As soybean cultivars are adapted to a relatively narrow range of latitude, the effects of climate changes are estimated to be severe. To address this issue, it is important to improve our understanding of the effects of climate change by applying the simulation model including both genetic and environmental factors with their interactions (G×E). To achieve this goal, we conducted the field experiments for soybean core collections using multiple sowing times in multi-latitudinal fields. Sowing time shifts altered the flowering time (FT) and growth phenotypes, and resulted in increasing the combinations of genotypes and environments. Genome-wide association studies for the obtained phenotypes revealed the effects of field and sowing time to the significance of detected alleles, indicating the presence of G×E. By using accumulated phenotypic and environmental data in 2018 and 2019, we constructed multiple regression models for FT and growth pattern. Applicability of the constructed models was evaluated by the field experiments in 2020 including a novel field, and high correlation between the predicted and measured values was observed, suggesting the robustness of the models. The models presented here would allow us to predict the phenotype of the core collections in a given environment.

中文翻译：

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基于不同基因型和环境组合表型的大豆品种生长性状预测模型的构建[J].

由于大豆品种适应相对较窄的纬度范围，估计气候变化的影响是严重的。为了解决这个问题，重要的是通过应用包括遗传和环境因素及其相互作用（G×E）的模拟模型来提高我们对气候变化影响的理解。为了实现这一目标，我们在多纬度田间使用多个播种时间对大豆核心种质进行了田间试验。播种时间变化改变了开花时间 (FT) 和生长表型，并导致基因型和环境的组合增加。获得的表型的全基因组关联研究揭示了田间和播种时间对检测到的等位基因重要性的影响，表明存在 G×E。通过使用 2018 年和 2019 年积累的表型和环境数据，我们构建了 FT 和增长模式的多元回归模型。所构建模型的适用性通过 2020 年包括一个新领域的现场实验进行了评估，并观察到预测值和测量值之间的高度相关性，表明模型的稳健性。这里介绍的模型将使我们能够预测给定环境中核心集合的表型。