生物技术通报 ›› 2024, Vol. 40 ›› Issue (3): 1-13.doi: 10.13560/j.cnki.biotech.bull.1985.2023-1079

• 特约综述 •    下一篇

作物全基因组选择育种技术研究进展

王欣1,2(), 徐一亿1, 徐扬1, 徐辰武1()   

  1. 1.扬州大学农学院,扬州 225009
    2.扬州大学信息工程学院,扬州 225009
  • 收稿日期:2023-11-17 出版日期:2024-03-26 发布日期:2024-04-08
  • 通讯作者: 徐辰武,男,博士,教授,研究方向:作物数量遗传;E-mail: cwxu@yzu.edu.cn
  • 作者简介:王欣,男,博士,副教授,研究方向:全基因组选择;E-mail: seuwangxin@163.com
  • 基金资助:
    国家重点研发计划项目(2022YFD1201804);江苏省种业振兴揭榜挂帅项目(JBGS[2021]009);江苏省重点研发计划项目(BE2022343)

Research Progress in Genomic Selection Breeding Technology for Crops

WANG Xin1,2(), XU Yi-yi1, XU Yang1, XU Chen-wu1()   

  1. 1. Agricultural College, Yangzhou University, Yangzhou 225009
    2. College of Information Engineering, Yangzhou University, Yangzhou 225009
  • Received:2023-11-17 Published:2024-03-26 Online:2024-04-08

摘要:

全基因组选择(GS)育种是根据训练群体全基因组上的分子标记基因型和表型之间的关联构建遗传模型,进而对基因型已知的待选群体进行育种值估计或表型预测,以实现对育种群体高效和精确的选择。相比于常用的分子标记辅助选择育种,GS育种无需进行标记显著性测验,特别适用于微效多基因控制的数量性状,可以缩短育种周期,降低育种成本,现已成为动、植物育种领域的一项前沿技术。然而,对受环境影响较大的作物产量等数量性状而言,仍面临着基因组预测准确性难以提升的瓶颈问题。本文首先分析了影响作物GS功效的主要因素,继而从非加性效应模型、群体构建方案、多性状与多环境预测、多组学预测和育种芯片技术现状等方面阐述了GS技术在作物育种中的研究进展,并指出研究所面临的问题和发展前景,为推动作物GS育种技术的进一步深入研究提供策略和思路。

关键词: 作物, 全基因组选择, 全基因组预测模型, 育种

Abstract:

Genome selection(GS)breeding builds a genetic model based on the association between genotypes of molecular markers on the whole genome and phenotypes of the training population, and then estimates the breeding values or predicts the phenotypes of the candidate population with known genotypes, so as to achieve efficient and accurate selection of the population for breeding. Compared with the commonly used molecular marker-assisted selection breeding, GS breeding does not require marker significance testing, and is particularly suitable for quantitative traits controlled by minor polygenes. It can shorten breeding cycle and reduce breeding cost, and has become a cutting-edge technology in the field of animal and plant breeding. However, for quantitative traits such as crop yield that are greatly affected by environment, it is still bottleneck issue to improve the accuracy of genomic prediction. This article first analyzes the main factors that affect the efficacy of GS in crop breeding, and then elaborates on the research progress of GS technology in crop breeding from the aspects of models with non-additive effects, population construction schemes, multi-trait and multi-environment prediction, multi-omic prediction and the current status of breeding chip technology. Then the article points out the issues and development prospects of the research, and provides the strategies and ideas for further research on crop GS breeding technology.

Key words: crop, genomic selection, genomic prediction model, breeding