Malaria that results from Plasmodium falciparum is among the most globallydevastating human diseases. The principle vector of malaria, mosquitoes of theAnopheles gambiae species complex, are thus central targets for controlling thehuman health burden of Plasmodium. For nearly two decades, there have beenlarge-scale, coordinated efforts to diminish mosquito populations, generallythrough spraying and insecticide treated bed nets. Indeed such control effortshave now led to a nearly 50% decrease in the rates of malaria infection in manyparts of sub-Saharan Africa. At present, however, control efforts of A. gambiaeare being threatened by evolutionary responses within mosquitos: A. gambiaepopulations have shown increases in insecticide resistance as well as behavioraladaptations that allow mosquitos to avoid spraying all together. Thus adaptationof mosquitos to the control efforts themselves is currently a risk to maintain thegains made in the fight against malaria.In this proposal we lay out an integrated population genomic approach forsystematically identifying regions of the A. gambiae genome that are evolvingadaptively in response to ongoing control efforts. Our approach centers uponstate-of-the-art supervised machine learning techniques that we have recentlyintroduced for finding the signatures of selective sweeps in genomes (Schriderand Kern, 2016), coupled with the large-scale population genomic datasetscurrently in production by the Ag1000G consortium.
|Effective start/end date||9/1/17 → 7/31/21|
- National Institutes of Health (NIH)
Africa South of the Sahara