优化培训套件,以识别混合种群中的优异基因型
Szu-Ping Chen1, Chen-Tuo Liao1
1Department of Agronomy, National Taiwan University, Taipei, Taiwan.
Frontiers in plant science
|February 2, 2026
概括
这项研究优化了混合繁殖中基因组选择 (GS) 的训练集. GV_average方法是有效的,但在小集中可能缺乏多样性,其中CD_mean(v2) 是准确预测的可靠替代方案.
科学领域:
- 植物育种和遗传学 植物育种和遗传学
- 基因组选择 (GS)
- 定量遗传学 是一种定量遗传学.
背景情况:
- 识别优质杂交物对于商业植物育种和大规模种植至关重要.
- 基因组选择 (GS) 使用标记数据来预测繁殖值,但训练集优化是准确性的关键.
- 现有的训练集选择方法需要对杂交种群进行评估.
研究的目的:
- 评估和扩展训练集优化方法,用于构建混合种群中的预测模型.
- 评估不同方法在识别具有高真实繁殖值 (TBV) 的表现最佳的基因型方面的表现.
- 提供灵活的框架,以优化实际育种计划中的培训套件.
主要方法:
- 研究了三个训练集优化方法:MSPE_Ridge (v2),CD_mean (v2) 和GV_average.
- 开发了三个评估指标来评估用于识别具有最高TBVs的基因型的预测性表现.
- 使用小麦,玉米和大米的真实基因型数据进行模拟实验.
主要成果:
- GV_average展示了高计算效率,并生成了各种尺寸的信息培训集.
- 在小型培训集中,GV_average偶尔会损害基因组多样性.
- CD_mean(v2) 证明在小型培训集中保持基因组多样性的更可靠的替代方案.
结论:
- 在基因组选择中,GV_average方法为训练集优化提供了计算优势.
- 当基因组多样性至关重要时,特别是在较小的训练集中,建议使用CD_mean{v2}.
- 拟议的框架提高了杂交育种计划中基因组预测的准确性.
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