Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics
Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics
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DOI：
10.1093/molbev/msn090
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发表时间：
2008-07-01
影响因子：
10.7
通讯作者：
Suchard, Marc A.
中科院分区：
文献类型：
--
作者：
Minin, Vladimir N.;Bloomquist, Erik W.;Suchard, Marc A.
金曼的聚结过程为从分子序列估计群体遗传学模型参数打开了大门。一个最重要的关注参数是有效种群规模。该数量的时间变化表征了人口的人口统计历史。由于研究人员很少能够先验地选择描述现有数据的有效种群规模动态的确定性模型，因此开发了基于多变点（MCP）模型的非参数曲线拟合方法。我们提出了一种变点建模的替代方案，利用高斯马尔可夫随机场来实现贝叶斯框架中有效种群规模的时间平滑。我们的方法的主要优点是，与 MCP 模型相比，显式时间平滑不需要强大的先验决策。为了近似种群动态的后验分布，我们使用专为高度结构化高斯模型设计的高效、快速混合马尔可夫链蒙特卡罗算法。在模拟研究中，我们证明了所提出的时间平滑方法（称为贝叶斯 Skyride）成功地恢复了所有模拟场景中的“真实”人口规模轨迹，并且与 MCP 方法竞争良好，而无需引起强烈的先验假设。我们将贝叶斯 Skyride 方法应用于 2 个真实数据集。我们分析了在埃及同时采样的丙型肝炎病毒序列，再现了病毒种群动态的所有已知关键方面。接下来，我们估计了在 3 个流感季节连续采样的人类甲型流感血凝素序列的人口统计历史。
Kingman's coalescent process opens the door for estimation of population genetics model parameters from molecular sequences. One paramount parameter of interest is the effective population size. Temporal variation of this quantity characterizes the demographic history of a population. Because researchers are rarely able to choose a priori a deterministic model describing effective population size dynamics for data at hand, nonparametric curve-fitting methods based on multiple change-point (MCP) models have been developed. We propose an alternative to change-point modeling that exploits Gaussian Markov random fields to achieve temporal smoothing of the effective population size in a Bayesian framework. The main advantage of our approach is that, in contrast to MCP models, the explicit temporal smoothing does not require strong prior decisions. To approximate the posterior distribution of the population dynamics, we use efficient, fast mixing Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. In a simulation study, we demonstrate that the proposed temporal smoothing method, named Bayesian skyride, successfully recovers "true" population size trajectories in all simulation scenarios and competes well with the MCP approaches without evoking strong prior assumptions. We apply our Bayesian skyride method to 2 real data sets. We analyze sequences of hepatitis C virus contemporaneously sampled in Egypt, reproducing all key known aspects of the viral population dynamics. Next, we estimate the demographic histories of human influenza A hemagglutinin sequences, serially sampled throughout 3 flu seasons.