Genetic evaluation of reproductive traits in Raeini Cashmere goat using structural ‎equation modeling

Document Type : Research Paper


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

2 Instructor, Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran

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


In the present study, data collected from 1993 to 2011 in Raeini Cashmere goat breeding station were used for genetic evaluation and inferring causal relationships among reproductive traits applying structural equation models. The studied lifetime reproductive traits were included the overall number of kids born (OLSB), the overall number of kids weaned (OLSW), the overall litter weight at birth (OLWB) and the overall litter weight at weaning (OLWW). Three models including standard (SMM), recursive based on prior knowledge (PRM) and recursive based on inductive causation algorithm (ICM) multivariate models were used for genetic evaluation of animals under the Bayesian approach. The comparisons of the investigated models via deviance information criterion (DIC), mean square error (MSE) and Pearson's correlations between observed and predicted records () revealed that ICM had lower DIC and MSE and higher () for all the studied lifetime reproductive traits; implying the more plausibility of ICM over SMM and PRM. Furthermore, re-raking of the animals under SMM and ICM confirmed the importance of considering the causal relationship among the traits for ensuring accurate ranking of animals according to their breeding values.


  1. Amou Posht-e Masari, H., Hafezian, S.H., Abdollahi-Arpanahi, R., Mokhtari, M.S. & Rahimi Mianji, G. (2018). Estimation of genetic parameters and genetic trends for growth traits in Lori Bakhtiari sheep using structural equation models. Animal Production Research, 7(2), 83-96. (in Farsi)
  2. Amou Posht-e Masari, H., Hafezian, S.H., Abdollahi-Arpanahi, R., Mokhtari, M.S., Rahimi Mianji, Gh. & Taheri Yeganeh, A. (2019). The comparison of alternative models for genetic evaluation of growth traits in Lori-Bakhtiari sheep: implications on predictive ability and ranking of animals. Small Ruminant Research, 173, 59-64.
  3. Gianola, D. & Sorensen, D. (2004). Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics, 167, 1407-1424.
  4. Jafari, S. & Manafiazar, G. (2016). Estimates of genetic parameters for lifetime reproductive performance traits in Makuei ewes. Small Ruminant Research, 139, 67-72.
  5. Konig, S., Wu, X.L., Gianola, D., Heringstad, B. & Simianer, H. (2008). Exploration of relationships between claw disorders and milk yield in Holstein cows via recursive linear and threshold models. Journal of Dairy Science, 91, 395-406.
  6. Kosgey, I.S. & Okeyo, A.M. (2007). Genetic improvement of small ruminants in low-input, smallholder production systems: technical and infrastructural issues. Small Ruminant Research, 70, 76-88.
  7. Maghsoudi, A., Vaez Torshizi, R. & Safi Jahanshahi, A. (2009). Estimates of (co)variance components for productive and composite reproductive traits in Iranian Cashmere goats. Livestock Science, 126, 162-167.
  8. Matos, C.A., Thomas, D.L., Gianola, D., Tempelman, R.J. & Young, L.D. (1997). Genetic analysis of discrete traits in sheep using linear and nonlinear models: I. Estimation of genetic parameters. Journal of Animal Science, 75, 76-87.
  9. Menezes, L.M., Sousa, W.H., Cavalcanti-Filho, E.P. & Gama, L.T. (2016). Genetic parameters for reproduction and growth traits in Boer goatsin Brazil. Small Ruminant Research, 136, 247-256.
  10. 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.
  11. Mohammadi, H., Moradi Sharebabak, M. & Moradi Sharebabak, H. (2012). Genetic parameter estimates for growth traits and prolificacy in Raeini Cashmere goats. Tropical Animal Health and Production, 44, 1213-1220.
  12. Mokhtari, M.S., Moradi Shahrbabak, M., Nejati Javaremi, A. & Rosa, G.J.M. (2018). Searching causal structure among calving traits in first-parity Holstein cattles of Iran. Iranian Journal of Animal Science, 49, 1-9. (in Farsi)
  13. Mokhtari, M.S., Moghbeli Damaneh, M. & Abdollahi Arpanahi, R. (2018). The application of recursive multivariate model for genetic evaluation of early growth traits in Raeini Chasmere goat: A comparison with standard multivariate model. Small Ruminant Research, 165, 54-61.
  14. 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.
  15. Sorensen, D.A. & Gianola, D. (2002). Likelihood, Bayesian and MCMC methods in quantitative genetics. Springer-Verlag, New York.
  16. 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.