Table_1_Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions.docx

Plant breeders evaluate their selection candidates in multi-environment trials to estimate their performance in contrasted environments. The number of genotype/environment combinations that can be evaluated is strongly constrained by phenotyping costs and by the necessity to limit the evaluation to a few years. Genomic prediction models taking the genotype by environment interactions (GEI) into account can help breeders identify combination of (possibly unphenotyped) genotypes and target environments optimizing the traits under selection. We propose a new prediction approach in which a secondary trait available on both the calibration and the test sets is introduced as an environment specific covariate in the prediction model (trait-assisted prediction, TAP). The originality of this approach is that the phenotyping of the test set for the secondary trait is replaced by crop-growth model (CGM) predictions. So there is no need to sow and phenotype the test set in each environment which is a clear advantage over the classical trait-assisted prediction models. The interest of this approach, called CGM-TAP, is highest if the secondary trait is easy to predict with CGM and strongly related to the target trait in each environment (and thus capturing GEI). We tested CGM-TAP on bread wheat with heading date as secondary trait and grain yield as target trait. Simple CGM-TAP model with a linear effect of heading date resulted in high predictive abilities in three prediction scenarios (sparse testing, or prediction of new genotypes or of new environments). It increased predictive abilities of all reference GEI models, even those involving sophisticated environmental covariates.

植物育种家通常在多环境试验（multi-environment trials）中对候选选育材料开展表型评价，以评估其在各异环境下的表现。但可开展评价的基因型/环境组合数量，会受到表型鉴定成本与评价周期受限（仅能在有限年份内开展试验）的双重制约。考虑到基因型×环境互作（genotype by environment interactions, GEI）的基因组预测模型，能够辅助育种家筛选（即便未完成表型鉴定的）基因型与目标环境的组合，从而优化目标性状的选育工作。
我们提出了一种全新的预测方法：在校正集与测试集的预测模型中，引入一项在两类集合中均可获取的次级性状作为环境专属协变量，即性状辅助预测（trait-assisted prediction, TAP）。该方法的创新点在于，无需针对测试集在各环境中开展次级性状的表型鉴定，而是通过作物生长模型（crop-growth model, CGM）的预测结果替代该表型数据；相较于传统性状辅助预测模型，这一优势十分显著。
若次级性状可通过CGM便捷预测，且在各环境下与目标性状高度相关（进而能够捕捉基因型×环境互作信号），则该被命名为CGM-TAP的方法的应用价值将达到峰值。我们以面包小麦为研究材料，以抽穗期作为次级性状、籽粒产量作为目标性状，对CGM-TAP进行了实证测试。仅引入抽穗期线性效应的简单CGM-TAP模型，在三种预测场景（稀疏测试、新基因型预测或新环境预测）中均展现出了优异的预测能力，且其预测性能优于所有参考基因型×环境互作模型，即便那些引入了复杂环境协变量的参考模型亦是如此。