A Comparison of Methods to Detect and Quantify the Markers of Antimalarial Drug Resistance

Hastings, Ian M.; Nsanzabana, Christian; Smith, T.

doi:10.4269/ajtmh.2010.10-0072

Cited by 38 publications

(61 citation statements)

References 22 publications

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“…Using prevalence data, various counting strategies have been used to estimate the frequency of the marker in the parasite population [22,23]. To assess these approaches, the frequency estimates from the statistical model were compared with those from commonly used counting methods using the data from Uganda (see Section 3, Additional file 2 for full details).…”

Section: Resultsmentioning

confidence: 99%

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Estimation of malaria haplotype and genotype frequencies: a statistical approach to overcome the challenge associated with multiclonal infections

Taylor

Flegg

Nsobya

et al. 2014

Malar J

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BackgroundReliable measures of anti-malarial resistance are crucial for malaria control. Resistance is typically a complex trait: multiple mutations in a single parasite (a haplotype or genotype) are necessary for elaboration of the resistant phenotype. The frequency of a genetic motif (proportion of parasite clones in the parasite population that carry a given allele, haplotype or genotype) is a useful measure of resistance. In areas of high endemicity, malaria patients generally harbour multiple parasite clones; they have multiplicities of infection (MOIs) greater than one. However, most standard experimental procedures only allow measurement of marker prevalence (proportion of patient blood samples that test positive for a given mutation or combination of mutations), not frequency. It is misleading to compare marker prevalence between sites that have different mean MOIs; frequencies are required instead.MethodsA Bayesian statistical model was developed to estimate Plasmodium falciparum genetic motif frequencies from prevalence data collected in the field. To assess model performance and computational speed, a detailed simulation study was implemented. Application of the model was tested using datasets from five sites in Uganda. The datasets included prevalence data on markers of resistance to sulphadoxine-pyrimethamine and an average MOI estimate for each study site.ResultsThe simulation study revealed that the genetic motif frequencies that were estimated using the model were more accurate and precise than conventional estimates based on direct counting. Importantly, the model did not require measurements of the MOI in each patient; it used the average MOI in the patient population. Furthermore, if a dataset included partially genotyped patient blood samples, the model imputed the data that were missing. Using the model and the Ugandan data, genotype frequencies were estimated and four biologically relevant genotypes were identified.ConclusionsThe model allows fast, accurate, reliable estimation of the frequency of genetic motifs associated with resistance to anti-malarials using prevalence data collected from malaria patients. The model does not require per-patient MOI measurements and can easily analyse data from five markers. The model will be a valuable tool for monitoring markers of anti-malarial drug resistance, including markers of resistance to artemisinin derivatives and partner drugs.

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Section: Resultsmentioning

confidence: 99%

“…Additional counting methods to estimate gene frequency also exist [22,23]. These include discarding all discernibly multiclonal blood samples, which can lead to large losses of data; or discounting minority alleles at mixed SNPs.…”

Section: Introductionmentioning

confidence: 99%

Estimation of malaria haplotype and genotype frequencies: a statistical approach to overcome the challenge associated with multiclonal infections

Taylor

Flegg

Nsobya

et al. 2014

Malar J

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“…(g) Frequency of resistance We employ two measures of the frequency of resistance in complex infections: (i) the fraction of the population that is infected by at least one resistant strain, 1 À P i X i;0 ; and (ii) the fraction of the parasite load that is resistant, P ij j=ði þ jÞX i ; j: The former is important because it tracks nearly exactly with the frequency that a treated clinical infection is resistant, which is the measure by which resistance is often tracked in a population [45,46]. On the other hand, the latter is particularly useful when discussing the role that asymptomatic infections play in the spread of resistance.…”

Section: (E) Equationsmentioning

confidence: 99%

Superinfection and the evolution of resistance to antimalarial drugs

et al. 2012

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A major issue in the control of malaria is the evolution of drug resistance. Ecological theory has demonstrated that pathogen superinfection and the resulting within-host competition influences the evolution of specific traits. Individuals infected with Plasmodium falciparum are consistently infected by multiple parasites; however, while this probably alters the dynamics of resistance evolution, there are few robust mathematical models examining this issue. We developed a general theory for modelling the evolution of resistance with host superinfection and examine: (i) the effect of transmission intensity on the rate of resistance evolution; (ii) the importance of different biological costs of resistance; and (iii) the best measure of the frequency of resistance. We find that within-host competition retards the ability and slows the rate at which drug-resistant parasites invade, particularly as the transmission rate increases. We also find that biological costs of resistance that reduce transmission are less important than reductions in the duration of drugresistant infections. Lastly, we find that random sampling of the population for resistant parasites is likely to significantly underestimate the frequency of resistance. Considering superinfection in mathematical models of antimalarial drug resistance may thus be important for generating accurate predictions of interventions to contain resistance.

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“…Because of these limitations, it has not been possible to study within-host evolution of human malaria parasites by using these methods. To study the competition and selection between variants in a mixed malaria infection, new tools are required that are sensitive to minority populations and quantitative for relative parasite population sizes in the host (17). Nextgeneration sequencing techniques, such as massively parallel pyrosequencing (MPP), provide the increased resolution of in-host diversity necessary for more thorough ecological studies of inhost selection.…”

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confidence: 99%

Exposing malaria in-host diversity and estimating population diversity by capture-recapture using massively parallel pyrosequencing

Juliano

Porter

Mwapasa

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

116

126

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Malaria infections commonly contain multiple genetically distinct variants. Mathematical and animal models suggest that interactions among these variants have a profound impact on the emergence of drug resistance. However, methods currently used for quantifying parasite diversity in individual infections are insensitive to lowabundance variants and are not quantitative for variant population sizes. To more completely describe the in-host complexity and ecology of malaria infections, we used massively parallel pyrosequencing to characterize malaria parasite diversity in the infections of a group of patients. By individually sequencing single strands of DNA in a complex mixture, this technique can quantify uncommon variants in mixed infections. The in-host diversity revealed by this method far exceeded that described by currently recommended genotyping methods, with as many as sixfold more variants per infection. In addition, in paired pre-and posttreatment samples, we show a complex milieu of parasites, including variants likely upselected and down-selected by drug therapy. As with all surveys of diversity, sampling limitations prevent full discovery and differences in sampling effort can confound comparisons among samples, hosts, and populations. Here, we used ecological approaches of species accumulation curves and capture-recapture to estimate the number of variants we failed to detect in the population, and show that these methods enable comparisons of diversity before and after treatment, as well as between malaria populations. The combination of ecological statistics and massively parallel pyrosequencing provides a powerful tool for studying the evolution of drug resistance and the in-host ecology of malaria infections.Plasmodium falciparum | next generation sequencing

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A Comparison of Methods to Detect and Quantify the Markers of Antimalarial Drug Resistance

Cited by 38 publications

References 22 publications

Estimation of malaria haplotype and genotype frequencies: a statistical approach to overcome the challenge associated with multiclonal infections

Estimation of malaria haplotype and genotype frequencies: a statistical approach to overcome the challenge associated with multiclonal infections

Superinfection and the evolution of resistance to antimalarial drugs

Exposing malaria in-host diversity and estimating population diversity by capture-recapture using massively parallel pyrosequencing

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