Neutral vs. non-neutral genetic footprints of Plasmodium falciparum multiclonal infections

At a time when effective tools for monitoring malaria control and eradication efforts are crucial, the increasing availability of molecular data motivates their application to epidemiology. The multiplicity of infection (MOI), defined as the number of genetically distinct parasite strains co-infecting a host, is one key epidemiological parameter for evaluating malaria interventions. Estimating MOI remains a challenge for high-transmission settings where individuals typically carry multiple co-occurring infections. Several quantitative approaches have been developed to estimate MOI, including two cost-effective ones relying on molecular data: i) THE REAL McCOIL method is based on putatively neutral single nucleotide polymorphism loci, and ii) the var coding method is a fingerprinting approach that relies on the diversity and limited repertoire overlap of the var multigene family encoding the major Plasmodium falciparum blood-stage antigen PfEMP1 and is therefore under selection. In this study, we assess the robustness of the MOI estimates generated with these two approaches by simulating P. falciparum malaria dynamics under three transmission conditions using an extension of a previously developed stochastic agent-based model. We demonstrate that these approaches are complementary and best considered across distinct transmission intensities. While var coding can underestimate MOI, it allows robust estimation, especially under high-transmission where repertoire overlap is extremely limited from frequency-dependent selection. In contrast, THE REAL McCOIL often considerably overestimates MOI, but still provides reasonable estimates for low- and moderate-transmission. As many countries pursue malaria elimination targets, defining the most suitable approach to estimate MOI based on sample size and local transmission intensity is highly recommended for monitoring the impact of intervention programs. Author Summary Despite control and elimination efforts, malaria continues to be a serious public health threat especially in high-transmission regions. Molecular tools for evaluating these efforts include those seeking to estimate multiplicity (or complexity) of infection (MOI), the number of genetically distinct parasite strains co-infecting a host, a key epidemiological parameter. MOI estimation remains challenging in high-transmission regions where hosts typically carry multiple co-infections by Plasmodium falciparum . THE REAL McCOIL and the var coding are two cost-effective methods relying on distinct parts of the parasite genome, those respectively under neutrality and selection. The more recent var coding approach relies on the var multigene family encoding for the major blood-stage antigen and contributing to a complex immune evasion strategy of the parasite. We compare the performance of the two methods by simulating disease dynamics under different transmission intensities with a stochastic agent-based model tracking infection by different parasite genomes and immune memory in individual hosts, then sampling resulting infections to estimate MOI. Although THE REAL McCOIL provides reasonable estimates for low- and moderate-transmission, var coding allows more robust estimates especially under high-transmission. Defining the most suitable approach to estimate MOI based on local transmission intensity is highly recommended for hyper-diverse pathogens such as malaria.