GSA-GxE: A Framework of Global Sensitivity Analysis of Maize Coupled with Genetics by Environments (GxE) Model

We present the coupled Global Sensitivity Analysis (GSA) and Genetics by Environment model (GxE) framework (Sarzaeim and Muñoz-Arriola, submitted). GSA-GxE uses the sensitivity analysis method PAWN (Pianosi and Wagener, 2015) coupled with the environmental covariance matrix used in GxE modeling (Jarquin et al., 2014). GSA-GxE estimates the relative sensitivity of maize yield predictability to hydroclimate variables that interact with maize genetics from the environmental covariances and genetic marker structures. We include hydroclimate variables like temperature (T), solar radiation (SR), rainfall (R), and relative humidity (RH). The data, codes, and scripts presented here were used to develop and test the GSA-GxE framework. They were built upon an enhanced version of the multi-dimensional Genomes to Fields (G2F) database consisting of maize genetic, phenotypic, environmental, and metadata in 84 field experiments in 2014-2017 across the U.S. and province of Ontario, Canada (Sarzaeim et al., 2020, 2022, 2023). This digital package contains a multi-dimensional Climate and Omics dataset, the GSA-GxE framework created in Python, and the GxE model developed in R. Acknowledgement This work was supported by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database; and Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln.本研究提出耦合全局敏感性分析（Global Sensitivity Analysis, GSA）与环境互作遗传模型（Genetics by Environment, GxE）的分析框架（Sarzaeim与Muñoz-Arriola, 已投稿）。 GSA-GxE框架采用敏感性分析方法PAWN（Pianosi与Wagener, 2015），并结合了GxE建模中使用的环境协方差矩阵（Jarquin等人, 2014）。 GSA-GxE可基于环境协方差与遗传标记结构，量化玉米产量可预测性对与玉米遗传互作的水文气候变量的相对敏感性。 本研究纳入的水文气候变量包括气温（T）、太阳辐射（SR）、降雨量（R）与相对湿度（RH）。 本研究提供的数据、代码与脚本均用于开发与验证GSA-GxE框架，其基于升级后的多维基因组到田间（Genomes to Fields, G2F）数据库构建。该数据库涵盖2014-2017年美国及加拿大安大略省境内84项田间试验的玉米遗传、表型、环境数据与元数据（Sarzaeim等人, 2020、2022、2023）。 本数字化数据包包含多维气候与组学数据集、基于Python编写的GSA-GxE框架，以及基于R语言开发的GxE模型。 致谢 本研究得到美国农业部（United States Department of Agriculture, USDA）国家食品与农业研究院下设“植物健康与生产及植物产品：农业生产用植物育种”项目的农业与食品研究倡议基金（项目编号：NEB-21-176与NEB-21-166）资助。 此外，本研究感谢基因组到田间（G2F）联盟提供数据库，以及内布拉斯加大学林肯分校量化生命科学倡议团队的支持。