回归方法用于建模基因型与环境的相互作用,并对未见的环境进行预测
Maksym Hrachov1, Hans-Peter Piepho2, Niaz Md Farhat Rahman3
1Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany.
概括
环境共变量改善了植物育种预测. 这项研究统一了回归方法,用于在新环境中提高预测准确性和不确定性估计,这对于品种测试至关重要.
科学领域:
- 农业科学 农业科学
- 生物识别信息 生物识别信息
- 遗传学 是一个遗传学.
背景情况:
- 植物育种越来越多地使用环境数据来在新环境中做出更好的预测.
- 像芬莱-威尔金森回归这样的传统方法是基础的,但也有局限性.
- 基因型与环境的相互作用是品种测试中的一个关键挑战.
研究的目的:
- 审查和统一各种线性混合模型用于植物育种中的预测.
- 为了证明看似不同的回归方法之间的密切关系.
- 提出一种新的方法,用于在新的环境中估计预测不确定性.
主要方法:
- 对线性混合模型的审查,包括因数回归,芬莱-威尔金森回归,减少等级回归和基于内核/亲属关系的方法.
- 开发一个共同的基于模型的预测框架.
- 应用交叉验证和基于新型模型的方法来估计不确定性.
主要成果:
- 环境共变量显著提高了预测的准确性.
- 几种不同的回归方法被证明在统一的框架内是密切相关的.
- 一种新的方法改善了预测方差的估计.
结论:
- 一个统一的基于模型的预测框架简化和增强了基因型-环境相互作用分析.
- 提出的方法提高了植物品种测试的预测准确性和不确定性评估.
- 这些发现用一种大米品种试验数据集来说明,证明了其实际适用性.
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