一种最大概率的方法,用SNP数据来估计单双型频率和流行率以及感染的多重性
Henri Christian Junior Tsoungui Obama1, Kristan Alexander Schneider1
1Department of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany.
Frontiers in epidemiology
|March 8, 2024
概括
这项研究引入了一个使用预期-最大化算法的统计框架,以准确估计疟疾单元型频率和流行率,即使是多次感染 (MOI). 该方法比分子疾病监测的传统方法提供了不那么有偏见的结果.
科学领域:
- 基因组流行病学基因组流行病学
- 分子疾病监测监测分子疾病的监测.
- 寄生虫学的寄生虫学
背景情况:
- 基因组方法可以实现标准化的分子疾病监测,这对于了解病原体传播动态至关重要.
- 疟疾寄生虫中的单核酸多态 (SNP) 条形码,如*Plasmodium falciparum*和*Plasmodium vivax*,有助于描述单核酸多态和传播模式.
- 感染多重性 (MOI),即在单一宿主中存在多种病原体变体,使得对遗传频率和流行率的准确估计变得复杂.
研究的目的:
- 开发一个统计框架,以获得哈普洛型频率和流行率的最大概率估计 (MLE),并考虑疟疾中的MOI.
- 为了比较使用所有遗传数据和仅使用明确数据得出的估计,突出了临时方法中的偏差.
- 为在传染病监测中分析SNP数据提供强大且数值稳定的方法.
主要方法:
- 引入使用预期-最大化 (EM) 算法来计算单型频率和流行率的MLE的统计框架.
- 由于模型参数的几何增加与遗传标记的数量,防止封闭式解决方案的MLE的数值导出.
- 使用所有遗传信息,并将其与仅从明确数据的估计值进行比较,推导出单 haplotype 的流行表达式.
主要成果:
- 使用EM算法开发的统计框架提供了数值稳定和准确的MLE,用于单种类型的频率和流行率,即使有MOI.
- 模拟表明,该方法对现实的样本大小和遗传位置的数量具有最小的偏差.
- 将其应用于来自喀麦隆的 * P. falciparum * 关于耐药性的数据集,证实了该方法的实用性.
结论:
- 拟议的统计框架有效地解决了MOI在分子监测中的挑战,产生了对哈普洛型频率和流行率的不太偏见的估计.
- 该方法比传统的临时方法显著改进,这些方法不考虑模糊的遗传信息.
- 这种方法广泛适用于任何使用类似遗传标记数据的传染病监测,并提供R脚本实现.
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