Genomic prediction of growth traits in scallops using convolutional neural networks

Abstract

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.