneity variance for milk yield and genetic and residual variances are not constant between herd and different production level. The level of herd production, management and nutritional practices and breeding strategies are heterogeneous variance factors. The problem of heterogeneous variances in genetic evaluation of dairy cows is that above average animals in the more variable herds may be over evaluated and larger proportion of animals is selected from these herds than when variance is homogeneous. When heterogeneity is not properly taken into account, differences in variance components can result in biased estimates of breeding values, in-correct ranking of sire and dams elite and reduced genetic progress. In evaluation of dairy cows, a simple method to reduce heterogeneous phenotypic variance, is pre-adjustment of records prior to fitting the animal model. The objectives of this study were to investigate the heterogeneity variance components for milk test-day records as well as the impact of data pre-adjustment method on on genetic parameters, breeding values and re-ranking of the top sires and dams in the Iranian Holstein cows.
Materials and methods: In this research, heterogeneity of variance components for milk production trait was investigated by using 1843985 test-day records of first parity of Iranian Holstein cows. Collected records were including 199353 cows in 983 herds that were collected by the Animal Breeding Center and Promotion of Animal Products of Iran. According to the average of herd-year production three groups (high, medium and low) were identified. The heterogeneity of variances was studied using the Bartlett’s test. A pre-correction method was used to correct heterogeneity of variance. Variance components, genetic correlations, heritabilities as well as animals’ breeding values (BVs) were estimated by ASreml program, using animal model. Spearman's rank correlations and re-ranking of top sire and dam before and after data pre-correction were estimated. The number of best sires and cows removed from top animal list after adjustment the heterogeneity of variance was obtained in order to investigate the benefit of this adjustment.
Results: The results of Leven’s test for milk test-day records before data pre-correction were significant (P<0.001) which indicated the heterogenous of variances. Data correction did not result in homogeneity of variance across the herd-year classes but the heterogeneity of variance was decreased by 25%. Applying of the pre-correction method resulted in slightly higher heritabilities so that estimates of total heritabilities were 0.319±0.007 and 0.351 ±0.009 before and after adjustment for heterogeneity of variance, respectively. Additive genetic correlations for milk test-day records were estimated in the range of 0.345 to 0.985 before and 0.403 to 0.996 after pre-correction, respectively. Adjustment of data had a considerable effects on the top 1% of animals and caused that 14% and 20% of top sires and dams, respectively, to be excluded from selection when compared to the homogenous variance scenario. Substantially different in re-ranking of top animals occurred and the best of sire and dam cows had rank 8 and 23 after adjustment of heterogeneity of variance
Conclusion: The results showed that variance components of test-day records based on different production levels in Holstein cow population are not homogeneous and heterogeneity of variance may influence the ranking and genetic evaluation of top animals.

Keywords


  1. Aliloo, H., Miraie-Ashtiani, S.R., Moradi Shahrebabak, M., Urioste, J.I. and Sadeghi, M. 2014. Accounting for heterogeneity of variance in Iranian Holstein test-day milk yield records. Journal of Livestock 167: 25-32.
  2. Biassus, I.O., Cobuci, J.A., Costa, C.N., Rorato, P.R.N., Neto, J.B. and Cardoso, L.L. 2011. Genetic parameters for production traits in primiparous Holstein cows estimated by random regression models. Revista Brasileira de Zootecnia. 40: 85-94.
  3. Bignardi, A., Faro, L., Cardoso, V., Machado, P. and De Albuquerque, L. 2008. Random regression models to estimate test day milk yield genetic parameters of Holstein cows in southeastern Brazil. Journal of Livestock Production Science. 123: 1-7.
  4. Bignardi, A.B., El Faro, L., Cardoso, V.L., Machado, P.F. and Albuquerque, L.G. 2009. Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil. Journal of Livestock 123: 1-7.
  5. Cho, C.I., Alam, M., Choi, T.J., Choy, Y.H., Choi, J.G., Lee, S.S. and Cho. K.H. 2016. Models for estimating genetic parameters of milk production traits using random regression models in Korean Holstein cattle. Asian-Australas. Journal of Animal Science. 29(5): 607-614.
  6. Cobuci, J.A., Costa, C.N., Netoand, J.B. and Freitas, A.F. 2011. Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling. Revista Brasileira de Zootecnia. 40: 557-567.
  7. Cobuci, J.A., Euclydes, R.F., Lopes, P.S., Costa, C.N., Torres, R.D. and Pereira, C.S. 2005. Estimation of genetic parameters for test-day milk yield in Holstein cows using a random regression model. Journal of Genetics and Molecular Biology. 28: 75-83.
  8. Costa, C.N., Melo, D.E., Packer, I.U., Freitas, A., Teixeira, N. and Cobuci, J.A. 2008. Genetic parameters for test day milk yield of first lactation Holstein cows estimated by random regression using Legendre polynomials. Revista Brasileira de Zootecnica. 4: 602-608.
  9. De Roos, A.P.W., Harbers, A.G.F. and De Jong, G. 2004. Random herd curves in a test-day model for milk, fat, and protein production of dairy cattle in the Netherlands. Journal of Dairy Science. 87: 2693-2701.
  10. Dodenhoff, J. and Swalve, H.H. 1998. Heterogeneity of variances across regions of northern Germany and adjustment in genetic evaluation. Journal of Livestock Production Science. 53: 225-236.
  11. Ehsaninia, J., Ghavi Hossein-Zadeh, N. and Shadparvar, A.A. 2016. Homogeneity and heterogeneity of variance components for milk and protein yield at different cluster sizes in Iranian Holsteins. Journal of Livestock 188: 174-181.
  12. Ehsaninia, J., Ghavi Hossein-zadeh, N. and Shadparvar, A.A. 2016. The effect of heterogeneity of variance components on genetic evaluation of protein yield in Holstein top sires and dams. Animal Science Journal. 114: 101-114. (In Persian).
  13. Fujii, C. and Suzuki, M. 2006. Comparison of homogeneity and heterogeneity of residual variance using random regression test-day models for first lactation Japanese Holstein cows. Journal of Animal Science. 77: 28-32.
  14. Gengler, N., Dusseldorf, T., Wiggans, G.R., Wright, J.R. and Druet, T. 2001. Heterogeneity of (co)variance components for Jersey type traits. Journal of Dairy Science. 84: 1772 -1790.
  15. Gengler, N., Wiggans, G.R. and Gillon, A. 2005. Adjustment for heterogeneous covariance due to herd milk yield by transformation of test-day random regressions. Journal of Dairy Science. 88: 2981- 2990.
  16. Gengler, N., Wiggans, G.R. and Gillon, A. 2004. Estimated heterogeneity of phenotypic variance of test-day yield with a structural variance model. Journal of Dairy Science. 87(6): 1908-1916.
  17. Gilmour, A.R., Gogel, B.J., Cullis, B.R. and Thompson, R. 2009. ASReml User Guide Release. VSN International Ltd: Hemel Hempstead.
  18. Ibanez, M.A., Carabano, M.J. and Alenda, R. 1999. Identification of sources of heterogeneous residual and genetic variances in milk yield data from the Spanish Holstein-Friesian population and impact on genetic evaluation. Journal of Livestock Production Science. 59(1): 33-49.
  19. Ilatsia, E.D., Muasya, T.K., Muhuyi, W.B. and Kahi, A.K. 2007. Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics. Animal. 1: 185-192.
  20. Jafari Torbaghan, M., Farhangfar, H., Bashtni, M., Mohammad Nazari, B. and Sarir, H. 2012. Genetic evaluation of cows for milk protein yield trait using fixed and random regression test day models. Animal Production Research. 2: 9-20. (In Persian).
  21. Jakobsen, J.H., Madsen, P., Jensen, J., Pedersen, J., Christensen, L.G. and Sorensen, D.A. 2002. Genetic parameters for milk production and persistency for Danish Holsteins estimated in random regression models using REML. Journal of Dairy Science. 85: 1607-1616.
  22. Kettunen, A., Mantysaari, E.A. and Poso, J. 2000. Estimation of genetic parameters for daily milk yield of primiparous Ayrshire cows by random regression test-day models. Journal of Livestock Production Science. 6: 251-261.
  23. Lidauer, M., Emmerling, R. and Mantysaari, E.A. 2008. Multiplicative random regression model for heterogeneous variance adjustment in genetic evaluation for milk yield in Simmental. Journal of Animal Breeding and Genetics. 125(3): 147-159.
  24. Markus, S., Mantysaari, E.A., Stranden, I., Eriksson, J.A. and Lidauer, M.H. 2014. Comparison of multiplicative heterogeneous variance adjustment models for genetic evaluations. Journal of Animal Breeding and Genetic. 131(3): 237-246.
  25. Microsoft Visual FoxPro 9.0. Copyright 1988-2004. Microsoft Corporation.
  26. Miglior, F., Gong, W., Wang, Y., Kistemaker, G.J., Sewalem, A. and Jamrozik, J. 2009. Genetic parameters of production traits in Chinese Holsteins using a random regression test-day model. Journal of Dairy Science. 92: 4697-706.
  27. Muir, B.L., Kistemaker, G., Jamrozik, J. and Canavesi, F. 2007. Genetic parameters for a multiple-trait multiple- lactation random regression test-day model in Italian Holsteins. Journal of Dairy Science. 90: 1564-1574.
  28. Nikolaou, M., Kominakis, A.P., Rogdakis, E. and Zampitis, S. 2004. Effect of mean and variance heterogeneity on genetic evaluations of Lesbos dairy sheep. Journal of Livestock Production Science. 88: 107-115.
  29. Panetto, J.C.C., Val, E., Marcondes, C.R., Peixoto, M.G.C.D., Verneque, R.S., Ferraz, J.B.S. and Golden, B.L. 2012. Female fertility in a Guzerat dairy subpopulation: Heterogeneity of variance components for calving intervals. Journal of Livestock Science. 145: 87-94.
  30. Pool, M.H., Janss L.L.G. and Meuwissen, T.H.E. 2000. Genetic parameters of Legendre polynomials for first-parity lactation curves. Journal of Dairy Science. 83: 2640-2649.
  31. Reents, R., Dopp, L., Schmutz, M. and Reinhardt, F. 1998. Impact of application of a test-day model to dairy production traits on genetic evaluations of cows. Interbull Bull. 17: 49-54.
  32. Sargolzaei, M., Iwaisaki, H. and Colleau, J.J. 2006. CFC: A tool for monitoring genetic diversity. Proc. 8th World Congr. Genetic Applied Livestock Production. CD-ROM Communication 27-28. Belo Horizonte Brazil. Aug. 13-18.
  33. SAS Institute Inc. 2009. Statistical Analysis System (SAS) User's Guide. SAS Institute. Cary. N.C. USA.
  34. Savar sofla, S., Varkohi, S. and Karkhaneh, A. 2017. Investigation of genetic parameters for milk production trait of Holstein cows in Kermanshah province using random regression model. Journal of Animal Science. 114: 11-20. (In Persian).
  35. Shadparvar, A.A. and Yazdanshenas, M.S. 2005. Genetic parameters of milk yield and milk fat percentage test day records of Iranian Holstein cows. Asian-Australian Journal of Animal Science. 18: 1231-1236.
  36. Stanton, T.L. 1990. Genotype by environment interaction for Holstein milk yield in Colombia, Mexico and Puerto Rico. Journal of Dairy Science. 74(5): 1700-1714.
  37. Strabel, T. and Mistral, I. 1999. Genetic parameters for first and second lactation milk yield of Polish Black and White cattle with random regression test-day models. Journal of Dairy Science. 82: 2805-2810.
  38. Strabel, T., Jankowski, T. and Jamrozik, J. 2006. Adjustments for heterogeneous herd-year variances in a random regression model for genetic evaluations of polish Black-and-White cattle. Journal of Applied Genetics. 47(2): 125-130.
  39. Strabel, T., Szyda, J., Ptak, E. and Jamrozik, J. 2005. Comparison of random regression test-day models for Polish Black and White cattle. Journal of Dairy Science. 88(10): 3688-3699.
  40. Szydowski, M. and Szwaczkowski, T. 1993. The effect of grouping herds according to production level on the heritability of milk traits in cattle. Journal of Animal Science. 11: 295-300.
  41. Urioste, J.I., Gianola, D., Rekaya, R., Fikse, W.F. and Weigel, K.A. 2001. Evaluation of extent and amount of heterogeneous variance for milk yield in Uruguayan Holsteins. Journal of Animal Science. 72(2): 259-268.
  42. Urioste, J.I., Rekaya, R., Gianola, D., Fikse, F. and Weigel, K.A. 2003. Model comparison for genetic evaluation of milk yield in Uruguayan Holsteins. Journal of Livestock Production Science. 84: 63-73.
  43. Visscher, P.M. and Hill, G.H. 1992. Heterogeneity of variance and dairy cattle breeding. Journal of Animal Production. 55(3): 321-329.
  44. Zavadilová, L., Jamrozik, J. and Schaeffer, L.R. 2005. Genetic parameters for test-day model with random regressions for production traits of Czech Holstein cattle. Czech Journal of Animal Science 50: 142-154.