India-EMBO Lecture Course: understanding malaria from molecular epidemiology, population genetics, and evolutionary perspectives

Sinha, Abhinav; Kar, S. K.; Deora, Nimita; Dash, Manoswini; Tiwari, Aparna; Kori, Lokesh; Schneider, Kristan A.; Ranford-Cartwright, Lisa

doi:10.1016/j.pt.2023.02.010

Cited by 4 publications

(9 citation statements)

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“…Especially, for diseases, which are endemic and not considered a sudden health emergency, for which asymptomatic infections are common, and/or vector-borne diseases, tracing transmission events directly is almost impossible. Malaria is a typical example of a disease, for which the benefits of molecular surveillance are apparent because only limited information can be retrieved from pure epidemiological surveillance [ 22 ]. In fact, some of the most urgent problems in malaria have genetic causes or need fine-grained information, which can only be mined from genetic/molecular data.…”

Section: Discussionmentioning

confidence: 99%

“…In the context of malaria elimination, monitoring routes of transmission is also highly relevant. Concerning transmission, MOI is an important quantity, which scales with transmission intensities and which is commonly monitored in malaria [ 2 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…In practice, such a marker can be a SNP, STR, or micro haplotype. Such methods are commonly used in malaria molecular surveillance [ 22 ]. Estimating the frequency distribution of single markers is often preferential to full haplotype-based methods.…”

Section: Discussionmentioning

confidence: 99%

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Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data

Hashemi,

Schneider

2024

PLoS ONE

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Background Molecular surveillance of infectious diseases allows the monitoring of pathogens beyond the granularity of traditional epidemiological approaches and is well-established for some of the most relevant infectious diseases such as malaria. The presence of genetically distinct pathogenic variants within an infection, referred to as multiplicity of infection (MOI) or complexity of infection (COI) is common in malaria and similar infectious diseases. It is an important metric that scales with transmission intensities, potentially affects the clinical pathogenesis, and a confounding factor when monitoring the frequency and prevalence of pathogenic variants. Several statistical methods exist to estimate MOI and the frequency distribution of pathogen variants. However, a common problem is the quality of the underlying molecular data. If molecular assays fail not randomly, it is likely to underestimate MOI and the prevalence of pathogen variants. Methods and findings A statistical model is introduced, which explicitly addresses data quality, by assuming a probability by which a pathogen variant remains undetected in a molecular assay. This is different from the assumption of missing at random, for which a molecular assay either performs perfectly or fails completely. The method is applicable to a single molecular marker and allows to estimate allele-frequency spectra, the distribution of MOI, and the probability of variants to remain undetected (incomplete information). Based on the statistical model, expressions for the prevalence of pathogen variants are derived and differences between frequency and prevalence are discussed. The usual desirable asymptotic properties of the maximum-likelihood estimator (MLE) are established by rewriting the model into an exponential family. The MLE has promising finite sample properties in terms of bias and variance. The covariance matrix of the estimator is close to the Cramér-Rao lower bound (inverse Fisher information). Importantly, the estimator’s variance is larger than that of a similar method which disregards incomplete information, but its bias is smaller. Conclusions Although the model introduced here has convenient properties, in terms of the mean squared error it does not outperform a simple standard method that neglects missing information. Thus, the new method is recommendable only for data sets in which the molecular assays produced poor-quality results. This will be particularly true if the model is extended to accommodate information from multiple molecular markers at the same time, and incomplete information at one or more markers leads to a strong depletion of sample size.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data

Hashemi,

Schneider

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Especially, for diseases which are endemic and not considered a sudden health emergency, for which asymptomatic infections are common, and/or vector-borne diseases, tracing transmission events directly is almost impossible. Malaria is a typical example of a disease, for which the benefits of molecular surveillance are apparent because only limited information can be retrieved from pure epidemiological surveillance [19]. In fact, some of the most urgent problems in malaria have genetic causes or need the fine-grained information, which can only be mined from genetic/molecular data.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data

Hashemi

Schneider

2023

Preprint

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Background: Molecular surveillance of infectious diseases allows the monitoring of pathogens beyond the granularity of traditional epidemiological approaches and is well-established for some of the most relevant infectious diseases such as malaria. The presence of genetically distinct pathogenic variants within an infection, referred to as multiplicity of infection (MOI) or complexity of infection (COI) is common in malaria and similar infectious diseases. It is an important metric that scales with transmission intensities, potentially affects the clinical pathogenesis, and a confounding factor when monitoring the frequency and prevalence of pathogenic variants. Several statistical methods exist to estimate MOI and the frequency distribution of pathogen variants. However, a common problem is the quality of the underlying molecular data. If molecular assays fail not randomly, it is likely to underestimate MOI and the prevalence of pathogen variants. Methods and findings: A statistical model is introduced which explicitly addresses data quality, by assuming a probability by which a pathogen variant remains undetected in a molecular assay. This is different from the assumption of missing at random, for which a molecular assay either performs perfectly or fails completely. The method is applicable to a single molecular marker and allows to estimate allele-frequency spectra, the distribution of MOI, and the probability of variants to remain undetected (incomplete information).Based on the statistical model, expressions for the prevalence of pathogen variants are derived and differences between frequency and prevalence are discussed. The usual desirable asymptotic properties of the maximum-likelihood estimator (MLE) are established by rewriting the model into an exponential family. The MLE has promising finite sample properties in terms of bias and variance. The covariance matrix of the estimator is close to the Cramér-Rao lower bound (inverse Fisher information). Importantly, the estimator’s variance is larger than that of a similar method which disregards incomplete information, but its bias is smaller. Conclusions: Although the model introduced here has convenient properties, in terms of the mean squared error it does not outperform a simple standard method that neglects missing information. Thus, the new method is recommendable only for data sets in which the molecular assays produced poor quality results. This will be particularly true if the model is extended to accommodate information from multiple molecular markers at the same time, and incomplete information at one or more markers leads to strong depletion of sample size.

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Estimating multiplicity of infection, haplotype frequencies, and linkage disequilibria from multi-allelic markers for molecular disease surveillance

Tsoungui Obama,

Schneider

2023

Preprint

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Molecular/genetic methods are becoming increasingly important for surveillance of diseases like malaria. Such methods allow to monitor routes of disease transmission or the origin and spread of variants associated with drug resistance. A confounding factor in molecular disease surveillance is the presence of multiple distinct variants in the same infection (multiplicity of infection – MOI), which leads to ambiguity when reconstructing which pathogenic variants are present in an infection. Heuristic approaches often ignore ambiguous infections, which leads to biased results. To avoid such bias, we introduce a statistical framework to estimate haplotype frequencies alongside MOI from a pair of multi-allelic molecular markers. Estimates are based on maximum-likelihood using the expectation-maximization (EM)-algorithm. The estimates can be used as plug-ins to construct pairwise linkage disequilibrium (LD) maps. The finite-sample properties of the proposed method are studied by systematic numerical simulations. These reveal that the EM-algorithm is a numerically stable method in our case and that the proposed method is accurate (little bias) and precise (small variance) for a reasonable sample size. In fact, the results suggest that the estimator is asymptotically unbiased. Furthermore, the method is appropriate to estimate LD (byD′, r2,Q*, or conditional asymmetric LD). Furthermore, as an illustration, we apply the new method to a previously-published dataset from Cameroon concerning sulfadoxine-pyrimethamine (SP) resistance. The results are in accordance with the SP drug pressure at the time and the observed spread of resistance in the country, yielding further evidence for the adequacy of the proposed method. The method is particularly useful for deriving LD maps from data with many ambiguous observations due to MOI. Importantly, the method per se is not restricted to malaria, but applicable to any disease with a similar transmission pattern. The method and several extensions are implemented in an easy-to-use R script.Author summaryAdvances in genetics render molecular disease surveillance increasingly popular. Unlike traditional incidence-based epidemiological data, genetic information provides fine-grained resolution, which allows monitoring and reconstructing routes of transmission, the spread of drug resistance, etc. Molecular surveillance is particularly popular in highly relevant diseases such as malaria. The presence of multiple distinct pathogenic variants within one infection, i.e., multiplicity of infection (MOI), is a confounding factor hampering the analysis of molecular data in the context of disease surveillance. Namely, due to MOI ambiguity concerning the pathogenic variants being present in mixed-clone infections arise. These are often disregarded by heuristic approaches to molecular disease surveillance and lead to biased results. To avoid such bias we introduce a method to estimate the distribution of MOI and frequencies of pathogenic variants based on a concise probabilistic model. The method is designed for two multi-allelic genetic markers, which is the appropriate genetic architecture to derive pairwise linkage-disequilibrium maps, which are informative on population structure or evolutionary processes, such as the spread of drug resistance. We validate the appropriateness of our method by numerical simulations and apply it to a malaria dataset from Cameroon, concerning sulfadoxine-pyrimethamine resistance, the drug used for intermittent preventive treatment during pregnancy.

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India-EMBO Lecture Course: understanding malaria from molecular epidemiology, population genetics, and evolutionary perspectives

Cited by 4 publications

References 0 publications

Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data

Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data

Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data

Estimating multiplicity of infection, haplotype frequencies, and linkage disequilibria from multi-allelic markers for molecular disease surveillance

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