coiaf: Directly estimating complexity of infection with allele frequencies.
coiaf: Directly estimating complexity of infection with allele frequencies.
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DOI：
10.1371/journal.pcbi.1010247
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发表时间：
2023-06
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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在疟疾中，个体经常感染不同的寄生虫菌株。感染的复杂性(COI)被定义为个体中遗传上不同的寄生虫菌株的数量。种群中平均COI的变化已被证明是传播强度变化的信息，目前已开发了许多概率似然和贝叶斯模型来估计COI。然而，基于杂合性或FWS的快速、直接的测量不能正确地代表COI。在这项工作中，我们提出了两种新的方法，这两种方法使用容易计算的度量来直接从等位基因频率数据估计COI。通过使用一个模拟框架，我们证明了我们的方法在计算上是有效的，并且与文献中的现有方法相比具有相当的准确性。通过敏感性分析，我们表征了寄生虫密度的分布、假设的测序深度和采样基因座的数量如何影响我们两种方法的偏差和准确性。使用我们开发的方法，我们进一步从恶性疟原虫测序数据中全局估计COI，并将结果与文献进行比较。我们显示，全球各大洲之间估计的COI有很大差异，疟疾流行率和COI之间的关系很弱。与快速发展的测序技术结合使用的计算模型正越来越多地被用于帮助监测工作和了解疟疾的流行病学动态。感染的复杂性(COI)是一项这样的重要指标，它间接地量化了传播水平。现有的“黄金标准”COI衡量标准依赖于复杂的概率似然和贝叶斯模型。作为另一种选择，我们开发了统计和软件包COIAF，它具有两种快速、直接的测量方法来估计个体(COI)中遗传上不同的寄生虫菌株的数量。我们的方法使用模拟数据进行了评估，随后与当前最先进的方法进行了比较，产生了类似的结果。最后，我们研究了COI在世界各地的分布情况，确定了各大洲之间COI的显著差异。因此，COIAF为快速鉴定多克隆感染提供了一个新的、有希望的框架。
In malaria, individuals are often infected with different parasite strains. The complexity of infection (COI) is defined as the number of genetically distinct parasite strains in an individual. Changes in the mean COI in a population have been shown to be informative of changes in transmission intensity with a number of probabilistic likelihood and Bayesian models now developed to estimate the COI. However, rapid, direct measures based on heterozygosity or FwS do not properly represent the COI. In this work, we present two new methods that use easily calculated measures to directly estimate the COI from allele frequency data. Using a simulation framework, we show that our methods are computationally efficient and comparably accurate to current approaches in the literature. Through a sensitivity analysis, we characterize how the distribution of parasite densities, the assumed sequencing depth, and the number of sampled loci impact the bias and accuracy of our two methods. Using our developed methods, we further estimate the COI globally from Plasmodium falciparum sequencing data and compare the results against the literature. We show significant differences in the estimated COI globally between continents and a weak relationship between malaria prevalence and COI. Computational models, used in conjunction with rapidly advancing sequencing technologies, are increasingly being used to help inform surveillance efforts and understand the epidemiological dynamics of malaria. One such important metric, the complexity of infection (COI), indirectly quantifies the level of transmission. Existing “gold-standard” COI measures rely on complex probabilistic likelihood and Bayesian models. As an alternative, we have developed the statistics and software package coiaf, which features two rapid, direct measures to estimate the number of genetically distinct parasite strains in an individual (the COI). Our methods were evaluated using simulated data and subsequently compared to current state-of-the-art methods, yielding comparable results. Lastly, we examined the distribution of the COI in several locations across the world, identifying significant differences in the COI between continents. coiaf, therefore, provides a new, promising framework for rapidly characterizing polyclonal infections.
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影响因子：
3
作者：
Andrade BB;Reis-Filho A;Barros AM;Souza-Neto SM;Nogueira LL;Fukutani KF;Camargo EP;Camargo LM;Barral A;Duarte A;Barral-Netto M
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通讯作者：
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