MODELS OF COMPLEX GENETIC SYSTEMS

复杂遗传系统模型

• 批准号: 6018731
• 负责人: MONTGOMERY Wilson SLATKIN
• 金额: $ 19.87万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-09-01 至 2001-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6018731
• 关键词: alleles; biochemical evolution; computer simulation; gene duplication; gene frequency; gene mutation; genetic markers; genetic models; genetic polymorphism; genetic recombination; human data; human population distribution; human population dynamics; human population genetics; linkage disequilibriums; mathematical model; model design /development; natural selections; nucleic acid sequence; statistics /biometry

The overall goal of this research program is the development and application of mathematical and statistical methods that can be applied to genetic data from human and non-human populations with the intention of understanding the past and present forces that determine their genetic composition. The kinds of data that will be considered are DNA sequences from different individuals or distinguishable genetic variants such as alleles at microsatellite loci, a relatively new and extremely useful class of genetic markers. This proposal will focus on four interrelated classes of problems: (l) the mutation process at microsatellite loci, (2) the history of individual alleles, including disease-causing alleles, (3) the extent of variation among disease-causing alleles at a single genetic locus, (4) the degree of non-random association among alleles at closely linked genetic loci. Microsatellite loci are of considerable practical interest because they are extremely abundant and highly variable in humans and other mammals. As a result, they are widely used in genetic mapping and in the analysis of population structure. Yet their use for these purposes depends on untested assumptions about the process of mutation. Further theoretical work is needed before population-level variation can be used to test hypotheses about the mutation process. Several parts of this proposal address the problem of the nonrandom association, or linkage disequilibrium, of closely linked genes, including genes that are closely linked to disease- causing loci. The extent of linkage disequilibrium depends in a complex way on various factors, including mutation, natural selection, migration among populations, and the history of population sizes. Mathematical models are needed that predict the extent of linkage disequilibrium under different conditions. Such models are needed now because of the widespread use of linkage disequilibrium for mapping disease-causing genes in human populations. The proposed research will be carried out using a combination of analytic theory, particularly the theory for Markov processes, and computer simulations. Many of the results will be in terms of mathematical likelihood functions that have desirable properties for estimating parameters and for testing hypotheses.

该研究计划的总体目标是开发和 应用数学和统计方法 应用于人类和非人类群体的遗传数据 旨在了解过去和现在的力量 确定他们的遗传组成。将会出现的数据类型 考虑的是来自不同个体的DNA序列或 可区分的遗传变异，例如微卫星位点的等位基因， 一类相对较新且极其有用的遗传标记。这 提案将侧重于四类相互关联的问题：(l) 微卫星位点的突变过程，（2）个体的历史 等位基因，包括致病等位基因，(3) 变异程度 在单个遗传位点的致病等位基因中，(4) 紧密相连的等位基因之间的非随机关联程度 遗传位点。微卫星位点具有相当大的实际意义 因为它们在人类中极其丰富且高度可变 和其他哺乳动物。因此，它们被广泛应用于遗传研究 绘制地图和人口结构分析。然而他们的用途 为了这些目的取决于未经检验的假设 突变的过程。在此之前还需要进一步的理论工作 群体水平的变异可以用来检验关于 突变过程。该提案的几个部分解决了该问题 密切相关的非随机关联或连锁不平衡 连锁基因，包括与疾病密切相关的基因 引起位点。连锁不平衡的程度取决于 各种因素的复杂方式，包括突变、自然 人口的选择、迁移和历史 人口规模。需要数学模型来预测 不同条件下连锁不平衡的程度。这样的 由于链接的广泛使用，现在需要模型 绘制人类致病基因的不平衡图谱 人口。拟议的研究将使用 分析理论的结合，特别是马尔可夫理论 过程和计算机模拟。许多结果将在 具有理想的数学似然函数项 用于估计参数和检验假设的属性。