Resilient animals can remain productive under different environmental conditions. Rearing in increasingly heterogeneous environmental conditions increases the need of selecting resilient animals. Detection of environmental challenges that affect an entire population can provide a unique opportunity to select animals that are more resilient to these events. The objective of this study was two-fold: (1) to present a simple and practical data-driven approach to estimate the probability that, at a given date, an unrecorded environmental challenge occurred; and (2) to evaluate the genetic determinism of resilience to such events. Our method consists of inferring the existence of highly variable days (indicator of environmental challenges) via mixture models applied to frequently recorded phenotypic measures and then using the inferred probabilities of the occurrence of an environmental challenge in a reaction norm model to evaluate the genetic determinism of resilience to these events. These probabilities are estimated for each day (or other time frame). We illustrate the method by using an ovine dataset with daily feed intake (DFI) records. Using the proposed method, we estimated the probability of the occurrence of an unrecorded environmental challenge, which proved to be informative and useful for inclusion as a covariate in a reaction norm animal model. We estimated the breeding values for sensitivity of the genetic potential for DFI of animals to environmental challenges. The level and slope of the reaction norm were negatively correlated (− 0.46 ± 0.21). Our method is promising and appears to be viable to identify unrecorded events of environmental challenges, which is useful when selecting resilient animals and only productive data are available. It can be generalized to a wide variety of phenotypic records from different species and used with large datasets. The negative correlation between level and slope indicates that a hypothetical selection for increased DFI may not be optimal depending on the presence or absence of stress. We observed a reranking of individuals along the environmental gradient and low genetic correlations between extreme environmental conditions. These results confirm the existence of a G $$\times$$ E interaction and show that the best animals in one environmental condition are not the best in another one.

中文翻译：

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检测高频记录性状中未记录的环境挑战，以及挑战恢复力的遗传决定论，并应用于羔羊的采食量


有弹性的动物可以在不同的环境条件下保持生产力。在日益多样化的环境条件下饲养增加了选择有弹性的动物的需要。检测影响整个种群的环境挑战可以为选择对这些事件更有弹性的动物提供独特的机会。这项研究的目标有两个：（1）提出一种简单实用的数据驱动方法来估计在给定日期发生未记录的环境挑战的概率； (2) 评估对此类事件的恢复力的遗传决定论。我们的方法包括通过应用于频繁记录的表型测量的混合模型来推断高度可变的日子（环境挑战的指标）的存在，然后使用反应规范模型中推断的环境挑战发生的概率来评估遗传决定论对这些事件的恢复能力。这些概率是针对每天（或其他时间范围）进行估计的。我们通过使用带有每日采食量 (DFI) 记录的绵羊数据集来说明该方法。使用所提出的方法，我们估计了未记录的环境挑战发生的概率，这被证明是信息丰富的，并且对于将其作为协变量纳入反应正常动物模型中很有用。我们估计了动物 DFI 遗传潜力对环境挑战的敏感性的育种值。反应范数的水平和斜率呈负相关（− 0.46 ± 0.21）。我们的方法很有前途，并且似乎可以识别未记录的环境挑战事件，这在选择有弹性的动物并且只有生产性数据可用时非常有用。 它可以推广到来自不同物种的各种表型记录，并与大型数据集一起使用。水平和斜率之间的负相关表明，根据是否存在压力，增加 DFI 的假设选择可能不是最佳的。我们观察到个体沿着环境梯度重新排名，并且极端环境条件之间的遗传相关性较低。这些结果证实了 G $$\times$$ E 相互作用的存在，并表明在一种环境条件下最好的动物在另一种环境条件下并不是最好的。