Bussiman Fernando, Alves Anderson A C, Richter Jennifer, Hidalgo Jorge, Veroneze Renata, Oliveira Tiago
Animal and Dairy Science Department, University of Georgia, Athens, GA 30602, USA.
Animal Science Department, Federal University of Viçosa, Viçosa 36570-900, Brazil.
Animals (Basel). 2024 Sep 20;14(18):2723. doi: 10.3390/ani14182723.
Gait scores are widely used in the genetic evaluation of horses. However, the nature of such measurement may limit genetic progress since there is subjectivity in phenotypic information. This study aimed to assess the application of machine learning techniques in the prediction of breeding values for five visual gait scores in Campolina horses: dissociation, comfort, style, regularity, and development. The dataset contained over 5000 phenotypic records with 107,951 horses (14 generations) in the pedigree. A fixed model was used to estimate least-square solutions for fixed effects and adjusted phenotypes. Variance components and breeding values (EBV) were obtained via a multiple-trait model (MTM). Adjusted phenotypes and fixed effects solutions were used to train machine learning models (using the EBV from MTM as target variable): artificial neural network (ANN), random forest regression (RFR) and support vector regression (SVR). To validate the models, the linear regression method was used. Accuracy was comparable across all models (but it was slightly higher for ANN). The highest bias was observed for ANN, followed by MTM. Dispersion varied according to the trait; it was higher for ANN and the lowest for MTM. Machine learning is a feasible alternative to EBV prediction; however, this method will be slightly biased and over-dispersed for young animals.
步态评分在马匹的遗传评估中被广泛应用。然而，由于表型信息存在主观性，这种测量的性质可能会限制遗传进展。本研究旨在评估机器学习技术在预测坎波拉马五种视觉步态评分（分离度、舒适度、风格、规律性和发育情况）育种值中的应用。数据集包含5000多条表型记录，系谱中有107951匹马（14代）。使用固定模型估计固定效应和调整后表型的最小二乘解。通过多性状模型（MTM）获得方差成分和育种值（EBV）。使用调整后的表型和固定效应解来训练机器学习模型（以MTM的EBV作为目标变量）：人工神经网络（ANN）、随机森林回归（RFR）和支持向量回归（SVR）。为了验证模型，使用了线性回归方法。所有模型的准确性相当（但ANN略高）。ANN的偏差最高，其次是MTM。离散度因性状而异；ANN的离散度较高，MTM的离散度最低。机器学习是EBV预测的一种可行替代方法；然而，这种方法对幼龄动物会有轻微偏差和过度离散。
