通过基于现场的高通量表型化与基因组数据的整合,以现象数据为驱动的玉米生物预测
Alper Adak1, Myeongjong Kang2, Steven L Anderson3
1Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA.
Journal of experimental botany
|June 6, 2023
概括
使用无人机进行基于现场的高通量表型 (HTP) 改进了对玉米特征的预测. 时间现象数据揭示了基因与环境的相互作用,有助于发展弹性作物.
科学领域:
- 植物科学 植物科学
- 遗传学 遗传学 是一个
- 农业技术 农业技术
背景情况:
- 高通量表型化 (HTP) 产生了大量数据,但发现很少.
- 使用无人机的现场HTP (FHTP) 有效地监测植物与环境的相互作用.
研究的目的:
- 使用基因组和现象数据预测植物特征.
- 发现影响植物弹性的基因环境相互作用.
主要方法:
- 在使用无人机 (UAV) 的玉米重组杂交线 (RIL) 上收集了花期和植物高度.
- 综合无人机 (UAV) 现象数据与单核酸多态 (SNP) 基因组数据进行预测.
- 通过使用时间反射现象类型进行全基因组关联研究 (GWAS).
主要成果:
- 综合现象和基因组数据显著提高了对开花时间和植物高度的预测能力.
- 通过时间现象数据分析发现了一种与热量相关的候选基因 (hsp18f).
- 揭示了玉米基因型和非生物压力之间的时间依赖关系.
结论:
- 高维的现象数据可以预测跨环境的复杂特征.
- 时间现象数据对于理解基因型对非生物压力的特定反应和开发弹性作物至关重要.
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