Guy S Z Y, Li L, Thomson P C, Hermesch S
School of Life and Environmental Sciences, University of Sydney, Camden, NSW, Australia.
Animal Genetics and Breeding Unit, a Joint Venture of NSW Department of Primary Industries and University of New England, University of New England, Armidale, NSW, Australia.
J Anim Breed Genet. 2017 Dec;134(6):520-530. doi: 10.1111/jbg.12282. Epub 2017 Jul 10.
Environmental descriptors derived from mean performances of contemporary groups (CGs) are assumed to capture any known and unknown environmental challenges. The objective of this paper was to obtain a finer definition of the unknown challenges, by adjusting CG estimates for the known climatic effects of monthly maximum air temperature (MaxT), minimum air temperature (MinT) and monthly rainfall (Rain). As the unknown component could include infection challenges, these refined descriptors may help to better model varying responses of sire progeny to environmental infection challenges for the definition of disease resilience. Data were recorded from 1999 to 2013 at a piggery in south-east Queensland, Australia (n = 31,230). Firstly, CG estimates of average daily gain (ADG) and backfat (BF) were adjusted for MaxT, MinT and Rain, which were fitted as splines. In the models used to derive CG estimates for ADG, MaxT and MinT were significant variables. The models that contained these significant climatic variables had CG estimates with a lower variance compared to models without significant climatic variables. Variance component estimates were similar across all models, suggesting that these significant climatic variables accounted for some known environmental variation captured in CG estimates. No climatic variables were significant in the models used to derive the CG estimates for BF. These CG estimates were used to categorize environments. There was no observable sire by environment interaction (Sire×E) for ADG when using the environmental descriptors based on CG estimates on BF. For the environmental descriptors based on CG estimates of ADG, there was significant Sire×E only when MinT was included in the model (p = .01). Therefore, this new definition of the environment, preadjusted by MinT, increased the ability to detect Sire×E. While the unknown challenges captured in refined CG estimates need verification for infection challenges, this may provide a practical approach for the genetic improvement of disease resilience.
从当代群体（CGs）的平均性能得出的环境描述符被假定为能够捕捉任何已知和未知的环境挑战。本文的目的是通过针对每月最高气温（MaxT）、最低气温（MinT）和月降雨量（Rain）的已知气候影响调整CG估计值，从而对未知挑战进行更精确的定义。由于未知成分可能包括感染挑战，这些经过改进的描述符可能有助于更好地模拟父系后代对环境感染挑战的不同反应，以定义疾病恢复力。数据于1999年至2013年在澳大利亚昆士兰州东南部的一个养猪场记录（n = 31,230）。首先，针对MaxT、MinT和Rain对平均日增重（ADG）和背膘（BF）的CG估计值进行调整，将其拟合为样条函数。在用于得出ADG的CG估计值的模型中，MaxT和MinT是显著变量。与不包含显著气候变量的模型相比，包含这些显著气候变量的模型的CG估计值方差更低。所有模型的方差成分估计值相似，表明这些显著气候变量解释了CG估计值中捕捉到的一些已知环境变异。在用于得出BF的CG估计值的模型中，没有气候变量是显著的。这些CG估计值被用于对环境进行分类。当使用基于BF的CG估计值的环境描述符时，ADG没有观察到父系与环境的相互作用（Sire×E）。对于基于ADG的CG估计值的环境描述符，只有当模型中包含MinT时才有显著的Sire×E（p = 0.01）。因此，这个由MinT预先调整的环境新定义提高了检测Sire×E的能力。虽然在经过改进的CG估计值中捕捉到的未知挑战需要针对感染挑战进行验证，但这可能为疾病恢复力的遗传改良提供一种实用方法。
