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
Rice yield is a complex trait affected by many related traits.Traditional single-trait genome-wide association studies(GWAS)have limitations when studying complex traits,as they cannot account for the genetic relation...Rice yield is a complex trait affected by many related traits.Traditional single-trait genome-wide association studies(GWAS)have limitations when studying complex traits,as they cannot account for the genetic relationships among multiple traits.Multi-trait GWAS can consider the relationships among multiple traits and identify pleiotropic loci,so it is more suitable for complex traits such as rice yield than single-trait GWAS.In this study,we conducted a multi-trait GWAS on 11 two-trait combinations of yield and yield-related traits with 575 hybrid rice varieties across two environments.All these yield-related traits showed significant genetic correlations with yield(YD),including filled grains per panicle(FGPP),1,000-grain weight(KGW),tillers per plant(TP),primary branch number(PB),secondary branch number(SB),and main panicle length(MPL).In total,we identified 44 pleiotropic quantitative trait loci(p QTLs),including 29 new p QTLs not found in a single-trait GWAS.We then screened 23 p QTLs showing common effects in two traits as key p QTLs.These key p QTLs were subsequently analyzed by haplotype analysis,which identified 13 pleiotropic candidate genes.Finally,we identified two optimal yieldenhancing allele combinations by pyramiding the superior haplotypes:GS3-GL3.1-Os CIPK17 for the YD-KGW combination and GNP12 for the YD-FGPP and YD-SB combinations.This study provides pleiotropic candidate genes and allele combinations that exhibit superior differences in both yield and yield-related traits,offering valuable information for future high-yielding rice breeding.展开更多
Agronomic traits in maize(Zea mays L.)are complex and modulated by pleiotropic loci and interconnected genetic networks.However,the traditional single-trait genome-wide association study(GWAS)method often misses genet...Agronomic traits in maize(Zea mays L.)are complex and modulated by pleiotropic loci and interconnected genetic networks.However,the traditional single-trait genome-wide association study(GWAS)method often misses genetic associations among traits,overlooks pleiotropic effects,and underestimates shared regulatory mechanisms.In the current study,we employed multi-trait analysis of GWAS(MTAG)and constructed a genetic network to dissect the genetic architecture of 18 agronomic traits across a genetically diverse panel of 2,448 maize inbred lines.Incorporating MTAG significantly improved the detection of pleiotropic loci that had not been detected by single-trait GWAS.Using a genetic network,we uncovered numerous previously unrecognized connections among traits related to plant architecture,yield,and flowering time.The 49 detected hub nodes,including Zm00001d028840 and Zm00001d033859(knotted1),influence multiple traits.Co-expression analysis of candidate genes across two developmental stages validated their distinct yet complementary roles,with Zm00001d028840 linked to early cell wall remodeling and Zm00001d033849 involved in chromatin remodeling during tasseling.Moreover,we integrated results from GWAS,MTAG,and genetic network analyses to prioritize pleiotropic loci and hub genes that regulate multiple agronomic traits.This integrative approach offers a practical framework for selecting stable,multi-trait-associated targets,thereby supporting more precise and efficient crop improvement strategies.展开更多
Yield prediction is the primary goal of genomic selection(GS)-assisted crop breeding.Because yield is a complex quantitative trait,making predictions from genotypic data is challenging.Transfer learning can produce an...Yield prediction is the primary goal of genomic selection(GS)-assisted crop breeding.Because yield is a complex quantitative trait,making predictions from genotypic data is challenging.Transfer learning can produce an effective model for a target task by leveraging knowledge from a different,but related,source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data.However,it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework.We therefore developed TrG2P,a transfer-learning-based framework.TrG2P first employs convolutional neural networks(CNN)to train models using non-yield-trait phenotypic and genotypic data,thus obtaining pre-trained models.Subsequently,the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task,and the fully connected layers are retrained,thus obtaining fine-tuned models.Finally,the convolutional layer and the first fully connected layer of the fine-tuned models are fused,and the last fully connected layer is trained to enhance prediction performance.We applied TrG2P to five sets of genotypic and phenotypic data from maize(Zea mays),rice(Oryza sativa),and wheat(Triticum aestivum)and compared its model precision to that of seven other popular GS tools:ridge regression best linear unbiased prediction(rrBLUP),random forest,support vector regression,light gradient boosting machine(LightGBM),CNN,DeepGS,and deep neural network for genomic prediction(DNNGP).TrG2P improved the accuracy of yield prediction by 39.9%,6.8%,and 1.8%in rice,maize,and wheat,respectively,compared with predictions generated by the best-performing comparison model.Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data.We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation.The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P.The web-based tool is available at http://trg2p.ebreed.cn:81.展开更多
Two clonal trial stands of Chinese Fir （Cunninghamia lanceolata） were used in this study, one was 19-year-old stand which included 38 clones, and the other was 17-year-old stand including 102 clones.The statistical ...Two clonal trial stands of Chinese Fir （Cunninghamia lanceolata） were used in this study, one was 19-year-old stand which included 38 clones, and the other was 17-year-old stand including 102 clones.The statistical analyses showed that there were very significant genetic variations in height, DBH,volume and ratio of heartwood(R<sub>hw</sub>),wood basic density（ρ<sub>b</sub> ） of the clones in the two stands. The repeatability of clones was in median to high level,and the genetic CV was different over the all five traits.There were very significant phenotypic and genetic correlations among height,DBH and volume,and negative correlations among growth, R<sub>hw</sub> andρ<sub>b</sub>.The selection method experiment indicated that index selection could improve volume, R<sub>hw</sub> andρ<sub>b</sub>,showing synthetically superior selection effects compared to any individual trait selection methods.展开更多
京津冀地区作为我国重要的早熟夏玉米生产区,对该区域早熟夏玉米品种开展多性状综合评价与筛选,对促进夏玉米品种资源的科学利用具有重要意义。本研究在品种-产量×性状组合(GYT,genotype by yield×trait)双标图模型基础上,将...京津冀地区作为我国重要的早熟夏玉米生产区,对该区域早熟夏玉米品种开展多性状综合评价与筛选,对促进夏玉米品种资源的科学利用具有重要意义。本研究在品种-产量×性状组合(GYT,genotype by yield×trait)双标图模型基础上,将“产量”拓展为“主要目标性状”,创新性提出品种-主性状×多性状(GMT,genotype by major trait×multi-trait)双标图方法。以2017-2024年完成京津冀地区京科联合体早熟夏玉米品种试验程序的72个参试品种为材料,分别基于主性状(产量、蛋白质含量、脂肪含量和赖氨酸含量)与产量、生育期、株高、百粒重、出籽率、籽粒含水量、容重、淀粉含量、蛋白质含量、脂肪含量、赖氨酸含量、抗病指数共12个目标性状的组合水平,采用新提出的GMT双标图方法对参试品种进行综合评价与选择。结果表明:(1)以产量为主要目标性状的品种-产量×性状组合双标图筛选出京农科458、京科628、MC921、鑫玉农812、京科383、京科938、京科597和京农科809共8个产量理想指数表现优秀的品种;(2)以蛋白质含量为主要目标性状的品种-蛋白质×性状组合(GPT,genotype by protein×trait)双标图筛选出综合表现突出的品种京农科836和MC921;(3)以脂肪含量为主要目标性状的品种-脂肪×性状组合(GFT,genotype by fat×trait)双标图筛选出表现优异的品种京农科458、京科383和MC616;(4)以赖氨酸含量为主要目标性状的品种-赖氨酸×性状组合(GLT,genotype by lysine×trait)双标图筛选出表现较好的品种MC921、MC167、京农科836和京农科801;(5)蛋白质理想指数和赖氨酸理想指数呈极显著正相关,以蛋白质含量和赖氨酸含量为共同目标性状时,筛选出“蛋白质-赖氨酸特专型”优秀品种MC921、京农科836和京农科458;产量理想指数和脂肪理想指数也呈极显著正相关,以产量和脂肪含量为共同目标性状时,筛选出京农科458和京科383“产量-脂肪特专型”优秀品种;基于上述4个主性状的理想指数同步筛选,选出“全能型”核心品种农科458和MC921。本研究提出的GMT双标图方法为多目标性状协同评价提供了新工具,筛选出的特专型品种和全能型品种可为京津冀地区玉米品种高效利用和高品质育种提供参考。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supported by the Science and Technology Innovation Program of Hunan Province,China(2023NK2001)the Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement,China(2022LZJJ08)the Changsha Science and Technology Project,China(kq2402109)。
文摘Rice yield is a complex trait affected by many related traits.Traditional single-trait genome-wide association studies(GWAS)have limitations when studying complex traits,as they cannot account for the genetic relationships among multiple traits.Multi-trait GWAS can consider the relationships among multiple traits and identify pleiotropic loci,so it is more suitable for complex traits such as rice yield than single-trait GWAS.In this study,we conducted a multi-trait GWAS on 11 two-trait combinations of yield and yield-related traits with 575 hybrid rice varieties across two environments.All these yield-related traits showed significant genetic correlations with yield(YD),including filled grains per panicle(FGPP),1,000-grain weight(KGW),tillers per plant(TP),primary branch number(PB),secondary branch number(SB),and main panicle length(MPL).In total,we identified 44 pleiotropic quantitative trait loci(p QTLs),including 29 new p QTLs not found in a single-trait GWAS.We then screened 23 p QTLs showing common effects in two traits as key p QTLs.These key p QTLs were subsequently analyzed by haplotype analysis,which identified 13 pleiotropic candidate genes.Finally,we identified two optimal yieldenhancing allele combinations by pyramiding the superior haplotypes:GS3-GL3.1-Os CIPK17 for the YD-KGW combination and GNP12 for the YD-FGPP and YD-SB combinations.This study provides pleiotropic candidate genes and allele combinations that exhibit superior differences in both yield and yield-related traits,offering valuable information for future high-yielding rice breeding.
基金supported by the National Key R&D Program of China(2022ZD0115703)the Hainan Provincial Natural Science Foundation of China(725QN518)+1 种基金the project of Sanya Yazhou Bay Science and Technology City(SKIC-JYRC-2024-55)the Agricultural Science and Technology Innovation Program(CAAS-CSIAF-202303).
文摘Agronomic traits in maize(Zea mays L.)are complex and modulated by pleiotropic loci and interconnected genetic networks.However,the traditional single-trait genome-wide association study(GWAS)method often misses genetic associations among traits,overlooks pleiotropic effects,and underestimates shared regulatory mechanisms.In the current study,we employed multi-trait analysis of GWAS(MTAG)and constructed a genetic network to dissect the genetic architecture of 18 agronomic traits across a genetically diverse panel of 2,448 maize inbred lines.Incorporating MTAG significantly improved the detection of pleiotropic loci that had not been detected by single-trait GWAS.Using a genetic network,we uncovered numerous previously unrecognized connections among traits related to plant architecture,yield,and flowering time.The 49 detected hub nodes,including Zm00001d028840 and Zm00001d033859(knotted1),influence multiple traits.Co-expression analysis of candidate genes across two developmental stages validated their distinct yet complementary roles,with Zm00001d028840 linked to early cell wall remodeling and Zm00001d033849 involved in chromatin remodeling during tasseling.Moreover,we integrated results from GWAS,MTAG,and genetic network analyses to prioritize pleiotropic loci and hub genes that regulate multiple agronomic traits.This integrative approach offers a practical framework for selecting stable,multi-trait-associated targets,thereby supporting more precise and efficient crop improvement strategies.
基金This research was funded by the STI2030-Major Projects(no.2023ZD0406104)the Beijing Postdoctoral Research Foundation(no.2023-ZZ-116).
文摘Yield prediction is the primary goal of genomic selection(GS)-assisted crop breeding.Because yield is a complex quantitative trait,making predictions from genotypic data is challenging.Transfer learning can produce an effective model for a target task by leveraging knowledge from a different,but related,source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data.However,it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework.We therefore developed TrG2P,a transfer-learning-based framework.TrG2P first employs convolutional neural networks(CNN)to train models using non-yield-trait phenotypic and genotypic data,thus obtaining pre-trained models.Subsequently,the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task,and the fully connected layers are retrained,thus obtaining fine-tuned models.Finally,the convolutional layer and the first fully connected layer of the fine-tuned models are fused,and the last fully connected layer is trained to enhance prediction performance.We applied TrG2P to five sets of genotypic and phenotypic data from maize(Zea mays),rice(Oryza sativa),and wheat(Triticum aestivum)and compared its model precision to that of seven other popular GS tools:ridge regression best linear unbiased prediction(rrBLUP),random forest,support vector regression,light gradient boosting machine(LightGBM),CNN,DeepGS,and deep neural network for genomic prediction(DNNGP).TrG2P improved the accuracy of yield prediction by 39.9%,6.8%,and 1.8%in rice,maize,and wheat,respectively,compared with predictions generated by the best-performing comparison model.Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data.We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation.The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P.The web-based tool is available at http://trg2p.ebreed.cn:81.
文摘Two clonal trial stands of Chinese Fir （Cunninghamia lanceolata） were used in this study, one was 19-year-old stand which included 38 clones, and the other was 17-year-old stand including 102 clones.The statistical analyses showed that there were very significant genetic variations in height, DBH,volume and ratio of heartwood(R<sub>hw</sub>),wood basic density（ρ<sub>b</sub> ） of the clones in the two stands. The repeatability of clones was in median to high level,and the genetic CV was different over the all five traits.There were very significant phenotypic and genetic correlations among height,DBH and volume,and negative correlations among growth, R<sub>hw</sub> andρ<sub>b</sub>.The selection method experiment indicated that index selection could improve volume, R<sub>hw</sub> andρ<sub>b</sub>,showing synthetically superior selection effects compared to any individual trait selection methods.
文摘京津冀地区作为我国重要的早熟夏玉米生产区,对该区域早熟夏玉米品种开展多性状综合评价与筛选,对促进夏玉米品种资源的科学利用具有重要意义。本研究在品种-产量×性状组合(GYT,genotype by yield×trait)双标图模型基础上,将“产量”拓展为“主要目标性状”,创新性提出品种-主性状×多性状(GMT,genotype by major trait×multi-trait)双标图方法。以2017-2024年完成京津冀地区京科联合体早熟夏玉米品种试验程序的72个参试品种为材料,分别基于主性状(产量、蛋白质含量、脂肪含量和赖氨酸含量)与产量、生育期、株高、百粒重、出籽率、籽粒含水量、容重、淀粉含量、蛋白质含量、脂肪含量、赖氨酸含量、抗病指数共12个目标性状的组合水平,采用新提出的GMT双标图方法对参试品种进行综合评价与选择。结果表明:(1)以产量为主要目标性状的品种-产量×性状组合双标图筛选出京农科458、京科628、MC921、鑫玉农812、京科383、京科938、京科597和京农科809共8个产量理想指数表现优秀的品种;(2)以蛋白质含量为主要目标性状的品种-蛋白质×性状组合(GPT,genotype by protein×trait)双标图筛选出综合表现突出的品种京农科836和MC921;(3)以脂肪含量为主要目标性状的品种-脂肪×性状组合(GFT,genotype by fat×trait)双标图筛选出表现优异的品种京农科458、京科383和MC616;(4)以赖氨酸含量为主要目标性状的品种-赖氨酸×性状组合(GLT,genotype by lysine×trait)双标图筛选出表现较好的品种MC921、MC167、京农科836和京农科801;(5)蛋白质理想指数和赖氨酸理想指数呈极显著正相关,以蛋白质含量和赖氨酸含量为共同目标性状时,筛选出“蛋白质-赖氨酸特专型”优秀品种MC921、京农科836和京农科458;产量理想指数和脂肪理想指数也呈极显著正相关,以产量和脂肪含量为共同目标性状时,筛选出京农科458和京科383“产量-脂肪特专型”优秀品种;基于上述4个主性状的理想指数同步筛选,选出“全能型”核心品种农科458和MC921。本研究提出的GMT双标图方法为多目标性状协同评价提供了新工具,筛选出的特专型品种和全能型品种可为京津冀地区玉米品种高效利用和高品质育种提供参考。
