Random regression model of growth during the first three months of age in Spanish Merino sheep1,2

Molina, A.; Menéndez-Buxadera, A.; Valera, M.; Serradilla, J. M.

doi:10.2527/jas.2006-647

Cited by 27 publications

(16 citation statements)

References 21 publications

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“…The results demonstrated in this studies that a large proportion of the total variance (56-68% and 80-94%, by homogeneous and heterogeneous residual variances, Table 4 and 5) explained by the first eigenfunction of each covariance function for each random effect. These results was in accordance with other reports (BARAZANDEH et al, 2012;BOHLOULI et al, 2013;MOLINA et al, 2007;ABEGAZ et al, 2010;KARIUKI et al, 2010). This implies that changes in the growth curve would be more likely achieved by selection based on this constant term (BOHLOULI et al, 2013;KARIUKI et al, 2010).…”

Section: Regression Coefficients and Variance Componentsupporting

confidence: 93%

“…There are two important recommendation method for genetic evaluation of growth in sheep: fitting nonlinear regression (e.g. logistic, exponential, Gompertz or Richards models) to the data and estimating genetic parameters for growth curves (LAMBE et al, 2006) or using random regression model (RRM) (LEWIS and BROTHERSTONE, 2002;FISCHER et al, 2004;MOLINA et al, 2007). Currently RRM is being applied for genetic evaluation in growth trait such cattle (KREJCOVA et al, 2007;NESER et al, 2012;BOHLOULI et al, 2013), sheep (LEWIS and BROTHERSTON, 2002;GHAFOURI KESBI et al, 2008;ABEGAZ et al, 2010;KARIUKI et al, 2010;WOLC et al, 2011 ) and pig (HUISMAN et al, 2002) data.…”

Section: Introductionmentioning

confidence: 99%

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Genetic evaluation of growth traits in Iranian Kordi Sheep using random regression model with homogeneous and heterogeneous residual variances

Mohammadi

Farhadian

2017

Genetika

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The purpose of this study was estimation of genetic parameter using random regression model (RRM) with various error variance in Iranian Kordi sheep. The data (consisting of 7875 weight records from birth to 360 days of age) were collected during the period 2000 to 2013 from the rearing and breeding station of Kordi sheep in Shirvan, Iran. The independent variables were Legendre polynomials (LP) of age at weighing and orders of fit from 2 to 5 were considered. Analyses were carried out fitting sets of random regression coefficients due to direct additive genetic, direct and maternal permanent environmental effects, with heterogeneous and homogeneous error variances. To compare the model were used different criteria such as LogL, AIC, BIC and LRT. The best fitting RRM among homogeneous error variance was the model with a LP of fourth order for fixed effect, fourth order for direct additive genetic and fifth order for direct and maternal permanent environmental effects (model 4455). Among the models with heterogeneous error variances different, model 7 (Heterogeneous error variances of 72 various classes), was selected as the best model. The variances increased along the trajectory from 3.75 to 12.81, 4.43 to 30.28 and 1.49 to 8.49; 0.25 to 27.94, 0.03 to 12.32 and 0.15 to 22.66 for direct additive genetic, direct and maternal permanent environmental effect by homogeneous and heterogeneous error variances, respectively. The direct heritability ranged from 0.15 to 0.41 and 0.11 to 0.56 by homogeneous and heterogeneous error variances, respectively. Genetic correlation between adjacent test days was more than between distant test days. This research has demonstrated the possibility of application of RRM with heterogeneous error variance for genetic evaluation of Iranian Kordi Sheep.

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Section: Regression Coefficients and Variance Componentsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Genetic evaluation of growth traits in Iranian Kordi Sheep using random regression model with homogeneous and heterogeneous residual variances

Mohammadi

Farhadian

2017

Genetika

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“…Molina et al . () identified in the Merino sheep by RRM a substantial genetic variation, not only in the general breeding values but also in the shape of the lamb growth curve. Such an analysis will therefore provide more information to breeders in selecting animals that better fulfil the market requirements in slaughter age and weight.…”

Section: Discussionmentioning

confidence: 99%

Direct and maternal genetic effects for preweaning growth in Retinta cattle estimated by a longitudinal approach throughout the calving trajectory of the cow

Morales

Menéndez-Buxadera

Avilés

et al. 2013

J Animal Breeding Genetics

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The direct and maternal genetic effects were estimated for the preweaning growth of Retinta calves with a multitrait model across parities, using a longitudinal approach with random regression models (RRM). The 120 (P120) and 180 days (P180) weights (5972 calves) were considered as different traits in each calving. The heritability of direct effect across parities was on average 0.37 for P120 and 0.58 for P180, slightly higher than the estimates by univariate (0.30 and 0.56) and bivariate models (0.30 and 0.51, respectively). The heritability for maternal effects was 0.16 for P120 and 0.26 for P180 and very similar by uni- (0.16 and 0.23) and multivariate model (0.16 and 0.22, respectively). The correlation between direct and maternal effects by RRM showed a pronounced antagonism -0.64 for P120 and -0.78 for P180), likewise uni- (-0.62 and -0.72) and multivariate case (-0.64 and -0.74, respectively). The preweaning weights should be considered as different traits across parities, because the genetic correlations were different from unity. The RRM also allowed us to estimate all the parameters throughout the calving trajectory of the cow. The use of multiple traits RRM across parities can provide very useful information for the breeding programmes.

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“…In meat sheep, studies have been conducted to evaluate the order of fit required for continuous functions applied to random regression models, as developed by Lewis and Brotherstone (2002), Fischer et al (2004), Sarmento et al (2006a), Molina et al (2007), and Sarmento et al (2010), and these differ in the order of fit of the functions adopted.…”

Section: Introductionmentioning

confidence: 99%

Random regression models for the estimation of genetic and environmental covariance functions for growth traits in Santa Ines sheep

et al. 2016

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ABSTRACT. Polynomial functions of different orders were used to model random effects associated with weight of Santa Ines sheep from birth to 196 days. Fixed effects included in the models were contemporary groups, age of ewe at lambing, and fourth-order Legendre polynomials for age to represent the average growth curve. In the random part, functions of different orders were included to model variances associated with direct additive and maternal genetic effects and with permanent environmental effects of the animal and mother. Residual variance was fitted by a sixth-order ordinary polynomial for age. The higher the order of the functions, the better the model fit the data. According to the Akaike information criterion and likelihood ratio test, a continuous function of order, five, five, seven, and three for direct additive genetic, maternal genetic, animal permanent environmental, and maternal permanent environmental effects (k = 5573), respectively, was sufficient to model changes in (co)variances with age. However, a more parsimonious model of order three, three, five, and three (k = 3353) was suggested based on Schwarz's Bayesian information criterion for the same effects. Since it was a more flexible model, model k = 5573 provided inconsistent genetic parameter estimates when compared to the biologically expected result. Predicted breeding values obtained with models k = 3353 and k = 5573 differed, especially at young ages. Model k = 3353 adequately fit changes in variances and covariances with time, and may be used to describe changes in variances with age in the Santa Ines sheep studied.

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Random regression model of growth during the first three months of age in Spanish Merino sheep1,2

Cited by 27 publications

References 21 publications

Genetic evaluation of growth traits in Iranian Kordi Sheep using random regression model with homogeneous and heterogeneous residual variances

Genetic evaluation of growth traits in Iranian Kordi Sheep using random regression model with homogeneous and heterogeneous residual variances

Direct and maternal genetic effects for preweaning growth in Retinta cattle estimated by a longitudinal approach throughout the calving trajectory of the cow

Random regression models for the estimation of genetic and environmental covariance functions for growth traits in Santa Ines sheep

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