结构方程混合模型在SNP分析中的应用

采用结构方程混合模型(SEMM)对实际SNP数据进行分析,为遗传统计学提供一种新的有效的分析方法。本研究的数据是由GAW17提供的,包含697个个体的22条常染色体的上万个SNP和根据这些SNP所模拟的697个个体的性状特点。随机挑选了1号染色体上的4个SNP和3个定量性状作为研究变量,分别进行潜在类别分析和结构方程混合模型分析。根据4个SNP数据,人群被分为3个潜在类别,概率分别为0.53,0.34,0.13。潜在类别1、2和3中的因子均值Q分别为-4.029、-2.052和0,潜在类别1、2的因子均值均低于3(<0.001)。研究表明:结构方程混合模型(SEMM)综合了结构方程模型和潜在类别模型的思想,形成了自己的优势,可用于处理同时包含分类潜变量和连续潜变量的数据。     

SNPs在水生动物遗传多样性分析的应用

Lander于1996年提出的单核苷酸多态性(single nucleotide polymorphisms,SNPs)被认为是第三代理想的遗传标记.SNPs是基因组水平上由单个碱基变异引起的DNA序列多态性,广泛应用于生物的遗传多样性研究.本文就SNPs定义、特性,及其在水生动物遗传多样性分析的应用进行综述.     

基于Petri网T不变量的PHB新陈代谢建模分析(英文)

从拓扑结构的角度分析生化反应网络是生物信息学研究中的一个热点问题。通过将两种传统的途径分析方法(基元模式和极端途径)与Petri网的T不变量分析进行了比较,结果表明:它们本质上是一致的,但是采用Petri网的T不变量分析更便捷。然后,利用Petri网技术构建了PHB代谢模型。对该模型作了结构分析,将计算得到的23个T不变量进行了分组:I组表示简单的可逆反应,II组表示循环的反应,III组可用于调控ATP/ADP比率,IV组是与PHB生产直接相关的反应,可用于代谢工程以提高PHB的产率。最后讨论了Petri网的T不变量分析在这个领域中的应用。     

野猪、家猪及野家杂种猪Leptin基因2、3外显子的SNPs分析

Leptin是一种内分泌激素（167个氨基酸）,主要在脂肪组织中表达,通过下丘脑对机体内能量的消耗和摄取起着重要的调节作用^[1]。鉴于此本文对野猪、家猪及野家杂种猪Leptin的2、3外显子进行了SNPs分析,并检测到了多态性。对纯合子片段进行克隆和测序,结果表明在编码区的214位碱基由T突变为C,编码的氨基酸没有发生改变;364位碱基由C突变为G,365位碱基由G突变为C,编码的Arg转变为Ala;426位由G突变为A,编码的氨基酸没有发生改变;451位插入碱基T,造成移码突变;462位由G突变为T。     

浅析基于形态特征的贝叶斯系统发育分析的不确定性*

系统发育分析通过形态特征或分子性状帮助理解不同生物类群的演化历史。化石作为许多已灭绝生物的唯一遗存, 为研究地球生命多样化提供了宝贵的机会。这使得基于离散型形态特征的系统发育分析在探讨生物的起源与早期演化方面发挥着不可或缺的作用。系统发育分析方法多样, 早期常常依赖最大简约法对化石形态特征进行分析。近年来, 基于模型的贝叶斯系统发育分析在古生物学中获得越来越多的重视。贝叶斯系统发育分析可以提供准确且易解释的系统发育关系, 并为不确定性提供评估指标。本文旨在讨论系统发育分析过程中的不确定性影响因素, 如数据集、替换模型以及马尔科夫链, 以此提高对系统发育分析结果的可信度。在本文的案例中, 利用已发表的苔藓虫数据集探讨不同版本的Mk模型和马尔可夫链的稳定性对系统发育树的影响。研究结果表明这些因素对系统发育树的后验概率和拓扑结构都有一定影响, 这意味着在系统发育分析中需要仔细分析这些因素, 选择合适的模型。     

简单贝叶斯决策的计算神经模型

大脑根据贝叶斯理论处理信息已经得到诸多心理学和神经生理学实验的支持.贝叶斯推理过程往往需要处理复杂的概率计算,最近Shadlen和Gold提出基于对数似然比可以简化基于贝叶斯的两可决策任务.然而,目前并不清楚如何在神经回路中实现基于对数似然比的贝叶斯决策.通过建立具有信息整合与胜者独享特性的决策神经回路,结合奖励调制的突触可塑性学习算法,得到决策行为与突触可塑性之间的对应关系,由此可实现简单贝叶斯决策的计算神经模型.最后,利用该模型模拟出最近Yang和Shadlen关于恒河猴可进行贝叶斯决策的实验结果.     

基于贝叶斯模型平均的蒸散遥感产品集成——以三江源国家公园为例

蒸散发(ET)是陆表水热过程的一个基础通量,不同模型基于的概念、假设、应用尺度等诸多差异给ET的准确模拟带来了多种不确定性.本研究以三江源国家公园为例,应用贝叶斯模型平均(BMA)方法,通过通量塔观测值对模型进行训练,并综合PT-JPL、ARTS-GIMMS3g、ARTS-MODIS、MOD16和SSEBo 5个模型结...     

基于层次贝叶斯似乎不相关回归的人工长白落叶松生物量模型构建

准确估算森林生物量对碳储量评估和森林资源管理具有重要意义,层次贝叶斯法作为一种可以有效提高参数稳定性的统计学方法,在森林生物量精准估算中展现出显著潜力。本研究基于黑龙江省孟家岗林场143株长白落叶松解析木数据,采用层次贝叶斯似乎不相关回归方法,构建了以胸径为自变量的一元似乎不相关混合效应模型(SURM1)和以胸径与树高为自变量的二元似乎不相关混合效应模型(SURM2),对比分析了限制最大似然估计(REML)与无先验信息(Br1)、基于数据自身先验信息(Br2)、基于历史先验信息(Br3)3种层次贝叶斯方法的拟合与预测效果。结果表明: SURM2模型在树干生物量和单木总生物量预测方面显著优于SURM1,平均绝对偏差百分比(MAPE)分别减少了7.8%和7.6%。基于数据自身先验信息的层次贝叶斯法(Br2)在参数估计稳定性方面(标准差为0.003～0.108)显著优于REML(标准差为0.052～0.540)、Br1(标准差为0.033～0.819)和Br3(标准差为0.038～0.771)。使用Br2进行预测时会产生更高的预测精度,SURM1模型在树干、树枝、树叶、树根和总生物量预测的MAPE分别为17.6%、45.1%、48.3%、25.2%、17.1%。与SURM1相比,SURM2模型在树干和总生物量的预测精度显著提升,MAPE分别减小7.3%和6.7%。在样本量较小(<60)时,有效的先验信息可以增加预测的稳定性。基于数据自身先验信息的贝叶斯方法在提高长白落叶松生物量模型预测精度与稳定性方面具有显著优势,为黑龙江地区长白落叶松生物量的精准估算提供了有效支持。     

类褛网蛛的扫描电镜观察

用扫描电镜观察了类褛网蛛的螯肢与纺器、筛器的微细结构。     

日本沼虾ITS1序列分析及SNPs位点的筛选

研究采用直接测序法,分析日本沼虾(Macrobrachium nipponense)rDNA基因内转录间隔区ITS1的DNA序列,以筛选日本沼虾SNPs位点。共分析了32个太湖水域野生日本沼虾样本,结果表明,日本沼虾ITS1序列平均长度为1749.8bp,是迄今已报道的最长的ITS1序列,A、G、T和C的平均含量分别为29.9%、28.3%、27.7%、14.0%,G+C的含量平均为42.3%。通过序列比对,共筛选出22个SNPs位点,SNPs位点出现频率为0.0126,其中9个为C/T转换(占40.91%),4个为A/G转换(占18.18%),2个为A/T颠换(占9.09%),5个为T/G颠换(占22.73%),1个为A/C颠换(占4.55%),1个A/T或C颠换(占4.55%)。日本沼虾ITS1序列的22个SNP位点中,21个位点为2个等位基因,1个位点出现了3个等位基因,为复等位基因位点。日本沼虾ITS1序列中还发现3个具有多态性的微卫星位点、1个高度变异区以及大量的缺失、插入。研究首次对日本沼虾ITS1序列进行了分析,并发现了大量的SNP位点,为日本沼虾遗传育种研究提供了新的分子标记。     

Linear Logistic Latent Class Analysis

In the present paper the linear logistic extension of latent class analysis is described. Thereby it is assumed that the item latent probabilities as well as the class sizes can be attributed to some explanatory variables. The basic equations of the model state the decomposition of the log-odds of the item latent probabilities and of the class sizes into weighted sums of basic parameters representing the effects of the predictor variables. Further, the maximum likelihood equations for these effect parameters and statistical tests for goodness-of-fit are given. Finally, an example illustrates the practical application of the model and the interpretation of the model parameters.     

Latent transition regression for mixed outcomes

Health status is a complex outcome, often characterized by multiple measures. When assessing changes in health status over time, multiple measures are typically collected longitudinally. Analytic challenges posed by these multivariate longitudinal data are further complicated when the outcomes are combinations of continuous, categorical, and count data. To address these challenges, we propose a fully Bayesian latent transition regression approach for jointly analyzing a mixture of longitudinal outcomes from any distribution. Health status is assumed to be a categorical latent variable, and the multiple outcomes are treated as surrogate measures of the latent health state, observed with error. Using this approach, both baseline latent health state prevalences and the probabilities of transitioning between the health states over time are modeled as functions of covariates. The observed outcomes are related to the latent health states through regression models that include subject-specific effects to account for residual correlation among repeated measures over time, and covariate effects to account for differential measurement of the latent health states. We illustrate our approach with data from a longitudinal study of back pain.     

具有潜伏期的无免疫型传染病动力学模型的分析

研究了一类具有潜伏期的无免疫型传染病动力学模型,用摄动理论讨论分析了相应的非线性系统,得到了不同群体生存变化的渐近表达式,从而揭示了各种作用对不同群体生存影响的规律.本文的研究为解决一些类型的非线性模型提供了一种有效的方法.     

Bayesian Neural Network Models for Censored Data

Neural networks are considered by many to be very promising tools for classification and prediction. The flexibility of the neural network models often result in over-fit. Shrinking the parameters using a penalized likelihood is often used in order to overcome such over-fit. In this paper we extend the approach proposed by FARAGGI and SIMON (1995a) to modeling censored survival data using the input-output relationship associated with a single hidden layer feed-forward neural network. Instead of estimating the neural network parameters using the method of maximum likelihood, we place normal prior distributions on the parameters and make inferences based on derived posterior distributions of the parameters. This Bayesian formulation will result in shrinking the parameters of the neural network model and will reduce the over-fit compared with the maximum likelihood estimators. We illustrate our proposed method on a simulated and a real example.     

Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data isvery important to understand the underlying biological system,and it has been a challenging task in bioinformatics.TheBayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determinethe network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithmwhich integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use ofboth simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of theknown real regulatory relationships from literature and predict the others unknown with high validity and accuracy.     

Bayesian biomarker identification based on marker-expression proteomics data

We are studying variable selection in multiple regression models in which molecular markers and/or gene-expression measurements as well as intensity measurements from protein spectra serve as predictors for the outcome variable (i.e., trait or disease state). Finding genetic biomarkers and searching genetic–epidemiological factors can be formulated as a statistical problem of variable selection, in which, from a large set of candidates, a small number of trait-associated predictors are identified. We illustrate our approach by analyzing the data available for chronic fatigue syndrome (CFS). CFS is a complex disease from several aspects, e.g., it is difficult to diagnose and difficult to quantify. To identify biomarkers we used microarray data and SELDI-TOF-based proteomics data. We also analyzed genetic marker information for a large number of SNPs for an overlapping set of individuals. The objectives of the analyses were to identify markers specific to fatigue that are also possibly exclusive to CFS. The use of such models can be motivated, for example, by the search for new biomarkers for the diagnosis and prognosis of cancer and measures of response to therapy. Generally, for this we use Bayesian hierarchical modeling and Markov Chain Monte Carlo computation.     

缺失数据和贝叶斯系统发育分析精确度之探索

The effect of missing data on phylogenetic methods is a potentially important issue in our attempts to reconstruct the Tree of Life. If missing data are truly problematic, then it may be unwise to include species in an analysis that lack data for some characters (incomplete taxa) or to include characters that lack data for some species. Given the difficulty of obtaining data from all characters for all taxa (e.g., fossils), missing data might seriously impede efforts to reconstruct a comprehensive phylogeny that includes all species. Fortunately, recent simulations and empirical analyses suggest that missing data cells are not themselves problematic, and that incomplete taxa can be accurately placed as long as the overall number of characters in the analysis is large. However, these studies have so far only been conducted on parsimony, likelihood, and neighbor-joining methods. Although Bayesian phylogenetic methods have become widely used in recent years, the effects of missing data on Bayesian analysis have not been adequately studied. Here, we conduct simulations to test whether Bayesian analyses can accurately place incomplete taxa despite extensive missing data. In agreement with previous studies of other methods, we find that Bayesian analyses can accurately reconstruct the position of highly incomplete taxa (i.e., 95% missing data), as long as the overall number of characters in the analysis is large. These results suggest that highly incomplete taxa can be safely included in many Bayesian phylogenetic analyses.     

A Bayesian analysis of multivariate doubly-interval-censored dental data

A Bayesian survival analysis is presented to examine the effect of fluoride-intake on the time to caries development of the permanent first molars in children between 7 and 12 years of age using a longitudinal study conducted in Flanders. Three problems needed to be addressed. Firstly, since the emergence time of a tooth and the time it experiences caries were recorded yearly, the time to caries is doubly interval censored. Secondly, due to the setup of the study, many emergence times were left-censored. Thirdly, events on teeth of the same child are dependent. Our Bayesian analysis is a modified version of the intensity model of Harkanen et al. (2000, Scandinavian Journal of Statistics 27, 577-588). To tackle the problem of the large number of left-censored observations a similar Finnish data set was introduced. Our analysis shows no convincing effect of fluoride-intake on caries development.