Genotype × environment interaction (GEI) is one of the key factors affecting breeding value estimation accuracy for agronomic traits in plant breeding. Measures of GEI include fitting prediction models with various kernels to capture the variance resulting from GEI, and characterizing trials into megaenvironment (ME) clusters within which breeding values can be estimated to remove the main GEI effects. However, many of the current approaches require observations of common genotypes across all trials, which is unavailable in most breeding programs. Our study introduces two methods that can be implemented on unbalanced data to categorize trials into clusters, where both need a correlation matrix between trials: one estimated via a factor analytic (FA) model and another estimated via weather variables. The methods were tested using empirical barley (Hordeum vulgare L.) yield data in a commercial breeding program from 102 trials over 5 yr spread across multiple locations in Australia. Leave-one-year-out cross-validation achieved comparable predictive accuracies using either trials or clusters as the observed variable in GEI FA models (max. 0.45), which was higher than the accuracy achieved using the non-GEI model (0.37). In the random cross-validations, accuracies achieved within clusters (0.42–0.64) were mostly comparable with those achieved in the full population (0.62). In the within-cluster validations, higher predictive accuracies were achieved when the training population was from the same cluster (mean 0.22) than outside of the cluster (mean 0.16). Our proposed methods of characterizing multienvironment trials into clusters provides a novel way to define training populations by reducing the variance resulting from GEI and could be implemented in any plant breeding program.

中文翻译：

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使用基因型×环境相互作用簇对大麦育种计划中谷物产量的基因组预测

基因型×环境互作（GEI）是影响植物育种农艺性状育种值估计精度的关键因素之一。GEI 的衡量标准包括使用各种内核拟合预测模型以捕获 GEI 产生的方差，并将试验表征为超级环境 (ME) 集群，其中可以估计育种值以消除主要的 GEI 影响。然而，当前的许多方法需要在所有试验中观察共同基因型，这在大多数育种计划中是不可用的。我们的研究介绍了两种可以在不平衡数据上实施的方法，将试验分类到集群中，这两种方法都需要试验之间的相关矩阵：一种通过因子分析（FA）模型估计，另一种通过天气变量估计。这些方法使用经验大麦（大麦L.) 商业育种计划中的产量数据来自 5 年内分布在澳大利亚多个地点的 102 项试验。使用试验或集群作为 GEI FA 模型中的观察变量（最大值 0.45），留出一年的交叉验证实现了可比的预测准确度（最高 0.45），高于使用非 GEI 模型（0.37）实现的准确度。在随机交叉验证中，集群内实现的准确度 (0.42-0.64) 与在整个群体中实现的准确度 (0.62) 大致相当。在集群内验证中，当训练群体来自同一集群时（平均 0.22）比集群外（平均 0.16）获得更高的预测准确度。