POPULATION MIXTURES EFFECTS ON GENETIC VARIATION

种群混合对遗传变异的影响

• 批准号: 2022278
• 负责人: Ranajit Chakraborty
• 金额: $ 16.94万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-04-01 至 1998-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2022278
• 关键词: alleles; biochemical evolution; computer assisted sequence analysis; computer program /software; computer simulation; gene frequency; gene mutation; genetic library; genetic markers; genetic models; genetic polymorphism; human data; human population genetics; model design /development; nucleic acid sequence; restriction fragment length polymorphism; tissue /cell culture

During the past four years our research has demonstrated that mixtures of subpopulations. the main constituent of most genetic surveys of human populations. differentially affect various measures of genetic variation. Recognizing the presence of mixtures and identifying the mixture components. therefore. are important for understanding the maintenance of genetic variation and for reconstructing evolutionary history. We demonstrated that the effects of population mixture are more efficiently detected through the use of the class of polymorphic loci known as variable number of tandem repeat (VNTR) loci. Since new variation at these loci is generated differently from new variation in classical markers (blood groups and proteins). new analytical tools are needed to study the evolutionary dynamics and statistical properties of summary measures of genetic variation at these hypervariable loci. VNTR loci (microsatellites. short tandem repeats. and minisatellites) appear to be maintained by different mutational mechanisms. which will be the focus of our proposed studies. Using both types of population mixtures (amalgamation and gene flow), we propose to develop methods for studying intrapopulation and interpopulation variation and so to investigate evolutionary relatedness between individuals and between populations. Multilocus measures will also be developed that can be used for evolutionary studies using DNA fingerprint data. Models of different molecular mechanisms of mutations (involving intrastrand and interstrand processes) will be used. The effects of incomplete identification of alleles and of convergent changes (independent mutations producing the same copy-number of repeat units at a VNTR locus) will be examined. The techniques used will be analytical tools of stochastic processes (incorporating drift and mutations) and computer simulations. Data on human populations with different histories of mixtures. as well as data from other species. will be used to validate the theory. New experimental data will be used to design the simulation procedures and parameters. Such studies will also help us to understand the etiology of complex diseases in populations with different mixture histories. and to develop survey strategies for mapping such disease genes using hypervariable loci.

在过去的四年里，我们的研究表明， 亚群。大多数人类基因调查的主要组成部分 人口。不同地影响不同的遗传变异措施。 识别混合物的存在并识别混合物 组件。因此。对于理解如何维护 基因变异和重建进化史。我们 证明了种群混合的效果更有效 通过使用一类称为 可变数目的串联重复序列(VNTR)基因座。因为这些新的变种 基因座的产生与经典标记的新变异不同 (血型和蛋白质)。需要新的分析工具来研究 综合度量的演化动力学和统计性质 这些高变数基因座的遗传变异。Vntr基因座 (微卫星。短串联重复。和小卫星)似乎 由不同的突变机制维持。这将是 我们提议的研究。 使用两种类型的种群混合(融合和基因流动)，我们 建议开发研究种群内和种群内的方法 种群间变异，从而研究进化的关联性 在个体之间和种群之间。多轨迹测量也将 开发出可用于使用DNA进行进化研究的 指纹数据。突变的不同分子机制模型 将使用(涉及线内和线间过程)。这个 等位基因不完全识别和趋同变化的影响 (产生相同拷贝数的重复单位的独立突变 一个VNTR基因座)将被检查。所使用的技术将是分析性的 随机过程的工具(包括漂移和突变)和 计算机模拟。关于不同历史人口的数据 一种混合物。以及来自其他物种的数据。将用于验证 这个理论。新的实验数据将被用来设计模拟 程序和参数。这样的研究也将有助于我们理解 不同混合人群复杂疾病的病原学研究 历史。并制定绘制此类疾病基因图谱的调查策略 使用高变数基因座。