Estimating population parameters using the structured serial coalescent with Bayesian MCMC inference when some demes are hidden.
Estimating population parameters using the structured serial coalescent with Bayesian MCMC inference when some demes are hidden.
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DOI：
10.4137/ebo.s0
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发表时间：
2007-02
影响因子：
2.6
通讯作者：
Greg Ewing-;Allen G. Rodrigo-
中科院分区：
文献类型：
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作者：
Greg Ewing-;Allen G. Rodrigo-
利用贝叶斯MCMC和序列样本的结构化序列聚结，我们估计了一些未采样或隐藏的群落（即幽灵群落）的种群规模。研究发现，即使存在鬼影，如果用真实模型估计参数，也可以得到准确的推断。然而，一个不正确的模型，估计是有偏见的，可能是积极的误导。我们将这些结果扩展到最后一次样本中有鬼魂序列的情况。这种情况可能发生在艾滋病毒患者身上，因为一些组织样本和病毒序列只有在死后才能获得。当在最后一次采样时，来自鬼deme的一些序列可用时，估计偏差减小，并且可以在采样偏差的情况下准确估计与鬼deme相关的参数。当迁移值较低时，这种情况下的迁移率也是很好的估计。
Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the true model. However with an incorrect model, estimates were biased and can be positively misleading. We extend these results to the case where there are sequences from the ghost at the last time sample. This case can arise in HIV patients, when some tissue samples and viral sequences only become available after death. When some sequences from the ghost deme are available at the last sampling time, estimation bias is reduced and accurate estimation of parameters associated with the ghost deme is possible despite sampling bias. Migration rates for this case are also shown to be good estimates when migration values are low.