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.

成功管理和保护自然人口需要了解人口结构和迁移率。 通过相对非侵入性方法获得的广泛遗传数据的可用性不断提高，在人群层面上提供了监测和管理的新机会。这些标记也使使用档案材料的使用。 但是，很少有计算方法可以完全利用人口数据。 使用马尔可夫链蒙特卡洛（MCMC）的蒙特卡洛可能性方法已被证明在系统发育分析中的进化量表上成功，在血统分析中在个体量表上获得了成功。 该项目开发了一种蒙特卡洛的可能性方法来推论，从遗传样本随着时间的流逝监测人群中。 这将允许估算有效繁殖种群实际尺寸的比率，并为估计混合或迁移结构的估计提供了可能性。一般的方法是在鲑鱼种群上开发的，鲑鱼种群既有良好的数据和档案材料，该项目将导致通常适用于人群研究的软件，而不论研究生物如何。