The sustainability of malaria control in Africa is threatened by the rise of insecticide resistance in Anopheles mosquitoes that transmit the disease1. To gain a deeper understanding of how mosquito populations are evolving, we sequenced the genomes of 765 specimens of Anopheles gambiae and Anopheles coluzzii sampled from 15 locations across Africa, identifying over 50 million single nucleotide polymorphisms within the accessible genome. These data revealed complex population structure and patterns of gene flow, with evidence of ancient expansions, recent bottlenecks, and local variation in effective population size. Strong signals of recent selection were observed in insecticide resistance genes, with multiple sweeps spreading over large geographical distances and between species. The design of novel tools for mosquito control using gene drive will need to take account of high levels of genetic diversity in natural mosquito populations.

The widespread distribution and relapsing nature of Plasmodium vivax infection present major challenges for malaria elimination. To characterise the genetic diversity of this parasite within individual infections and across the population, we performed deep genome sequencing of >200 clinical samples collected across the Asia-Pacific region, and analysed data on >300,000 SNPs and 9 regions of the genome with large copy number variations. Individual infections showed complex patterns of genetic structure, with variation not only in the number of dominant clones but also in their level of relatedness and inbreeding. At the population level, we observed strong signals of recent evolutionary selection both in known drug resistance genes and at novel loci, and these varied markedly between geographical locations. These findings reveal a dynamic landscape of local evolutionary adaptation in P. vivax populations, and provide a foundation for genomic surveillance to guide effective strategies for control and elimination.

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

The sequencing of modern and ancient genomes from around the world has revolutionized our understanding of human history and evolution. However, the problem of how best to characterize ancestral relationships from the totality of human genomic variation remains unsolved. Here, we address this challenge with nonparametric methods that enable us to infer a unified genealogy of modern and ancient humans. This compact representation of multiple datasets explores the challenges of missing and erroneous data and uses ancient samples to constrain and date relationships. We demonstrate the power of the method to recover relationships between individuals and populations as well as to identify descendants of ancient samples. Finally, we introduce a simple nonparametric estimator of the geographical location of ancestors that recapitulates key events in human history.