Department of Animal Science, Faculty of Agronomy Eliseu Maciel, Federal University of Pelotas, PO Box 354, 96010-900 Pelotas, RS, Brazil.
Institute of Agriculture and Food Research and Technology, Animal Breeding and Genetics, Caldes de Montbui 68140, Spain.
Poult Sci. 2019 Apr 1;98(4):1601-1609. doi: 10.3382/ps/pey548.
This study aimed to compare different nonlinear functions to describe the growth curve of European quails and to estimate growth curve parameters, (co)variance components, and genetic and systematic effects that affected the curve using a hierarchical Bayesian model that allows joint estimation. Three different models were fitted in the first stage (Gompertz, Logístic, and von Bertalanffy). The analyzed data set had 45,965 records of 6,838 meat quails selected for higher body weight at 42 d of age for 15 successive generations, weighed at birth, 7, 14, 21, 28, 35, and 42 d of age. Comparisons of the overall goodness of fit were based on deviance information criterion (DIC) and mean square error. Gelfand's check function compared the models at different points of the growth curve. In the second stage, the systematic (sex and generation) and genetic effects were considered in an animal model. Random samples of the a posteriori distributions were obtained by Metropolis-Hastings and Gibbs sampling algorithms. The Gompertz function presented lower DIC and better adjustment at different ages and was defined as the best fit. The heritabilities of A, b, and k parameters were moderate (0.32, 0.29, and 0.18, respectively). The genetics correlations were A and b (0.25), A and k (-0.50), and b and k (0.03). The samples of the posterior marginal distributions for the differences between the estimates of the parameters of the Gompertz model, for generation, A, b, k, age at inflexion point (APOI), and weight at inflexion point (WPOI) showed differences in relation to sex, the females are heavier, A, WPOI, and APOI for females were also higher. In conclusion, 15 generations of selection and changes in the environmental conditions altered the growth curve, leaving the quails heavier and with greater WPOI and APOI, decreased growth rate, and increased the birth weight. The curve parameters could be used in a selection index, despite the difficulty in selecting quails with higher rate of growth and adult body weight.
本研究旨在比较不同的非线性函数来描述欧洲鹌鹑的生长曲线，并使用允许联合估计的层次贝叶斯模型来估计生长曲线参数、（协）方差分量以及影响曲线的遗传和系统效应。在第一阶段拟合了三种不同的模型（Gompertz、Logístic 和 von Bertalanffy）。分析数据集包含 6838 只肉鹌鹑的 45965 条记录，这些鹌鹑在 42 日龄时选择体重更高，经过 15 个连续世代，在出生、7、14、21、28、35 和 42 日龄时称重。基于偏差信息准则（DIC）和均方误差比较了整体拟合优度。Gelfand 检验函数比较了不同生长曲线点的模型。在第二阶段，系统（性别和世代）和遗传效应在动物模型中得到了考虑。通过 Metropolis-Hastings 和 Gibbs 抽样算法获得了后验分布的随机样本。在不同年龄时，Gompertz 函数表现出更低的 DIC 和更好的调整，被定义为最佳拟合。A、b 和 k 参数的遗传力适中（分别为 0.32、0.29 和 0.18）。遗传相关性为 A 和 b（0.25）、A 和 k（-0.50）以及 b 和 k（0.03）。Gompertz 模型参数估计值之间、世代、A、b、k、拐点年龄（APOI）和拐点体重（WPOI）的差异的后验边缘分布样本显示了与性别有关的差异，雌性更重，A、WPOI 和 APOI 也更高。总之，15 个世代的选择和环境条件的变化改变了生长曲线，使鹌鹑更重，WPOI 和 APOI 更大，生长速度降低，出生体重增加。尽管选择生长速度和成年体重更高的鹌鹑具有一定难度，但曲线参数仍可用于选择指数。
