Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.
Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.
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DOI：
10.1093/molbev/msv123
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发表时间：
2015-09
影响因子：
10.7
通讯作者：
Poon AF
中科院分区：
文献类型：
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作者：
Poon AF
与病毒种群相关的系统发育树的形状取决于病毒在每个宿主内的适应性，以及病毒在宿主之间的传播。系统动力学推断试图逆转这一信息流，根据从疫情的遗传序列样本重建的病毒系统发育图来估计这些过程的参数。系统动力学推断的一个关键挑战是以有效和全面的方式量化两棵树之间的相似性。在这项研究中，我证明了一种新的距离度量，它基于计算语言学中的子集树核函数，在分类不同流行病学场景下生成的树时，比以前的树形状度量有了显著的改进。接下来，我将这种基于核的距离度量纳入到用于系统动力学推断的近似贝叶斯计算(ABC)框架中。ABC不需要模型可能性的解析解，因为它只需要模拟来自模型的数据的能力。我通过从一个简单的流行病学模型下模拟的数据估计参数来验证这种用于系统动力学推断的“核-ABC”方法。结果表明，在与病毒传播相关的参数上，内核-ABC在相同数据集上比领先的软件获得了更高的准确性。最后，我应用核-ABC框架研究了最近在中国暴发的一种重组艾滋病毒亚型。内核-ABC为系统动力学推理提供了一个通用的框架，因为它比依赖精确似然计算的方法可以适应更广泛的模型范围。
The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this “kernel-ABC” method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods.