MOIRE: A software package for the estimation of allele frequencies and effective multiplicity of infection from polyallelic data

Abstract

AbstractMalaria 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 athttps://eppicenter.github.io/moire/.Author SummaryRecent advances in amplicon sequencing have enabled reliable high throughput generation of polyallelic data from malaria causing parasites. However, few methods exist to analyze these information rich genetic markers in the context of multiply infected individuals. To address this gap in analyzing data from polyclonal samples, we have developed MOIRE, an R package implementing a Bayesian approach to simultaneously estimate allele frequencies, multiplicity of infection (MOI), and within-host relatedness from noisy polyallelic data. We also introduce a new metric of within-host diversity, the effective MOI (eMOI), a continuous metric that integrates MOI and within-host relatedness into a single value that can be readily compared. We evaluated our method on simulated data from idealized high diversity genetic loci, as well as on recently developed high diversity amplicon sequencing panels. We also evaluated our method on real world data collected from 4 health districts across Namibia, demonstrating our ability to recover heterogeneity in multiplicity of infection as well as within-host relatedness, suggesting detectable differences in transmission dynamics. MOIRE serves as a new tool to better understand transmission dynamics and provide fundamental quantities such as population allele frequencies for downstream analysis.