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Abstract：
The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there\'s no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.
摘要：
现代基因分型技术的出现彻底改变了动物育种中的基因组选择。大型标记数据集显示了传统基因组预测方法在灵活性方面的几个缺点，准确度,和计算能力。最近,机器学习模型在动物育种中的应用由于其巨大的灵活性和在大型嘈杂数据集中捕获模式的能力而获得了极大的兴趣。这里,我们对一些机器学习算法及其在基因组预测中的应用进行了概述，以提供其在基因组估计育种值估计中的性能的元图，基因型插补，和特征选择。最后,我们讨论了机器学习模型在发展中国家基因组预测中的潜在应用。审查的研究结果表明，机器学习模型在一些研究中确实在拟合大型嘈杂数据集和建模次要非加性效应方面表现良好。然而,有时传统方法优于机器学习模型，这证实了基因组预测没有通用的方法。总之,机器学习模型在从单核苷酸多态性数据集中提取模式方面具有巨大的潜力。尽管如此，由于数据限制，它们在动物育种中的采用水平仍然很低，复杂的遗传相互作用，缺乏标准化和可重复性，以及在使用生物数据进行训练时缺乏机器学习模型的可解释性。因此,在基因组预测中，与传统方法相比，机器学习方法没有显著的优势。因此,应该进行更多的研究，以发现可以增强牲畜育种计划的新见解。
