Residual networks without pooling layers improve the accuracy of genomic predictions

Abstract With the decrease in gene sequencing cost and the development of deep learning, phenotype prediction accuracy by genomic selection (GS) has been continuously improved. Residual networks, a widely validated deep learning technique, are introduced to deep learning for GS. Since each locus has a different weighted impact on the phenotype, strided convolutions are more suitable for GS problems than pooling layers. Through the above technological innovations, we propose a deep learning algorithm for GS, residual neural network genomic selection (ResGS). ResGS is the first neural network to reach 50 layers in GS. In 15 cases with four public data, the prediction accuracy of ResGS is higher than that of ridge-regression best linear unbiased prediction, support vector regression, random forest, gradient boosting regressor, and deep neural network genomic prediction in most cases. ResGS performs well in dealing with gene-environment interaction. Phenotypes from other environments are imported into ResGS along with genetic data. The prediction results are much better than just providing genetic data as input, which demonstrates the effectiveness of GS multi-modal learning. Standard deviation is recommended as an auxiliary GS evaluation metric, which could improve the distribution of predicted results. Deep learning for GS, such as ResGS, has increasingly apparent advantages compared with traditional GS algorithms.