Knowledge about potential functional relationships among traits of interest offers a unique opportunity to understand causal mechanisms and to optimize breeding goals, management practices, and prediction accuracy. In this study, we inferred the phenotypic causal networks among five traits in a turkey population and assessed the effect of the use of such causal structures on the accuracy of predictions of breeding values. Phenotypic data on feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking score in addition to genotype data from a commercial breeding population were used. Causal links between the traits were detected using the inductive causation algorithm based on the joint distribution of genetic effects obtained from a standard Bayesian multiple trait model. Then, a structural equation model was implemented to infer the magnitude of causal structure coefficients among the phenotypes. Accuracies of predictions of breeding values derived using pedigree- and blending-based multiple trait models were compared to those obtained with the pedigree- and blending-based structural equation models. In contrast to the two unconditioned traits (i.e., feed conversion ratio and breast meat yield) in the causal structures, the three conditioned traits (i.e., residual feed intake, body weight, and walking score) showed noticeable changes in estimates of genetic and residual variances between the structural equation model and the multiple trait model. The analysis revealed interesting functional associations and indirect genetic effects. For example, the structural coefficient for the path from body weight to walking score indicated that a 1-unit genetic improvement in body weight is expected to result in a 0.27-unit decline in walking score. Both structural equation models outperformed their counterpart multiple trait models for the conditioned traits. Applying the causal structures led to an increase in accuracy of estimated breeding values of approximately 7, 6, and 20% for residual feed intake, body weight, and walking score, respectively, and different rankings of selection candidates for the conditioned traits. Our results suggest that structural equation models can improve genetic selection decisions and increase the prediction accuracy of breeding values of selection candidates. The identified causal relationships between the studied traits should be carefully considered in future turkey breeding programs.

中文翻译：

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使用或不使用基因组学的递归模型对火鸡（Meleagris gallopavo）生产性状的育种值的准确性

有关感兴趣的性状之间潜在功能关系的知识为了解因果机制并优化育种目标，管理实践和预测准确性提供了独特的机会。在这项研究中，我们推断了火鸡群体中五个性状的表型因果网络，并评估了这种因果结构对繁殖值预测准确性的影响。除了来自商业繁殖群体的基因型数据外，还使用了关于饲料转化率，剩余饲料摄入量，体重，胸肉产量和步行得分的表型数据。使用归纳因果算法基于从标准贝叶斯多重性状模型获得的遗传效应的联合分布，检测性状之间的因果关系。然后，构造结构方程模型以推断表型之间的因果结构系数的大小。将基于系谱和混合的多重性状模型得出的育种值预测准确性与基于系谱和混合的结构方程模型获得的预测准确性进行比较。与因果结构中的两个非条件性状（即饲料转化率和胸肉产量）相比，三个条件性状（即残留饲料摄入量，体重和步行得分）在遗传和残留估计值方面显示出显着变化结构方程模型和多重特征模型之间的差异。分析揭示了有趣的功能关联和间接遗传效应。例如，从体重到步行分数的路径的结构系数表明，体重的1个单位遗传改善预计会导致步行分数下降0.27个单位。对于条件性状，两个结构方程模型均优于其对应的多重性状模型。应用因果结构导致估计的育种值的准确性分别提高了大约7％，6％和20％，分别用于残余饲料摄入量，体重和步行得分，以及条件性状的不同候选选择等级。我们的结果表明，结构方程模型可以改善遗传选择决策，并提高选择候选物的育种值的预测准确性。