Biology subjects

Midega, J.

Publications and source records attributed to Midega, J..

5 recordsLinked to original sources

bioRxiv2026

Genomic signatures of selection in Anopheles funestus reveal shared and population-specific adaptive variation across African populations

Insecticide resistance in Anopheles funestus threatens malaria vector control across sub-Saharan Africa, yet genomic signatures of selection across geographically structured populations remain poorly understood. We analysed whole-genome sequence data from 635 An. funestus mosquitoes from West (Senegal, Guinea, Nigeria), Central (Cameroon), and East Africa (Kenya) to characterise population structure and identify targets of recent positive selection. Population genomic analyses revealed strong differentiation between East African and West/Central African populations, with finer-scale structuring within West Africa. Genome-wide selection scans using H12 identified sweep regions on chromosome arms 2RL and 3RL, overlapping a cytochrome P450 cluster and the gamma-aminobutyric acid (GABA) receptor locus respectively. iSAFE prioritised candidate variants within these sweeps: non-synonymous substitutions in CYP6A14 were identified at high frequencies in Guinea, Nigeria, and Cameroon, while population-restricted variants implicated octopamine receptor genes on 2RL and the GABA receptor on 3RL. Diplotype clustering and copy number variation analyses confirmed the causal role of candidate variants, embedded within extended haplotypes of reduced heterozygosity consistent with recent positive selection. These findings demonstrate that adaptive evolution in An. funestus reflects both shared and population-specific selective processes shaped by geography and ecological context. Whereas selection on detoxification pathways appears widespread, localised signals in neuromodulatory loci, including the GABA receptor and octopamine-related genes, reveal that biological systems beyond metabolic resistance contribute to mosquito adaptation. The convergence of selective signals across these gene classes highlights neuromodulatory pathways as potential complementary targets for next-generation vector control strategies.

genomics↗

bioRxiv2023

Spatially explicit sampling frameworks to identify regions of increased mosquito abundance

Vector control interventions often lack comprehensive information on vector population distribution and dynamics. This knowledge gap poses challenges in targeting interventions effectively, especially in areas with heterogeneous transmission and where complementary vector control tools may be required to achieve sustained impact on disease transmission. In this study, we implemented a spatially explicit sampling framework for improved vector surveillance in coastal Kenya. Our stratified lattice with close pair sampling design aimed to characterise the vector dynamics of the primary malaria-transmitting species in the area and assess the ecotype classifications effectiveness at identifying clear population patterns. The study collected 3,621 mosquitoes, with An. funestus s.l. being the most abundant malaria vector. The inclusion of the ecotype classification significantly improved spatial abundance model estimates for An. gambiae and Culex spp. Wetlands, topographic wetness index, and proximity to rivers were associated with increased mosquito abundance. Spatial modelling revealed high abundance regions near the Galana-Sabaki River. Our study demonstrates the applicability of a reproducible spatial sampling approach to identify areas with high vector abundance and inform targeted vector control strategies. The study highlights the importance of ecological stratification and a spatial explicit sampling approach for predicting mosquito presence when prior data is limited and underscores the potential for refining future sampling for control efforts.

ecology↗

bioRxiv2019

Genome variation and population structure among 1,142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii

Mosquito control remains a central pillar of efforts to reduce malaria burden in sub-Saharan Africa. However, insecticide resistance is entrenched in malaria vector populations, and countries with high malaria burden face a daunting challenge to sustain malaria control with a limited set of surveillance and intervention tools. Here we report on the second phase of a project to build an open resource of high quality data on genome variation among natural populations of the major African malaria vector species Anopheles gambiae and Anopheles coluzzii. We analysed whole genomes of 1,142 individual mosquitoes sampled from the wild in 13 African countries, and a further 234 individuals comprising parents and progeny of 11 lab crosses. The data resource includes high confidence single nucleotide polymorphism (SNP) calls at 57 million variable sites, genome-wide copy number variation (CNV) calls, and haplotypes phased at biallelic SNPs. We used these data to analyse genetic population structure, and characterise genetic diversity within and between populations. We also illustrate the utility of these data by investigating species differences in isolation by distance, genetic variation within proposed gene drive target sequences, and patterns of resistance to pyrethroid insecticides. This data resource provides a foundation for developing new operational systems for molecular surveillance, and for accelerating research and development of new vector control tools.

genomics↗

bioRxiv2018

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

Vector-borne disease control relies on efficient vector surveillance, mostly carried out using traps whose number and locations are often determined by expert opinion rather than a rigorous quantitative sampling design. In this work we first propose a framework for ecological sampling design which in its preliminary stages can take into account environmental conditions obtained from open data (i.e. remote sensing and meteorological stations). These environmental data are used to delimit the area into ecologically homogenous strata. By employing a model-based sampling design, the traps are deployed among the strata using a mixture of random and grid locations which allows balancing predictions and fitting accuracies. Sample sizes and the effect of ecological strata on sample sizes are estimated from previous sampling campaigns. Notably, we found that a configuration of 30 locations with 4 households each (120 traps) will have a similar accuracy in the estimates of mosquito abundance as 300 random samples. In addition, we show that random sampling independently from ecological strata, produces biased estimates of the mosquito abundance. Finally, we propose standardizing reporting of sampling designs to allow transparency and repetition / re-use in subsequent sampling campaigns.

ecology↗

bioRxiv2016

Natural diversity of the malaria vector Anopheles gambiae

The sustainability of malaria control in Africa is threatened by rising levels of insecticide resistance, and new tools to prevent malaria transmission are urgently needed. To gain a better understanding of the mosquito populations that transmit malaria, we sequenced the genomes of 765 wild specimens of Anopheles gambiae and Anopheles coluzzii sampled from 15 locations across Africa. The data reveal high levels of genetic diversity, with over 50 million single nucleotide polymorphisms across the 230 Mbp genome. We observe complex patterns of population structure and marked variations in local population size, some of which may be due at least in part to malaria control interventions. Insecticide resistance genes show strong signatures of recent selection associated with multiple independent mutations spreading over large geographical distances and between species. The genetic variability of natural populations substantially reduces the target space for novel gene-drive strategies for mosquito control. This large dataset provides a foundation for tracking the emergence and spread of insecticide resistance and developing new vector control tools.

genomics↗