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
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:
WANG Xin1,2(), XU Yi-yi1, XU Yang1, XU Chen-wu1()
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。
王欣, 徐一亿, 徐扬, 徐辰武. 作物全基因组选择育种技术研究进展[J]. 生物技术通报, 2024, 40(3): 1-13.
WANG Xin, XU Yi-yi, XU Yang, XU Chen-wu. Research Progress in Genomic Selection Breeding Technology for Crops[J]. Biotechnology Bulletin, 2024, 40(3): 1-13.
影响因素 Affecting factors | 优化策略 Optimizating strategies | |
---|---|---|
遗传因素 | 目标性状遗传力 | 增加训练样本田间试验的重复次数;增加优选群体的数目;多性状或多组学预测 |
训练群体和育种群体间的关系 | 科学开展遗传交配设计;优化训练样本的选择 | |
标记密度 | 开发GS专用芯片;选用全基因组上适当密度的代表性标记 | |
标记和QTL间连锁不平衡的程度 | 候选基因遴选;单倍型划分 | |
非遗传因素 | 训练样本数量 | 增加训练样本数量;多环境联合预测 |
模型和算法的选择 | 参考模型和算法的比较研究成果;考虑性状的遗传结构;训练集内的交叉验证 | |
参数的选择 | 基于多组数据集,进行网格搜索、随机搜索或人工调参,优化参数组合 | |
数据的清洗方案 | 数据的标准化或归一化等预处理;单倍型划分;主成分分析;因子分析;聚类分析 |
表1 影响全基因组选择功效的因素及其优化策略
Table 1 Factors affecting GS efficacy and corresponding optimizating strategies
影响因素 Affecting factors | 优化策略 Optimizating strategies | |
---|---|---|
遗传因素 | 目标性状遗传力 | 增加训练样本田间试验的重复次数;增加优选群体的数目;多性状或多组学预测 |
训练群体和育种群体间的关系 | 科学开展遗传交配设计;优化训练样本的选择 | |
标记密度 | 开发GS专用芯片;选用全基因组上适当密度的代表性标记 | |
标记和QTL间连锁不平衡的程度 | 候选基因遴选;单倍型划分 | |
非遗传因素 | 训练样本数量 | 增加训练样本数量;多环境联合预测 |
模型和算法的选择 | 参考模型和算法的比较研究成果;考虑性状的遗传结构;训练集内的交叉验证 | |
参数的选择 | 基于多组数据集,进行网格搜索、随机搜索或人工调参,优化参数组合 | |
数据的清洗方案 | 数据的标准化或归一化等预处理;单倍型划分;主成分分析;因子分析;聚类分析 |
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