MaizeGEP: A Maize Hybrids Dataset with Genotype, Phenotype, and Envirotype to Develop Genomic Selection Models

The integration of genotype with envirotype is essential for achieving precise phenotypic prediction. However, there is currently a scarcity of datasets derived from variety approval testing trials that encompass a broad spectrum of trial locations, conform to standardized management protocols, and which incorporate comprehensive records of environmental variables. This study introduces MaizeGEP, a dataset consisting of 260 hybrid maize varieties from national maize variety regional trials. This dataset includes 12,233 selected tag single nucleotide polymorphisms for each variety, phenotypic survey data on 11 traits across 2382 year-county locations, and daily meteorological records. We utilized this dataset to conduct analyses on the clustering of 2382 year-county locations, the population structure of the 260 varieties, and genome-wide association studies. Furthermore, a novel mixture of experts (MoE) framework incorporating genotype-envirotype to phenotype (GE2P) algorithms was employed for best linear unbiased estimator (BLUE) value prediction and phenotypic prediction. Additionally, several machine learning and deep learning algorithms, including Bayesian methods, support vector machines (SVM), LightGBM, multilayer perceptron (MLP), DeepGS, DEM, and Cropformer, were utilized to validate the effectiveness of the dataset and improve phenotypic prediction accuracy. The findings suggest that the MaizeGEP dataset serves as a valuable resource for investigating the relationship among genotype, envirotype, and phenotype, as well as predicting cross-environmental performance. This underscores the importance of encouraging researchers to utilize this dataset in developing sophisticated GE2P models. Such models can aid plant breeders in selecting new varieties and facilitating their deployment across diverse regions. MaizeGEP is publicly accessible at http://user.ebreed.cn:9992/scb/.