Genomic selection (GS) is a statistical and breeding methodology designed to improve genetic gain. It has proven to be successful in animal breeding; however, key points of difference have not been fully considered in the transfer of GS from animal to plant breeding. In plant breeding, individuals (varieties) are typically evaluated across a number of locations in multiple years (environments) in formally designed comparative experiments, called multi-environment trials (METs). The design structure of individual trials can be complex and needs to be modelled appropriately. Another key feature of MET data sets is the presence of variety by environment interaction (VEI), that is the differential response of varieties to a change in environment. In this paper, a single-step factor analytic linear mixed model is developed for plant breeding MET data sets that incorporates molecular marker data, appropriately accommodates non-genetic sources of variation within trials and models VEI. A recently developed set of selection tools, which are natural derivatives of factor analytic models, are used to facilitate GS for a motivating data set from an Australian plant breeding company. The power and versatility of these tools is demonstrated for the variety by environment and marker by environment effects.

中文翻译：

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使用因子分析线性混合模型的多环境植物育种试验中的基因组选择

基因组选择 (GS) 是一种统计和育种方法，旨在提高遗传增益。它已被证明在动物育种方面是成功的；然而，在将GS从动物育种转移到植物育种过程中，尚未充分考虑差异的关键点。在植物育种中，通常在正式设计的比较实验（称为多环境试验 (MET)）中，在多年（环境）的多个地点对个体（品种）进行评估。个别试验的设计结构可能很复杂，需要适当建模。MET 数据集的另一个关键特征是环境相互作用（VEI）的多样性，即品种对环境变化的不同反应。在本文中，为植物育种 MET 数据集开发了一个单步因子分析线性混合模型，该模型结合了分子标记数据，适当地适应了试验和模型 VEI 中的非遗传​​变异来源。最近开发的一组选择工具是因子分析模型的自然衍生物，用于促进 GS 对来自澳大利亚植物育种公司的激励数据集。这些工具的强大功能和多功能性通过环境和环境影响标记的多样性得到了证明。用于促进 GS 获得来自澳大利亚植物育种公司的激励数据集。这些工具的强大功能和多功能性通过环境和环境影响标记的多样性得到了证明。用于促进来自澳大利亚植物育种公司的激励数据集的 GS。这些工具的强大功能和多功能性通过环境和环境影响标记的多样性得到了证明。