🧬 biology

Genomic Connectivity and Adaptive Gene Flow of Anopheles gambiae s. s. Across East and Central Africa

This study utilizes whole-genome data to reveal that while *Anopheles gambiae* s. s. populations across East and Central Africa generally exhibit high gene flow with distinct ecological barriers limiting coastal and southern dispersal, the spread of insecticide resistance alleles varies by locus, positioning Ethiopia as a critical convergence zone for monitoring emerging adaptations.

Original authors: Fekadu Gemechu, Kelly L. Bennett, Araya Eukubay, Sisay Dugassa, Anastasia Hernandez-Koutoucheva, Luna Kamau, Tadesse Kebede Zeleke, Alistair Miles, Deriba Abera, Chris S. Clarkson, Lemu Golassa

Published 2026-10-01

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Fekadu Gemechu, Kelly L. Bennett, Araya Eukubay, Sisay Dugassa, Anastasia Hernandez-Koutoucheva, Luna Kamau, Tadesse Kebede Zeleke, Alistair Miles, Deriba Abera, Chris S. Clarkson, Lemu Golassa

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Mosquitoes are not just annoying pests; they are the world's most effective disease vectors, carrying malaria across vast landscapes. To stop them, public health officials rely on tools like insecticide-treated bed nets and indoor sprays. However, mosquitoes are evolving. Just as bacteria learn to resist antibiotics, mosquitoes can develop genetic changes that allow them to survive the chemicals meant to kill them. These survival traits are not always stuck in one place. Because mosquitoes fly and breed in connected environments, they can carry these resistance genes from one region to another, much like a traveler carrying a souvenir across borders. If a mosquito population in one country is highly connected to a population in a neighboring country, a resistance gene that appears in one spot can quickly spread to the other. Understanding how these populations connect, and how they move their genetic defenses, is critical for designing control strategies that do not fail.

A team of researchers set out to map these connections for Anopheles gambiae, a primary mosquito responsible for malaria transmission in East and Central Africa. While this mosquito is well-known in many parts of the continent, its presence and behavior in Ethiopia had remained a mystery until very recently. The scientists wanted to know: are the mosquitoes in Ethiopia isolated, or do they mix freely with those in Uganda, Kenya, Tanzania, and the Democratic Republic of the Congo? To find the answer, they gathered nearly one thousand mosquitoes from these regions, including fresh samples collected in western Ethiopia in 2023. They then sequenced the entire genetic code of each insect, looking for patterns that would reveal how closely related the different groups were to one another.

The results painted a clear picture of a vast, interconnected network. The mosquitoes found in western Ethiopia were not isolated; they shared a high degree of genetic similarity with populations in inland Uganda, northern Kenya, northern Tanzania, and the northern part of the Democratic Republic of the Congo. This suggests that the landscape between these areas—characterized by open savannas and seasonal water pools—allows mosquitoes to move and breed continuously across borders. The genetic differences between these inland groups were so small that they essentially formed one large, connected population. However, the story changed when the researchers looked at the edges of this network. Mosquitoes living in the coastal regions of Kenya and Tanzania, as well as those in the deep, dense rainforests of central and southern Democratic Republic of the Congo, were genetically distinct. These groups were separated from the main inland population, likely because the coastal areas have different, fragmented habitats, and the rainforests create a barrier that the mosquitoes cannot easily cross.

The study then investigated how insecticide resistance spreads through these populations. The researchers looked at specific genes known to help mosquitoes survive chemical attacks. They found that the spread of resistance depended heavily on the type of gene and the geography. For genes that help mosquitoes detoxify chemicals, the pattern of sharing matched the movement of the mosquitoes themselves. The resistance variants found in the inland populations were shared broadly across Ethiopia, Uganda, and the northern Democratic Republic of the Congo, mirroring the high connectivity of the mosquitoes in those areas. In contrast, the isolated coastal and deep-forest populations had their own unique versions of these resistance genes, suggesting they evolved independently due to their separation.

Yet, a different pattern emerged when the scientists examined the genes that act as the direct target for insecticides. One specific genetic change, which alters the mosquito's nervous system to prevent the insecticide from working, was found to be shared between Ethiopia and the Democratic Republic of the Congo, but was absent in the populations of Kenya and Tanzania. This indicates that while the mosquitoes themselves move freely across the inland region, the spread of this particular resistance trait does not follow the same path. It suggests that historical events or specific local pressures have shaped the distribution of this trait differently than the movement of the mosquitoes themselves.

The researchers concluded that Ethiopia acts as a crucial meeting point, or a "melting pot," where resistance traits from both East and Central Africa can converge. Because the mosquitoes in this region are connected to populations on both sides, any new resistance trait that appears in Ethiopia has the potential to spread widely across the continent. This makes the region a critical zone for monitoring. The findings suggest that fighting malaria resistance requires a dual approach: coordinated, cross-border strategies to manage the rapid spread of resistance in connected inland areas, and specific, localized plans for the isolated populations where resistance evolves on its own. By understanding these genetic highways and barriers, health officials can better predict where resistance will appear next and how to stop it before it undermines the tools used to save lives.

Technical Summary: Genomic Connectivity and Adaptive Gene Flow of Anopheles gambiae s. s. Across East and Central Africa

Problem Statement
Population connectivity is a critical determinant of the adaptive capacity of disease vectors, specifically influencing the speed and trajectory of insecticide resistance allele dissemination. While high gene flow can accelerate the spread of resistance, compromising localized intervention strategies, the extent of connectivity for Anopheles gambiae s. s. in East and Central Africa remains poorly characterized, particularly for understudied populations in Ethiopia. Although An. gambiae is a primary malaria vector in the region, its presence in Ethiopia was only recently confirmed, leaving a gap in understanding its population structure, gene flow with neighboring countries, and the origins of its insecticide resistance markers. Furthermore, it is unclear whether resistance alleles in Ethiopia result from adaptive gene flow across the broader region or represent independent evolutionary events.

Methodology
This study utilized whole-genome sequencing (WGS) data to investigate population connectivity and adaptive gene flow.

Data Sources: The analysis included 913 An. gambiae s. s. samples. This comprised 79 newly sequenced samples from western Ethiopia (Benishangul-Gumuz and Gambella regions) collected in 2023, combined with publicly available data from the Anopheles gambiae 1000 Genomes (Ag1000G) project. The comparative dataset included 50 samples from Kenya, 252 from Uganda, 291 from Tanzania, and 241 from the Democratic Republic of the Congo (DRC), collected between 2000 and 2020.
Sequencing and Processing: DNA was extracted and sequenced on the Illumina HiSeq X platform to generate 150-bp paired-end reads. Reads were mapped to the AgamP4 reference genome using BWA-mem. Variant calling was performed using GATK v.3.7.0, with strict quality filtering (minimum 10X coverage, <50% genome representation, <4.5% contamination). Haplotypes were constructed using WhatsHap and SHAPEIT, and Copy Number Variants (CNVs) were identified using a Gaussian Hidden Markov Model.
Analytical Framework:
- Population Structure: Unrooted neighbor-joining trees (NJT) and Principal Component Analysis (PCA) were constructed using 100,000 biallelic SNPs from the 3L chromosome arm (minimally impacted by inversions). Pairwise Hudson's $F_{ST}$ was calculated to measure genetic differentiation.
- Adaptive Gene Flow: Diplotype clustering was performed on specific loci under selection: the voltage-gated sodium channel gene (Vgsc), the Cyp6aa/p gene cluster, Cyp9k1, and Gste2. Clustering utilized hierarchical methods based on city-block genetic distance to identify shared haplotypes and CNVs.

Key Results

Genomic Population Structure:
- Inland Connectivity: Ethiopian populations (Benishangul-Gumuz and Gambella) formed a single cluster with low genetic differentiation ( $F_{ST}$ 0.000–0.003) among themselves. They exhibited high gene flow with inland East African populations, including Uganda, inland Kenya, inland Tanzania, and northern DRC (Nord-Ubangi). These groups formed a broad "inland East African" grouping in PCA and NJT analyses.
- Geographical Barriers: Distinct clusters were observed in geographically isolated regions. Coastal populations (Kilifi, Kenya; Tanga, Tanzania) and central/southern DRC populations (Kongo-Central, Central Kasai, Haut-Katanga) showed restricted gene flow and higher genetic differentiation ( $F_{ST}$ up to 0.074) compared to the inland group.
- Subtle Differentiation: While broadly connected, Ethiopian populations and Nord-Ubangi (northern DRC) showed subtle genetic differentiation ( $F_{ST}$ 0.003–0.01) relative to other inland populations, likely due to intervening arid valleys and escarpments.
Genetic Diversity: Inland populations displayed comparable nucleotide diversity ( $\theta\pi \approx 0.018–0.026$ ). In contrast, coastal and central/southern DRC populations exhibited lower diversity and less negative Tajima's D values, suggesting smaller effective population sizes and potential bottlenecks.
Adaptive Gene Flow Patterns:
- Metabolic Resistance (Cyp6aa/p and Cyp9k1): CNV variant sharing at these loci broadly mirrored genome-wide connectivity. Inland populations (Ethiopia, Kenya, Uganda, Tanzania, Nord-Ubangi) shared common CNV-associated clusters, while isolated populations (central/southern DRC) possessed distinct clusters.
- Target-Site Resistance (Vgsc): Allele sharing at the Vgsc locus did not follow genome-wide connectivity patterns. The Vgsc-L995S allele was broadly shared across inland East Africa. Conversely, the Vgsc-L995F allele (typically associated with West/Central Africa) was shared between Ethiopia and DRC populations but was absent in Kenya, Tanzania, and Uganda. Notably, Ethiopia acted as a convergence point where Vgsc-L995F and Vgsc-L995S alleles co-occurred.
- Gste2: No clear low-heterozygosity clusters associated with specific resistance markers were identified in the Gste2 region.

Significance and Claims
The authors claim that this study provides the first comprehensive genomic characterization of An. gambiae connectivity involving Ethiopian populations. The findings highlight that resistance evolution in this region is locus-specific:

Metabolic resistance (CNVs in Cyp6aa/p and Cyp9k1) spreads efficiently across ecologically continuous inland regions, mirroring genome-wide gene flow.
Target-site mutations (e.g., Vgsc-L995F) can exhibit geographically restricted patterns that diverge from overall population structure, likely driven by specific selection pressures or historical introduction events.

The paper identifies Ethiopia as a critical "crossover point" or "melting pot" where resistance alleles from East and Central Africa converge. This suggests Ethiopia is a vital region for monitoring emergent adaptations. The authors conclude that effective resistance management requires a dual strategy: coordinated cross-border monitoring to address rapid allele dissemination in highly connected inland populations, and site-specific interventions tailored to ecologically isolated regions (such as the Congo Basin and coastal zones) where divergent resistance trajectories are likely to emerge. The study emphasizes the necessity of integrating genomic surveillance into routine vector control programs to anticipate both regional connectivity and local divergence.

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