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Revealing GE Interactions from Trial Data without Replications

机译:在没有复制的情况下从试验数据显示GE交互

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摘要

Detecting genotype-by-environment (GE) interaction effects or yield stability is one of the most important components for crop trial data analysis, especially in historical crop trial data. However, it is statistically challenging to discover the GE interaction effects because many published data were just entry means under each environment rather than repeated field plot data. In this study, we propose a new methodology, which can be used to impute replicated trial data sets to reveal GE interactions from the original data. As a demonstration, we used a data set, which includes 28 potato genotypes and six environments with three replications to numerically evaluate the properties of this new imputation method. We compared the phenotypic means and predicted random effects from the imputed data with the results from the original data. The results from the imputed data were highly consistent with those from the original data set, indicating that imputed data from the method we proposed in this study can be used to reveal information including GE interaction effects harbored in the original data. Therefore, this study could pave a way to detect the GE interactions and other related information from historical crop trial reports when replications were not available.
机译:检测基因型-环境(GE)相互作用效应或产量稳定性是作物试验数据分析(尤其是历史作物试验数据)中最重要的组成部分之一。但是,发现GE相互作用的影响在统计学上具有挑战性,因为许多公开的数据只是每种环境下的输入手段,而不是重复的田地图数据。在这项研究中,我们提出了一种新方法,可用于估算重复的试验数据集,以揭示原始数据中的GE相互作用。作为演示,我们使用了一个数据集,该数据集包含28种马铃薯基因型和六个环境,其中三个重复进行了模拟,以数值方式评估了这种新插补方法的特性。我们将表型平均值和推算数据的预测随机效应与原始数据的结果进行了比较。推算数据的结果与原始数据集高度一致,这表明我们在本研究中提出的方法的推算数据可用于揭示原始数据中包含的GE相互作用效应等信息。因此,这项研究可为无法获得复制品的历史作物试验报告中的GE相互作用和其他相关信息铺平道路。

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