Estimates of genetics parameters of egg weight and body weight due to social interaction in a commercial broiler chicken line

Document Type : Research Paper


1 M.Sc. Student, Department of Animal and poultry science, Aburaihan Campus, University of Tehran, Iran

2 Assistant Professor, Department of Animal and poultry science, Aburaihan Campus, University of Tehran, Iran

3 Associate Professor, Department of Animal and poultry science, Aburaihan Campus, University of Tehran, Iran


Social interaction among individuals can have important influences on the production and fitness traits in livestock population. Therefore, the aim of this study was to estimate variance components due to social effects for two production traits including egg weight and body weight in a commercial broiler chicken line. In this study 29,645 production records provided by Arian commercial broiler chicken line center of Iran were used. Data were collected during years 2007 to 2016 and analyzed with four different single-trait animal models including or excluding random genetic social and pen effects, using AI-REML algorithm. For each trait, the most appropriate model was chosen based on Akaike information criteria. Based on the most appropriate fitted models, direct heritability estimates ranged from 0.60±0.02 (egg weight) to 0.55±0.02 (body weight). Even though social genetic variance was small, it was significantly different from zero for body weight and egg weight. The variance due to random pen effect was significant for egg production traits. In general, social genetic and pen effects were important effects regardless of trait under study. Correlation between direct and social genetic effects was negative (-0.51) for egg weight and positive (0.27) for body weight. In general, we concluded that social genetic and pen effects should be included in the statistical models for genetic evaluation of egg weight and body weight in broiler chicken.


  1. Bergsma, R., Kanis, E., Knol, E. F. & Bijma, P. (2008). The contribution of social effects to heritable variation in finishing traits of domestic pigs (Sus scrofa). Genetics, 178(3), 1559-1570.
  2. Bijma, P., Muir, W. M. & Van Arendonk, J. A. (2007). Multilevel selection 1: quantitative genetics of inheritance and response to selection. Genetics, 175(1), 277-288.
  3. Bouwman, A., Bergsma, R., Duijvesteijn, N. & Bijma, P. (2010). Maternal and social genetic effects on average daily gain of piglets from birth until weaning. Journal of Animal Science, 88(9), 2883-2892.
  4. Ellen, E. D., Ducrocq, V., Ducro, B. J., Veerkamp, R. F. & Bijma, P. (2010). Genetic parameters for social effects on survival in cannibalistic layers: combining survival analysis and a linear animal model. Genetics Selection Evolution, 42(1), 27.
  5. Ellen, E. D., Muir, W. M., Teuscher, F. & Bijma, P. (2007). Genetic improvement of traits affected by interactions among individuals: sib selection schemes. Genetics, 176(1), 489-499.
  6. Ellen, E. D., Rodenburg, T. B., Albers, G. A., Bolhuis, J. E., Camerlink, I., Duijvesteijn, N., Knol, E. F., Muir, W. M., Peeters, K. & Reimert, I. (2014). The prospects of selection for social genetic effects to improve welfare and productivity in livestock. Frontiers in genetics 5.
  7. Griffing, B. (1967). Selection in reference to biological groups I. Individual and group selection applied to populations of unordered groups. Australian Journal of Biological Sciences, 20(1), 127-140.
  8. Khaw, H. L., Ponzoni, R. W. & Bijma, P. (2014). Indirect genetic effects and inbreeding: consequences of BLUP selection for socially affected traits on rate of inbreeding. Genetics Selection Evolution, 46(1), 39.
  9. Liu, J. & Tang, G. (2016). Investigating the contribution of social genetic effect to longer selection response in a ten generations breeding programme simulated. Italian Journal of Animal Science, 15(4), 610-616.
  10. Meyer, K. (2007). WOMBAT-A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University-Science, B, 8(11), 815-821.
  11. Muir, W. M. (2005). Incorporation of competitive effects in forest tree or animal breeding programs. Genetics, 170(3), 1247-1259.
  12. Peeters, K., Ellen, E. D. & Bijma, P. (2013). Using pooled data to estimate variance components and breeding values for traits affected by social interactions. Genetics Selection Evolution, 45(1), 27.
  13. Team, R. C. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2014.
  14. Wilson, A. J., Gelin, U., Perron, M.-C. & Réale, D. (2009). Indirect genetic effects and the evolution of aggression in a vertebrate system. In: Proceedings of the Royal Society of London B: Biological Sciences, 276(1656), 533-541.
  15. Wolf, J. B. (2003). Genetic architecture and evolutionary constraint when the environment contains genes. In: Proceedings of the National Academy of Sciences, 100(8), 4655-4660.