Document Type : Research Paper

Authors

1 Former Ph.D. Student, Department of Animal Science, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

2 Assistant Professor, Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran

3 Professor, Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Karaj, Iran

4 Associate Professor, Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Karaj, Iran

5 Professor, Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA

Abstract

In this research the causal structure among calving traits of 29950 first-parity Holstein cattles of Iran including calving difficulty (CD), birth weight of calves (BW) and gestation length (GL) was revealed applying data collected by Iranian Animal Breeding in 131 herds from 1995 to 2004 by Inductive Causation (IC) searching algorithm. Significant structural coefficients were found for causal effects of BW on CD (0.060±0.002) and of GL on CD (0.007±0.002). Furthermore, the causal effect of GL on BW was significant (0.219±0.005). Considering the revealed causal structure, standard and recursive multivariate models were compared applying deviance Information criterion (DIC) and predictive ability of models in terms of two measures including mean square of error and correlation between observed and predicted values. The obtained results revealed the causal effect of BW and GL on CD and the plausibility of recursive multivariate model over standard multivariate one. Therefore, considering the causal structure among calving traits is of crucial importance.

Keywords

  1. Dekkers, J. C. M. (1994). Optimal breeding strategies for calving ease. Journal of Dairy Science, 77, 3441-3453.
  2. Gianola, D. & Sorensen, D. (2004). Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics, 167, 1407-1424.
  3. Ghoreishi, S. Sh, Rokouei, M., Sargolzaee, M. & Moghimi Esfandabadi, A. (2013). Studying the effect of calf birth weight on some economically important traits in Holstein dairy cows of Iran. Iranian Journal of Animal Sciences, 44 (1), 35-43. (in Farsi)
  4. Groen, A. F., Steine, T., Colleau, J. J., Pedersen, J. Pribyl, J. & Reinsch, N. (1997). Economic values in dairy cattle breeding, with special reference to functional traits. Report of an EAAP-working group. Livestock Production Science, 49, 1-21.
  5. Hansen, M., Lund, M.S., Pedersen, J. & Christensen, L. G. (2004). Gestation length in Danish Holsteins has weak genetic associations with stillbirth, calving difficulty, and calf size. Livestock Production Science, 91, 23-33.
  6. Jamrozik, J., Fatehi, J., Kistemaker, G. J. & Schaeffer, L. R. (2005). Estimates of genetic parameters for Canadian Holstein female reproduction traits. Journal of Dairy Science, 88, 2199-2208.
  7. Jamrozik, J. & Miller, S. P. (2014a). Genetic evaluation of calving ease in Canadian Simmentals using birth weight and gestation length as correlated traits. Livestock Science, 162, 42-49.
  8. Jamrozik, J. & Miller, S. P. (2014b). Partitioning of multiple-trait model parameters with respect to phenotypic recursion: case study of birth weight and calving ease in Canadian Simmentals. In: Proceedings of 10th World Congress of Genetics Applied to Livestock Production, 17-22 Aug., Vancouver, British Columbia, Canada.
  9. Johanson, J. M. & Berger, P. J. (2003). Birth weight as a predictor of calving ease and perinatal mortality in Holstein cattle. Journal of Dairy Science, 86, 3745-3755.
  10. Lee, D., Misztal, I., Bertrand, K. & Rekaya, R. (2002). National evaluation for calving ease, gestation length and birth weight by linear and threshold model methodologies. Journal of Applied Genetics, 43(2), 209-216.
  11. Lopez de Maturana, E., Legarra, A., Varona, L. & Ugarte, E. (2007). Analysis of fertility and Dystocia in Holsteins using recursive models to handle censored and categorical data. Journal of Dairy Science, 90, 2012-2024.
  12. Lopez de Maturana, E., Wu, X-L., Gianola, D., Weigel, K. W. & Rosa, G. J. M. (2009). Exploring biological relationships between calving traits in primiparous cattle with a Bayesian recursive model. Genetics, 181, 277-287.
  13. Lopez de Maturana, E., de los Campos, G., Wu, X. L., Gianola, D., Weigel, K. A. & Rosa, G. J. M. (2010). Modeling relationships between calving traits: a comparison between standard and recursive mixed models. Genetics Selection Evolution, 42(1).
  14. Mark, T. (2004). Applied genetic evaluations for production and functional traits in dairy cattle. Journal of Dairy Science, 87, 2641-2652.
  15. Misztal, I., Tsuruta, S., Strabel, T., Auvray, B., Druet, T. & Lee, D. (2002). BLUPF90 and related programs (BGF90). In: Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, 19-23 Aug., Montpellier, France.
  16. Moreno, C., Sorensen, D., Garcia-Cortes, L. A., Varna, L. & Altarriba, J. (1997). On biased inferences about variance components in the binary threshold model. Genetics Selection Evolution, 29, 145-160.
  17. Mrode, R. & Thompson, R. (2005). Linear models for the prediction of animal breeding values. CABI publishing. USA. Pp. 344.
  18. Nogalski, Z. & Piwszynski, D. (2012). Association of length of pregnancy with other reproductive traits in dairy cattle. Asian-Australian Journal of Animal Sciences, 25(1), 22-27.
  19. Rosa, G. J. M., Valente, B. D.,  de los Campos, G.,  Wu, X. L., Gianola, D. & Silva, M. A. (2011). Inferring causal phenotype networks using structural equation models. Genetics Selection Evolution, 43:6.
  20. Sorensen, D. A. & Gianola, D. (2002). Likelihood, Bayesian and MCMC methods in quantitative genetics. Springer-Verlag, New York.
  21. Statistical Analysis System (SAS). (2004). SAS Users’ Guide, Version 9.1. SAS Institute Inc., Cary, North Carolina, USA.
  22. Valente, B. D. & Rosa, G. J. M. (2013). Mixed effects structural equation models and phenotypic causal networks,In: C. Gondro, (Ed), Genome-Wide Association Studies and Genomic Prediction, Methods in Molecular Biology. (pp. 449-464.)  Springer Sciences.