Improving Bayesian Population Dynamics Inference: A Coalescent-Based Model for Multiple Loci
Improving Bayesian Population Dynamics Inference: A Coalescent-Based Model for Multiple Loci
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DOI：
10.1093/molbev/mss265
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发表时间：
2013-03-01
影响因子：
10.7
通讯作者：
Suchard, Marc A.
中科院分区：
文献类型：
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作者：
Gill, Mandev S.;Lemey, Philippe;Suchard, Marc A.
有效种群规模是种群遗传学的基础，也是遗传多样性的特征。为了从分子序列数据推断过去的种群动态，人们开发了基于合并的模型来估计随时间变化的有效种群大小的贝叶斯非参数估计。其中最成功的是针对单基因座的高斯马尔可夫随机场(GMRF)模型。在这里，我们提出了一个推广的GMRF模型，允许分析多位点序列数据。使用模拟数据，我们证明了我们的方法在恢复真实的种群轨迹和到最近共同祖先的时间(TMRCA)方面的改进性能。我们分析了来自喀麦隆的HIV-1 CRF02_AG基因序列的多位点比对。我们的结果与HIV流行数据是一致的，并揭示了在贝叶斯参数估计中未被发现的人口历史的某些方面。最后，我们为一个经典的古代DNA数据集恢复了一个更老、更协调的TMRCA。
Effective population size is fundamental in population genetics and characterizes genetic diversity. To infer past population dynamics from molecular sequence data, coalescent-based models have been developed for Bayesian nonparametric estimation of effective population size over time. Among the most successful is a Gaussian Markov random field (GMRF) model for a single gene locus. Here, we present a generalization of the GMRF model that allows for the analysis of multilocus sequence data. Using simulated data, we demonstrate the improved performance of our method to recover true population trajectories and the time to the most recent common ancestor (TMRCA). We analyze a multilocus alignment of HIV-1 CRF02_AG gene sequences sampled from Cameroon. Our results are consistent with HIV prevalence data and uncover some aspects of the population history that go undetected in Bayesian parametric estimation. Finally, we recover an older and more reconcilable TMRCA for a classic ancient DNA data set.