Predicting the performance of new plant genotypes under new environmental conditions could accelerate the development of locally adapted and climate resilient cultivars. Enabling these predictions may rely on extending the genomewide prediction framework to include environmental covariates (EC), but such models have generally been tested under limited, less breeding-realistic contexts. Using a barley (Hordeum vulgare L.) multi-environment dataset, our objectives were to compare multi-environment prediction models and scenarios that target genotypes from different breeding generations, use different levels and timescales of ECs, and are applied to different agronomic and quality traits. When predicting the phenotypes of previously tested genotypes in untested environments, models that included the interaction of genomewide markers and pre-selected in-season ECs resulted in more accurate predictions (r_MG or r_MP) within (r_MG = 0.56–0.94) and across (r_MP = 0.63–0.87) environments; similar accuracy was achieved within (r_MP = 0.46–0.89) and across (r_MP = 0.87–0.95) locations when using only ECs from realistically available historical climate data. Shifting the prediction target to a distinct, untested offspring population slightly reduced model performance within environments or locations, but r_MP across environments (r_MP = 0.60–0.86) or locations (r_MP = 0.87–0.94) remained very high. Though we achieved moderately high r_MP for most traits in the challenging scenario of predicting the offspring population in holdout environments, the similarity between training and target environments, like that between populations, will be a limiting factor for enabling accurate predictions of new genotypes under new growing conditions.

中文翻译：

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使用全基因组标记和环境协变量准确预测大麦表型

预测新的植物基因型在新的环境条件下的表现可以加速当地适应和气候适应品种的发展。启用这些预测可能依赖于扩展全基因组预测框架以包括环境协变量 (EC)，但此类模型通常已在有限的、不太现实的育种环境下进行了测试。使用大麦（Hordeum vulgareL.) 多环境数据集，我们的目标是比较多环境预测模型和场景，这些模型和场景针对不同育种世代的基因型，使用不同水平和时间尺度的 EC，并应用于不同的农艺和质量性状。在未经测试的环境中预测先前测试的基因型的表型时，包括全基因组标记和预选的季节性 ECs 相互作用的_模型在( r MG ₌  0.56–0.94 )和跨 ( r _MP  = 0.63–0.87) 环境；在 ( r _MP  = 0.46–0.89) 和跨 (r _MP  = 0.87–0.95) 仅使用来自现实可用历史气候数据的 EC 的位置。将预测目标转移到一个不同的、未经测试的后代群体会略微降低环境或位置内的模型性能，但跨环境 ( r _MP = 0.60–0.86) 或位置 ( r _{MP  = 0.87–0.94) 的}r _MP 仍然非常高。虽然我们达到了中等高的r _MP对于在保留环境中预测后代种群这一具有挑战性的场景中的大多数性状，训练环境和目标环境之间的相似性（如种群之间的相似性）将成为在新的生长条件下准确预测新基因型的限制因素。