Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
Faculty of Animal Science, Vietnam National University of Agriculture, Gia Lam, Hanoi, Vietnam.
Genet Sel Evol. 2023 Aug 7;55(1):58. doi: 10.1186/s12711-023-00829-8.
Maternal effects influence juvenile traits such as body weight and early growth in broilers. Ignoring significant maternal effects leads to reduced accuracy and inflated predicted breeding values. Including genetic and environmental direct-maternal covariances into prediction models in broilers can increase the accuracy and limit inflation of predicted breeding values better than simply adding maternal effects to the model. To test this hypothesis, we applied a model accounting for direct-maternal genetic covariance and direct-maternal environmental covariance to estimate breeding values.
This model, and simplified versions of it, were tested using simulated broiler populations and then was applied to a large broiler population for validation. The real population analyzed consisted of a commercial line of broilers, for which body weight at a common slaughter age was recorded for 41 selection rounds. The direct-maternal genetic covariance was negative whereas the direct-maternal environmental covariance was positive. Simulated populations were created to mimic the real population. The predictive ability of the models was assessed by cross-validation, where the validation birds were all from the last five selection rounds. Accuracy of prediction was defined as the correlation between the predicted breeding values estimated without the phenotypic records of the validation population and a predictor. The predictors were the breeding values estimated using all the phenotypic information and the phenotypes corrected for the fixed effects, and for the simulated data, the true breeding values. In the real data, adding the environmental covariance, with or without also adding the genetic covariance, increased the accuracy, or reduced deflation of breeding values compared with a model not including dam-offspring covariance. Nevertheless, in the simulated data, reduction in the inflation of breeding values was possible and was associated with a gain in accuracy of up to 6% compared with a model not including both forms of direct-maternal covariance.
In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values.
母体效应对肉鸡的幼体特征，如体重和早期生长有影响。忽略显著的母体效应会降低准确性并夸大预测的育种值。在肉鸡的预测模型中纳入遗传和环境直接母体协方差，可以比简单地将母体效应添加到模型中更好地提高准确性并限制预测育种值的膨胀。为了检验这一假设，我们应用了一个考虑直接母体遗传协方差和直接母体环境协方差的模型来估计育种值。
该模型及其简化版本已在模拟肉鸡群体中进行了测试，然后应用于大型肉鸡群体进行验证。所分析的实际群体是一个商业肉鸡品系，其在一个共同的屠宰年龄的体重被记录了 41 个选择轮次。直接母体遗传协方差为负，而直接母体环境协方差为正。模拟群体是为了模拟真实群体而创建的。通过交叉验证评估了模型的预测能力，其中验证鸟类均来自最后五个选择轮次。预测的准确性定义为没有验证群体的表型记录估计的预测育种值与预测因子之间的相关性。预测因子是使用所有表型信息估计的育种值和校正固定效应的表型，对于模拟数据，预测因子是真实的育种值。在实际数据中，与不包括母-子协方差的模型相比，添加环境协方差，无论是否同时添加遗传协方差，都可以提高准确性或减少育种值的低估。然而，在模拟数据中，降低育种值的膨胀是可能的，与不包括两种形式的直接母体协方差的模型相比，准确性提高了高达 6%。
在本文中，我们提出了一种使用 REML 分析的标准软件估计环境直接母体协方差的简单方法。母与子之间的遗传协方差为负，而相应的环境协方差为正。在遗传评估模型中考虑这两种协方差可以提高预测的育种值的准确性。
