Genetic relatedness analysis reveals the cotransmission of genetically related Plasmodium falciparum parasites in Thiès, Senegal. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Wong, Wesley; Griggs, Allison; Daniels, Rachel; Schaffner, Stephen; Ndiaye, Daouda; Bei, Amy; Deme, Awa; MacInnis, Bronwyn; Volkman, Sarah; Hartl, Daniel; Neafsey, Daniel; Wirth, Dyann
• Is Part Of: Genome medicine. Volume 9:Issue 1(2017)
• Page Start: 1
• Page End: 12
• Publication Date: 2017-12
• Subjects: Malaria -- Genomics -- Epidemiology -- Relatedness -- Transmission -- Coinfection -- Polygenomic infection -- Cotransmission -- Superinfection; Genomics -- Periodicals; Medical genetics -- Periodicals; 616.042
• DOI: 10.1186/s13073-017-0398-0
• Languages: English
• ISSNs: 1756-994X

Abstract

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.