Yu Xiaofei, Faggion Sara, Liu Yuxiang, Wang Bo, Zeng Qifan, Lu Chunzhe, Hu Jingjie, Bargelloni Luca, Fang Lingzhao, Bao Zhenmin
Ministry of Education Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China.
Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, 35020, Italy.
Sci China Life Sci. 2025 May 28. doi: 10.1007/s11427-024-2828-8.
Aquaculture, a fast-growing sector, plays an important role in the supply of nutrient-rich food for humans. Selective breeding is a promising approach to ensure the development and sustainability of intensive aquaculture systems by achieving cumulative and permanent improvements in desirable traits. The advancement of omics technologies offers unprecedented opportunities for genetic improvement, especially in the prioritization of SNPs to be used in the genomic selection and editing of economically important traits. This review highlights novel breeding strategies in aquaculture, emphasizing how multi-omics data can be integrated into selective breeding programs. Specifically, we discuss the current achievements in integrating functional data into conventional genomic prediction models and highlight the potential of artificial intelligence to efficiently map genes and predict phenotypes or genetic merit using multi-omics data. Ultimately, we discuss genome editing methods for their potential to fix existing alleles, introduce alleles from wild populations or related species, and create de novo alleles, with the general goal of improving commercially important traits in aquaculture species.
水产养殖是一个快速发展的领域，在为人类提供营养丰富的食物方面发挥着重要作用。选择性育种是一种很有前景的方法，通过在理想性状上实现累积和永久的改进，来确保集约化水产养殖系统的发展和可持续性。组学技术的进步为遗传改良提供了前所未有的机会，特别是在用于经济重要性状的基因组选择和编辑的单核苷酸多态性（SNP）的优先级确定方面。本综述重点介绍了水产养殖中的新型育种策略，强调了多组学数据如何能够整合到选择性育种计划中。具体而言，我们讨论了将功能数据整合到传统基因组预测模型中的当前成果，并强调了人工智能利用多组学数据有效定位基因以及预测表型或遗传价值的潜力。最终，我们讨论基因组编辑方法，其潜力在于修复现有等位基因、引入野生种群或相关物种的等位基因以及创造全新的等位基因，总体目标是改善水产养殖物种的商业重要性状。
