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
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
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
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
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
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。
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.
