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Genomic Selection Improves Response to Selection in Resilience by Exploiting Genotype by Environment Interactions

机译:基因组选择通过环境相互作用利用基因型来提高对选择力的反应。

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摘要

Genotype by environment interactions (GxE) are very common in livestock and hamper genetic improvement. On the other hand, GxE is a source of genetic variation: genetic variation in response to environment, e.g., environmental perturbations such as heat stress or disease. In livestock breeding, there is tendency to ignore GxE because of increased complexity of models for genetic evaluations and lack of accuracy in extreme environments. GxE, however, creates opportunities to increase resilience of animals toward environmental perturbations. The main aim of the paper is to investigate to which extent GxE can be exploited with traditional and genomic selection methods. Furthermore, we investigated the benefit of reaction norm (RN) models compared to conventional methods ignoring GxE. The questions were addressed with selection index theory. GxE was modeled according to a linear RN model in which the environmental gradient is the contemporary group mean. Economic values were based on linear and non-linear profit equations. Accuracies of environment-specific (G)EBV were highest in intermediate environments and lowest in extreme environments. RN models had higher accuracies of (G)EBV in extreme environments than conventional models ignoring GxE. Genomic selection always resulted in higher response to selection in all environments than sib or progeny testing schemes. The increase in response was with genomic selection between 9 and 140% compared to sib testing and between 11 and 114% compared to progeny testing when the reference population consisted of 1 million animals across all environments. When the aim was to decrease environmental sensitivity, the response in slope of the RN model with genomic selection was between 1.09 and 319 times larger than with sib or progeny testing and in the right direction in contrast to sib and progeny testing that still increased environmental sensitivity. This shows that genomic selection with large reference populations offers great opportunities to exploit GxE to increase resilience of animals.
机译:环境相互作用的基因型(GxE)在牲畜中非常常见,并阻碍了遗传改良。另一方面,GxE是遗传变异的来源:遗传变异是对环境的响应,例如对环境的干扰,例如热应激或疾病。在牲畜育种中,由于遗传评估模型的复杂性增加以及在极端环境中缺乏准确性,因此倾向于忽略GxE。然而,GxE创造了机会来提高动物对环境干扰的适应力。本文的主要目的是研究可以在多大程度上利用传统和基因组选择方法开发GxE。此外,我们调查了反应规范(RN)模型与忽略GxE的常规方法相比的好处。选择索引理论解决了这些问题。 GxE是根据线性RN模型建模的,其中环境梯度是当代群体均值。经济价值基于线性和非线性利润方程。特定于环境的(G)EBV的精度在中间环境中最高,而在极端环境中最低。相对于忽略GxE的传统模型,RN模型在极端环境中的(G)EBV精度更高。与同胞或后代测试方案相比,基因组选择总是在所有环境中对选择产生更高的响应。当参考种群由在所有环境中的一百万只动物组成时,响应的增加是与同胞测试相比,基因组选择介于9%和140%之间,与后代测试相比,介于11%和114%之间。当目的是降低环境敏感性时,选择基因组的RN模型在斜率上的响应是同胞或后代测试的1.09到319倍之间,并且在正确的方向上与同胞和后代测试相比仍能提高环境敏感性。这表明具有大量参考种群的基因组选择提供了利用GxE增强动物适应力的巨大机会。

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