Cecchinato Alessio, Toledo-Alvarado Hugo, Pegolo Sara, Rossoni Attilio, Santus Enrico, Maltecca Christian, Bittante Giovanni, Tiezzi Francesco
Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Padua, Italy.
Department of Genetics and Biostatistics, National Autonomous University of Mexico, Mexico City, Mexico.
Front Genet. 2020 Sep 29;11:563393. doi: 10.3389/fgene.2020.563393. eCollection 2020.
The objective of this study was to evaluate the contribution of Fourier-transformed infrared spectroscopy (FTIR) data for dairy cattle breeding through two different approaches: (i) estimating the genetic parameters for 30 measured milk traits and their FTIR predictions and investigating the additive genetic correlation between them and (ii) evaluating the effectiveness of FTIR-derived phenotyping to replicate a candidate bull's progeny testing or breeding value prediction at birth. Records were available from 1,123 cows phenotyped using gold standard laboratory methodologies (LAB data). This included phenotypes related to fine milk composition and milk technological characteristics, milk acidity, and milk protein fractions. The dataset used to generate FTIR predictions comprised 729,202 test-day records from 51,059 Brown Swiss cows (FIELD data). A first approach consisted of estimating genetic parameters for phenotypes available from LAB and FIELD datasets. To do so, a set of bivariate animal models were run, and genetic correlations between LAB and FIELD phenotypes were estimated using FIELD information obtained at the population level. Heritability estimates were generally higher for FIELD predictions than for the corresponding LAB measures. The additive genetic correlations (r ) between LAB and FIELD phenotypes had different magnitudes across traits but were generally strong. Overall, these results demonstrated the potential of using FIELD information as indicator traits for the indirect genetic improvement of LAB measures. In the second approach, we included genotype information for 1,011 cows from the LAB dataset, 1,493 cows from the FIELD dataset, 181 sires with daughters in both LAB and FIELD datasets, and 540 sires with daughters in the FIELD dataset only. Predictions were obtained using the single-step GBLUP method. A four fold cross-validation was used to assess the predictive ability of the different models, assessed as the ability to predict masked LAB records from daughters of progeny testing bulls. The correlation between observed and predicted LAB measures in validation was averaged over the four training-validation sets. Different sets of phenotypic information were used sequentially in cross-validation schemes: (i) LAB cows from the training set; (ii) FIELD cows from the training set; and (iii) FIELD cows from the validation set. Models that included FIELD records showed an improvement for the majority of traits. This study suggests that breeding programs for difficult-to-measure traits could be implemented using FTIR information. While these programs should use progeny testing, acceptable values of accuracy can be achieved also for bulls without phenotyped progeny. Robust calibration equations are, deemed as essential.
本研究的目的是通过两种不同方法评估傅里叶变换红外光谱（FTIR）数据对奶牛育种的贡献：（i）估计30个实测牛奶性状及其FTIR预测值的遗传参数，并研究它们之间的加性遗传相关性；（ii）评估FTIR衍生表型在出生时复制候选公牛后代测试或育种值预测的有效性。记录来自1123头使用金标准实验室方法进行表型分析的奶牛（LAB数据）。这包括与精细牛奶成分、牛奶技术特性、牛奶酸度和牛奶蛋白组分相关的表型。用于生成FTIR预测值的数据集包括来自51059头瑞士褐牛的729202条测定日记录（FIELD数据）。第一种方法包括估计LAB和FIELD数据集中可用表型的遗传参数。为此，运行了一组二元动物模型，并使用在群体水平获得的FIELD信息估计LAB和FIELD表型之间的遗传相关性。FIELD预测值的遗传力估计值通常高于相应的LAB测量值。LAB和FIELD表型之间的加性遗传相关性（r ）因性状而异，但总体上较强。总体而言，这些结果证明了使用FIELD信息作为LAB测量值间接遗传改良指标性状的潜力。在第二种方法中，我们纳入了来自LAB数据集的1011头奶牛、来自FIELD数据集的1493头奶牛、在LAB和FIELD数据集中均有女儿的181头公牛以及仅在FIELD数据集中有女儿的540头公牛的基因型信息。使用单步GBLUP方法进行预测。采用四折交叉验证来评估不同模型的预测能力，评估指标为预测后代测试公牛女儿的隐藏LAB记录的能力。验证中观察到的和预测的LAB测量值之间的相关性在四个训练 - 验证集上进行平均。在交叉验证方案中依次使用不同的表型信息集：（i）训练集中的LAB奶牛；（ii）训练集中的FIELD奶牛；以及（iii）验证集中的FIELD奶牛。包含FIELD记录的模型在大多数性状上表现出改进。本研究表明，可以使用FTIR信息实施针对难以测量性状的育种计划。虽然这些计划应使用后代测试，但对于没有表型后代的公牛也可以实现可接受的准确性值。稳健的校准方程被认为是必不可少的。
