Malaria that results from Plasmodium falciparum is among the most globally devastating human diseases. The principle vector of malaria, mosquitoes of the Anopheles gambiae species complex, are thus central targets for controlling the human health burden of Plasmodium. For nearly two decades, there have been large-scale, coordinated efforts to diminish mosquito populations, generally through spraying and insecticide treated bed nets. Indeed such control efforts have now led to a nearly 50% decrease in the rates of malaria infection in many parts of sub-Saharan Africa. At present, however, control efforts of A. gambiae are being threatened by evolutionary responses within mosquitos: A. gambiae populations have shown increases in insecticide resistance as well as behavioral adaptations that allow mosquitos to avoid spraying all together. Thus adaptation of mosquitos to the control efforts themselves is currently a risk to maintain the gains made in the fight against malaria. In this proposal we lay out an integrated population genomic approach for systematically identifying regions of the A. gambiae genome that are evolving adaptively in response to ongoing control efforts. Our approach centers upon state-of-the-art supervised machine learning techniques that we have recently introduced for finding the signatures of selective sweeps in genomes (Schrider and Kern, 2016), coupled with the large-scale population genomic datasets currently in production by the Ag1000G consortium.

Public Health Relevance

Malaria is a mosquito-borne infectious disease that has enormous impacts on human health globally. For the past 16 years, large gains have been made in decreasing the rate of malaria transmission through control of its mosquito vector Anopheles gambiae; unfortunately at present these control efforts are in danger of collapse due to the evolution of insecticide resistance in the mosquitos. We aim to discover the genomic targets of such resistance through the development of sophisticated population genomic approaches and their application to state-of- the-art genome sequence datasets from Anopheles gambiae.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM117241-04
Application #
9753261
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Janes, Daniel E
Project Start
2017-09-01
Project End
2021-07-31
Budget Start
2019-08-01
Budget End
2020-07-31
Support Year
4
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Oregon
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
City
Eugene
State
OR
Country
United States
Zip Code
97403
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Schrider, Daniel R; Kern, Andrew D (2018) Supervised Machine Learning for Population Genetics: A New Paradigm. Trends Genet 34:301-312
Kern, Andrew D; Hahn, Matthew W (2018) The Neutral Theory in Light of Natural Selection. Mol Biol Evol 35:1366-1371
Kern, Andrew D; Schrider, Daniel R (2018) diploS/HIC: An Updated Approach to Classifying Selective Sweeps. G3 (Bethesda) 8:1959-1970
Price, Nicholas; Moyers, Brook T; Lopez, Lua et al. (2018) Combining population genomics and fitness QTLs to identify the genetics of local adaptation in Arabidopsis thaliana. Proc Natl Acad Sci U S A 115:5028-5033
Anopheles gambiae 1000 Genomes Consortium; Data analysis group; Partner working group et al. (2017) Genetic diversity of the African malaria vector Anopheles gambiae. Nature 552:96-100