Genetic Parameters for Milk Production and Somatic Cell Score of First Lactation in Holstein Cattle with Random Regression Test-Day Models

임의회귀 검정일 모형을 이용한 홀스타인 젖소의 1산차 산유형질 및 체세포지수에 대한 유전모수

  • Lee, D.H. (Hankyong National University) ;
  • Jo, J.H. (National Agricultural Co-operative Federation) ;
  • Han, K.G. (National Agricultural Co-operative Federation)
  • Published : 2003.10.31


The objective of this study was to estimate genetic parameters for test-day milk production and somatic cell score using field data collected by dairy herd improvement program in Korea. Random regression animal models were applied to estimate genetic variances for milk production and somatic cell score. Heritabilities for milk yields, fat percentage, protein percentage, solid-not-fat percentage, and somatic cell score from test day records of 5,796 first lactation Holstein cows were estimated by REML algorithm in single trait random regression test-day animal models. For these analyses, Legendre polynomial covariate function was applied to model the fixed effect of age-season, the additive genetic effect and the permanent environment effect as random. Homogeneous residual variance was assumed to be equal throughout lactation. Heritabilities as a function of time were calculated from the estimated curve parameters from univariate analyses. Heritability estimates for milk yields were in range of 0.13 to 0.29 throughout first lactation. Heritability estimates for fat percentage, protein percentage and solid-not-fat percentage were within 0.09 to 0.11, 0.12 to 0.19 and 0.17 to 0.23, respectively. For somatic cell score, heritabilities were within 0.02 to 0.04. Heritabilities for milk productions and somatic cell score were fluctuated by days in milk with comparing 305d milk production.


Random regression model;Test day;Genetic variance;Heritability;Persistency


  1. Gengler, N., Tijani, A., Wiggans, G. R. and Misztal, I. 1999. Estimation of (co)variance function coefficients for test day yields with expectation-maximization resticted maximum likelihood alforithm. J. Dairy Sci. 82(Aug.) Online. Available:
  2. 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. J. Dairy Sci. 85:1607-1616.
  3. Jamrozik, J., Kistemaker, G. J., Dekkers, J. C. M., and Schaeffer, L. R. 1997. Comparison of possible covariates for use in random regression model for analyses of test day yields. J. Dairy Sci. 80: 2550-2556.
  4. Jamrozik, J. and Schaeffer, L. R. 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.
  5. Jamrozik, J. and Schaeffer, L. R. 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. Jensen, J. 2001. Genetic evaluation of dairy cattle using test-day models. J. Dairy Sci. 84:2803-2812.
  7. Liu, Z., Reinhardt, F. and Reents, R. 2000a. 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.
  8. Liu, Z., Reinhardt, F. and Reents, R. 2000b. Parameter estimates of a Random Regression Test Day Model for first tree lactations somatic cell scores. INTERBULL Bulletin No.26:61-65.
  9. Misztal, I. 2001. BLUPF90 family package(Access at sep. 2001).
  10. Ptak, E. and Schaeffer, L. R. 1993. Use of test day yields for genetic evaluation of dairy sires and cows. Livest. Prod. Sci. 34:23-34.
  11. Reents, R., Jamrozik, R. J., Schaeffer, L. R. and Dekkers, J. C. M. 1995. Estimation of genetic parameters for test day records of somatic cell score. J. Dairy Sci. 78:2847-2857.
  12. Reinhardt, F., Liu, Z., Bünger, A., Dopp, L. and Reents, R. 2002. Impact of Application of a Random Regression Test Day Model to Production Trait Genetic Evaluations in Dairy Cattle. INTERBULL Bulletin No.29:103-107.
  13. Swalve, H. H. 2000. Theoretical basis and computational methods for different test-day genetic evaluation methods. J. Dairy Sci. 2000 83:1115-1124.
  14. Strabel, T. and Misztal, I. 1999. Genetic parameters for first and second lactation milk yields of Polish black and white cattle with random regression test-day models. J. Dairy Sci. 82:2805-2810.
  15. Tijani, A., Wiggans, G. R., VanTassel, C. P., Philpot, J. C. and Gengler, N. 1999. Use of (co)variance functions to describe (co)variance for test day yield. J. Dairy Sci. 82(Jan.) Online. Available:
  16. VanTassel, C. P., Wiggans, G. R. and Norman, H.D. 1999. Method R estimates of heritability for milk, fat, and protein yields of United tates dairy cattle. J. Dairy Sci. 82:2231-2237.
  17. 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.
  18. 이득환, 한광진. 2001. 결측기록을 포함한 홀스타인종 젖소에 대한 다형질 개체모형에서 이중사슬 깁스샘플링 방법을 이용한 비유형질에 대한 유전모수 추정. 한국동물자원과학회지 43(1):53-64.
  19. 축산기술연구소. 2001. 2001년 하반기 젖소 유전능력 평가 보고서. 농촌진흥청 축산기술연구소.

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