对因果网络的条件化阻止了对选择的间接反应
Martin Bonamy1,2, María Elena Fernández2, Guillermo Giovambattista2
1Cátedra de Producción de Bovinos, Departamento de Producción Animal, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata (UNLP), La Plata, Argentina.
概括
结构方程模型 (SEM) 提供了一个比多特征动物模型 (MTM) 更准确的方法,用于估计因果网络中的繁殖值 (EBV). 通过正确解释因果关系,SEM可以改善选择反应,特别是上游特征.
科学领域:
- 动物遗传学 动物遗传学
- 定量遗传学 是一个量子遗传学.
- 统计建模 统计建模
背景情况:
- 多特征动物模型 (MTM) 通过考虑遗传和环境相关性来估计繁殖值 (BV).
- 特征之间的因果关系可能需要不同的方法,例如结构方程模型 (SEM).
- 虽然在参数上相当,但MTM和SEM产生EBV,需要不同的解释.
研究的目的:
- 调查MTM与SEM对选择反应在五特征因果网络中的影响.
- 为了比较因果网络中不同位置 (上游,中游,下游) 的特征的选择结果.
- 评估纯因果关系和因果和遗传相关性结合的场景.
主要方法:
- 随机模拟实验以建模一个五个特征的因果网络.
- 应用MTM和SEM用于估计育种价值.
- 分析不同选择目标和网络结构下的选择反应.
主要成果:
- 与SEM相比,MTM吸收了因果关系作为遗传相关性,改变了选择反应.
- 当针对顶部特征时,没有观察到选择反应的差异.
- 在针对中游或下游特征时,上游特征出现了显著的差异.
- 在存在因果关系和遗传相关性时,SEM减少了上游特征的间接反应.
结论:
- 在因果网络中,SEM提供了更准确的EBV解释,从而改善了选择反应.
- 在MTM和SEM之间做出选择会对选择结果产生重大影响,特别是对于受上游因素影响的特征.
- 在复杂的遗传架构中,SEM对于准确剖析直接和间接选择反应至关重要.
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