Carabaño M J, Ramón M, Díaz C, Molina A, Pérez-Guzmán M D, Serradilla J M
J Anim Sci. 2017 Apr;95(4):1813-1826. doi: 10.2527/jas.2016.1114.
Selection for heat tolerant (HT) animals in dairy production has been so far linked to estimation of declines in production using milk recording and meteorological information on the day of control using reaction norms. Results from these models show that there is a reasonable amount of genetic variability in the individual response to high heat loads, which makes feasible selection of HT animals at low costs. However, the antagonistic relationship between level of production and response to heat stress (HS) implies that selection for HT animals under this approach must be done with caution so that productivity is not damaged. Decomposition of the genetic variability in principal components (PC) can provide selection criteria independent of milk production level although biological interpretation of PC is difficult. Moreover, given that response to heat stress for each animal is estimated with very sparse information collected under different physiological and management circumstances, biased (normally underestimation) and lack of accuracy may be expected. Alternative phenotypic characterization of HT can come from the use of physiological traits, which have also shown moderate heritability. However, costs of a large scale implementation based on physiological characteristics has precluded its use. Another alternative is the use of biomarkers that define heat tolerance. A review of biomarkers of HS from more recent studies is provided. Of particular interest are milk biomarkers, which together with infrared spectra prediction equations can provide useful tools for genetic selection. In the 'omics' era, genomics, transcriptomics, proteomics and metabolomics have been already used to detect genes affecting HT. A review of findings in these areas is also provided. Except for the slick hair gene, there are no other genes for which variants have been clearly associated with HT. However, integration of omics information could help in pointing at knots of the HS control network and, in the end, to a panel of markers to be used in the selection of HT animals. Overall, HT is a complex phenomenon that requires integration of fine phenotypes and omics information to provide accurate tools for selection without damaging productivity. Technological developments to make on-farm implementation feasible and with greater insight into the key biomarkers and genes involved in HT are needed.
在奶牛生产中，耐热（HT）动物的选育至今一直与利用产奶记录以及使用反应规范在对照日的气象信息来估计产量下降情况相关联。这些模型的结果表明，个体对高热负荷的反应存在合理程度的遗传变异，这使得低成本选育耐热动物成为可能。然而，生产水平与热应激（HS）反应之间的拮抗关系意味着，采用这种方法选育耐热动物时必须谨慎，以免损害生产力。尽管主成分（PC）中遗传变异的生物学解释较为困难，但对其进行分解可以提供独立于产奶水平的选择标准。此外，鉴于每头动物的热应激反应是根据在不同生理和管理条件下收集的非常稀疏的信息进行估计的，可能会出现偏差（通常是低估）且缺乏准确性。耐热性的另一种表型特征可以来自生理性状的使用，生理性状也显示出中等遗传力。然而，基于生理特征进行大规模实施的成本使其无法得到应用。另一种选择是使用定义耐热性的生物标志物。本文对近期研究中的热应激生物标志物进行了综述。特别值得关注的是乳生物标志物，其与红外光谱预测方程一起可为遗传选择提供有用工具。在“组学”时代，基因组学、转录组学、蛋白质组学和代谢组学已被用于检测影响耐热性的基因。本文还对这些领域的研究结果进行了综述。除了光滑毛基因外，没有其他基因的变异与耐热性有明确关联。然而，组学信息的整合有助于指出热应激控制网络的节点，并最终确定用于选育耐热动物的一组标记。总体而言，耐热性是一个复杂的现象，需要整合精细表型和组学信息，以提供准确的选择工具，同时不损害生产力。需要进行技术开发，以使农场实施可行，并更深入了解参与耐热性的关键生物标志物和基因。
