在一个小小的小麦种群中进行基因组预测,可以从有关种群的培训中受益
Cathrine Kiel Skovbjerg1,2, Pernille Sarup1, Ellen Margrethe Wahlström1
1Nordic Seed A/S, Kornmarken 1, Galten 8464, Denmark.
G3 (Bethesda, Md.)
|October 22, 2025
概括
在新的大麦育种计划中,基因组预测 (GP) 的准确性在早期阶段通过外部数据得到改善. 多变量GP对于新程序或特征是最有益的,单变量模型往往优于多变量模型.
科学领域:
- 植物育种和遗传学
- 定量遗传学 是一种定量遗传学.
- 农业科学 农业科学
背景情况:
- 基因组预测 (GP) 对于遗传改进至关重要,但需要大量的培训人群.
- 新成立的育种计划往往缺乏足够的数据来提供强大的GP模型.
- 跨程序的数据组合可以提高资源不足程序的GP准确性.
研究的目的:
- 评估多种群基因组预测 (GP) 对新的6行冬季大麦 (6RW) 育种计划的好处.
- 调查训练群体大小,模型选择,特征和遗传距离如何影响多人群GP准确性.
- 确定将外部数据纳入正在开发的育种计划的最佳时间.
主要方法:
- 结合了12年来来自四个大麦育种计划的数据集.
- 专注于谷物产量,植物高度和耐性特征.
- 对多种群的GP进行单变体和多变体模型的比较,考虑遗传距离.
主要成果:
- 早期的6RW育种受益于外部种群数据,优势因种群和特征而异.
- 经过四年的数据积累,多人群GP的表现与人群内GP相匹配.
- 多变量模型在多人群GP中经常表现不佳,特别是在稀疏数据的情况下.
结论:
- 多种群GP提供了显著的优势,主要是在新的育种计划或新的育种目标的初始阶段.
- 多种群GP的有效性取决于诸如人口相关性和特征遗传性等因素.
- 对于多人群GP,建议使用单变量模型,特别是当数据有限以避免参数估计问题时.
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