On the use of generative models for evolutionary inference of malaria vectors from genomic data

Malaria in sub-Saharan Africa is transmitted by mosquitoes, in particular the Anopheles gambiae complex. Efforts to control the spread of malaria have often focused on these vectors, but relatively little is known about the relationships between populations and species in the Anopheles complex. Here, we first quantify the genetic structure of mosquito populations in sub-Saharan Africa using unsupervised machine learning. We then adapt and apply an innovative generative deep learning algorithm to infer the joint evolutionary history of populations sampled in Guinea and Burkina Faso, West Africa. We further develop a novel model selection approach and discover that an evolutionary model with migration fits this pair of populations better than a model without post-split migration. For the migration model, we find that our method outperforms earlier work based on summary statistics, especially in capturing population genetic differentiation. These findings demonstrate that machine learning and generative models are a valuable direction for future understanding of the evolution of malaria vectors, including the joint inference of demography and natural selection. Understanding changes in population size, migration patterns, and adaptation in hosts, vectors, and pathogens will assist malaria control interventions, with the ultimate goal of predicting nuanced outcomes from insecticide resistance to population collapse.