To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-envi...To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.展开更多
Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq ...Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq to detect QTL is often limited by inappropriate experimental designs, as evidenced by numerous practical studies. Most BSA-seq studies have utilized small to medium-sized populations, with F2populations being the most common choice. Nevertheless, theoretical studies have shown that using a large population with an appropriate pool size can significantly enhance the power and resolution of QTL detection in BSA-seq, with F_(3)populations offering notable advantages over F2populations. To provide an experimental demonstration, we tested the power of BSA-seq to identify QTL controlling days from sowing to heading(DTH) in a 7200-plant rice F_(3)population in two environments, with a pool size of approximately 500. Each experiment identified 34 QTL, an order of magnitude greater than reported in most BSA-seq experiments, of which 23 were detected in both experiments, with 17 of these located near41 previously reported QTL and eight cloned genes known to control DTH in rice. These results indicate that QTL mapping by BSA-seq in large F_(3)populations and multi-environment experiments can achieve high power, resolution, and reliability.展开更多
At present, with the rapid development of science and technology, based on the requirements of weapon test and identification tasks, the experimental data acquisition and processing space station, which is suitable fo...At present, with the rapid development of science and technology, based on the requirements of weapon test and identification tasks, the experimental data acquisition and processing space station, which is suitable for a variety of extreme natural environments such as alpine, plateau, mountain, jungle, desert, island and reef, has been studied theoretically and in practice. The space station is a dome-shaped structure with scale-shaped modules and basalt reinforced fiber composite materials, providing thermal insulation, ventilation and continuous power supply. It can provide support and guarantee for the real-time monitoring, recovery and information transmission of test data, and meet the basic work and life needs of test personnel.展开更多
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 State Key Laboratory of Tree Genetics and Breeding(Northeast Forestry University)(K2013204)co-financed with NSFC project(31470673)Guangdong Science and Technology Planning Project(2016B070701008)
文摘To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.
基金supported by Natural Science Foundation of Fujian Province (CN) (2020I0009, 2022J01596)Cooperation Project on University Industry-Education-Research of Fujian Provincial Science and Technology Plan (CN) (2022N5011)+1 种基金Lancang-Mekong Cooperation Special Fund (2017-2020)International Sci-Tech Cooperation and Communication Program of Fujian Agriculture and Forestry University (KXGH17014)。
文摘Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq to detect QTL is often limited by inappropriate experimental designs, as evidenced by numerous practical studies. Most BSA-seq studies have utilized small to medium-sized populations, with F2populations being the most common choice. Nevertheless, theoretical studies have shown that using a large population with an appropriate pool size can significantly enhance the power and resolution of QTL detection in BSA-seq, with F_(3)populations offering notable advantages over F2populations. To provide an experimental demonstration, we tested the power of BSA-seq to identify QTL controlling days from sowing to heading(DTH) in a 7200-plant rice F_(3)population in two environments, with a pool size of approximately 500. Each experiment identified 34 QTL, an order of magnitude greater than reported in most BSA-seq experiments, of which 23 were detected in both experiments, with 17 of these located near41 previously reported QTL and eight cloned genes known to control DTH in rice. These results indicate that QTL mapping by BSA-seq in large F_(3)populations and multi-environment experiments can achieve high power, resolution, and reliability.
文摘At present, with the rapid development of science and technology, based on the requirements of weapon test and identification tasks, the experimental data acquisition and processing space station, which is suitable for a variety of extreme natural environments such as alpine, plateau, mountain, jungle, desert, island and reef, has been studied theoretically and in practice. The space station is a dome-shaped structure with scale-shaped modules and basalt reinforced fiber composite materials, providing thermal insulation, ventilation and continuous power supply. It can provide support and guarantee for the real-time monitoring, recovery and information transmission of test data, and meet the basic work and life needs of test personnel.
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.
