Chafai Narjice, Hayah Ichrak, Houaga Isidore, Badaoui Bouabid
Laboratory of Biodiversity, Ecology, and Genome, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco.
Centre for Tropical Livestock Genetics and Health, The Roslin Institute, Royal (Dick) School of Veterinary Medicine, The University of Edinburgh, Edinburgh, United Kingdom.
Front Genet. 2023 Sep 6;14:1150596. doi: 10.3389/fgene.2023.1150596. eCollection 2023.
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.
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。大型标记数据集在灵活性、准确性和计算能力方面显示出传统基因组预测方法存在的几个缺点。最近，机器学习模型在动物育种中的应用因其极大的灵活性以及在大型噪声数据集中捕捉模式的能力而备受关注。在此，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，以呈现它们在基因组估计育种值估计、基因型填充和特征选择方面的性能全貌。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。综述研究结果表明，机器学习模型在拟合大型噪声数据集以及在一些研究中对微小非加性效应建模方面确实表现良好。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不存在通用方法。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潜力。尽管如此，由于数据限制、复杂的基因相互作用、缺乏标准化和可重复性以及使用生物数据训练时机器学习模型缺乏可解释性，它们在动物育种中的应用水平仍然较低。因此，在基因组预测中，机器学习方法与传统方法相比并没有显著的优势。所以，应该进行更多研究以发现能够改进家畜育种计划的新见解。
