Bayesian Inference under the Structured Coalescent Model

结构化合并模型下的贝叶斯推理

• 批准号: 2435782
• 金额: --
• 依托单位: University of Warwick
• 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-2435782
• 关键词: Bayesian; Inference; under; Structured; Coalescent

The coalescent is a population genetics model for the inheritance of genetic material over time. Genetic sequences are taken at known times and based on genetic similarities between sequences, the times until pairs of lineages coalesce at a common ancestor can be estimated. An important factor, which is not accounted for in the ordinary coalescent model, is the spatial constraints. For example, if two lineages have been separated geographically at some time in the past, they cannot find a common ancestor until both lineages exist in a common location. This motivates an extension to the ordinary coalescent model which factors in spatial constraints, known as the structured coalescent. Individuals are assumed to exist in a fixed, and possibly unknown, number of distinct demes, with migrations occurring between demes at fixed rates backwards in time. A dataset consists of a number of genomes sampled at various timepoints and from various demes. From this, there are several evolutionary parameters which we would like to infer in the structured coalescent model, including the migration rates between demes and effective population sizes of each deme. The migration history that led to the current locations of the samples is also often of interest. The uncertainty in at least some of these parameters is likely to be important, which motivates a Bayesian approach to inference. Currentmethods to infer these parameters are either computationally expensive, or rely on approximations of the structured coalescent in place of the full model which can introduce significant biases (Muller et al, 2017).To combat this lack of scalable approaches to perform inference under the structured coalescent, I intend to construct a reversible jump Markov chain Monte Carlo algorithm which will infer migration histories and evolutionary parameters for a fixed coalescent genealogy. There are multiple robust methods currently available to infer a genealogy from genomic data, including BEAST (Suchard et al, 2018), LSD (To et al, 2016) and TreeTime (Sagulenko et al, 2018). My work will build upon previous MCMC schemes proposed by Drummond et al. (2002) and Ewing et al. (2004) for the coalescent and structured coalescent respectively. Further, I will release an implementation of my algorithm as an open source R package.The correctness and computational efficiency of my algorithm will be assessed by benchmarking on simulated datasets. Applications to state-of-the-art real datasets from infectious disease pathogens will demonstrate the usefulness of my algorithm, for example a global dataset of cholera genomes from the seventh pandemic (Didelot et al 2015) and a collection of Ebola genomes from the 2013-2016 West African epidemic (Dudas et al 2017). I anticipate that this project will contribute to advances in the accuracy of statistical methods for genetic sequences. It will also be relevant for generic MCMC methods on constrained and non-Euclidean spaces, which have applications across applied sciences and engineering.

聚合体是遗传物质随时间遗传的群体遗传学模型。遗传序列是在已知时间获取的，并基于序列之间的遗传相似性，可以估计成对的谱系在共同祖先处结合的时间。在普通的联合模型中没有考虑的一个重要因素是空间限制。例如，如果两个血统在过去的某个时间在地理上被分开，他们就无法找到共同的祖先，直到两个血统都存在于共同的位置。这促使了对普通合并模型的扩展，该模型考虑了空间约束，称为结构化合并。假设个体以固定的、可能未知的数量存在于不同的Deme中，在Deme之间以固定的速率向后迁移。数据集由在不同时间点从不同Deme采样的多个基因组组成。由此，我们希望在结构合并模型中推断出几个进化参数，包括Deme之间的迁移率和每个Deme的有效种群规模。导致样本当前位置的迁移历史也常常令人感兴趣。这些参数中至少有一些参数的不确定性可能很重要，这促使人们采用贝叶斯方法进行推理。目前推断这些参数的方法要么计算昂贵，要么依赖于结构化合并的近似，而不是可能引入显著偏差的完整模型(Muller等人，2017)。为了解决这种缺乏可扩展的方法来在结构化合并下执行推理的问题，我打算构建一个可逆跳跃马尔可夫链蒙特卡罗算法，该算法将推断固定合并谱系的迁移历史和进化参数。目前有多种稳健的方法可以从基因组数据中推断家谱，包括Beast(Suchard等人，2018年)、LSD(to et al，2016)和TreeTime(Sagulenko等人，2018年)。我的工作将建立在以前由Drummond等人提出的MCMC方案的基础上。(2002)和Ewing et al.(2004)分别针对聚结剂和结构化聚结剂。此外，我将以开源R包的形式发布我的算法的实现。我的算法的正确性和计算效率将通过在模拟数据集上进行基准测试来评估。对传染病病原体的最新真实数据集的应用将证明我的算法的实用性，例如第七次霍乱大流行的全球基因组数据集(Didelot等人2015年)和2013-2016年西非疫情的埃博拉基因组集合(Dudas等人2017年)。我预计这个项目将有助于提高基因序列统计方法的准确性。它也将与约束和非欧几里德空间上的通用MCMC方法相关，这些方法在应用科学和工程中都有应用。