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 res...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 across34 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 crossvalidation,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 for97%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 GBLUPGE_(PCA))provided similar prediction accuracies but could reduce the computational burden.In addition,increasing the number of test set environments in the training set from 8 to 14 increased the prediction accuracy of GBLUP-GE_(19CF)trained with monthly average climate data for 2020-2021.Examining prediction accuracy based on concordance,the proportion of overlapping hybrids between the top 50%of predicted and observed values for GMC and GY,indicated that concordance exceeded 50%for the GBLUP-GE_(19CF)model,confirming the reliability of our predictions.This study can provide practical guidance for optimizing GPs for maize breeding programs in multi-environment selection.展开更多
基金supported by grants from the Biological Breeding-National Science and Technology Major Project(2023ZD0407501)National Natural Science Foundation of China(32361143514)+2 种基金Nanfan Special Project,CAAS(YBXM2408)Key R&D Programs of Hainan Province(ZDYF2024XDNY210)the Innovation Program of Chinese Academy of Agricultural Sciences(CAAS-CSIAF202303)。
文摘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 across34 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 crossvalidation,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 for97%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 GBLUPGE_(PCA))provided similar prediction accuracies but could reduce the computational burden.In addition,increasing the number of test set environments in the training set from 8 to 14 increased the prediction accuracy of GBLUP-GE_(19CF)trained with monthly average climate data for 2020-2021.Examining prediction accuracy based on concordance,the proportion of overlapping hybrids between the top 50%of predicted and observed values for GMC and GY,indicated that concordance exceeded 50%for the GBLUP-GE_(19CF)model,confirming the reliability of our predictions.This study can provide practical guidance for optimizing GPs for maize breeding programs in multi-environment selection.
