Last Updated

08 May 2019

Genome-wide association studies to map genetic variation underlying mosquito susceptibility to human malaria


Targeting the mosquitoes that transmit the disease has proven to be the most effective strategy towards reducing the number of malaria cases. As mosquitoes tend to bite at night and indoors, the increased use of bednets in recent years has contributed to the decrease of malaria cases. However, some mosquitoes are showing evidence of increased biting in the daytime and outdoors where bednets offer no protection, so new methods of control must be developed.

Based on the fact that some mosquitoes are better malaria transmitters than others, this project aims to investigate the differences in the genetic make-up of these mosquitoes using a technology that intimately examines the mosquito genome. This comparison and follow-up tests will identify genes that are associated with mosquito capacity to carry the most deadly of the human malarias. Discovering the mosquito genes that have a big impact on malaria transmission is an essential step towards the promising strategy of developing drugs and vaccines that target the parasite while it is in the mosquito.

Principal Institution(s)

Principal Investigator
Rationale and Abstract

Understanding the molecular basis of mosquito susceptibility to Plasmodium infection and thus malaria transmission is fundamental to implementing vector-based malaria control strategies.

To date, genotypic interactions between Anopheles gambiae and Plasmodium falciparum, the deadliest of the human malarias, have only been examined in one study. This work detected that genotype-by-genotype (g*g) interactions were the major determinants of the outcome of infection such that no parasite genotype was able to infect all mosquito families, and likewise, no mosquito family was able to resist all parasite genotypes. This finding highlights the complexity of the interaction and motivates research particularly aimed at dissecting the molecular basis of g*g interactions: which molecules lead to different infection outcomes due to their genetic variation?

The molecular nature of g*g interactions and the role of mosquito species in P. falciparum susceptibility is crucial to examine because success of any vector control strategy will depend on the application to all of the malaria vectors. Molecules found to be consistently involved in infection outcomes become prime targets for transmission blocking interventions, such as drugs and vaccines. Targeting mosquitoes is an effective malaria transmission reduction strategy as evidenced by the historical and current successes of insecticides and bednets. However, insecticides and bednets will fail as mosquitoes evolve insecticide resistance and bednet avoidance. New vector-based malaria control strategies will depend on a deep understanding of the molecular interactions between vectors and parasites. The efforts described here will further our understanding of the connection between genotype and phenotype in a system that is of great human interest.

Study Design

The research team will use genome-wide association studies (GWAS) in the two major sub-Saharan African malaria vectors, A. gambiae and A. arabiensis, to map genetic variation associated with P. falciparum susceptibility. Lab mosquitoes will be infected with different combinations of recently collected Senegalese parasites that will constitutively express green or red fluorescent protein, allowing the infection success of individual parasite genotypes to be assessed when mosquitoes are fed mixtures of two parasite genotypes. Field mosquitoes will be fed parasites from local gametocyte carriers. GWAS will be carried out using a genotyping chip that interrogates 400,000 SNPs in the mosquito genome. By comparing susceptible to refractory mosquitoes, small regions of the mosquito genome associated with large impacts on the outcome of the infection will be identified. Candidate genes falling in these regions and potentially involved in mosquito susceptibility will be phenotypically characterised using RNA-interference gene knockdown techniques.


2012 Jan - 2018 Oct

Total Project Funding


Funding Details

Project Site