Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany.
Theor Appl Genet. 2024 Jul 10;137(8):181. doi: 10.1007/s00122-024-04684-z.
We investigate a method of extracting and fitting synthetic environmental covariates and pedigree information in multilocation trial data analysis to predict genotype performances in untested locations. Plant breeding trials are usually conducted across multiple testing locations to predict genotype performances in the targeted population of environments. The predictive accuracy can be increased by the use of adequate statistical models. We compared linear mixed models with and without synthetic covariates (SCs) and pedigree information under the identity, the diagonal and the factor-analytic variance-covariance structures of the genotype-by-location interactions. A comparison was made to evaluate the accuracy of different models in predicting genotype performances in untested locations using the mean squared error of predicted differences (MSEPD) and the Spearman rank correlation between predicted and adjusted means. A multi-environmental trial (MET) dataset evaluated for yield performance in the dry lowland sorghum (Sorghum bicolor (L.) Moench) breeding program of Ethiopia was used. For validating our models, we followed a leave-one-location-out cross-validation strategy. A total of 65 environmental covariates (ECs) obtained from the sorghum test locations were considered. The SCs were extracted from the ECs using multivariate partial least squares analysis and subsequently fitted in the linear mixed model. Then, the model was extended accounting for pedigree information. According to the MSEPD, models accounting for SC improve predictive accuracy of genotype performances in the three of the variance-covariance structures compared to others without SC. The rank correlation was also higher for the model with the SC. When the SC was fitted, the rank correlation was 0.58 for the factor analytic, 0.51 for the diagonal and 0.46 for the identity variance-covariance structures. Our approach indicates improvement in predictive accuracy with SC in the context of genotype-by-location interactions of a sorghum breeding in Ethiopia.
我们研究了一种在多地点试验数据分析中提取和拟合综合环境协变量和系谱信息的方法，以预测未测试地点的基因型表现。植物育种试验通常在多个测试地点进行，以预测目标环境中的基因型表现。通过使用适当的统计模型可以提高预测准确性。我们比较了线性混合模型，以及在基因型-地点互作的恒等、对角和因子分析方差-协方差结构下，有无综合协变量（SCs）和系谱信息的模型。通过预测差异的均方误差（MSEPD）和预测均值与调整均值之间的斯皮尔曼秩相关来比较不同模型在预测未测试地点基因型表现的准确性。使用埃塞俄比亚高粱（ Sorghum bicolor (L.) Moench）育种计划的产量表现评估的多环境试验（MET）数据集进行了比较。为了验证我们的模型，我们采用了一种留一位置交叉验证策略。共考虑了来自高粱测试地点的 65 个环境协变量（ECs）。使用多元偏最小二乘法分析从 ECs 中提取 SCs，然后将其拟合到线性混合模型中。然后，扩展模型以考虑系谱信息。根据 MSEPD，在三种方差-协方差结构中，考虑 SC 的模型比不考虑 SC 的模型提高了基因型表现的预测准确性。有 SC 的模型的秩相关度也更高。当拟合 SC 时，因子分析的秩相关度为 0.58，对角的为 0.51，恒等的为 0.46。我们的方法表明，在埃塞俄比亚高粱育种中，考虑到基因型-地点互作，SC 可以提高预测准确性。
