缺少的环境变量的统计抽样改善了小麦的生物物理基因组预测
Abdulqader Jighly1,2, Thabo Thayalakumaran3, Surya Kant4,5
1AgriBio, Centre for AgriBiosciences, Agriculture Victoria, Bundoora, VIC, 3083, Australia. a.jighly@sustatability.com.
概括
将基因组预测与作物生长模型 (CGM-WGP) 整合起来,可以通过估计缺少的环境数据来改善谷物产量预测. 这种方法提高了对历史作物育种种群的全基因组预测 (WGP) 的准确性.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 植物育种 植物育种
背景情况:
- 全基因组预测 (WGP) 通过利用广泛的参考种群来推进作物育种.
- 作物生长模型 (CGMs) 需要详细的环境数据,限制其应用在缺乏此类输入的历史WGP数据上.
- CGM-WGP模型整合了CGM和WGP,但由于环境记录不完整而面临准确性挑战.
研究的目的:
- 调查在CGM-WGP框架内近似缺失的环境变量的有效性.
- 通过使用历史的育种数据,提高作物特征的预测准确度,特别是谷物产量.
- 评估归纳特定环境变量的对模型性能的影响.
主要方法:
- 开发并将CGM-WGP算法应用于小麦数据.
- 调查了土壤初始含水量 (InitlSoilWCont) 和初始酸盐概况的归算.
- 使用采样与实际环境变量进行比较的预测准确度.
主要成果:
- 单独采样土壤初始含水量 (InitlSoilWCont) 显著提高了对谷粒数 (0.07),产量 (0.06) 和蛋白质含量 (0.03) 的预测准确度.
- 输入InitlSoilWCont将基因型特定参数的平均狭义遗传性增加了0.05.
- 谷物数量和产量的根平均平方误差在CGM下降了7%,在CGM-WGP下降了31%,使用采样InitlSoilWCont.
结论:
- 接近缺失的环境变量,特别是土壤初始含水量,是提高CGM-WGP准确性的可行策略.
- 这种方法扩大了WGP的实用性,允许包含具有不完整环境记录的历史数据集.
- 这些发现表明,在基因组预测模型中利用环境数据的归算方法具有显著的优势.
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