我国的人类群体遗传学研究

介绍了什么是人类群体遗传学,我国人类群体遗传学的研究历史、现状、初步得出的一些主要结论以及以今后的展望。     

浅议遗传学教学改革

在剖析传统教学体系弊病的基础上,从教学内容、教学方法和实践教学等几方面讨论了遗传学教学改革的必要性和作者的尝试。认为遗传学教学应当讲授一门课,而不只是一本书;梳理框架,讲授知识体系,而不只是知识点;讲授获得知识的方法,而不只是知识本身。实验教学应当开设综合性大实验,而不只是小实验。     

植物分子群体遗传学研究动态

分子群体遗传学是当代进化生物学研究的支柱学科, 也是遗传育种和关于遗传关联作图和连锁分析的基础理论学科。分子群体遗传学是在经典群体遗传的基础上发展起来的, 它利用大分子主要是DNA序列的变异式样来研究群体的遗传结构及引起群体遗传变化的因素与群体遗传结构的关系, 从而使得遗传学家能够从数量上精确地推知群体的进化演变, 不仅克服了经典的群体遗传学通常只能研究群体遗传结构短期变化的局限性, 而且可检验以往关于长期进化或遗传系统稳定性推论的可靠程度。同时, 对群体中分子序列变异式样的研究也使人们开始重新审视达尔文的以“自然选择”为核心的进化学说。到目前为止, 分子群体遗传学已经取得长足的发展, 阐明了许多重要的科学问题, 如一些重要农作物的DNA多态性式样、连锁不平衡水平及其影响因素、种群的变迁历史、基因进化的遗传学动力等, 更为重要的是, 在分子群体遗传学基础上建立起来的新兴的学科如分子系统地理学等也得到了迅速的发展。文中综述了植物分子群体遗传研究的内容及最新成果。     

遗传学的发展与遗传学教学改革诌议

本文概述了遗传学的最近发展及其对遗传学教学提出的新问题。针对面临的问题,从遗传学教学的目标、要求及几大分支的内容处理等方面的改革提出了作者的观点,根据教学与市场的关系提出教学改革的初步意见。 Abstract：New questions are now put forward to genetics teaching by the advance of genetics recently,and summarized in this discussion. According to relation between market requirement and education,the author suggested tentative proposal of reforming genetics teaching and improving contents of main parts in genetics.     

面向中学生物教育的高师遗传学教学改革

遗传学知识,在中学生物教学中占有较大比重,随着中学新课改的实施,生物教材在教学内容上也有了一些变化。为使高师院校的遗传学教学适应中学生物教学的需要以及高师院校对师范生的培养要求,作者对遗传学教学内容进行了改革,使其与中学生物教学内容和特点紧密结合,同时在遗传学教学中渗透了对学生生物教学技能的培养。     

医学遗传学“PBL”教学改革初试

目的:探讨PBL(Problem-based learning)在医学遗传学教学中的应用效果.方法:随机选取临床专业中的两个小班学生作为实验组,采用PBL教学方法,另选择进行传统教学的两个小班学生作为对照组.对PBL教学效果的主观评价采用实验组学生问卷调查方式,客观评价通过比较两组考核成绩的方式.结果:实验组80％以上的学生认为PBL教学方法在学习兴趣、学习主动性、临床思维培养和知识面扩展方面优于传统教学方法,两组学生的试卷在临床症状、诊断方法和发病机制的题目得分率,均明显高于对照组(P＜0.05).结论:PBL教学在医学遗传学中取得较好的教学效果,与传统教学相结合的综合教学,是未来医学遗传学教学改革的趋势.     

贵州彝族、白族4项群体遗传学特征的调查

调查了贵州彝族、白族879人（彝族472人、白族407人）4项群体遗传学特征。结果显示：1）贵州彝族和白族群体间无显著性差异（P〉0.05）。2）彝族男女间利眼有显著性差异（P〈0.05）;白族男女间门齿类型存在显著性差异（P〈0.05）。3）4项群体遗传学特征间相关性不大。     

临床遗传学的教学方法探讨

临床遗传学是一门新兴学科,是研究对遗传性疾病的预防、诊断和治疗的学科.加强医学生的临床遗传学专业知识的培养以及临床医生遗传学知识的普及,对临床遗传学学科的发展至关重要.本文分析了我国高校临床遗传学的教学现状,总结了临床遗传学教学方法的改革探索与体会,不仅有助于提高临床遗传学教学效果,更有利于培养高素质医学人才.     

群体遗传学模拟软件应用现状

随着下一代测序技术的不断进步与测序价格的不断下降,越来越多物种的全基因组信息被公开。作为研究群体遗传变异模式工具之一的模拟软件必然将发挥越来越重要的作用。依据时间推演方向的不同,模拟软件可以分为依时间向前和向后推演,二者各有所长,功能上互相补充,分别适合于不同的模拟需求。这些软件在研究进化动力的影响、估计进化动力参数与验证不同进化假设以及新方法有效性等方面起着重要作用。本文简要介绍了群体遗传学相关理论知识,详细比较了近10年来发表的32款模拟软件,并对模拟软件的未来发展方向给出了建议。     

寄生蠕虫的群体遗传学研究

寄生蠕虫群体遗传学研究常用的遗传标记有等位酶、线粒体DNA、随机扩增多态性DNA或扩增性片段长度多态性和微卫星DNA等。应用这些遗传标记的研究表明,大多数寄生蠕虫群体遗传结构有不同水平的变异,这些变异的产生主要与寄生虫的生活史和群体生态、宿主的地理分布和环境等因素有关,并因此提出了有关遗传变异的一些假说。本文对寄生蠕虫群体遗传学的研究作一综述。 Abstract:Genetic markers including allozyme,mtDNA,RAPD/RFLP and micro DNA have been used in the research of helminth population genetics.Available data on helminth genetic variability have shown that most helminth populations exhibit different levels of genetic variation resulting mainly from the pattern of life cycle,geographical distribution and parasite-host interaction,and several hypotheses have been proposed to explain the genetic variation.     

遗传学教学中Morgan定律讲解新探

运用广义和狭义的定义模式,对Morgan定律中的几个基本概念做了新的注释,深入地分析了经典三点测交的使用条件,并找出了粗糙脉孢菌的经典三点测交中不相邻两点间的交换值(FC)与其重组值（FR）差的出处。 Abstract:Using the model of broad and narrow way,the paper introduces a new approach in the explaining of a few basic concepts in the Morgan Law.The paper introduces a thorough inquiry into the applying condition of the three-factor crosses,and finds the source of the difference between the crossover frequency (FC) and recombination frequency (FR) of the non-adjacent factors in the three-factor crosses in Neurospora crassa.     

A Principled Approach to Deriving Approximate Conditional Sampling Distributions in Population Genetics Models with Recombination

The multilocus conditional sampling distribution (CSD) describes the probability that an additionally sampled DNA sequence is of a certain type, given that a collection of sequences has already been observed. The CSD has a wide range of applications in both computational biology and population genomics analysis, including phasing genotype data into haplotype data, imputing missing data, estimating recombination rates, inferring local ancestry in admixed populations, and importance sampling of coalescent genealogies. Unfortunately, the true CSD under the coalescent with recombination is not known, so approximations, formulated as hidden Markov models, have been proposed in the past. These approximations have led to a number of useful statistical tools, but it is important to recognize that they were not derived from, though were certainly motivated by, principles underlying the coalescent process. The goal of this article is to develop a principled approach to derive improved CSDs directly from the underlying population genetics model. Our approach is based on the diffusion process approximation and the resulting mathematical expressions admit intuitive genealogical interpretations, which we utilize to introduce further approximations and make our method scalable in the number of loci. The general algorithm presented here applies to an arbitrary number of loci and an arbitrary finite-alleles recurrent mutation model. Empirical results are provided to demonstrate that our new CSDs are in general substantially more accurate than previously proposed approximations.THE probability of observing a sample of DNA sequences under a given population genetics model—which is referred to as the sampling probability or likelihood—plays an important role in a wide range of problems in a genetic variation study. When recombination is involved, however, obtaining an analytic formula for the sampling probability has hitherto remained a challenging open problem (see Jenkins and Song 2009, 2010 for recent progress on this problem). As such, much research (Griffiths and Marjoram 1996; Kuhner et al. 2000; Nielsen 2000; Stephens and Donnelly 2000; Fearnhead and Donnelly 2001; De Iorio and Griffiths 2004a,b; Fearnhead and Smith 2005; Griffiths et al. 2008; Wang and Rannala 2008) has focused on developing Monte Carlo methods on the basis of the coalescent with recombination (Griffiths 1981; Kingman 1982a,b; Hudson 1983), a well-established mathematical framework that models the genealogical history of sample chromosomes. These Monte Carlo-based full-likelihood methods mark an important development in population genetics analysis, but a well-known obstacle to their utility is that they tend to be computationally intensive. For a whole-genome variation study, approximations are often unavoidable, and it is therefore important to think of ways to minimize the trade-off between scalability and accuracy.A popular likelihood-based approximation method that has had a significant impact on population genetics analysis is the following approach introduced by Li and Stephens (2003): Given a set Φ of model parameters (e.g., mutation rate, recombination rate, etc.), the joint probability p(h₁, … , h_n | Φ) of observing a set {h₁, … , h_n} of haplotypes sampled from a population can be decomposed as a product of conditional sampling distributions (CSDs), denoted by π,(1)where π(h_k+1|h₁, …, h_k, Φ) is the probability of an additionally sampled haplotype being of type h_k+1, given a set of already observed haplotypes h₁, …, h_k. In the presence of recombination, the true CSD π is unknown, so Li and Stephens proposed using an approximate CSD in place of π, thus obtaining the following approximation of the joint probability:(2)Li and Stephens referred to this approximation as the product of approximate conditionals (PAC) model. In general, the closer is to the true CSD π, the more accurate the PAC model becomes. Notable applications and extensions of this framework include estimating crossover rates (Li and Stephens 2003; Crawford et al. 2004) and gene conversion parameters (Gay et al. 2007; Yin et al. 2009), phasing genotype data into haplotype data (Stephens and Scheet 2005; Scheet and Stephens 2006), imputing missing data to improve power in association mapping (Stephens and Scheet 2005; Li and Abecasis 2006; Marchini et al. 2007; Howie et al. 2009), inferring local ancestry in admixed populations (Price et al. 2009), inferring human colonization history (Hellenthal et al. 2008), inferring demography (Davison et al. 2009), and so on.Another problem in which the CSD plays a fundamental role is importance sampling of genealogies under the coalescent process (Stephens and Donnelly 2000; Fearnhead and Donnelly 2001; De Iorio and Griffiths 2004a,b; Fearnhead and Smith 2005; Griffiths et al. 2008). In this context, the optimal proposal distribution can be written in terms of the CSD π (Stephens and Donnelly 2000), and as in the PAC model, an approximate CSD may be used in place of π. The performance of an importance sampling scheme depends critically on the proposal distribution and therefore on the accuracy of the approximation . Often in conjunction with composite-likelihood frameworks (Hudson 2001; Fearnhead and Donnelly 2002), importance sampling has been used in estimating fine-scale recombination rates (McVean et al. 2004; Fearnhead and Smith 2005; Johnson and Slatkin 2009).So far, a significant scope of intuition has gone into choosing the approximate CSDs used in these problems (Marjoram and Tavaré 2006). In the case of completely linked loci, Stephens and Donnelly (2000) suggested constructing an approximation by assuming that the additional haplotype h_k+1 is an imperfect copy of one of the first k haplotypes, with copying errors corresponding to mutation. Fearnhead and Donnelly (2001) generalized this construction to include crossover recombination, assuming that the haplotype h_k+1 is an imperfect mosaic of the first k haplotypes (i.e., h_k+1 is obtained by copying segments from h₁, …, h_k, where crossover recombination can change the haplotype from which copying is performed). The associated CSD, which we denote by , can be interpreted as a hidden Markov model and so admits an efficient dynamic programming solution. Finally, Li and Stephens (2003) proposed a modification to Fearnhead and Donnelly''s model that limits the hidden state space, thereby providing a computational simplification; we denote the corresponding approximate CSD by .Although these approaches are computationally appealing, it is important to note that they are not derived from, though are certainly motivated by, principles underlying typical population genetics models, in particular the coalescent process (Griffiths 1981; Kingman 1982a,b; Hudson 1983). The main objective of this article is to develop a principled technique to derive an improved CSD directly from the underlying population genetics model. Rather than relying on intuition, we base our work on mathematical foundation. The theoretical framework we employ is the diffusion process. De Iorio and Griffiths (2004a,b) first introduced the diffusion-generator approximation technique to obtain an approximate CSD in the case of a single locus (i.e., no recombination). Griffiths et al. (2008) later extended the approach to two loci to include crossover recombination, assuming a parent-independent mutation model at each locus. In this article, we extend the framework to develop a general algorithm that applies to an arbitrary number of loci and an arbitrary finite-alleles recurrent mutation model.Our work can be summarized as follows. Using the diffusion-generator approximation technique, we derive a recursion relation satisfied by an approximate CSD. This recursion can be used to construct a closed system of coupled linear equations, in which the conditional sampling probability of interest appears as one of the unknown variables. The system of equations can be solved using standard numerical analysis techniques. However, the size of the system grows superexponentially with the number of loci and, consequently, so does the running time. To remedy this drawback, we introduce additional approximations to make our approach scalable in the number of loci. Specifically, the recursion admits an intuitive genealogical interpretation, and, on the basis of this interpretation, we propose modifications to the recursion, which then can be easily solved using dynamic programming. The computational complexity of the modified algorithm is polynomial in the number of loci, and, importantly, the resulting CSD has little loss of accuracy compared to that following from the full recursion.The accuracy of approximate CSDs has not been discussed much in the literature, except in the application-specific context for which they are being employed. In this article, we carry out an empirical study to explicitly test the accuracy of various CSDs and demonstrate that our new CSDs are in general substantially more accurate than previously proposed approximations. We also consider the PAC framework and show that our approximations also produce more accurate PAC-likelihood estimates. We note that for the maximum-likelihood estimation of recombination rates, the actual value of the likelihood may not be so important, as long as it is maximized near the true recombination rate. However, in many other applications—e.g., phasing genotype data into haplotype data, imputing missing data, importance sampling, and so on—the accuracy of the CSD and PAC-likelihood function over a wide range of parameter values may be important. Thus, we believe that the theoretical work presented here will have several practical implications; our method can be applied in a wide range of statistical tools that use CSDs, improving their accuracy.The remainder of this article is organized as follows. To provide intuition for the ensuing mathematics, we first describe a genealogical process that gives rise to our CSD. Using our genealogical interpretation, we consider two additional approximations and relate these to previously proposed CSDs. Then, in the following section, we derive our CSD using the diffusion-generator approach and provide mathematical statements for the additional approximations; some interesting limiting behavior is also described there. This section is self-contained and may be skipped by the reader uninterested in mathematical details. Finally, in the subsequent section, we carry out a simulation study to compare the accuracy of various approximate CSDs and demonstrate that ours are generally the most accurate.     

An Approach to Ecology Teaching at University Level

The Virus. A History of the Concept S. S. HUGHES 140 pp., illustrated. London: Heinemann Educational (New York: Science History Publications), 1977. £3.90. Reviewed by H. V. WYATT

Ecology and Archaeology Studies to Biology, No. 77 G. W. DIMBLEBY 55 pp., illustrated. London: Edward Arnold, 1977. £3.00 boards, £1.50 paper. Reviewed by CHARLES BRADY

Colonization of Industrial Wasteland Studies in Biology, No. 80 R. P. GEMMELL 75 pp., illustrated. London: Edward Arnold, 1977. £3.20 boards, £1.60. paper. Reviewed by JOHN M. AYERST

Energy and the Living Cell. An Introduction to Bioenergetics W. M. BECKER 346 pp., illustrated. Philadelphia: J. B. Lippincott (Oxford: Blackwell Scientific), 1977. £6.40. Reviewed by J. PREBBLE

Microbial and Molecular Genetics Second edition J. R. FINCHAM 150 pp. London: Hodder and Stoughton, 1976. £3.95 boards, £2.25 paper. Reviewed by J. H. CROFT

Genetics M. W. ROBERTS 90 pp., illustrated. Plymouth: Macdonald and Evans, 1977. £1.25. Reviewed by CECILY A. GALE

The Differentiation of Cells N. MACLEAN 216 pp., illustrated. London: Edward Arnold, 1977. £12.00 boards, £5.95 paper. Reviewed by K. R. TYLER

Mechanics of the Mind C. BLAKEMORE 208 pp., illustrated. Cambridge: Cambridge University Press, 1977. £10.50 boards, £3.95 paper.Reviewed by O. LOWENSTEIN

The Pursuit of Nature. Informal Essays on the History of Physiology A. L. HODGKIN, A. F. HUXLEY, et al. 180 pp. Cambridge: Cambridge University Press, 1977. £7.50. Reviewed by DIANA E. MANUEL

Signs of Life I. RIDPATH 190 pp., illustrated. Harmondsworth, Middlesex: Penguin, 1977. £1.25. Reviewed by O. LOWENSTEIN     

植物化学遗传学：一种崭新的植物遗传学研究方法

化学遗传学（chemical genetics,也称为化学基因组学,chemical genomics）研究方法是利用生物活性小分子扰动蛋白分子互作过程来研究有关的生命现象,是常规遗传学研究方法的补充和延伸。化学遗传学在植物科学中的应用——植物化学遗传学的研究在短短几年内,凭借其作为一种新的遗传学研究方法所具备的独特优势（如能够克服常规遗传学研究中的遗传冗余、突变致死难题及可提供特异强度、作用时间点上的条件性遗传扰动等）,已开始解决一些植物分子生物学中长期存在的研究难题。本文就植物化学遗传学的一般原理及其方法,以及它作为一种新的遗传学研究方法的优势及特点作一个综述．     

Population Genetics of Gene Function

This paper shows that differentiating the lifetimes of two phenotypes independently from their fertility can lead to a qualitative change in the equilibrium of a population: since survival and reproduction are distinct functional aspects of an organism, this observation contributes to extend the population-genetical characterisation of biological function. To support this statement a mathematical relation is derived to link the lifetime ratio T ₁/T ₂, which parameterizes the different survival ability of two phenotypes, with population variables that quantify the amount of neutral variation underlying a population’s phenotypic distribution.