发表机构
University of California San Francisco; Yale University; Stanford University（旧金山加利福尼亚大学; 耶鲁大学; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对有界溯祖过程，提出基于点过程视角的模拟与马尔可夫链蒙特卡罗推断算法，用于估计有效群体大小轨迹，并以新冠序列数据验证。
AI 中文摘要
溯祖过程是群体遗传学中的一个核心框架，用于通过基因谱系（表示为有根且有序的二叉树）对样本个体之间的祖先关系进行建模。在该模型中，谱系以与有效群体大小成反比的速率发生溯祖，而有效群体大小是一个随时间变化的主要关注量。有界溯祖过程将基因谱系的条件设定为最近共同祖先的时间被一个固定时间上界所限制。该模型在各种情境下都很有用，例如已知引入时间的传染病系统动力学，以及合成条形码实验中的单细胞谱系追踪。据我们所知，目前没有现有工具能够在有界溯祖过程下推断可变的有效群体大小轨迹。我们将有界溯祖过程下的估计视为对非齐次点过程强度函数的估计。我们利用点过程方法提供了一种在有界溯祖过程下进行溯祖模拟的高效算法，保留了朴素拒绝采样的精确性，同时大幅降低了计算成本，并避免了对有界累积风险函数的重复数值求逆。随后，我们开发了一种马尔可夫链蒙特卡罗程序，用于有效群体大小轨迹的后验推断，该程序避免了对似然积分的离散化。在模拟中，以该界为条件在三种设置中的两种降低了中位平方误差和，而在变化最剧烈的设置中结果较不理想。我们使用华盛顿州的严重急性呼吸综合征冠状病毒2序列数据对该方法进行了说明。
英文摘要
The coalescent is a central framework in population genetics for modelling the ancestral relationships among sampled individuals through a genealogy, represented as a rooted and ranked binary tree. In this model, lineages coalesce at a rate inversely proportional to the effective population size, a time-varying quantity of primary interest. The bounded coalescent conditions genealogies on the time to the most recent common ancestor being bounded above by a fixed time. This model is useful in various contexts, such as phylodynamics of infectious diseases with known introduction times and single-cell lineage tracing in synthetic barcoding experiments. To our knowledge, there is no existing tool that infers variable effective population size trajectories under the bounded coalescent. We view estimation under the bounded coalescent as equivalent to estimation of the intensity function of an inhomogeneous point process. We provide an efficient algorithm for coalescent simulation under the bounded coalescent using point process methods, retaining the exactness of naive rejection sampling while substantially reducing computational cost and avoiding repeated numerical inversion of the bounded cumulative hazard. We then develop a Markov chain Monte Carlo procedure for posterior inference of effective population size trajectories that avoids discretization of the likelihood integrals. In simulations, conditioning on the bound reduces the median sum of squared errors in two of three settings, with less favourable results in the most rapidly varying setting. We illustrate the method using severe acute respiratory syndrome coronavirus 2 sequence data from Washington State.
Comments37 pages, 6 figures
