从不完整的观测数据中推导表型的方法的比较与乳牛青春期的年龄的应用
Melissa A Stephen1,2, Chris R Burke3, Jennie E Pryce4,5
1DairyNZ Ltd, 605 Ruakura Road, Hamilton, 3240, New Zealand. melissa.stephen@dairynz.co.nz.
Journal of animal science and biotechnology
|September 8, 2023
概括
准确的动物繁殖值可以从不完整的数据来估计时间依赖的特征. 这项研究表明,使用更少的测量,比如每后代一个用于孕激素第一次升高的年龄 (AGEP4),可以产生可靠的 sire 排名.
科学领域:
- 动物育种与遗传学
- 生殖生物学 生殖生物学
- 量化遗传学 量化遗传学
背景情况:
- 许多关键的动物育种特征难以或昂贵地精确测量,导致数据不完整.
- 诸如生殖状态和寿命之类的特征是时间依赖的,并且经常表现出左边,间隔或右边的审查.
- 孕激素第一次升高的年龄 (AGEP4) 是公牛青春期开始的关键指标,通常来自不完整的测量.
研究的目的:
- 从不完整的数据中推导出表型的三个不同的方法进行比较.
- 评估数据审查对遗传性估计和育种价值预测的影响.
- 评估在大型动物育种计划中使用减少表型化策略对时间依赖性特征的可行性.
主要方法:
- AGEP4的表型来自5000头奶牛的血液样本.
- 模拟了7个访问场景,省略了三个血液样本采集点中的一个或两个,增加了数据审查.
- 对比了三种表型衍生方法:顺序分类变量,连续变量与审查处罚,并对采样连续变量进行数据增强.
主要成果:
- 遗传性估计显示了跨方法和审查级别的重叠可信度间隔.
- 估计的遗传能力通常更高,左侧审查减少.
- 从三次访问与减少数据场景中估计的繁殖值 (EBV) 之间的相关性在0.65到0.95之间,具体取决于方法.
结论:
- 从每个后代的单一观察来得出的表型,对于像AGEP4这样的时间依赖性特征,可以产生与使用多个观察结果相比较的 sire 排名.
- 这一发现对于在动物育种中设计具有成本效益的,大规模的表型化计划具有重大意义.
- 减少表型化策略可以用于估计差异参数和挑战性特征的EBV.
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