Background: Different production systems and climates could lead to genotype-by-environment(G × E) interactions between populations, and the inclusion of G × E interactions is becoming essential in breeding ...Background: Different production systems and climates could lead to genotype-by-environment(G × E) interactions between populations, and the inclusion of G × E interactions is becoming essential in breeding decisions. The objective of this study was to investigate the performance of multi-trait models in genomic prediction in a limited number of environments with G × E interactions.Results: In total, 2,688 and 1,384 individuals with growth and reproduction phenotypes, respectively, from two Yorkshire pig populations with similar genetic backgrounds were genotyped with the PorcineSNP80 panel.Single-and multi-trait models with genomic best linear unbiased prediction(GBLUP) and BayesC π were implemented to investigate their genomic prediction abilities with 20 replicates of five-fold cross-validation.Our results regarding between-environment genetic correlations of growth and reproductive traits(ranging from 0.618 to 0.723) indicated the existence of G × E interactions between these two Yorkshire pig populations. For single-trait models, genomic prediction with GBLUP was only 1.1% more accurate on average in the combined population than in single populations, and no significant improvements were obtained by BayesC π for most traits. In addition, single-trait models with either GBLUP or BayesC π produced greater bias for the combined population than for single populations. However, multi-trait models with GBLUP and BayesC π better accommodated G × E interactions,yielding 2.2% – 3.8% and 1.0% – 2.5% higher prediction accuracies for growth and reproductive traits, respectively,compared to those for single-trait models of single populations and the combined population. The multi-trait models also yielded lower bias and larger gains in the case of a small reference population. The smaller improvement in prediction accuracy and larger bias obtained by the single-trait models in the combined population was mainly due to the low consistency of linkage disequilibrium between the two populations, which also caused the BayesC π method to always produce the largest standard error in marker effect estimation for the combined population.Conclusions: In conclusion, our findings confirmed that directly combining populations to enlarge the reference population is not efficient in improving the accuracy of genomic prediction in the presence of G × E interactions, while multi-trait models perform better in a limited number of environments with G × E interactions.展开更多
The maintenance of genetic variation in mutualism-related traits is key for understanding mutualism evolution,yet the mechanisms maintaining variation remain unclear.We asked whether genotype-byenvironment(G3E)interac...The maintenance of genetic variation in mutualism-related traits is key for understanding mutualism evolution,yet the mechanisms maintaining variation remain unclear.We asked whether genotype-byenvironment(G3E)interaction is a potential mechanism maintaining variation in the model legume–rhizobia system,Medicago truncatula–Ensifer meliloti.We planted 50 legume genotypes in a greenhouse under ambient light and shade to reflect reduced carbon availability for plants.We found an expected reduction under shaded conditions for plant performance traits,such as leaf number,aboveground and belowground biomass,and a mutualism-related trait,nodule number.We also found G3E for nodule number,with83%of this interaction due to shifts in genotype fitness rank order across light environments,coupled with strong positive directional selection on nodule number regardless of light environment.Our results suggest that G3E can maintain genetic variation in a mutualism-related trait that is under consistent positive directional selection across light environments.展开更多
Global climate change poses a severe threat to mountain biodiversity.Phenotypic plasticity and local adaptation are two common strategies for alpine plant to cope with such change.They may facilitate organismal adapta...Global climate change poses a severe threat to mountain biodiversity.Phenotypic plasticity and local adaptation are two common strategies for alpine plant to cope with such change.They may facilitate organismal adaptation to contrasting environments,depending on the influences of the environment or genotype or their interacted effects.In this study,we use an endemic alpine plant(Rorippa elata)in the Hengduan mountains(HDM)to unravel its phenotypic basis of adaptation strategy and evaluate the relative contributions of environment and genotype to its phenotype.We transplanted 37 genotypes of R.elata into two common gardens across low and high elevations(2800 vs.3800 m)during 2021-2022.Nine fitness-related traits were measured,including flowering probability and glucosinolates(GS)content.We estimated the environmental or genotypic contributions to the phenotype and identified the main environmental components.Our results revealed that both environment and genotype-by-environment interactions contributed to the phenotypes of R.elata.Latitudinal heterogeneity was identified as a key factor that explained 24%of the total phenotypic variation.In particular,genotypes of the northern HDM showed significantly higher plasticity in flowering probability than those of the southern HDM.Furthermore,within the southern HDM,GS content indicated local adaptation to herbivory stresses for R.elata genotypes along elevations.In conclusion,our results suggest that R.elata may have adapted to the alpine environment through species-level plasticity or regional-level local adaptation.These processes were shaped by either complex topography or interactions between genotype and mountain environments.Our study provides empirical evidence on the adaptation of alpine plants.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
Phenotypic plasticity,the ability of an individual to alter its phenotype in response to changes in the environment,has been proposed as a target for breeding crop varieties with high environmental fitness.Here,we use...Phenotypic plasticity,the ability of an individual to alter its phenotype in response to changes in the environment,has been proposed as a target for breeding crop varieties with high environmental fitness.Here,we used phenotypic and genotypic data from multiple maize(Zea mays L.)populations to mathematically model phenotypic plasticity in response to the environment(PPRE)in inbred and hybrid lines.PPRE can be simply described by a linear model in which the two main parameters,intercept a and slope b,reflect two classes of genes responsive to endogenous(class A)and exogenous(class B)signals that coordinate plant development.Together,class A and class B genes contribute to the phenotypic plasticity of an individual in response to the environment.We also made connections between phenotypic plasticity and hybrid performance or general combining ability(GCA)of yield using 30 F_(1) hybrid populations generated by crossing the same maternal line with 30 paternal lines from different maize heterotic groups.We show that the parameters a and b from two given parental lines must be concordant to reach an ideal GCA of F_(1) yield.We hypothesize that coordinated regulation of the two classes of genes in the F_(1) hybrid genome is the basis for high GCA.Based on this theory,we built a series of predictive models to evaluate GCA in silico between parental lines of different heterotic groups.展开更多
Background:The performance of oat genotypes differs across environments due to variations in biotic and abiotic factors.Thus,evaluation of oat genotypes across diverse environments is very important to identify superi...Background:The performance of oat genotypes differs across environments due to variations in biotic and abiotic factors.Thus,evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement.Methods:The study aimed to assess the interaction(genotype-by-environment interaction;GEI)effect and determine the stability of grain yield in oat(Avena sativa L.)genotypes in Ethiopia using parametric and nonparametric stability statistics.Twenty-four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times.Results:The pooled analysis of the variance of grain yield showed significant variations among genotypes,environments,and their interaction effects.Significant GEI revealed the rank order change of genotypes across environments.The environment main effect captured 44.62%of the total grain yield variance,while genotype and GEI effects explained 28.84%and 26.54%of the total grain yield variance,respectively.The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics.The results indicated that genotypes with superior grain yield-showed stable performance on the basis of the stability parameters of the genotypic superiority index(P_(i)),the Perkins and Jinks adjusted linear regression coefficient(B_(i)),and the yield stability index(YSI),indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes.Spearman's rank correlation coefficients also showed that the stability parameters of P_(i),B_(i),and YSI had a significant positive association with grain yield.However,grain yield had an inverse correlation with the stability parameters of standard deviation,deviation from regression (S_(di)^(2)),the Hernandez desirability index(D_(ji)),Wricke ecovalence(W_(i)),the Shukla stability variance(σ_(i)^(2)),the AMMI stability value(ASV),and environmental variance (S_(i)^((2))),indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low-yielding genotypes more,compared to high-yielding ones.Conclusions:Therefore,G5,G8,G11,G12,G14,G16,G17,G19,and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi,Bi,and YSI,and selection of these superior genotypes would improve grain yield in oat genotypes.However,the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.展开更多
基金supported by grants from the earmarked fund for China Agriculture Research System (CARS-35)Modern Agriculture Science and Technology Key Project of Hebei Province (19226376D)+2 种基金the National Key Research and Development Project (SQ2019YFE00771)the National Natural Science Foundation of China (31671327)Major Project of Selection for New Livestock and Poultry Breeds of Zhejiang Province (2016C02054–5)。
文摘Background: Different production systems and climates could lead to genotype-by-environment(G × E) interactions between populations, and the inclusion of G × E interactions is becoming essential in breeding decisions. The objective of this study was to investigate the performance of multi-trait models in genomic prediction in a limited number of environments with G × E interactions.Results: In total, 2,688 and 1,384 individuals with growth and reproduction phenotypes, respectively, from two Yorkshire pig populations with similar genetic backgrounds were genotyped with the PorcineSNP80 panel.Single-and multi-trait models with genomic best linear unbiased prediction(GBLUP) and BayesC π were implemented to investigate their genomic prediction abilities with 20 replicates of five-fold cross-validation.Our results regarding between-environment genetic correlations of growth and reproductive traits(ranging from 0.618 to 0.723) indicated the existence of G × E interactions between these two Yorkshire pig populations. For single-trait models, genomic prediction with GBLUP was only 1.1% more accurate on average in the combined population than in single populations, and no significant improvements were obtained by BayesC π for most traits. In addition, single-trait models with either GBLUP or BayesC π produced greater bias for the combined population than for single populations. However, multi-trait models with GBLUP and BayesC π better accommodated G × E interactions,yielding 2.2% – 3.8% and 1.0% – 2.5% higher prediction accuracies for growth and reproductive traits, respectively,compared to those for single-trait models of single populations and the combined population. The multi-trait models also yielded lower bias and larger gains in the case of a small reference population. The smaller improvement in prediction accuracy and larger bias obtained by the single-trait models in the combined population was mainly due to the low consistency of linkage disequilibrium between the two populations, which also caused the BayesC π method to always produce the largest standard error in marker effect estimation for the combined population.Conclusions: In conclusion, our findings confirmed that directly combining populations to enlarge the reference population is not efficient in improving the accuracy of genomic prediction in the presence of G × E interactions, while multi-trait models perform better in a limited number of environments with G × E interactions.
基金NSERC Canada for funding that supports our science,Megan Frederickson,Art WeisHelen Rodd for comments on our workBill Cole and Thomas Gludovacz for horticultural assistance。
文摘The maintenance of genetic variation in mutualism-related traits is key for understanding mutualism evolution,yet the mechanisms maintaining variation remain unclear.We asked whether genotype-byenvironment(G3E)interaction is a potential mechanism maintaining variation in the model legume–rhizobia system,Medicago truncatula–Ensifer meliloti.We planted 50 legume genotypes in a greenhouse under ambient light and shade to reflect reduced carbon availability for plants.We found an expected reduction under shaded conditions for plant performance traits,such as leaf number,aboveground and belowground biomass,and a mutualism-related trait,nodule number.We also found G3E for nodule number,with83%of this interaction due to shifts in genotype fitness rank order across light environments,coupled with strong positive directional selection on nodule number regardless of light environment.Our results suggest that G3E can maintain genetic variation in a mutualism-related trait that is under consistent positive directional selection across light environments.
基金supported by the National Natural Science Foundation of China(32170224,32225005)the NSFC-ERC International Cooperation and Exchange Programs(32311530331)the Youth Innovation Promotion Association CAS(2020391).
文摘Global climate change poses a severe threat to mountain biodiversity.Phenotypic plasticity and local adaptation are two common strategies for alpine plant to cope with such change.They may facilitate organismal adaptation to contrasting environments,depending on the influences of the environment or genotype or their interacted effects.In this study,we use an endemic alpine plant(Rorippa elata)in the Hengduan mountains(HDM)to unravel its phenotypic basis of adaptation strategy and evaluate the relative contributions of environment and genotype to its phenotype.We transplanted 37 genotypes of R.elata into two common gardens across low and high elevations(2800 vs.3800 m)during 2021-2022.Nine fitness-related traits were measured,including flowering probability and glucosinolates(GS)content.We estimated the environmental or genotypic contributions to the phenotype and identified the main environmental components.Our results revealed that both environment and genotype-by-environment interactions contributed to the phenotypes of R.elata.Latitudinal heterogeneity was identified as a key factor that explained 24%of the total phenotypic variation.In particular,genotypes of the northern HDM showed significantly higher plasticity in flowering probability than those of the southern HDM.Furthermore,within the southern HDM,GS content indicated local adaptation to herbivory stresses for R.elata genotypes along elevations.In conclusion,our results suggest that R.elata may have adapted to the alpine environment through species-level plasticity or regional-level local adaptation.These processes were shaped by either complex topography or interactions between genotype and mountain environments.Our study provides empirical evidence on the adaptation of alpine plants.
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.
基金funded by the Hainan Yazhou Bay Seed Laboratory(B21HJ0505)the Chinese Universities Scientific Fund(2022TC139)the 2115 Talent Development Program of China Agricultural University.
文摘Phenotypic plasticity,the ability of an individual to alter its phenotype in response to changes in the environment,has been proposed as a target for breeding crop varieties with high environmental fitness.Here,we used phenotypic and genotypic data from multiple maize(Zea mays L.)populations to mathematically model phenotypic plasticity in response to the environment(PPRE)in inbred and hybrid lines.PPRE can be simply described by a linear model in which the two main parameters,intercept a and slope b,reflect two classes of genes responsive to endogenous(class A)and exogenous(class B)signals that coordinate plant development.Together,class A and class B genes contribute to the phenotypic plasticity of an individual in response to the environment.We also made connections between phenotypic plasticity and hybrid performance or general combining ability(GCA)of yield using 30 F_(1) hybrid populations generated by crossing the same maternal line with 30 paternal lines from different maize heterotic groups.We show that the parameters a and b from two given parental lines must be concordant to reach an ideal GCA of F_(1) yield.We hypothesize that coordinated regulation of the two classes of genes in the F_(1) hybrid genome is the basis for high GCA.Based on this theory,we built a series of predictive models to evaluate GCA in silico between parental lines of different heterotic groups.
基金financed by the Ethiopian Institute of Agricultural Research(EIAR)and the Bill and Melinda Gates Foundation through the Equip-Strengthening Smallholder Livestock Systems for the Future Project(sub-award agreement no.UFDSP00012156 between the University of Florida(UF)and EIAR)。
文摘Background:The performance of oat genotypes differs across environments due to variations in biotic and abiotic factors.Thus,evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement.Methods:The study aimed to assess the interaction(genotype-by-environment interaction;GEI)effect and determine the stability of grain yield in oat(Avena sativa L.)genotypes in Ethiopia using parametric and nonparametric stability statistics.Twenty-four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times.Results:The pooled analysis of the variance of grain yield showed significant variations among genotypes,environments,and their interaction effects.Significant GEI revealed the rank order change of genotypes across environments.The environment main effect captured 44.62%of the total grain yield variance,while genotype and GEI effects explained 28.84%and 26.54%of the total grain yield variance,respectively.The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics.The results indicated that genotypes with superior grain yield-showed stable performance on the basis of the stability parameters of the genotypic superiority index(P_(i)),the Perkins and Jinks adjusted linear regression coefficient(B_(i)),and the yield stability index(YSI),indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes.Spearman's rank correlation coefficients also showed that the stability parameters of P_(i),B_(i),and YSI had a significant positive association with grain yield.However,grain yield had an inverse correlation with the stability parameters of standard deviation,deviation from regression (S_(di)^(2)),the Hernandez desirability index(D_(ji)),Wricke ecovalence(W_(i)),the Shukla stability variance(σ_(i)^(2)),the AMMI stability value(ASV),and environmental variance (S_(i)^((2))),indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low-yielding genotypes more,compared to high-yielding ones.Conclusions:Therefore,G5,G8,G11,G12,G14,G16,G17,G19,and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi,Bi,and YSI,and selection of these superior genotypes would improve grain yield in oat genotypes.However,the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.
