Hay El Hamidi
USDA Agricultural Research Service, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT 59301, USA.
Animals (Basel). 2024 Oct 18;14(20):3014. doi: 10.3390/ani14203014.
The adoption of genomic selection is prevalent across various plant and livestock species, yet existing models for predicting genomic breeding values often remain suboptimal. Machine learning models present a promising avenue to enhance prediction accuracy due to their ability to accommodate both linear and non-linear relationships. In this study, we evaluated four machine learning models-Random Forest, Support Vector Machine, Convolutional Neural Networks, and Multi-Layer Perceptrons-for predicting genomic values related to birth weight (BW), weaning weight (WW), and yearling weight (YW), and compared them with other conventional models-GBLUP (Genomic Best Linear Unbiased Prediction), Bayes A, and Bayes B. The results demonstrated that the GBLUP model achieved the highest prediction accuracy for both BW and YW, whereas the Random Forest model exhibited a superior prediction accuracy for WW. Furthermore, GBLUP outperformed the other models in terms of model fit, as evidenced by the lower mean square error values and regression coefficients of the corrected phenotypes on predicted values. Overall, the GBLUP model delivered a superior prediction accuracy and model fit compared to the machine learning models tested.
基因组选择在各种植物和家畜物种中广泛应用，然而现有的预测基因组育种值的模型往往仍不尽人意。机器学习模型因其能够处理线性和非线性关系，为提高预测准确性提供了一条有前景的途径。在本研究中，我们评估了四种机器学习模型——随机森林、支持向量机、卷积神经网络和多层感知器——用于预测与出生体重（BW）、断奶体重（WW）和周岁体重（YW）相关的基因组值，并将它们与其他传统模型——基因组最佳线性无偏预测（GBLUP）、贝叶斯A和贝叶斯B进行比较。结果表明，GBLUP模型在BW和YW的预测准确性方面均最高，而随机森林模型在WW的预测准确性方面表现更优。此外，GBLUP在模型拟合方面优于其他模型，校正表型对预测值的均方误差值和回归系数较低证明了这一点。总体而言，与所测试的机器学习模型相比，GBLUP模型具有更高的预测准确性和更好的模型拟合度。
