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

随着古代基因组测序数量的快速增长，人们对利用这些新信息研究过去和现在的适应性越来越感兴趣。这种额外的时间组件有望提供改进的推断选择的能力。近年来，从古代DNA （aDNA）数据中检测和定量选择的统计方法得到了发展。大多数现有的方法只能估计一个选择系数和可能选择的等位基因的年龄。其他重要因素，例如邻近基因座的额外选择系数、更复杂的选择方案或时变选择，都无法有效估计。此外，大多数现有方法忽略了自然种群几乎总是结构化的事实，这可能导致对潜在选择系数的高估。为了解决这些问题，我们的目标是开发一个新的贝叶斯框架，用于从aDNA数据中联合推断多个选择系数和迁移。我们的目标是结合扩散过程和大都会-黑斯廷斯型采样方案的技术。

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.