人类群体遗传空间结构的"克立格"模型

通过将“克立格”技术应用于人类群体遗传学领域,构建了人类群体遗传空间结构的“克立格”模型,并论述了其原理和计算方法。以HLA-A基因座为例,应用“克立格”模型,定量分析了中国人群HLA-A基因座的空间遗传异质性;对HLA-A基因频率的空间数据矩阵进行了主成分分析,进而定义了人类群体遗传结构的综合遗传测度(SPC),绘制了综合遗传测度和主成分(PC)的“克立格”地图,分析了其群体遗传空间结构特性。与其他空间插值或平滑方法相比,人类群体遗传空间结构的“克立格”模型具有明显优点：1)“克立格”估计以空间遗传变异函数模型为基础,在绘制空间遗传结构地图之前,可利用变异函数模型定量分析所研究基因座(或多基因座)的空间遗传异质性;2)“克立格”插值方法是真正意义上的无偏估计模型,它利用待估区域周围的已知群体遗传调查点数据,并充分考虑调查点的空间影响范围,给出待估区域的最优估计值;3)“克立格”模型允许估计插值误差,这种插值误差既可用于评价空间估计效果,又可通过绘制误差地图指导在误差过高的地点增加新的群体遗传调查样本点,以优化估计效果。然而,人类群体遗传空间结构的“克立格”模型也存在一定缺点：1)若不能用任何理论遗传变异函数模型拟合观察遗传变异函数值,则不能建立“克立格”模型;2)若理论遗传变异函数的拟合优度很低,则据此建立的“克立格”模型的估计标准差在整个空间范围内会很大,此时“克立格”模型不适用于估计群体遗传空间结构。出现上述两种情形时,应选用不考虑空间相关性的空间随机插值方法绘制群体遗传结构地图,如基因绘图软件中的Cavalli-Sforza方法,反向距离加权法和条样函数插值法等。     

基于Median函数的分段回归模型及其在生物学上的应用

在生物学科研工作中,经常会遇到因变量和自变量之间存在着多种不同的趋势,分段回归模型可以很好的拟合变量间这种非线性趋势.本文介绍了基于Median函数的分段回归模型,可以同时对各项回归参数和转折点进行估计,最后,本文结合生物学上的实例运用SAS统计软件进行了分段回归模型的拟合.     

稻纵卷叶螟卷叶有虫率与化蛹率关系的研究

探索利用稻纵卷叶螟 Cnaphalocrocis medinalis（Guenée）卷叶有虫率估测化蛹率的方法和途径。本文基于稻纵卷叶螟化蛹进度的田间调查数据,用化蛹率（y）和卷叶有虫率（x）分别拟合直线函数、一元二次函数、一元三次函数、对数函数、指数函数和幂函数6种不同模型。结果表明,以一元三次函数方法估计精度最高,误差最小,应用效果最好,并根据最优数学模型建立化蛹率（y）与卷叶有虫率（x）的关系速查表。生产中可以应用拟合的 最优数学模型对田间稻纵卷叶螟化蛹进度进行监测。     

准好氧填埋场的温度空间变异性

利用半变异函数, 对准好氧填埋装置中温度的空间变异特性进行了研究,并对装置内部温度进行了Kriging（克里格法）插值,得到纵剖面的等温线图.对不同理论模型进行优化拟合结果表明,剖面上温度半变异函数用线性有基台值模型拟合, 效果最好.得到的理论模型参数为：变程3.5 m,基台值83.6.利用克里格法进行最优内插估值得到的温度等温线表明,高温区域一般位于填埋体中段4～16 m的3 m以上部分,低温区域一般位于填埋体两端及中段1 m以下部分.可在高温区加大通风散热效果, 以降低过高的温度; 在低温区改善好氧环境, 增大氧气含量, 从而提高温度.对剖面上温度进行加权平均后,得到的准好氧填埋堆体的温度为59.8 ℃.这一温度可作为准好氧填埋结构基本形成的参考值.     

几种光合作用光响应典型模型的比较研究

光响应曲线是判定植物光合效率的重要方法,通过曲线可以获得植物光合特性的相关生理参数,但不同模型提取的光响应参数和指标存在差异。本文选择直角双曲线、非直角双曲线和两种指数曲线模型,分别对3个品系常绿杨光响应数据进行拟合,提取了各光响应曲线模型的主要特征参数,对比分析了各模型参数问的差异,并对光饱和点（LSP）的不同计算方法进行了讨论。最后用巨尾桉光响应数据对分析结果作了进一步验证。结果表明,直角和非直角双曲线模型拟合的最大净光合速率（P＇max）、表观量子效率（a）及暗呼吸速率（Rd）值高于指数模型拟合值,且直角双曲线拟合的各参数均比非直角双曲线拟合的各参数的值大,而两指数模型各参数拟合值基本一致;在LSP计算方法中,用光通量200μmol·m^-2·s^-1以下的点拟合的Pn-I直线与其它模型相结合得到光饱和点的方法不可靠,会使计算结果明显偏小,用接近最大总光合速率Pmax一定比例的方法估计LSP也存在较大偏差,而P＇max由于消除了Rd的影响,计算光饱和点时各模型的估计比例相对固定,是一个比较理想的LSP估计方法,初步得出直角、非直角及指数模型用P＇max来估计光饱和点时应选取的比例分别为（78±1）％、（82±1）％及（96±1）％。     

模型选择中交叉验证量的改进

对模型选择中交叉验证量CV进行改进,得到新的验证模型是否合适的准则RCV,RCV包含了CV的信息,并包含了拟合程度,模型中的待估参数个数和样本容量等等,比起AIC,BIC和CV具有更好的稳定性和分辨功能.     

基于集合卡尔曼滤波的南雄烟草LAI数据同化研究

叶面积指数(LAI)是表征烟草生长健康状态的重要指标之一,获取准确的LAI数据是监测烟草生长走势的重要步骤。以广东省南雄地区为试验区开展了集合卡尔曼滤波同化方法在烟草LAI的应用研究。通过野外实测得到南雄烟草生长期内的高光谱数据,并计算每个生长期的归一化植被指数(NDVI),依据NDVI值获得LAI测量数据;通过积温数据和实测LAI数据构建了符合南雄地区烟草LAI变化规律的LOGISTIC模型;并以LAI为研究变量,利用集合卡尔曼滤波数据同化技术融合NDVI数据计算得到的LAI和简化LOGSITIC模型拟合得到的LAI这两种不同的数据信息,获取实验区烟草生长期时间序列上的连续LAI数据。最后,进一步对比了数据同化方法、NDVI计算LAI方法和LOGISTIC模型拟合这3种方法获取烟草LAI的效果。结果显示:数据同化方法、NDVI计算LAI方法和LOGISTIC模型拟合3种方法均可一定程度上表征烟草LAI的变化状态,其中数据同化方法拟合效果最优。实验发现NDVI计算LAI方法在烟草生长前后期LAI值出现偏大或偏小的异常情况;LOGISTIC模型拟合则不能有效的描述烟草LAI的突发性变化;同化方法综合作物生长模型和遥感监测的优势,能够动态调节参数得到LAI优化结果,同化后LAI结果和真实值吻合,变化曲线更符合烟草的实际生长状况。     

R法遗传参数估值置信区间的计算方法和重复估计次数的研究

探讨R法遗传参数估值置信区间的计算方法和重复估计次数(NORE)对参数估值的影响,利用4种模型通过模拟产生数据集。基础群中公、母畜数分别为200和2000头,BLUP育种值选择5个世代。利用多变量乘法迭代(MMI)法,结合先决条件的共扼梯度(PCG)法求解混合模型方程组估计方差组分。用经典方法、Box-Cox变换后的经典方法和自助法计算参数估值的均数、标准误和置信区间。结果表明,重复估计次数较多时,3种方法均可;重复估计次数较少时,建议使用自助法。简单模型下需要较少的重复估计,但对于复杂模型则需要较多的重复估计。随模型中随机效应数的增加,直接遗传力高估。随着PCG和MMI轮次的增大,参数估值表现出低估的趋势。     

利用NEMG信号AR模型参数对局部肌疲劳的研究

探讨如何利用针电极肌电信号来评价人体的肌肉疲劳。采用参数化模型的方法提取针电极肌电信号的AR模型a1参数,并对反映肌肉疲劳时间过程的a1参数进行直线拟合。实验结果发现拟合的直线变化趋势能够较好地反映肌肉的疲劳过程。     

基于线性混合模型的红松人工林一级枝条大小预测模拟

基于黑龙江省孟家岗林场60株人工红松955个标准枝数据,采用线性混合效应模型理论和方法,考虑树木效应,利用SAS软件中的MIXED模块拟合红松人工林一级枝条各因子(基径、枝长、着枝角度)的预测模型.结果表明： 通过选择合适的随机参数和方差协方差结构能够提高模型的拟合精度;把相关性结构包括复合对称结构CS、一阶自回归结构AR(1)及一阶自回归与滑动平均结构ARMA(1,1)加入到一级枝条大小最优混合模型中,AR(1)可显著提高枝条基径和角度混合模型的拟合精度,但3种结构均不能提高枝条角度混合模型的精度.为了描述混合模型构建过程中产生的异方差现象,把CF₁和CF₂函数加入到枝条混合模型中,CF₁函数显著提高了枝条角度混合模型的拟合效果,CF₂函数显著提高了枝条基径和长度混合模型拟合效果.模型检验结果表明：对于红松人工林一级枝条大小预测模型,混合效应模型的估计精度比传统回归模型估计精度明显提高.
     

Spatial models for spatial statistics: some unification

A general statistical framework is proposed for comparing linear models of spatial process and pattern. A spatial linear model for nested analysis of variance can be based on either fixed effects or random effects. Greig-Smith (1952) originally used a fixed effects model, but there are also examples of random effects models in the soil science literature. Assuming intrinsic stationarity for a linear model, the expectations of a spatial nested ANOVA and two term local variance (TTLV, Hill 1973) are functions of the variogram, and several examples are given. Paired quadrat variance (PQV, Ludwig & Goodall 1978) is a variogram estimator which can be used to approximate TTLV, and we provide an example from ecological data. Both nested ANOVA and TTLV can be seen as weighted lag-1 variogram estimators that are functions of support, rather than distance. We show that there are two unbiased estimators for the variogram under aggregation, and computer simulation shows that the estimator with smaller variance depends on the process autocorrelation.     

Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data

Genomic data provide a valuable source of information for modeling covariance structures, allowing a more accurate prediction of total genetic values (GVs). We apply the kriging concept, originally developed in the geostatistical context for predictions in the low-dimensional space, to the high-dimensional space spanned by genomic single nucleotide polymorphism (SNP) vectors and study its properties in different gene-action scenarios. Two different kriging methods ["universal kriging" (UK) and "simple kriging" (SK)] are presented. As a novelty, we suggest use of the family of Matérn covariance functions to model the covariance structure of SNP vectors. A genomic best linear unbiased prediction (GBLUP) is applied as a reference method. The three approaches are compared in a whole-genome simulation study considering additive, additive-dominance, and epistatic gene-action models. Predictive performance is measured in terms of correlation between true and predicted GVs and average true GVs of the individuals ranked best by prediction. We show that UK outperforms GBLUP in the presence of dominance and epistatic effects. In a limiting case, it is shown that the genomic covariance structure proposed by VanRaden (2008) can be considered as a covariance function with corresponding quadratic variogram. We also prove theoretically that if a specific linear relationship exists between covariance matrices for two linear mixed models, the GVs resulting from BLUP are linked by a scaling factor. Finally, the relation of kriging to other models is discussed and further options for modeling the covariance structure, which might be more appropriate in the genomic context, are suggested.     

One-step analysis of seed storage data and the longevity of Arabidopsis thaliana seeds

Seeds of two ecotypes of Arabidopsis thaliana, NW20 and N1601, were aged over a range of saturated salt solutions at temperatures between 6 degrees C and 55 degrees C. For each ecotype, the results from 37 storage experiments were summarized using the Ellis and Roberts viability equations and a modified version of these equations which allows for a proportion of 'non-respondents'. For both models, two approaches were taken in order to model the effect of moisture content (MC) and temperature on seed longevity. The first, a two-step approach, involved fitting individual survival curves and then multiple regression analysis of the fitted parameters on moisture content and temperature. For the second approach, the full viability models were fitted in one step, including the multiple regression for the effects of MC and temperature within the generalized linear model used to describe each survival curve. This one-step approach takes into account the full variability of the data and provides the best predictions of seed longevity based on the original assumptions of the Ellis and Roberts viability equations. As a consequence of taking into account all the variation, this one-step approach is more sensitive and thus more likely to detect changes due to reducing the number of parameters in the model as being significant. Whilst both approaches indicated that seeds from the two Arabidopsis ecotypes have the same response to MC and temperature, parameter values did differ between the approaches, with the one-step approach providing the better fit. The best model for these two ecotypes, from the one-step approach, confirmed a quadratic relationship between temperature and longevity, but the magnitude of the non-linearity is not as large as indicated by the universal value for the quadratic term.     

Comparison of joint versus purebred genomic evaluation in the French multi-breed dairy goat population

Background

All progeny-tested bucks from the two main French dairy goat breeds (Alpine and Saanen) were genotyped with the Illumina goat SNP50 BeadChip. The reference population consisted of 677 bucks and 148 selection candidates. With the two-step approach based on genomic best linear unbiased prediction (GBLUP), prediction accuracy of candidates did not outperform that of the parental average. We investigated a GBLUP method based on a single-step approach, with or without blending of the two breeds in the reference population.

Methods

Three models were used: (1) a multi-breed model, in which Alpine and Saanen breeds were considered as a single breed; (2) a within-breed model, with separate genomic evaluation per breed; and (3) a multiple-trait model, in which a trait in the Alpine was assumed to be correlated to the same trait in the Saanen breed, using three levels of between-breed genetic correlations (ρ): ρ = 0, ρ = 0.99, or estimated ρ. Quality of genomic predictions was assessed on progeny-tested bucks, by cross-validation of the Pearson correlation coefficients for validation accuracy and the regression coefficients of daughter yield deviations (DYD) on genomic breeding values (GEBV). Model-based estimates of average accuracy were calculated on the 148 candidates.

Results

The genetic correlations between Alpine and Saanen breeds were highest for udder type traits, ranging from 0.45 to 0.76. Pearson correlations with the single-step approach were higher than previously reported with a two-step approach. Correlations between GEBV and DYD were similar for the three models (within-breed, multi-breed and multiple traits). Regression coefficients of DYD on GEBV were greater with the within-breed model and multiple-trait model with ρ = 0.99 than with the other models. The single-step approach improved prediction accuracy of candidates from 22 to 37% for both breeds compared to the two-step method.

Conclusions

Using a single-step approach with GBLUP, prediction accuracy of candidates was greater than that based on parent average of official evaluations and accuracies obtained with a two-step approach. Except for regression coefficients of DYD on GEBV, there were no significant differences between the three models.     

影响动物模型MBLUP评定准确性的主要因素

标记辅助最佳线性无偏预测（marker-assisted best linear unbiased prediction,MBLUP）是对动物实施标记辅助选择（marker-assisted selection,MAS）的一种重要方法。通过计算机随机模拟研究了所选性状的遗传力、QTL方差和相邻两个标记间图距3个因素对动物模型MBLUP评定准确性的影响。结果表明,性状的遗传力越高、QTL方差和相邻两个标记间图距越小时,动物模型MBLUP评定的准确性越高;相反,当性状的遗传力较低、QTL方差和相邻两个标记间图距较大时,动物模型MBLUP评定的准确性则较低。     

Kinetics of binding the mRNA cap analogues to the translation initiation factor eIF4E under second-order reaction conditions

The kinetics of binding of five analogues of the 5'-mRNA cap, differing in size and electric charge, to the eukaryotic initiation factor eIF4E, at 20 degrees C, pH 7.2, and ionic strength of 150 mM, were measured, after mixing solutions of comparable concentrations of the reagents, in a stopped-flow spectrofluorimeter. The registered stopped-flow signals were fitted using an efficient software package, called Dyna Fit, based on a numerical solution of the kinetic rate equations for assumed reaction mechanisms. One-, two-, and three-step binding models were considered. The quality of fits for these models were compared using two statistical criteria: Akaike's Information Criterion and Bayesian Information Criterion. Based on resulting probabilities of the models, it was concluded that for all investigated ligands a one-step binding model has essentially no support in the experimental observations. Our conclusions were also analysed from the perspective of kinetic transients obtained for cap-eIF4E systems under the so called pseudo-first order reaction condition, which result in the linear correlation of the observed association rate constant with ligand concentration. The existence of such a linear correlation is usually considered as proof of a one-step binding mechanism. The kinetic and optical parameters, derived from fitting a two-step cap-binding model with the DynaFit, were used to simulate kinetic transients under pseudo-first order reaction conditions. It appeared that the observed association rate constants derived from these simulated transients are also linearly correlated with the ligand concentration. This indicated that these linear dependencies are not sufficient to conclude a one-step binding.     

The generalized linear model for spatial data: assessing the effects of environmental covariates on population density in the field

With interest in spatial ecology growing, correlational field studies are likely to become increasingly important. Unfortunately, ecological field data often do not follow the assumptions of classical statistics, so techniques like the popular and powerful multiple linear regression and its variants are often unreliable, and results can be misleading. The generalized linear model (GLM) is a flexible extension of linear regression that has proved especially useful for discrete data. In this paper, the technique is adapted to accommodate spatially correlated, discrete data. Specifically, to demonstrate the approach, Japanese beetle grub [Popillia japonica Newman (Coleoptera, Scarabaeidae)] population density in the field is modeled as a function of soil organic matter content. The response variable (grub counts in small soil samples) was a spatially autocorrelated, discrete random variable. Three classes of GLMs of the association between soil organic matter content and grub density were compared: (i) regression (assuming normally distributed response variable), (ii) GLM assuming negative binomial counts, and (iii) GLM based on the assumption that the counts conformed to Taylor's power law (TPL). Because the grubs were distributed in patches rather than at random, models that explicitly accounted for the spatial autocorrelation of grub counts were constructed, and compared with models that assumed independent observations. The fitted values for the discrete GLMs [viz., (ii) and (iii)] differed noticeably from the fitted values from multiple regression; but fitted values among the negative binomial and TPL GLMs were virtually identical, regardless of whether the spatial covariance was incorporated into a model, whether a spherical or exponential variogram model was used, or whether variance function parameters were estimated over a large or small scale. However, P‐values for the overall significance of the models depended heavily on whether the GLM assumed a discrete or continuous response variable, and whether or not spatial autocorrelation in the response variable was accounted for. On average, P‐values were 45‐fold higher in the spatial GLMs than in the non‐spatial and 23‐fold higher in the discrete GLMs than in the continuous.     

Non-isothermal thermogravimetric analysis of oil-palm solid wastes

Thermal decomposition of oil-palm solid wastes, including oil-palm shell, fibre and kernel, was studied by thermogravimetric analysis (TGA). Effect of heating rate and sample particle size on the behaviour of thermogram and kinetic parameters were investigated. The one-step global model, two-step consecutive model and two-parallel reactions model were used to simulate the pyrolysis process of the three materials studied. The one-step global model was able to describe the fractional weight loss upon pyrolysis of oil-palm kernel reasonably well but gave a large deviation for oil-palm shell and fibre. The two-step consecutive model could improve the fitting for oil-palm shell and fibre, but it cannot account for the inflection characteristic of the thermogram. Prediction by the two-parallel reactions model gave the best fitting with the experimental data of all oil-palm wastes under all pyrolysis conditions investigated. This proposed model was also tested with other biomass materials and proved to be satisfactory.     

A method to optimize selection on multiple identified quantitative trait loci

A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models.     

Detecting and cleaning outliers for robust estimation of variogram models in insect count data

Outlier detection and cleaning procedures were evaluated to estimate mathematical restricted variogram models with discrete insect population count data. Because variogram modeling is significantly affected by outliers, methods to detect and clean outliers from data sets are critical for proper variogram modeling. In this study, we examined spatial data in the form of discrete measurements of insect counts on a rectangular grid. Two well-known insect pest population data were analyzed; one data set was the western flower thrips, Frankliniella occidentalis (Pergande) on greenhouse cucumbers and the other was the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) on greenhouse cherry tomatoes. A spatial additive outlier model was constructed to detect outliers in both the isolated and patchy spatial distributions of outliers, and the outliers were cleaned with the neighboring median cleaner. To analyze the effect of outliers, we compared the relative nugget effects of data cleaned of outliers and data still containing outliers after transformation. In addition, the correlation coefficients between the actual and predicted values were compared using the leave-one-out cross-validation method with data cleaned of outliers and non-cleaned data after unbiased back transformation. The outlier detection and cleaning procedure improved geostatistical analysis, particularly by reducing the nugget effect, which greatly impacts the prediction variance of kriging. Consequently, the outlier detection and cleaning procedures used here improved the results of geostatistical analysis with highly skewed and extremely fluctuating data, such as insect counts.