Computational Methods for Inference of Population Parameters

群体参数推断的计算方法

• 批准号: 9807747
• 负责人: Elizabeth Thompson
• 金额: $ 16.63万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-15 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9807747&HistoricalAwards=false
• 关键词: Computational; Methods; Inference; Population; Parameters

Successful management and conservation of natural populations requires knowledge of population structure and migration rates. The increasing availability of extensive genetic data obtained through relatively non-invasive methods, present new opportunities for monitoring and management at a population level. These markers also make possible the use of archive material. However, there are few computational methods available to fully exploit population data. Methods of Monte Carlo likelihood, using Markov chain Monte Carlo (MCMC) have proven successful on the evolutionary scale in phylogenetic analysis, and on the individual scale in pedigree analysis. This project develops a Monte Carlo likelihood approach to inferences from genetic samples taken in monitoring a population over time. This will permit estimation of the ratio of effective to actual sizes of breeding populations and will also provide likelihoods for the estimation of admixture or migration structure. The general approach is being developed on salmonid populations, for which there exist both good data and archive material, and the project will result in software generally applicable for population studies regardless of the study organism.

自然种群的成功管理和保护需要了解种群结构和迁移率。通过相对非侵入性的方法获得的大量遗传数据越来越多，为在种群一级进行监测和管理提供了新的机会。这些标记也使档案材料的使用成为可能。然而，很少有计算方法可以充分利用人口数据。利用马尔科夫链蒙特卡罗（MCMC）的蒙特卡罗似然方法在系统发育分析的进化尺度和谱系分析的个体尺度上都取得了成功。该项目开发了蒙特卡罗似然方法，从监测种群的遗传样本中推断出一段时间。这将允许估计繁殖种群的有效规模与实际规模的比例，也将为估计混合或迁移结构提供可能性。目前正在拟订关于鲑鱼种群的一般办法，这方面既有良好的数据和档案材料，而且该项目将产生一般适用于种群研究的软件，而不管所研究的有机体是什么。