Hardie L C, Armentano L E, Shaver R D, VandeHaar M J, Spurlock D M, Yao C, Bertics S J, Contreras-Govea F E, Weigel K A
Department of Animal Sciences, Iowa State University, Ames 50011.
Department of Dairy Science, University of Wisconsin, Madison 53706.
J Dairy Sci. 2015 Apr;98(4):2727-37. doi: 10.3168/jds.2014-8580. Epub 2015 Feb 7.
Prior to genomic selection on a trait, a reference population needs to be established to link marker genotypes with phenotypes. For costly and difficult-to-measure traits, international collaboration and sharing of data between disciplines may be necessary. Our aim was to characterize the combining of data from nutrition studies carried out under similar climate and management conditions to estimate genetic parameters for feed efficiency. Furthermore, we postulated that data from the experimental cohorts within these studies can be used to estimate the net energy of lactation (NE(L)) densities of diets, which can provide estimates of energy intakes for use in the calculation of the feed efficiency metric, residual feed intake (RFI), and potentially reduce the effect of variation in energy density of diets. Individual feed intakes and corresponding production and body measurements were obtained from 13 Midwestern nutrition experiments. Two measures of RFI were considered, RFI(Mcal) and RFI(kg), which involved the regression of NE(L )intake (Mcal/d) or dry matter intake (DMI; kg/d) on 3 expenditures: milk energy, energy gained or lost in body weight change, and energy for maintenance. In total, 677 records from 600 lactating cows between 50 and 275 d in milk were used. Cows were divided into 46 cohorts based on dietary or nondietary treatments as dictated by the nutrition experiments. The realized NE(L) densities of the diets (Mcal/kg of DMI) were estimated for each cohort by totaling the average daily energy used in the 3 expenditures for cohort members and dividing by the cohort's total average daily DMI. The NE(L) intake for each cow was then calculated by multiplying her DMI by her cohort's realized energy density. Mean energy density was 1.58 Mcal/kg. Heritability estimates for RFI(kg), and RFI(Mcal) in a single-trait animal model did not differ at 0.04 for both measures. Information about realized energy density could be useful in standardizing intake data from different climate conditions or management systems, as well as investigating potential genotype by diet interactions.
在对某一性状进行基因组选择之前，需要建立一个参考群体，以便将标记基因型与表型联系起来。对于成本高昂且难以测量的性状，可能需要开展国际合作并进行跨学科的数据共享。我们的目标是对在相似气候和管理条件下开展的营养研究数据进行整合，以估计饲料效率的遗传参数。此外，我们推测这些研究中实验队列的数据可用于估计日粮的泌乳净能（NE(L)）密度，这可为计算饲料效率指标——剩余采食量（RFI）时的能量摄入量提供估计值，并有可能降低日粮能量密度变化的影响。个体采食量以及相应的生产性能和体尺测量数据取自13项中西部营养实验。我们考虑了两种RFI测量方法，即RFI(Mcal)和RFI(kg)，这两种方法涉及将NE(L)摄入量（Mcal/天）或干物质摄入量（DMI；kg/天）对三项支出进行回归分析：产奶能量、体重变化中获得或损失的能量以及维持能量。总共使用了来自600头泌乳奶牛在产奶50至275天期间的677条记录。根据营养实验规定的日粮或非日粮处理方式，奶牛被分为46个队列。通过将队列成员三项支出中使用的平均每日能量相加，再除以该队列的总平均每日DMI，来估计每个队列日粮的实际NE(L)密度（Mcal/kg DMI）。然后，将每头奶牛的DMI乘以其所在队列的实际能量密度，计算出每头奶牛的NE(L)摄入量。平均能量密度为1.58 Mcal/kg。在单性状动物模型中，RFI(kg)和RFI(Mcal)的遗传力估计值在两种测量方法下均为0.04，没有差异。关于实际能量密度的信息可能有助于标准化来自不同气候条件或管理系统的采食量数据，以及研究潜在的基因型与日粮互作。
