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Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle
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 Title & Authors
Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle
Cho, C.I.; Alam, M.; Choi, T.J.; Choy, Y.H.; Choi, J.G.; Lee, S.S.; Cho, K.H.;
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 Abstract
The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.
 Keywords
Random Regression Model;Test Day Yield;Milk Production;Heritability;Holstein;
 Language
English
 Cited by
 References
1.
Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory (Eds. B. N. Petrov and F. Csaki). Akademiai Kaido, Budapest, Hungary. pp. 267-281.

2.
Andonov, S., J. odegArd, M. Svendsen, T. Adnoy, M. Vegara, and G. Klemetsdal. 2013. Comparison of random regression and repeatability models to predict breeding values from test-day records of Norwegian goats. J. Dairy Sci. 96:1834-1843. crossref(new window)

3.
Bahreini Behzadi, M. R., A. Amini, A. A. Aslaminejad, and M. Tahmoorespour. 2013. Estimation of genetic parameters for production traits of Iranian Holstein dairy cattle. Livest. Res. Rural Dev. 25, Article #156.

4.
Bignardi, A. B., L. El Faro, V. L. Cardoso, P. F. Machado, and L. G. de Albuquerque. 2009. Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil. Livest. Sci. 123:1-7. crossref(new window)

5.
Bignardi, A. B., L. El Faro, R. A. A. Torres Junior, V. L. Cardoso, P. F. Machado, and L. G. D. Albuquerque. 2011. Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows. Genet. Mol. Res. 10:3565-3575. crossref(new window)

6.
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. crossref(new window)

7.
Cho, J. H., K. H. Cho, and K. J. Lee. 2005. The effect of the incomplete lactation records for genetic evaluations with random regression test-day models (RRTDM) in Holstein cattle. J. Anim. Sci. Technol. (Kor.). 47:147-158. crossref(new window)

8.
Cobuci, J. A., C. N. Costa, J. Braccini Neto, and A. F. D. Freitas. 2011. Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling. R. Bras. Zootec. 40:557-567. crossref(new window)

9.
Costa, C. N., C. M. R. de Melo, I. U. Packer, A. F. de Freitas, N. M. Teixeira, and J. A. Cobuci. 2008. Genetic parameters for test day milk yield of first lactation Holstein cows estimated by random regression using Legendre polynomials. R. Bras. Zootec. 37:602-608. crossref(new window)

10.
De Roos, A. P. W., A. G. F. Harbers, and G. De Jong. 2004. Random herd curves in a test-day model for milk, fat, and protein production of dairy cattle in the Netherlands. J. Dairy Sci. 87:2693-2701. crossref(new window)

11.
Dziak, J. J., D. L. Coffman, S. T. Lanza, and R. Li. 2012. Sensitivity and specificity of information criteria. The Methodology Center and Department of Statistics, Penn State, The Pennsylvania State University, State College, PA, USA. pp. 1-10.

12.
Henderson Jr, C. R. 1982. Analysis of covariance in the mixed model: higher-level, nonhomogeneous, and random regressions. Biometrics 38:623-640. crossref(new window)

13.
Herrera, A. C., O. D. Munera, and M. F. Ceron-Munoz. 2013. Variance components and genetic parameters for milk production of Holstein cattle in Antioquia (Colombia) using random regression models. Rev. Colom. Cienc. Pecua. 26:90-97.

14.
Hurtado-Lugo, N. A., S. C. de Sousa, R. R. Aspilcueta, S. Y. Gutierrez, M. F. Ceron-Munoz, and H. Tonhati. 2013. Estimation of genetic parameters for test-day milk yield in first calving buffaloes. Rev. Colom. Cienc. Pecua. 26:177-185.

15.
Jakobsen, J. H., P. Madsen, J. Jensen, J. Pedersen, L. G. Christensen, and D. A. Sorensen. 2002. Genetic parameters for milk production and persistency for Danish Holsteins estimated in random regression models using REML. J. Dairy Sci. 85:1607-1616. crossref(new window)

16.
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. crossref(new window)

17.
Kheirabadi, K., A. Rashidi, S. Alijani, and I. Imumorin. 2014. Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple-trait random regression models. Anim. Sci. J. 85:925-934. crossref(new window)

18.
Kim, B. W., D. Lee, J. T. Jeon, and J. G. Lee 2009. Estimation of genetic parameters for milk production traits using a random regression test-day model in Holstein cows in Korea. Asian Australas. J. Anim. Sci. 22:923-930. crossref(new window)

19.
Lee, D. H., J. H. Jo, and K. G. Han. 2003. Genetic parameters for milk production and somatic cell score of first lactation in Holstein cattle with random regression test-day models. J. Anim. Sci. Technol. (Kor.). 45:739-748 crossref(new window)

20.
Liu, Z., F. Reinhardt, and R. Reents. 2000a. Parameter estimates of a random regression test day model for first three lactation somatic cell scores. Interbull Bull. 26:61-65.

21.
Liu, Z., F. Reinhardt, and R. Reents. 2000b. Estimating parameters of a random regression test day model for first three lactation milk production traits using the covariance function approach. Interbull Bull. 25:74-80.

22.
Lopez-Romero, P. and M. J. Carabano. 2003. Comparing alternative random regression models to analyse first lactation daily milk yield data in Holstein-Friesian cattle. Livest. Prod. Sci. 82:81-96. crossref(new window)

23.
Lopez-Romero, P., R. Rekaya, and M. J. Carabano. 2003. Assessment of homogeneity vs. heterogeneity of residual variance in random regression test-day models in a Bayesian analysis. J. Dairy Sci. 86:3374-3385. crossref(new window)

24.
Meyer, K. 1999. Estimates of genetic and phenotypic covariance functions for postweaning growth and mature weight of beef cows. J. Anim. Breed. Genet. 116:181-205. crossref(new window)

25.
Meyer, K. 2007. WOMBAT - A tool for mixed model analyses in quantitative genetics by REML. J. Zhejiang Univ. Sci. B. 8:815-821. crossref(new window)

26.
Meyer, K., H. U. Graser, and K. Hammond. 1989. Estimates of genetic parameters for first lactation test day production of Australian Black and White cows. Livest. Prod. Sci. 21:177-199. crossref(new window)

27.
Olori, V. E. and W. G. Hill. 1999. The structure of the residual error variance of test day milk in random regression models. Interbull Bull. 20:103-108.

28.
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. crossref(new window)

29.
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. crossref(new window)

30.
Ptak, E. and L. R. Schaeffer. 1993. Use of test day yields for genetic evaluation of dairy sires and cows. Livest. Prod. Sci. 34:23-34. crossref(new window)

31.
Rupp, R. and D. Boichard. 2003. Genetics of resistance to mastitis in dairy cattle. Vet. Res. 34:671-688. crossref(new window)

32.
Schaeffer, L. R. and J. C. M. Dekkers. 1994. Random regression in animal models for test-day production in dairy cattle. 5th World Congr. Genet. Appl. Livest. Prod. 18:443-446.

33.
Schwarz, G. 1978. Estimating the dimension of a model. Ann. Statist. 6:461-464. crossref(new window)

34.
Schaeffer, L. R., J. Jamrozik, G. J. Kistemaker, and J. Van Doormaal. 2000. Experience with a test-day model. J. Dairy Sci. 83:1135-1144. crossref(new window)

35.
Strabel, T., J. Szyda, E. Ptak, and J. Jamrozik. 2005. Comparison of random regression test-day models for Polish Black and White cattle. J. Dairy Sci. 88:3688-3699. crossref(new window)

36.
Тakma, C. and Y. Akbas. 2009. Heterogeneity of residual variances of test day milk yields estimated by random regression model in Turkish Holsteins. J. Anim. Vet. Adv. 8:782-787.