Soybean is a source of edible oil for humans and provides a third of the vegetable oil consumed worldwide.Increasing seed oil content in seeds is thus a key objective in soybean breeding.In the present study,a four-wa...Soybean is a source of edible oil for humans and provides a third of the vegetable oil consumed worldwide.Increasing seed oil content in seeds is thus a key objective in soybean breeding.In the present study,a four-way recombinant inbred line(FW-RIL)population comprising 144 lines,planted in 10 environments,and a germplasm panel of 455 accessions,planted in two environments,were used to collect oil-content phenotypes.First,59 quantitative trait loci(QTL)were detected in the FW-RIL population by inclusive complete interval mapping on a linkage map consisting of 2232 single-nucleotide polymorphism(SNP)markers.Also in the FW-RILs,44 quantitative trait nucleotides(QTNs)were detected by association analysis using 109,676 SNP markers and fivemethods of multi-locus genome-wide association study.Second,77 QTN were detected by association analysis in the germplasm panel using 63,306 markers.Comparison of the QTL and QTN suggested four QTN controlling oil content.Pathway analysis was performed on genes in attenuation regions of these four QTN,and two candidate genes involved in the synthesis or metabolism of soybean oil were identified.These findings provide useful information about the genetics of oil content and may contribute to its genetic improvement by marker-assisted selection.展开更多
Heading date is a key trait in rice domestication and adaption, and a number of quantitative trait loci（QTLs）have been identified. The rice（Oryza sativa L.） cultivars in the Heilongjiang Province, the northernmost...Heading date is a key trait in rice domestication and adaption, and a number of quantitative trait loci（QTLs）have been identified. The rice（Oryza sativa L.） cultivars in the Heilongjiang Province, the northernmost region of China,have to flower extremely early to fulfill their life cycle.However, the critical genes or different gene combinations controlling early flowering in this region have not been determined. QTL and candidate gene analysis revealed that Hd2/Ghd7.1/Os PRR37 plays a major role in controlling rice distribution in Heilongjiang. Further association analysis with a collection of rice cultivars demonstrated that another three major QTL genes（Hd4/Ghd7, Hd5/DTH8/Ghd8, and Hd1）also participate in regulating heading date under natural long day（LD） conditions. Hd2/Ghd7.1/Os PRR37 and Hd4/Ghd7 are two major QTLs and function additively. With the northward rice cultivation, the Hd2/Ghd7.1/Os PRR37 and Hd4/Ghd7 haplotypes became non-functional alleles. Hd1 might be non-functional in most Heilongjiang rice varieties,implying that recessive hd1 were selected during local rice breeding. Non-functional Hd5/DTH8/Ghd8 is very rare, but constitutes a potential target for breeding extremely early flowering cultivars. Our results indicated that diverse genetic combinations of Hd1, Hd2, Hd4, and Hd5 determined the different distribution of rice varieties in this northernmost province of China.展开更多
Closed production systems,such as plant factories and vertical farms,have emerged to ensure a sustainable supply of fresh food,to cope with the increasing consumption of natural resource for the growing population.In ...Closed production systems,such as plant factories and vertical farms,have emerged to ensure a sustainable supply of fresh food,to cope with the increasing consumption of natural resource for the growing population.In a plant factory,a microclimate model is one of the direct control components of a whole system.In order to better realize the dynamic regulation for the microclimate model,energy-saving and consumption reduction,it is necessary to optimize the environmental parameters in the plant factory,and thereby to determine the influencing factors of atmosphere control systems.Therefore,this study aims to identify accurate microclimate models,and further to predict temperature change based on the experimental data,using the classification and regression trees(CART)algorithm.A random forest theory was used to represent the temperature control system.A mechanism model of the temperature control system was proposed to improve the performance of the plant factories.In terms of energy efficiency,the main influencing factors on temperature change in the plant factories were obtained,including the temperature and air volume flow of the temperature control device,as well as the internal relative humidity.The generalization error of the prediction model can reach 0.0907.The results demonstrated that the proposed model can present the quantitative relationship and prediction function.This study can provide a reference for the design of high-precision environmental control systems in plant factories.展开更多
基金This research was financially supported by Hundredthousand and Million Project of Heilongjiang Province for Engineering and Technology Science(2019ZX16B01)We thank Professor Jiankang Wang(Institute of Crop Sciences,Chinese Academy of Agricultural Sciences)for valuable suggestions.
文摘Soybean is a source of edible oil for humans and provides a third of the vegetable oil consumed worldwide.Increasing seed oil content in seeds is thus a key objective in soybean breeding.In the present study,a four-way recombinant inbred line(FW-RIL)population comprising 144 lines,planted in 10 environments,and a germplasm panel of 455 accessions,planted in two environments,were used to collect oil-content phenotypes.First,59 quantitative trait loci(QTL)were detected in the FW-RIL population by inclusive complete interval mapping on a linkage map consisting of 2232 single-nucleotide polymorphism(SNP)markers.Also in the FW-RILs,44 quantitative trait nucleotides(QTNs)were detected by association analysis using 109,676 SNP markers and fivemethods of multi-locus genome-wide association study.Second,77 QTN were detected by association analysis in the germplasm panel using 63,306 markers.Comparison of the QTL and QTN suggested four QTN controlling oil content.Pathway analysis was performed on genes in attenuation regions of these four QTN,and two candidate genes involved in the synthesis or metabolism of soybean oil were identified.These findings provide useful information about the genetics of oil content and may contribute to its genetic improvement by marker-assisted selection.
基金supported by the Hundred-Talent Program of the Chinese Academy of SciencesNational Natural Science Foundation of China(31070255,31371588)+1 种基金Excellent Academic Leaders of Harbin(RC2014XK002003)the High Tech Program of Ministry of Science and Technology of China(2014AA10A602-5)
文摘Heading date is a key trait in rice domestication and adaption, and a number of quantitative trait loci（QTLs）have been identified. The rice（Oryza sativa L.） cultivars in the Heilongjiang Province, the northernmost region of China,have to flower extremely early to fulfill their life cycle.However, the critical genes or different gene combinations controlling early flowering in this region have not been determined. QTL and candidate gene analysis revealed that Hd2/Ghd7.1/Os PRR37 plays a major role in controlling rice distribution in Heilongjiang. Further association analysis with a collection of rice cultivars demonstrated that another three major QTL genes（Hd4/Ghd7, Hd5/DTH8/Ghd8, and Hd1）also participate in regulating heading date under natural long day（LD） conditions. Hd2/Ghd7.1/Os PRR37 and Hd4/Ghd7 are two major QTLs and function additively. With the northward rice cultivation, the Hd2/Ghd7.1/Os PRR37 and Hd4/Ghd7 haplotypes became non-functional alleles. Hd1 might be non-functional in most Heilongjiang rice varieties,implying that recessive hd1 were selected during local rice breeding. Non-functional Hd5/DTH8/Ghd8 is very rare, but constitutes a potential target for breeding extremely early flowering cultivars. Our results indicated that diverse genetic combinations of Hd1, Hd2, Hd4, and Hd5 determined the different distribution of rice varieties in this northernmost province of China.
文摘Closed production systems,such as plant factories and vertical farms,have emerged to ensure a sustainable supply of fresh food,to cope with the increasing consumption of natural resource for the growing population.In a plant factory,a microclimate model is one of the direct control components of a whole system.In order to better realize the dynamic regulation for the microclimate model,energy-saving and consumption reduction,it is necessary to optimize the environmental parameters in the plant factory,and thereby to determine the influencing factors of atmosphere control systems.Therefore,this study aims to identify accurate microclimate models,and further to predict temperature change based on the experimental data,using the classification and regression trees(CART)algorithm.A random forest theory was used to represent the temperature control system.A mechanism model of the temperature control system was proposed to improve the performance of the plant factories.In terms of energy efficiency,the main influencing factors on temperature change in the plant factories were obtained,including the temperature and air volume flow of the temperature control device,as well as the internal relative humidity.The generalization error of the prediction model can reach 0.0907.The results demonstrated that the proposed model can present the quantitative relationship and prediction function.This study can provide a reference for the design of high-precision environmental control systems in plant factories.
