整合作物模型,单核酸多态和气候指数来开发基因型与环境相互作用模型:关于大米开花时间的案例研究
Jinhan Zhang1, Shaoyuan Zhang1, Yubin Yang2
1National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Ministry of Education, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China.
基因型与环境相互作用模型通过将基因型特异性参数与生长模型联系起来来预测作物表型. 机器学习提高了预测,帮助数字育种和大米中的分子特征选择.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 计算生物学 计算生物学
背景情况:
- 基因型与环境相互作用 (G × E) 模型对于数字育种和预测作物表型至关重要.
- 基因型特异性参数 (GSP) 可以弥合作物生长模型和G × E相互作用,模拟植物发育.
研究的目的:
- 整合米生长模型,SNP和气候数据来预测开花时间.
- 使用GWAS研究GSP和定量性质核酸 (QTNs) 之间的关联.
- 评估基于SNP的GSP和机器学习对预测准确性的影响.
主要方法:
- 使用了169种大米基因型的数据集,其中包括700K个SNP标记和多环境开花数据.
- 整合了三种米生长模型 (ORYZA,CERES-Rice,RiceGrow) 与SNP和气候指数.
- 采用全基因组关联研究 (GWAS) 来将GSP与QTN和机器学习 (ML) 联系起来,以改进预测.
主要成果:
- 确定了GSP与已知的水开花基因 (例如DTH2,DTH3,OsCOL15) 之间的显著关联.
- 在米模型中,基于SNP的GSP导致与传统校准相比,适合性降低 (RMSE增加).
- 经过ML修改的预测和多模型合集的准确性与传统方法相提并论.
结论:
- 全国优惠制度提供了遗传解释性和在大米中数字育种应用的潜力.
- 将作物模型与ML和气候数据相结合,可以改善表型预测.
- 这些发现支持分子育种策略的进步和在大米中准确的表型预测.
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