Deep learning for genomic selection of aquatic animals.

Abstract

Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic progress than pedigree-based methods. The genetic analysis of complex traits in GS does not escape the current excitement around artificial intelligence, including a renewed interest in deep learning (DL), such as deep neural networks (DNNs), convolutional neural networks (CNNs), and autoencoders. This article reviews the current status and potential of DL applications in phenotyping, genotyping and genomic estimated breeding value (GEBV) prediction of GS. It can be seen from this article that CNNs obtain phenotype data of aquatic animals efficiently, and without injury; DNNs as single nucleotide polymorphism (SNP) variant callers are critical to have shown higher accuracy in assessments of genotyping for the next-generation sequencing (NGS); autoencoder-based genotype imputation approaches are capable of highly accurate genotype imputation by encoding complex genotype relationships in easily portable inference models; sparse DNNs capture nonlinear relationships among genes to improve the accuracy of GEBV prediction for aquatic animals. Furthermore, future directions of DL in aquaculture are also discussed, which should expand the application to more aquaculture species. We believe that DL will be applied increasingly to molecular breeding of aquatic animals in the future.

Supplementary information: The online version contains supplementary material available at 10.1007/s42995-024-00252-y.