系谱分析中概率问题的贝叶斯分析一例

《生物学通报》2007年第2期“遗传概率的一个认识误区”一文,给出了一个家系概率问题的答案,但没有详尽的分析过程,不利于初学者的学习掌握,本文对此予以补充和完善。[第一段]     

怎样判别组成雌蕊的心皮数目

在多年的植物学教学实践过程中 ,发现学生在书写植物花程式时 ,感到最困难 ,最容易出错的地方就是对组成雌蕊的基本单位——心皮数目的判别上。为此 ,笔者经过不断探索 ,总结出一套较为有效的判别组成雌蕊的心皮数目的方法。具体作法如下 :1　确定雌蕊的类型在 1朵花中 ,依据雌蕊的数目及组成每个雌蕊的心皮数目的多少 ,将雌蕊分为单雌蕊、离生单雌蕊和复雌蕊等 3种类型。1)在具体观察 1朵花时 ,如花中有两个或两个以上雌蕊时 ,该雌蕊类型为离生单雌蕊 ;如花中只有 1个雌蕊时 ,则要判断该雌蕊是属于单雌蕊还是复雌蕊。2 )如在 1朵花中只有 1…     

介绍一种野外判别高原鼢鼠性别的方法

高原鼢鼠(Myospalax baileyi)广泛分布于青藏高原和川西北草原,终年营地下生活,为寻求建立有效的防治方法和技术,我们于1990年和1991年的4—6月,在四川省阿坝州若尔盖县阿西乡测量和统计了雄性和雌性高原鼢鼠的洞道和土丘直径,剖洞观察并比较雄性和雌性鼢鼠的洞道结构和分布特点,分析了野外判别高原鼢鼠性别的方法。     

动物的双亲判别与DNA指纹图谱

在亲缘识别和婚配选择等动物行为学研究中,需要知道动物之间的亲缘关系。通过分析以往的各种双亲判别方法,如蛋白电泳和血清学技术等,其中DNA指纹图谱法被认为是目前最好的。这种方法已在许多种动物(狗、猫、小家鼠、家燕、蓠雀、天鹅等)和人的研究中得到应用。DNA指纹图谱方法的基本原理是这样,两个动物的DNA 片断具有完全相同的碱基排序的情形从理论上讲其可能性极小,所以每个个体(除同卵双生子外)的DNA经提取、酶切、凝胶电泳、RNA或DNA探针杂交和放射性自显影后所形成的条带分布应该是有区别的、具有个体的特异性,这些条带就是DNA指纹图谱。动物个体DNA 指纹图谱中的每一条带,除了偶然发生的基因突变, 都可以从父母双方、或父方、或母方找到。据此,本文介绍了如何使用DNA指纹图谱进行包括父方和母方亲代判别的方法,如条带比较法和相似性系数法。     

基于贝叶斯网潜类模型的高维SNPs分析

采用贝叶斯(Bayesian)网的潜类模型对GAW17高维SNPs数据进行分析,为复杂性状疾病遗传以及基因定位等方面的研究提供新的方法支持。本研究从GAW17提供的包含697个个体22条常染色体的上万个SNP中,随机挑选出1号染色体上12个基因的29个SNPs作为研究对象。按照累计信息贡献率达到95%的原则,应用贝叶斯网潜变量模型选出C1S11408,C1S3201,C1S1786等15个与X0互信息量大的SNPs位点来对研究人群进行分类与解释。结果表明697个个体总的被分为2个潜在类别,各类别的概率分别为0.68和0.32。对两类人群的疾病分布状况进行分析,结果表明二者不一致,第二个类别人群患病率(38.64%)明显高于第一个类别人群(25.99%)(χ2=11.46,P=0.001)。由此可见,两类人群疾病患病率的差别正是由选出的15个SNPs造成的,从而有理由认为这些SNPs为可疑致病位点,为进一步的研究提供明确的思路。     

基因组育种值估计的贝叶斯方法

基因组育种值估计是基因组选择的重要环节,基因组育种值的准确性是基因组选择成功应用的关键,而其准确性在很大程度上取决于估计方法。目前研究和应用最多的基因组育种值估计方法是贝叶斯(Bayes)和最佳线性无偏预测(BLUP)两大类方法。文章系统介绍了目前已提出的各种Bayes方法,并总结了该类方法的估计效果和各方面的改进。模拟数据和实际数据研究结果都表明,Bayes类方法估计基因组育种值的准确性优于BLUP类方法,特别对于存在较大效应QTL的性状其优势更明显。由于Bayes方法的理论和计算过程相对复杂,目前其在实际育种中的运用不如BLUP类方法普遍,但随着快速算法的开发和计算机硬件的改进,计算问题有望得到解决;另外,随着对基因组和性状遗传结构研究的深入开展,能为Bayes方法提供更为准确的先验信息,从而使Bayes方法估计基因组育种值准确性的优势更加突出,应用将会更加广泛。     

天然草地合理利用的判别模型研究

天然草地具有资源和生境的双重功能 ,合理利用与否直接关系着资源的可持续性及生态环境的优劣。在短花针茅荒漠草原群落 ,实地观测研究了建群种短花针茅、优势种无芒隐子草和碱韭的枝条密度、单枝重及株丛枝条数目对不同放牧制度的响应 ,初步建立了草地合理利用的判别模型。不同放牧制度下植物的枝条密度、单枝重存在差异 ,轮牧区植物的单枝重高于自由放牧区 ,枝条生长也较优越。株丛单枝重与枝条数目之间负相关 ,单枝重随枝条数目的增加最终出现负增长。绝对不利用会造成草地资源的浪费 ,并且对植物的生长不利。轮牧实现了草地资源的合理利用 ,同时加强了草地的生态功能     

贝叶斯推论重建乳头瘤病毒的进化系统

基于90种乳头瘤病毒的L1基因和E2基因的DNA全序列,应用似然比检验和贝叶斯推论重建病毒的系统发育树,并在此基础上讨论病毒分子系统学研究的一般原则和方法。同时比较研究贝叶斯推论和过去常用的最小进化、最大简约、最大似然三种建树算法的优劣。结果表明：乳头瘤病毒可分为4超群,不同超群的乳头瘤病毒宿主和组织特异性不同。引起人宫颈癌的高危和低危乳头瘤病毒在进化上分别具有相近的亲缘关系。高危类群起源于共同祖先。算法的比较研究表明,贝叶斯推论作为进化生物学的最新进展,在计算速度和可靠性上明显优于其它算法。     

如何判别雌蕊的心皮数

植物花程式是将组成花的各部分,用符号和数字列成的公式。如何准确地写出各种植物的花程式,是学生学习植物分类学必须掌握的基本技能之一。有关花萼、花冠和雄蕊的组成特点,均可通过肉眼观察便能基本掌握,而惟独雌蕊的组成特点,特别是组成雌蕊的基本单位——心皮的数目,则需     

中国汉族成人髌骨性别判别初步研究

对256副中国汉族成人左右侧髌骨分别观测,显示各项指标测量值的平均值都是男性大于女性,并具有显著性差异;各指标以髌骨体积判别性别效果最好,单项判别率达94％以上;运用Fisher线性两类判别分析、逐步判别分析方法得出多项指标判别性别的公式,判别率最高达96.1％。本研究还设计了髌骨体积及髌骨内外关节面高指标的测量方法。本研究为髌骨性别判别提供了简单、实用、科学、判别率较高的新方法。     

Prediction of protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and Naive Bayes Feature Fusion

Summary. The interaction of non-covalently bound monomeric protein subunits forms oligomers. The oligomeric proteins are superior to the monomers within the scope of functional evolution of biomacromolecules. Such complexes are involved in various biological processes, and play an important role. It is highly desirable to predict oligomer types automatically from their sequence. Here, based on the concept of pseudo amino acid composition, an improved feature extraction method of weighted auto-correlation function of amino acid residue index and Naive Bayes multi-feature fusion algorithm is proposed and applied to predict protein homo-oligomer types. We used the support vector machine (SVM) as base classifiers, in order to obtain better results. For example, the total accuracies of A, B, C, D and E sets based on this improved feature extraction method are 77.63, 77.16, 76.46, 76.70 and 75.06% respectively in the jackknife test, which are 6.39, 5.92, 5.22, 5.46 and 3.82% higher than that of G set based on conventional amino acid composition method with the same SVM. Comparing with Chou’s feature extraction method of incorporating quasi-sequence-order effect, our method can increase the total accuracy at a level of 3.51 to 1.01%. The total accuracy improves from 79.66 to 80.83% by using the Naive Bayes Feature Fusion algorithm. These results show: 1) The improved feature extraction method is effective and feasible, and the feature vectors based on this method may contain more protein quaternary structure information and appear to capture essential information about the composition and hydrophobicity of residues in the surface patches that buried in the interfaces of associated subunits; 2) Naive Bayes Feature Fusion algorithm and SVM can be referred as a powerful computational tool for predicting protein homo-oligomer types.     

Fidelity Estimation for a Hierarchical Classifier

To estimate the correct classification rate of a classifier, many different methods exist (test sample, bootstrap, cross validation). The test sample is a method with very small expense. Sometimes, only a small number of objects is available (seldom diseases, high costs for experiments). When we split the sample in training set and test set, we get good or bad fidelity estimations but, unfortunately, vice versa a big or small confidence interval for the estimation. Overcoming this dilemma is only possible for simple classifiers. Such a simple classifier is investigated and a direct fidelity estimation is proposed.     

Miscellanea Kernel-Type Density Estimation on the Unit Interval

We consider kernel-type methods for the estimation of a densityon 0,1 which eschew explicit boundary correction. We proposeusing kernels that are symmetric in their two arguments; thesekernels are conditional densities of bivariate copulas. We giveasymptotic theory for the version of the new estimator usingGaussian copula kernels and report on simulation comparisonsof it with the beta-kernel density estimator of Chen ([1]).We also provide automatic bandwidth selection in the form of‘rule-of-thumb’ bandwidths for both estimators.As well as its competitive integrated squared error performance,advantages of the new approach include its greater range ofpossible values at 0 and 1, the fact that it is a bona fidedensity and that the individual kernels and resulting estimatorare comprehensible in terms of a single simple picture.     

A new approach to reconstruction models of dendritic branching patterns

Quantitative models for characterising the detailed branching patterns of dendritic trees aim to explain these patterns either in terms of growth models based on principles of dendritic development or reconstruction models that describe an existing structure by means of a canonical set of elementary properties of dendritic morphology, which when incorporated into an algorithmic procedure will generate samples of dendrites that are statistically indistinguishable in both canonical and emergent features from those of the original sample of real neurons. This article introduces a conceptually new approach to reconstruction modelling based on the single assumption that dendritic segments are built from sequences of units of constant diameter, and that the distribution of the lengths of units of similar diameter is independent of location within a dendritic tree. This assumption in combination with non-parametric methods for estimating univariate and multivariate probability densities leads to an algorithm that significantly reduces the number of basic parameters required to simulate dendritic morphology. It is not necessary to distinguish between stem and terminal segments or to specify daughter branch ratios or dendritic taper. The procedure of sampling probability densities conditioned on local morphological features eliminates the need, for example, to specify daughter branch ratios and dendritic taper since these emerge naturally as a consequence of the conditioning process. Thus several basic parameters of previous reconstruction algorithms become emergent parameters of the new reconstruction process. The new procedure was applied successfully to a sample of 51 interneurons from lamina II/III of the spinal dorsal horn.