[Objective] The aim was to study characters of pollen grains of tetraploid lines and diploid control line of Chrysanthemum cinerariifolium (Trev.) Vis.,morphological characters,fertility of pollen grain and germinatio...[Objective] The aim was to study characters of pollen grains of tetraploid lines and diploid control line of Chrysanthemum cinerariifolium (Trev.) Vis.,morphological characters,fertility of pollen grain and germination percentage of seeds. [Method] Pollen grains were prepared by sulphuric acid-acetyl oxide decomposition method. The lengths of polar axis and equatorial axis of pollen grains were determined with general optical microscope. The morphology of pollen grains was observed with SEM (scanning electron microscope) and the typical visual fields of 2 500× (or 2 000×),7 000× were taken pictures. [Result] Comparing with the diploid control line,the pollen grains of five tetraploid lines which were tested were different from the diploid line in morphology,sculpture,etc.. 4 of the 5 tested samples were significant larger than the diploid line in size and one was similar to the diploid line. [Conclusion] This research provided references for breeding tetraploid improved varieties of Chrysanthemum cinerariifolium (Trev.) Vis. with good fertility and high germination percentage.展开更多
The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly re...The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.展开更多
基金Supported by the Fundamental Research Funds for the Central Universities (SWJTU09BR221)~~
文摘[Objective] The aim was to study characters of pollen grains of tetraploid lines and diploid control line of Chrysanthemum cinerariifolium (Trev.) Vis.,morphological characters,fertility of pollen grain and germination percentage of seeds. [Method] Pollen grains were prepared by sulphuric acid-acetyl oxide decomposition method. The lengths of polar axis and equatorial axis of pollen grains were determined with general optical microscope. The morphology of pollen grains was observed with SEM (scanning electron microscope) and the typical visual fields of 2 500× (or 2 000×),7 000× were taken pictures. [Result] Comparing with the diploid control line,the pollen grains of five tetraploid lines which were tested were different from the diploid line in morphology,sculpture,etc.. 4 of the 5 tested samples were significant larger than the diploid line in size and one was similar to the diploid line. [Conclusion] This research provided references for breeding tetraploid improved varieties of Chrysanthemum cinerariifolium (Trev.) Vis. with good fertility and high germination percentage.
基金supported by the National Natural Science Foundation of China(32160782 and 32060737).
文摘The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.
