Structural equation modeling for genetic analysis of body weight traits in Moghani sheep

Document Type : Research Article (Regular Paper)

Authors

1 Payame Noor University

2 Kerman Agricultural and Natural Resources Research and Education Center, AREEO, Kerman, Iran

3 Faculty of Animal Science, Hamedan university

4 Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran

Abstract

The aim of the present study was to investigate the advantages of structural equation modeling for genetic evaluation of body weight traits in Moghani sheep, using data collected on 6,320 Moghani lambs during a 23-year period (1988 to 2011) in Jafarabad Breeding Station of Moghani Sheep. Traits investigated were the body weight at birth (BW), weaning (WW), six-month (6MW), nine-month (9MW) and yearling weight (YW). Three multivariate animal models including the standard (SMM), fully recursive (FRM) and temporal recursive (TRM) models were compared in terms of deviance information criterion (DIC) and predictive ability measures including mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y, )) of records. Spearman's rank correlation coefficients between posterior means of direct genetic effects for the studied traits were also calculated across all, 50% top-ranked, 10% top-ranked and 1% top-ranked animals. In general, TRM performed better than SMM and FRM in terms of DIC, MSE and r(y, ): resulting in the lowest DIC and MSE and the highest r(y, ). All structural coefficients estimated by TRM were statistically significant. Comparisons of Spearman's rank correlations between posterior means of direct genetic effects of lambs for the studied body weight traits under SMM and TRM showed that considering the causal relationships among the studied growth traits resulted in considerable re-ranking of the animals based on the estimated breeding values, especially for the top-ranked animals; implying that TRM had more plausibility over SMM for genetic evaluation of these traits in Moghani sheep.

Keywords

Main Subjects


References
Amou Posht-e Masari, H., Hafezian, S.H., Abdollahi-Arpanahi, R., Mokhtari, M.S., Rahimi Mianji, G.H. and 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. and Kansari, J., 2002. Estimates of (co)variances due to direct and maternal effects for body weights in Timahdite sheep. Animal Science 74, 409-414.
Cardellino, R.A., 2009. Introduction and overview to the special issue on animal genetic resources. Livestock Science 120, 163-165.
Gianola, D. And Sorensen, D., 2004. Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 167, 1407-1424.
Jafaroghli, M., Rashidi, A., Mokhtari, M.S., Shadparvar, A.A., 2010. (Co)Variance components and genetic parameter estimates for growth traits in Moghani sheep. Small Ruminant Research 91, 170-177.
Konig, S., Wu, X.L., Gianola, D., Heringstad, B. and 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. And 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 Programme 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. and Lee, D., 2002. BLUPF90 and related programs (BGF90). In: Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France.
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.
Mokhtari, M.S., Moghbeli Damaneh, M. and Abdollahi Arpanahi, R., 2018. The application of recursive multivariate model for genetic evaluation of early growth traits in Raeini Cashmere goat: A comparison with standard multivariate model. Small Ruminant Research 165, 54-61.
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 Technology 7, 29-35.
Razmkabir, M., Mokhtari, M., Mahmoudim P., Rashidi, M., 2020. The comparison of standard and fully recursive multivariate models for genetic evaluation of growth traits in Markhoz goat: predictive ability of models and ranking of animals. The Journal of Agricultural Science 158, 211-217.
Rosa, G.J.M., Valente, B.D.,  de los Campos, G.,  Wu, X.L., Gianola, D. and 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.
SAS (Statistical Analysis System). 2004. SAS User’s Guide, Version 9.1. SAS Institute Inc. Cary, North Carolina, USA.
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., de los Campos, G., Gianola, D. and Silva, M.A., 2010. Searching for recursive causal structures in multivariate quantitative genetics mixed models. Genetics 185, 633-644.
Valente, B.D., Rosa, G.J.M., Gianola, D., Wu, X.L. and 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.
Wright, S., 1934. An analysis of variability in number of digits in an inbred strain of guinea pigs. Genetics, 19, 506-536.