Advancing aquaculture breeding through genomic selection: models, tools, and challenges

Genomic selection (GS) is a powerful breeding tool that utilizes statistical models to predict breeding values for candidate populations by leveraging genotype–phenotype relationships. In contrast to marker-assisted selection, GS eliminates the need for the mapping of genes associated with traits, making it especially suitable for complex quantitative traits controlled by numerous small-effect genes. By facilitating faster breeding cycles and reducing costs, GS is poised to revolutionize aquaculture breeding programs. This paper provides a comprehensive review of the latest statistical models for GS, focusing on their application in aquaculture species. It also highlights the most widely used computational tools for implementing these models. Furthermore, it compares the genomic prediction accuracy of different models across multiple aquaculture species and provides practical guidance on model selection based on model assumptions, strengths, and suitable application scenarios. Additionally, it discusses the challenges faced by GS models and offers insights into their future prospects. The paper provides strategies and ideas for future research to advance GS in aquaculture breeding.