The comparison of standard multiple-trait and structural equation modeling approaches for the estimation of genetic and phenotypic parameters of growth traits in Arman sheep

Document Type : Original Research Article (Regular Paper)

Authors

1 Department of Animal Science, Faculty of Animal and Food Science, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz, Iran

2 Department of Sheep and Goat Breeding, National Animal Breeding Center and Promotion of Animal Products, Tehran, Iran

3 Department of Animal Science, Faculty of Agriculture, University of Jiroft, P.O. Box 364, Jiroft, Iran

Abstract

The current investigation was performed to compare the performance of standard multivariate and structural equation models for the estimation of genetic parameters of growth traits in Arman sheep. Data was collected on 2194 Arman lambs in 13 years (1999 to 2012) at Abbasabad Sheep Breeding Station, Khorasan Razavi province, north-eastern Iran. The studied growth traits were body weight at birth (BWT), weaning (WWT), six months (6MWT), nine months (9MWT), and yearling weight (YWT). The predictive abilities of three multivariate animal models comprising standard (SMM), temporal recursive (TRM), and fully recursive (FRM) models were evaluated by applying two statistical criteria including the mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted records (r(y,y ̂)). In general, TRM performed better than SMM and FRM. The lowest MSE and the highest r(y,y ̂) were found under TRM. All the posterior means for the structural coefficients were statistically significant. Spearman's rank correlation coefficients between the estimated breeding values for the body weight traits were also computed across all, 50% top-ranked, and 10% top-ranked animals. Comparisons of these correlations between posterior means of estimated breeding values of individuals for the growth traits under SMM and TRM revealed that taking the causal relationships among these traits into account could result in significant re-ranking of the animals according to the estimated breeding values; showing that TRM had more advantage over SMM for the estimation of genetic parameters and the breeding values of the studied traits in Arman sheep.

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References

References
Abegaz, S., van Wyk, J.B., Olivier, J.J., 2005. Model comparisons and genetic and environmental parameter estimates of growth and the Kleiber ratio in Horro sheep. South African Journal of Animal Science 35, 30-40.
Amou Posht-e Masari, H., Hafezian, S. H., Arpanahi, R. A., 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, 83-96.
Amou Posht-e Masari, H., Hafezian, S.H., Abdollahi-Arpanahi, R., Mokhtari, M.S., Rahimi Mianji, G.H., 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.
Boujenane, I., Kansari, J., 2002. Estimates of (co)variances due to direct and maternal effects for body weights in Timahdite sheep. Animal Science 74, 409-414.
Gianola, D., Sorensen, D., 2004. Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 167, 1407-1424.
Jafaroghli, M., Soflaee Shahrbabak, M., Ghafouri-Kesbi, F., Mokhtari, M., 2021. Structural equation modeling for genetic analysis of body weight traits in Moghani sheep. Journal of Livestock Science and Technologies 9, 59-65.
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.
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.
Meyer, K. 2013. WOMBAT- A Program for Mixed Model Analyses by Restricted Maximum Likelihood. User Notes, Animal Genetics and Breeding Unit, Armidale, Australia.
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, (Montpellier, France).
Mokhtari, M.S., Moradi Shahrebabak, M., Moradi Shahrebabak, H., Sadeghi, M., 2013. Estimation of (co) variance components and genetic parameters for growth traits in Arman sheep. Journal of Livestock Science and Technologies 1, 35-43.
Mokhtari, M., Barazandeh, A., Moghbeli Damaneh, M., Roudbari, Z., Kargar Borzi, N., 2020. Inferring causal relationships among growth traits in Kermani sheep applying structural equation modeling. Journal of Livestock Science and Technologies 7, 29-35.
Mohammadi, Y., Rashidi, A., Mokhtari, M. S., Esmailizadeh, A. K., 2010. Quantitative genetic analysis of growth traits and Kleiber ratios in Sanjabi sheep. Small Ruminant Research 93, 88-93.
Mohammadi, Y., Saghi, D.A., Shahdadi, A.R., Rosa, G.J.M., Mokhtari, M.S., 2020. Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep. Acta Scientiarum. Animal Sciences 42, e48823.
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.
Schwarz, G., 1978. Estimating the dimension of a model. The Annals of Statistics 6, 461-464.
Tosh, J.J. and Kemp, R.A., 1994. Estimation of variance components for lamb weights in three sheep populations. Journal of Animal Science 72, 1184-1190.
Valente, B.D., Rosa, G.J.M., Gianola, D., Wu, X.L., Weigel, K., 2013. Is structural equation modeling advantageous for the genetic improvement of multiple traits? Genetics 194, 561-572.
Wright, S., 1921. Correlation and causation. Journal of Agricultural Research 20, 557-585.
Journal of Livestock Science and Technologies
Volume 11, Issue 2
December 2023
Pages 69-76

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