Estimation of Genetic Parameters for Milk Production Traits Using a Random Regression Test-day Model in Holstein Cows in Korea

  • Kim, Byeong-Woo (Division of Applied Life Science . Institute of Agriculture and Life Sciences, Gyeongsang National University) ;
  • Lee, Deukhwan (Hankyong National University, Dept. of Animal Life and Resources) ;
  • Jeon, Jin-Tae (Division of Applied Life Science . Institute of Agriculture and Life Sciences, Gyeongsang National University) ;
  • Lee, Jung-Gyu (Division of Applied Life Science . Institute of Agriculture and Life Sciences, Gyeongsang National University)
  • Received : 2008.01.17
  • Accepted : 2008.08.04
  • Published : 2009.07.01


This study was conducted to compare three models: two random regression models with and without considering heterogeneity in the residual variances and a lactation model (LM) for evaluating the genetic ability of Holstein cows in Korea. Two datasets were prepared for this study. To apply the test-day random regression model, 94,390 test-day records were prepared from 15,263 cows. The second data set consisted of 14,704 lactation records covering milk production over 305 days. Raw milk yield and composition data were collected from 1998 to 2002 by the National Agricultural Cooperative Federation' dairy cattle improvement center by way of its milk testing program, which is nationally based. The pedigree information for this analysis was collected by the Korean Animal Improvement Association. The random regression models (RRMs) are single-trait animal models that consider each lactation record as an independent trait. Estimates of covariance were assumed to be different ones. In order to consider heterogeneity of residual variance in the analysis, test-days were classified into 29 classes. By considering heterogeneity of residual variance, variation for lactation performance in the early lactation classes was higher than during the middle classes and variance was lower in the late lactation classes than in the other two classes. This may be due to feeding management system and physiological properties of Holstein cows in Korea. Over classes e6 to e26 (covering 61 to 270 DIM), there was little change in residual variance, suggesting that a model with homogeneity of variance be used restricting the data to these days only. Estimates of heritability for milk yield ranged from 0.154 to 0.455, for which the estimates were variable depending on different lactation periods. Most of the heritabilities for milk yield using the RRM were higher than in the lactation model, and the estimate of genetic variance of milk yield was lower in the late lactation period than in the early or middle periods.


Supported by : Korea Animal Improvement Association


  1. Gelfand, A. E. and A. F. M. Smith. 1990. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85:398-409
  2. Han, Kwang-Jin. 1995. Study on estimation of genetic parameters and breeding value of sire for economic traits in Holstein-Friesian cattle. PhD Thesis, Seoul National University. Korea
  3. Jamrozik, J., L. R. Schaeffer, Z. Liu and G. Jansen. 1997. Multiple trait random regression test-day model for production traits. Proc. Interbull Mtg., Vienna, Austria. Interbull Bull. 16:43-47
  4. Jamrozik, J. and L. R. Schaeffer. 1997. Estimates of genetic parameters for a test day model with random regressions for production of first lactation Holsteins. J. Dairy Sci. 80:762-770
  5. Jamrozik, J. and L. R. Schaeffer. 2000. Comparison of two computing algorithms for solving mixed model equations for multiple trait Random Regression test-day models. Livest. Prod. Sci. 67:143-153
  6. Kirkpatric, M., D. Lofsvold and M. Bulmer. 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124:979-993
  7. Lee, D. H. and K. J. Han. 2001. Genetic parameters for lactation using the coupling chains with gibbs sampler in multivariate animal models with missing traits in Korean Holstein cattle. J. Anim. Sci. Technol. (Kor.) 43(1):53-64
  8. Liu, Z., F. Reinhardt and R. Reents. 2000. Estimating parameters of a random regression test-day model for first three lactation milk production traits using the covariance function approach. INTERBULL Bulletin NO.25:74-80
  9. Meyer, K. 2002. 'RRGIBBS - A program for simple random regression analyses via Gibbs sampling'. Proc. 7th WCGALP, Montpellier, France, Paper 28-27
  10. Misztal, I., S. Tsuruta, T. Strabel, B. Auvray, T. Druet and D. H. Lee. 2002. BLUPF90 and related programs (BGF90). Proc. 7th WCGALP, Montpellier, France. CD-ROM communication 28:07
  11. National Livestock Research Institute. 2003. Report for dairy cattle genetic evaluation. Korea
  12. Park, Byoungho and Deukhwan Lee. 2006. Prediction of future milk yield with random regression model using test-day records in Holstein cows. Asian-Aust. J. Anim. Sci. 19(7):915-921
  13. 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
  14. 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

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