To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-envi...To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.展开更多
Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq ...Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq to detect QTL is often limited by inappropriate experimental designs, as evidenced by numerous practical studies. Most BSA-seq studies have utilized small to medium-sized populations, with F2populations being the most common choice. Nevertheless, theoretical studies have shown that using a large population with an appropriate pool size can significantly enhance the power and resolution of QTL detection in BSA-seq, with F3populations offering notable advantages over F2populations. To provide an experimental demonstration, we tested the power of BSA-seq to identify QTL controlling days from sowing to heading(DTH) in a 7200-plant rice F3population in two environments, with a pool size of approximately 500. Each experiment identified 34 QTL, an order of magnitude greater than reported in most BSA-seq experiments, of which 23 were detected in both experiments, with 17 of these located near41 previously reported QTL and eight cloned genes known to control DTH in rice. These results indicate that QTL mapping by BSA-seq in large F3populations and multi-environment experiments can achieve high power, resolution, and reliability.展开更多
At present, with the rapid development of science and technology, based on the requirements of weapon test and identification tasks, the experimental data acquisition and processing space station, which is suitable fo...At present, with the rapid development of science and technology, based on the requirements of weapon test and identification tasks, the experimental data acquisition and processing space station, which is suitable for a variety of extreme natural environments such as alpine, plateau, mountain, jungle, desert, island and reef, has been studied theoretically and in practice. The space station is a dome-shaped structure with scale-shaped modules and basalt reinforced fiber composite materials, providing thermal insulation, ventilation and continuous power supply. It can provide support and guarantee for the real-time monitoring, recovery and information transmission of test data, and meet the basic work and life needs of test personnel.展开更多
Incorporating genotype-by-environment(GE)interaction effects into genomic prediction(GP)models with multi-environment climate data can improve selection accuracy to accelerate crop breeding but has received little res...Incorporating genotype-by-environment(GE)interaction effects into genomic prediction(GP)models with multi-environment climate data can improve selection accuracy to accelerate crop breeding but has received little research attention.Here,we conducted a cross-region GP study of grain moisture content(GMC)and grain yield(GY)in maize hybrids in two major Chinese growing regions using data for 19 climatic factors across34 environments in 2020 and 2021.Predictions were conducted in 2,126 hybrids generated from 475 maize inbred lines,using 9,355 single nucleotide polymorphism markers for genotyping.Models based on genomic best linear unbiased prediction(GBLUP)incorporating GE interaction effects of 19 climatic factors associated with day length,transpiration,temperature,and radiation(GBLUP-GE19CF)trained on whole data set outperformed the traditional GBLUP or BayesB models in predicting GMC or GY by 10-fold crossvalidation,achieving prediction accuracies of 0.731 and 0.331,respectively.To refine the climate data,we examined 84 statistical features associated with these climatic factors and identified nine factors most correlated with GMC or GY.Principal component analysis of climate data yielded nine principal components responsible for97%of the variability in the data.Incorporating these nine factors or principal components into the GBLUP-GE framework with a similarity matrix of environments(GBLUP-GE9CFand GBLUPGEPCA)provided similar prediction accuracies but could reduce the computational burden.In addition,increasing the number of test set environments in the training set from 8 to 14 increased the prediction accuracy of GBLUP-GE19CFtrained with monthly average climate data for 2020-2021.Examining prediction accuracy based on concordance,the proportion of overlapping hybrids between the top 50%of predicted and observed values for GMC and GY,indicated that concordance exceeded 50%for the GBLUP-GE19CFmodel,confirming the reliability of our predictions.This study can provide practical guidance for optimizing GPs for maize breeding programs in multi-environment selection.展开更多
目的调查与分析居住环境对成年人肥胖的影响,为优化建成环境以防治超重肥胖提供参考依据。方法本研究为横断面研究,于2023年5—8月选取在武汉大学人民医院健康体检中心进行体检的1672名体检人群,最终纳入符合标准且无数据缺失的1349名...目的调查与分析居住环境对成年人肥胖的影响,为优化建成环境以防治超重肥胖提供参考依据。方法本研究为横断面研究,于2023年5—8月选取在武汉大学人民医院健康体检中心进行体检的1672名体检人群,最终纳入符合标准且无数据缺失的1349名体检人员作为研究对象并进行分析。通过调查问卷收集研究对象的长期住址、居住楼层、性别、年龄、身体质量指数(body mass index,BMI)等一般资料,使用空间分析方法计算研究对象常住地址的环境因素。研究分别采用最小截平方和模型和logistic回归模型分析环境因素对研究对象BMI和是否超重/肥胖的影响,使用广义线性混合效应模型分析研究对象常住楼层与研究对象是否超重/肥胖的相关性。结果1349位研究对象的超重率为36.4%,肥胖率为11.0%。更高的交通设施可接近性(coef.=0.324,P<0.001)和餐饮设施可接近性(coef.=0.233,P=0.006)是BMI升高的促进因素;更高的交通设施可接近性(OR=1.141,95%CI:1.000~1.302)和更高的运动设施可接近性(OR=1.177,95%CI:1.017~1.362)是超重/肥胖的风险因素。随着居住楼层的升高,楼层对研究对象超重、肥胖概率呈现出先促进后抑制的趋势,于14层左右达到峰值。结论研究对象的BMI与超重/肥胖的发生概率与环境因素存在相关关系。常住楼层对研究对象的超重/肥胖概率存在影响,其原因可能与楼梯体力活动水平、不同楼层个人特征的间接影响有关。展开更多
延安市常年受沙尘天气影响,在陕西省内属沙尘天气影响高发城市。2023年4月和2024年2月,延安市出现了两次沙尘天气,基于地面气象及环境观测、第五代欧洲中期天气预报中心大气再分析全球气候数据(fifth generation ECMWF atmospheric rean...延安市常年受沙尘天气影响,在陕西省内属沙尘天气影响高发城市。2023年4月和2024年2月,延安市出现了两次沙尘天气,基于地面气象及环境观测、第五代欧洲中期天气预报中心大气再分析全球气候数据(fifth generation ECMWF atmospheric reanalysis of the global climate,ERA5)及现代研究与应用回顾分析第2版再分析数据集(Modern-Era Retrospective Analysis for Research and Applications Version 2,MERRA-2)再分析资料、后向轨迹模型(Hybrid Single Particle Lagrangian Integrated Trajectory Model,HYSPLIT)、激光雷达等数据,对两次沙尘进行综合对比分析。结果显示,两个过程均呈现“起沙—峰值—减弱”3个阶段。2023年沙尘过程污染时间更久,PM10质量浓度峰值为1137μg/m3,而2024年PM10质量浓度峰值高达3283μg/m3,但扩散速度快,2024年沙尘过程在阶段一及阶段三相对于2023年沙尘过程更洁净。沙尘气溶胶柱质量密度监测也有同样结果,且污染物均以粗颗粒物为主。在气象条件方面,两次沙尘过程均在阶段一时风场静稳,在阶段二时盛行强北风,气温、相对湿度下降,气压上升。但在2023年,沙尘过程在阶段三由强北风清除污染物,2024年沙尘过程则由高湿度偏南风清除。两次沙尘过程均在西北或偏北风向时PM10质量浓度较高。激光雷达观测显示,在2023年沙尘期间,多个高度层均受沙尘影响明显,呈现复杂的垂直输送与下沉过程;而在2024年,沙尘主要集中在近地500 m层,垂直结构稳定,层间传输不显著,降水则在末期起关键清除作用。在高空形势方面,2023年沙尘爆发与冷槽东移、高空风速增强密切相关,污染清除主要依靠平稳沉降;而2024年沙尘在高空槽前强西北风与上升气流协同下快速爆发,末期由850 hPa暖湿东南风触发沉降,污染迅速结束。轨迹分析显示,2023年沙尘在500 m处表现出偏南路径特征,区域性扬尘显著;1000 m以上则为典型西北远距离传输。而2024年过程则整体由偏西北路径主导,呈现出一致的远距离输送特征。展开更多
基金supported by State Key Laboratory of Tree Genetics and Breeding(Northeast Forestry University)(K2013204)co-financed with NSFC project(31470673)Guangdong Science and Technology Planning Project(2016B070701008)
摘要To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.
基金supported by Natural Science Foundation of Fujian Province (CN) (2020I0009, 2022J01596)Cooperation Project on University Industry-Education-Research of Fujian Provincial Science and Technology Plan (CN) (2022N5011)+1 种基金Lancang-Mekong Cooperation Special Fund (2017-2020)International Sci-Tech Cooperation and Communication Program of Fujian Agriculture and Forestry University (KXGH17014)。
摘要Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq to detect QTL is often limited by inappropriate experimental designs, as evidenced by numerous practical studies. Most BSA-seq studies have utilized small to medium-sized populations, with F2populations being the most common choice. Nevertheless, theoretical studies have shown that using a large population with an appropriate pool size can significantly enhance the power and resolution of QTL detection in BSA-seq, with F3populations offering notable advantages over F2populations. To provide an experimental demonstration, we tested the power of BSA-seq to identify QTL controlling days from sowing to heading(DTH) in a 7200-plant rice F3population in two environments, with a pool size of approximately 500. Each experiment identified 34 QTL, an order of magnitude greater than reported in most BSA-seq experiments, of which 23 were detected in both experiments, with 17 of these located near41 previously reported QTL and eight cloned genes known to control DTH in rice. These results indicate that QTL mapping by BSA-seq in large F3populations and multi-environment experiments can achieve high power, resolution, and reliability.
摘要At present, with the rapid development of science and technology, based on the requirements of weapon test and identification tasks, the experimental data acquisition and processing space station, which is suitable for a variety of extreme natural environments such as alpine, plateau, mountain, jungle, desert, island and reef, has been studied theoretically and in practice. The space station is a dome-shaped structure with scale-shaped modules and basalt reinforced fiber composite materials, providing thermal insulation, ventilation and continuous power supply. It can provide support and guarantee for the real-time monitoring, recovery and information transmission of test data, and meet the basic work and life needs of test personnel.
基金supported by grants from the Biological Breeding-National Science and Technology Major Project(2023ZD0407501)National Natural Science Foundation of China(32361143514)+2 种基金Nanfan Special Project,CAAS(YBXM2408)Key R&D Programs of Hainan Province(ZDYF2024XDNY210)the Innovation Program of Chinese Academy of Agricultural Sciences(CAAS-CSIAF202303)。
摘要Incorporating genotype-by-environment(GE)interaction effects into genomic prediction(GP)models with multi-environment climate data can improve selection accuracy to accelerate crop breeding but has received little research attention.Here,we conducted a cross-region GP study of grain moisture content(GMC)and grain yield(GY)in maize hybrids in two major Chinese growing regions using data for 19 climatic factors across34 environments in 2020 and 2021.Predictions were conducted in 2,126 hybrids generated from 475 maize inbred lines,using 9,355 single nucleotide polymorphism markers for genotyping.Models based on genomic best linear unbiased prediction(GBLUP)incorporating GE interaction effects of 19 climatic factors associated with day length,transpiration,temperature,and radiation(GBLUP-GE19CF)trained on whole data set outperformed the traditional GBLUP or BayesB models in predicting GMC or GY by 10-fold crossvalidation,achieving prediction accuracies of 0.731 and 0.331,respectively.To refine the climate data,we examined 84 statistical features associated with these climatic factors and identified nine factors most correlated with GMC or GY.Principal component analysis of climate data yielded nine principal components responsible for97%of the variability in the data.Incorporating these nine factors or principal components into the GBLUP-GE framework with a similarity matrix of environments(GBLUP-GE9CFand GBLUPGEPCA)provided similar prediction accuracies but could reduce the computational burden.In addition,increasing the number of test set environments in the training set from 8 to 14 increased the prediction accuracy of GBLUP-GE19CFtrained with monthly average climate data for 2020-2021.Examining prediction accuracy based on concordance,the proportion of overlapping hybrids between the top 50%of predicted and observed values for GMC and GY,indicated that concordance exceeded 50%for the GBLUP-GE19CFmodel,confirming the reliability of our predictions.This study can provide practical guidance for optimizing GPs for maize breeding programs in multi-environment selection.
摘要目的调查与分析居住环境对成年人肥胖的影响,为优化建成环境以防治超重肥胖提供参考依据。方法本研究为横断面研究,于2023年5—8月选取在武汉大学人民医院健康体检中心进行体检的1672名体检人群,最终纳入符合标准且无数据缺失的1349名体检人员作为研究对象并进行分析。通过调查问卷收集研究对象的长期住址、居住楼层、性别、年龄、身体质量指数(body mass index,BMI)等一般资料,使用空间分析方法计算研究对象常住地址的环境因素。研究分别采用最小截平方和模型和logistic回归模型分析环境因素对研究对象BMI和是否超重/肥胖的影响,使用广义线性混合效应模型分析研究对象常住楼层与研究对象是否超重/肥胖的相关性。结果1349位研究对象的超重率为36.4%,肥胖率为11.0%。更高的交通设施可接近性(coef.=0.324,P<0.001)和餐饮设施可接近性(coef.=0.233,P=0.006)是BMI升高的促进因素;更高的交通设施可接近性(OR=1.141,95%CI:1.000~1.302)和更高的运动设施可接近性(OR=1.177,95%CI:1.017~1.362)是超重/肥胖的风险因素。随着居住楼层的升高,楼层对研究对象超重、肥胖概率呈现出先促进后抑制的趋势,于14层左右达到峰值。结论研究对象的BMI与超重/肥胖的发生概率与环境因素存在相关关系。常住楼层对研究对象的超重/肥胖概率存在影响,其原因可能与楼梯体力活动水平、不同楼层个人特征的间接影响有关。
摘要延安市常年受沙尘天气影响,在陕西省内属沙尘天气影响高发城市。2023年4月和2024年2月,延安市出现了两次沙尘天气,基于地面气象及环境观测、第五代欧洲中期天气预报中心大气再分析全球气候数据(fifth generation ECMWF atmospheric reanalysis of the global climate,ERA5)及现代研究与应用回顾分析第2版再分析数据集(Modern-Era Retrospective Analysis for Research and Applications Version 2,MERRA-2)再分析资料、后向轨迹模型(Hybrid Single Particle Lagrangian Integrated Trajectory Model,HYSPLIT)、激光雷达等数据,对两次沙尘进行综合对比分析。结果显示,两个过程均呈现“起沙—峰值—减弱”3个阶段。2023年沙尘过程污染时间更久,PM10质量浓度峰值为1137μg/m3,而2024年PM10质量浓度峰值高达3283μg/m3,但扩散速度快,2024年沙尘过程在阶段一及阶段三相对于2023年沙尘过程更洁净。沙尘气溶胶柱质量密度监测也有同样结果,且污染物均以粗颗粒物为主。在气象条件方面,两次沙尘过程均在阶段一时风场静稳,在阶段二时盛行强北风,气温、相对湿度下降,气压上升。但在2023年,沙尘过程在阶段三由强北风清除污染物,2024年沙尘过程则由高湿度偏南风清除。两次沙尘过程均在西北或偏北风向时PM10质量浓度较高。激光雷达观测显示,在2023年沙尘期间,多个高度层均受沙尘影响明显,呈现复杂的垂直输送与下沉过程;而在2024年,沙尘主要集中在近地500 m层,垂直结构稳定,层间传输不显著,降水则在末期起关键清除作用。在高空形势方面,2023年沙尘爆发与冷槽东移、高空风速增强密切相关,污染清除主要依靠平稳沉降;而2024年沙尘在高空槽前强西北风与上升气流协同下快速爆发,末期由850 hPa暖湿东南风触发沉降,污染迅速结束。轨迹分析显示,2023年沙尘在500 m处表现出偏南路径特征,区域性扬尘显著;1000 m以上则为典型西北远距离传输。而2024年过程则整体由偏西北路径主导,呈现出一致的远距离输送特征。