The aim of present study was to investigate the performance of standard multivariate model
(SMM), fully recursive models (FRM) and temporal recursive model (TRM) for genetic
evaluation of growth traits in Lori-Bakhtiari sheep. The pedigree and phenotypic data of growth
traits including birth weight (BW), average daily gain from birth to weaning (ADG1), weaning
weight (WW), average daily gain from weaning to six-month weight (ADG2) and six-month
weight (6MW) collected from 1995 to 2012 were used. Firstly, three models (SMM, FRM and
TRM) were fitted via Bayesian approach with 200000 Gibbs samples and the first 50000 samples
were considered as burn-in period with thinning intervals of 10 samples. Contrary to FRM and
TRM, in SMM causal relationships between the studied traits were ignored. Deviance
Information Criterion (DIC) values obtained under three considered models indicated superiority
of models with causal relationships on SMM. Also, based on DIC, within models containing
causal relationships, TRM performed better than FRM for genetic evaluation of the studied
growth traits. The posterior means for structural coefficients between BW-ADG1, ADG1-WW,
WW-ADG2 and ADG2-6MW were 9.343, 0.03, 10.632 and 0.14, respectively and were
statistically significant (P <0.05). Results of comparisons of rank correlations between posterior
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means of direct genetic effects for the studied growth traits under SMM and TRM revealed that
taking the causal relationships among the studied growth traits into account may cause
considerable re-ranking for the animals in terms of the estimated breeding values, especially for
the top-ranked animals. Model comparisons based on goodness of fit and predictive ability
applying mean square error (MSE) and Pearson’s correlation coefficients between the observed
and predicted records ( ( revealed that in general TRM performed better than SMM in
terms of goodness of fit and predictive ability. In general, the results indicated th