Genome-based diagnostics for mapping, monitoring and management of insecticide resistance in major African malaria vectors

Malaria is a major cause of mortality and morbidity in Sub-Saharan Africa (SSA) and one of the biggest impediments to the economic development. The major method for controlling these malaria-transmitting mosquitoes is through the use of chemical insecticides but resistance has emerged and is a major threat to the recent reductions in both deaths and malaria infections. A major challenge facing malaria control program managers is knowing to what extent insecticide resistance is impacting control and when to take action eg by switching to a new intervention. In the first cycle of this award we exploited the advent of population genomic technologies to develop an improved understanding of the evolution and distribution of insecticide resistance mechanisms. In this proposal we describe how we will integrate this resistance marker discovery work with new functional genomic approaches and large vector control trials to demonstrate how genomic surveillance can be used to guide vector control. 

We will leverage our work on two large vector control trials in East Africa. In Uganda we embedded a cluster- randomised control trial (RCT) of long-lasting insecticidal nets (LLINs) with, and without, the synergist PBO into a countrywide distribution campaign. In Kenya together with KEMRI we are conducting an RCT of novel intervention, Attractive Targeted Sugar Baits. We will use whole genome sequencing of the three major malaria vectors Anopheles gambiae, An. funestus and An. arabiensis from these trial sites to identify genomic regions that are associated with insecticide resistance. We will then develop two contrasting models of the genetics of resistance. The first that assumes that we can accurately describe the likelihood of mosquito being insecticide resistant to by examining a small number of well characterised markers. This model capitalises on our recent developments in CRISPR/Cas9 transformation of Anopheles. The second model uses a polygenic score approach that requires a far larger number of markers, significantly-associated with resistance, but with no need for an understanding of causal mechanisms. By screening mosquito collections from the clusters within the RCTs and by, re-analysing the epidemiological data with the inclusion of the two resistance models, we will quantify the impact of resistance on the intervention efficacy. We will test whether the model based on a small number of genetic variants has sufficient predictive power for resistance monitoring or whether a larger number of loci provides superior predictive power. The former would aid widespread adoption of genetic surveillance of programmes. 

Project details

Enabling Technologies & Assays
Genetics and Genomics
Insecticide Resistance
Measurement of Transmission
Modeling
Surveillance

Feb 2016 — May 2027

$3.7M

Kenya
Uganda
United Kingdom

Malaria Molecular Surveillance (MMS)