质量—数量性状的遗传分析 Ⅲ.受三倍体遗传控制的胚乳性状

将二倍体质量—数量性状的遗传分析方法推广应用于受三倍体遗传控制的胚乳性状。给出了加性—显性模型下有关胚乳世代的平均数和遗传方差的分量。以稻米胚乳的糊化温度（以碱扩散值表示,简记为ASV）为例演示了分析程序。结果表明被研究组合ASV的遗传涉及一个主基因和基干微基因。根据分离世代中胚乳ASV的株平均数和方差,可     

玉米人工合成群体配合力效应及遗传潜势研究

选用6个自交系为测验种, 采用NCII遗传交配设计, 对以我国西南地区优良玉米地方种质巫溪14, 兰花早和北美, 热、 亚热带种质为主体的人工合成群体LBM、 WBM、 LLS、 WLS和两个美国优良群体BSSSR、 BS16等6个群体的配合力效应及遗传潜势进行了系统评估。 两年两地(武汉、 安阳)的研究结果表明： LBM、 WBM、 WLS群体的单株     

Genotype × Environment Interacion in multi-environment Trials using shrinkage factors for ammi models

Shrinkage factors applied to the additive main effects and multiplicative interaction (AMMI) models improve prediction of cultivar responses in multi-environment trials (MET). Estimates of shrinkage factors based on the eigenvalue partition (EVP) method may get a further improvement in the predictions of cell means. Objectives of this work were: (1) to compare the EVP-based shrinkage method with unshrunken AMMI, best linear unbiased predictor (BLUP) and other shrunken method (herein named CCC), when they were applied to five maize MET and simulation data; (2) to assess by cross validation the equation which estimates the standard error of predicted means (SEPM) based on the EVP theory; (3) to estimate the genotype × environment interaction (GEI) variance components after applying the EVP shrinkage method to the five maize MET. Empirical data of five maize MET and simulation data were used for cross validation of the methods using the root mean square predictive difference (RMSPD) criterion. The RMSPD of the shrunken EVP predicted cell means was generally smaller than those of the other methods, suggesting that the EVP method was generally better predictor than the other methods. The truncated AMMI was the worst among the four methods studied. The EVP-based equation, which predicts the SEPM, was a good predictor as determined by the RMSPD cross validation criterion, with the advantage that it does not need one replication for validation. Estimates of mean squares, and GEI and error variances associated with the GEI effects were smaller for the shrunken EVP predicted effects than for the original data. This revised version was published online in July 2006 with corrections to the Cover Date.     

有缺失数据两因素随机区组试验资料的最小二乘分析法及其应用

在缺失数据两因素随机区组试验资料是两因素水平组合重复数不等的非平衡资料。本文根据最小二乘原理提出分析这类资料的新方法。利用此法，可以估计出两因素的主效互作效应，人而获得两因素各水平及其合作的最小二乘均数，并精确地进行方关分析与多重比较，最后，以一实例介绍该方法的应用。     

作物品种区域试验中品种均值估计的模型和方法——算术平均值、加权最小二乘估值和BLUP的比较

针对作物区域试验中的品种均值估计问题,根据混合线性模型的一般原理,总结和提出多种加权最小二乘估计 (WLSE)和最佳线性无偏预测(BLUP)的方法,推导了这些方法的平衡数据计算简式;同时,利用14套2年多点的棉花区试资料和一套4年多点的棉花品种试验对这些方法的预测效果进行验证比较.结果表明,与算术平均值相比,以环境内误差方     

European Regional Convergence Revisited: A Weighted Least Squares Approach

A plethora of past studies have concluded that unconditional β‐convergence is present in a broad sample of regions, implying that poor regions grow faster than rich ones. All these econometric studies tend to overlook the relative importance or size of each region in the national setting, treating all regional observations as equal. However, this assumption might lead to unrealistic or misleading results. Convergence analysis could be more meaningful if it included a weighting mechanism taking into account the size of regions. The aim of this paper is to investigate whether the inclusion of a weighting mechanism in β‐convergence analysis, giving more weight to larger regions and less to smaller ones, can result in sharply different implications for the regional convergence‐divergence process. For this reason, both unweighted ordinary least squares (OLS) and weighted least squares estimators are used in the analysis of regional (intra‐national) convergence within 10 European Union (EU) countries over the period 1990–2000. The comparison between the two methods reveals that when regions are appropriately weighted for their size, intra‐national divergence, rather than convergence found with the OLS approach, seems to be the dominant experience in the EU.     

蜂蜜中水分 果糖和葡萄糖的近红外定量分析模型

采用近红外光谱(NIRS)技术和偏最小二乘回归(PLS)方法分别建立了蜂蜜中的水分、果糖和葡萄糖含量的近红外定量分析模型,并对模型进行评价和考察。结果表明,所建模型具有一定的实用性。     

Component analysis of complex characters in plant breeding

Summary A method is described by which the contribution of individual components to the variation of a complex character is quantified. The method is adapted for use in plant breeding from the sequential component analysis, developed for agronomic experiments by Eaton & Kyte (1978). It applies to a situation in which the complex character y is the product of n components (x₁,...x_n). The components are ratios of observed primary characters, introduced in a logical sequence. The proposed method differs from that of Eaton & Kyte in that: (1) the data are not log-transformed, and (2) the complementary determinations of y by its components are obtained directly from differences between the coefficients of determination of y by the successive primary characters rather than from a stepwise multiple regression of ln y on its log-transformed components.A comparison of the two methods suggests that the differences in results are caused mainly by the logtransformation which Eaton & Kyte apply to transform the relationship between y and its components into a linear one. The proposed alternative procedure avoids the transformation of the data; the multiplicative relationship between y and its components is retained and so is the assumed additive inheritance of the components. These two features of component analysis allow an active exploitation of specific combining ability based on recombinative heterosis.Present address: Giltaylaan 12, 6703 GB Wageningen, The Netherlands.     

Long-Term Experiments with cropping systems: Case studies on data analysis

Data analysis for Long-Term Experiments (LTEs) with cropping systems requires some careful thinking, especially for the most complex designs, characterised by rotations with different durations and/or a different number of test-crops per rotation cycle. This paper takes an example-based approach, built upon a number of datasets, covering the main types of LTEs, with increasing levels of complexity. A procedure is outlined to build statistical models for data analysis that is useful for all LTEs characterised by the simultaneous presence of all rotation phases in all years, together with within-year replication. This procedure is based on the assumption that correct analyses can be performed separately for each year. The use of mixed models and REML estimation is advocated for model fitting with all LTEs, due to the fact that most designs are non-orthogonal, as plots may not produce data for the test-crop under study in all years. Mixed models are also useful to account for the autocorrelation of residuals over time and hints are given for the selection of an appropriate variance-covariance structure. For all our examples, variances were not constant across years and compound symmetry correlation structures with variance heterogeneity of years proved to be the best compromise between parsimony and statistical accuracy. Methods are outlined to test for the need of other more complex correlation structures and examples are also given on how to test for fixed effects, model fertility trends and assess the long-term stability of cropping systems.     

玉米抗丝黑穗病的基因效应

利用2个抗玉米丝黑穗病自交系（齐319和Mo17）与2个感病系（E28和黄早四）,组配4个抗感杂交组合。2004年,对4个组合的亲本、F₁、F₂、BCR（F₁与抗病亲本回交1代）和BCS（F₁与感病亲本回交1代）6个世代群体分别在北京和黑龙江进行丝黑穗病的人工接种鉴定,采用世代平均值分析玉米抗丝黑穗病的遗传特点。研究表明,各杂交组合的抗病性均含有显著的加性效应,其中3个组合有显著的显性效应。不同杂交组合的抗病遗传模式表现不同,用感病亲本E28组配的2个组合（齐319×E28）和（Mo17×E28）的抗病性基本符合加性-显性遗传模型;而用另一个感病亲本黄早四组配的组合（齐319×黄早四）和（Mo17×黄早四）的抗病性存在明显的上位性效应。这说明玉米对丝黑穗病的抗性呈现较复杂的遗传模式。因此,在抗玉米丝黑穗病育种中既要重视对自交系抗病水平的鉴定,也要加强杂种F₁的合理组配及抗病性评估。     

Generation means analysis of right-censored response-time traits: Low temperature seed germination in tomato

Summary In studies to determine the inheritance of response-time traits, such as time to seed germination, some viable individuals may fail to respond during an experiment. If these right-censored observations are ignored, sample means and variances will be underestimated. This is illustrated using data from time to seed germination at 9°C for Lycopersicon esculentum (Mill.) fast germinating PI 120256, slow-germinating T3 and their reciprocal F₁, F₂ and backcross progeny. This paper presents methods to detect and to accommodate right-censored data in generation means analysis. Genetic interpretations derived from corrected and uncorrected estimates of generation means and variances are compared. Correction for right-censoring increased estimates of environmental and phenotypic variances, and decreased heritability estimates.     

基于遗传算法结合偏最小二乘的潮土碱解氮高光谱特征及含量估测

提取土壤碱解氮特征光谱是利用高光谱数据进行其含量估测的关键。对山东省典型潮土土壤样本测试高光谱并进行变换;采用遗传算法（GA）结合偏最小二乘法（PLS）,在筛选潮土碱解氮含量特征谱区的基础上,构建潮土碱解氮含量偏最小二乘（PLS）回归估测模型;优选最佳模型并与相关分析、逐步回归分析和单纯偏最小二乘回归分析的模型进行比较。结果表明：潮土碱解氮特征波段为449~469nm,988~1001nm,1065~1078nm,1716~1736nm,1912~1925nm,2213~2233nm,2262~2275nm;基于各输入光谱特征谱区构建的估测模型决定系数R²均较高,其中基于反射率一阶导数光谱筛选的特征谱区,构建的模型精度最高,数据点（147个）为原始全谱的7.17%,建模R²达到0.97,均方根误差RMSE为4.78mg/kg,验证R²为0.95,RMSE为5.49mg/kg,对潮土碱解氮含量具有较好的预测准确性;在光谱变换形式中,反射率的一阶导数表现最佳;方法比较显示采用遗传算法结合偏最小二乘（GA-PLS）获得较高预测精度的同时,可简化模型。说明遗传算法结合偏最小二乘法（GA-PLS）,可有效筛选土壤碱解氮的特征波段,减少模型参与变量,提高估测精度。     

世代平均值分析的多元回归程序

群体遗传结构特征决定育种方法与选择效果,群体遗传特征又与基因效应有相当关系。研究表明:加性——显性模式不能适用于数量性状遗传的所有场合,估计上位效应的重要性日益明显。世代平均值分析是估计上位效应的重要方法之一,通过最小二乘多元回归程序进行世代平均值分析既可估计基因效应,又可估计其相应方差。文中对世代平均     

Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content

Spectral scattering is useful for assessing the firmness and soluble solids content (SSC) of apples. In previous research, mean reflectance extracted from the hyperspectral scattering profiles was used for this purpose since the method is simple and fast, and also gives relatively good predictions. The objective of this study was to improve firmness and SSC prediction for ‘Golden Delicious’ (GD), ‘Jonagold’ (JG), and ‘Delicious’ (RD) apples by integration of critical spectral and image features extracted from the hyperspectral scattering images over the wavelength region of 500-1000 nm, using spectral scattering profile and image analysis techniques. Scattering profile analysis was based on mean reflectance method and discrete and continuous wavelet transform decomposition, while image analysis included textural features based on first order statistics, Fourier analysis, co-occurrence matrix and variogram analysis, as well as multi-resolution image features obtained from discrete and continuous wavelet analysis. A total of 294 parameters were extracted by these methods from each apple, which were then selected and combined for predicting fruit firmness and SSC using partial least squares (PLS) method. Prediction models integrating spectral scattering and image characteristics significantly improved firmness and SSC prediction results compared with the mean reflectance method when used alone. The standard errors of prediction (SEP) for GD, JG, and RD apples were reduced by 6.6, 16.1, 13.7% for firmness (R_pred-values of 0.87, 0.95, and 0.84 and the SEPs of 5.9, 7.1, and 8.7 N), and by 11.2, 2.8, and 3.0% for SSC (R_pred-values of 0.88, 0.78, and 0.66 and the SEPs of 0.7, 0.7, and 0.9%), respectively. Hence, integration of spectral and image analysis methods provides an effective means for improving hyperspectral scattering prediction of fruit internal quality.     

Genetic variation within eight populations of perennial forage grasses

Information on both mean performance and genetic variation is important in determining the value of germplasm for breeding via recurrent selection. The objectives of this experiment were to evaluate different methods of controlling spatial variation for forage yield, and to estimate genetic variances and heritability in eight perennial forage grass populations. Polycross families of two populations of smooth bromegrass (Bromus inermis Leyss.), two populations of hybrid wheatgrass (Elytrigia×muctonata (Opiz ex. Bercht.) Prokud.), and four populations of orchardgrass (Dactylis glomerata L.) were field tested for 2 years. Spatial variation was present in all three progeny tests, but was well accounted for by either the incomplete block design or a nearest neighbour analysis (NNA) of yield potentials. Low rank correlations between adjusted family means for the incomplete block design vs. the NNA created the unanswered question of which would provide a more appropriate estimate of family mean performance. Genetic variation was detected in six out of eight populations for forage yield, four out of six populations for ground cover, all four orchardgrass populations for maturity and one of four orchardgrass populations for leafspot reaction (caused by Drechslera spp.). Excessive year-family interactions prevented detection of genetic variation for leafspot reaction in the other populations. Heritability estimates ranged from 0.30 to 0.42 for forage yield. In all three species, there was a trend toward a negative relationship between population mean and genetic variance, suggesting that less elite populations may need to contribute other traits to be useful in the short term.     

Matromorphy in Brassica oleracea L. V. Studies on quantitative characters of matromorphic plants and their progeny

Summary A theoretical review is given of genotypical means and variances of matromorphic populations which developed in different ways.From combining ability analyses of variance and comparisons of means and variances of quantitative characters of matromorphic and control plants or their progeny it is concluded that matromorphic plants do develop in different ways.It seems likely that matromorphic embryos develop from egg-cells which were diploid as a result of pre-meiotic endomitosis (followed by autobivalent formation) or absense of meiosis and from egg-cells diploid as a result of failing of the first or second meiotic division. Homozygous matromorphic plants appear not to occur.     

Application of partial least squares regression in the forecast of ground subsidence

Taking into account many influence factors of ground subsidence induced by underground exploitation,based on partial least squares multinomial regression,a forecast analysis on the maximum of ground subsidence is carried out.Taking height,depth,obliquity of coal clay and rigidity coefficient as independent variables,and maximum of ground subsidence as dependent variable,the forecast model of maximum of ground subsidence is obtained.It is found that,Press residual value decreases with the increase of number of latent variables,and the number of latent variables is four by Press residual value versus number of latent variables.The normal regression coefficient of height is the largest in the four influence factors,and this indicates that the influence of height is the largest on maximum of ground subsidence.The determination coefficient of forecast model obtained in this paper is 0.915 7,the error of forecast model is ±10.41%.The following conclusion can be drawn that the model based on partial least squares multinomial regression is a better and feasible non linear method.     

Detection of donor effects in a rye introgression population with genome‐wide prediction

Introgression populations are developed to make genetic resources for breeding purposes available. In the case that the number of donor segments exceeds the number of lines, genome‐wide prediction (GWP) methods are suggested as promising for the analysis of such populations. Our objectives were to characterize a rye introgression population with the Rye5K SNP assay and to apply a GWP model with a modification of the restricted maximum likelihood procedure that yields heteroscedastic variances to detect significant donor effects. The introgression lines (ILs) carried on average 4.6 donor segments with a mean length of 27 cM and represented 94% of the donor genome. Two donor effects were detected that significantly increased thousand‐kernel weight. We found four donor effects for protein, total pentosan and starch content that can improve baking quality. Three donor effects for protein content were observed for improving feeding purposes and one donor effect for starch content to improve ethanol production. The effects were localized to small genomic regions. Consequently, these ILs can improve rye breeding by directly employing them in breeding programmes for variety development.     

Prediction of deoxynivalenol and zearalenone concentrations in Fusarium graminearum inoculated backcross populations of maize by symptom rating and near‐infrared spectroscopy

Gibberella ear rot (GER) caused by Fusarium graminearum is a destructive disease in maize of temperate regions resulting in yield reduction and contamination by the mycotoxins deoxynivalenol (DON) and zearalenone (ZON). We wanted to analyse whether prediction of DON and ZON concentrations is feasible either by GER severity ratings or by near‐infrared spectroscopy (NIRS). We analysed 80 and 102 lines developed by backcrossing doubled‐haploid lines from segregating populations to the resistant and susceptible parent, respectively, by artificial infection at three locations in Germany and France. Both backcross (BC) populations differed substantially in their means for all traits with significant (P < 0.01) genotypic variances. DON and ZON concentrations measured by immunotests were significantly (P < 0.01) correlated with each other and with GER severity within each BC population (0.6 ≤ r ≤ 0.9, P < 0.01). DON concentration measured by immunotest and NIRS significantly correlated (r ≈ 0.9, P < 0.01). In conclusion, DON and ZON concentrations could be reliably predicted by GER severity. Additional NIRS analysis of DON concentration might be useful for the positively selected fraction.     

EXCEL在裂区试验统计分析中的应用

介绍一种利用EXCEL进行裂区试验统计分析的方法。根据方差分析原理,利用EXCEL编制了裂区试验统计分析计算程序。用户只需输入试验的原始数据,即可快速、准确地计算出各因素的平方和、均方和检验值F,其计算结果与专业统计软件DPS处理的结果基本一致。该程序使用方便,并可用于进行裂区试验统计分析。