Genetic analysis of growth curve traits in Iran-Black sheep breed: Comparison of standard multivariate and structural equation models

Document Type : 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, Jiroft, Iran

Abstract

In this study, a dataset comprising 20,328 body weight records from birth to 360 days of age was utilized to compare five growth curve models and genetic analysis of growth curve parameters in Iran-Black sheep. The data and genealogical information were collected between 1981 and 2007 from the Breeding Station of Abbasabad in the Khorasan Razavi province of northeast Iran. The performance of five statistical models including Brody, negative exponential, von Bertalanffy, Logistic, and Gompertz for describing the growth curve of the studied population was evaluated by applying the SAS software. The statistics l measures used for model comparisons were Akaike's information criterion (AIC), root mean square error (RMSE), and adjusted coefficient of determination ( ). The Brody model, exhibiting the highest  and the lowest values for both AIC and RMSE, was selected as the best model for characterizing the growth curve in this breed. Consequently, the parameters of the growth curve, including parameter A (considered as weight at maturity), B (considered as an integration constant), and K (maturation rate) were predicted by applying the Brody model. To investigate the effect of maternal components on the growth curve parameters, nine univariate animal models, including different combinations of the direct additive genetic, maternal additive genetic, maternal permanent environmental, and maternal temporary environmental effects, were fitted. Subsequently, two multivariate animal models, comprising the standard (SMM) and fully recursive (FRM) models were analyzed by using the Bayesian inference. The FRM outperformed SMM in terms of lower means square error (MSE) and higher Pearson's correlation coefficients between the actual and predicted records (r(y, )) values, indicating better goodness of fit. The posterior means for heritability of A, B, and K parameters were low but statistically significant under SMM and FRM. It may be concluded that the growth trajectory traits of Iran-Black sheep are influenced mainly by non-additive genetic and environmental effects, emphasizing the importance of considering these effects for developing the corresponding breeding strategies. The Spearman's rank correlation coefficients between the estimated breeding values for growth curve traits under SMM and FRM indicated significant re-ranking of animals, favoring FRM for genetic evaluation in Iran-Black sheep.

Keywords

Main Subjects

References

References
Abegaz, S., van Wyk, J.B., Olivier, J.J., 2010. Estimation of genetic and phenotypic parameters of growth curve and their relationship with early growth and productivity in Horro sheep. Archive Tierzucht 53, 85-94.
Ahmadpanah, J., Ghaderi-Zefrehei, M., Zakizadeh, S., Rafeie, F., 2023. Genetic analysis of growth parameters and optimum age and weight slaughter prediction in Kurdi sheep. Small Ruminant Research 229, 107132.
Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716-723.
Amou Posht-e Masari, H., Hafezian, S.H., Mokhtari, M., Rahimi Mianji, G., Abdollahi-Arpanahi, R., 2021. Inferring causal relationships among growth curve traits of Lori-Bakhtiari sheep using structural equation models. Small Ruminant Research 203, 106489.
Bathaei, S.S., Leroy, P.L., 1998. Genetic and phenotypic aspects of the growth curve characteristics in Mehraban Iranian fat-tailed sheep. Small Ruminant Research 29, 261-269.
Brody, S., 1945. Bioenergetics and Growth. Reinhold Publishing Corp, NY, USA.
Brown, J.E., Fitzhugh, Jr.H.A., Cartwright, T.C., 1976. A comparison of nonlinear models for describing weight–
age relationships in cattle. Journal of Animal Science 42, 810-818.
da Silva, L.S.A., Fraga, A.B., da Silva, F.D.L., Beelen, P.M.G., Silva, R.M.D.O., Tonhati, H., Barros, C.D.C., 2012. Growth curve in Santa Ines sheep. Small Ruminant Research 105, 182-185.
Eisen, E.J., 1976. Result of growth curve analysis in mice and rats. Journal of Animal Science 42, 1008-1023.
Gbangboche, A.B., Gleke-Kalai, R., Albuquerque, L.G., Leroy, P., 2008. Comparison of non-linear growth models to describe the growth curve in West African Dwarf sheep. Animal 2, 1003-1012.
Ghavi Hossein-Zadeh, N., 2015a. Bayesian estimates of genetic relationships between growth curve parameters in Shall sheep via Gibbs sampling. Iranian Journal of Applied Animal Science 5, 897-904.
Ghavi Hossein-Zadeh, N., 2015b. Modeling the growth curve of Iranian Shall sheep using non-linear growth models. Small Ruminant Research 130, 60-66.
Ghavi Hossein-Zadeh, N., 2017. Modelling growth curve in Moghani sheep: comparison of non-linear mixed growth models and estimation of genetic relationship between growth curve parameters. Journal of Agricultural Science 155, 1150-1159.
Gianola, D., Sorensen, D., 2004. Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 167, 1407-1424.
Laird, A.K., 1965. Dynamics of relative growth. Growth 29, 249-263.
London, J.C., Weniger, J.H., 1995. Investigations into traditionally managed Djallonke-sheep production in the humid and subhumid zones of Asante, Ghana. III. Relationship between birth weight, preweaning growth, and post-weaning growth of lambs. Journal of Animal Breeding and Genetics 112, 431-453.
 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.
Lupi, T.M., Leon, J.M., Nogales, S., Barba, C., Delgado, J.V., 2016. Genetic parameters of traits associated with the growth curve in Segurena sheep. Animal 10, 729-735.
Malhado, C.H.M., Carneiroa, P.L.S., Affonso, P.R.A.M., Souza Jr., A.A.O., Sarmento, J.L.R., 2009. Growth curves in Dorper sheep crossed with the local Brazilian breeds, Morada Nova, Rabo Largo, and Santa Ines. Small Ruminant Research 84, 16-21.
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, 815-821.
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., KargarBorzi, N., Asadifozi, M., Bahreini Behzadi, M.R., 2019. Evaluation of non-linear models for genetic parameter estimation of growth curve traits in Kermani sheep. Tropical Animal Health and Production 51, 2203-2212.
 Mohammadi, Y., Mokhtari, M.S., Saghi, D.A., Shahdadi, A.R., 2019. Modeling the growth curve in Kordi sheep: The comparison of non-linear models and estimation of genetic parameters for the growth curve traits. Small Ruminant Research 177, 117-123.
Nelder, J.A., 1961. The fitting of a generalization of the logistic curve. Biometrics 17, 89-110.
Rashedi Dehsahraei, A., Ghaderi-Zefrehei, M., Rafeie, F., Zakizadeh, S., Shirani Shamsabadi, J., Elahi Torshizi, M., Neysi, S., Rahmatalla, S.A., 2023. Genetic analysis of growth curve in Moghani sheep using Bayesian and REML. Journal of Animal Science 101, skad203.
Rashidi, A., 2012. Genetic parameter estimates of body weight traits in Iran-Black sheep. Journal of Livestock Science and Technologies 1, 54-60.
Richards, F.J., 1959. A flexible growth function for empirical use. Journal of Experimental Botany 10, 290-300.
Sargolzaei, M., Iwaisaki, H., Colleau, J.J., 2006. CFC: A tool for monitoring genetic diversity, Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil.
SAS (Statistical Analysis System). 2004. SAS User’s Guide, Version 9.1. SAS Institute Inc. Cary, North Carolina, USA.
Tekelen, J.T., Galvao, A. C., da Silva Robazza, W., 2017. Comparing non-linear mathematical models to describe growth of different animals. Acta Scientiarum. Animal Sciences 39, 73-81.
Vazquez, J. A., Lorenzo, J. M., Fucinos, P., Franco, D. 2012. Evaluation of non-linear equations to model different animal growths with mono and bisigmoid profiles. Journal of Theoretical Biology 314, 95-105.
von Bertalanffy, L., 1957. Quantitative laws in metabolism and growth. The Quarterly Review of Biology 32, 217-230.
Waheed, A., Khan, M.S., Ali, S., Sarwar, M., 2011. Estimation of growth curve parameters in Beetal goats. Archive Tierzucht 54, 287-296.
Wright, S., 1921. Correlation and causation. Journal of Agricultural Research 20, 557-585.
Zamani, P., Moradi, M.R., Alipour, D., Ghafouri-Kesbi, F., 2016. Combination of B-Spline and Legendre functions in random regression models to fit growth curve of Moghani sheep. Small Ruminant Research 145, 115-122.
 

Files

Share

How to cite

Statistics