Modelos de regressão aleatória para descrição da curva de crescimento de codornas de corte

Bonafé, Cristina Moreira; Torres, R.A.; Sarmento, José Lindenberg Rocha; Silva, L.P.; Ribeiro, Jéferson Corrêa; Teixeira, Rafael Bastos; Silva, F.G.; Sousa, Mariele Freitas

doi:10.1590/s1516-35982011000400009

Cited by 10 publications

(14 citation statements)

References 9 publications

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“…The highest genetic correlation (0.972) was observed between 21 and 28 days of age, suggesting that selection at 28 days of age can be effective, considering that correlation between 28 and 35 days of age (selection age) was also high (0.965). These results are consistent with those reported by Dionello et al (2008) and Bonafé et al (2011), who also reported high genetic correlation from 7 days of age. The permanent environmental correlations were, in general, similar to the genetic correlations, which highlights the importance of strong permanent effect on body weight, indicating that environmental factors may influence body weight at all ages.…”

Section: Resultssupporting

confidence: 93%

“…However, animal permanent environmental variance displayed lower values (Figure 1). Similar results were reported by Akbas et al (2004), Dionello et al (2008) and Bonafé et al (2011), while studying meat quails, in which both variances also increased with age.…”

Section: Resultssupporting

confidence: 90%

“…Similar results were reported by Bonafé et al (2011), who also reported the sixth order Legendre polynomial as the best fit for growth rate curves of meat quail. Akbas et al (2004), when assessing the weight of meat quails, also reported best fit for the sixth order Legendre polynomial for random effects and constant residual variance at all ages.…”

Section: Resultssupporting

confidence: 90%

“…These results contradict Bonafé et al (2011), who reported that heritability decreased up to 14 days, increased from 14 to 35 days and decreased again after 35 days (Figure 2). …”

Section: Resultscontrasting

confidence: 82%

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Genetic evaluation of European quails by random regression models

et al. 2012

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-The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (co)variance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6) to determine which model had the best fit to describe the (co)variance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT), the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day) to 0.16 (42 days). Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.

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Section: Resultssupporting

confidence: 93%

Section: Resultssupporting

confidence: 90%

Section: Resultssupporting

confidence: 90%

“…These results contradict Bonafé et al (2011), who reported that heritability decreased up to 14 days, increased from 14 to 35 days and decreased again after 35 days (Figure 2). …”

Section: Resultscontrasting

confidence: 82%

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Genetic evaluation of European quails by random regression models

et al. 2012

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“…Segundo BONAFÉ et al (2011a), modelos de regressão aleatória permitem a obtenção de diferentes curvas de valores genéticos associadas aos diferentes indivíduos e consideram as mudanças nas variâncias genéticas e residuais, ao longo do tempo.…”

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Modelos de regressão aleatória para avaliação da curva de crescimento em matrizes de codorna de corte

et al. 2012

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Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials

Mota

Martins²,

Littiere

et al. 2018

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The objective was to estimate (co)variance functions using random regression models (RRM) with Legendre polynomials, B-spline function and multi-trait models aimed at evaluating genetic parameters of growth traits in meat-type quail. A database containing the complete pedigree information of 7000 meat-type quail was utilized. The models included the fixed effects of contemporary group and generation. Direct additive genetic and permanent environmental effects, considered as random, were modeled using B-spline functions considering quadratic and cubic polynomials for each individual segment, and Legendre polynomials for age. Residual variances were grouped in four age classes. Direct additive genetic and permanent environmental effects were modeled using 2 to 4 segments and were modeled by Legendre polynomial with orders of fit ranging from 2 to 4. The model with quadratic B-spline adjustment, using four segments for direct additive genetic and permanent environmental effects, was the most appropriate and parsimonious to describe the covariance structure of the data. The RRM using Legendre polynomials presented an underestimation of the residual variance. Lesser heritability estimates were observed for multi-trait models in comparison with RRM for the evaluated ages. In general, the genetic correlations between measures of BW from hatching to 35 days of age decreased as the range between the evaluated ages increased. Genetic trend for BW was positive and significant along the selection generations. The genetic response to selection for BW in the evaluated ages presented greater values for RRM compared with multi-trait models. In summary, RRM using B-spline functions with four residual variance classes and segments were the best fit for genetic evaluation of growth traits in meat-type quail. In conclusion, RRM should be considered in genetic evaluation of breeding programs.

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Modelos de regressão aleatória para descrição da curva de crescimento de codornas de corte

Cited by 10 publications

References 9 publications

Genetic evaluation of European quails by random regression models

Genetic evaluation of European quails by random regression models

Modelos de regressão aleatória para avaliação da curva de crescimento em matrizes de codorna de corte

Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials

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