A model-based Bayesian estimation of the rate of evolution of VNTR loci in Mycobacterium tuberculosis.
A model-based Bayesian estimation of the rate of evolution of VNTR loci in Mycobacterium tuberculosis.
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DOI：
10.1371/journal.pcbi.1002573
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发表时间：
2012
影响因子：
4.3
通讯作者：
Tanaka MM
中科院分区：
文献类型：
--
作者：
Aandahl RZ;Reyes JF;Sisson SA;Tanaka MM
可变数目串联重复序列（VNTR）分型被广泛用于研究结核病的细菌病因。了解VNTR位点的突变率有助于结核分枝杆菌的进化和流行病学研究。以前的研究应用群体遗传模型来估计突变率，导致估计值从每年每个位点到每个位点变化很大。使用更详细的模型和统计方法解决这个问题将导致改进结核病分子流行病学的推断。在这里，我们使用一个基于模型的方法，结合了两种替代形式的逐步突变过程中的VNTR演变的流行病学模型的疾病传播。在贝叶斯框架下使用该模型，我们估计了M。来自阿尔巴尼亚、伊朗、摩洛哥和委内瑞拉的四个已发表的VNTR概况数据集的结核病。在第一种变体中，突变率相对于重复数线性增加（线性模型）;在第二种变体中，突变率在重复数之间是恒定的（恒定模型）。我们发现，在常数模型下，每个位点的平均突变率为（95%CI：，），在线性模型下，每个重复单位的每个位点的平均突变率为（95%CI：，）。这些新的估计值代表了与以前的估计值相比VNTR基因座的高突变率。为了比较这两个模型，我们使用后验预测检查来确定这两个模型中的哪一个能够更好地再现观察到的数据。从这个过程中，我们发现线性模型比常数模型表现得更好。我们使用的一般框架允许在未来将分析扩展到更复杂的模型。对引起结核病的细菌进行基因分型有助于了解该疾病的进化和流行病学特征。基于可变数目串联重复（VNTR）基因座的分型方法正越来越多地被使用。这些基因座由重复单元组成，通过增加或减少这些重复的数量而突变。分子标记突变率的知识有助于对细菌分离物样品中观察到的遗传变异进行流行病学解释。很少有研究检查这些标记的突变率，迄今为止的估计差异很大。为了解决这个问题，我们开发了一个随机模型，这些标记的演变，然后估计其突变率使用近似贝叶斯计算。我们研究了突变过程的两种替代形式。观察到的数据来自于阿尔巴尼亚、伊朗、摩洛哥和委内瑞拉四个已发表的结核病细菌分离株数据集。我们发现，这些标志物有相当高的突变率相比，从以前的研究估计。
Variable numbers of tandem repeats (VNTR) typing is widely used for studying the bacterial cause of tuberculosis. Knowledge of the rate of mutation of VNTR loci facilitates the study of the evolution and epidemiology of Mycobacterium tuberculosis. Previous studies have applied population genetic models to estimate the mutation rate, leading to estimates varying widely from around to per locus per year. Resolving this issue using more detailed models and statistical methods would lead to improved inference in the molecular epidemiology of tuberculosis. Here, we use a model-based approach that incorporates two alternative forms of a stepwise mutation process for VNTR evolution within an epidemiological model of disease transmission. Using this model in a Bayesian framework we estimate the mutation rate of VNTR in M. tuberculosis from four published data sets of VNTR profiles from Albania, Iran, Morocco and Venezuela. In the first variant, the mutation rate increases linearly with respect to repeat numbers (linear model); in the second, the mutation rate is constant across repeat numbers (constant model). We find that under the constant model, the mean mutation rate per locus is (95% CI: ,)and under the linear model, the mean mutation rate per locus per repeat unit is (95% CI: ,). These new estimates represent a high rate of mutation at VNTR loci compared to previous estimates. To compare the two models we use posterior predictive checks to ascertain which of the two models is better able to reproduce the observed data. From this procedure we find that the linear model performs better than the constant model. The general framework we use allows the possibility of extending the analysis to more complex models in the future. Genetically typing the bacterium responsible for tuberculosis is useful for understanding the evolutionary and epidemiological characteristics of the disease. Typing methods based on variable number tandem repeat (VNTR) loci are increasingly being used. These loci, which are composed of repeated units, mutate by increasing or decreasing in the number of these repeats. Knowledge of the mutation rate of molecular markers facilitates the epidemiological interpretation of the observed genetic variation in a sample of bacterial isolates. Few studies have examined the rate of mutation at these markers and estimates to date have varied considerably. To address this problem we develop a stochastic model of evolution of these markers and then estimate their mutation rate using approximate Bayesian computation. We examine two alternative forms of the mutation process. The observed data are from four published data sets of tuberculosis bacterial isolates sampled in Albania, Iran, Morocco and Venezuela. We find that these markers have fairly high rates of mutation compared with estimates from previous studies.
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影响因子：
3.7
作者：
Abadía E;Sequera M;Ortega D;Méndez MV;Escalona A;Da Mata O;Izarra E;Rojas Y;Jaspe R;Motiwala AS;Alland D;de Waard J;Takiff HE
通讯作者：
Takiff HE
影响因子：
7
作者：
Kelkar, Yogeshwar D.;Tyekucheva, Svitlana;Makova, Kateryna D.
通讯作者：
Makova, Kateryna D.
影响因子：
9.4
作者：
Kamerbeek, J;Schouls, L;vanEmbden, J
通讯作者：
vanEmbden, J
影响因子：
1.4
作者：
Cornuet, JM;Beaumont, MA;Solignac, M
通讯作者：
Solignac, M
影响因子：
4.3
作者：
JACQUEZ, JA;SIMON, CP
通讯作者：
SIMON, CP