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

随着已测序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有望提供更好的能力来推断选择。最近，从古代DNA(ADNA)数据中检测和量化选择的统计方法已经发展起来。现有的大多数方法只能估计一个选择系数，并可能估计所选等位基因的年龄。其他重要因素，例如附近座位的额外选择系数、更复杂的选择方案或时变选择，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，即自然种群几乎总是有结构的，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种新的贝叶斯框架，用于联合推断多个选择系数和从DNA数据进行迁移。我们的目标是将扩散过程和Metropolis-Hastings类型抽样方案的技术结合起来。

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.