Interaction of genotype and environment effects on important traits of cassava(Manihot esculenta Crantz)

General and specific environmental adaptation of genotypes is the main goal of breeders.However, genotype-by-environment(G x E) interaction complicates the identification of genotypes for release. This study aimed at analyzing the effects of G x E interaction on the expression of important cassava traits using two multivariate analyses: additive main effects and multiplicative interaction(AMMI) and genotype stability index(GSI). Total carotene content(TCC), postharvest physiological deterioration(PPD), and reaction to viral diseases were significantly affected by G x E interaction effects. The low percent(%)variation due to genotype for cassava brown streak disease(GBSD) explained the influence of environment on CBSD expression. The % variation due to genotype for TCC was higher(96%) than variation due to environment(1.7%) and G x E interaction(2.4%) indicating a low interaction effect of environment on TCC accumulation. The % variation due to genotype was higher than % variation due to environment for all traits but CBSD root necrosis and CBSD on stems, indicating the influence of environment on the severity of the viral diseases. These findings indicate that screening for disease resistance requires multi-environment trials, whereas a single-environment trial suffices to screen for total carotene content.

One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

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.

Detection and Analysis of High Temperature Sensitivity of TGMS Lines in Rice Using AMMI Model

With the AMMI (additive main effects and multiplicative interaction) analysis model, thedetermination of the sensitivity to temperature among different TGMS (thermo-sensitivegenic male sterile) lines was performed. To assess the genetic differences due to hightemperature stress at the fertility-sensitive stage (10-20d before heading), sevengenotypes (six TGMS lines and the control Pei-Ai64S) were grown from May 4 at sevendifferent stages with 10d intervals. The temperatures at the fertility-sensitive stagesinvolved twelve levels from<20 to>℃ under the regime natural conditions in Hangzhou,China. There was considerable variation in pollen fertility among genotypes in responseto high temperature. Five genotypes identified as TGMS lines as their percentages offertile pollens were lower than or close to that of the control except for the unstableline RTS19 (V6). When the temperatures at the fertility-sensitive stage were at Ⅰ-Ⅳ,Ⅴ-Ⅵ and Ⅶ-Ⅻ, the percentages of fertile pollens varied in the ranges of 46.46-48.49%,19.62-22.79% and 3.49-5.87%, respectively. The critical temperatures of sterility andfertility in the five TGMS lines were 25.1 and 23.0℃, respectively. Considering theamounts and directions of main effect and their IPCA (interaction principal componentsanalysis), we can classify the lines and temperature levels into different groups, anddescribe the characteristics of genotypetemperature interaction, offering the informationand tools for the development and utility of thermo-sensitive male sterile lines.Several TGMS rice lines with their reproductive sensitivity to high temperature that canbe screened using the AMMI model may add valuable germplasm to the breeding program ofhybrid rice.

Genotype×year interaction of pod and seed mass and stability of Pongamia pinnata families in a semi-arid region

Sixteen pongamia families were evaluated in a field experiment for eight consecutive years in dryland conditions to identify stable,high-yielding families.The trial was conducted in a randomized complete block design with three replications.Each family,consisting of nine trees per replication,was planted at a spacing of3 m x 3 m.Yield stability was analyzed using(1)Eberhart and Russel’s regression coefficient(β_i)and deviation from regression(S_d^2),(2)Wrike’s ecovalence(W_i);(3)Shukla stability variance(σ_i^2);and(4)Piepho and Lotito’s stability index(L_i).Families were also analyzed for adaptability and stability using AMMI and GGE biplots graphical methods.The study revealed significant variances due to family and family x year interaction for pod and seed yield.Families performed differently and ranked differently across years.The performance of families was influenced by both genetic factor and environmental conditions in different years.Among families tested,TNMP20,Acc14,TNMP14 and Acc30 were high yielders for pods,and Acc14,Acc30,TNMP6,RAK19 and TNMP14 were high for seed yield.According to the Eberhart and Russell model,Acc30,TNMP14 and TNMP3 were stable across years.In the graphical view of family x year interaction based on AMMI methods,TNMP3,TNMP4 and TNMP14 had greater stability with moderate seed yield,and Acc14 and Acc30 had moderate stability with high seed yield.On the other hand,GGE biplots revealed Acc14,Acc30 and TNMP14 as high yielders with moderate stability.AMMI and GGE biplots were able to capture nonlinear parts of the family x year interaction that were not be captured by the Eberhart and Russel model while also identifying stable families.Based on different methodologies,Acc14,Acc30 and TNMP14 were identified as high yielding and stable families for promoting pongamia cultivation as a biofuel crop for semi-arid regions.

Multi-Environment Evaluation and Genotype ×Environment Interaction Analysis of Sorghum [<i>Sorghum bicolor</i>(L.) Moench] Genotypes in Highland Areas of Ethiopia

Sorghum [Sorghum bicolor (L.) Moench] is a high-yielding, nutrient-use efficient, and drought tolerant crop that can be cultivated on over 80 per cent of the world’s agricultural land. However, a number of biotic and abiotic factors are limiting grain yield increase. Diseases (leaf and grain) are considered as one of the major biotic factors hindering sorghum productivity in the highland and intermediate altitude sorghum growing areas of Ethiopia. In addition, the yield performance of crop varieties is highly influenced by genotype × environment (G × E) interaction which is the major focus of researchers while generating improved varieties. In Ethiopia, high yielding and stable varieties that withstand biotic stress in the highland areas are limited. In line with this, the yield performance of 21 sorghum genotypes and one standard check were evaluated across 14 environments with the objectives of estimating magnitude G × E interaction for grain yield and to identify high yielder and stable genotypes across environments. The experiment was laid out using Randomized Complete Block Design with three replications in all environments. The combined analysis of variance across environments revealed highly significant differences among environments, genotypes and G × E interactions of grain yield suggesting further analysis of the G × E interaction. The results of the combined AMMI analysis of variance indicated that the total variation in grain yield was attributed to environments effects 71.21%, genotypes effects 4.52% and G × E interactions effects 24.27% indicating the major sources of variation. Genotypes 2006AN7010 and 2006AN7011 were high yielder and they were stable across environments and one variety has been released for commercial production and can be used as parental lines for genetic improvement in the sorghum improvement program. In general, this research study revealed the importance of evaluating sorghum genotypes for their yield and stability across diverse highland areas of Ethiopia before releasing for commercial production.

水稻品种抗褐飞虱不同生物型稳定性的AMMI模型分析

采用AMMI模型分析水稻品种抗褐飞虱不同生物型的稳定性,结果表明:水稻品种对褐飞虱不同生物型的抗感性均存在极显著的环境效应、基因型效应和互作效应。但是,不同水稻品种对褐飞虱不同生物型的抗感性稳定性程度不一样,同一水稻品种对褐飞虱不同生物型的抗性稳定性程度也存在差异。抗感褐飞虱生物型Ⅱ的品种中,Rathu Heenati(RHT)的抗虫性最稳定,RP1976-18-6-4-2的抗虫性最不稳定;TN1的感虫性最稳定,国粳4号的感虫性最不稳定。抗感褐飞虱孟加拉型的品种中,Ptb33的抗虫性最稳定,IR56的抗虫性最不稳定;IR26的感虫性最稳定,ASD7的感虫性最不稳定。

基于AMMI模型的烤烟品种丰产性和稳定性评价

对2010年湖南省烤烟品种区域试验结果进行方差分析,利用AMMI模型进行品种稳定性分析,发现HY-9J、C6212两品种在3个试验点产量均较高,具有稳定丰产的特性,品种HY-5X有较好的丰产性,对各地的适应性很好,HY-9X和HY-9CH丰产性和稳定性较差,利用价值不大;湖南农业大学基地环境对品种的鉴别力较高,而永州烟科所环境对品种的适应性较强。

基于AMMI模型的内蒙古苜蓿品种稳定性及适应性分析

为了全面评价内蒙古自治区苜蓿品种产量的稳定性、适应性以及各个试验点的代表性,为当地建立优良苜蓿栽培草地提供优良种质。利用AMMI模型对种植在内蒙古自治区4个不同地区的国内外10个紫花苜蓿品种干草进行分析研究。结果表明:苜蓿品种和试验地点的选择均具有一定的代表性,AMMI模型中IPCA1和IPCA2差异极显著(P<0.01),能够较好地分析基因型和环境的互作效应。WL343HQ、驯鹿属于高产稳定型的苜蓿品种,赤峰、鄂尔多斯地区的环境条件具有较好的品种分辨力。AMMI模型可以为内蒙古自治区苜蓿品种区域试验合理布局提供理论依据。

利用加性主效应和乘积交互作用模型对国际杂交水稻圃数据的分析

国际水稻遗传评价网（INGE）的1994年杂交水稻圃（IRHON）采用增广设计，包括了6个国家的17个地点和89个品种。为充分了解这类多点试验中关于品种和地点的交互作用，用加性主效应和乘积交互作用（AMMI）模型，以若干项乘积之和估算品种和地点的交互作用。借助双标图，可鉴别对地点有特殊适应性的品种。如果有参试地点的环境l因子的数据，AMMI模型还可帮助我们用环境因子解释品种和地点的交互作用。通过本文对1994年杂交水稻圃数据的分析，我们看到AMMI模型是分析多点试验数据的非常n效的工具，能为育种项目和决策部门提供非常有价值的品种适应信息。

应用基因型与播期互作效应分析水稻光温敏核不育系对光周期和温度的育性敏感性

应用AMMI模型、线性回归模型和系统聚类分析方法分析了9个水稻光温敏核不育系和2个对照不育系(培矮64S,浙农大11S),在9个播期环境下花粉和种子育性的变化动态。从AMMI两维图可直观看到基因型与播期的交互模式,根据最大互作效应主成分轴IPCA1值和花粉育性平均值将不育系分成3个集团。第1集团是培矮64S(1)、浙大21S(10)和浙大22S(11),具有较低花粉育性平均值和较高IPCA1值,表明它们对温度变化比较敏感,且有较大的负向互作效应。第2集团是浙农大11S(2)、浙大4S(3)、浙大5S(4)、浙大7S(6)、浙大8S(7)和浙大9S(8),其花粉育性平均值和互作值相对较低,变动在0.013～0.276间,暗示花粉育性对播期敏感度低。第3集团是浙大6S(5)和浙大10S(9),花粉育性平均值高,互作效应大,该2个不育系尚有分离,且花粉育性对播期反映敏感度高。对光敏型和温敏型不育系而言,基因型IPCA值大小主要反映它们对光周期和温度敏感性强弱。花粉和种子育性在基因型、播期及其互作效应上都存在极显著差异。本文还提出利用育性相对稳定性的定量指标Di界定光温敏核不育系的育性稳定性,分析表明,Di值与育种实践结果较为接近。基于AMMI模型的基因型主效应和互作效应分析可以明确划分不育系的不育期、育性转换期和可育期,并将水稻光敏型与温敏型不育系区分开,因而该模型可为不育系应用于种子生产提供信息和依据。

水稻籼粳交衍生系产量相关性状的遗传效应与杂种优势

采用包括基因型与环境互作效应的加性—显性遗传模型(AD模型),对不同环境下水稻籼粳交衍生系产量相关性状的遗传效应与杂种优势进行研究。结果表明,播抽天数、每穗总粒数、每穗实粒数和千粒重性状以基因的加性主效应为主;结实率和株高性状以基因的显性主效应为主;基因型×环境互作效应明显影响产量相关性状表现,以显性×环境互作为主,其中播抽天数、单株谷重、千粒重和穗长的显性×环境互作效应尤为明显。杂种优势研究表明,由遗传主效应控制的杂种优势除单株有效穗数外,其余8个性状均表现正向群体平均优势;基因型×环境互作杂种优势分析表明,单株谷重、单株有效穗数、每穗实粒数、千粒重、株高和穗长6个性状杂种优势的稳定性较好。遗传效应分析结果表明,明恢413、97gk1037、明恢118和明恢417在多个性状上表现以遗传主效应为主,环境互作效应较弱,具有较好的环境稳定性,表明该4个籼粳交衍生品系在籼粳杂种优势利用中具有较高的利用价值。

多药合用药效学协同、相加和拮抗定量计算新方法的建立

许多严重疾病如癌症的病因复杂,属于多靶点疾病,针对多个靶点联合用药比单靶点用药更容易实现治疗目的。药物联用可产生多层面的相互作用,最终表现为药效发生协同、相加或拮抗。对其进行定量评价的前提是获得多药合用组合的预期相加效应值。但长期以来一直缺乏精确计算协同、相加和拮抗的可靠方法,使新复方药物研究等发展受阻。通过等效剂量兑换和引入药理学中的药效的序贯和集合原理,作者发现了多药合用组合预期相加效应的数学规律,简述如下:多药合用剂量组合的预期相加效应值是一个连续的数值范围,即预期相加效应值是药物组合剂量的闭区间数集函数,建立了通用型计算公式。在二维坐标系中显示为一条量效曲带,与多药合用组合的实际量效曲线构成一幅"一条曲带和一条曲线"的图像。组成预期相加量效曲带的量效曲线的数量,随着合用药物数量的增加呈指数性增长。通过计算实际量效曲线和相加量效曲带的交点坐标,能够得出多药合用组合药效发生协同、相加或拮抗的剂量范围;通过比较实际量效曲线中的效应值(或剂量值)与相加量效曲带的偏离状况,能够得出发生协同、相加或拮抗程度的指标,如剂量合用指数和效应合用指数等。该方法能为新复方药物研发、多药联合效应的定量评价和中药复方的效应评价等提供可靠通用的计算方法,简称为"一带一线"法。

接受理论与提高思想政治课有效性研究

接受理论在思想政治课教学中已有的研究成果,并不能明显改善当前思想政治课的效率、效果和效益,其根本原因在于未能根据思想政治课的特点对接受理论进行完善。因此,从思想政治课有效性的角度出发,对接受理论进行重新认识,并在教学模式上进行创新具有重要的现实意义。

粳稻光敏型核不育系对日长敏感性的鉴定分析

提出利用自然日长鉴定粳稻光敏不育系的方法。应用AMMI(additive main effects and multiplicative interaction)模型,对8个水稻光敏不育系对日长和温度反应的遗传敏感性进行比较分析,将其育性敏感期日长分成6个梯度,温度分成11个梯度。结果表明:在杭州自然条件下种植参试的8个不育系均为光敏核不育系,具有以下特性,抽穗前10～20d是育性转化敏感期,不育系的可育临界日长为13.0～13.8h,育性的临界高温为25.0℃,临界低温为21.0℃。

MINITAB统计程序中方差分析指令的巧用 Ⅰ.方差分析中的应用

本文利用试验设计理论与统计分析理论,指导 MINITAB 统计程序中的方差分析指令ONEWAY 与 TWOWAY 的使用,圆满地解决了单因子试验与复因子试验的各种方差分析问题。

Multiple Imputation of Missing Data:A Simulation Study on a Binary Response

Currently, a growing number of programs become available in statistical software for multiple imputation of missing values. Among others, two algorithms are mainly implemented: Expectation Maximization (EM) and Multiple Imputation by Chained Equations (MICE). They have been shown to work well in large samples or when only small proportions of missing data are to be imputed. However, some researchers have begun to impute large proportions of missing data or to apply the method to small samples. A simulation was performed using MICE on datasets with 50, 100 or 200 cases and four or eleven variables. A varying proportion of data (3% - 63%) was set as missing completely at random and subsequently substituted using multiple imputation by chained equations. In a logistic regression model, four coefficients, i.e. non-zero and zero main effects as well as non-zero and zero interaction effects were examined. Estimations of all main and interaction effects were unbiased. There was a considerable variance in the estimates, increasing with the proportion of missing data and decreasing with sample size. The imputation of missing data by chained equations is a useful tool for imputing small to moderate proportions of missing data. The method has its limits, however. In small samples, there are considerable random errors for all effects.

《金属-有机框架》专辑序言──金属-有机框架:新型多功能材料

金属-有机框架是由金属离子与有机配体通过配位键形成的三维框架材料,是近几十年来配位化学领域中发展较快的新型多功能材料。自上世纪90年代以来,金属-有机框架的研究呈现空前的增长,目前已有大于20000例的金属-有机框架被报道。金属-有机框架可变的金属中心及有机配体使其结构与功能具有多样性。金属中心的选择几乎覆盖了所有金属,包括主族元素、过渡元素和镧系金属。而配体的选择,除了传统的氮杂环和羧酸类配体外,还可以引入一些官能团对其进行修饰,

铝、硒、硅和磷复合处理对水稻幼苗生长的影响

采用二次正交旋转组合设计,以铝(Al)、硒(Se)、硅(Si)、磷(P)为处理因子,建立了多因子复合处理对水稻品种金优725幼苗存活率、百株苗鲜质量、百株根鲜质量和根脯氨酸含量影响的回归模型,并分析了各因子的主效应和互作效应.结果表明:在供试条件下,影响水稻幼苗生长的单因子主效应大小为Al>P>Se>Si.其中,Al表现为负效应,而P、Se和Si表现为正效应;除Al与Si的互作外,各因子间的互作效应均未达到显著水平.模拟寻优结果表明,Al在0.587～0.913mmol·L-1、Se在0.478～0.564mg·L-1、Si在0.613～1.069mmol·L-1、P在2.252～2.657mmol·L-1浓度范围时,可使水稻幼苗在一个存在铝胁迫的营养液环境下达到一个较佳的生长状态.