Models of Complex Genetic Systems

复杂遗传系统模型

• 批准号: 6789906
• 负责人: MONTGOMERY Wilson SLATKIN
• 金额: $ 24.31万
• 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/6789906
• 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)：建议的研究将在 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。特别强调的是 被置于对内部个体之间的差异模式的核算上 种群和不同种群之间的变异。这个项目的总体目标是 研究正在开发使用变异模式的方法来 了解人类和其他人口的历史，并协助 努力识别影响遗传病的基因。人口史 生长、扩散和与其他种群的混合反映在 整个基因组的遗传变异模式。个人的历史 突变反映在密切相关的遗传基因座的变异模式中。 与这种突变有关。具体目标是(1)模拟 导致遗传病的个体突变，以便了解在哪里和 以及这些突变的携带者是否有优势 生存和繁殖，(2)更好地利用非随机关联 不同遗传座位的等位基因定位(称为连锁不平衡) 导致遗传病的基因，(3)模拟自然选择的影响 以保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)基因座，以及(4)至 发展与近亲患遗传病风险相关的理论 与影响疾病的遗传基因座的数量有关 疾病和这些基因座之间相互作用的种类。数学上的 本研究使用的方法主要来自概率论 和随机过程。只要有可能，就会得到分析结果。 并将得到计算机模拟的补充。数据分析的方法将 用模拟数据和真实数据进行测试。找到的计算机程序 将免费提供对数据分析有用的信息。