整合使用单核近红外光谱和基因组选择进行现象选择,以改善玉米的育种
Rafaela P Graciano1,2,3, Marco Antônio Peixoto1,3, Kristen A Leach1
1Sweet Corn and Potato Breeding and Genomics Lab, University of Florida, Gainesville, FL, 32611, USA.
概括
使用近红外光谱学 (NIRS) 完整种子的现象选择提供了一种具有成本效益的方法来改善玉米育种. 将现象和基因组数据结合起来,最大限度地提高了对增强遗传收益的预测能力.
科学领域:
- 植物育种 植物育种
- 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:
- 现象选择 (PS) 使用现象数据预测复杂的特征,补充了基因组选择 (GS).
- 传统的近红外光谱法 (NIRS) 的PS通常需要破坏性采样或选择后数据收集.
- 非破坏性,单核NIRS为PS在育种计划中提供了更有效的方法.
研究的目的:
- 探索非破坏性,单核NIRS用于PS在甜玉米育种中的应用.
- 用完整种子的现象数据来预测未观察到的,基于现场的特征.
- 评估PS模型的预测能力,并将其与GS模型进行比较.
主要方法:
- 采用了使用NIRS数据的基因组最佳线性无偏预测 (GBLUP),现象最佳线性无偏预测 (PBLUP) 和组合模型.
- 利用SNP和NIRS数据来构建用于预测模型的关系矩阵.
- 应用PS来选择芽的双倍哈普洛伊德 (DH) 线,用观察到的数据验证预测.
主要成果:
- 对于植物高度 (例如0.46) 等特征,PS表现出良好的预测能力.
- 在未经测试的DH线上,PS成功地区分了高和低的发芽率.
- 组合的基因组和现象模型实现了最高的预测能力,在低标记密度下表现优于GS.
结论:
- 非破坏性,单核NIRS是玉米育种中PS的可行工具.
- 将基因组和现象数据结合起来,最大限度地提高了预测准确性和遗传收益.
- PS为GS提供了成本效益高的替代品或补充,特别是当基因型定型有限时.
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