Estimates of (co)variance function for growth to yearling in Horro sheep of Ethiopia using random regression model

Abegaz, Solomon; Wyk, J.B. van; Olivier, J.J.

doi:10.5194/aab-53-689-2010

Cited by 7 publications

(12 citation statements)

References 26 publications

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“…Competition for milk consumption among twins and triplets leads to a significant effect of birth type of lambs on the studied traits. Significant influences of investigated fixed effects on body weight of different sheep breed have been confirmed by others (Ghafouri-Kesbi et al 2008, Abegaz et al 2010.…”

Section: Fixed Effectssupporting

confidence: 56%

“…Such appropriate selective procedure requires accurate (co)variance components and genetic parameter estimates. Genetic parameters for growth traits of different sheep breeds have been reported (Prince et al 2010, Di et al 2011, Abegaz et al 2010. The results of these studies have shown that the inclusion of maternal effects on the models considered for genetic analysis of growth traits, especially for pre-weaning traits, is of crucial importance.…”

Section: Introductionmentioning

confidence: 99%

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Model comparisons and genetic parameter estimates of growth and the Kleiber ratio in Shal sheep

Mohammadi

Shahrebabak

et al. 2013

Arch. Anim. Breed.

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Genetic and phenotypic parameters for lamb growth traits were estimated for the Shal sheep used as animal model. Data on lamb growth performance were extracted from available performance records at the Shal sheep Station in Qazvin, Iran. Studied traits were the body weight of lambs at birth, at three months of age as weaning weight, the six months weight, nine months weight, yearling weight, average daily gain from birth to weaning and Kleiber ratio from birth to weaning. Significant random effects for each trait were determined by fitting additive direct genetic effects, additive maternal effects, the covariance between additive direct and additive maternal effects, maternal permanent environmental and maternal temporary environmental (common litter) effects of twelve animal models. Univariate analyses were carried out under the most appropriate model, determined by the Akaike information criterion test. Direct heritability estimates for birth weight, weaning weight, average daily gain, Kleiber ratio, six months weight, nine months weight and yearling weight were 0.13, 0.19, 0.18, 0.05, 0.16, 0.18 and 0.19, respectively. Maternal additive genetic effects were fitted only for birth weight and weaning weight. Corresponding estimates of 0.12 and 0.10 were obtained for maternal heritability of birth weight and weaning weight, respectively. Maternal permanent environmental effects have low contribution to the expression of Kleiber ratio and lead to estimates of 0.06 and 0.06 for permanent maternal environmental variance as a proportion of phenotypic variance (c 2 ) of these traits, respectively. All pre-weaning traits, except Kleiber ratio, were affected by litter effects. The magnitude of ratio of common litter variance to phenotypic variance (l 2 ) was 0.05, 0.12 and 0.14 for birth weight, weaning weight and average daily gain, respectively. Direct genetic correlations were positive and ranged from 0.09 for Kleiber ratio-yearling weight to 0.80 for weaning weight-average daily gain. Phenotypic correlations ranged from 0.18 for Kleiber ratio-yearling weight to 0.87 for weaning weight-average daily gain.

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Section: Fixed Effectssupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Model comparisons and genetic parameter estimates of growth and the Kleiber ratio in Shal sheep

Mohammadi

Shahrebabak

et al. 2013

Arch. Anim. Breed.

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“…The results showed that the criteria of values decreased when the order of fit increased in the models, agreeing with the results presented by MOHAMMADI et al (2014a, b); LOPEZ-ROMERO and CARABANO 2003; BIGNARDI et al (2009); EL FARO et al (2008) and ALBUQUERQUE and MEYER (2005); BOHLOULI et al (2013). Therefore, the results showed a significant improvement in the level of fit when the heterogeneous residual variance was included in the model, in comparison to homogeneous residual variance (ABEGAZ et al, 2010;BOHLOULI et al, 2013) and model fit improved with increasing polynomial regression order.…”

Section: The Values Of Comparison Criteriasupporting

confidence: 90%

“…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. These models use polynomials in time to describe mean profiles with random coefficients to generate a correlation among the repeated observations on each individual (ROBERT GRANIE et al, 2002).…”

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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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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“…The order of fit for the random effects was kept constant to define the best variance structure to model the residual variances. The results of log L, AIC, BIC and LRT indicated a significant improvement in the level of fit when the heterogeneous residual variance was included in the model, in comparison to homogeneous residual variance (ABEGAZ et al, 2010) and model fit improved with increasing polynomial regression order to 3.3.het3 model and then worsened to 6.6.het3 model. These indicated that residual variances had different behavior along the age.…”

Section: Discussion Log Likelihoods and Information Criteriamentioning

confidence: 96%

Estimation of genetic parameters for body weight traits in Mazandaran native breeder hens by random regression models

Naderi

2019

Genetika

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The primary concern of this study is to investigate appropriate random regression model for estimate genetic parameters body weight at hatch (BW1), eight (BW8), twelve (BW12) and thirty two (BW32) weeks of ages by the restricted maximum likelihood method. The body weight records set included 39872 during 16 generations of hens kept at the Mazandaran Breeding Center of Iran. Random regression were modelled using generation-hatch as a fixed effect and additive genetic and permanent environmental effects as random effects Residual variances were modeled through a step function with 1 and 3 classes. The model was considered to be the most appropriate with the highest significant log likelihood ratio test (LRT) and the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC). Heritability values increased from 0.21 for BW1, to 0.40 for BW32. Genetic correlations of body weight at different record keeping were often higher than permanent environmental correlations. Genetic correlations between pairs of body weight measures were moderate to high with a range from 0.25 to 0.97. The largest genetic correlation, as well as permanent environmental correlation, was observed between BW12and BW32. High and moderate broad sense heritability values for all studied traits shows that these traits are less influenced by residual effects which make them effectively transmitted to the progeny. Findings show that genetic improvement for body weight can be achieved by selection. The Heritability of body weight at thirty two weeks of ages and its relatively 18

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Estimates of (co)variance function for growth to yearling in Horro sheep of Ethiopia using random regression model

Cited by 7 publications

References 26 publications

Model comparisons and genetic parameter estimates of growth and the Kleiber ratio in Shal sheep

Model comparisons and genetic parameter estimates of growth and the Kleiber ratio in Shal sheep

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

Estimation of genetic parameters for body weight traits in Mazandaran native breeder hens by random regression models

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