Novel malaria control strategies using genetically engineered mosquitoes (GEMs) are on the horizon. Population modification is one approach wherein mosquitoes are engineered with genes rendering them refractory to the malaria parasite, Plasmodium falciparum, coupled with a low‐threshold, Cas9‐based gene drive. When released into a wild vector population, GEMs preferentially transmit these parasite‐blocking genes to their offspring, ultimately modifying a vector population into a nonvector one. Deploying this technology awaits ecologically contained field trial evaluations. Here, we consider a process for site selection, the first critical step in designing a trial. Our goal is to identify a site that maximizes prospects for success, minimizes risk, and serves as a fair, valid, and convincing test of efficacy and impacts of a GEM product intended for large‐scale deployment in Africa. We base site selection on geographic, geological, and biological, rather than social or legal, criteria. We recognize the latter as critically important but not as a first step in selecting a site. We propose physical islands as being the best candidates for a GEM field trial and present an evaluation of 22 African islands. We consider geographic and genetic isolation, biological complexity, island size, and topography and identify two island groups that satisfy key criteria for ideal GEM field trial sites.

Anopheles coluzzii is a major malaria vector throughout its distribution in west-central Africa. Here we present a whole-genome study of 142 specimens from nine countries in continental Africa and three islands in the Gulf of Guinea. This sample set covers a large part of this species’ geographic range. Our population genomic analyses included a description of the structure of mainland populations, island populations, and connectivity between them. Three genetic clusters are identified among mainland populations and genetic distances (FST) fits an isolation-by-distance model. Genomic analyses are applied to estimate the demographic history and ancestry for each island. Taken together with the unique biogeography and history of human occupation for each island, they present a coherent explanation underlying levels of genetic isolation between mainland and island populations. We discuss the relationship of our findings to the suitability of São Tomé and Príncipe islands as candidate sites for potential field trials of genetic-based malaria control strategies.

Using high-depth whole genome sequencing of F0 mating pairs and multiple individual F1 offspring, we estimated the nuclear mutation rate per generation in the malaria vectors Anopheles coluzzii and Anopheles stephensi by detecting de novo genetic mutations. A purpose-built computer program was employed to filter actual mutations from a deep background of superficially similar artifacts resulting from read misalignment. Performance of filtering parameters was determined using software-simulated mutations, and the resulting estimate of false negative rate was used to correct final mutation rate estimates. Spontaneous mutation rates by base substitution were estimated at 1.00 × 10−9 (95% confidence interval, 2.06 × 10−10—2.91 × 10−9) and 1.36 × 10−9 (95% confidence interval, 4.42 × 10−10—3.18 × 10−9) per site per generation in A. coluzzii and A. stephensi respectively. Although similar studies have been performed on other insect species including dipterans, this is the first study to empirically measure mutation rates in the important genus Anopheles, and thus provides an estimate of µ that will be of utility for comparative evolutionary genomics, as well as for population genetic analysis of malaria vector mosquito species.