通过基因组学,现象学和环境数据增强冬季小麦预测
Osval A Montesinos-López1, Andrew W Herr2, José Crossa3,4
1Facultad de Telemática, Universidad de Colima, Colima, 28040, México.
BMC genomics
|May 31, 2024
概括
整合环境数据显著提高了小麦育种中的基因组选择精度. 这种方法可以改善各种环境的预测,提高植物育种计划的整体选择效率.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 遗传学 遗传学 是一个
背景情况:
- 在多环境预测中,基因组选择 (GS) 的效率是有限的,因为基因型因环境相互作用而产生.
- 提高预测准确性需要整合各种数据类型,如现象学,环境和omics数据.
研究的目的:
- 评估将环境信息纳入预测模型的影响.
- 评估在多种环境中对软白冬季小麦的预测准确度的提高.
主要方法:
- 使用了五个软白色冬季小麦数据集.
- 将基因组,现象和环境数据集成到预测模型中.
- 使用规范化根平均平方误差 (NRMSE) 测量预测准确度.
主要成果:
- 纳入环境信息显著提高了预测准确性.
- 在NRMSE的数据集中,预测准确度的平均增长为49.19%.
- 根据数据集,预测准确度的增长范围从5.68%到60.36%不等.
结论:
- 环境数据集成在多环境试验中大大提高了预测准确度.
- 植物育种计划可以通过结合基因组,现象和环境数据来提高选择效率.
- 这种综合方法对于在不同农业地区进行有效的繁殖至关重要.
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