1Department of Animal Science, Faculty of Agricultural and Veterinary Science,State University of São Paulo,Jaboticabal,SP 14884-900,Brazil.
2Federal Institute of Northern Minas Gerais,Almenara,MG 39900-000,Brazil.
Animal. 2018 Apr;12(4):667-674. doi: 10.1017/S1751731117001951. Epub 2017 Aug 14.
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.
本研究旨在使用随机回归模型（RRM）结合勒让德多项式、B 样条函数和多性状模型估计（协）方差函数，从而评估肉用型鹌鹑生长性状的遗传参数。研究使用了一个包含 7000 只肉用型鹌鹑完整系谱信息的数据库。模型包括当代群体和世代的固定效应。直接加性遗传和持久环境效应被视为随机效应，使用 B 样条函数进行建模，考虑到每个个体片段的二次和三次多项式以及年龄的勒让德多项式。残差方差分为四个年龄组。直接加性遗传和持久环境效应使用 2 到 4 个片段进行建模，使用拟合阶数为 2 到 4 的勒让德多项式进行建模。直接加性遗传和持久环境效应的二次 B 样条调整模型，使用 4 个片段，是描述数据协方差结构最合适和最简约的模型。使用勒让德多项式的 RRM 对残差方差存在低估。与评估年龄的 RRM 相比，多性状模型的遗传力估计值较低。一般来说，从孵化到 35 日龄的 BW 测量值之间的遗传相关性随着评估年龄范围的增加而降低。BW 的遗传趋势在选择世代中呈正相关且显著。在评估年龄时，BW 的遗传响应在 RRM 中比多性状模型具有更大的选择值。综上所述，使用 B 样条函数和四个残差方差类和片段的 RRM 是肉用型鹌鹑生长性状遗传评估的最佳选择。总之，在遗传评估中应考虑 RRM 来制定选育计划。
