多环境田间测试是选育高产稳产品种的重要途径, 但因其成本高逐渐成为植物育种的瓶颈问题。将稀疏测试与全基因组预测方法相结合可实现对未测表型的预测, 进而减少田间测试的工作量和成本。利用244份玉米(Zea mays)自然群体自交系在两年(2022年和2023年)两点(北京顺义和黑龙江密山) 4个环境下, 针对散粉期、株高、穗位高、穗长、穗行数和行粒数6个代表性农艺性状开展研究, 比较了4种模型(Single、Across、M×E和R-norm)、2种训练群体组成方案(CV1和CV2)以及3种训练集抽样比例(0.5、0.7和0.9)对预测精度的影响。结果表明,上述6个农艺性状的平均预测精度分别为0.67、0.58、0.50、0.33、0.33和0.48; Single模型、Across模型、M×E模型和R-norm模型的平均预测精度分别为0.36、0.52、0.53和0.53; 其中CV1各模型在不同性状中的预测精度范围在0.19-0.65之间, CV2预测精度范围在0.47-0.89之间; 不同抽样比例比较显示, 不同模型中训练集比例的提升对6个性状的预测精度提升有限, 最大提升幅度仅为0.05。综上表明, 在进行多环境预测时, 利用CV2训练群体组成方案并在预测模型中纳入多个环境下的表型数据可提升预测精度。

Multi-environment field testing is an important way to select optimize maize yield and yield stability varieties. However, because of its high cost, it has gradually become a challenge in plant breeding. The combination of field sparse testing and genome-wide prediction method can be used to predict untested phenotypes, reduced the effort and cost on field testing. In this experiment, 244 inbred lines of natural populations were planted in Shunyi, Beijing and Mishan, Heilongjiang in 2022 and 2023. Six agronomic traits were studied, including days to anthesis, plant height, ear height, ear length, kernel number per row and ear row number. The effects of four different models (Single, Across, M×E and R-norm), two different cross-validation schemes (CV1 and CV2) and three different training sets sampling ratios (0.5, 0.7 and 0.9) on the prediction accuracy were compared. The results showed that the average prediction accuracy of the six agronomic traits was 0.67, 0.58, 0.50, 0.33, 0.33 and 0.48. The average prediction accuracy of the Single model, Across model, M×E model and R-norm model was 0.36, 0.52, 0.53 and 0.53 for each trait. In CV1, the average prediction accuracy of each model in six traits ranged from 0.19 to 0.65, and in CV2, the average prediction accuracy ranged from 0.47 to 0.89. The comparison of different training set sampling ratios shows that the improvement of the proportion of the training sets has limited improvement in the prediction accuracy of different traits in different models, and the maximum is only 0.05. The results show that the CV2 training set can be used to form a scheme and include phenotypic data from multiple environments in the prediction model to provide good prediction accuracy for multi-environment prediction.