coiaf: Directly estimating complexity of infection with allele frequencies.
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 inten...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010247 |
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| author | Aris Paschalidis Oliver J Watson Ozkan Aydemir Robert Verity Jeffrey A Bailey |
| author_facet | Aris Paschalidis Oliver J Watson Ozkan Aydemir Robert Verity Jeffrey A Bailey |
| author_sort | Aris Paschalidis |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ba06723fb3be41d68905715e87d81af6 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-ba06723fb3be41d68905715e87d81af62025-08-20T02:23:18ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-06-01196e101024710.1371/journal.pcbi.1010247coiaf: Directly estimating complexity of infection with allele frequencies.Aris PaschalidisOliver J WatsonOzkan AydemirRobert VerityJeffrey A BaileyIn 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.https://doi.org/10.1371/journal.pcbi.1010247 |
| spellingShingle | Aris Paschalidis Oliver J Watson Ozkan Aydemir Robert Verity Jeffrey A Bailey coiaf: Directly estimating complexity of infection with allele frequencies. PLoS Computational Biology |
| title | coiaf: Directly estimating complexity of infection with allele frequencies. |
| title_full | coiaf: Directly estimating complexity of infection with allele frequencies. |
| title_fullStr | coiaf: Directly estimating complexity of infection with allele frequencies. |
| title_full_unstemmed | coiaf: Directly estimating complexity of infection with allele frequencies. |
| title_short | coiaf: Directly estimating complexity of infection with allele frequencies. |
| title_sort | coiaf directly estimating complexity of infection with allele frequencies |
| url | https://doi.org/10.1371/journal.pcbi.1010247 |
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