谈系谱分析中的一个概率问题

人类遗传学中常涉及到系谱分析问题。通过对某个家系的遗传分析,不仅可以了解这个家系中各个成员的基因型分布情况,而且还可估计他们婚配后所生子女出现各种基因型的概率,因此,系谱分析与概率分析有着密切的关系,如果对这类问题不加注意,很容易得出错误结论。特别当...     

详析遗传系谱的判别方法

遗传系谱的遗传方式的判别,是中学生学习生物学的一个重点和难点,学生较难理解和掌握。遗传系谱涉及的遗传方式大概有6种类型：常显、常隐、X显、X隐、伴Y和细胞质遗传。纵观绝大多数遗传系谱,大致可分两大类：①一类是可以判断显隐性的．则该系谱的遗传方式就从6种可能性减为2种．即常隐     

快速准确判断遗传系谱的有效方法

高中生物学教学中,遗传系谱复杂、多样,是教学中的重点和难点。对遗传系谱的判定,有关报刊、资料介绍的经验、方法不下10余种。本文拟对所见方法试作评析,并在此基础上提出一种快速准确判断遗传系谱的有效方法。     

解遗传系谱类题型的方法

许多学生在做生物遗传系谱类题型时感到无从下手 ,或者急于得出结论 ,而不去考虑其它情况的可能性。其实做这类题型是完全有规律可循的 ,介绍这方面解题技巧的文章经常可以见到 ,但往往缺乏系统性和灵活性。现介绍解遗传系谱类题型的最佳方法如下 :思路　演绎法排除法确定法遗传类型1男性患者的儿子都是患者 ,女性无患者。 女性有患者 1Y染色体遗传2隔代遗传(两名非患者后代出现患者 )男性患者比女性多男女患病率相等两名患者后代出现非患者女患者父亲或儿子不是患者 两名非患者的女儿是患者2 X染色体隐性遗传3常染色体隐性遗传3不隔代遗传…     

关于遗传系谱的分析

遗传系谱题形式简洁,条件隐蔽,灵活多变,已成为高考、会考和平时训练作为考查学生分析问题、解决问题能力的热门题型。因试题中的遗传病（指单基因遗传病）种类多,既有显性遗传,又有隐性遗传,既有常染色体遗传,又有伴性遗传。故常使学生不能简捷明快地判断出某个遗传系谱反映的是什么遗传病类型．而无从下手分析解决有关问题。如何分析解答这种类型的问题呢？下面就是我在教学中的一点体会。     

圈养大熊猫的系谱分析

应用Sparks Verl.4软件结合手工算法对现有的圈养大熊猫系谱进行了遗传分析。结果表明,圈养大熊猫群体规模小,漂变是导致遗传多样性丢失的主要因素。由于分散管理,现有群体正面临着近亲交配、种源枯竭的危险。因此,应统一遗传管理,加强各繁殖系之间的基因交流,使圈养大熊猫的遗传多样性能够保持在较高的水平之上。     

对一个概率计算问题的分析

《生物学通报》2006年第4期“遗传学中基因频率和基因型频率的有关问题”一文中,例2的解法明显错误,分析如下:例2某人群中,每10 000人中有一白化病患者。有一正常女性(其父母均正常,其弟患白化病)与一正常     

小议遗传概率的计算

所谓概率,就是在一定条件下某一独立事件或相关事件所能产生的理论上的比率,通称几率,即机会。概率可能发生,也可能不发生。因此,概率是随机的,而不是固定的。概率只能大于或等于0,或者是小于或等于1。即概率只能在0与1之间。近几年,我在高中生物教学和复习过程中,根据概率的概念与基本定律,对常见的三种类型遗传几率的计算题的解题的思路、分析和方法步骤,给学生进行了讲述,收到较好的效果。一、单一的基本事件的概率的计算,即亲本只存在一种基因型时子代发生某一基因     

河南省主要小麦品种的系谱分析及利用

经系谱分析表明,自建国以来,河南省小麦品种选育中起骨干作用的亲本为三大系：地方品种辉县红及其衍生系,意大利“St．”系统及其衍生系,意大利阿夫及其衍生系。这项分析说明加强对地方品种和从意大利引进品种的收集、鉴定与利用是非常重要的。今后必须注重收集小麦近缘植物,同时对地方品种作深入的鉴定分析,并进行远缘杂交和特殊性状材料的复交,以创造出更多的优异种质,供育种和生产利用。     

巨峰葡萄系谱的SSR与RAPD分析

利用RAPD和SSR 2种标记分别对系谱关系明确的22个巨峰系葡萄品种进行聚类分析,以验证2种标记方法的正确性与可靠性.结果显示,11条RAPD引物和12对SSR引物,分别扩增了82和43个清晰的条带.RAPD与SSR引物扩增的条带中分别有58和31条多态性带,其多态性比率分别为70.7％和72.1％.2种分子标记聚类结果基本一致,并且都能有效地将品种区分开来以及正确反映22个葡萄品种的亲缘关系.研究结果说明RAPD和SSR 2种分子标记均适合于巨峰系葡萄的系谱分析和种质遗传多样性研究.     

An Algorithm of Step-by-step Pedigree Drawing

An algorithm for drawing large, complex pedigrees containing inbred loops and multiple-mate families is presented. The algorithm is based on a step-by-step approach to imaging, when the researcher determines the direction of further extension of the scheme. The algorithm is implemented as the PedigreeQuery software package written in Java. The software has a convenient graphical interface. The software package permits constructing not only whole pedigrees, but also their fragments that are particularly interesting for research. It also allows for adding new information on the phenotypes and genotypes of pedigree members. PedigreeQuery is distributed free of charge; it is available at http://mga.bionet.msc.ru/PedigreeQuery/PedigreeQuery.html and ftp://mga.bionet.nsc.ru/PedigreeQuery/.     

Bayesian Linkage Analysis of Categorical Traits for Arbitrary Pedigree Designs

Background

Pedigree studies of complex heritable diseases often feature nominal or ordinal phenotypic measurements and missing genetic marker or phenotype data.

Methodology

We have developed a Bayesian method for Linkage analysis of Ordinal and Categorical traits (LOCate) that can analyze complex genealogical structure for family groups and incorporate missing data. LOCate uses a Gibbs sampling approach to assess linkage, incorporating a simulated tempering algorithm for fast mixing. While our treatment is Bayesian, we develop a LOD (log of odds) score estimator for assessing linkage from Gibbs sampling that is highly accurate for simulated data. LOCate is applicable to linkage analysis for ordinal or nominal traits, a versatility which we demonstrate by analyzing simulated data with a nominal trait, on which LOCate outperforms LOT, an existing method which is designed for ordinal traits. We additionally demonstrate our method''s versatility by analyzing a candidate locus (D2S1788) for panic disorder in humans, in a dataset with a large amount of missing data, which LOT was unable to handle.

Conclusion

LOCate''s accuracy and applicability to both ordinal and nominal traits will prove useful to researchers interested in mapping loci for categorical traits.     

A Variance-Component Framework for Pedigree Analysis of Continuous and Categorical Outcomes

Variance-component methods are popular and flexible analytic tools for elucidating the genetic mechanisms of complex quantitative traits from pedigree data. However, variance-component methods typically assume that the trait of interest follows a multivariate normal distribution within a pedigree. Studies have shown that violation of this normality assumption can lead to biased parameter estimates and inflations in type-I error. This limits the application of variance-component methods to more general trait outcomes, whether continuous or categorical in nature. In this paper, we develop and apply a general variance-component framework for pedigree analysis of continuous and categorical outcomes. We develop appropriate models using generalized-linear mixed model theory and fit such models using approximate maximum-likelihood procedures. Using our proposed method, we demonstrate that one can perform variance-component pedigree analysis on outcomes that follow any exponential-family distribution. Additionally, we also show how one can modify the method to perform pedigree analysis of ordinal outcomes. We also discuss extensions of our variance-component framework to accommodate pedigrees ascertained based on trait outcome. We demonstrate the feasibility of our method using both simulated data and data from a genetic study of ovarian insufficiency.     

General Statistical Issues in the Analysis of Pedigree Data

When analysing pedigree data, it is just as important to detect deficiencies in the fitting and application of the current model as to be able to specify the model, estimate its parameters and test appropriate hypotheses. Some of the statistical issues that arise in the fitting and application of genetic models to pedigree data are discussed in this paper. A particular set of data and a non-controversial characteristic have been chosen to highlight these issues.     

Cattle Sex-Specific Recombination and Genetic Control from a Large Pedigree Analysis

Meiotic recombination is an essential biological process that generates genetic diversity and ensures proper segregation of chromosomes during meiosis. From a large USDA dairy cattle pedigree with over half a million genotyped animals, we extracted 186,927 three-generation families, identified over 8.5 million maternal and paternal recombination events, and constructed sex-specific recombination maps for 59,309 autosomal SNPs. The recombination map spans for 25.5 Morgans in males and 23.2 Morgans in females, for a total studied region of 2,516 Mb (986 kb/cM in males and 1,085 kb/cM in females). The male map is 10% longer than the female map and the sex difference is most pronounced in the subtelomeric regions. We identified 1,792 male and 1,885 female putative recombination hotspots, with 720 hotspots shared between sexes. These hotspots encompass 3% of the genome but account for 25% of the genome-wide recombination events in both sexes. During the past forty years, males showed a decreasing trend in recombination rate that coincided with the artificial selection for milk production. Sex-specific GWAS analyses identified PRDM9 and CPLX1 to have significant effects on genome-wide recombination rate in both sexes. Two novel loci, NEK9 and REC114, were associated with recombination rate in both sexes, whereas three loci, MSH4, SMC3 and CEP55, affected recombination rate in females only. Among the multiple PRDM9 paralogues on the bovine genome, our GWAS of recombination hotspot usage together with linkage analysis identified the PRDM9 paralogue on chromosome 1 to be associated in the U.S. Holstein data. Given the largest sample size ever reported for such studies, our results reveal new insights into the understanding of cattle and mammalian recombination.