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.

Data availabilitySummary statistics generated by COVID-19 Host Genetics Initiative are available online (https://www.covid19hg.org/results/r6/). The analyses described here use the freeze 6 data. The COVID-19 Host Genetics Initiative continues to regularly release new data freezes. Summary statistics for samples from individuals of non-European ancestry are not currently available owing to the small individual sample sizes of these groups, but the results for 23 loci lead variants are reported in Supplementary Table 3. Individual-level data can be requested directly from the authors of the contributing studies, listed in Supplementary Table 1.

Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC’s performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics.

Impacts of introgressive hybridisation may range from genomic erosion and species collapse to rapid adaptation and speciation but opportunities to study these dynamics are rare. We investigated the extent, causes and consequences of a hybrid zone between Anopheles coluzzii and Anopheles gambiae in Guinea-Bissau, where high hybridisation rates appear to be stable at least since the 1990s. Anopheles gambiae was genetically partitioned into inland and coastal subpopulations, separated by a central region dominated by A. coluzzii. Surprisingly, whole genome sequencing revealed that the coastal region harbours a hybrid form characterised by an A. gambiae-like sex chromosome and massive introgression of A. coluzzii autosomal alleles. Local selection on chromosomal inversions may play a role in this process, suggesting potential for spatiotemporal stability of the coastal hybrid form and providing resilience against introgression of medically-important loci and traits, found to be more prevalent in inland A. gambiae.