Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference
Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference
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DOI：
10.1371/journal.pcbi.1004789
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发表时间：
2016-03-01
影响因子：
4.3
通讯作者：
Minin, Vladimir N.
中科院分区：
文献类型：
--
作者：
Karcher, Michael D.;Palacios, Julia A.;Minin, Vladimir N.
系统动力学旨在从感兴趣的种群中采样的个体的分子序列中估计有效的种群大小波动。完成这项任务的一种方法是利用采样个体家谱的聚结模型来制定观察到的序列数据可能性，然后通过蒙特卡罗对所有可能的家谱进行整合，或者通过对从序列数据估计的一个家谱进行调节，效率较低。然而，当分析随时间连续采样的序列时，当前的方法隐含地假设采样时间是由数据收集协议确定的，或者它们的分布不依赖于总体的大小。通过模拟，我们首先表明，当采样时间概率依赖于有效总体大小时，估计方法可能存在系统偏差。为了纠正这一缺陷，我们提出了一个新的模型，通过将采样时间建模为依赖于有效种群大小的非齐次泊松过程，明确地解释了优先抽样。结果表明，在存在优先抽样的情况下，新模型不仅降低了偏差，而且提高了估计精度。最后，我们通过临床相关的季节性人类流感实例，比较了目前使用的系统动力学方法与我们提出的模型的性能。
Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting a coalescent model for the sampled individuals' genealogy and then integrating over all possible genealogies via Monte Carlo or, less efficiently, by conditioning on one genealogy estimated from the sequence data. However, when analyzing sequences sampled serially through time, current methods implicitly assume either that sampling times are fixed deterministically by the data collection protocol or that their distribution does not depend on the size of the population. Through simulation, we first show that, when sampling times do probabilistically depend on effective population size, estimation methods may be systematically biased. To correct for this deficiency, we propose a new model that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size. We demonstrate that in the presence of preferential sampling our new model not only reduces bias, but also improves estimation precision. Finally, we compare the performance of the currently used phylodynamic methods with our proposed model through clinically-relevant, seasonal human influenza examples.