Local adaptation is critical for plant survivals and reproductions in the context of global environmental change.Heterogeneous environments impose various selection pressures that influence the fitness of organisms an...Local adaptation is critical for plant survivals and reproductions in the context of global environmental change.Heterogeneous environments impose various selection pressures that influence the fitness of organisms and leave genomic signatures during the process of adaptation to local environments.However,unveiling the genomic signatures of adaptation still poses a major challenge especially for perennials due to limited genomic resources.Here,we utilized Actinidia eriantha,a Chinese endemic liana,as a model case to detect drivers of local adaptation and adaptive signals through landscape genomics for 311 individuals collected from 25 populations.Our results demonstrated precipitation and solar radiation were two crucial factors influencing the patterns of genetic variations and driving adaptive processes.We further uncovered a set of genes involved in adaptation to heterogeneous environments.Among them,AeERF110 showed high genetic differentiation between populations and was confirmed to be involved in local adaptation via changes in allele frequency along with precipitation(Prec_03)and solar radiation(Srad_03)in native habitats separately,implying that adaptive loci frequently exhibited environmental and geographic signals.In addition,we assessed genetic offsets of populations under four future climate models and revealed that populations from middle and east clusters faced higher risks in adapting to future environments,which should address more attentions.Taken together,our study opens new perspectives for understanding the genetic underpinnings of local adaptation in plants to environmental changes in a more comprehensive fashion and offered the guides on applications in conservation efforts.展开更多
Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simula...Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simulate plant growth and development processes.In this study,a dataset containing multi-environmental planting and flowering data for 169 genotypes,each with 700K single nucleotide polymorphism(SNP)markers was used.Three rice growth models(ORYZA,CERES-Rice,and RiceGrow),SNPs,and climatic indices were in-tegrated for flowering time prediction.Significant associations between GSPs and quantitative trait nucleotides(QTNs)were investigated using genome-wide association study(GWAS)methods.Several GSPs were associated with previously reported rice flowering genes,including DTH2,DTH3 and OsCOL15,demonstrating the genetic interpretability of the models.The rice models driven by SNPs-based GSPs showed a decrease in goodness of fit as reflected by increased root mean square errors(RMSE),compared to the traditional model calibration.The predictions of crop model were further modified using the machine learning(ML)methods and climate indicators.The accuracy of the modified predictions were comparable to what was achieved using the traditional calibration approach.In addition,the Multi-model ensemble(MME)was comparable to that of the best individual model.Implications of our findings can potentially facilitate molecular breeding and phenotypic prediction of rice.展开更多
The integration of genotypic and environmental data can enhance genomic prediction accuracy for crop field traits.Existing genomic prediction methods fail to consider environmental factors and the real growth environm...The integration of genotypic and environmental data can enhance genomic prediction accuracy for crop field traits.Existing genomic prediction methods fail to consider environmental factors and the real growth environments of crops,resulting in low genomic prediction accuracy.In this work,we developed GEFormer,a genotype-environment interaction genomic prediction method that integrates gating multilayer perceptron(gMLP)and linear attention mechanisms.First,GEFormer uses gMLP to extract local and global features among SNPs.Then,Omni-dimensional Dynamic Convolution is used to extract the dynamic and comprehensive features of multiple environmental factors within each day,taking into consideration the real growth pattern of crops.A linear attention mechanism is used to capture the temporal features of environmental changes.Finally,GEFormer uses a gating mechanism to effectively fuse the genomic and environmental features.We examined the accuracy of GEFormer for predicting important agronomic traits of maize,rice,and wheat under three experimental scenarios:untested genotypes in tested environments,tested genotypes in untested environments,and untested genotypes in untested environments.The results showed that GEFormer outperforms six cutting-edge statistical learning methods and four machine learning methods,especially with great advantages under the scenario of untested genotypes in untested environments.In addition,we used GEFormer for three realworld breeding applications:phenotype prediction in unknown environments,hybrid phenotype prediction using an inbred population,and cross-population phenotype prediction.The results showed that GEFormer had better prediction performance in actual breeding scenarios and could be used to assist in crop breeding.展开更多
[Objective] This study aimed to analyze the interaction between genotype of flavonoids of barley grain and environment, to increase the flavonoid content of barley grain in cultivation and breeding. [Method] In this s...[Objective] This study aimed to analyze the interaction between genotype of flavonoids of barley grain and environment, to increase the flavonoid content of barley grain in cultivation and breeding. [Method] In this study, the content of cate- chin, myricetin, quercetin and kaempferol of barley grain planted in Kunming, Qujing and Baoshan were determined by HPLC, and the genotype, environment, genotype- environment interaction of the flavonoid content of barley grain were analyzed. [Result] According to the experimental results, the genotype variance, environmental variance and G x E interaction variance of catechin and kaempferol contents show the same trend： genotype variation 〉 environmental variation 〉 G × E interaction variation, which all reach a extremely significant level; the genotype variance, envi- ronmental variance and G × E interaction variance of quercetin and total flavonoid contents show the same trend： genetype variation 〉 G × E interaction variation 〉 environmental variation, which all reach a extremely significant level; the genotype variance and environmental variance of myricetin content both reach a extremely sig- nificant level, while the G × E interaction variance reaches a significant level, showing an order of genotype variation 〉 environmental variation 〉 G × E interaction variation; the genotype variance, environmental variance and G x E interaction vari- ance of total flavonoid content show an order of genotype variation 〉 environmental variation 〉 G × E interaction variation. Among different barley varieties, Ziguang- mangluoerling and Kuanyingdamai in Qujing, Kunming and Baoshan have relatively high content of quercetin, while other barley varieties barely contain any quercetin. The grains of Ziguangmangluoerling and Kuanyingdamai are purple, while the grains of other barley varieties are yellow. [Conclusion] Four main flavonoids and the total flavonoids of barley grain are mainly under genetic control and affected by genetic- environment interactions; the purple barley grains contain high content of quercetin.展开更多
Larix gmelinii var.principis-rupprechtii(Mayr.)Pilger is an important native tree species in North China with advantages of fast growth,straight trunk,and good wood properties.The multi-year and multi-site breeding re...Larix gmelinii var.principis-rupprechtii(Mayr.)Pilger is an important native tree species in North China with advantages of fast growth,straight trunk,and good wood properties.The multi-year and multi-site breeding research of families of the species has not been reported previously.Based on diameter at breast height(DBH),height and volume of 25 families on four experimental sites,we calculated variance components,genetic parameters,juvenile and mature trait correlations and made genotype main effect plus genotype×environment interaction effect(GGE)biplot based on the breeding values estimated using the method of best linear unbiased prediction(BLUP).Compared with height,DBH and volume had higher heritability and larger variation coefficients,making them the more suitable traits for family selection and evaluation.Based on these,GGE biplots containing 20 combinations of site×age were drawn using data at 13 to 17 years when the interactions between family and location were strong.Test sites classifications based on DBH,and volume were inconsistent,with two categories for DBH and one for volume.The Guyuan site was the most suitable with strong discriminating ability,high representativeness and stability among tree ages.Integrating the ranking results of DBH and volume,families 66,76,82 and 111 were high-yielding and stable,families 78 and96 were high-yielding with above average stability,families72 and 79 were high-yielding with below average stability,whereas stability of family 100 was inconsistent between DBH and volume.Early selection based on DBH was convenient and reliable,and can be made at seven years.This study provides support for the selection of Larix gmelinii var.principis-rupprechtii families in Hebei province and an example for the application of stem analysis data from multiple sites in tree breeding.展开更多
Through the widespread implementation of next-generation sequencing （NGS）, analyses of the whole genome （the entire DNA content） and the whole transcriptome （the genes being expressed） are becoming commonplace. ...Through the widespread implementation of next-generation sequencing （NGS）, analyses of the whole genome （the entire DNA content） and the whole transcriptome （the genes being expressed） are becoming commonplace. NGS enables the analysis of a vast amount of previously unattainable genetic information. Despite this potential, NGS has yet to be widely imple- mented in genetic studies of biological invasions. The study of the genomic causes and consequences of biological invasions al- lows a deeper understanding of the molecular mechanisms underpinning the invasion process. In this review, we present a brief introduction to NGS followed by a synthesis of current research in the genomics and transcriptomics of adaptation and coloniza- tion. We then highlight research opportunities in the field, including： （1） assembling genomes and transcriptomes of non-model organisms, （2） identifying genomic regions and candidate genes underlying evolutionary processes, and （3） studying the adaptive role of gene expression variation. In particular, because introduced species face a broad range of physiological and biotic chal- lenges when colonizing novel and variable environments, transcriptomics will enable the study of gene regulatory pathways that may be responsible for acclimation or adaptation. To conclude, we identify a number of research approaches that will aid our fu- ture understanding of biological invasions展开更多
Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during t...Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.展开更多
The stability of soybean genotypes is very important in breeding programs for not only the evaluation, selection, and production of cultivars but also the establishment of parameters required for the classification of...The stability of soybean genotypes is very important in breeding programs for not only the evaluation, selection, and production of cultivars but also the establishment of parameters required for the classification of genotypes into relative maturity groups (RMG). The aim of this study was to define stable genotypes for traits, such as days to flowering, days to maturity, and length of the reproductive period, and to classify them into RMG. For this purpose, 20 commercial soybean cultivars were evaluated in 12 environments distributed in the major producing regions of Brazil. Assessments according to the Eberhart and Russell method and the additive main effects and multiplicative interaction (AMMI) method were effective in the identification of stable genotypes and their classification into RMG. These methods can also be used collectively for this purpose. Our results showed that the AMMI method led to a better interpretation of genotype-environment interactions. Thus, RMG obtained on the basis of stable genotypes represented a good estimate of the relative maturity of soybean crops throughout Brazil. *Corresponding author.展开更多
Phenotypic plasticity, or the ability to adapt to and thrive in changing climates and variable environments, is essential for developmental programs in plants. Despite its importance, the genetic underpinnings of phen...Phenotypic plasticity, or the ability to adapt to and thrive in changing climates and variable environments, is essential for developmental programs in plants. Despite its importance, the genetic underpinnings of phenotypic plasticity for key agronomic traits remain poorly understood in many crops. In this study, we aim to fill this gap by using genome-wide association studies to identify genetic variations associated with phenotypic plasticity in upland cotton (Gossypium hirsutum L.). We identified 73 additive quantitative trait loci (QTLs), 32 dominant QTLs, and 6799 epistatic QTLs associated with 20 traits. We also identified 117 additive QTLs, 28 dominant QTLs, and 4691 epistatic QTLs associated with phenotypic plasticity in 19 traits. Our findings reveal new genetic factors, including additive, dominant, and epistatic QTLs, that are linked to phenotypic plasticity and agronomic traits. Meanwhile, we find that the genetic factors controlling the mean phenotype and phenotypic plasticity are largely independent in upland cotton, indicating the potential for simultaneous improvement. Additionally, we envision a genomic design strategy by utilizing the identified QTLs to facilitate cotton breeding. Taken together, our study provides new insights into the genetic basis of phenotypic plasticity in cotton, which should be valuable for future breeding.展开更多
Budding speciation is a process wherein a new species arises from a small,isolated population within or at the margin of an ancestral species.Well-documented cases of budding speciation are rare,and the roles of vario...Budding speciation is a process wherein a new species arises from a small,isolated population within or at the margin of an ancestral species.Well-documented cases of budding speciation are rare,and the roles of various evolutionary factors in this process remain controversial.Based on whole-genome resequencing data from 272 individuals across 27 populations,we reconstructed the evolutionary history of Rhodiola sect.Trifida and explored the relative contributions of natural selection,genetic drift,and chromosomal rearrangements as drivers of lineage divergence.We found that all samples of R.chrysanthemifolia(including R.alterna and R.sinuata)were clustered into three clades.Rhodiola liciae was sister to all other samples in the section,likely due to post-divergence gene flow and the minimal population structure of the progenitor species,while it shared the same ancestry with R.ch-I in population structure analyses.The two populations of R.sinuata were not monophyletic,instead clustering with geographically proximate populations of R.ch-III.Demographic analyses revealed that R.liciae underwent a contraction in population size following its divergence from R.ch-I approximately 0.34 million years ago(Mya),and has remained stable since around 0.1 Mya.Genomic islands and genotype-environment association analyses suggested that genetic drift and the assorting of ancestral polymorphism may have played a more significant role in the speciation of R.liciae than nature selection or chromosomal rearrangements.We propose that R.liciae diverged from R.chrysanthemifolia through budding speciation,although post-divergence gene flow has obscured its phylogenetic signal.Additionally,we identified two potential parallel budding speciation events in R.sinuata at an earlier stage than R.liciae.Our study highlights budding speciation as a prevalent yet poorly characterized mode of plant speciation,with assorting of ancestral polymorphism as a key stochastic mechanism in the process.展开更多
Maize（Zea mays L.） root morphology exhibits a high degree of phenotypic plasticity to nitrogen（N） de ficiency,but the underlying genetic architecture remains to be investigated Using an advanced BC_4F_3 population...Maize（Zea mays L.） root morphology exhibits a high degree of phenotypic plasticity to nitrogen（N） de ficiency,but the underlying genetic architecture remains to be investigated Using an advanced BC_4F_3 population,we investigated the root growth plasticity under two contrasted N levels and identi fied the quantitative trait loci（QTLs） with QTL-environment（Q×E）interaction effects. Principal components analysis（PCA） on changes of root traits to N de ficiency（D LN-HN） showed that root length and biomass contributed for 45.8% in the same magnitude and direction on the first PC,while root traits scattered highly on PC_2 and PC_3. Hierarchical cluster analysis on traits for D LN-HN further assigned the BC_4F_3 lines into six groups,in which the special phenotypic responses to N de ficiency was presented These results revealed the complicated root plasticity of maize in response to N de ficiency that can be caused by genotype environment（G×E） interactions. Furthermore,QTL mapping using a multi-environment analysis identi fied 35 QTLs for root traits. Nine of these QTLs exhibited signi ficant Q×E interaction effects. Taken together,our findings contribute to understanding the phenotypic and genotypic pattern of root plasticity to N de ficiency,which will be useful for developing maize tolerance cultivars to N de ficiency.展开更多
Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviro...Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and management.To support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops.Already,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and terrain.This wealth of information,coupled with freely available climate data,fuels innovative enviromics research.While enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research institutions.Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.展开更多
Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques,...Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques,while plant phenotyping has lagged far behind and it has become the rate-limiting factor in genetics, large-scale breeding and development of new cultivars. In this paper,we consider crop phenotyping technology under three categories. The first is high-throughput phenotyping techniques in controlled environments such as greenhouses or specifically designed platforms. The second is a phenotypic strengthening test in semi-controlled environments, especially for traits that are difficult to be tested in multi-environment trials(MET), such as lodging, drought and disease resistance. The third is MET in uncontrolled environments, in which crop plants are managed according to farmer's cultural practices. Research and application of these phenotyping techniques are reviewed and methods for MET improvement proposed.展开更多
基金funded by the National Natural Science Foundation of China(grants number 32070377 and 31770374)Science Fund for Creative Research Groups of the Natural Science Foundation of Hubei Province(2024AFA035).
文摘Local adaptation is critical for plant survivals and reproductions in the context of global environmental change.Heterogeneous environments impose various selection pressures that influence the fitness of organisms and leave genomic signatures during the process of adaptation to local environments.However,unveiling the genomic signatures of adaptation still poses a major challenge especially for perennials due to limited genomic resources.Here,we utilized Actinidia eriantha,a Chinese endemic liana,as a model case to detect drivers of local adaptation and adaptive signals through landscape genomics for 311 individuals collected from 25 populations.Our results demonstrated precipitation and solar radiation were two crucial factors influencing the patterns of genetic variations and driving adaptive processes.We further uncovered a set of genes involved in adaptation to heterogeneous environments.Among them,AeERF110 showed high genetic differentiation between populations and was confirmed to be involved in local adaptation via changes in allele frequency along with precipitation(Prec_03)and solar radiation(Srad_03)in native habitats separately,implying that adaptive loci frequently exhibited environmental and geographic signals.In addition,we assessed genetic offsets of populations under four future climate models and revealed that populations from middle and east clusters faced higher risks in adapting to future environments,which should address more attentions.Taken together,our study opens new perspectives for understanding the genetic underpinnings of local adaptation in plants to environmental changes in a more comprehensive fashion and offered the guides on applications in conservation efforts.
基金supported by the National Key Research and Development Program of China(2022YFD2001001)the Jiangsu Independent Innovation Fund Project of Agricultural Science and Technology[CX(21)1006]+1 种基金the Jiangsu Collaborative Innovation Center for Modern Crop Production(JCICMCP)the 111 Project.
文摘Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simulate plant growth and development processes.In this study,a dataset containing multi-environmental planting and flowering data for 169 genotypes,each with 700K single nucleotide polymorphism(SNP)markers was used.Three rice growth models(ORYZA,CERES-Rice,and RiceGrow),SNPs,and climatic indices were in-tegrated for flowering time prediction.Significant associations between GSPs and quantitative trait nucleotides(QTNs)were investigated using genome-wide association study(GWAS)methods.Several GSPs were associated with previously reported rice flowering genes,including DTH2,DTH3 and OsCOL15,demonstrating the genetic interpretability of the models.The rice models driven by SNPs-based GSPs showed a decrease in goodness of fit as reflected by increased root mean square errors(RMSE),compared to the traditional model calibration.The predictions of crop model were further modified using the machine learning(ML)methods and climate indicators.The accuracy of the modified predictions were comparable to what was achieved using the traditional calibration approach.In addition,the Multi-model ensemble(MME)was comparable to that of the best individual model.Implications of our findings can potentially facilitate molecular breeding and phenotypic prediction of rice.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04076)the Hubei Provincial Natural Science Foundation(2023AFB832)+2 种基金the Natural Science Foundation of Guizhou Province Science and Technology Agency(ZK[2025]096)the Major Project of Hubei Hongshan Laboratory(2022HSZD031)the Yingzi Tech&Huazhong Agricultural University Intelligent Research Institute of Food Health(IRIFH202209).
文摘The integration of genotypic and environmental data can enhance genomic prediction accuracy for crop field traits.Existing genomic prediction methods fail to consider environmental factors and the real growth environments of crops,resulting in low genomic prediction accuracy.In this work,we developed GEFormer,a genotype-environment interaction genomic prediction method that integrates gating multilayer perceptron(gMLP)and linear attention mechanisms.First,GEFormer uses gMLP to extract local and global features among SNPs.Then,Omni-dimensional Dynamic Convolution is used to extract the dynamic and comprehensive features of multiple environmental factors within each day,taking into consideration the real growth pattern of crops.A linear attention mechanism is used to capture the temporal features of environmental changes.Finally,GEFormer uses a gating mechanism to effectively fuse the genomic and environmental features.We examined the accuracy of GEFormer for predicting important agronomic traits of maize,rice,and wheat under three experimental scenarios:untested genotypes in tested environments,tested genotypes in untested environments,and untested genotypes in untested environments.The results showed that GEFormer outperforms six cutting-edge statistical learning methods and four machine learning methods,especially with great advantages under the scenario of untested genotypes in untested environments.In addition,we used GEFormer for three realworld breeding applications:phenotype prediction in unknown environments,hybrid phenotype prediction using an inbred population,and cross-population phenotype prediction.The results showed that GEFormer had better prediction performance in actual breeding scenarios and could be used to assist in crop breeding.
基金Supported by National Barley Industrial Technology System of China(CARS-05)National Natural Science Foundation of China(No.31260326)~~
文摘[Objective] This study aimed to analyze the interaction between genotype of flavonoids of barley grain and environment, to increase the flavonoid content of barley grain in cultivation and breeding. [Method] In this study, the content of cate- chin, myricetin, quercetin and kaempferol of barley grain planted in Kunming, Qujing and Baoshan were determined by HPLC, and the genotype, environment, genotype- environment interaction of the flavonoid content of barley grain were analyzed. [Result] According to the experimental results, the genotype variance, environmental variance and G x E interaction variance of catechin and kaempferol contents show the same trend： genotype variation 〉 environmental variation 〉 G × E interaction variation, which all reach a extremely significant level; the genotype variance, envi- ronmental variance and G × E interaction variance of quercetin and total flavonoid contents show the same trend： genetype variation 〉 G × E interaction variation 〉 environmental variation, which all reach a extremely significant level; the genotype variance and environmental variance of myricetin content both reach a extremely sig- nificant level, while the G × E interaction variance reaches a significant level, showing an order of genotype variation 〉 environmental variation 〉 G × E interaction variation; the genotype variance, environmental variance and G x E interaction vari- ance of total flavonoid content show an order of genotype variation 〉 environmental variation 〉 G × E interaction variation. Among different barley varieties, Ziguang- mangluoerling and Kuanyingdamai in Qujing, Kunming and Baoshan have relatively high content of quercetin, while other barley varieties barely contain any quercetin. The grains of Ziguangmangluoerling and Kuanyingdamai are purple, while the grains of other barley varieties are yellow. [Conclusion] Four main flavonoids and the total flavonoids of barley grain are mainly under genetic control and affected by genetic- environment interactions; the purple barley grains contain high content of quercetin.
基金supported by the Key Research and Development Program of Hebei Province[20326333D]Science and Technology Promotion Demonstration Project of forestry and grassland in Hebei Province[Hebei TG[2019]001]Science and Technology Promotion Demonstration Project of forestry and grassland in Hebei Province[Hebei TG[2020]013]。
文摘Larix gmelinii var.principis-rupprechtii(Mayr.)Pilger is an important native tree species in North China with advantages of fast growth,straight trunk,and good wood properties.The multi-year and multi-site breeding research of families of the species has not been reported previously.Based on diameter at breast height(DBH),height and volume of 25 families on four experimental sites,we calculated variance components,genetic parameters,juvenile and mature trait correlations and made genotype main effect plus genotype×environment interaction effect(GGE)biplot based on the breeding values estimated using the method of best linear unbiased prediction(BLUP).Compared with height,DBH and volume had higher heritability and larger variation coefficients,making them the more suitable traits for family selection and evaluation.Based on these,GGE biplots containing 20 combinations of site×age were drawn using data at 13 to 17 years when the interactions between family and location were strong.Test sites classifications based on DBH,and volume were inconsistent,with two categories for DBH and one for volume.The Guyuan site was the most suitable with strong discriminating ability,high representativeness and stability among tree ages.Integrating the ranking results of DBH and volume,families 66,76,82 and 111 were high-yielding and stable,families 78 and96 were high-yielding with above average stability,families72 and 79 were high-yielding with below average stability,whereas stability of family 100 was inconsistent between DBH and volume.Early selection based on DBH was convenient and reliable,and can be made at seven years.This study provides support for the selection of Larix gmelinii var.principis-rupprechtii families in Hebei province and an example for the application of stem analysis data from multiple sites in tree breeding.
文摘Through the widespread implementation of next-generation sequencing （NGS）, analyses of the whole genome （the entire DNA content） and the whole transcriptome （the genes being expressed） are becoming commonplace. NGS enables the analysis of a vast amount of previously unattainable genetic information. Despite this potential, NGS has yet to be widely imple- mented in genetic studies of biological invasions. The study of the genomic causes and consequences of biological invasions al- lows a deeper understanding of the molecular mechanisms underpinning the invasion process. In this review, we present a brief introduction to NGS followed by a synthesis of current research in the genomics and transcriptomics of adaptation and coloniza- tion. We then highlight research opportunities in the field, including： （1） assembling genomes and transcriptomes of non-model organisms, （2） identifying genomic regions and candidate genes underlying evolutionary processes, and （3） studying the adaptive role of gene expression variation. In particular, because introduced species face a broad range of physiological and biotic chal- lenges when colonizing novel and variable environments, transcriptomics will enable the study of gene regulatory pathways that may be responsible for acclimation or adaptation. To conclude, we identify a number of research approaches that will aid our fu- ture understanding of biological invasions
文摘Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.
文摘The stability of soybean genotypes is very important in breeding programs for not only the evaluation, selection, and production of cultivars but also the establishment of parameters required for the classification of genotypes into relative maturity groups (RMG). The aim of this study was to define stable genotypes for traits, such as days to flowering, days to maturity, and length of the reproductive period, and to classify them into RMG. For this purpose, 20 commercial soybean cultivars were evaluated in 12 environments distributed in the major producing regions of Brazil. Assessments according to the Eberhart and Russell method and the additive main effects and multiplicative interaction (AMMI) method were effective in the identification of stable genotypes and their classification into RMG. These methods can also be used collectively for this purpose. Our results showed that the AMMI method led to a better interpretation of genotype-environment interactions. Thus, RMG obtained on the basis of stable genotypes represented a good estimate of the relative maturity of soybean crops throughout Brazil. *Corresponding author.
基金This study was supported by the National Key Research and Development Program of China(2021YFF1000900)the National Natural Science Foundation of China(32170645)This study was also supported by the Foundation of Hubei Hongshan Laboratory(2021hszd014)。
文摘Phenotypic plasticity, or the ability to adapt to and thrive in changing climates and variable environments, is essential for developmental programs in plants. Despite its importance, the genetic underpinnings of phenotypic plasticity for key agronomic traits remain poorly understood in many crops. In this study, we aim to fill this gap by using genome-wide association studies to identify genetic variations associated with phenotypic plasticity in upland cotton (Gossypium hirsutum L.). We identified 73 additive quantitative trait loci (QTLs), 32 dominant QTLs, and 6799 epistatic QTLs associated with 20 traits. We also identified 117 additive QTLs, 28 dominant QTLs, and 4691 epistatic QTLs associated with phenotypic plasticity in 19 traits. Our findings reveal new genetic factors, including additive, dominant, and epistatic QTLs, that are linked to phenotypic plasticity and agronomic traits. Meanwhile, we find that the genetic factors controlling the mean phenotype and phenotypic plasticity are largely independent in upland cotton, indicating the potential for simultaneous improvement. Additionally, we envision a genomic design strategy by utilizing the identified QTLs to facilitate cotton breeding. Taken together, our study provides new insights into the genetic basis of phenotypic plasticity in cotton, which should be valuable for future breeding.
基金supported by the National Natural Science Foundation of China(grant nos.:32070236 and 32370226)the Innovation Capability Support Program of Shaanxi(No.2023KJXX-019)the Fundamental Research Funds for the Central Universities(No.GK202301008 to J.Q.Zhang,and No.LHRCCX23183 to L.Huang).
文摘Budding speciation is a process wherein a new species arises from a small,isolated population within or at the margin of an ancestral species.Well-documented cases of budding speciation are rare,and the roles of various evolutionary factors in this process remain controversial.Based on whole-genome resequencing data from 272 individuals across 27 populations,we reconstructed the evolutionary history of Rhodiola sect.Trifida and explored the relative contributions of natural selection,genetic drift,and chromosomal rearrangements as drivers of lineage divergence.We found that all samples of R.chrysanthemifolia(including R.alterna and R.sinuata)were clustered into three clades.Rhodiola liciae was sister to all other samples in the section,likely due to post-divergence gene flow and the minimal population structure of the progenitor species,while it shared the same ancestry with R.ch-I in population structure analyses.The two populations of R.sinuata were not monophyletic,instead clustering with geographically proximate populations of R.ch-III.Demographic analyses revealed that R.liciae underwent a contraction in population size following its divergence from R.ch-I approximately 0.34 million years ago(Mya),and has remained stable since around 0.1 Mya.Genomic islands and genotype-environment association analyses suggested that genetic drift and the assorting of ancestral polymorphism may have played a more significant role in the speciation of R.liciae than nature selection or chromosomal rearrangements.We propose that R.liciae diverged from R.chrysanthemifolia through budding speciation,although post-divergence gene flow has obscured its phylogenetic signal.Additionally,we identified two potential parallel budding speciation events in R.sinuata at an earlier stage than R.liciae.Our study highlights budding speciation as a prevalent yet poorly characterized mode of plant speciation,with assorting of ancestral polymorphism as a key stochastic mechanism in the process.
基金supported by the Ministry of Science and Technology of China(2011CB100305,2012AA100304)National Natural Science Foundation of China(31172015,31421092,31572186)+2 种基金Danish Strategic Research Council(NUTRIEFFICIENT 10-093498)European Community the Seventh Framework Programme for Research(NUE-CROPSFP7-CP-IP 222645)Chinese Universities Scientific Fund(2015ZH001)
文摘Maize（Zea mays L.） root morphology exhibits a high degree of phenotypic plasticity to nitrogen（N） de ficiency,but the underlying genetic architecture remains to be investigated Using an advanced BC_4F_3 population,we investigated the root growth plasticity under two contrasted N levels and identi fied the quantitative trait loci（QTLs） with QTL-environment（Q×E）interaction effects. Principal components analysis（PCA） on changes of root traits to N de ficiency（D LN-HN） showed that root length and biomass contributed for 45.8% in the same magnitude and direction on the first PC,while root traits scattered highly on PC_2 and PC_3. Hierarchical cluster analysis on traits for D LN-HN further assigned the BC_4F_3 lines into six groups,in which the special phenotypic responses to N de ficiency was presented These results revealed the complicated root plasticity of maize in response to N de ficiency that can be caused by genotype environment（G×E） interactions. Furthermore,QTL mapping using a multi-environment analysis identi fied 35 QTLs for root traits. Nine of these QTLs exhibited signi ficant Q×E interaction effects. Taken together,our findings contribute to understanding the phenotypic and genotypic pattern of root plasticity to N de ficiency,which will be useful for developing maize tolerance cultivars to N de ficiency.
基金R.T.R.,L.L.P.,and G.E.M.thank the Brazilian agencies Coordenac¸ao de Aperfeic¸oamento de Pessoal de Nıvel Superior(CAPES)and Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico(CNPq)for the financial support,which was instrumental in the successful execution of this project.L.H.was supported through an ARC Future Fellowship(FT220100350)from the Australian Research Council.C.H.A.was supported by The University of Colorado Boulder Grand ChallengeCIRES Earth Lab.Y.X.was supported by the Agricultural Science and Technology Innovation Program(ASTIP)of the Chinese Academy of Agricultural Sciences,Shenzhen Science and Technology Program(KQTD202303010928390070)Hebei Science and Technology Program(215A7612D),and the Provincial Technology Innovation Program of Shandong,China.
文摘Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and management.To support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops.Already,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and terrain.This wealth of information,coupled with freely available climate data,fuels innovative enviromics research.While enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research institutions.Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
基金supported by the National Natural Science Foundation(Spatial Distribution of Multi-environment Trial Stations for Maize Cultivar,41301075)the National Science-technology Support Plan Projects(Research and Demonstration of North China Corn Commercialized Breeding Technique,2014BAD01B01)Key Laboratory of Agricultural Information Acquisition Technology,Ministry of Agriculture
文摘Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques,while plant phenotyping has lagged far behind and it has become the rate-limiting factor in genetics, large-scale breeding and development of new cultivars. In this paper,we consider crop phenotyping technology under three categories. The first is high-throughput phenotyping techniques in controlled environments such as greenhouses or specifically designed platforms. The second is a phenotypic strengthening test in semi-controlled environments, especially for traits that are difficult to be tested in multi-environment trials(MET), such as lodging, drought and disease resistance. The third is MET in uncontrolled environments, in which crop plants are managed according to farmer's cultural practices. Research and application of these phenotyping techniques are reviewed and methods for MET improvement proposed.
