With the support by the National Natural Science Foundation of China and the'Strategic Priority Research Program'of the Chinese Academy of Sciences,a collaborative study by the research groups led by Professor...With the support by the National Natural Science Foundation of China and the'Strategic Priority Research Program'of the Chinese Academy of Sciences,a collaborative study by the research groups led by Professors Tian Zhixi(田志喜),Wang Guodong(王国栋),and Zhu Baoge(朱保葛)from the展开更多
Brassica napus,commonly known as rapeseed or canola,is a major oil crop contributing over 13%to the stable supply of edible vegetable oil worldwide.Identification and understanding the gene functions in the B.napus ge...Brassica napus,commonly known as rapeseed or canola,is a major oil crop contributing over 13%to the stable supply of edible vegetable oil worldwide.Identification and understanding the gene functions in the B.napus genome is crucial for genomic breeding.A group of genes controlling agronomic traits have been successfully cloned through functional genomics studies in B.napus.In this review,we present an overview of the progress made in the functional genomics of B.napus,including the availability of germplasm resources,omics databases and cloned functional genes.Based on the current progress,we also highlight the main challenges and perspectives in this field.The advances in the functional genomics of B.napus contribute to a better understanding of the genetic basis underlying the complex agronomic traits in B.napus and will expedite the breeding of high quality,high resistance and high yield in B.napus varieties.展开更多
High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine ...High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.展开更多
文摘With the support by the National Natural Science Foundation of China and the'Strategic Priority Research Program'of the Chinese Academy of Sciences,a collaborative study by the research groups led by Professors Tian Zhixi(田志喜),Wang Guodong(王国栋),and Zhu Baoge(朱保葛)from the
基金supported by grants from the National Science Fund for Distinguished Young Scholars(32225037)Hubei Hongshan Laboratory(2021HSZD004)+1 种基金HZAU-AGIS Cooperation Fund(SZYJY2022008)Higher Education Discipline Innovation Project(B20051)。
文摘Brassica napus,commonly known as rapeseed or canola,is a major oil crop contributing over 13%to the stable supply of edible vegetable oil worldwide.Identification and understanding the gene functions in the B.napus genome is crucial for genomic breeding.A group of genes controlling agronomic traits have been successfully cloned through functional genomics studies in B.napus.In this review,we present an overview of the progress made in the functional genomics of B.napus,including the availability of germplasm resources,omics databases and cloned functional genes.Based on the current progress,we also highlight the main challenges and perspectives in this field.The advances in the functional genomics of B.napus contribute to a better understanding of the genetic basis underlying the complex agronomic traits in B.napus and will expedite the breeding of high quality,high resistance and high yield in B.napus varieties.
基金supported by the JST CREST[grant number:JPMJCR16O2]and MEXT KAKENHI[grant number:JP22H02306].The funders had no role in the study design,data collection and analysis,decision to publish,or manuscript preparation.
文摘High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.
