The objective of this study was to investigate the genetic diversity of barley accessions. Additionally, association trait analysis was conducted for grain yield under salinity, heading date and plant height. For this...The objective of this study was to investigate the genetic diversity of barley accessions. Additionally, association trait analysis was conducted for grain yield under salinity, heading date and plant height. For this purpose, 48 barley genotypes were analyzed with 22 microsatellite simple sequence repeat （SSR） markers. Four of the 22 markers （Bmac316, scssr03907, HVM67 and Bmag770） were able to differentiate all barley genotypes. Cluster and principal coordinate analysis allowed a clear grouping between countries from the same region. The genotypes used in this study have been evaluated for agronomic performance in different environments. Conducting association analysis for grain yield under salinity conditions using TASSEL software revealed a close association of the marker Bmag749 （2H, bin 13） in two different environments with common significant alleles （175, 177）, whereas the HVHOTR1 marker （2H, bin 3） was only significant in Sakhar Egypt with alleles size being 158 and 161. Heading date also showed an association with scssr03907 through the common significant specific allele 111 and EBmac0415 markers in three different agro climatic locations, whereas HVCMA, scssr00103 and HVM67 were linked to heading date in the Egyptian environment only. The plant height association analysis revealed significant markers Bmag770 via the significant allele 152 and scssr09398.展开更多
Main-effect QTL, epistatic effects and their interactions with environment are important genetic components of quantitativetraits. In this study, we analyzed the QTL, epistatic effects and QTL by environment interacti...Main-effect QTL, epistatic effects and their interactions with environment are important genetic components of quantitativetraits. In this study, we analyzed the QTL, epistatic effects and QTL by environment interactions (QE) underlying plantheight and heading date, using a doubled-haploid ( DH) population consisting of 190 lines from the cross between anindica parent Zhenshan 97 and a japonica parent Wuyujing 2, and tested in two-year replicated field trials. A geneticlinkage map with 179 SSR (simple sequence repeat) marker loci was constructed. A mixed linear model approach wasapplied to detect QTL, digenic interactions and QEs for the two traits. In total, 20 main-effect QTLs, 9 digenic interactionsinvolving 18 loci, and 5 QTL by environment interactions were found to be responsible for the two traits. No interactionswere detected between the digenic interaction and environment. The amounts of variations explained by QTLs of maineffect were 53.9% for plant height and 57.8% for heading date, larger than that explained by epistasis and QEs. However,the epistasis and QE interactions sometimes accounted for a significant part of phenotypic variation and should not bedisregarded.展开更多
Buckthorns(Glossy buckthorn,Frangula alnus and common buckthorn,Rhamnus cathartica)represent a threat to biodiversity.Their high competitivity lead to the replacement of native species and the inhibition of forest reg...Buckthorns(Glossy buckthorn,Frangula alnus and common buckthorn,Rhamnus cathartica)represent a threat to biodiversity.Their high competitivity lead to the replacement of native species and the inhibition of forest regeneration.Early detection strategies are therefore necessary to limit invasive alien plant species’impacts,and remote sensing is one of the techniques for early invasion detection.Few studies have used phenological remote sensing approaches to map buckthorn distribution from medium spatial resolution images.Those studies highlighted the difficulty of detecting buckthorns in low densities and in understory using this category of images.The main objective of this study was to develop an approach using multi-date very high spatial resolution satellite imagery to map buckthorns in low densities and in the understory in the Québec city area.Three machine learning classifiers(Support Vector Machines,Random Forest and Extreme Gradient Boosting)were applied to WorldView-3,GeoEye-1 and SPOT-7 satellite imagery.The Random Forest classifier performed well(Kappa=0.72).The SVM and XGBoost’s coefficient Kappa were 0.69 and 0.66,respectively.However,buckthorn distribution in understory was identified as the main limit to this approach,and LiDAR data could be used to improve buckthorn mapping in similar environments.展开更多
基金the Generation Challenge Program Project, and the North Africa Regional Program
文摘The objective of this study was to investigate the genetic diversity of barley accessions. Additionally, association trait analysis was conducted for grain yield under salinity, heading date and plant height. For this purpose, 48 barley genotypes were analyzed with 22 microsatellite simple sequence repeat （SSR） markers. Four of the 22 markers （Bmac316, scssr03907, HVM67 and Bmag770） were able to differentiate all barley genotypes. Cluster and principal coordinate analysis allowed a clear grouping between countries from the same region. The genotypes used in this study have been evaluated for agronomic performance in different environments. Conducting association analysis for grain yield under salinity conditions using TASSEL software revealed a close association of the marker Bmag749 （2H, bin 13） in two different environments with common significant alleles （175, 177）, whereas the HVHOTR1 marker （2H, bin 3） was only significant in Sakhar Egypt with alleles size being 158 and 161. Heading date also showed an association with scssr03907 through the common significant specific allele 111 and EBmac0415 markers in three different agro climatic locations, whereas HVCMA, scssr00103 and HVM67 were linked to heading date in the Egyptian environment only. The plant height association analysis revealed significant markers Bmag770 via the significant allele 152 and scssr09398.
基金We gratefully acknowledge Prof.Zhu Jun for kind pro-V1sion of software QTLMapper 1.0.The work was in part supported by the National High Tech R&D Pro-gram of China(863 Program)the National Natural Sci-ence Foundation of China and the National Program on Key Basic Research Project of China(973 Program).
文摘Main-effect QTL, epistatic effects and their interactions with environment are important genetic components of quantitativetraits. In this study, we analyzed the QTL, epistatic effects and QTL by environment interactions (QE) underlying plantheight and heading date, using a doubled-haploid ( DH) population consisting of 190 lines from the cross between anindica parent Zhenshan 97 and a japonica parent Wuyujing 2, and tested in two-year replicated field trials. A geneticlinkage map with 179 SSR (simple sequence repeat) marker loci was constructed. A mixed linear model approach wasapplied to detect QTL, digenic interactions and QEs for the two traits. In total, 20 main-effect QTLs, 9 digenic interactionsinvolving 18 loci, and 5 QTL by environment interactions were found to be responsible for the two traits. No interactionswere detected between the digenic interaction and environment. The amounts of variations explained by QTLs of maineffect were 53.9% for plant height and 57.8% for heading date, larger than that explained by epistasis and QEs. However,the epistasis and QE interactions sometimes accounted for a significant part of phenotypic variation and should not bedisregarded.
文摘Buckthorns(Glossy buckthorn,Frangula alnus and common buckthorn,Rhamnus cathartica)represent a threat to biodiversity.Their high competitivity lead to the replacement of native species and the inhibition of forest regeneration.Early detection strategies are therefore necessary to limit invasive alien plant species’impacts,and remote sensing is one of the techniques for early invasion detection.Few studies have used phenological remote sensing approaches to map buckthorn distribution from medium spatial resolution images.Those studies highlighted the difficulty of detecting buckthorns in low densities and in understory using this category of images.The main objective of this study was to develop an approach using multi-date very high spatial resolution satellite imagery to map buckthorns in low densities and in the understory in the Québec city area.Three machine learning classifiers(Support Vector Machines,Random Forest and Extreme Gradient Boosting)were applied to WorldView-3,GeoEye-1 and SPOT-7 satellite imagery.The Random Forest classifier performed well(Kappa=0.72).The SVM and XGBoost’s coefficient Kappa were 0.69 and 0.66,respectively.However,buckthorn distribution in understory was identified as the main limit to this approach,and LiDAR data could be used to improve buckthorn mapping in similar environments.
