Accurate genomic prediction for grain yield and grain moisture content of maize hybrids using multi‐environment data

作者：Jingxin Wang, Liwei Liu, Kunhui He, Takele Weldu Gebrewahid, Shang Gao, Qingzhen Tian, Zhanyi Li, Yiqun Song, Yiliang Guo, Yanwei Li, Qinxin Cui, Luyan Zhang, Jiankang Wang, Changling Huang, Liang Li, Tingting Guo, Huihui Li · 发表于：Journal of Integrative Plant Biology · 年份：2025 · DOI：10.1111/jipb.13857 · 被引用次数：7 · 研究领域：Genetic and phenotypic traits in livestock、Genetics and Plant Breeding、Genetic Mapping and Diversity in Plants and Animals

ABSTRACT Incorporating genotype‐by‐environment (GE) interaction effects into genomic prediction (GP) models with multi‐environment climate data can improve selection accuracy to accelerate crop breeding but has received little research attention. Here, we conducted a cross‐region GP study of grain moisture content (GMC) and grain yield (GY) in maize hybrids in two major Chinese growing regions using data for 19 climatic factors across 34 environments in 2020 and 2021. Predictions were conducted in 2,126 hybrids generated from 475 maize inbred lines, using 9,355 single nucleotide polymorphism markers for genotyping. Models based on genomic best linear unbiased prediction (GBLUP) incorporating GE interaction effects of 19 climatic factors associated with day length, transpiration, temperature, and radiation (GBLUP‐GE 19CF ) trained on whole data set outperformed the traditional GBLUP or BayesB models in predicting GMC or GY by 10‐fold cross‐validation, achieving prediction accuracies of 0.731 and 0.331, respectively. To refine the climate data, we examined 84 statistical features associated with these climatic factors and identified nine factors most correlated with GMC or GY. Principal component analysis of climate data yielded nine principal components responsible for 97% of the variability in the data. Incorporating these nine factors or principal components into the GBLUP‐GE framework with a similarity matrix of environments (GBLUP‐GE 9CF and GBLUP‐GE PCA ) provided similar...