coiaf: Directly estimating complexity of infection with allele frequencies
File(s)
Published version
Author(s)
Paschalidis, Aris
Watson, Oliver
Aydemir, Ozkan
Verity, Robert
Bailey, Jeffrey
Type
Journal Article
Abstract
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.
Editor(s)
Larremore, Daniel B
Date Issued
2023-06-09
Date Acceptance
2023-05-01
Citation
PLoS Computational Biology, 2023, 19 (6)
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS Computational Biology
Volume
19
Issue
6
Copyright Statement
© 2023 Paschalidis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/37294835
PII: PCOMPBIOL-D-22-00780
Subjects
ARTEMISININ
Biochemical Research Methods
Biochemistry & Molecular Biology
CHALLENGES
GENETIC DIVERSITY
Life Sciences & Biomedicine
Mathematical & Computational Biology
MULTIPLICITY
PLASMODIUM-FALCIPARUM
Science & Technology
Publication Status
Published
Coverage Spatial
United States
Article Number
e1010247
Date Publish Online
2023-06-09