The use of recombining genetic markers for demographic inference

使用重组遗传标记进行人口统计推断

• 批准号: 7320276
• 负责人: JOHN E POOL
• 金额: $ 0.49万
• 依托单位: UNIVERSITY OF COPENHAGEN
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-11-16 至 2007-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7320276
• 关键词: Admixture; Affect; African; African American; Asians; Biological Markers; Chromosomes; Complex; Computing Methodologies; Condition; DNA; Data; Data Set; Development; Disease; Disease Association; Event; Frequencies; Generations; Genetic Drift; Genetic Markers; Genetic Models; Genetic Polymorphism; Genetic Population Study; Genetic Variation; Goals; Hispanics; Human; Immigration; Island; Length; Link; Maps; Markov chain Monte Carlo methodology; Methods; Metric; Microsatellite Repeats; Modeling; Mutation; Natural History; Nomads; Numbers; Pattern; Phase; Population; Population Genetics; Population Sizes; Probability; Public Health; Rate; Recording of previous events; Research; Research Personnel; Sampling; Source; Structure; Testing; Time; computer program; genetic variant; migration; novel; population migration; programs; simulation

DESCRIPTION (provided by applicant): The proposed research concerns the inference of historical patterns of migration. Traditional population genetic models of migration assume that populations have been exchanging migrants at a constant rate over long periods of time. For many species, however, this assumption may not be appropriate. Therefore, the development of a computational method to test for recent changes in migration rate and to estimate the relevant demographic parameters is proposed. While most methods of demographic inference assume that all of the genetic markers being studied are independent (unlinked), this approach will take advantage of the patterns of linkage along a recombining chromosome. By considering this linkage information (specifically, the lengths of DNA segments that inferred to have migrant origin), one can go beyond estimating how much migration has occurred between two populations, and say something about when, historically, this migration occurred. During the first phase of this project, the effect of various population histories on the length distribution of migrant DNA segments will be investigated, making use of an existing simulation program (ms) and inference method (structure 2.0). Next, the new inference method described above will be developed, using Markov chain Monte Carlo methodology in a maximum likelihood or Bayesian framework. Finally, this method will be applied to existing human polymorphism data sets (both SNP and microsatellite) in order to test the null hypothesis that migration among human populations has been constant since their divergence. This analysis will permit the estimation of demographic parameters for admixed human populations, and will therefore aid in the selection of populations for admixture mapping studies of disease association. Relevance to public health: The goal of the proposed research is to test for historical changes in the rate of migration between populations, and to estimate quantities such as the time since a migration rate change and the magnitude of such a change. The computational method developed will have a variety of applications, including the estimation of demographic parameters in human populations with a history of recent admixture (ancestry from multiple sources), such as African-American, Hispanic, Central Asian and Northern African populations. That information will be relevant in assessing the utility of such populations for admixture mapping studies, which aim to identify genetic variants associated with complex diseases that occur at different frequencies in different populations.

描述（由申请人提供）：拟议的研究涉及历史移民模式的推断。传统的人口迁移遗传模型假设人口在很长一段时间内以恒定的速度交换移民。然而，对于许多物种来说，这个假设可能并不合适。因此，建议开发一种计算方法来测试迁移率的近期变化并估计相关的人口统计参数。虽然大多数人口统计推断方法都假设所研究的所有遗传标记都是独立的（不相关），但这种方法将利用沿重组染色体的连锁模式。通过考虑这种关联信息（具体来说，推断具有移民起源的 DNA 片段的长度），人们不仅可以估计两个种群之间发生了多少迁移，还可以了解历史上这种迁移发生的时间。在该项目的第一阶段，将利用现有的模拟程序（ms）和推理方法（结构2.0）研究不同人口历史对移民DNA片段长度分布的影响。接下来，将在最大似然或贝叶斯框架中使用马尔可夫链蒙特卡罗方法来开发上述新的推理方法。最后，该方法将应用于现有的人类多态性数据集（SNP 和微卫星），以检验人类群体之间的迁移自分歧以来一直保持不变的零假设。该分析将允许估计混合人群的人口参数，因此将有助于选择人群进行疾病关联的混合绘图研究。 与公共卫生的相关性：拟议研究的目标是测试人口之间迁移率的历史变化，并估计迁移率变化以来的时间和变化的幅度等数量。开发的计算方法将具有多种应用，包括估计具有近期混合历史（来自多个来源的祖先）的人口统计参数，例如非洲裔美国人、西班牙裔、中亚人和北非人口。这些信息将有助于评估此类人群在混合图谱研究中的效用，该研究旨在识别与不同人群中以不同频率发生的复杂疾病相关的遗传变异。