Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals.
Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals.
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DOI：
10.3389/fgene.2023.1127530
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发表时间：
2023
影响因子：
3.7
通讯作者：
Pong-Wong, Ricardo
中科院分区：
文献类型：
--
作者：
Zefreh, Masoud Ghaderi;Doeschl-Wilson, Andrea B.;Riggio, Valentina;Matika, Oswald;Pong-Wong, Ricardo
可持续畜牧业生产要求动物具有很高的生产潜力，但也对环境挑战具有很强的适应力。通过遗传选择同时改善这些性状的第一步是准确预测它们的遗传价值。在本文中，我们使用绵羊种群的模拟来评估基因组数据，不同的遗传评估模型和表型分析策略对生产潜力和恢复力的预测精度和偏差的影响。此外，我们还评估了不同选择策略对这些性状的改良效果。结果表明，这两个性状的估计大大受益于采取重复测量和使用基因组信息。然而，生产潜力的预测准确性受到损害，弹性估计往往是向上偏置的，当家庭聚集在群体中，即使使用基因组信息。当环境挑战水平未知时，预测精度也较低，无论是性状，弹性和生产潜力。然而，我们观察到，即使在未知的环境挑战的情况下，当家庭分布在大范围的环境中，这两个性状的遗传增益也可以实现。然而，在广泛的环境中使用基因组评估、反应规范模型和表型分析，这两种性状的同时遗传改良大大受益。在弹性和生产潜力之间存在权衡的情况下使用没有反应规范的模型，并且从狭窄的环境中收集表型可能会导致一个性状的损失。该研究表明，基因组选择与反应规范模型相结合，即使在权衡的情况下，也为同时提高养殖动物的生产力和恢复力提供了很好的机会。
Sustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simulations of sheep populations to assess the effect of genomic data, different genetic evaluation models and phenotyping strategies on prediction accuracies and bias for production potential and resilience. In addition, we also assessed the effect of different selection strategies on the improvement of these traits. Results show that estimation of both traits greatly benefits from taking repeated measurements and from using genomic information. However, the prediction accuracy for production potential is compromised, and resilience estimates tends to be upwards biased, when families are clustered in groups even when genomic information is used. The prediction accuracy was also found to be lower for both traits, resilience and production potential, when the environment challenge levels are unknown. Nevertheless, we observe that genetic gain in both traits can be achieved even in the case of unknown environmental challenge, when families are distributed across a large range of environments. Simultaneous genetic improvement in both traits however greatly benefits from the use of genomic evaluation, reaction norm models and phenotyping in a wide range of environments. Using models without the reaction norm in scenarios where there is a trade-off between resilience and production potential, and phenotypes are collected from a narrow range of environments may result in a loss for one trait. The study demonstrates that genomic selection coupled with reaction-norm models offers great opportunities to simultaneously improve productivity and resilience of farmed animals even in the case of a trade-off.
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DOI：
10.1186/s12711-022-00712-y
发表时间：
2022-03-18
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Sánchez-Mayor M;Riggio V;Navarro P;Gutiérrez-Gil B;Haley CS;De la Fuente LF;Arranz JJ;Pong-Wong R
通讯作者：
Pong-Wong R
影响因子：
3.7
作者：
Bishop SC;Woolliams JA
通讯作者：
Woolliams JA
影响因子：
3.6
作者：
Knap, P. W.;Su, G.
通讯作者：
Su, G.
影响因子：
4.1
作者：
Carvalheiro, Roberto;Costilla, Roy;Hayes, Ben J.
通讯作者：
Hayes, Ben J.
影响因子：
2.4
作者：
Kijas, James W.;Porto-Neto, Laercio;McEwan, John
通讯作者：
McEwan, John