Plasmodium vivax Populations Are More Genetically Diverse and Less Structured than Sympatric Plasmodium falciparum Populations

Introduction The human malaria parasite, Plasmodium vivax, is proving more difficult to control and eliminate than Plasmodium falciparum in areas of co-transmission. Comparisons of the genetic structure of sympatric parasite populations may provide insight into the mechanisms underlying the resilience of P. vivax and can help guide malaria control programs. Methodology/Principle findings P. vivax isolates representing the parasite populations of four areas on the north coast of Papua New Guinea (PNG) were genotyped using microsatellite markers and compared with previously published microsatellite data from sympatric P. falciparum isolates. The genetic diversity of P. vivax (H-e = 0.83-0.85) was higher than that of P. falciparum (H-e = 0.64-0.77) in all four populations. Moderate levels of genetic differentiation were found between P. falciparum populations, even over relatively short distances (less than 50 km), with 21-28% private alleles and clear geospatial genetic clustering. Conversely, very low population differentiation was found between P. vivax catchments, with less than 5% private alleles and no genetic clustering observed. In addition, the effective population size of P. vivax (30353; 13043-69142) was larger than that of P. falciparum (18871; 8109-42986). Conclusions/Significance Despite comparably high prevalence, P. vivax had higher diversity and a panmictic population structure compared to sympatric P. falciparum populations, which were fragmented into subpopulations. The results suggest that in comparison to P. falciparum, P. vivax has had a long-term large effective population size, consistent with more intense and stable transmission, and limited impact of past control and elimination efforts. This underlines suggestions that more intensive and sustained interventions will be needed to control and eventually eliminate P. vivax. This research clearly demonstrates how population genetic analyses can reveal deeper insight into transmission patterns than traditional surveillance methods.