Genetic relatedness analysis reveals the cotransmission of genetically related Plasmodium falciparum parasites in Thiès, Senegal

Abstract

BackgroundAs public health interventions drive parasite populations to elimination, genetic epidemiology models that incorporate population genomics can be powerful tools for evaluating the effectiveness of continued intervention. However, current genetic epidemiology models may not accurately simulate the population genetic profile of parasite populations, particularly with regard to polygenomic (multi-strain) infections. Current epidemiology models simulate polygenomic infections via superinfection (multiple mosquito bites), despite growing evidence that cotransmission (a single mosquito bite) may contribute to polygenomic infections.MethodsHere, we quantified the relatedness of strains within 31 polygenomic infections collected from patients in Thiès, Senegal using a hidden Markov model to measure the proportion of the genome that is inferred to be identical by descent.ResultsWe found that polygenomic infections can be composed of highly related parasites and that superinfection models drastically underestimate the relatedness of strains within polygenomic infections.ConclusionsOur findings suggest that cotransmission is a major contributor to polygenomic infections in Thiès, Senegal. The incorporation of cotransmission into existing genetic epidemiology models may enhance our ability to characterize and predict changes in population structure associated with reduced transmission intensities and the emergence of important phenotypes like drug resistance that threaten to undermine malaria elimination activities.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0398-0) contains supplementary material, which is available to authorized users.

Identity-by-descent analyses for measuring population dynamics and selection in recombining pathogens

“…The first probabilistic model for identifying IBD between pairs of haploid genomes was introduced by Daniels et al [9], who implemented a hidden Markov model (HMM) for IBD detection in Plasmodium. This model has since been used for in-depth analyses of population structure and disease transmission in malaria [8, 9], and was recently made available as the tool hmmIBD [13]. However, as it is only applicable to haploid genomes, it is limited to MIOI = 1 isolates only.…”

Evaluating the performance of malaria genomics for inferring changes in transmission intensity using transmission modelling

“…If the individual's pre-erythrocytic immunity was boosted in the last days no new parasite barcodes will be passed to the individual, otherwise more than one different asexual parasite barcode that will be observed in the ensuing gametocyte genotypes may be introduced during an infection event, representing cotransmission of genetically related parasites (if the mosquito was infected with more than one sporozoite genotype). The precise distribution describing the number of genotypes is unknown, 8 but the mean number of sporozoites within an inoculation event is well characterised by a geometric distribution with mean equal to 10. The geometric mean will then be used to estimate the proportion of sporozoites that are successful, , which yields the maximum number of successful sporozoites in an individual with no preerythrocytic immunity.…”

hmmIBD: software to infer pairwise identity by descent between haploid genotypes

“…Increasingly, IBD-based analyses are also being used to study haploid organisms such as the malaria parasite. Examples include studies of malaria disease transmission (Daniels et al, 2015), of malaria parasites within multiple-genotype infections (Wong et al, 2017), to aid surveillance of antimalarial resistance (Cerqueira et al, 2017), and to detect signals of selection (Henden,L. et al (2016).…”