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Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data

People living in endemic areas often habour several malaria infections at once. High-resolution genotyping can distinguish between infections by detecting the presence of different alleles at a polymorphic locus. However the number of infections may not be accurately counted since parasites from mul...

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Autores principales: Ross, Amanda, Koepfli, Cristian, Li, Xiaohong, Schoepflin, Sonja, Siba, Peter, Mueller, Ivo, Felger, Ingrid, Smith, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3430681/
https://www.ncbi.nlm.nih.gov/pubmed/22952595
http://dx.doi.org/10.1371/journal.pone.0042496
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author Ross, Amanda
Koepfli, Cristian
Li, Xiaohong
Schoepflin, Sonja
Siba, Peter
Mueller, Ivo
Felger, Ingrid
Smith, Thomas
author_facet Ross, Amanda
Koepfli, Cristian
Li, Xiaohong
Schoepflin, Sonja
Siba, Peter
Mueller, Ivo
Felger, Ingrid
Smith, Thomas
author_sort Ross, Amanda
collection PubMed
description People living in endemic areas often habour several malaria infections at once. High-resolution genotyping can distinguish between infections by detecting the presence of different alleles at a polymorphic locus. However the number of infections may not be accurately counted since parasites from multiple infections may carry the same allele. We use simulation to determine the circumstances under which the number of observed genotypes are likely to be substantially less than the number of infections present and investigate the performance of two methods for estimating the numbers of infections from high-resolution genotyping data. The simulations suggest that the problem is not substantial in most datasets: the disparity between the mean numbers of infections and of observed genotypes was small when there was 20 or more alleles, 20 or more blood samples, a mean number of infections of 6 or less and where the frequency of the most common allele was no greater than 20%. The issue of multiple infections carrying the same allele is unlikely to be a major component of the errors in PCR-based genotyping. Simulations also showed that, with heterogeneity in allele frequencies, the observed frequencies are not a good approximation of the true allele frequencies. The first method that we proposed to estimate the numbers of infections assumes that they are a good approximation and hence did poorly in the presence of heterogeneity. In contrast, the second method by Li et al estimates both the numbers of infections and the true allele frequencies simultaneously and produced accurate estimates of the mean number of infections.
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spelling pubmed-34306812012-09-05 Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data Ross, Amanda Koepfli, Cristian Li, Xiaohong Schoepflin, Sonja Siba, Peter Mueller, Ivo Felger, Ingrid Smith, Thomas PLoS One Research Article People living in endemic areas often habour several malaria infections at once. High-resolution genotyping can distinguish between infections by detecting the presence of different alleles at a polymorphic locus. However the number of infections may not be accurately counted since parasites from multiple infections may carry the same allele. We use simulation to determine the circumstances under which the number of observed genotypes are likely to be substantially less than the number of infections present and investigate the performance of two methods for estimating the numbers of infections from high-resolution genotyping data. The simulations suggest that the problem is not substantial in most datasets: the disparity between the mean numbers of infections and of observed genotypes was small when there was 20 or more alleles, 20 or more blood samples, a mean number of infections of 6 or less and where the frequency of the most common allele was no greater than 20%. The issue of multiple infections carrying the same allele is unlikely to be a major component of the errors in PCR-based genotyping. Simulations also showed that, with heterogeneity in allele frequencies, the observed frequencies are not a good approximation of the true allele frequencies. The first method that we proposed to estimate the numbers of infections assumes that they are a good approximation and hence did poorly in the presence of heterogeneity. In contrast, the second method by Li et al estimates both the numbers of infections and the true allele frequencies simultaneously and produced accurate estimates of the mean number of infections. Public Library of Science 2012-08-29 /pmc/articles/PMC3430681/ /pubmed/22952595 http://dx.doi.org/10.1371/journal.pone.0042496 Text en © 2012 Ross et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
spellingShingle Research Article
Ross, Amanda
Koepfli, Cristian
Li, Xiaohong
Schoepflin, Sonja
Siba, Peter
Mueller, Ivo
Felger, Ingrid
Smith, Thomas
Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data
title Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data
title_full Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data
title_fullStr Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data
title_full_unstemmed Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data
title_short Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data
title_sort estimating the numbers of malaria infections in blood samples using high-resolution genotyping data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3430681/
https://www.ncbi.nlm.nih.gov/pubmed/22952595
http://dx.doi.org/10.1371/journal.pone.0042496
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