1000-Grain weight and spikelet number per panicle are two important components for rice grain yield. In our previous study, eight quantitative trait loci （QTLs） conferring spikelet number per panicle and 1000-grain ...1000-Grain weight and spikelet number per panicle are two important components for rice grain yield. In our previous study, eight quantitative trait loci （QTLs） conferring spikelet number per panicle and 1000-grain weight were mapped through sequencing-based genotyping of 150 rice recombinant inbred lines （RILs）. In this study, we validated the effects of four QTLs from Nipponbare using chromosome segment substitution lines （CSSLs）, and pyramided eight grain yield related QTLs. The new lines containing the eight QTLs with positive effects showed increased panicle and spikelet size as compared with the parent variety 93-11. We further proposed a novel pyramid breeding scheme based on marker-assistant and phenotype selection （MAPS）. This scheme allowed pyramiding of as many as 24 QTLs at a single hybridization without massive cross work. This study provided insights into the molecular basis of rice grain yield for direct wealth for high-yielding rice breeding.展开更多
Background:Artificial insemination is a preferred breeding method for beef heifers as it advances the genetic background,produces a predictive and profitable calving season,and extends the heifer’s reproductive life ...Background:Artificial insemination is a preferred breeding method for beef heifers as it advances the genetic background,produces a predictive and profitable calving season,and extends the heifer’s reproductive life span.As reproductive efficiency in heifers is key for the success of beef cattle production systems,following artificial insemination,heifers are exposed to a bull for the remainder of the breeding season.Altogether,up to 95%of heifers might become pregnant in their first breeding season.Heifers that do not become pregnant at the end of the breeding season represent an irreparable economical loss.Additionally,heifers conceiving late in the breeding season to natural service,although acceptable,poses serious losses to producers.To minimize losses due to reproductive failure,different phenotypic parameters can be assessed and utilized as selection tools.Here,we tested the hypothesis that in a group of pre-selected heifers,records of weaning weight,age at weaning,age at artificial insemination,and age of dam differ among heifers of varied reproductive outcomes during the first breeding season.Results:None of the parameters tested presented predictive ability to discriminate the heifers based on the response variable(‘pregnant to artificial insemination’,‘pregnant to natural service’,‘not pregnant’).Heifers categorized with body condition score=6 and reproductive tract score≥4 had the greatest proportion of pregnancy to artificial insemination(49%and 44%,respectively).Furthermore,it was notable that heifers presenting body condition score=6 and reproductive tract score=5 presented the greatest pregnancy rate at end of the breeding season(89%).Heifers younger than 368 d at the start of the breeding season did not become pregnant to artificial insemination.Those young heifers had 12.5%chance to become pregnant in their first breeding season,compared to 87.5%if the heifers were older than 368 days.Conclusion:Our results suggest that beef heifers with body condition score=6 and reproductive tract score≥4 are more likely to become pregnant to artificial insemination.Careful assessment should be undertaken when developing replacement heifers that will not reach 12 months of age by the beginning of the breeding season.展开更多
Erratic rainfall often results in intermittent drought and/or waterlogging and limits maize(Zea mays L.)productivity in many parts of the Asian tropics.Developing climate-resilient maize germplasm possessing tolerance...Erratic rainfall often results in intermittent drought and/or waterlogging and limits maize(Zea mays L.)productivity in many parts of the Asian tropics.Developing climate-resilient maize germplasm possessing tolerance to these key abiotic stresses without a yield penalty under optimal growing conditions is a challenge for breeders working in stress-vulnerable agro-ecologies in the region.Breeding stress-resilient maize for rainfed stress-prone ecologies is identified as one of the priority areas for CIMMYT-Asia maize program.We applied rapid cycle genomic selection(RCGS)on two multiparent yellow synthetic populations(MYS-1 and MYS-2)to improve grain yield simultaneously under drought and waterlogging conditions using genomic-estimated breeding values(GEBVs).Also,the populations were simultaneously advanced using recurrent phenotypic selection(PS)by exposing them to managed drought and waterlogging and intermating tolerant plants from the two selection environments.Selection cycles per se(C1,C2,and C3)of the two populations developed using RCGS and PS approach and their test-cross progenies were evaluated separately in multilocation trials under managed drought,waterlogging,and optimal moisture conditions.Significant genetic gains were observed with both GS and PS,except with PS in MYS-2 under drought and with GS in MYS-1 under waterlogging.Realized genetic gains from GS were relatively higher under drought conditions(110 and 135 kg ha^(-1) year^(-1))compared to waterlogging(38 and 113 kg ha^(-1) year^(-1))in both MYS-1 and MYS-2,respectively.However,under waterlogging stress PS showed at par or better than GS as gain per year with PS was 80 and 90 kg ha^(-1),whereas with GS it was 90 and 43 kg ha^(-1) for MYS-1 and MYS-2,respectively.Our findings suggested that careful constitution of a multiparent population by involving trait donors for targeted stresses,along with elite highyielding parents from diverse genetic background,and its improvement using RCGS is an effective breeding approach to build multiple stress tolerance without compromising yield when tested under optimal conditions.展开更多
Genomic selection(GS)and phenotypic selection(PS)are widely used for accelerating plant breeding.However,the accuracy,robustness,and transferability of these two selection methods are underexplored,especially when add...Genomic selection(GS)and phenotypic selection(PS)are widely used for accelerating plant breeding.However,the accuracy,robustness,and transferability of these two selection methods are underexplored,especially when addressing complex traits.In this study,we introduce a novel data fusion framework,GPS(genomic and phenotypic selection),designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies:data fusion,feature fusion,and result fusion.The GPS framework was rigorously tested using an extensive suite of models,including statistical approaches(GBLUP and BayesB),machine learning models(Lasso,RF,SVM,XGBoost,and LightGBM),a deep learning method(DNNGP),and a recent phenotype-assisted prediction model(MAK).These models were applied to large datasets from four crop species,maize,soybean,rice,and wheat,demonstrating the versatility and robustness of the framework.Our results indicated that:(1)data fusion achieved the highest accuracy compared with the feature fusion and result fusion strategies.The top-performing data fusion model(Lasso_D)improved the selection accuracy by 53.4%compared to the best GS model(LightGBM)and by 18.7%compared to the best PS model(Lasso).(2)Lasso_D exhibited exceptional robustness,achieving high predictive accuracy even with a sample size as small as 200 and demonstrating resilience to single-nucleotide polymorphism(SNP)density variations,underscoring its adaptability to diverse data conditions.Moreover,the model’s accuracy improved with the number of auxiliary traits and their correlation strength with target traits,further highlighting its adaptability to complex trait prediction.(3)Lasso_D demonstrated broad transferability,with substantial improvements in predictive accuracy when incorporating multi-environmental data.This enhancement resulted in only a 0.3%reduction in accuracy compared to predictions generated using data from the same environment,affirming the model’s reliability in crossenvironmental scenarios.This study provides groundbreaking insights,pushing the boundaries of predictive accuracy,robustness,and transferability in trait prediction.These findings represent a significant contribution to plant science,plant breeding,and the broader interdisciplinary fields of statistics and artificial intelligence.展开更多
Aims Reproductive fitness of different floral phenotypes varies within and/or among populations.These variations are important to understand the process of natural selection and the evolution of floral traits.In this ...Aims Reproductive fitness of different floral phenotypes varies within and/or among populations.These variations are important to understand the process of natural selection and the evolution of floral traits.In this study,we focused on a distylous,self-incompat-ible species,Primula poissonii,to investigate fitness-related selec-tion on floral traits.Our aim was to determine how traits vary as targets of natural selection and whether morph-specific selection occurs.Methods This study was conducted at two sites(Yushuizhai at 2700 m and Haligu at 3200 m)in the Lijiang Alpine Botanical Garden,northwest-ern Yunnan,southwestern China.Insects visiting flowers of P.pois-sonii were observed,captured and identified.Randomly selected plants of long-and short-styled morphs were labeled.Five floral/inflorescence traits were measured including floral display,corolla width(CW),floral tube length(FTL),tube opening width(TOW)and floral scape height.Fruit and seed set were recorded.The total num-ber of seeds per individual plant(plant fitness)and seed production per capsule(flower fitness)were calculated.Multiple regression analyses were used to quantify selection gradients.Important Findings The frequencies of the two morphs did not deviate from the expected 1:1 ratio at both sites.Except for FTL,the four other traits did not dif-fer significantly between the long-and short-styled morphs.Floral scape height,floral display and FTL differed between two sites.The selection regimes differed between two morphs and between two sites.At the Yushuizhai site,linear selection for shorter floral tubes was stronger in the short-styled morph.However,nonlinear selec-tion on the floral display was stronger in the long-styled morph than selection on the short-styled morph.At the Haligu site,linear selec-tion for a smaller corolla was stronger in the long-styled morph.A morph-specific nonlinear selection on CW and floral display was also detected.Morph-specific selections were detected through the estimation of flower fitness only in Haligu population.In this site,morph-specific linear selection was also detected for CW and floral display.Morph-specific nonlinear selection on traits was detected only in CW.We found that butterflies and sphingid moths dominated at Yushuizhai,while long-tongued bees dominated at Haligu.The difference in pollinator fauna suggested that selection on floral tubes may be due to differences in pollinator assemblages.Overall,variation of floral and/or inflorescence traits in P.poissonii was probably driven by pollinator selection.Selection regime dif-ferences between two morphs,in part,due the inter-morph diver-gences of sexual functions in distylous plant.展开更多
Aims In multiflowered species,the architecture of inflorescences is of primary importance in shaping plant attractiveness.The aim of this study was to disentangle the role of inflorescence traits in plant female repro...Aims In multiflowered species,the architecture of inflorescences is of primary importance in shaping plant attractiveness.The aim of this study was to disentangle the role of inflorescence traits in plant female reproductive success and pollination patterns along the inflorescence in the lax-flowered orchid Anacamptis laxiflora,a terrestrial species exploiting a deceptive pollination strategy.We also evaluated whether the relationship between inflorescence traits and female reproductive success was modified by the height of surrounding vegetation and/or by population density.Methods We delimited experimental plots in a natural population of A.laxiflora.We tallied the individuals within each plot and categorized low-density plots and high-density plots;then,in part of the plots we manually removed surrounding grass thus producing an equal number of plots with high grass and low grass.Within these plots,we recorded inflorescence traits and female reproductive success(i.e.the number of fruit and their position along the inflorescence).We analyzed these data using generalized linear mixed-effects models(GLMMs)and calculated selection gradients.Important Findings We found that all the investigated inflorescence traits influenced female reproductive success.In particular,our GLMMs showed that'average flower distance'was the best predictor for shaping reproductive success patterns.We detected significant positive selection on the investigated inflorescence traits,but these selective trends were strictly linked to both the height of the surrounding vegetation and the population density,suggesting a significant influence of local environmental context in shaping selective patterns.Female reproductive success was not linked to the position of flowers along the inflorescence,suggesting that pollinators visit flowers randomly along the inflorescence without a detectable preference for a specific part.This study highlights the importance of inflorescence traits in shaping female reproductive success of multiflowered deceptive orchids,and confirms a primary role for the environmental context in modifying pollinator-mediated selection patterns.展开更多
基金supported by the Ministry of Science and Technology(Grant No. 2011 CB 100205)the Ministry of Agriculture of China (Grant Nos.2011ZX08001-004 and 2011ZX08009-002)the National Natural Science Foundation of China(Grant No. 31121063)
文摘1000-Grain weight and spikelet number per panicle are two important components for rice grain yield. In our previous study, eight quantitative trait loci （QTLs） conferring spikelet number per panicle and 1000-grain weight were mapped through sequencing-based genotyping of 150 rice recombinant inbred lines （RILs）. In this study, we validated the effects of four QTLs from Nipponbare using chromosome segment substitution lines （CSSLs）, and pyramided eight grain yield related QTLs. The new lines containing the eight QTLs with positive effects showed increased panicle and spikelet size as compared with the parent variety 93-11. We further proposed a novel pyramid breeding scheme based on marker-assistant and phenotype selection （MAPS）. This scheme allowed pyramiding of as many as 24 QTLs at a single hybridization without massive cross work. This study provided insights into the molecular basis of rice grain yield for direct wealth for high-yielding rice breeding.
基金partially funded by the Alabama Agricultural Experiment Stationthe Hatch program of the National Institute of Food and Agriculture,U.S.Department of Agriculture
文摘Background:Artificial insemination is a preferred breeding method for beef heifers as it advances the genetic background,produces a predictive and profitable calving season,and extends the heifer’s reproductive life span.As reproductive efficiency in heifers is key for the success of beef cattle production systems,following artificial insemination,heifers are exposed to a bull for the remainder of the breeding season.Altogether,up to 95%of heifers might become pregnant in their first breeding season.Heifers that do not become pregnant at the end of the breeding season represent an irreparable economical loss.Additionally,heifers conceiving late in the breeding season to natural service,although acceptable,poses serious losses to producers.To minimize losses due to reproductive failure,different phenotypic parameters can be assessed and utilized as selection tools.Here,we tested the hypothesis that in a group of pre-selected heifers,records of weaning weight,age at weaning,age at artificial insemination,and age of dam differ among heifers of varied reproductive outcomes during the first breeding season.Results:None of the parameters tested presented predictive ability to discriminate the heifers based on the response variable(‘pregnant to artificial insemination’,‘pregnant to natural service’,‘not pregnant’).Heifers categorized with body condition score=6 and reproductive tract score≥4 had the greatest proportion of pregnancy to artificial insemination(49%and 44%,respectively).Furthermore,it was notable that heifers presenting body condition score=6 and reproductive tract score=5 presented the greatest pregnancy rate at end of the breeding season(89%).Heifers younger than 368 d at the start of the breeding season did not become pregnant to artificial insemination.Those young heifers had 12.5%chance to become pregnant in their first breeding season,compared to 87.5%if the heifers were older than 368 days.Conclusion:Our results suggest that beef heifers with body condition score=6 and reproductive tract score≥4 are more likely to become pregnant to artificial insemination.Careful assessment should be undertaken when developing replacement heifers that will not reach 12 months of age by the beginning of the breeding season.
基金funders BMZ/GIZ,Germany for the financial support to the project “Climate-resilient maize for Asia”(Project No.15.7860.8-001.00)Financial support from the CGIAR Research Program MAIZE towards supporting part of staff time through W1/2。
文摘Erratic rainfall often results in intermittent drought and/or waterlogging and limits maize(Zea mays L.)productivity in many parts of the Asian tropics.Developing climate-resilient maize germplasm possessing tolerance to these key abiotic stresses without a yield penalty under optimal growing conditions is a challenge for breeders working in stress-vulnerable agro-ecologies in the region.Breeding stress-resilient maize for rainfed stress-prone ecologies is identified as one of the priority areas for CIMMYT-Asia maize program.We applied rapid cycle genomic selection(RCGS)on two multiparent yellow synthetic populations(MYS-1 and MYS-2)to improve grain yield simultaneously under drought and waterlogging conditions using genomic-estimated breeding values(GEBVs).Also,the populations were simultaneously advanced using recurrent phenotypic selection(PS)by exposing them to managed drought and waterlogging and intermating tolerant plants from the two selection environments.Selection cycles per se(C1,C2,and C3)of the two populations developed using RCGS and PS approach and their test-cross progenies were evaluated separately in multilocation trials under managed drought,waterlogging,and optimal moisture conditions.Significant genetic gains were observed with both GS and PS,except with PS in MYS-2 under drought and with GS in MYS-1 under waterlogging.Realized genetic gains from GS were relatively higher under drought conditions(110 and 135 kg ha^(-1) year^(-1))compared to waterlogging(38 and 113 kg ha^(-1) year^(-1))in both MYS-1 and MYS-2,respectively.However,under waterlogging stress PS showed at par or better than GS as gain per year with PS was 80 and 90 kg ha^(-1),whereas with GS it was 90 and 43 kg ha^(-1) for MYS-1 and MYS-2,respectively.Our findings suggested that careful constitution of a multiparent population by involving trait donors for targeted stresses,along with elite highyielding parents from diverse genetic background,and its improvement using RCGS is an effective breeding approach to build multiple stress tolerance without compromising yield when tested under optimal conditions.
基金supported in part by the National Key Research and Development Program of China(2022YFD2300700)the Fundamental Research Funds for the Central Universities(YDZX2025021,KYT2024005,QTPY2025006)+9 种基金the Jiangsu Province Key Research and Development Program(BE2023369)the Natural Science Foundation of Jiangsu Province(BK20231469)the Hainan Yazhou Bay Seed Laboratory(B21H J1005)the National Natural Science Foundation of China(32201656)the Sichuan Provincial Finance Department Project of China(1+3 ZYGG001)the JBGS Project of Seed Industry Revitalization in Jiangsu Province(JBGS[2021]007)the Young Elite Scientists Sponsorship Program by CAST(YESS)the Science and Technology Innovation 2030-Major Project(2023ZD04034,2023ZD0405605)the Zhongshan Biological Breeding Laboratory(ZSBBL-KY2023-03)the Jiangsu Provincial Special Fund for Basic Research(Major Innovation Platform Plan)(BM2024005).
文摘Genomic selection(GS)and phenotypic selection(PS)are widely used for accelerating plant breeding.However,the accuracy,robustness,and transferability of these two selection methods are underexplored,especially when addressing complex traits.In this study,we introduce a novel data fusion framework,GPS(genomic and phenotypic selection),designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies:data fusion,feature fusion,and result fusion.The GPS framework was rigorously tested using an extensive suite of models,including statistical approaches(GBLUP and BayesB),machine learning models(Lasso,RF,SVM,XGBoost,and LightGBM),a deep learning method(DNNGP),and a recent phenotype-assisted prediction model(MAK).These models were applied to large datasets from four crop species,maize,soybean,rice,and wheat,demonstrating the versatility and robustness of the framework.Our results indicated that:(1)data fusion achieved the highest accuracy compared with the feature fusion and result fusion strategies.The top-performing data fusion model(Lasso_D)improved the selection accuracy by 53.4%compared to the best GS model(LightGBM)and by 18.7%compared to the best PS model(Lasso).(2)Lasso_D exhibited exceptional robustness,achieving high predictive accuracy even with a sample size as small as 200 and demonstrating resilience to single-nucleotide polymorphism(SNP)density variations,underscoring its adaptability to diverse data conditions.Moreover,the model’s accuracy improved with the number of auxiliary traits and their correlation strength with target traits,further highlighting its adaptability to complex trait prediction.(3)Lasso_D demonstrated broad transferability,with substantial improvements in predictive accuracy when incorporating multi-environmental data.This enhancement resulted in only a 0.3%reduction in accuracy compared to predictions generated using data from the same environment,affirming the model’s reliability in crossenvironmental scenarios.This study provides groundbreaking insights,pushing the boundaries of predictive accuracy,robustness,and transferability in trait prediction.These findings represent a significant contribution to plant science,plant breeding,and the broader interdisciplinary fields of statistics and artificial intelligence.
基金The Joint Fund of the National Natural Science Foundation of China-Yunnan Province(U1502261)the National Key Basic Research Program of China(2014CB954100)+1 种基金the Major International Joint Research Project of National Natural Science Foundation of China(31320103919)a visiting professorship for senior international scientists of Chinese Academy of Sciences to L.M.W(2012T1S0006).
文摘Aims Reproductive fitness of different floral phenotypes varies within and/or among populations.These variations are important to understand the process of natural selection and the evolution of floral traits.In this study,we focused on a distylous,self-incompat-ible species,Primula poissonii,to investigate fitness-related selec-tion on floral traits.Our aim was to determine how traits vary as targets of natural selection and whether morph-specific selection occurs.Methods This study was conducted at two sites(Yushuizhai at 2700 m and Haligu at 3200 m)in the Lijiang Alpine Botanical Garden,northwest-ern Yunnan,southwestern China.Insects visiting flowers of P.pois-sonii were observed,captured and identified.Randomly selected plants of long-and short-styled morphs were labeled.Five floral/inflorescence traits were measured including floral display,corolla width(CW),floral tube length(FTL),tube opening width(TOW)and floral scape height.Fruit and seed set were recorded.The total num-ber of seeds per individual plant(plant fitness)and seed production per capsule(flower fitness)were calculated.Multiple regression analyses were used to quantify selection gradients.Important Findings The frequencies of the two morphs did not deviate from the expected 1:1 ratio at both sites.Except for FTL,the four other traits did not dif-fer significantly between the long-and short-styled morphs.Floral scape height,floral display and FTL differed between two sites.The selection regimes differed between two morphs and between two sites.At the Yushuizhai site,linear selection for shorter floral tubes was stronger in the short-styled morph.However,nonlinear selec-tion on the floral display was stronger in the long-styled morph than selection on the short-styled morph.At the Haligu site,linear selec-tion for a smaller corolla was stronger in the long-styled morph.A morph-specific nonlinear selection on CW and floral display was also detected.Morph-specific selections were detected through the estimation of flower fitness only in Haligu population.In this site,morph-specific linear selection was also detected for CW and floral display.Morph-specific nonlinear selection on traits was detected only in CW.We found that butterflies and sphingid moths dominated at Yushuizhai,while long-tongued bees dominated at Haligu.The difference in pollinator fauna suggested that selection on floral tubes may be due to differences in pollinator assemblages.Overall,variation of floral and/or inflorescence traits in P.poissonii was probably driven by pollinator selection.Selection regime dif-ferences between two morphs,in part,due the inter-morph diver-gences of sexual functions in distylous plant.
文摘Aims In multiflowered species,the architecture of inflorescences is of primary importance in shaping plant attractiveness.The aim of this study was to disentangle the role of inflorescence traits in plant female reproductive success and pollination patterns along the inflorescence in the lax-flowered orchid Anacamptis laxiflora,a terrestrial species exploiting a deceptive pollination strategy.We also evaluated whether the relationship between inflorescence traits and female reproductive success was modified by the height of surrounding vegetation and/or by population density.Methods We delimited experimental plots in a natural population of A.laxiflora.We tallied the individuals within each plot and categorized low-density plots and high-density plots;then,in part of the plots we manually removed surrounding grass thus producing an equal number of plots with high grass and low grass.Within these plots,we recorded inflorescence traits and female reproductive success(i.e.the number of fruit and their position along the inflorescence).We analyzed these data using generalized linear mixed-effects models(GLMMs)and calculated selection gradients.Important Findings We found that all the investigated inflorescence traits influenced female reproductive success.In particular,our GLMMs showed that'average flower distance'was the best predictor for shaping reproductive success patterns.We detected significant positive selection on the investigated inflorescence traits,but these selective trends were strictly linked to both the height of the surrounding vegetation and the population density,suggesting a significant influence of local environmental context in shaping selective patterns.Female reproductive success was not linked to the position of flowers along the inflorescence,suggesting that pollinators visit flowers randomly along the inflorescence without a detectable preference for a specific part.This study highlights the importance of inflorescence traits in shaping female reproductive success of multiflowered deceptive orchids,and confirms a primary role for the environmental context in modifying pollinator-mediated selection patterns.
