Wang Yangfan, Ni Ping, Sturrock Marc, Zeng Qifan, Wang Bo, Bao Zhenmin, Hu Jingjie
MOE Key Laboratory of Marine Genetics and Breeding, Ocean University of China, Qingdao, 266003 China.
Key Laboratory of Tropical Aquatic Germplasm of Hainan Province, Sanya Oceanographic Institution, Ocean University of China, Sanya, 572024 China.
Mar Life Sci Technol. 2024 Sep 27;6(4):631-650. doi: 10.1007/s42995-024-00252-y. eCollection 2024 Nov.
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.
The online version contains supplementary material available at 10.1007/s42995-024-00252-y.
近年来，基因组选择（GS）应用于水产养殖育种备受关注，因为它比基于系谱的方法具有更高的准确性和更快的遗传进展。GS中复杂性状的遗传分析也未能避开当前围绕人工智能的热潮，包括对深度学习（DL）重新产生的兴趣，如深度神经网络（DNN）、卷积神经网络（CNN）和自动编码器。本文综述了DL在GS的表型分析、基因分型和基因组估计育种值（GEBV）预测中的应用现状及潜力。从本文可以看出，CNN能够高效且无损地获取水产动物的表型数据；DNN作为单核苷酸多态性（SNP）变异调用工具，在下一代测序（NGS）基因分型评估中已显示出更高的准确性，至关重要；基于自动编码器的基因型填充方法能够通过在易于移植的推理模型中编码复杂的基因型关系，实现高精度的基因型填充；稀疏DNN能够捕捉基因间的非线性关系，提高水产动物GEBV预测的准确性。此外，还讨论了DL在水产养殖中的未来发展方向，应将其应用扩展到更多水产养殖物种。我们相信，DL未来将越来越多地应用于水产动物的分子育种。
在线版本包含可在10.1007/s42995-024-00252-y获取的补充材料。
