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<p>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 <i>de novo</i> alleles, with the general goal of improving commercially important traits in aquaculture species.</p>

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Role of multi-omics in aquaculture genetics and breeding: current status and future perspective

  • 期刊论文

  • 发表日期：

    2025年5月28日

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  • Xiaofei Yu,
  • Sara Faggion,
  • Yuxiang Liu,
  • Bo Wang,
  • Qifan Zeng,
  • Chunzhe Lu,
  • Jingjie Hu,
  • Luca Bargelloni,
  • Lingzhao Fang,
  • Zhenmin Bao

摘要

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.