计算和建模方法为美国值基因评估的分娩容易度的方法
J M Tabet1, M Bermann1, D Lourenco1
1Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602.
JDS communications
|March 6, 2026
概括
美国乳牛生育容易性 (CE) 遗传评估使用父母祖母 (SMGS) 和父母祖母 (SMAT) 模型. 这两种模型,使用牛顿-拉普森 (NR) 或期望最大化 (EM) 算法,为遗传改进提供可靠和高效的解决方案.
科学领域:
- 动物遗传学动物遗传学
- 乳制品科学 乳制品科学
- 量化遗传学 量化遗传学
背景情况:
- 对于美国奶牛的分娩容易性 (CE) 的遗传评估对于群体的健康和生产率至关重要.
- 目前的评估采用了包括直接和母遗传效应在内的父母祖父 (SMGS) 模型.
- 替代模型和计算算法需要对效率和可靠性进行调查.
研究的目的:
- 为了比较美国乳牛生育容易 (CE) 基因评估的父母祖父 (SMGS) 和父母祖父 (SMAT) 模型.
- 在这些模型中评估牛顿-拉普森 (NR) 和预期最大化 (EM) 算法的性能.
- 评估不同CE评估方法的计算效率和可靠性.
主要方法:
- 分析了超过2400万份美国乳制品生育容易度 (CE) 记录.
- 两种遗传评估模型的比较:父-母祖父 (SMGS) 和父-母祖父 (SMAT).
- 牛顿-拉普森 (NR) 和预期最大化 (EM) 解决算法的应用用于遗传参数估计.
主要成果:
- 在每种模型中的算法中,GEBV的高一致性 (相关性>0.99) 对于父亲和母亲的祖父.
- 牛顿-拉普森 (NR) 在代和计算时间方面展示了优越的计算效率,而不是预期最大化 (EM).
- 无论是SMGS还是SMAT模型都产生了可靠的结果,NR和EM都为单一特征分析提供了高效的解决方案.
结论:
- 无论是SMGS还是SMAT模型都适合在美国乳牛中进行常规基因评估,以测试美国乳牛的分娩容易度 (CE).
- 牛顿-拉普森 (NR) 算法在计算上比预期最大化 (EM) 算法更有效.
- NR和EM算法为单一特征CE遗传评估提供可靠和高效的解决方案,支持知情的育种决策.
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