The genetics of behavioral resistance in Anopheles gambiae (M)

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DESCRIPTION (provided by applicant): Malaria control depends crucially on two widespread control methods; long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS). Both methods rely on the indoor application of insecticides, and successfully target vectors that feed and rest indoors. The efficacy of LLINs and IRS is threatened by insecticide resistance and changes in the mosquito's behavior. This behavioral resistance involves vectors shifting towards outdoor biting and resting. While the biological basis of insecticide resistance is well-studied, nothing is known about the genetic basis of behavioral resistance. The malaria vector Anopheles gambiae (M) on Bioko Island has been subjected to IRS under the Bioko Island Malaria Control Project (BIMCP) since 2004. This has resulted in a shift of this mosquito's behavior towards outdoor host seeking. Because the bendiocarb insecticide used on Bioko has low repellency, and because the outdoor biting rate was the same one vs. five months following spraying, we hypothesize that the behavioral resistance of this population has a strong genetic basis. We will use a DNA pooling and whole genome sequencing approach to map loci involved in outdoor biting in the An. gambiae (M) population on Bioko Island. Outdoor and indoor biting mosquitoes were collected using human landing catches as part of the vector monitoring of the BIMCP from 2009 to 2013. Outdoor and indoor samples from 2009 and 2013 will be pooled and sequenced. This will produce > 2,000,000 SNPs that will be used to identify alleles that occur at significantly higher frequency in the outdoor vs the indoor biting mosquitoes. We have developed a novel simulation approach for establishing the false discovery rate (=significance threshold). This approach will identify loci responsible for variation in host seeking behavior in post-control populations. Preliminary results show that we will be able to identify alleles that differ in frequency by 0.069. In addition, we will identify loci that increased in frequency followng the vector control by comparing pre-intervention samples collected in 2004 to post-control 2009 and 2013 samples. Simulations that incorporate genetic drift and sampling effects will provide the false discovery rate, allowing the identification of loci under positive selection. These two approaches will provide a list of candidate loci underlying behavioral resistance in a major malaria vector. This will be the first study into the biological basis of behavioral resistance and takes the first step towards developing the tools to monitor or counteract this major threat to our vector control tools.