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Estimating the numbers of malaria infections in blood samples using high-resolution genotyping data

Ross, Amanda and Koepfli, Cristian and Li, Xiaohong and Schoepflin, Sonja and Siba, Peter and Mueller, Ivo and Felger, Ingrid and Smith, Thomas. (2012) Estimating the numbers of malaria infections in blood samples using high-resolution genotyping data. PLoS ONE, 7 (8). e42496.

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Official URL: http://edoc.unibas.ch/dok/A6094158

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Abstract

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.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Household Economics and Health Systems Research
UniBasel Contributors:Felger, Ingrid and Smith, Thomas A.
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Public Library of Science
e-ISSN:1932-6203
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:11 Feb 2019 14:36
Deposited On:19 Jul 2013 07:39

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