Fighting and controlling epidemic and endemic diseases represents a considerable cost to livestock production. Much research is dedicated to breeding disease resilient livestock, but this is not yet a common objective in practical breeding programs. In this paper, we investigate how future breeding programs may benefit from recent research on disease resilience. We define disease resilience in terms of its component traits resistance (R: the ability of a host animal to limit within-host pathogen load (PL)) and tolerance (T: the ability of an infected host to limit the damage caused by a given PL), and model the host's production performance as a reaction norm on PL, depending on R and T. Based on this, we derive equations for the economic values of resilience and its component traits. A case study on porcine respiratory and reproductive syndrome (PRRS) in pigs illustrates that the economic value of increasing production in infectious conditions through selection for R and T can be more than three times higher than by selection for production in disease-free conditions. Although this reaction norm model of resilience is helpful for quantifying its relationship to its component traits, its parameters are difficult and expensive to quantify. We consider the consequences of ignoring R and T in breeding programs that measure resilience as production in infectious conditions with unknown PL—particularly, the risk that the genetic correlation between R and T is unfavourable (antagonistic) and that a trade-off between them neutralizes the resilience improvement. We describe four approaches to avoid such antagonisms: (1) by producing sufficient PL records to estimate this correlation and check for antagonisms—if found, continue routine PL recording, and if not found, shift to cheaper proxies for PL; (2) by selection on quantitative trait loci (QTL) known to influence both R and T in favourable ways; (3) by rapidly modifying towards near-complete resistance or tolerance, (4) by re-defining resilience as the animal's capacity to resist (or recover from) the perturbation caused by an infection, measured as temporal deviations of production traits in within-host longitudinal data series. All four alternatives offer promising options for genetic improvement of disease resilience, and most rely on technological and methodological developments and innovation in automated data generation.

中文翻译：

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为什么要培育抗病牲畜，如何培育？


防治流行病和地方病给畜牧业生产带来了相当大的成本。许多研究致力于培育抗病牲畜，但这还不是实际育种计划的共同目标。在本文中，我们研究了未来的育种计划如何从最近的疾病恢复力研究中受益。我们根据疾病抵抗力（R：宿主动物限制宿主内病原体负荷（PL）的能力）和耐受性（T：受感染宿主限制特定病原体造成的损害的能力）来定义疾病恢复力。 PL），并将宿主的生产绩效建模为 PL 的反应范数，具体取决于 R 和 T。基于此，我们推导出弹性及其组成特征的经济价值方程。猪呼吸与繁殖综合症 (PRRS) 的案例研究表明，通过选择 R 和 T 在传染性条件下增加产量的经济价值可能比在无病条件下选择产量高出三倍以上。尽管这种弹性反应规范模型有助于量化其与其组成特征的关系，但其参数量化起来很困难且昂贵。我们考虑在育种计划中忽略 R 和 T 的后果，这些育种计划衡量弹性为未知 PL 的感染条件下的生产，特别是 R 和 T 之间的遗传相关性不利（拮抗）以及它们之间的权衡中和的风险弹性的提高。 我们描述了避免此类对抗的四种方法：（1）通过生成足够的 PL 记录来估计这种相关性并检查对抗性 - 如果发现，则继续常规 PL 记录，如果没有找到，则转向更便宜的 PL 代理； (2) 通过选择已知能以有利方式影响 R 和 T 的数量性状基因座 (QTL)； (3) 通过快速向接近完全的抵抗或耐受转变， (4) 通过重新定义复原力，即动物抵抗（或恢复）由感染引起的扰动的能力，以内部生产性状的时间偏差来衡量-主机纵向数据系列。所有四种替代方案都为疾病恢复力的遗传改善提供了有前途的选择，并且大多数依赖于技术和方法的发展以及自动化数据生成的创新。