  The var genes of the human malaria parasite Plasmodium falciparum present a
challenge to population geneticists due to their extreme diversity, which is
generated by high rates of recombination. These genes encode a primary antigen
protein called PfEMP1, which is expressed on the surface of infected red blood
cells and elicits protective immune responses. Var gene sequences are
characterized by pronounced mosaicism, precluding the use of traditional
phylogenetic tools that require bifurcating tree-like evolutionary
relationships. We present a new method that identifies highly variable regions
(HVRs), and then maps each HVR to a complex network in which each sequence is a
node and two nodes are linked if they share an exact match of significant
length. Here, networks of var genes that recombine freely are expected to have
a uniformly random structure, but constraints on recombination will produce
network communities that we identify using a stochastic block model. We
validate this method on synthetic data, showing that it correctly recovers
populations of constrained recombination, before applying it to the Duffy
Binding Like-{\alpha} (DBL{\alpha}) domain of var genes. We find nine HVRs
whose network communities map in distinctive ways to known DBL{\alpha}
classifications and clinical phenotypes. We show that the recombinational
constraints of some HVRs are correlated, while others are independent. These
findings suggest that this micromodular structuring facilitates independent
evolutionary trajectories of neighboring mosaic regions, allowing the parasite
to retain protein function while generating enormous sequence diversity. Our
approach therefore offers a rigorous method for analyzing evolutionary
constraints in var genes, and is also flexible enough to be easily applied more
generally to any highly recombinant sequences.
