Rovere Gabriel, de Los Campos Gustavo, Gebreyesus Grum, Savegnago Rodrigo Pelicioni, Buitenhuis Albert J
Department of Animal Science, Michigan State University, East Lansing, MI 48824.
Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824; Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824.
J Dairy Sci. 2024 Mar;107(3):1561-1576. doi: 10.3168/jds.2023-23772. Epub 2023 Oct 6.
Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infrared (FT-MIR) spectroscopy is a noninvasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp using the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype (r) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5% to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, DMIp and EBp, and 0.42 for BW. The genetic correlation between EnM and BW was -0.17, with DMIp was 0.40 and with EBp was -0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance.
在动物和畜群层面，有关干物质摄入量（DMI）和能量平衡（EB）的信息对于管理和育种决策非常重要。然而，在商业农场常规记录这些性状可能具有挑战性且成本高昂。傅里叶变换中红外（FT-MIR）光谱技术是一种适用于大量动物群体的非侵入性技术，常用于分析牛奶成分，便于预测通常难以大规模获取且成本高昂的复杂表型。我们旨在开发能量平衡的预测模型，并将预测的表型用于遗传分析。首先，我们使用来自一个实验站的167头荷斯坦奶牛的4485条表型记录评估预测方程。可用的表型包括体重（BW）、产奶量（MY）和牛奶成分、每周平均DMI以及所有可用牛奶样本的FT-MIR数据。我们采用贝叶斯方法实施混合模型，并通过5折交叉验证的50次随机重复对其进行评估。其次，我们使用最佳预测模型在5个商业农场获取能量平衡（EBp）和干物质摄入量（DMIp）的预测表型，这些农场有来自1441头荷斯坦奶牛的2365条产奶量、牛奶成分和FT-MIR数据以及体重的表型记录。第三，我们进行了全基因组关联研究（GWAS），并利用商业农场奶牛的基因组信息估计了牛奶能量含量（EnM）、体重、DMIp和EBp的遗传力和遗传相关性。预测表型与观察到的表型之间的最高相关性（r）在DMI方面为0.88，在EB方面为0.86，而正如预期的那样，预测体重更具挑战性（0.69）。在我们的研究中，在所分析的3个性状中，包含FT-MIR信息的模型比没有光谱信息的模型表现更好，预测相关性提高了5%至10%。对于通过预测方程计算出的预测表型以及来自商业农场的数据，EnM、DMIp和EBp的遗传力在0.11至0.16之间，体重的遗传力为0.42。EnM与体重之间的遗传相关性为-0.17，与DMIp为0.40，与EBp为-0.39。从GWAS中，我们检测到一个EnM的显著QTL区域，3个体重的显著QTL区域，但未检测到DMIp和EBp的显著QTL区域。我们研究中获得的结果支持了先前的证据，即来自牛奶样本的FT-MIR信息有助于改进DMI、BW和EB的预测方程，这些预测表型可用于畜群管理，并有助于制定提高奶牛性能的育种策略。
