标题： 一种在混合模型中构建环境矩阵的框架 显示英文标题

标题： A Framework for Building Enviromics Matrices in Mixed Models

摘要： 本研究介绍了一个构建环境组学矩阵的框架，用于混合模型中整合遗传和环境数据，以提高植物育种中的表型预测。 环境组学利用多种数据源，如气候和土壤，来表征基因型-环境（GxE）互作。 该方法在设计矩阵中采用块对角结构，以纳入不同试验中来自遗传和环境表型协变量的随机效应。 协方差结构通过遗传关系矩阵和表示环境表型效应的身份矩阵的Kronecker乘积进行建模，捕捉遗传和环境变异。 这种双重表示方法能够在不同环境中更准确地预测作物表现，改进育种计划中的选择策略。 该框架与现有的混合模型软件（包括rrBLUP和BGLR）兼容，并可扩展用于更复杂的互作。 通过结合遗传关系和环境影响，这种方法为推进GxE研究和加速改良作物品种的开发提供了一个强大的工具。 显示英文摘要

摘要： This study introduces a framework for constructing enviromics matrices in mixed models to integrate genetic and environmental data to enhance phenotypic predictions in plant breeding. Enviromics utilizes diverse data sources, such as climate and soil, to characterize genotype-by-environment (GxE) interactions. The approach employs block-diagonal structures in the design matrix to incorporate random effects from genetic and envirotypic covariates across trials. The covariance structure is modeled using the Kronecker product of the genetic relationship matrix and an identity matrix representing envirotypic effects, capturing genetic and environmental variability. This dual representation enables more accurate crop performance predictions across environments, improving selection strategies in breeding programs. The framework is compatible with existing mixed model software, including rrBLUP and BGLR, and can be extended for more complex interactions. By combining genetic relationships and environmental influences, this approach offers a powerful tool for advancing GxE studies and accelerating the development of improved crop varieties.