Novel statistical techniques for joint estimation of selection and migration from time series genetic data

用于从时间序列遗传数据中联合估计选择和迁移的新统计技术

• 批准号: 2445962
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2445962
• 关键词: Novel; statistical; techniques; joint; estimation

With the rapid growth of the number of sequenced ancient genomes, there has been increasing interest in using this new information to study past and present adaptation. Such an additional temporal component has the promise of providing improved power to infer selection. Recently, statistical approaches for detection and quantification of selection from ancient DNA (aDNA) data have been developed. Most existing methods can only estimate one selection coefficient and possibly the age of the selected allele. Other important factors, e.g. additional selection coefficients at nearby loci, more complicated selection schemes, or time-varying selection, cannot be effectively estimated. Moreover, most existing approaches ignore the fact that natural population are almost always structured, which can result in the overestimation of the underlying selection coefficient. To address these issues, we aim to develop a novel Bayesian framework for the joint inference of multiple selection coefficients and migration from aDNA data. We aim to combine techniques from diffusion processes and Metropolis-Hastings type sampling schemes.

随着已测序的古代基因组数量的快速增长，人们越来越有兴趣利用这些新信息来研究过去和现在的适应。这种额外的时间分量具有提供改进的推断选择的能力的前景。最近，已经开发了用于检测和定量从古DNA（aDNA）数据选择的统计方法。大多数现有的方法只能估计一个选择系数和可能的年龄选择等位基因。其他重要的因素，如在附近的基因座，更复杂的选择方案，或随时间变化的选择，额外的选择系数，不能有效地估计。此外，大多数现有的方法忽略了这样一个事实，即自然群体几乎总是结构化的，这可能会导致高估的潜在选择系数。为了解决这些问题，我们的目标是开发一种新的贝叶斯框架的多重选择系数和迁移从aDNA数据的联合推断。我们的目标是结合联合收割机技术从扩散过程和大都会黑斯廷斯型采样计划。