Models of Complex Genetic Systems

复杂遗传系统模型

• 批准号: 6525605
• 负责人: MONTGOMERY Wilson SLATKIN
• 金额: $ 24.15万
• 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 2005-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6525605
• 关键词: alleles; biochemical evolution; computer program /software; computer simulation; data collection methodology /evaluation; 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

DESCRIPTION (provided by applicant): The proposed research will be on mathematical and statistical models that can be used to used for the analysis of genetic data in human and other populations. Particular emphasis will be placed on accounting for patterns on variation among individuals within populations and variation among different populations. The overall goal of this research is the development of methods for using patterns of variation to understand the history of human and other populations and to assist with efforts to identify genes affecting genetic diseases. The history of population growth, dispersal and intermixture with other populations is reflected in patterns of genetic variation across the genome. The history of an individual mutation is reflected in patterns of variation at genetic loci that are closely linked to that mutation. The specific goals are (1) to model the history of individual mutations causing genetic diseases in order to understand where and when they arose and whether carriers of those mutations have an advantage survival and reproduction, (2) to make better use of non-random associations of alleles at different genetic loci (called linkage disequilibrium) to locate gene causing genetic diseases, (3) to model the effects of natural selection that acts to maintain genetic diversity at genetic loci such as the loci in the major histocompatibility (MHC) loci in humans and other species, and (4) to develop theories that relate the risk of a genetic disease in close relatives of an individual who has the disease to the number of genetic loci affecting the disease and to the kinds of interactions among those loci. The mathematical methods used in this research will be primarily from the theory of probability and stochastic processes. Analytic results will be obtained whenever possible and will be supplemented by computer simulations. Methods of data analysis will be tested both with simulated and real data. Computer programs that are found to be useful for data analysis will be made freely available.

描述（由申请人提供）：拟议的研究将是 可用于分析的数学和统计模型 人类和其他人群的遗传数据。将特别强调的是 放在考虑内部个体之间差异的模式上 人群以及不同人群之间的差异。本次活动的总体目标 研究是开发利用变异模式来 了解人类和其他人群的历史并协助 努力识别影响遗传疾病的基因。人口历史 增长、分散以及与其他种群的混合反映在 整个基因组的遗传变异模式。一个人的历史 突变反映在紧密相连的基因位点的变异模式中。 与该突变有关。具体目标是（1）对历史进行建模 导致遗传疾病的个体突变，以便了解其发生地点和 它们何时出现以及这些突变的携带者是否具有优势 生存和繁殖，（2）更好地利用非随机关联 不同遗传位点（称为连锁不平衡）的等位基因来定位 导致遗传疾病的基因，(3) 模拟自然选择的影响 其作用是维持遗传基因座的遗传多样性，例如 人类和其他物种的主要组织相容性 (MHC) 位点，以及 (4) 发展与近亲遗传病风险相关的理论 患有该疾病的个体的遗传位点数量影响 疾病以及这些位点之间的相互作用类型。数学的 本研究中使用的方法主要来自概率论 和随机过程。将尽可能获得分析结果 并将通过计算机模拟进行补充。数据分析方法将 使用模拟和真实数据进行测试。找到的计算机程序 对数据分析有用的信息将免费提供。