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
Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize （Zea mays L.）. Thus, investigating the genetic basis of leaf area will a...Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize （Zea mays L.）. Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines （RILs） derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area （TELA） under multi-environments. Inclusive composite interval map- ping （ICIM） was used to identify quantitative trait loci （QTLs） for TELA under a single environment and estimated breeding value （EBV）. A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL （qTELA_2-9） located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate （ranging from 10.79 to 16.51%） that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.展开更多
Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice ge...Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years(2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment(GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of AMMI model for partitioning the GE interaction effects showed that only the first two terms of AMMI were significant based on Gollob's Ftest. The lowest AMMI-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. AMMI stability value introduced G6 as the most stable one. According to AMMI biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments.展开更多
Background Globally,the cultivation of cotton is constrained by its tendency for extended periods of growth.Early maturity plays a potential role in rainfed-based multiple cropping system especially in the current era...Background Globally,the cultivation of cotton is constrained by its tendency for extended periods of growth.Early maturity plays a potential role in rainfed-based multiple cropping system especially in the current era of climate change.In the current study,a set of 20 diverse Gossypium hirsutum genotypes were evaluated in two crop seasons with three planting densities and assessed for 11 morphological traits related to early maturity.The study aimed to identify genotype(s)that mature rapidly and accomplish well under diverse environmental conditions based on the two robust multivariate techniques called multi-trait stability index(MTSI)and multi-trait genotype-ideotype distance index(MGIDI).Results MTSI analysis revealed that out of the 20 genotypes,three genotypes,viz.,NNDC-30,A-2,and S-32 accomplished well in terms of early maturity traits in two seasons.Furthermore,three genotypes were selected using MGIDI method for each planting densities with a selection intensity of 15%.The strengths and weaknesses of the genotypes selected based on MGIDI method highlighted that the breeders could focus on developing early-maturing genotypes with specific traits such as days to first flower and boll opening.The selected genotypes exhibited positive genetic gains for traits related to earliness and a successful harvest during the first and second pickings.However,there were negative gains for traits related to flowering and boll opening.Conclusion The study identified three genotypes exhibiting early maturity and accomplished well under different planting densities.The multivariate methods(MTSI and MGIDI)serve as novel approaches for selecting desired genotypes in plant breeding programs,especially across various growing environments.These methods offer exclusive benefits and can easily construe and minimize multicollinearity issues.展开更多
Large-scale genetic population used for genetic breeding researches covers a large area in the field experiment,and the effect of local control would be gradually weakened.The block in replication(BIR)design is suitab...Large-scale genetic population used for genetic breeding researches covers a large area in the field experiment,and the effect of local control would be gradually weakened.The block in replication(BIR)design is suitable for large population,which is applied to the field experiment of genetic population.The statistical methods of analysis of variance(ANOVA)and heritability estimation in single and multiple environments were derived and implemented using the statistical analysis system(SAS)program for the analysis of BIR.As a work example,a comparison of statistical analysis between BIR design and the completely random block(CRB)design were conducted for the protein content from a panel containing 455 soybean germplasms.The results indicated the different estimates of average heritability in multiple environments.The research results provided technical support for the application of BIR design in genetics and breeding studies.展开更多
Assessment of yield stability is an important issue for maize （Zea mays L.） cultivar evaluation and recommendation. Many parametric procedures are available for stability analysis, each of them allowing for differen...Assessment of yield stability is an important issue for maize （Zea mays L.） cultivar evaluation and recommendation. Many parametric procedures are available for stability analysis, each of them allowing for different interpretations. The objective of the present study was to assess yield stability of maize hybrids evaluated in the National Maize Cultivar Regional Trials in southwestern China using 20 parametric stability statistics proposed by various authors at different times, and to investigate their interrelationships. Two yield datasets were obtained from the 2003 and 2004 national maize cultivar regional trials in southwestern China. A combined analysis of variance, stability statistics, and rank correlations among these stability statistics were determined. Effects of location, cultivar, and cultivar by location interaction were highly significant （P〈0.01）. Different stability statistics were used to determine the stability of the studied cultivars. Cultivar mean yield （Y） was significantly correlated to the Lin and Binns stability statistic （LP, r=0.98^＊＊ and 0.97^＊＊ for 2003 and 2004 trials, respectively） and desirability index （HD, r=0.38 and 0.84^＊＊ for the 2003 and 2004 trials, respectively）. The statistics LP and HD would be useful for simultaneously selecting for high yield and stability. Based on a principal component analysis, the parametric stability statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability.展开更多
Analysis of multi-environment trials （METs） of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yiel...Analysis of multi-environment trials （METs） of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yield is essential in MET analyses. The objective of the present investigation was to compare 11 nonparametric stability statistics and apply nonparametric tests for genotype-by-environment interaction （GEI） to 14 maize （Zea mays L.） genotypes grown at 25 locations in southwestern China during 2005. Results of nonparametric tests of GEl and a combined ANOVA across locations showed that both crossover and noncrossover GEI, and genotypes varied highly significantly for yield. The results of principal component analysis, correlation analysis of nonparametric statistics, and yield indicated the nonparametric statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability. Furthermore, high values of TOP and low values of rank-sum were associated with high mean yield, but the other nonparametric statistics were not positively correlated with mean yield. Therefore, only rank-sum and TOP methods would be useful for simultaneously selection for high yield and stability. These two statistics recommended JY686 and HX168 as desirable and ND108, CM12, CN36, and NK6661 as undesirable genotypes.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
The expression of quantitative traits of a line of a crop depends on its genetics,the environment where it is sown and the interaction between the genetic information and the environment known as GxE.Thus to maximize ...The expression of quantitative traits of a line of a crop depends on its genetics,the environment where it is sown and the interaction between the genetic information and the environment known as GxE.Thus to maximize food production,new varieties are developed by selecting superior lines of seeds suitable for a specific environment.Genomic selection is a computational technique for developing a new variety that uses whole genome molecular markers to identify top lines of a crop.A large number of statistical and machine learning models are employed for single environment trials,where it is assumed that the environment does not have any effect on the quantitative traits.However,it is essential to consider both genomic and environmental data to develop a new variety,as these strong assumptions may lead to failing to select top lines for an environment.Here we devised three novel deep learning frameworks incorporating GxE within the deep learning model and predicted line-specific yield for an environment.In the process,we also developed a new technique for identifying environmentspecific markers that can be useful in many applications of environment-specific genomic selection.The result demonstrates that our best framework obtains 1.75 to 1.95 times better correlation coefficients than other deep learning models that incorporate environmental data depending on the test scenario.Furthermore,the feature importance analysis shows that environmental information,followed by genomic information,is the driving factor in predicting environment-specific yield for a line.We also demonstrate a way to extend our framework for new data types,such as text or soil data.The extended model also shows the potential to be useful in genomic 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 financially by the National Natu ral Science Foundation of China(31301830)the Natural Science Basic Research Plan in Shaanxi Province of China(2014JQ3108)+1 种基金the Special Fund for Basic Research in Northwest A&F University,China(QN2012001)the Chinese Scholarship Council(CSC)
文摘Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize （Zea mays L.）. Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines （RILs） derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area （TELA） under multi-environments. Inclusive composite interval map- ping （ICIM） was used to identify quantitative trait loci （QTLs） for TELA under a single environment and estimated breeding value （EBV）. A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL （qTELA_2-9） located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate （ranging from 10.79 to 16.51%） that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.
文摘Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years(2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment(GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of AMMI model for partitioning the GE interaction effects showed that only the first two terms of AMMI were significant based on Gollob's Ftest. The lowest AMMI-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. AMMI stability value introduced G6 as the most stable one. According to AMMI biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments.
文摘Background Globally,the cultivation of cotton is constrained by its tendency for extended periods of growth.Early maturity plays a potential role in rainfed-based multiple cropping system especially in the current era of climate change.In the current study,a set of 20 diverse Gossypium hirsutum genotypes were evaluated in two crop seasons with three planting densities and assessed for 11 morphological traits related to early maturity.The study aimed to identify genotype(s)that mature rapidly and accomplish well under diverse environmental conditions based on the two robust multivariate techniques called multi-trait stability index(MTSI)and multi-trait genotype-ideotype distance index(MGIDI).Results MTSI analysis revealed that out of the 20 genotypes,three genotypes,viz.,NNDC-30,A-2,and S-32 accomplished well in terms of early maturity traits in two seasons.Furthermore,three genotypes were selected using MGIDI method for each planting densities with a selection intensity of 15%.The strengths and weaknesses of the genotypes selected based on MGIDI method highlighted that the breeders could focus on developing early-maturing genotypes with specific traits such as days to first flower and boll opening.The selected genotypes exhibited positive genetic gains for traits related to earliness and a successful harvest during the first and second pickings.However,there were negative gains for traits related to flowering and boll opening.Conclusion The study identified three genotypes exhibiting early maturity and accomplished well under different planting densities.The multivariate methods(MTSI and MGIDI)serve as novel approaches for selecting desired genotypes in plant breeding programs,especially across various growing environments.These methods offer exclusive benefits and can easily construe and minimize multicollinearity issues.
基金Supported by Key Research and Development Project of Heilongjiang Province(GA21B009-6)Heilongjiang Province Natural Science Foundation(C2015009)。
文摘Large-scale genetic population used for genetic breeding researches covers a large area in the field experiment,and the effect of local control would be gradually weakened.The block in replication(BIR)design is suitable for large population,which is applied to the field experiment of genetic population.The statistical methods of analysis of variance(ANOVA)and heritability estimation in single and multiple environments were derived and implemented using the statistical analysis system(SAS)program for the analysis of BIR.As a work example,a comparison of statistical analysis between BIR design and the completely random block(CRB)design were conducted for the protein content from a panel containing 455 soybean germplasms.The results indicated the different estimates of average heritability in multiple environments.The research results provided technical support for the application of BIR design in genetics and breeding studies.
基金the Program for the Changjiang Scholars and Innovative Research Team in University, China (IRT0453)the Youth Fund of Sichuan Provincial Department of Education (2006B005)
文摘Assessment of yield stability is an important issue for maize （Zea mays L.） cultivar evaluation and recommendation. Many parametric procedures are available for stability analysis, each of them allowing for different interpretations. The objective of the present study was to assess yield stability of maize hybrids evaluated in the National Maize Cultivar Regional Trials in southwestern China using 20 parametric stability statistics proposed by various authors at different times, and to investigate their interrelationships. Two yield datasets were obtained from the 2003 and 2004 national maize cultivar regional trials in southwestern China. A combined analysis of variance, stability statistics, and rank correlations among these stability statistics were determined. Effects of location, cultivar, and cultivar by location interaction were highly significant （P〈0.01）. Different stability statistics were used to determine the stability of the studied cultivars. Cultivar mean yield （Y） was significantly correlated to the Lin and Binns stability statistic （LP, r=0.98^＊＊ and 0.97^＊＊ for 2003 and 2004 trials, respectively） and desirability index （HD, r=0.38 and 0.84^＊＊ for the 2003 and 2004 trials, respectively）. The statistics LP and HD would be useful for simultaneously selecting for high yield and stability. Based on a principal component analysis, the parametric stability statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability.
基金the Program for Changjiang Scholars and Innovative Research Team in University,China(IRT0453)the Youth Foundation of Sichuan Province Office of Education(2006B005) of China,for supporting this research
文摘Analysis of multi-environment trials （METs） of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yield is essential in MET analyses. The objective of the present investigation was to compare 11 nonparametric stability statistics and apply nonparametric tests for genotype-by-environment interaction （GEI） to 14 maize （Zea mays L.） genotypes grown at 25 locations in southwestern China during 2005. Results of nonparametric tests of GEl and a combined ANOVA across locations showed that both crossover and noncrossover GEI, and genotypes varied highly significantly for yield. The results of principal component analysis, correlation analysis of nonparametric statistics, and yield indicated the nonparametric statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability. Furthermore, high values of TOP and low values of rank-sum were associated with high mean yield, but the other nonparametric statistics were not positively correlated with mean yield. Therefore, only rank-sum and TOP methods would be useful for simultaneously selection for high yield and stability. These two statistics recommended JY686 and HX168 as desirable and ND108, CM12, CN36, and NK6661 as undesirable genotypes.
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.
文摘The expression of quantitative traits of a line of a crop depends on its genetics,the environment where it is sown and the interaction between the genetic information and the environment known as GxE.Thus to maximize food production,new varieties are developed by selecting superior lines of seeds suitable for a specific environment.Genomic selection is a computational technique for developing a new variety that uses whole genome molecular markers to identify top lines of a crop.A large number of statistical and machine learning models are employed for single environment trials,where it is assumed that the environment does not have any effect on the quantitative traits.However,it is essential to consider both genomic and environmental data to develop a new variety,as these strong assumptions may lead to failing to select top lines for an environment.Here we devised three novel deep learning frameworks incorporating GxE within the deep learning model and predicted line-specific yield for an environment.In the process,we also developed a new technique for identifying environmentspecific markers that can be useful in many applications of environment-specific genomic selection.The result demonstrates that our best framework obtains 1.75 to 1.95 times better correlation coefficients than other deep learning models that incorporate environmental data depending on the test scenario.Furthermore,the feature importance analysis shows that environmental information,followed by genomic information,is the driving factor in predicting environment-specific yield for a line.We also demonstrate a way to extend our framework for new data types,such as text or soil data.The extended model also shows the potential to be useful in genomic selection.
