Abstract：
BACKGROUND:As 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. METHODS:Here, 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. RESULTS:We found that polygenomic infections can be composed of highly related parasites and that superinfection models drastically underestimate the relatedness of strains within polygenomic infections. CONCLUSIONS:Our 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.
journal_name
Genome Medjournal_title
Genome medicineauthors
Wong W,Griggs AD,Daniels RF,Schaffner SF,Ndiaye D,Bei AK,Deme AB,MacInnis B,Volkman SK,Hartl DL,Neafsey DE,Wirth DFdoi
10.1186/s13073-017-0398-0subject
Has Abstractpub_date
2017-01-24 00:00:00pages
5issue
1issn
1756-994Xpii
10.1186/s13073-017-0398-0journal_volume
9pub_type
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