随机回归与有限维模型相比,用于估计山羊生长特征的遗传参数
Zeleke Tesema1, Belay Derbie2, Tesfaye Getachew3
1Debre Birhan Agricultural Research Center, P.O.Box 112, Debre Birhan, Ethiopia. zeleke.t2007@gmail.com.
Tropical animal health and production
|March 15, 2025
概括
随机回归模型为山羊生长特征提供了比传统模型更准确的遗传参数估计. 虽然多变量动物模型最适合有限的数据,但随机回归模型对综合遗传评估具有更大的多功能性.
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:
- 准确的遗传参数估计对于有效的畜牧养殖计划至关重要.
- 传统的有限维模型可能无法完全捕捉随着时间的推移增长特征发展的复杂性.
- 随机回归模型 (RRM) 提供了一个更灵活的方法来建模纵向数据.
研究的目的:
- 为了比较随机回归模型 (RRM) 与单变体 (UNI) 和多变体 (MUV) 动物模型的性能,以估计山羊生长特征的遗传参数.
- 评估来自不同建模方法的育种价值估计的准确性,可靠性和精度.
主要方法:
- 利用了来自875只山羊的2888个体重记录,从出生到年龄的年龄.
- 配备RRM的莱根德多项式 (第1至第3顺序) 和评估的同质/异质残余方差.
- 包括所有模型中的直接添加基因和母性遗传效应作为随机效应.
主要成果:
- 最适合的RRM用于随机效应的第三阶多项式.
- 与UNI和MUV模型相比,RRM提供了中等到高的直接遗传概率估计,标准误差较低.
- 与UNI和MUV模型相比,RRM对育种价值估计的准确性和可靠性更高,尽管MUV显示了更好的信息标准,适合较小的数据集.
结论:
- RRM是一种多功能工具,用于对山羊生长特征的遗传评估,提供更高的精度和可靠性.
- 对于较小,较少测量的数据集,建议使用MUV动物模型,因为它具有优越的信息标准.
- 需要对更大,频繁测量的数据集进行进一步的研究,以充分阐明RRM在有限维模型上的优势.
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