Biased phylodynamic inferences from analysing clusters of viral sequences
Biased phylodynamic inferences from analysing clusters of viral sequences
复制标题
通过分析病毒序列簇得出的有偏差的系统动力学推论
DOI：
10.1101/095661
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Dearlove B
中科院分区：
文献类型：
--
作者：
Dearlove B
系统发生学方法正越来越多地用于帮助了解可测量进化病毒（包括艾滋病毒）的传播动态。经常观察到高度相似序列的集群，这些集群似乎遵循“幂律”行为，具有少量非常大的集群。这些集群可能有助于确定流行病中的亚群，并告知应在何处实施干预战略。然而，样本聚集并不一定意味着存在高传播率的亚群，因为由于过度采样等非流行病学影响，也可能出现密切相关的病毒组。重要的是要确保观察到的系统发育聚类反映了传播人群的真正异质性，而不是由非流行病学影响驱动的。我们有资格的效果，使用一个错误识别的“传输集群”的序列，以估计包括有效的人口规模和指数增长率在几个人口情景下的动态参数。我们的模拟研究表明，采取最大规模集群重新估计参数的随机混合下，恒定的人口规模的聚结过程模拟的树木系统低估了整体有效人口规模。此外，传输集群错误地类似于指数或逻辑增长模型的99%的时间。我们还说明了假集群的后果，在指数增长的聚结和出生死亡树，再次，增长率是向上倾斜。这对于识别大型病毒数据库中的簇具有明显的影响，其中错误的簇可能会导致干预资源的浪费。
Phylogenetic methods are being increasingly used to help understand the transmission dynamics of measurably evolving viruses, including HIV. Clusters of highly similar sequences are often observed, which appear to follow a ‘power law’ behaviour, with a small number of very large clusters. These clusters may help to identify subpopulations in an epidemic, and inform where intervention strategies should be implemented. However, clustering of samples does not necessarily imply the presence of a subpopulation with high transmission rates, as groups of closely related viruses can also occur due to non-epidemiological effects such as over-sampling. It is important to ensure that observed phylogenetic clustering reflects true heterogeneity in the transmitting population, and is not being driven by non-epidemiological effects. We qualify the effect of using a falsely identified ‘transmission cluster’ of sequences to estimate phylodynamic parameters including the effective population size and exponential growth rate under several demographic scenarios. Our simulation studies show that taking the maximum size cluster to re-estimate parameters from trees simulated under a randomly mixing, constant population size coalescent process systematically underestimates the overall effective population size. In addition, the transmission cluster wrongly resembles an exponential or logistic growth model 99% of the time. We also illustrate the consequences of false clusters in exponentially growing coalescent and birth-death trees, where again, the growth rate is skewed upwards. This has clear implications for identifying clusters in large viral databases, where a false cluster could result in wasted intervention resources.
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影响因子：
3.3
作者：
M. Kuhner;Jon A Yamato;J. Felsenstein
通讯作者：
M. Kuhner;Jon A Yamato;J. Felsenstein
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H
影响因子：
3.8
作者：
Bezemer, Daniela;van Sighem, Ard;de Wolf, Frank
通讯作者：
de Wolf, Frank
影响因子：
3
作者：
Vrbik I;Stephens DA;Roger M;Brenner BG
通讯作者：
Brenner BG
DOI：
10.1097/qai.0b013e3181e8c7b0
发表时间：
2010-09-01
影响因子：
3.6
作者：
Ragonnet-Cronin, Manon;Ofner-Agostini, Marianna;Brooks, James I.
通讯作者：
Brooks, James I.