奶牛抗逆相关性状及遗传选育研究进展

娄文琦 ,  王见宝 ,  王雅春

中国农业大学学报 ›› 2026, Vol. 31 ›› Issue (3) : 136 -143.

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中国农业大学学报 ›› 2026, Vol. 31 ›› Issue (3) : 136 -143. DOI: 10.11841/j.issn.1007-4333.2026.03.12

奶牛抗逆相关性状及遗传选育研究进展

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Advances in genetic aspects of resilience related traits in dairy cows

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摘要

在气候变化持续加剧与养殖日趋集约化背景下,提升奶牛抗逆性已成为全球奶牛育种的重点战略方向。我国在奶牛抗逆育种方面的研究起步相对较晚,遗传评估与育种体系构建尚不完善。本综述系统梳理了抗逆性的概念、内涵及其表型量化方法,并围绕抗逆相关性状在奶牛群体遗传分析、全基因组关联分析和选育模拟方面的国内外研究进展进行总结。主要发现包括:1)基于表型的动态波动可有效识别奶牛对环境扰动的响应能力,由此衍生的抗逆性状具有中低遗传力,与繁殖、健康、体况和长寿等性状之间存在有利的遗传相关;2)抗逆性受微效多基因共同调控,全基因组关联研究逐步揭示其背后复杂的生物学机制,包括肠道微生物组、免疫调节和代谢稳态等关键通路;3)将抗逆性纳入育种目标不仅能提升动物福利和饲养效率,也能在劳动力资源趋紧和牧场规模化发展的背景下产生长期经济效益。综上,抗逆性作为一类具有重要选育价值的新型功能性状,未来可结合传感器采集的高通量纵向表型数据与分子数量遗传学手段,构建更精准的评估与选育体系,为推动绿色、高效、可持续的奶牛生产体系发展提供坚实支撑。

Abstract

Against the backdrop of intensifying climate change and the trend toward intensive farming, enhancing resilience has become a growing focus in global dairy cattle breeding programs. Yet in China, studies on resilience breeding in dairy cows remain in their infancy, with limited foundational research. Therefore, this review systematically summarizes recent domestic and international advances in phenotype and genetics for resilience in dairy cows. The main findings included that: 1) Dynamic fluctuations in phenotype can effectively capture dairy cows’ responses to environmental disturbances, while derived resilience traits generally exhibit low to moderate heritability and perform favorable and moderate genetic correlations with reproduction, health,body condition, longevity and other traits. 2) As a complex polygenic trait, the development of resilience involves multiple mechanisms including the gut microbiota, immune regulation and maintenance of metabolic homeostasis. 3) Incorporating resilience into dairy cattle breeding indices can improve animal welfare and management efficiency and also generate long-term economic benefits in dairy farms, especially under labor-limited and large-scale production systems. In summary, resilience is a novel and valuable functional trait in cows’ breeding, future efforts should integrate high-throughput longitudinal phenotypes and techniques of molecular quantitative genetics to improve resilience evaluation and breeding in dairy cows, supporting the sustainable, efficient, and welfare-oriented development in dairy production.

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关键词

奶牛 / 抗逆性状 / 遗传选育

Key words

dairy cows / resilience trait / genetic selection

引用本文

引用格式 ▾
娄文琦,王见宝,王雅春. 奶牛抗逆相关性状及遗传选育研究进展[J]. 中国农业大学学报, 2026, 31(3): 136-143 DOI:10.11841/j.issn.1007-4333.2026.03.12

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过去几十年,基于少数关键性状的长期遗传改良和现代繁育技术的广泛应用显著提高了奶牛生产性能,但也导致奶牛群体繁殖、健康、长寿等功能性状出现负向遗传进展 [1]。随着气候变化加剧、牧场日益集约化以及社会对环境保护的高度关注,全球育种目标正逐步朝向更平衡、可持续的方向转变,这对畜牧业发展提出了更高要求[2-3]。为应对上述挑战,牧场管理策略及育种规划亟需纳入复杂且综合的新性状,其中抗逆性是奶牛育种领域的研究重点[4-6]。
目前,已有研究将抗逆性定义为动物个体在中短期扰动下受影响最小或能快速恢复至正常状态的能力[4]。从生物学角度看,抗逆性体现为机体通过内部缓冲机制吸收外界扰动,以维持机体稳态的过程。当奶牛遭受应激时,会启动涉及多个生理系统的复杂生理生化调控网络,通过神经和体液调节实现高度协调(图1)[5]。另一方面,抗逆性可理解为奶牛在资源有限条件下,为应对外界扰动而动态调整不同生命功能间资源分配的能力[7]。在此过程中,机体通常优先将能量与营养资源分配给应对应激的关键功能,而暂时抑制如产奶等非关键功能表达。因此,抗逆奶牛面对环境扰动时无需额外管理干预,便能维持稳定的生产性能,且在繁殖、健康、长寿和动物福利方面表现优异。有研究系统评估了奶牛不同性状的碳足迹与经济价值,发现育种中更注重繁殖、长寿和饲料效率等功能性状,可有效降低奶牛生产的碳排放[8]。那些具备较强抗逆能力的个体,不仅能带来更稳定的经济回报,也有助于环境可持续,对牧场乃至其他畜牧业利益相关者具有重要意义。
近年来,国内外越来越多的育种工作者重视通过遗传选择和基因组管理策略提升奶牛群体的抗逆能力[9]。然而,我国在该领域的研究仍存在起步较晚和基础薄弱等问题。为此,本文系统梳理了国内外奶牛抗逆相关遗传育种中的研究进展,以期为我国奶牛特色选育与平衡育种的构建提供理论参考。

1 奶牛抗逆性状定义

如何系统监测与评估奶牛的整体抗逆表型,并进一步将其纳入遗传育种体系成为当前研究的核心问题。其他学科如土壤科学[10]、自然生态系统[11]和社会生态学[12]等已在系统抗逆性评估方面开展了广泛探索,并建立了若干评价指标。然而,这些跨领域抗逆指标大多难以直接应用于畜禽,原因在于实际生产中奶牛常面临多种环境应激因素的叠加作用,其复杂程度远高于单一扰动情境。Colditz和Hine[4]提出应从动物对多种环境扰动的综合响应角度来衡量其整体抗逆性状。处于环境扰动下的奶牛,其采食量、生长性能及其他性状的实际纵向表现往往偏离其遗传决定的潜在纵向水平[13]。基于此,不同表型的纵向偏差(y¯)被用于定义抗逆性状以反映个体的抗逆能力,计算公式如下:

y¯=y-y^

式中:y为一段时间内个体实际纵向表现;y^为一段时间内个体潜在纵向表现。

Scheffer等[5]首次提出抗逆性的多个量化性状,包括偏差方差的对数转换(Log-transformed variance of deviation, LnVar)、偏差的自相关系数(Autocorrelation, Autocorr)、偏差的偏度(Skewness, Skew)以及偏差的互相关系数(Cross-correlation, Cr)。上述性状的基本理论假设是抗逆性较弱个体在面对环境扰动时表现更多且强烈的波动,主要以负偏差为主,而抗逆性强的个体表现更为稳定。各性状具体解释[6]如表1所示,LnVar体现个体受环境扰动影响的程度,在无扰动或未受影响的个体中该值较低;Autocorr反映扰动的持续性,若个体恢复较慢,其自相关系数趋近于-1或1;Skew指示偏差的方向,积极应对扰动时偏度为正值,消极应对则为负值;偏差的Cr用于评估个体内部不同子系统之间抗逆能力的一致性,抗逆性较弱者该值更接近-1或1。综上所述,抗逆性强的个体在上述性状上均更接近零值,而抗逆性弱者则偏离程度更大。除上述核心性状外,性能下降次数与下降斜率等常规参数也常被用作评估个体抗逆能力的辅助参考。

2 奶牛抗逆性状的遗传研究进展

传统的营养与管理调控手段虽能部分缓解环境压力,但效果有限且成本高昂。因此,从遗传本质上改良提升奶牛自身的抗逆能力,成为实现畜牧业可持续发展的根本出路。通过阐明抗逆性的遗传基础与生物学机制,可以精准筛选出与抗应激、抗病和适应性强相关的关键基因和分子标记。这将推动基因组选择育种技术,加速培育具有高抗逆性的奶牛新品种,从根源上增强牛群的整体健康水平与生产稳定性,降低兽药使用,有效应对未来气候变化与其他挑战,为保障奶业的优质、高效和绿色发展提供核心驱动力。

2.1 抗逆性状的遗传参数估计

日产奶量(Daily milk yield,DMY)是反映奶牛响应环境扰动强度最直观的性状且易于收集。最早荷兰Elgersma等[14]利用荷斯坦牛泌乳期DMY结合泌乳曲线拟合定义了DMY下降次数和LnVar,遗传力(Heritability, h2)为0.06~0.10,其中LnVar与乳房健康(-0.36)、酮病(-0.52)、泌乳持久性(-0.29)和长寿(-0.30)等功能性状呈中等负遗传相关(Genetic correlation, rg),表明抗逆性强的个体通常具备更好的乳房健康状况、较低的酮病发生率、更优的泌乳能力和更长的生产寿命。Poppe等[15]基于DMY数据,比较了多种泌乳曲线拟合方法下抗逆指标的遗传表现,包括LnVar、Lag-1 Autocorr和Skew。结果显示在分位数回归下各抗逆指标h2最高,其中LnVar和Lag-1 Autocorr的h2为0.10~0.20,与乳房健康(-0.26~-0.32)、肢蹄健康(-0.10)、寿命(-0.13~-0.29)、繁殖(-0.12~-0.40)、体况评分(-0.32~-0.41)、酮病(-0.21~-0.48)以及MY(0.20~0.79)存在显著rg。Poppe等[16-18]进一步探究了抗逆性指标在不同泌乳阶段、胎次及牧场条件下的变化规律,验证了LnVar和Lag-1 Autocorr在子代群体中的选育效果。随后,Kok等[19]利用DMY数据和分位数回归计算了LnVar和Lag-1 Autocorr,发现泌乳早期患乳房炎的奶牛有更高的LnVar和更低的Lag-1 Autocorr。而Wang等[20-21]、Chen等[22]、Oloo等[23]、Keßler等[24]分别在中国、美国、非洲、德国等各国的DMY数据基础上,提出并验证了适用本地奶牛群体的抗逆指标。由于其余性状的纵向数据难以收集,相关研究非常少。Poppe等[25]和Rodrigues等[26]分别聚焦于奶牛日活动和日均体重数据定义抗逆指标,研究结果与DMY类似。相较于DMY,其余性状捕捉了个体抗逆其他方面,可作为补充指标纳入抗逆育种。在其他畜种上陆续开展抗逆相关研究,包括猪[27-29]和蛋鸡[30-31]。综上,抗逆相关指标均表现出良好的遗传特性,与其他性状的遗传关联符合抗逆性假设,验证了其作为奶牛抗逆育种的可行性。

2.2 抗逆性状遗传机制的基因组解析

为深入解析奶牛抗逆的遗传基础,基因组分析已成为关键手段。早期研究已有多项全基因组关联分析聚焦于特定应激下相关基因及其生物途径的挖掘,涵盖热应激[32]和疾病[9]等(表2)。例如,Liu 等[33]研究中国荷斯坦牛群体的热应激抗逆性机制,鉴定出14个显著差异表达基因。Cheruiyot等[34]进一步采用反应规范模型,将热应激下生产性状的下降速率量化为耐热性状,并发现61个潜在遗传变异位点。此外,Bohlouli等[35]的研究基于德国荷斯坦牛群体鉴定出30个气候敏感相关变异位点。Passamonti等[36]揭示了奶牛围产期代谢应激的遗传机制,发现相关变异位点与血浆代谢物(如γ-谷氨酰转移酶、对氧磷酶、碱性磷酸酶)及锌浓度密切有关,为解析其生理机制提供了线索。相较之下,基于纵向表现的偏差定义的抗逆性状,为在常规生产环境中系统性选育抗逆性提供了可行途径,但研究仍相对有限。Chen等[37]基于DMY定义的LnVar和Autocorr(Lag=1)开展关联分析,指出肠道稳态调节是影响个体抗逆表现的核心生物学途径。Punturiero等[38]基于LnVar和Autocorr(Lag=1)表型,进一步鉴定到多个与免疫应答、能量代谢和组织完整性等方面的候选基因与变异位点。其他畜禽的研究为揭示抗逆性的遗传机制提供了宝贵参考。在猪的研究中,Li等[39]和Shi等[40]均发现抗逆性与免疫调节、激素信号转导等通路密切相关,这与奶牛中的发现高度一致,提示了跨物种共通的抗逆生理基础。然而,Doekes等[41]在鸡产蛋数相关抗逆研究中未发现显著关联的遗传位点和基因。总体而言,研究表明奶牛抗逆性状作为一种数量性状受微效多基因调控。抗逆性可能由多种潜在机制(如肠道、生理和免疫)及相关遗传因素共同决定,这为后续深入解析抗逆性的复杂遗传基础奠定了基础,但仍需更大规模的群体研究和功能试验进行验证并整合。

2.3 抗逆性状效益评估和育种规划

尽管传统的生产和疾病性状可在一定程度上间接反映奶牛部分抗逆特性,但它们往往无法全面反映抗逆性所包含的潜在经济价值。这部分隐性价值包括疾病或其他问题监测的人工成本[6]和牧场员工的工作满意度[42-43]等。此外,研究表明抗逆性与生产性状间存在遗传拮抗,这使得在育种中明确考虑抗逆性变得尤为必要。为了量化抗逆性育种的综合经济效益,一系列研究采用经济学模型进行分析。Mulder和Rashidi[44]证明,针对抗逆性的选择策略可有效降低群体发病率。Berghof等[6]基于奶牛和猪养殖的人工成本估计了育种中引入抗逆性状的经济价值,发现当劳动力成为限制性资源时,抗逆性的经济权重随牧场规模扩大而显著增加。此外,环境扰动通常未知且复杂,经济权重的推导困难,如何将抗逆性状纳入选择指数仍是一个问题。Bengtsson等[45]模拟将抗逆性状纳入欧洲育种指数,发现奶牛的功能性状表现如胴体重、繁殖性能和乳房健康等均有提升。Meijer等[46]首次尝试将抗逆性状纳入荷兰和比利时奶牛的常规育种指数中,发现选择的奶牛受环境干扰较小,恢复速度更快。Keßler等[47]整合多个抗逆性状制定了德国的抗逆选择指数,以提高奶牛群体健康状况。以上结果均表明,将抗逆性纳入育种目标,不仅能够提高动物福利和管理效率,更可在长期内为牧场带来显著而持久的经济效益。

3 当前挑战

3.1 实际纵向表型测定有待提升

在构建奶牛抗逆性状评估体系时,科学选择具有代表性的实际纵向表型(y)至关重要。理想纵向表型应具备明确的生物学意义,能够在个体或群体层面实现稳定的长时间重复测定,并对内外环境扰动表现出足够的敏感性,从而为准确解析抗逆性及其遗传变异提供可靠依据。受限于牧场纵向数据获取的难度,目前多数研究主要聚焦于奶牛的日产奶量DMY[14-24]、日活动量[25]和日体重[26]等易于记录的表型。其他畜种中,猪的日采食量[27-29]和持续时间[27,29]以及鸡的日体重[30-31]被广泛采用。未来可以进一步拓展至免疫反应[48]和微生物组成[49]等更具机制代表性的表型维度。需注意的是,奶牛在不同泌乳阶段或年龄区间内其性状的偏差大小(y¯)模式存在显著差异。因此,建议针对不同生理阶段分别进行抗逆性状量化,并在必要时将其视为不同性状加以处理。为全面评估奶牛的整体抗逆性,实际纵向表型测定策略需兼顾周期长度与采样频率[50]。较长的观测周期有助于覆盖多样化的环境扰动,从而更充分地捕获个体对微环境变化的遗传响应[51],而适当的测定频率则可有效避免遗漏短期干扰引起的实际表型波动。例如,Elgersma等[14]研究表明,DMY记录间隔太长,可能导致抗逆指标遗传估计值偏低。因此,整个生产周期内实施高频、长期的实际纵向表型监测水平有待提高,这将有助于获取高通量纵向数据,有利于抗逆信息捕获。

尽管基于生产或行为等单一维度纵向数据定义的抗逆性状已展现出良好的遗传前景,但抗逆性本身作为一种复杂的综合性状,其形成可能源于多种潜在的生理机制。这意味着在面对不同类型的环境扰动时,不同个体可能激活或依赖于不同的生理缓冲路径。因此,仅依靠单一来源的纵向信息难以全面捕捉其生物学本质,可能限制育种选择的准确性。为突破此局限,构建能够整合多维纵向表型信息的综合评估体系已成为该领域的重要发展方向。已有研究在此方向进行了有益探索,例如丹麦Højsgaard和Friggens[52]基于动态线性模型提出一种整合多个纵向时序数据的新方法,结合牛奶电导率、体细胞数以及免疫相关乳酸脱氢酶以细化和重新定义奶牛乳房炎。法国Ben Abdelkrim等[53]提出了一种识别多时间序列中扰动的方法,评估了奶牛泌乳期体重和DMY两个信息的波动变化关系。法国Ithurbide等[54]基于短期营养试验结合功能主成分分析整合了多种代谢物浓度的纵向变化信息,成功实现了对抗逆与非抗逆山羊个体的分组识别。目前,研究集中于单一表型定义抗逆相关指标及多表型信息融合方法的探索,未来有望借助机器学习等新型算法实现对个体抗逆性的全面评估,但这仍需进一步深入研究和技术积累。

3.2 潜在纵向表型拟合有待优化

科学估计遗传决定的潜在纵向水平(y^)也是当前奶牛研究的关键问题。从生物学角度看,当奶牛持续面临环境扰动时,其实际泌乳性能往往低于其遗传潜力所决定的理想水平(无干扰环境下真实泌乳水平)。仅以平均产奶量作为潜在表现的估计值,难以准确反映个体对环境扰动的真实响应,从而影响后续遗传评估的准确性。为更可靠地估计遗传决定的个体潜在表现,近年来研究提出了多种方法。有研究通过拟合标准泌乳曲线,计算实际DMY与模型预测值之间的偏差,以此表征个体泌乳的稳定性[14]。Codrea等[55]提出一种基于平滑技术的方法,用于识别DMY时间序列中由环境扰动引起的异常波动。Poppe等[15]采用分位数回归模型拟合DMY变化趋势,定义不同个体泌乳的偏差大小。除奶牛外,其他畜禽中类似方法也具有参考价值,例如Berghof等[30]在蛋鸡研究中以标准化体重为基础定义个体表现的偏差。Nguyen-Ba等[56]则利用育肥猪的累积采食量评估环境扰动对其生长的影响。如何准确界定遗传决定的潜在水平,是实现抗逆性指标无偏估计所面临的主要挑战之一,这方面仍需深入研究。

4 小结与展望

抗逆性是一个高度复杂的性状,在气候变化与集约化养殖日趋严峻的背景下其重要性日益凸显。目前全球奶牛相关研究仍处于探索阶段,研究表明,纵向表型的动态波动包含个体对环境扰动的反应信息,由此衍生的抗逆性状具有中低遗传力,与繁殖、健康、体况和长寿等性状存在正向的遗传关联。奶牛抗逆性受多基因微效共同调控,全基因组研究揭示了其复杂的生物学基础,涉及肠道菌群、免疫调节以及代谢稳态等多重机制。效益评估和育种规划进一步表明,将抗逆性纳入育种目标不仅有助于提升动物福利和群体管理效率,还能为牧场带来长期经济效益,尤其在人工资源有限和养殖规模不断扩大的背景下,其优势愈加明显。虽然在复杂多变环境中较高的抗逆性能够赋予奶牛生存优势,但某些特定环境中,其维护所需的额外代谢成本可能反而对其不利。因此,在实际育种和管理中,需要结合具体环境、生产目标和市场需求,谨慎权衡和选育奶牛的抗逆性水平[57-58]。未来,随着传感器技术发展,纵向外部数据和内源性指标的整合将极大丰富奶牛抗逆性表型信息。在此基础上,进一步通过多组学与验证等手段锁定核心标记基因,将为抗逆性遗传改良提供坚实基础,从而推动我国奶牛产业实现绿色高效和抗逆可持续的发展。

参考文献

[1]

Brito L F, Bedere N, Douhard F, Oliveira H R, Arnal M, Peñagaricano F, Schinckel A P, Baes C F, Miglior F. Review: Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world [J]. Animal, 2021, 15: 100292

[2]

Hansen J, Sato M, Ruedy R. Perception of climate change[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(37): 2415-2423

[3]

Mehrabi Z, Gill M, van Wijk M, Herrero M, Ramankutty N. Livestock policy for sustainable development[J]. Nature Food, 2020, 1(3): 160-165

[4]

Colditz I G, Hine B C. Resilience in farm animals: Biology, management, breeding and implications for animal welfare [J]. Animal Production Science, 2016, 56(12): 1961-1983

[5]

Scheffer M, Bolhuis J E, Borsboom D, Buchman T G, Gijzel S M W, Goulson D, Kammenga J E, Kemp B, van de Leemput I A, Levin S, Martin C M, Melis R J F, van Nes E H, Romero L M, Olde Rikkert M G M. Quantifying resilience of humans and other animals[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(47): 11883-11890

[6]

Berghof T V L, Poppe M, Mulder H A. Opportunities to improve resilience in animal breeding programs[J]. Frontiers in Genetics, 2019, 9: 692

[7]

Stearns S C. The Evolution of Life Histories[M]. Oxford; Tokyo: Oxford University Press, 1992

[8]

Bell M J. Breeding a sustainable future for milk production[J]. NPJ Sustainable Agriculture, 2024, 2:18

[9]

Knap P W, Doeschl-Wilson A. Why breed disease-resilient livestock, and how?[J]. Genetics, Selection, Evolution, 2020, 52(1): 60

[10]

Todman L C, Fraser F C, Corstanje R, Deeks L K, Harris J A, Pawlett M, Ritz K, Whitmore A P. Defining and quantifying the resilience of responses to disturbance: A conceptual and modelling approach from soil science[J]. Scientific Reports, 2016, 6: 28426

[11]

Thorogood R, Mustonen V, Aleixo A, Aphalo P J, Asiegbu F O, Cabeza M, Cairns J, Candolin U, Cardoso P, Eronen J T, Hällfors M, Hovatta I, Juslén A, Kovalchuk A, Kulmuni J, Kuula L, Mäkipää R, Ovaskainen O, Pesonen A-K, Primmer C R, Saastamoinen M, Schulman A H, Schulman L, Strona G, Vanhatalo J. Understanding and applying biological resilience, from genes to ecosystems[J]. NPJ Biodiversity, 2023, 2: 16

[12]

Grafton R Q, Doyen L, Béné C, Borgomeo E, Brooks K, Chu L, Cumming G S, Dixon J, Dovers S, Garrick D, Helfgott A, Jiang Q, Katic P, Kompas T, Little L R, Matthews N, Ringler C, Squires D, Steinshamn S I, Villasante S, Wheeler S, Williams J, Wyrwoll P R. Realizing resilience for decision-making[J]. Nature Sustainability, 2019, 2(10): 907-913

[13]

Van der Waaij E H, Bijma P, Bishop S C, Van Arendonk J A. Modeling selection for production traits under constant infection pressure[J]. Journal of Animal Science, 2000, 78(11): 2809-2820

[14]

Elgersma G G, Jong G D, Van der Linde R, Mulder H A. Fluctuations in milk yield are heritable and can be used as a resilience indicator to breed healthy cows[J]. Journal of Dairy Science, 2018, 101(2): 1240-1250

[15]

Poppe M, Veerkamp R F, Van Pelt M L, Mulder H A. Exploration of variance, autocorrelation, and skewness of deviations from lactation curves as resilience indicators for breeding [J]. Journal of Dairy Science, 2020, 103(2): 1667-1684

[16]

Poppe M, Bonekamp G, Van Pelt M L, Mulder H A. Genetic analysis of resilience indicators based on milk yield records in different lactations and at different lactation stages[J]. Journal of Dairy Science, 2021, 104(2): 1967-1981

[17]

Poppe M, Mulder H A, Veerkamp R F. Validation of resilience indicators by estimating genetic correlations among daughter groups and with yield responses to a heat wave and disturbances at herd level[J]. Journal of Dairy Science, 2021, 104(7): 8094-8106

[18]

Poppe M, Mulder H A, Kamphuis C, Veerkamp R F. Between-herd variation in resilience and relations to herd performance[J]. Journal of Dairy Science, 2021, 104(1): 616-627

[19]

Kok A, Tsousis G, Niozas G, Kemp B, Kaske M, van Knegsel A T M. Short communication: Variance and autocorrelation of deviations in daily milk yield are related with clinical mastitis in dairy cows[J]. Animal, 2021, 15(10): 100363

[20]

Wang A, Su G S, Brito L F, Zhang H L, Shi R, Liu D K, Guo G, Wang Y C. Investigating the relationship between fluctuations in daily milk yield as resilience indicators and health traits in Holstein cattle[J]. Journal of Dairy Science, 2024, 107(3): 1535-1548

[21]

Wang A, Brito L F, Zhang H L, Shi R, Zhu L, Liu D K, Guo G, Wang Y C. Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle[J]. Frontiers in Genetics, 2022, 13: 1031557

[22]

Chen S Y, Boerman J P, Gloria L S, Pedrosa V B, Doucette J, Brito L F. Genomic-based genetic parameters for resilience across lactations in North American Holstein cattle based on variability in daily milk yield records[J]. Journal of Dairy Science, 2023, 106(6): 4133-4146

[23]

Oloo R D, Mrode R, Bennewitz J, Ekine-Dzivenu C C, Ojango J M K, Gebreyohanes G, Mwai O A, Chagunda M G G. Potential for quantifying general environmental resilience of dairy cattle in sub-Saharan Africa using deviations in milk yield[J]. Frontiers in Genetics, 2023, 14: 1208158

[24]

Keßler F, Wellmann R, Chagunda M G G, Bennewitz J. Resilience indicator traits in 3 dairy cattle breeds in Baden-W ür ttemberg [J]. Journal of Dairy Science, 2024, 107(6): 3780-3793

[25]

Poppe M, Mulder H A, Van Pelt M L, Mullaart E, Hogeveen H, Veerkamp R F. Development of resilience indicator traits based on daily step count data for dairy cattle breeding[J]. Genetics Selection Evolution, 2022, 54(1): 21

[26]

Rodrigues G R D, Rezende V T, Mercadante M E Z, Bonilha S F M, Canesin R C, Raineri C, De Paula Soares Valents J, Ligori V A, Dos Santos Gonçalves Cyrillo J N. Animal growth models as a tool to estimate resilience indicators in Bos indicus and Bos taurus heifers: Selection effects and genetics parameters[J]. Livestock Science, 2024, 282: 105435

[27]

Putz A M, Harding J C S, Dyck M K, Fortin F, Plastow G S, Dekkers J C M. Novel resilience phenotypes using feed intake data from a natural disease challenge model in wean-to-finish pigs [J]. Frontiers in Genetics, 2019, 9: 660

[28]

Cheng J, Putz A M, Harding J C S, Dyck M K, Fortin F, Plastow G S, Canada P, Dekkers J C M. Genetic analysis of disease resilience in wean-to-finish pigs from a natural disease challenge model [J]. Journal of Animal Science, 2020, 98(8): skaa244

[29]

Homma C, Hirose K, Ito T, Kamikawa M, Toma S, Nikaido S, Satoh M, Uemoto Y. Estimation of genetic parameter for feed efficiency and resilience traits in three pig breeds[J]. Animal, 2021, 15(11): 100384

[30]

Berghof T V L, Bovenhuis H, Mulder H A. Body weight deviations as indicator for resilience in layer chickens[J]. Frontiers in Genetics, 2019, 10: 1216

[31]

Berghof T V L, Bedere N, Peeters K, Poppe M, Visscher J, Mulder H A. The genetics of resilience and its relationships with egg production traits and antibody traits in chickens[J]. Genetics Selection Evolution, 2024, 56(1): 20

[32]

Silpa M V, König S, Sejian V, Malik P K, Nair M R R, Fonseca V F C, Maia A S C, Bhatta R. Climate-resilient dairy cattle production: Applications of genomic tools and statistical models [J]. Frontiers in Veterinary Science, 2021, 8: 625189

[33]

Liu S H, Yue T T, Ahmad M J, Hu X W, Zhang X X, Deng T X, Hu Y, He C J, Zhou Y, Yang L G. Transcriptome analysis reveals potential regulatory genes related to heat tolerance in Holstein dairy cattle[J]. Genes, 2020, 11(1): 68

[34]

Cheruiyot E K, Haile-Mariam M, Cocks B G, MacLeod I M, Xiang R D, Pryce J E. New loci and neuronal pathways for resilience to heat stress in cattle[J]. Scientific Reports, 2021, 11(1): 16619

[35]

Bohlouli M, Halli K, Yin T, Gengler N, König S. Genome-wide associations for heat stress response suggest potential candidate genes underlying milk fatty acid composition in dairy cattle[J]. Journal of Dairy Science, 2022, 105(4): 3323-3340

[36]

Passamonti M M, Milanesi M, Cattaneo L, Ramirez-Diaz J, Stella A, Barbato M, Braz C U, Negrini R, Giannuzzi D, Pegolo S, Cecchinato A, Trevisi E, Williams J L, Ajmone Marsan P. Unraveling metabolic stress response in dairy cows: Genetic control of plasma biomarkers throughout lactation and the transition period[J]. Journal of Dairy Science, 2024, 107(11): 9602-9614

[37]

Chen S Y, Gloria L S, Pedrosa V B, Doucette J, Boerman J P, Brito L F. Unraveling the genomic background of resilience based on variability in milk yield and milk production levels in North American Holstein cattle through genome-wide association study and Mendelian randomization analyses[J]. Journal of Dairy Science, 2024, 107(2): 1035-1053

[38]

Punturiero C, Delledonne A, Ferrari C, Bagnato A, Strillacci M G. Mapping genomic regions affecting resilience traits in a large dairy farm of Holstein cows[j]. Frontiers in Animal Science, 2025, 6:1627086

[39]

Li M Y, Pu L, MacHugh D E, Tian J J, Wang X Q, Zhao Q Y, Shi L J, Gao H M, Yu Y, Wang L X, Zhao F P. Genome-wide association studies of novel resilience traits identify important immune QTL regions and candidate genes in duroc pigs[J]. Journal of Integrative Agriculture, 2025, 24(11): 4355-4369

[40]

Shi L, Hao W, Li Z, Chaolu H, Wang L. Genome-wide association study and genomic prediction of sow resilience based on reproductive traits[J]. Animal, 2025, 19(9): 101607

[41]

Doekes H P, Bovenhuis H, Berghof T V L, Peeters K, Visscher J, Mulder H A. Research Note: Genome-wide association study for natural antibodies and resilience in a purebred layer chicken line [J]. Poultry Science, 2023, 102(1): 102312

[42]

Nielsen H M, Olesen I, Navrud S, Kolstad K, Amer P. How to consider the value of farm animals in breeding goals. A review of current status and future challenges[J]. Journal of Agricultural and Environmental Ethics, 2011, 24(4): 309-330

[43]

Grimsrud K M, Nielsen H M, Navrud S, Olesen I. Households’ willingness-to-pay for improved fish welfare in breeding programs for farmed Atlantic salmon [J]. Aquaculture, 2013, 372-375: 19-27

[44]

Mulder H A, Rashidi H. Selection on resilience improves disease resistance and tolerance to infections[J]. Journal of Animal Science, 2017, 95(8): 3346-3358

[45]

Bengtsson C, Thomasen J R, Kargo M, Bouquet A, Slagboom M. Emphasis on resilience in dairy cattle breeding: Possibilities and consequences[J]. Journal of Dairy Science, 2022, 105(9): 7588-7599

[46]

Meijer N, Bouwmeester-Vosman J, van Pelt M, de Jong G. Breeding for resilience in the Netherlands and Flanders. Interbull Bulletin, 2024, 60: 103-107

[47]

Keßler F, Wellmann R, Chagunda M G G, Bennewitz J. Toward a resilience selection index with indicator traits in German Holstein dairy cattle[J]. Journal of Dairy Science, 2025, 108(1): 726-734

[48]

König S, May K. Invited review: Phenotyping strategies and quantitative-genetic background of resistance, tolerance and resilience associated traits in dairy cattle [J]. Animal, 2019, 13(5): 897-908

[49]

Bronzo V, Lopreiato V, Riva F, Amadori M, Curone G, Addis M F, Cremonesi P, Moroni P, Trevisi E, Castiglioni B. The role of innate immune response and microbiome in resilience of dairy cattle to disease: The mastitis model[J]. Animals, 2020, 10(8): 1397

[50]

Llonch P, Hoffmann G, Bodas R, Mirbach D, Verwer C, Haskell M J. Opinion paper: Measuring livestock robustness and resilience: Are we on the right track [J]. Animal, 2020, 14(4): 667-669

[51]

Mulder H A, Rönnegård L, Fikse W F, Veerkamp R F, Strandberg E. Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models [J]. Genetics, Selection, Evolution, 2013, 45(1): 23

[52]

Højsgaard S, Friggens N C. Quantifying degree of mastitis from common trends in a panel of indicators for mastitis in dairy cows[J]. Journal of Dairy Science, 2010, 93(2): 582-592

[53]

Ben Abdelkrim A, Tribout T, Martin O, Boichard D, Ducrocq V, Friggens N C. Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies[J]. Journal of Dairy Science, 2021, 104(1): 459-470

[54]

Ithurbide M, Wang H, Fassier T, Li Z, Pires J, Larsen T, Cao J, Rupp R, Friggens N C. Multivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes[J]. Journal of Dairy Science, 2023, 106(11): 8072-8086

[55]

Codrea M C, Højsgaard S, Friggens N C. Differential smoothing of time-series measurements to identify disturbances in performance and quantify animal response characteristics: An example using milk yield profiles in dairy cows1 [J]. Journal of Animal Science, 2011, 89(10): 3089-3098

[56]

Nguyen-Ba H, van Milgen J, Taghipoor M. A procedure to quantify the feed intake response of growing pigs to perturbations[J]. Animal, 2020, 14(2): 253-260

[57]

Strandberg E. The role of environmental sensitivity and plasticity in breeding for robustness: Lessons from evolutionary genetics[M].Breeding for robustness in cattle. Wageningen Academic, 2009: 15-33

[58]

Douhard F, Douhard M, Gilbert H, Monget P, Gaillard J M, Lemaître J F. How much energetic trade-offs limit selection? Insights from livestock and related laboratory model species [J]. Evolutionary Applications, 2021, 14(12): 2726-2749

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