Estimating effective population size changes from preferentially sampled genetic sequences.
Estimating effective population size changes from preferentially sampled genetic sequences.
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DOI：
10.1371/journal.pcbi.1007774
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发表时间：
2020-10
影响因子：
4.3
通讯作者：
Minin VN
中科院分区：
文献类型：
--
作者：
Karcher MD;Carvalho LM;Suchard MA;Dudas G;Minin VN
结合统计建模理论，使我们能够估计有效的人口规模波动的分子序列的个人从一个人口的利益。当序列在时间上连续采样并且采样时间的分布取决于有效总体大小时，采样时间的显式统计建模可以改善总体大小估计。以前的工作假设，家谱相关的采样序列是已知的和建模的采样时间作为一个非齐次泊松过程的对数强度等于一个线性函数的对数转换的有效人口规模。我们从两个方面改进这种方法。首先，我们扩展的方法，允许联合贝叶斯估计的系谱，有效的人口规模的轨迹，和其他模型参数。接下来，我们通过以随时间变化的协变量的形式引入额外的信息源来改进采样时间模型。我们使用模拟研究验证了我们的新建模框架，并将我们的新方法应用于季节性流感的人口动态分析和最近在西非爆发的埃博拉病毒。在某些情况下，估计给定人口中个体数量的变化是一个具有挑战性的问题。例如，估计被病原体感染的人数的人口规模轨迹（例如，流感病毒）是一个难题，因为大量人群中的许多感染仍然未被观察到/隐藏。评估群体大小变化的一种间接方式是从感兴趣的群体中采集个体样本并分析来自这些个体的遗传序列（例如，流感病毒基因组）。直觉上，遗传数据是关于群体大小变化的信息，因为遗传多样性随着群体大小而增加/减少。然而，如果我们在种群规模增加时抽样更多的个体，而在种群规模减少时抽样更少，这种策略会产生有偏差的结果。为了避免这种偏差，我们提出了一种方法，明确和灵活的模型的潜在依赖性的遗传序列采样的人口规模。这个新的建模框架的额外好处是更精确地估计人口规模的变化。我们展示了我们的新方法的模拟数据和流感和埃博拉病毒的基因序列的优势。
Coalescent theory combined with statistical modeling allows us to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. When sequences are sampled serially through time and the distribution of the sampling times depends on the effective population size, explicit statistical modeling of sampling times improves population size estimation. Previous work assumed that the genealogy relating sampled sequences is known and modeled sampling times as an inhomogeneous Poisson process with log-intensity equal to a linear function of the log-transformed effective population size. We improve this approach in two ways. First, we extend the method to allow for joint Bayesian estimation of the genealogy, effective population size trajectory, and other model parameters. Next, we improve the sampling time model by incorporating additional sources of information in the form of time-varying covariates. We validate our new modeling framework using a simulation study and apply our new methodology to analyses of population dynamics of seasonal influenza and to the recent Ebola virus outbreak in West Africa. Estimating changes in the number of individuals in a given population is a challenging problem in some settings. For example, estimating population size trajectories of the number of people infected by a pathogen (e.g., Influenza virus) is a difficult problem, because many infections in a large population remain unobserved/hidden. One indirect way of assessing population size changes is to take a sample of individuals from the population of interest and analyze genetic sequences from these individuals (e.g., Influenza virus genomes). Intuitively, genetic data is informative about population size changes, because genetic diversity increases/decreases together with the population size. However, if we sample more individuals when the population size increases and less when it decreases, this strategy produces biased results. To avoid this bias, we propose a method that explicitly and flexibly models potential dependency of genetic sequence sampling on the population size. An added bonus of this new modeling framework is more precise estimation of population size changes. We demonstrate strengths of our new methodology on simulated data and on genetic sequences of Influenza and Ebola viruses.
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