综合基因组和多谱数据的混合内核可以提高小麦基因组预测准确度
Osval A Montesinos-López1, Joel A Martínez-Regalado2, Cintia Leonora Murillo-Avalos3
1Facultad de Telemática, Universidad de Colima, Colima, México.
The plant genome
|December 30, 2025
概括
在植物育种中,基因组选择 (GS) 的准确性通过结合基因组和无人机 (UAV) 衍生的现象数据来提高. 混合内核模型捕获了更多的变异,大大提高了优越基因型的预测性能.
科学领域:
- 植物育种和遗传学
- 农业科学 农业科学
- 生物信息学是一种生物信息学.
背景情况:
- 基因组选择 (GS) 提高了植物育种效率,但在预测准确性方面面临挑战.
- 样本大小和特征复杂性等因素会影响GS模型的性能.
- 整合基因组和现象数据是改善GS准确性的有希望的策略.
研究的目的:
- 开发和评估基因组选择的混合内核建模策略.
- 提高在植物育种中识别优异基因型的效率和准确性.
- 为了利用无人驾驶飞行器 (UAV) 衍生的现象数据与基因组数据一起.
主要方法:
- 通过结合基因组和现象数据构建混合内核.
- 使用的多年冬季小麦 (Triticum aestivum L.) 繁殖数据.
- 应用混合内核建模到无人机衍生的现象和基因组数据.
主要成果:
- 混合内核建模显著提高了GS预测准确度.
- 平均改善率为17.52% (皮尔森相关性) 和30.36% (正常化的RMSE).
- 在识别表现最好的线路方面取得了实质性的收益 (28.94%为前10%,16.73%为前20%).
结论:
- 通过混合内核集成基因组和UAV衍生的现象数据是有效的改善GS.
- 这种方法为现代植物育种提供了一个有价值和可扩展的工具.
- 该方法捕捉了互补变异,从而更准确地预测了基因型.
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