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
Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques ar...Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.展开更多
The Tianshan orogenic belt is a major part of the southern Central Asian Orogenic Belt（CAOB）,extending from west to east for over 2500 km through Uzbekistan,Tajikistan,Kyrgyzstan and Kazakhstan to Xinjiang in NW Chi...The Tianshan orogenic belt is a major part of the southern Central Asian Orogenic Belt（CAOB）,extending from west to east for over 2500 km through Uzbekistan,Tajikistan,Kyrgyzstan and Kazakhstan to Xinjiang in NW China,and contains the record of multi-phase tectonothermal evolution.Till now.展开更多
目的:检验中文版植入心律转复除颤器患者自我效能期望及结局期望量表(Self-Efficacy Expectations and Outcome Expectations after ICD Implantation Scales,SEOE-ICD)在心脏电子设备植入人群中应用的信度和效度,为该人群期望的评估提...目的:检验中文版植入心律转复除颤器患者自我效能期望及结局期望量表(Self-Efficacy Expectations and Outcome Expectations after ICD Implantation Scales,SEOE-ICD)在心脏电子设备植入人群中应用的信度和效度,为该人群期望的评估提供参考。方法:遵循量表引进原则,对量表进行汉化;于2024年3月—12月采用便利抽样法纳入205例北京市某三级甲等医院心律失常病房行心脏电子设备植入术的患者为调查对象,使用中文版SEOE-ICD、一般自我效能量表、治疗期望问卷、Florida患者接受度调查表进行测试;采用验证性因子分析、多质多法、内部一致性分析对量表进行信度和效度评价。结果:中文版SEOE-ICD共包括自我效能期望与结局期望两个子量表,13个条目;验证性因子模型适配度指标表现良好(χ^(2)/df=2.929,TLI=0.951,CFI=0.961,SRMR=0.042,RMSEA=0.097);多质多法分析显示量表有较好的聚合效度及区分效度;两个子量表的Cronbach’sα系数分别为0.949、0.878。结论:中文版SEOE-ICD在心脏电子设备植入人群中应用的信度、效度良好,可作为该人群期望的评估工具,为健康策略制定提供有效依据。展开更多
Ultra-deep and complex formations are characterized by narrow safety density windows and challenging well control.The combined use of multiple well-killing methods or temporary adjustments to well-killing strategies i...Ultra-deep and complex formations are characterized by narrow safety density windows and challenging well control.The combined use of multiple well-killing methods or temporary adjustments to well-killing strategies is becoming common.However,conventional well-killing models often struggle to calculate the parameters required for these special cases.In this paper,a boundary matrix for wellkilling fluid density and volume is proposed to unify the driller's method,the engineer's method,and the weight-while-circulating method.Furthermore,a dynamic unified well-killing model is developed to enable the synergistic regulation of multiple well-killing methods.The model also can be applied with or without accounting for gas dissolution.Using this model,it is able to dynamically track key parameters during well killing and shut in the well at any time to determine the standpipe and casing pressures.The results indicate that the casing pressure drops to zero before the well-killing fluid returns to the annulus wellhead,and continued injection of the fluid leads to a gradual increase in standpipe pressure,a phenomenon not previously accounted for.The discrepancy between the actual and calculated standpipe/casing pressures after shut-in can be utilized to assess whether the downhole gas kick is effectively controlled.Through real-time adjustments to the boundary matrix,updated wellkilling parameters can be derived for conventional method,multi-method combination,temporary strategy modification,and other well-killing scenarios.The model was applied to two field wells under water-and oil-based drilling fluids.No secondary downhole complications occurred during well killing,and the calculated pressure curves closely matched the measured construction pressure curves,confirming the model's reliability and applicability.This study provides valuable theoretical guidance for enhancing well control safety in ultra-deep and complex formations.展开更多
Background Globally,the cultivation of cotton is constrained by its tendency for extended periods of growth.Early maturity plays a potential role in rainfed-based multiple cropping system especially in the current era...Background Globally,the cultivation of cotton is constrained by its tendency for extended periods of growth.Early maturity plays a potential role in rainfed-based multiple cropping system especially in the current era of climate change.In the current study,a set of 20 diverse Gossypium hirsutum genotypes were evaluated in two crop seasons with three planting densities and assessed for 11 morphological traits related to early maturity.The study aimed to identify genotype(s)that mature rapidly and accomplish well under diverse environmental conditions based on the two robust multivariate techniques called multi-trait stability index(MTSI)and multi-trait genotype-ideotype distance index(MGIDI).Results MTSI analysis revealed that out of the 20 genotypes,three genotypes,viz.,NNDC-30,A-2,and S-32 accomplished well in terms of early maturity traits in two seasons.Furthermore,three genotypes were selected using MGIDI method for each planting densities with a selection intensity of 15%.The strengths and weaknesses of the genotypes selected based on MGIDI method highlighted that the breeders could focus on developing early-maturing genotypes with specific traits such as days to first flower and boll opening.The selected genotypes exhibited positive genetic gains for traits related to earliness and a successful harvest during the first and second pickings.However,there were negative gains for traits related to flowering and boll opening.Conclusion The study identified three genotypes exhibiting early maturity and accomplished well under different planting densities.The multivariate methods(MTSI and MGIDI)serve as novel approaches for selecting desired genotypes in plant breeding programs,especially across various growing environments.These methods offer exclusive benefits and can easily construe and minimize multicollinearity issues.展开更多
Fusarium head blight(FHB),also known as scab,is a devastating fungal disease of wheat that causes significant losses in grain yield and quality.Quantitative inheritance and cumbersome phenotyping make FHB resistance a...Fusarium head blight(FHB),also known as scab,is a devastating fungal disease of wheat that causes significant losses in grain yield and quality.Quantitative inheritance and cumbersome phenotyping make FHB resistance a challenging trait for direct selection in wheat breeding.Genomic selection to predict FHB resistance traits has shown promise in several studies.Here,we used univariate and multivariate genomic prediction models to evaluate the prediction accuracy(PA)for different FHB traits using 476elite and advanced breeding lines developed by South Dakota State University hard winter wheat breeding program.These breeding lines were assessed for FHB disease index(DIS),and percentage of Fusarium damaged kernels(FDK)in three FHB nurseries in 2018,2019,and 2020(TP18,TP19,and TP20)and were evaluated as training populations(TP)for genomic prediction(GP)of FHB traits.We observed a moderate PA using univariate models for DIS(0.39 and 0.35)and FDK(0.35 and 0.37)using TP19 and TP20,respectively,while slightly higher PA was observed(0.41 for DIS and 0.38 for FDK)when TP19 and TP20(TP19+20)were combined to leverage the advantage of a large training population.Although GP with multivariate approach including plant height and days to heading as covariates did not significantly improve PA for DIS and FDK over univariate models,PA for DON increased by 20%using DIS,FDK,DTH as covariates using multi-trait model in 2020.Finally,we used TP19,TP20,and TP19+20 in forward prediction to calculate genomic-estimated breeding values(GEBVs)for DIS and FDK in preliminary breeding lines at an early stage of the breeding program.We observed moderate PA of up to 0.59 for DIS and 0.54for FDK,demonstrating the promise in genomic prediction for FHB resistance in earlier stages using advanced lines.Our results suggest GP for expensive FHB traits like DON and FDK can facilitate the rejection of highly susceptible materials at an early stage in a breeding program.展开更多
The objectives of this study were to estimate genetic parameters of lactation average somatic cell scores （LSCS） and examine genetic associations between LSCS and production traits in the first three lactations of C...The objectives of this study were to estimate genetic parameters of lactation average somatic cell scores （LSCS） and examine genetic associations between LSCS and production traits in the first three lactations of Chinese Holstein cows using single-parity multi-trait animal model and multi-trait repeatability animal model. There were totally 273605 lactation records of Chinese Holstein cows with first calving from 2001 to 2012. Heritability estimates for LSCS ranged from 0.144 to 0.187. Genetic correlations between LSCS and 305 days milk, protein percentage and fat percentage were -0.079, -0.082 and -0.135, respectively. Phenotypic correlation between LSCS and 305 days milk yield was negative （-0.103 to -0.190）. Genetic correlation between 305 days milk and fat percentage or protein percentage was highly negative. Genetic correlation between milk fat percentage and milk protein percentage was highly favorable. Heritabilities of production traits decreased with increase of parity, whereas heritability of LSCS increased with increase of parity.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
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 Fundamental Research Funds for the Central Universities” No.2018CUCTJ081
文摘Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.
基金supported by the Major Basic Research Project of the Ministry of Science and Technology of China(Grant No.2014CB448000)National Science Foundation of China(Grant Nos..41473053 and 41573045)a grant of Chinese Ministry of Land and Resources(Grant No.201211074–05)
文摘The Tianshan orogenic belt is a major part of the southern Central Asian Orogenic Belt（CAOB）,extending from west to east for over 2500 km through Uzbekistan,Tajikistan,Kyrgyzstan and Kazakhstan to Xinjiang in NW China,and contains the record of multi-phase tectonothermal evolution.Till now.
文摘目的:检验中文版植入心律转复除颤器患者自我效能期望及结局期望量表(Self-Efficacy Expectations and Outcome Expectations after ICD Implantation Scales,SEOE-ICD)在心脏电子设备植入人群中应用的信度和效度,为该人群期望的评估提供参考。方法:遵循量表引进原则,对量表进行汉化;于2024年3月—12月采用便利抽样法纳入205例北京市某三级甲等医院心律失常病房行心脏电子设备植入术的患者为调查对象,使用中文版SEOE-ICD、一般自我效能量表、治疗期望问卷、Florida患者接受度调查表进行测试;采用验证性因子分析、多质多法、内部一致性分析对量表进行信度和效度评价。结果:中文版SEOE-ICD共包括自我效能期望与结局期望两个子量表,13个条目;验证性因子模型适配度指标表现良好(χ^(2)/df=2.929,TLI=0.951,CFI=0.961,SRMR=0.042,RMSEA=0.097);多质多法分析显示量表有较好的聚合效度及区分效度;两个子量表的Cronbach’sα系数分别为0.949、0.878。结论:中文版SEOE-ICD在心脏电子设备植入人群中应用的信度、效度良好,可作为该人群期望的评估工具,为健康策略制定提供有效依据。
基金supported by the National Natural Science Foundation of China(52474018,52227804,U22B2072,52404012)the National Key Research and Development Program of China(2023YFC3009200)the Science Foundation of China University of Petroleum,Beijing(2462023BJRC008,2462024XKBH006)。
文摘Ultra-deep and complex formations are characterized by narrow safety density windows and challenging well control.The combined use of multiple well-killing methods or temporary adjustments to well-killing strategies is becoming common.However,conventional well-killing models often struggle to calculate the parameters required for these special cases.In this paper,a boundary matrix for wellkilling fluid density and volume is proposed to unify the driller's method,the engineer's method,and the weight-while-circulating method.Furthermore,a dynamic unified well-killing model is developed to enable the synergistic regulation of multiple well-killing methods.The model also can be applied with or without accounting for gas dissolution.Using this model,it is able to dynamically track key parameters during well killing and shut in the well at any time to determine the standpipe and casing pressures.The results indicate that the casing pressure drops to zero before the well-killing fluid returns to the annulus wellhead,and continued injection of the fluid leads to a gradual increase in standpipe pressure,a phenomenon not previously accounted for.The discrepancy between the actual and calculated standpipe/casing pressures after shut-in can be utilized to assess whether the downhole gas kick is effectively controlled.Through real-time adjustments to the boundary matrix,updated wellkilling parameters can be derived for conventional method,multi-method combination,temporary strategy modification,and other well-killing scenarios.The model was applied to two field wells under water-and oil-based drilling fluids.No secondary downhole complications occurred during well killing,and the calculated pressure curves closely matched the measured construction pressure curves,confirming the model's reliability and applicability.This study provides valuable theoretical guidance for enhancing well control safety in ultra-deep and complex formations.
文摘Background Globally,the cultivation of cotton is constrained by its tendency for extended periods of growth.Early maturity plays a potential role in rainfed-based multiple cropping system especially in the current era of climate change.In the current study,a set of 20 diverse Gossypium hirsutum genotypes were evaluated in two crop seasons with three planting densities and assessed for 11 morphological traits related to early maturity.The study aimed to identify genotype(s)that mature rapidly and accomplish well under diverse environmental conditions based on the two robust multivariate techniques called multi-trait stability index(MTSI)and multi-trait genotype-ideotype distance index(MGIDI).Results MTSI analysis revealed that out of the 20 genotypes,three genotypes,viz.,NNDC-30,A-2,and S-32 accomplished well in terms of early maturity traits in two seasons.Furthermore,three genotypes were selected using MGIDI method for each planting densities with a selection intensity of 15%.The strengths and weaknesses of the genotypes selected based on MGIDI method highlighted that the breeders could focus on developing early-maturing genotypes with specific traits such as days to first flower and boll opening.The selected genotypes exhibited positive genetic gains for traits related to earliness and a successful harvest during the first and second pickings.However,there were negative gains for traits related to flowering and boll opening.Conclusion The study identified three genotypes exhibiting early maturity and accomplished well under different planting densities.The multivariate methods(MTSI and MGIDI)serve as novel approaches for selecting desired genotypes in plant breeding programs,especially across various growing environments.These methods offer exclusive benefits and can easily construe and minimize multicollinearity issues.
基金collectively funded by the USDA hatch projects SD00H695-20,USDA-ARS agreement 59-0206-0-177(USDAUSWBSI)the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439(Wheat CAP)from the USDA National Institute of Food and AgricultureSouth Dakota Wheat Commission Grant 3X1340。
文摘Fusarium head blight(FHB),also known as scab,is a devastating fungal disease of wheat that causes significant losses in grain yield and quality.Quantitative inheritance and cumbersome phenotyping make FHB resistance a challenging trait for direct selection in wheat breeding.Genomic selection to predict FHB resistance traits has shown promise in several studies.Here,we used univariate and multivariate genomic prediction models to evaluate the prediction accuracy(PA)for different FHB traits using 476elite and advanced breeding lines developed by South Dakota State University hard winter wheat breeding program.These breeding lines were assessed for FHB disease index(DIS),and percentage of Fusarium damaged kernels(FDK)in three FHB nurseries in 2018,2019,and 2020(TP18,TP19,and TP20)and were evaluated as training populations(TP)for genomic prediction(GP)of FHB traits.We observed a moderate PA using univariate models for DIS(0.39 and 0.35)and FDK(0.35 and 0.37)using TP19 and TP20,respectively,while slightly higher PA was observed(0.41 for DIS and 0.38 for FDK)when TP19 and TP20(TP19+20)were combined to leverage the advantage of a large training population.Although GP with multivariate approach including plant height and days to heading as covariates did not significantly improve PA for DIS and FDK over univariate models,PA for DON increased by 20%using DIS,FDK,DTH as covariates using multi-trait model in 2020.Finally,we used TP19,TP20,and TP19+20 in forward prediction to calculate genomic-estimated breeding values(GEBVs)for DIS and FDK in preliminary breeding lines at an early stage of the breeding program.We observed moderate PA of up to 0.59 for DIS and 0.54for FDK,demonstrating the promise in genomic prediction for FHB resistance in earlier stages using advanced lines.Our results suggest GP for expensive FHB traits like DON and FDK can facilitate the rejection of highly susceptible materials at an early stage in a breeding program.
基金fundings from the National Natural Science Foundation of China (31200927)the National Modern Agricultural Industry Technology Fund for Scientists in Sheep Industry System, China (CARS-39-04B)+1 种基金the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2011BAD28B02, 2012BAD12B06)the Chinese Academy of Agricultural Sciences Foundation (2012cj-2)
文摘The objectives of this study were to estimate genetic parameters of lactation average somatic cell scores （LSCS） and examine genetic associations between LSCS and production traits in the first three lactations of Chinese Holstein cows using single-parity multi-trait animal model and multi-trait repeatability animal model. There were totally 273605 lactation records of Chinese Holstein cows with first calving from 2001 to 2012. Heritability estimates for LSCS ranged from 0.144 to 0.187. Genetic correlations between LSCS and 305 days milk, protein percentage and fat percentage were -0.079, -0.082 and -0.135, respectively. Phenotypic correlation between LSCS and 305 days milk yield was negative （-0.103 to -0.190）. Genetic correlation between 305 days milk and fat percentage or protein percentage was highly negative. Genetic correlation between milk fat percentage and milk protein percentage was highly favorable. Heritabilities of production traits decreased with increase of parity, whereas heritability of LSCS increased with increase of parity.
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.
