Inferring malaria parasite population structure from serological networks

The malaria parasite Plasmodium falciparum is characterized by high levels of genetic diversity at antigenic loci involved in virulence and immune evasion. Knowledge of the population structure and dynamics of these genes is important for designing control programmes and understanding the acquisition of immunity to malaria; however, high rates of homologous and non-homologous recombination as well as complex patterns of expression within hosts have hindered attempts to elucidate these structures experimentally. Here, we analyse serological data from Kenya using a novel network technique to deconstruct the relationships between patients' immune responses to different parasite isolates. We show that particular population structures and expression patterns produce distinctive signatures within serological networks of parasite recognition, which can be used to discriminate between competing hypotheses regarding the organization of these genes. Our analysis suggests that different levels of immune selection occur within different groups of the same multigene family leading to mixed population structures.