使用无人机检索的多谱数据用于马育种中的现象选择
Alessio Maggiorelli1, Nadia Baig1, Vanessa Prigge2
1Institute of Quantitative Genetics and Genomics of Plants (QGGP), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany.
概括
使用无人机数据的现象选择显示了土豆育种的前景,特别是在早期阶段. 将基因组和现象数据结合起来,可以提高大多数特征的预测准确度.
科学领域:
- 植物育种 植物育种
- 农业科学 农业科学
- 基因组学和现象学
背景情况:
- 马育种计划面临着大量入场和早期有限的茎的挑战.
- 预测性育种策略,如基因组选择 (GS) 和现象选择 (PS),提供了潜在的解决方案.
研究的目的:
- 评估无人机衍生的多光谱反射率,用于在各种特征的各种土豆材料中进行现象预测.
- 为了比较现象性与基因组选择的预测性能.
- 评估结合SNP和光谱数据的混合关系矩阵的实用性.
主要方法:
- 测试各种现象预测场景,使用基于无人机的多光谱数据对四形土豆板进行测试.
- 比较现象和基因组选择模型的预测能力.
- 使用混合关系矩阵集成SNP数组和多光谱反射率数据.
主要成果:
- 现象预测的准确性差异很大 (0.15到0.88),受环境,特征和场景的影响.
- 使用现象数据观察到对产量,成熟度,叶子发育和出现的高预测能力.
- 综合基因组和现象数据的混合模型改善了22个特征中的20个特征的预测,表明了互补的信息.
结论:
- 现象选择对于早期的土豆育种 (幼苗/单一山) 非常有价值,因为高通量基因型是成本高昂的.
- 无人机衍生的光谱数据可以有效地支持对特定土豆特征的现象预测.
- 整合基因组和现象数据提供了一种强大的方法,以提高马育种计划的选择收益.
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