A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species.
A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species.
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DOI：
10.3389/fpls.2022.932512
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发表时间：
2022
影响因子：
5.6
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中科院分区：
文献类型：
--
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基因组选择是育种者直接从基因型数据中准确选择植物的不可或缺的工具，从而导致更快和更具资源效率的育种计划。在过去的几年里，已经建立了几种预测方法。这些方法的范围从经典的线性混合模型到复杂的非线性机器学习方法，如支持向量回归，以及基于深度学习的现代体系结构。这些方法中的许多已经在不同的作物品种上进行了广泛的评估，结果各不相同。在这项工作中，我们的目标是系统地比较12种不同的表型预测模型，包括基本的基因组选择方法和更先进的基于深度学习的技术。更重要的是，我们评估了这些模型在模拟表型数据以及来自拟南芥和来自大豆和玉米的两个育种数据集的真实世界数据上的性能。合成的表型数据使我们能够在受控和预定义的设置下分析所有预测模型，特别是选定的标记。我们证明了稀疏约束下的贝叶斯B模型和线性回归模型在不同的模拟设置下相对于解释方差表现得最好。此外，我们可以从其他研究证实，与成熟的方法相比，基于更复杂的神经网络的体系结构在表型预测方面没有优势。然而，在现实世界的数据上，几个预测模型产生了类似的结果，弹性网络略有优势，但这种情况不太清楚，这表明未来的研究空间很大。
Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.
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