Rovadoscki Gregori A, Petrini Juliana, Ramirez-Diaz Johanna, Pertile Simone F N, Pertille Fábio, Salvian Mayara, Iung Laiza H S, Rodriguez Mary Ana P, Zampar Aline, Gaya Leila G, Carvalho Rachel S B, Coelho Antonio A D, Savino Vicente J M, Coutinho Luiz L, Mourão Gerson B
Department of Animal Science, University of São Paulo, Piracicaba, SP, 13.418-900, Brazil.
Department of Basic Sciences, University of São Paulo, Pirassununga, SP, 13.635-900, Brazil.
Poult Sci. 2016 Sep 1;95(9):1989-98. doi: 10.3382/ps/pew167. Epub 2016 May 20.
Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that genetic gain for body weight can be achieved by selection. Also, selection for body weight at 42 days of age can be maintained as a selection criterion.
在单变量或多变量分析中，已通过使用重复性和有限维度模型对来自同一动物个体的重复测量数据进行了分析。然而，在过去十年中，随机回归模型在纵向数据遗传研究中的应用变得更加普遍。因此，本研究的目的是通过使用单变量随机回归模型来估计四个实验鸡品系体重的遗传参数。使用了来自四个实验性放养鸡品系（7P、ESALQ的Caipirão、ESALQ的Caipirinha和Carijó Barbado）从孵化到84日龄的体重数据（n = 34,730）。分析模型包括当代组（性别和饲养系统）的固定效应、测量时年龄的固定回归系数，以及永久环境效应和加性遗传效应的随机回归系数。考虑了残差效应的异质性方差，并为测量时年龄的六个子类中的每一个指定了一个残差方差。使用二阶和三阶勒让德多项式对随机回归曲线进行建模，并根据赤池信息准则、贝叶斯信息准则和限制最大似然法选择最佳模型。还在相同的动物混合模型下进行了多变量分析，以验证随机回归模型。二阶勒让德多项式更适合描述所研究品系的生长曲线。估计1至84日龄体重的遗传力为中等至高（h² = 0.15至0.98），这表明在所有年龄对体重进行选择可作为选择标准。通过多变量分析获得的体重记录之间的遗传相关性在7P、ESALQ的Caipirão、ESALQ的Caipirinha和Carijó Barbado鸡品系中分别为0.18至0.96、0.12至0.89、0.06至0.96和0.28至0.96。结果表明，可以通过选择实现体重的遗传进展。此外，在42日龄时对体重的选择可作为一个选择标准继续保留。
