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

Abstract Malaria in sub-Saharan Africa is transmitted by mosquitoes from the Anopheles genus. Efforts to control the spread of malaria have often focused on these vectors, but little is known about the demographic history of populations and species of Anopheles mosquitoes. Here, we adapt and apply an innovative generative deep learning algorithm to infer the joint evolutionary history of Anopheles gambiae populations sampled in Guinea and Burkina Faso. We further develop a 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 accurately captures 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.