多特征和随机回归模型之间的比较,用于对澳大利亚肉羊的体重特征的遗传评估
Uddhav Paneru1, Nasir Moghaddar1, Julius van der Werf1
1School of Environment and Rural Science, University of New England, NSW 2351, Armidale, Australia.
Journal of animal science
|February 9, 2024
概括
随机回归模型 (RR) 与多特征模型 (MT) 相比,为绵羊生长特征提供灵活的遗传评估. 虽然没有显著提高预测准确性,但RR模型提供了诸如无限记录和避免年龄校正等好处.
科学领域:
- 动物遗传学动物遗传学
- 量化遗传学 量化遗传学
- 畜牧养殖 畜牧养殖 畜牧养殖
背景情况:
- 多重特征 (MT) 模型是对重复测量特征的遗传评估的标准.
- 随机回归 (RR) 模型提供了随着时间的推移改进差异-共差异结构捕获的潜力.
- 在羊的遗传收益中,RR与MT模型的实证比较是有限的.
研究的目的:
- 为了比较RR和MT模型对澳大利亚肉羊生长特征的预测准确度.
- 评估RR模型超出预测准确性的好处.
主要方法:
- 利用来自24872只澳大利亚肉羊 (60-525天) 的102579个体重记录.
- 使用MT和RR模型估计的方差组件和繁殖值.
- 通过前预测验证模型性能.
主要成果:
- MT模型的准确度在0.51到0.58之间;RR模型的准确度在0.51到0.56之间.
- 在MT和RR模型之间没有发现预测准确度的显著差异.
- 对估计的繁殖值调整后的表型的回归对于这两种模型来说都是相似的.
结论:
- 对于绵羊的生长特征,RR模型提供了与MT模型相似的预测准确度.
- RR模型提供了更大的灵活性,包括每只动物的无限记录,不需要年龄校正.
- RR模型是一种可行的,灵活的替代品,用于对绵羊生长特征的遗传评估.
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