Murphy Maxwell, Greenhouse Bryan
bioRxiv. 2024 Aug 2:2023.10.03.560769. doi: 10.1101/2023.10.03.560769.
Malaria parasite genetic data can provide insight into parasite phenotypes, evolution, and transmission. However, estimating key parameters such as allele frequencies, multiplicity of infection (MOI), and within-host relatedness from genetic data has been challenging, particularly in the presence of multiple related coinfecting strains. Existing methods often rely on single nucleotide polymorphism (SNP) data and do not account for within-host relatedness. In this study, we introduce a Bayesian approach called MOIRE (Multiplicity Of Infection and allele frequency REcovery), designed to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. Importantly, MOIRE is flexible in accommodating both polyallelic and SNP data, making it adaptable to diverse genotyping panels. We also introduce a novel metric, the effective MOI (eMOI), which integrates MOI and within-host relatedness, providing a robust and interpretable measure of genetic diversity. Using extensive simulations and real-world data from a malaria study in Namibia, we demonstrate the superior performance of MOIRE over naive estimation methods, accurately estimating MOI up to 7 with moderate sized panels of diverse loci (e.g. microhaplotypes). MOIRE also revealed substantial heterogeneity in population mean MOI and mean relatedness across health districts in Namibia, suggesting detectable differences in transmission dynamics. Notably, eMOI emerges as a portable metric of within-host diversity, facilitating meaningful comparisons across settings, even when allele frequencies or genotyping panels are different. MOIRE represents an important addition to the analysis toolkit for malaria population dynamics. Compared to existing software, MOIRE enhances the accuracy of parameter estimation and enables more comprehensive insights into within-host diversity and population structure. Additionally, MOIRE's adaptability to diverse data sources and potential for future improvements make it a valuable asset for research on malaria and other organisms, such as other eukaryotic pathogens. MOIRE is available as an R package at https://eppicenter.github.io/moire/.
疟原虫遗传数据能够为了解寄生虫表型、进化及传播情况提供线索。然而，从遗传数据中估算关键参数，如等位基因频率、感染复数（MOI）以及宿主内亲缘关系，一直颇具挑战，尤其是在存在多个相关的共感染菌株的情况下。现有方法通常依赖单核苷酸多态性（SNP）数据，且未考虑宿主内亲缘关系。在本研究中，我们引入了一种名为MOIRE（感染复数和等位基因频率恢复）的贝叶斯方法，旨在从存在实验误差的遗传数据中估算等位基因频率、MOI以及宿主内亲缘关系。重要的是，MOIRE在适应多等位基因和SNP数据方面具有灵活性，使其能够适用于各种基因分型平台。我们还引入了一种新的指标，即有效MOI（eMOI），它整合了MOI和宿主内亲缘关系，提供了一种稳健且可解释的遗传多样性度量。通过大量模拟以及来自纳米比亚一项疟疾研究的实际数据，我们证明了MOIRE相较于简单估算方法具有更优的性能，利用中等规模的不同位点（如微单倍型）面板能够准确估算高达7的MOI。MOIRE还揭示了纳米比亚各健康区人群平均MOI和平均亲缘关系存在显著异质性，这表明传播动态存在可检测到的差异。值得注意的是，eMOI成为了宿主内多样性的一个通用指标，有助于在不同环境下进行有意义的比较，即使等位基因频率或基因分型平台不同。MOIRE是疟疾种群动态分析工具包中的一项重要补充。与现有软件相比，MOIRE提高了参数估计的准确性，并能够更全面地洞察宿主内多样性和种群结构。此外，MOIRE对不同数据源的适应性以及未来改进的潜力使其成为疟疾及其他生物（如其他真核病原体）研究的宝贵资产。MOIRE可作为一个R包在https://eppicenter.github.io/moire/获取。
