Deep learning neural networks applied to the genomic prediction of complex traits have been of great interest in recent years. Previous studies primarily used simulated phenotypes or/and genotypes in plants and animals. The properties of deep learning models used in genomic selection are not well characterized and not well validated with real datasets. In the present study, we evaluated the performance of a class of deep learning methods called convolutional neural networks (CNNs) in the genomic prediction of four quantitative traits (e.g., shell length, shell height, shell width, and total weight) in a Bay scallop (Argopecten irradians irradians) population. The results were compared with those obtained from two linear models, RR-GBLUP and Bayes B, and multilayer perceptron neural networks (MLPs). One-convolutional layer CNNs with an optimal structure, which was obtained by using AIC or BIC method, had roughly comparable prediction accuracies on the four quantitive traits in the scallop population. Overall, CNNs outperformed RR-GBLUP, Bayes B and MLPs on shell height, shell width and total weight, and performed slightly worse than only Bayes B on shell length. MLPs gave the least accurate predictions on average among the four types of models. Because MLPs had far more parameters to estimate than the two linear models, and their predictions were challenged by the overfitting problem. Genomic prediction accuracy varied with SNP panel size and training population size.The impact of varied marker densities and two GWAS-based scenarios for SNP selection on genomic prediction accuracy was investigated as well. The present results provide evidence that supports the use of convolutional neural networks for genomic prediction of complex traits in scallops, yet the optimal structures of CNNs remained to be exploited in future studies.

近年来，应用于复杂性状基因组预测的深度学习神经网络引起了人们的极大兴趣。以前的研究主要在植物和动物中使用模拟表型或/和基因型。用于基因组选择的深度学习模型的特性没有得到很好的表征，也没有得到真实数据集的很好验证。在本研究中，我们评估了一类称为卷积神经网络 (CNN) 的深度学习方法在海湾中四个数量性状（例如，壳长、壳高、壳宽和总重量）的基因组预测中的性能。扇贝（Argopecten irradians irradians） 人口。将结果与从两个线性模型 RR-GBLUP 和贝叶斯 B 以及多层感知器神经网络 (MLP) 获得的结果进行比较。通过使用 AIC 或 BIC 方法获得的具有最佳结构的单卷积层 CNN 对扇贝种群中的四个数量性状的预测精度大致相当。总体而言，CNN 在壳高度、壳宽和总重量上的表现优于 RR-GBLUP、贝叶斯 B 和 MLP，并且在壳长上的表现略差于仅贝叶斯 B。MLP 在四种类型的模型中平均给出了最不准确的预测。因为 MLP 比两个线性模型要估计的参数要多得多，而且它们的预测受到过拟合问题的挑战。基因组预测准确性因 SNP 面板大小和训练种群大小而异。还研究了不同标记密度和两种基于 GWAS 的 SNP 选择方案对基因组预测准确性的影响。目前的结果提供了支持使用卷积神经网络对扇贝复杂性状进行基因组预测的证据，但 CNN 的最佳结构仍有待在未来的研究中加以利用。