Inoue Keiichi
National Livestock Breeding Center, Nishigo, Fukushima, Japan.
Anim Sci J. 2020 Jan-Dec;91(1):e13359. doi: 10.1111/asj.13359.
Optimized breeding goals and management practices for the improvement of target traits requires knowledge regarding any potential functional relationships between them. Fitting a structural equation model (SEM) allows for inferences about the magnitude of causal effects between traits to be made. In recent years, an adaptation of SEM was proposed in the context of quantitative genetics and mixed models. Several studies have since applied the SEM in the context of animal breeding. However, fitting the SEM requires choosing a causal structure with prior biological or temporal knowledge. The inductive causation (IC) algorithm can be used to recover an underlying causal structure from observed associations between traits. The results of the papers, which are introduced in this review, showed that using the IC algorithm to infer a causal structure is a helpful tool for detecting a causal structure without proper prior knowledge or with uncertain relationships between traits. The reports also presented that fitting the SEM could infer the effects of interventions, which are not given by correlations. Hence, information from the SEM provides more insights into and suggestions on breeding strategy than that from a multiple-trait model, which is the conventional model used for multitrait analysis.
为了改善目标性状，优化育种目标和管理实践需要了解它们之间任何潜在的功能关系。拟合结构方程模型（SEM）可以推断性状之间因果效应的大小。近年来，在数量遗传学和混合模型的背景下提出了SEM的一种改编形式。此后，一些研究在动物育种背景下应用了SEM。然而，拟合SEM需要根据先前的生物学或时间知识选择因果结构。归纳因果关系（IC）算法可用于从性状之间观察到的关联中恢复潜在的因果结构。本综述中介绍的论文结果表明，使用IC算法推断因果结构是一种在没有适当先验知识或性状之间关系不确定的情况下检测因果结构的有用工具。报告还指出，拟合SEM可以推断干预的效果，而相关性并不能给出这些效果。因此，与多性状模型（用于多性状分析的传统模型）相比，SEM提供的信息能为育种策略提供更多的见解和建议。
