Non-geographical population structure of malaria vector Anopheles gambiae and Anopheles coluzzii but weak structure in Anopheles arabiensis within Burkina Faso: Implications for vector control and gene drive implementation

Non-geographical population structure of malaria vector Anopheles gambiae and Anopheles coluzzii but weak structure in Anopheles arabiensis within Burkina Faso: Implications for vector control and gene drive implementation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Non-geographical population structure of malaria vector Anopheles gambiae and Anopheles coluzzii but weak structure in Anopheles arabiensis within Burkina Faso: Implications for vector control and gene drive implementation Honorine Kaboré, Jon Brenas, Mahamadi Kientega, Nouhoun Traoré, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9271625/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Malaria control tools are nowadays challenged by the expansion and persistence of insecticide resistance in the malaria vector along with the changes in vector behaviour. To complement and reinforce the existing control tools, innovative vector control methods, such as gene drives, are currently under development. Understanding the Anopheles gambiae s.l. population structure is crucial for the implementation of genetic control tools and improvement of existing control strategies. Here we investigated the genetic diversity and population structure of Anopheles gambiae s.l. populations from three climatic zones in Burkina Faso. Results The results revealed the absence of geographical population structure in An. coluzzii and An. gambiae s.s collected in the three climatic zones, implicating high gene flow within the country. Anopheles arabiensis showed a structure that separates Hauts-Bassins samples in the Soudanian zone from others An. arabiensis samples. The cryptic species ( An. goundry and An. tengrela ) previously identified in the country were not detected in this study. Our findings indicate also population expansion in An. gambiae s.l. however An. arabiensis populations show evidence of a smaller population size or a bottleneck. Conclusion Our findings indicate that An. arabiensis is not expanding as fast as An. gambiae s.s. and An. coluzzii which could be caused by vector control interventions. The population structure of An. gambiae s.s and An. coluzzii across the country is characterized by a lack of geographical differentiation. These findings have important implications for both current vector control strategies and the potential implementation of gene drive technologies. Indeed, the lack of geographical population structure offers the potential for rapid spread of gene drive construct. It demands also a coordinated and a regional approach to manage the insecticide resistance. Population structure malaria vectors gene drive population expansion vector control Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Malaria is a vector-borne disease endemic to tropical countries and epidemic to several countries, caused by the protozoan parasite species Plasmodium and transmitted via the bite of an infected Anopheles mosquito. Approximately 282 million cases of malaria have been reported worldwide in 2024 with 610 000 deaths ( 1 ). Most cases and deaths have been reported in Africa ( 1 ). Despite the implementation of expanded interventions as mass distribution and continuous provision of long-lasting insecticidal nets (LLINs), Burkina Faso continues to experience a high disease burden. Burkina Faso is one of the most affected countries with 10 241 637 cases and 3 523 deaths reported in 2024, with pregnant women and children the most vulnerable ( 2 ). In sub-Saharan Africa, malaria is primarily transmitted by mosquitoes in the An. gambiae sensu lato (s.l) and the An. funestus group ( 3 ). In Burkina Faso, An. gambiae sensu stricto (s.s.), An. coluzzii and An. arabiensis constitute the main species transmitting malaria ( 4 ). These mosquitoes coexist in sympatry, exhibiting divergence in their resting and feeding behaviours ( 4 , 5 ). Two cryptic species of An. gambiae complex have also been identified in Burkina Faso: An. tengrela and An. goundry from the villages of Tengrela and Goundry, respectively ( 6 , 7 ). Anopheles goundry were significantly susceptible to infection by wild Plasmodium falciparum ( 8 ). The presence of these cryptic species increases the challenge of reducing malaria in Burkina Faso and highlights the importance of understanding mosquito population structure in order to control the multiple malaria vector species simultaneously. Genetic control strategies are being developed to reduce the reproductive and vectorial potential of mosquitoes ( 9 , 10 ). One strategy under development is gene drive, a selfish genetic element capable of quickly disseminating a genetic trait in the field ( 11 ). However, gene drives and other genetic control strategies target specific DNA sequences, with little or no tolerance to variation ( 12 ). The genetic variation and structure of wild Anopheles populations may impede the spread of gene drives and the efficacy of genetic control and therefore require study prior to the application of these strategies. In areas with high population connectivity, the implementation of control measures can rapidly become ineffective due to the influx of vectors from neighbouring, uncontrolled regions. In the context of gene drive, low connectivity within a population could impede the dissemination of the gene drive construct; but high connectivity between populations increases the likelihood of unintended dissemination outside of the target population ( 13 , 14 ). Mosquito populations can differ in terms of behaviour, vector competence and insecticide resistance, influencing the efficacy of vector control tools between populations. Understanding the structure and genetic diversity of mosquito populations at both coarse and fine geographical scales is important for establishing appropriate methodology, not only for implementing new vector control tools, but also for continual improvement of LLINs and indoor residual spray (IRS) deployment. Population genetic structure is the result of interaction between ecological and genetic processes, and the living organism’s adaptation to different ecological environment. It is influenced by several factors, including organisms’ intrinsic factors, drift events in populations, selection pressure due to human activities, historical events, and eco-geographical factors ( 15 – 20 ). Numerous studies have shown that geographical barriers have a more significant impact on the genetic structure of the mosquito, compared to geographic distance ( 21 – 23 ). Geographic distance and barriers to gene flow can act in conjunction to generate population genetic structure (24). Strong population structure has been detected in An. bellator especially between Bahia and southern populations in Brazil, the distance between them was at least 980 km ( 25 ). St. Laurent et al. (2022) also demonstrated that An. minimus was structured in four distinct populations with two of them exhibiting a geographic overlap in Cambodia ( 26 ). Mwinyi et al (2025) found geographical isolation between An. gambiae s.s . populations from north-western and north-eastern Tanzania ( 27 ). Low genetic differentiation was observed among An. gambiae s.s . populations in West and Central Africa ( 28 ). The genetic diversity and population structure constitute key parameters in determining the level of mosquito adaptation to different environments, and their response to vector control method. Consequently, malaria control strategies may vary according to the population structure, with different subpopulations of mosquitoes exhibiting distinct behavioural patterns. A recent study in Burkina Faso showed no geographical structure in the An. gambiae s.l. population ( 29 ); however, the sampling area of this study was restricted to 3 villages located in the Hauts-Bassins region in Burkina Faso. To extend beyond the scope of the previous study and achieve a more thorough understanding of the population structure in this country, mosquitoes were collected in the three climatic zones in Burkina Faso such as Soudanian zone, Soudano-sahelian zone and Sahelian zone. Using these data, we investigated whether population structure exists within An. gambiae s.l. populations and determined the effects of vector control interventions in An. gambiae s.l .. An investigation is also conducted to determine whether we find the cryptic species outside of the range where they were described. Method Mosquito sampling Burkina Faso is characterised by the presence of three distinct climatic zones. The Sahelian zone, located in the northern region, the Sudano-Sahelian zone, situated in the central area, and the Soudanese zone, located in the southern region. The distinguishing characteristics of these regions include variations in rainfall, vegetation, and land use. The Sahelian zone, which is characterised by lower precipitation levels and a paucity of vegetative cover. The average temperature is 29.1°C, with an average rainfall of 627.1 mm per year. In contrast, the Sudano-Sahelian zone is characterised by moderate rainfall and wooded savannah vegetation. It is situated between the 900- and 600-mm isohyet, with an average temperature of 28.4°C and an average annual rainfall of 758.5 mm. The Soudanese zone is the wettest, with dense vegetation and rainfall ranging from 900 mm to 1200 mm per year. The average temperature is 27.7°C and the average rainfall is 900.8 mm per year. These ecological zones are characterised by a high presence of Anopheles , keeping the country highly endemic for malaria. Mosquito samples have been collected in the 3 climatic zone of country from September to November 2022. The mosquitoes were collected using the pyrethroid spray capture technique. Following each capture session, the mosquitoes were returned to the field laboratory for morphological identification of the species, employing reference keys by Gillies et Coetzee ( 30 ) and Gillies et Meillon ( 31 ). The mosquitoes that had been identified were stored in 1.5 ml tubes containing 80% of alcohol for subsequent molecular analysis. The specimens have been selected for whole genome sequencing (Fig. 1 , S Table 1 ). In addition, whole genome sequence data generated during phase 3 of the Ag1000G project previously collected in the country was available (Fig. 1 , S Table 2 ). Whole genome sequencing The libraries were prepared and sequenced at the Wellcome Sanger Institute. The mosquito samples were sequenced individually at high coverage using the Illumina HiSeq sequencing technology. Paired multiplex libraries were prepared in accordance with the manufacturer's instructions, except for genomic DNA, which was fragmented using the Covaris Adaptive Focused Acoustics system on preference to nebulisation. The multiplexes comprised 12 individual tagged mosquitoes, with three sequencing lanes generated for each multiplex to even out variations in throughput between sequencing runs. Cluster generation and sequencing were performed in accordance with the manufacturer's protocol for paired-end sequence reads with insertion sizes between 100 and 200 bp. All individuals were sequenced with a target coverage of 30×, resulting in the generation of paired reads of 100 bp and 150 bp. Data processing The sequences were subjected to analysis with the objective of identifying genetic variants, including single nucleotide polymorphisms (SNPs). The sequences were aligned against the An. gambiae AgamP4 reference genome using version 0.7.15 of the BWA Burrows-Wheeler Aligner (BWA) program. Deletions were realigned using the Genome Analysis Toolkit (GATK) version 3.7-0. Additionally, single nucleotide polymorphisms were identified using the same toolkit UnifiedGenotyper. Genotypes were identified on an independent basis for each sample. Genotypes were called for each sample independently, in genotyping mode, given all possible alleles at all genomic sites where the reference base was not “N. Coverage was capped at 250× by random down-sampling. Complete specifications of the alignment ( alignment ) and genotyping ( genotyping ) pipelines are available from the malariagen/pipelines GitHub repository . Once the variants had been identified, a series of quality control analyses were conducted on the samples and variants to ensure the quality of the data. Sample quality control Sequencing and SNP calling was performed according to the Ag1000G phase 3 project protocols. The depth of coverage at all genomic positions was calculated for each sample. Samples were excluded if the median coverage across all chromosomes was below 10×, or if less than 50% of the reference genome was covered by at least 1×. To identify samples that have been affected by cross-contamination, the model for detecting contamination in next generation sequencing (NGS) alignments, as described by Jun et al. (2012) ( 32 ), was implemented. In summary, the methodology entails the estimation of the probability of the observed alternate and reference allele counts under different contamination proportions, based on the approximation of population allele frequencies. The estimation of population allele frequencies was conducted using the Ag1000G phase 2 data release. Subsequently, the model computed the maximum likelihood value for the parameter alpha, which represents the percentage of contamination. Samples were excluded from the analysis if the alpha value was 4.5% or above. Pairwise genetic distance was calculated between all sample pairs within a sample set. Principal component analysis (PCA) was conducted using the scikit-allel version 1.2.0 to identify and exclude individual samples that were identified as population outliers. The number of SNPs was reduced to 100,000 segregating non-singleton sites from chromosomes 3R and 3L to circumvent regions that were complicated by the presence of known introgression loci or paracentric inversions. The sex of all samples was determined based on the modal coverage ratio between the X chromosome and the autosomal chromosome arm 3R. Species assignment Species assignment was performed according to the Ag1000G phase 3 project protocols. A species was assigned to each individual that passed sample quality control (QC) using their genomic data, via two independent methods: ancestry-informative markers (AIMs) and PCA ( 33 ). Data analysis The data analyses were conducted using JupyterLab, a software environment for interactive computing and data visualization based on the Python programming language. Malariagen_data Python package ( 33 ) were used. The malariagen_data module contains a suite of functions designed for the extraction of genomic and site-specific data types for subsequent analysis. The modules employed were NumPy, Pandas, Dask.array, Matplotlib, Seaborn, Malariagen_data and Scikit-allel. Population structure The principal component analysis (PCA) and unrooted neighbour joining tree (NJT) were used to investigate population structure. The PCA plot is a two- or three-dimensional representation of the mosquito samples, illustrating the degree of relatedness between them. This approach has been demonstrated to effectively illustrate the evolutionary relationships among mosquito samples. To investigate geographical structure, chromosome arm 3L was used, as it is not affected by large polymorphism inversions and rearrangements. The target range for the PCA and NJT analyses were set from position 15,000,000 to 41,000,000, with the exclusion of the telomeric and pericentromeric regions due to recombination and subsequent selection at linked sites. Conversely, all chromosome X region used to perform PCA and to construct NJT. The PCA analysis was conducted using 300,000 single-nucleotide polymorphisms (SNPs) that were distributed uniformly across each chromosome arm, with a minor allele count exceeding 2 and no missing calls. In addition, we analysed pairwise Fst using 3L from position 15,000,000 to 41,000,000. Genetic diversity The diversity statistics were analysed in order to ascertain further information regarding demographic differences and changes within mosquito populations. The genetic diversity between species and populations was evaluated using the summary statistics, namely the nucleotide diversity (𝜃𝜋), Watterson's theta (θW) and Tajima's D. Chromosome arm 3L is typically favoured for the genetic diversity study due to the absence of large polymorphic inversions in any of the An. gambiae subspecies. The genome region spanning from 15,000,000 to 41,000,000 was used for the diversity calculations. Furthermore, we opted to use sites that are less influenced by selection, employing 4-fold degenerate sites in our analysis. In addition, an investigation was conducted into the heterozygosity and homozygosity across the genome. These analyses have the potential to facilitate a more in-depth understanding of the demographic history of the mosquito population. We used a window size of 10,000 sites. Results Mosquito populations The entire genome of 631 An. gambiae s.l. collected in 2022 and 34 samples collected in 2020–2021 was sequenced using the Illumina HiSeq platform (Table 1 ). A species assignment analysis of the genomic data based on ancestry informative markers and principal components analysis revealed that most of the samples (477) were assigned to An. coluzzii , 114 samples were assigned to An. gambiae s.s ., and 72 samples were assigned to An. arabiensis . Two samples were not assigned to a taxon due to an insufficient level of confidence in the assignment. In addition to the collected samples, data from 418 An. gambiae s.l. from across Burkina Faso included in the MalariaGEN dataset were analysed (Table 2 ). The cryptic species subgroup An. tengrela and An. goundry are named gcx4 in the MalariaGen dataset. Table 1 Mosquito species distribution per region (new collection) Region An. arabiensis An. coluzzii An. gambiae gcx4 unassigned Total Boucle du Mouhoun 13 32 2 0 0 47 Cascades 8 14 31 0 1 54 Centre-Sud 11 20 18 0 0 49 Est 22 139 40 0 0 201 Hauts-Bassins 8 211 23 0 0 242 Sahel 10 61 0 0 1 72 Total 72 477 114 0 2 665 Table 2 Mosquito species distribution per region (MalariaGen dataset) Region An. arabiensis An. coluzzii An. gambiae gcx4 Total Cascades 0 167 0 41 208 Centre-Ouest 0 0 0 6 6 Plateau Central 4 3 0 6 13 Hauts-Bassins 54 79 53 0 186 Boucle de Mouhoun 1 4 0 0 5 Total 59 253 53 53 418 Geographical population structure To visualise the impact of geography on population structure, a principal component analysis was performed on chromosome 3L, which is used due to the low impact of chromosomal inversions and rearrangements. The analysis showed that there is a clear structure between species of the An. gambiae complex (Fig. 2 , S Fig. 1 ). Hybrid individuals, labelled as “unassigned” taxa, were seen between An. gambiae s.s. and An. coluzzii , and An. coluzzii and An. arabiensis (Fig. 2 ). The cryptic species (gcx4) included samples form a cluster without other samples very closed or mixed with them (Fig. 2 .a). To investigate the impact of fine geographical scale on population structure, a principal component analysis was conducted per species and per region on chromosome 3L. The analysis of An. gambiae s.s. and An. coluzzii samples from Burkina Faso reveal no evidence of a geographical structure in the population (Fig. 3 a & b, S Fig. 3 ). This suggests a lack of a substantial geographical isolation barrier within the country, and no impact of the different climate zones on mosquito population structure. We performed a PCA per year to check whether there were any discrepancies in the samples collected each year in the country. To this end, mosquito samples previously collected in Burkina Faso were analysed. Principal component analysis (PCA) revealed the absence of any distinct structure in the populations of An. coluzzii and An. gambiae s.s per year (S Fig. 2 a & b). Principal component analysis on the 3L chromosome arm show a weak structure in An. arabiensis population (Fig. 3 c). The Neighbours joining tree (NJT) was performed to ensure the structure of An. arabiensis population. We found a slight difference between An. arabiensis samples from the Hauts-Bassins region and An. arabiensis samples from other regions (Fig. 3 d, S Fig. 3 ). We performed a PCA per year to check whether the weak structure observed in An. arabiensis population is due to the year of collection. PCA revealed the absence of any distinct structure in the populations of An. arabiensis per year (S Fig. 2 c). The X chromosome plays an important role in mosquito speciation and reproductive isolation. The 3L chromosome gives the general geographical population structure while the X chromosome offers specific population genetic. The X chromosome may show clear structure even when autosomes show weak differentiation. PCA and NJT were performed on the X chromosome to ensure the population structure. A close affinity is observed between the gcx4 samples and An. coluzzii samples (Fig. 4 a). The analysis of the An. arabiensis samples revealthe presence of two distinct clusters on chromosome X separated Hauts-Bassins samples from other regions samples (Fig. 4 b & c). Demographic story of mosquito population We conducted the analysis to determine the effects of malaria control intervention in An. gambiae s.l. population Genetic diversity The nucleotide diversity refers to a molecular genomic notion which serves to estimate the level of polymorphism present in a genomic region within a given population. It quantifies the number of nucleotide differences per site between two DNA sequences, selected at random, from the same population. The genetic diversity analysis of the An. gambiae s.l. species showed that the cryptic species have demonstrated the lowest nucleotide diversity, at approximately 0.01, followed by the An. arabiensis populations, at 0.015, while the nucleotide diversity of the An. gambiae s.s. and An. coluzzii population was approximately 0.023 (Fig. 5 ). The analysis revealed that all An. arabiensis populations showed similar levels of nucleotide diversity, except for the population from Hauts-Bassins region, which demonstrated the lowest diversity of approximately 0.012. This finding suggests that the demographic histories of all An. arabiensis populations within these regions may be substantially similar, apart from the Hauts-Bassins population. All the An. coluzzii populations exhibited a comparable level of nucleotide diversity, with a slight increase observed in An. coluzzii mosquitoes collected in cascades region. Within the An. gambiae s.s. populations, mosquitoes collected in Hauts-Bassins exhibited the highest observed nucleotide diversity. The diversity estimators have grouped the An. arabiensis in single cluster except the An. arabiensis population from Hauts-Bassins. The An. gambiae s.s. and An. coluzzii have been grouped also into single cluster except An . coluzzii in from Cascades, as well as the An. gambiae s.s. from Hauts-Bassins. An investigation of the nucleotide diversity across region and years within each An. gambiae s.l. population revealed a near-smooth distribution, except for the An. coluzzii population at Hauts-Bassins in 2017 (S Fig. 7). In general, the An. arabiensis population in Hauts-Bassins region exhibits the lowest diversity, while the An. gambiae s.s. populations in the Hauts-Bassins region demonstrate the highest diversity. The taxon gcx4 has exhibited low nucleotide diversity 0.01 with a positive Tajima D value would be consistent with a reduction in population size, whereas all An. gambiae s.l. have demonstrated a negative Tajima D value which is consistent with an excess of rare variants and historical population expansion. The confidence interval of gcx4 goes into negatives, it is quite possible that the population is still expanding slowly. Mosquito heterozygosity A genome-wide measure of heterozygosity was defined as the ratio of heterozygous single-nucleotide polymorphisms (SNPs) to non-reference homozygous SNPs (Wang et al. 2015). The majority of An. gambiae s.s. and An. coluzzii samples exhibited a heterozygosity per window of 0.015 (1.5%), with a decrease towards chromosome ends as is expected due to reduced rates of recombination (S Fig. 4 ). Lower heterozygosity and the presence of blocks of run of homozygosity were observed at specific loci, including insecticide resistance loci such as the glutathione S-transferase epsilon (GSTe) locus in the An. coluzzii population indicative selective pressure on this locus within the population (S Fig. 5 ). Contrastingly, the majority of An. arabiensis samples exhibited heterozygosity levels of approximately 0.01 (1%) (S Fig. 6). This could be attributed to the comparatively smaller population size of An. arabiensis in the country when compared to An. gambiae s.s. and An. coluzzii . Discussion Impact of vector control The impact of the vector interventions is not always easy to measure. The use of innovative bednets should improve entomological intervention effectiveness, but their epidemiological impact on malaria control requires further evaluations including those based on genomics to track the potential link between the interventions and mosquito demography and evolution. Our study based on genomic analysis showed the different demographic changes in the major malaria vectors of the An. gambiae complex in Burkina Faso. The An. arabiensis population showed a clear distinction from the other members of the An. gambiae complex as An. gambiae s.s. and An. coluzzii characterized by lower levels of heterozygosity and nucleotide diversity. This contrasts with findings from Tanzania, where a previous study reported high levels of genetic diversity in An. arabiensis ( 34 ). This might suggest that inbreeding is more prevalent in An. arabiensis populations in Burkina. Anopheles arabiensis populations show evidence of a smaller population size or a bottleneck as demonstrated by their genetic diversity, heterozygosity and ROH. However, Tajima's D remains negative, suggesting that the population may still be expanding, although at a reduced rate. These findings indicate that An. arabiensis is not expanding as fast as An. gambiae s.s. and An. coluzzii which could be caused by vector control interventions especially for An. arabiensis from Hauts-Bassins. Anopheles gambiae s.s and An. coluzzii populations showed significant nucleotide diversity with a normal heterozygosity level and negative Tajima’s D. This is often indicative of either a large effective population size, which serves to maintain polymorphism, or a history of population admixture or gene flow, which in turn introduces novel alleles. The observation that mosquitoes exhibit normal heterozygosity suggests that there were extensive panmixia and gene flow among them. In such large, interconnected populations, any local reduction in heterozygosity is rapidly compensated for by migration or mutation. Indeed, low heterozygosity and the presence of blocks of run of homozygosity were observed at specific loci, for example, in the GSTE locus in the An. coluzzii population. This could be indicative of selective pressure on this locus within the population. The selection across the population facilitates the dissemination of advantageous variants, including resistance alleles, for adaptation and surviving purposes. The absence of extensive runs of homozygosity further suggests that breeding is predominantly outbred, and that populations across diverse environments remain genetically well-mixed. The combination of significant nucleotide diversity and negative Tajima's D in An. gambiae s.s. and An. coluzzii indicates a recent expansion in its population and /or pervasive selection sweep ( 35 ). The ecological context, characterised by intense insecticide pressure ( 29 ), ongoing malaria transmission, and extensive habitat variability, suggests that these genomic patterns emerge from the interplay of rapid population growth and continual adaptive bouts. The mosquito’s capacity for rapid adaptation to control measures could be an indicator of its significant standing diversity. The large effective population of these mosquitoes serves to buffer genetic diversity against even strong directional forces, while facilitating sustained evolution in response to environmental and anthropogenic pressures. These results suggest that the implementation of vector control measures in the country is not an effective method for preventing the expansion of An. gambiae s.s. and An. coluzzii populations. The taxon gcx4 has exhibited low nucleotide diversity with a positive Tajima D value, whereas all other An. gambiae s.l . have demonstrated a negative Tajima D value. The absence of novel samples may be attributable to the collection methodology used. It is noteworthy that gcx4 does not appear to be a significant vector, and they were detected at larval state. A positive Tajima D value indicates that gcx4 is experiencing a crash, but the Tajima’s D, whilst slightly positive, is not statistically significant. It is quite possible that the population is still expanding slowly. Gene drive implementation Gene drive technology is currently under development to strengthen vector control tools. Recently developed CRISPR–Cas9-based gene-drive systems are highly efficient in laboratory settings, offering the potential to reduce the prevalence of vector-borne diseases ( 36 , 37 ). Gene drives are designed to reduce insect vectors target population or to render them unable to transmit pathogen, in the face of the emergence of insecticide resistance, the novel technologies as gene drive could be worth for vector control. However, few countries have initiated the process of developing or adapting regulatory frameworks to oversee gene drive research and potential future applications. The scientific advances, combined with ethical and social considerations, will facilitate the transparent and responsible advancement of gene drive technology towards field implementation ( 36 ). Beyond communities or society acceptance, vector genomics need to be studied to understand how the gene drive will spread and better predict or attenuate the rise of potential resistance in the field. Population structure significantly affects the gene drive as the genetic barriers could lead to spread failure or altered dynamics. Our study showed a slight difference between An. arabiensis samples from the Hauts-Bassins region and An. arabiensis samples from other regions. The presence of geographical structure in An. arabiensis populations could impede the spread of the gene drive. This could be a potential initial target for the gene drive as it would limit its spread while allow scientists to evaluate the outcome in a more control fashion. The presence of population structure would need the development of a population specific gene drive ( 38 ). Geographical structure was not detected in An. coluzzii and An. gambiae s.s. in Burkina Faso. The genetic connectivity of mosquito populations has the potential to facilitate the rapid dissemination of a successful gene drive construct across extensive geographical areas. This is a key objective for population suppression drives that aim to have a substantial impact on malaria transmission. The capacity of propagation could represent a significant advantage in achieving malaria vector suppression or replacement. It has been demonstrated that even self-limiting gene drive systems, which are designed to spread for a limited number of generations, might achieve population elimination with repeated releases in the presence of high gene flow that helps to distribute the gene drive allele or construct ( 39 ).The interconnectedness of An. gambiae s.s. and An. coluzzii populations therefore present a potential pathway for a gene drive to exert a country-wide effect on malaria transmission. The absence of geographical structure ought to facilitate the spread of the gene drive construct in the mosquito population within the country. Therefore, it is crucial to evaluate gene drive constructs carefully in the context of the genetic diversity present in the target populations to accurately predict their impact and ensure their effectiveness across different ecological zones. Population structure connected to climatic conditions No geographical structure was identified in An. coluzzii and An. gambiae s.s. collected in the three climatic zones in Burkina Faso implicating a high gene flow of these vectors in the country. In the Sahelian zone, dryest and hottest, no An. gambiae s.s . were found which could indicate that it is less adapted to the climate. The absence of geographical structure suggests that the presence of a substantial geographical isolation barrier in the country is unlikely, and that mosquito migration may potentially occur in the absence of such barriers. A study in Tanzania showed a geographical structure of An. gambiae s.s. within the north-western and north- eastern population while structure was not found in An. arabiensis ( 27 ). The Burkina Faso ecological factors in the three zones do not impact much the genetic diversity or heterogeneity of the mosquitoes. The absence of geographical population structure within An. gambiae s.s. and An. coluzzii populations provides the evidence for spatial gene flow between these locations. This is an indication that the malaria control strategies must be monitored to avoid resistance that can spread quickly due to high gene flow. This is consistent with a study previously conducted by Kientega et al (2024) which showed no geographical structure within An. gambiae s.s. and An. coluzzii collected in the Hauts-Bassins region ( 29 ). Anopheles arabiensis showed a structure distinguishing the Hauts-Bassins individuals in the Soudanian zone from other individuals which is clearly visible on chromosome X. This may be due to the role of the X chromosome on the postzygotic isolation between species. The X chromosome plays a major role in postzygotic isolation between An. gambiae s.s . and An. arabiensis ( 40 , 41 ). Chromosome X is more divergent between these taxa than in the autosomes ( 42 – 44 ). The geographical structure observed in An. arabiensis may be due to the geographical factors since Hauts-Bassins region is in the Sudanian zone (humid zone). The Cascades region does not have many An. arabiensis , it might be due to the environment condition (wettest zone in the country). The cryptic taxa An. tengrela and An. goundry , previously identified in Burkina Faso, were not found in this study, despite the fact that samples were collected in areas including the two regions, Cascades (Sidéradougou near Tengrela) and Plateau-Central (Po Dongo near Goundry Village), where the cryptic species had previously been identified ( 6 , 7 ). This suggests that these cryptic species are not distributed throughout the country or the collection methods introduced a bias given that almost all known An. goundry and An. tengrela were collected as larvae. Further studies could be necessary to characterize the extent of the species distributions. Conclusion Our findings indicate that An. arabiensis is not expanding as fast as An. gambiae s.s. and An. coluzzii which could be caused by vector control interventions. The cryptic taxa An. tengrela and An. goundry , previously identified in Burkina Faso, were not found in this study, this suggests that the cryptic species are not distributed throughout the country or the collection methods introduced a bias given that almost all known An. goundry and An. tengrela were collected as larvae. Our study showed a geographical differentiation in An. arabiensis . Geographical population structure was not detected in An. coluzzii and An. gambiae s.s. collected in the three climatic zones in Burkina Faso implicating a high gene flow of these vectors in the country. These findings have important implications for both current vector control strategies and the potential implementation of gene drive technologies. The presence of geographical structure in An. arabiensis populations could impede the spread of the gene drive. This could be a potential initial target for the gene drive as it would limit its spread while allow scientists to evaluate the outcome in a more control fashion. The lack of geographical population structure in An. coluzzii and An. gambiae s.s. offers the potential for rapid spread of gene drive construct. It demands also a coordinated and a regional approach to manage the insecticide resistance. Abbreviations s.s: Sensu stricto s.l: Sensu lato An: Anopheles SNP: Single nucleotide polymorphism PCA: Principal component analysis GSTe: Glutathione S-transferase epsilon NJT: Neighbour joining tree 𝜃𝜋: Nucleotide diversity θW: Watterson's theta QC: Quality control AIM: Ancestry-informative marker Ag1000G: Anopheles gambiae 1000 genome BWA: Burrows-Wheeler aligner NGS: Next generation sequencing GATK: Genome analysis toolkit DNA: Deoxyribonucleic acid LLIN Long-lasting insecticidal net mm: Millimetre pb Pair base Declarations Ethics approval and consent to participate The protocol of the sampling was approved by the Institutional Ethics Committee of the Institut de Recherche en Sciences de la Santé (32-2022/CEIRES). All the activities related to this paper were not required any other ethics approval. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work is funded by the Gates Foundation [INV-037164] and the Wellcome [224487/Z/21/Z] which the Institut de Recherche en Sciences de la Santé received to carried out mosquito sampling and data analyses. Also, the Pan-African Mosquito Control Association’s was funded by the Bill and Melinda Gates Foundation (INV-031595). The MalariaGEN Vector Observatory is supported by multiple institutes and funders. The Wellcome Sanger Institute’s participation was supported by funding from Wellcome (220540/Z/20/A, 'Wellcome Sanger Institute Quinquennial Review 2021–2026') and the Bill & Melinda Gates Foundation (INV-001927 and INV-068808). Author Contribution HK, JB and MK conceived the study. HK, MK, HM, NT, GS performed samples collection. PP, AHK and AL provided edition. AM and CSC produced the genomic data. AD, AM and TN provided funding and resources. AD, HM, MN and CSC supervised the study. HK, JN and MK carried out data analysis and visualization. HK, JN and MK drafted the manuscript. All authors have read and approved this version of the manuscript. Acknowledgements The authors would like to acknowledge the international collaboration and funded as Bill Gate foundation and Wellcome Trust, which are supporting the safe and sustainable implementation of gene drive technology for malaria vector control in Africa. We greatly thank the Institut de Recherche en Sciences de la Santé and the Ministry of Health for the implementation of the nationwide sampling of malaria mosquitoes. Ministry of Health of Burkina Faso, the community health workers. We address our gratitude to the MalariaGEN Vector Observatory which is an international collaboration working to build capacity for malaria vector genomic research and surveillance. We would like to thank Liverpool School of Tropical Medicine, Broad Institute of Harvard and MIT. The authors would like to acknowledge the staff of the Wellcome Sanger Genomic Surveillance unit and the Wellcome Sanger Institute Sample Logistics, Sequencing and Informatics facilities for their contributions. Thank you to the health workers and the populations of the sampling sites for their cooperation during the sample collection. Data Availability Jupyter Notebooks and scripts to reproduce all the analyses are available in this link: https://drive.google.com/drive/folders/1I-gPhfqEY9Vn0pprrhgetc8z-41-3Ply?usp=sharing. The SNPs and haplotypes data are available on the homepage of MalariaGEN and can be accessed using the malariagen_data package. The raw sequences in FASTQ format and the aligned sequences in BAM format were stored in the European Nucleotide Archive (ENA, Study Accession n° ERR12776294-ERR12871281). References WHO. World Malaria Report 2025. World Health Organization; 2025. Ministère de la Santé. Annuaire Statistique 2024. 2025 Apr. Report. Coetzee M. 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Semipermeable species boundaries between Anopheles gambiae and Anopheles arabiensis: Evidence from multilocus DNA sequence variation [Internet]. 2003. Report. Available from: www.pnas.org. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfilepopulationstructure.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor invited by journal 02 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Koutoucheva","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Anastasia","middleName":"H.","lastName":"Koutoucheva","suffix":""},{"id":634141785,"identity":"6d37825d-69e7-4cba-bdaa-20533dc62c48","order_by":7,"name":"Alessandra Lanfancotti","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Alessandra","middleName":"","lastName":"Lanfancotti","suffix":""},{"id":634141786,"identity":"11bc2a92-f274-46e0-891d-071d273c56ae","order_by":8,"name":"Moussa Namountougou","email":"","orcid":"","institution":"Nazi Boni University","correspondingAuthor":false,"prefix":"","firstName":"Moussa","middleName":"","lastName":"Namountougou","suffix":""},{"id":634141788,"identity":"006a9b87-9eda-4d0a-b871-4e3fcaa75487","order_by":9,"name":"Hamidou Maiga","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Hamidou","middleName":"","lastName":"Maiga","suffix":""},{"id":634141790,"identity":"ea6b96c2-52c5-48c4-a6c0-17e621d8a52d","order_by":10,"name":"Tony Nolan","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tony","middleName":"","lastName":"Nolan","suffix":""},{"id":634141791,"identity":"6e4a8132-0abd-4c0a-a395-31f2b87fbfdd","order_by":11,"name":"Alistair Miles","email":"","orcid":"","institution":"Ellison Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Alistair","middleName":"","lastName":"Miles","suffix":""},{"id":634141792,"identity":"fbfcb598-ae17-4f19-bd53-b6b54143575b","order_by":12,"name":"Chris S. Clarkson","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"S.","lastName":"Clarkson","suffix":""},{"id":634141793,"identity":"011c2889-1c16-412d-8245-51779040ac93","order_by":13,"name":"Abdoulaye Diabaté","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Abdoulaye","middleName":"","lastName":"Diabaté","suffix":""}],"badges":[],"createdAt":"2026-03-30 19:53:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9271625/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9271625/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108572770,"identity":"c7243207-1286-44cd-a679-a464c2083e77","added_by":"auto","created_at":"2026-05-06 06:36:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":700381,"visible":true,"origin":"","legend":"\u003cp\u003emosquito collection site for whole genome sequencing\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9271625/v1/107ffc984719bf0952fab63c.png"},{"id":109067868,"identity":"b9bcc49b-649e-419f-a6aa-ace6dfd7b9a4","added_by":"auto","created_at":"2026-05-12 10:02:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":302576,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) plot showing the population structure of \u003cem\u003eAn. gambiae\u003c/em\u003e complex mosquitoes based on the analysis of chromosome 3L, a) PCA showing population structure of \u003cem\u003eAn. gambiae\u003c/em\u003e complex including gcx4 and b) PCA showing population structure of \u003cem\u003eAn. gambiae\u003c/em\u003e complex without gcx4 samples.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9271625/v1/646f5ec41651e9e9e92c3b66.png"},{"id":108572771,"identity":"ff9b172a-74b2-4b27-8408-247586313358","added_by":"auto","created_at":"2026-05-06 06:36:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":671290,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) plot shows the population structure of \u003cem\u003eAn. gambiae\u003c/em\u003e s.s (a), \u003cem\u003eAn. coluzzii \u003c/em\u003e(b), \u003cem\u003eAn. arabiensis \u003c/em\u003e(c)\u003cem\u003e \u003c/em\u003eon chromosome 3L per region d) Neighbours joining tree (NJT) on 3L, region:15,000,000-41,000,000, with 30,260 SNPs in \u003cem\u003eAn. arabiensis\u003c/em\u003e population.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9271625/v1/a9f78efc1d40e3aad8dae552.png"},{"id":108572769,"identity":"07c28854-ad0a-4dce-920a-17a6c4acad27","added_by":"auto","created_at":"2026-05-06 06:36:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":491901,"visible":true,"origin":"","legend":"\u003cp\u003ePCA showing the population structure of \u003cem\u003eAn. gambiae\u003c/em\u003e complex mosquitoes (a) and \u003cem\u003eAn. arabiensis \u003c/em\u003e(b) on chromosome c) NJT on chromosome X with 31,656 SNPs in \u003cem\u003eAn. arabiensis \u003c/em\u003epopulation\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9271625/v1/c34e62c62db647b830ad5d6c.png"},{"id":108572772,"identity":"c4eecd95-13a3-4eab-afe9-cbde3f84858c","added_by":"auto","created_at":"2026-05-06 06:36:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":642057,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic diversity summary statistics. (\u003cstrong\u003eA\u003c/strong\u003e) Nucleotide diversity, (\u003cstrong\u003eB\u003c/strong\u003e) Watterson estimator, (\u003cstrong\u003eC\u003c/strong\u003e) Tajima’s D, and (\u003cstrong\u003eD)\u003c/strong\u003e All diversity estimators showing the genetic diversity of each \u003cem\u003eAn. gambiae\u003c/em\u003e complex population.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9271625/v1/761d869d05a0a9821b650f7e.png"},{"id":109070127,"identity":"26e3eedd-f882-40c6-ad3e-03c7010db80a","added_by":"auto","created_at":"2026-05-12 10:29:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2886549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9271625/v1/95523622-ea1d-4b6c-9bbb-c1319b50314f.pdf"},{"id":108572767,"identity":"7a0b6ad7-3a27-4b10-96e8-971a4d1fc35d","added_by":"auto","created_at":"2026-05-06 06:36:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2614648,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfilepopulationstructure.docx","url":"https://assets-eu.researchsquare.com/files/rs-9271625/v1/6d363a104c4d473eda6e6c1a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eNon-geographical population structure of malaria vector \u003cem\u003eAnopheles gambiae\u003c/em\u003e and \u003cem\u003eAnopheles coluzzii\u003c/em\u003e but weak structure in \u003cem\u003eAnopheles arabiensis \u003c/em\u003ewithin Burkina Faso: Implications for vector control and gene drive implementation\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eMalaria is a vector-borne disease endemic to tropical countries and epidemic to several countries, caused by the protozoan parasite species \u003cem\u003ePlasmodium\u003c/em\u003e and transmitted via the bite of an infected \u003cem\u003eAnopheles\u003c/em\u003e mosquito. Approximately 282\u0026nbsp;million cases of malaria have been reported worldwide in 2024 with 610 000 deaths (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Most cases and deaths have been reported in Africa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite the implementation of expanded interventions as mass distribution and continuous provision of long-lasting insecticidal nets (LLINs), Burkina Faso continues to experience a high disease burden. Burkina Faso is one of the most affected countries with 10 241 637 cases and 3 523 deaths reported in 2024, with pregnant women and children the most vulnerable (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In sub-Saharan Africa, malaria is primarily transmitted by mosquitoes in the \u003cem\u003eAn. gambiae sensu lato\u003c/em\u003e (s.l) and the \u003cem\u003eAn. funestus\u003c/em\u003e group (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In Burkina Faso, \u003cem\u003eAn. gambiae sensu stricto\u003c/em\u003e (s.s.), \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. arabiensis\u003c/em\u003e constitute the main species transmitting malaria (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These mosquitoes coexist in sympatry, exhibiting divergence in their resting and feeding behaviours (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Two cryptic species of \u003cem\u003eAn. gambiae\u003c/em\u003e complex have also been identified in Burkina Faso: \u003cem\u003eAn. tengrela\u003c/em\u003e and \u003cem\u003eAn. goundry\u003c/em\u003e from the villages of Tengrela and Goundry, respectively (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). \u003cem\u003eAnopheles goundry\u003c/em\u003e were significantly susceptible to infection by wild \u003cem\u003ePlasmodium falciparum\u003c/em\u003e (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The presence of these cryptic species increases the challenge of reducing malaria in Burkina Faso and highlights the importance of understanding mosquito population structure in order to control the multiple malaria vector species simultaneously.\u003c/p\u003e \u003cp\u003eGenetic control strategies are being developed to reduce the reproductive and vectorial potential of mosquitoes (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). One strategy under development is gene drive, a selfish genetic element capable of quickly disseminating a genetic trait in the field (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, gene drives and other genetic control strategies target specific DNA sequences, with little or no tolerance to variation (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The genetic variation and structure of wild \u003cem\u003eAnopheles\u003c/em\u003e populations may impede the spread of gene drives and the efficacy of genetic control and therefore require study prior to the application of these strategies. In areas with high population connectivity, the implementation of control measures can rapidly become ineffective due to the influx of vectors from neighbouring, uncontrolled regions. In the context of gene drive, low connectivity within a population could impede the dissemination of the gene drive construct; but high connectivity between populations increases the likelihood of unintended dissemination outside of the target population (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Mosquito populations can differ in terms of behaviour, vector competence and insecticide resistance, influencing the efficacy of vector control tools between populations. Understanding the structure and genetic diversity of mosquito populations at both coarse and fine geographical scales is important for establishing appropriate methodology, not only for implementing new vector control tools, but also for continual improvement of LLINs and indoor residual spray (IRS) deployment.\u003c/p\u003e \u003cp\u003ePopulation genetic structure is the result of interaction between ecological and genetic processes, and the living organism\u0026rsquo;s adaptation to different ecological environment. It is influenced by several factors, including organisms\u0026rsquo; intrinsic factors, drift events in populations, selection pressure due to human activities, historical events, and eco-geographical factors (\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Numerous studies have shown that geographical barriers have a more significant impact on the genetic structure of the mosquito, compared to geographic distance (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Geographic distance and barriers to gene flow can act in conjunction to generate population genetic structure (24). Strong population structure has been detected in \u003cem\u003eAn. bellator\u003c/em\u003e especially between Bahia and southern populations in Brazil, the distance between them was at least 980 km (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). St. Laurent et al. (2022) also demonstrated that \u003cem\u003eAn. minimus\u003c/em\u003e was structured in four distinct populations with two of them exhibiting a geographic overlap in Cambodia (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Mwinyi et al (2025) found geographical isolation between \u003cem\u003eAn. gambiae s.s\u003c/em\u003e. populations from north-western and north-eastern Tanzania (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Low genetic differentiation was observed among \u003cem\u003eAn. gambiae s.s\u003c/em\u003e. populations in West and Central Africa (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The genetic diversity and population structure constitute key parameters in determining the level of mosquito adaptation to different environments, and their response to vector control method. Consequently, malaria control strategies may vary according to the population structure, with different subpopulations of mosquitoes exhibiting distinct behavioural patterns. A recent study in Burkina Faso showed no geographical structure in the \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e population (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e); however, the sampling area of this study was restricted to 3 villages located in the Hauts-Bassins region in Burkina Faso.\u003c/p\u003e \u003cp\u003eTo extend beyond the scope of the previous study and achieve a more thorough understanding of the population structure in this country, mosquitoes were collected in the three climatic zones in Burkina Faso such as Soudanian zone, Soudano-sahelian zone and Sahelian zone. Using these data, we investigated whether population structure exists within \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e populations and determined the effects of vector control interventions in \u003cem\u003eAn. gambiae s.l\u003c/em\u003e.. An investigation is also conducted to determine whether we find the cryptic species outside of the range where they were described.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMosquito sampling\u003c/h2\u003e \u003cp\u003eBurkina Faso is characterised by the presence of three distinct climatic zones. The Sahelian zone, located in the northern region, the Sudano-Sahelian zone, situated in the central area, and the Soudanese zone, located in the southern region. The distinguishing characteristics of these regions include variations in rainfall, vegetation, and land use. The Sahelian zone, which is characterised by lower precipitation levels and a paucity of vegetative cover. The average temperature is 29.1\u0026deg;C, with an average rainfall of 627.1 mm per year. In contrast, the Sudano-Sahelian zone is characterised by moderate rainfall and wooded savannah vegetation. It is situated between the 900- and 600-mm isohyet, with an average temperature of 28.4\u0026deg;C and an average annual rainfall of 758.5 mm. The Soudanese zone is the wettest, with dense vegetation and rainfall ranging from 900 mm to 1200 mm per year. The average temperature is 27.7\u0026deg;C and the average rainfall is 900.8 mm per year. These ecological zones are characterised by a high presence of \u003cem\u003eAnopheles\u003c/em\u003e, keeping the country highly endemic for malaria. Mosquito samples have been collected in the 3 climatic zone of country from September to November 2022. The mosquitoes were collected using the pyrethroid spray capture technique. Following each capture session, the mosquitoes were returned to the field laboratory for morphological identification of the species, employing reference keys by Gillies et Coetzee (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and Gillies et Meillon (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The mosquitoes that had been identified were stored in 1.5 ml tubes containing 80% of alcohol for subsequent molecular analysis. The specimens have been selected for whole genome sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, S Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, whole genome sequence data generated during phase 3 of the Ag1000G project previously collected in the country was available (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, S Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWhole genome sequencing\u003c/h3\u003e\n\u003cp\u003eThe libraries were prepared and sequenced at the Wellcome Sanger Institute. The mosquito samples were sequenced individually at high coverage using the Illumina HiSeq sequencing technology. Paired multiplex libraries were prepared in accordance with the manufacturer's instructions, except for genomic DNA, which was fragmented using the Covaris Adaptive Focused Acoustics system on preference to nebulisation. The multiplexes comprised 12 individual tagged mosquitoes, with three sequencing lanes generated for each multiplex to even out variations in throughput between sequencing runs. Cluster generation and sequencing were performed in accordance with the manufacturer's protocol for paired-end sequence reads with insertion sizes between 100 and 200 bp. All individuals were sequenced with a target coverage of 30\u0026times;, resulting in the generation of paired reads of 100 bp and 150 bp.\u003c/p\u003e\n\u003ch3\u003eData processing\u003c/h3\u003e\n\u003cp\u003eThe sequences were subjected to analysis with the objective of identifying genetic variants, including single nucleotide polymorphisms (SNPs). The sequences were aligned against the \u003cem\u003eAn. gambiae\u003c/em\u003e AgamP4 reference genome using version 0.7.15 of the BWA Burrows-Wheeler Aligner (BWA) program. Deletions were realigned using the Genome Analysis Toolkit (GATK) version 3.7-0. Additionally, single nucleotide polymorphisms were identified using the same toolkit UnifiedGenotyper. Genotypes were identified on an independent basis for each sample. Genotypes were called for each sample independently, in genotyping mode, given all possible alleles at all genomic sites where the reference base was not \u0026ldquo;N. Coverage was capped at 250\u0026times; by random down-sampling. Complete specifications of the alignment ( \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ealignment\u003c/span\u003e ) and genotyping (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003egenotyping\u003c/span\u003e) pipelines are available from the \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003emalariagen/pipelines GitHub repository\u003c/span\u003e. Once the variants had been identified, a series of quality control analyses were conducted on the samples and variants to ensure the quality of the data.\u003c/p\u003e\n\u003ch3\u003eSample quality control\u003c/h3\u003e\n\u003cp\u003eSequencing and SNP calling was performed according to the Ag1000G phase 3 project protocols. The depth of coverage at all genomic positions was calculated for each sample. Samples were excluded if the median coverage across all chromosomes was below 10\u0026times;, or if less than 50% of the reference genome was covered by at least 1\u0026times;.\u003c/p\u003e \u003cp\u003eTo identify samples that have been affected by cross-contamination, the model for detecting contamination in next generation sequencing (NGS) alignments, as described by Jun et al. (2012) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), was implemented. In summary, the methodology entails the estimation of the probability of the observed alternate and reference allele counts under different contamination proportions, based on the approximation of population allele frequencies. The estimation of population allele frequencies was conducted using the Ag1000G phase 2 data release. Subsequently, the model computed the maximum likelihood value for the parameter alpha, which represents the percentage of contamination. Samples were excluded from the analysis if the alpha value was 4.5% or above. Pairwise genetic distance was calculated between all sample pairs within a sample set.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was conducted using the scikit-allel version 1.2.0 to identify and exclude individual samples that were identified as population outliers. The number of SNPs was reduced to 100,000 segregating non-singleton sites from chromosomes 3R and 3L to circumvent regions that were complicated by the presence of known introgression loci or paracentric inversions. The sex of all samples was determined based on the modal coverage ratio between the X chromosome and the autosomal chromosome arm 3R.\u003c/p\u003e\n\u003ch3\u003eSpecies assignment\u003c/h3\u003e\n\u003cp\u003eSpecies assignment was performed according to the Ag1000G phase 3 project protocols. A species was assigned to each individual that passed sample quality control (QC) using their genomic data, via two independent methods: ancestry-informative markers (AIMs) and PCA (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe data analyses were conducted using JupyterLab, a software environment for interactive computing and data visualization based on the Python programming language. Malariagen_data Python package (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) were used. The malariagen_data module contains a suite of functions designed for the extraction of genomic and site-specific data types for subsequent analysis. The modules employed were NumPy, Pandas, Dask.array, Matplotlib, Seaborn, Malariagen_data and Scikit-allel.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation structure\u003c/h3\u003e\n\u003cp\u003eThe principal component analysis (PCA) and unrooted neighbour joining tree (NJT) were used to investigate population structure. The PCA plot is a two- or three-dimensional representation of the mosquito samples, illustrating the degree of relatedness between them. This approach has been demonstrated to effectively illustrate the evolutionary relationships among mosquito samples. To investigate geographical structure, chromosome arm 3L was used, as it is not affected by large polymorphism inversions and rearrangements. The target range for the PCA and NJT analyses were set from position 15,000,000 to 41,000,000, with the exclusion of the telomeric and pericentromeric regions due to recombination and subsequent selection at linked sites. Conversely, all chromosome X region used to perform PCA and to construct NJT. The PCA analysis was conducted using 300,000 single-nucleotide polymorphisms (SNPs) that were distributed uniformly across each chromosome arm, with a minor allele count exceeding 2 and no missing calls. In addition, we analysed pairwise Fst using 3L from position 15,000,000 to 41,000,000.\u003c/p\u003e\n\u003ch3\u003eGenetic diversity\u003c/h3\u003e\n\u003cp\u003eThe diversity statistics were analysed in order to ascertain further information regarding demographic differences and changes within mosquito populations. The genetic diversity between species and populations was evaluated using the summary statistics, namely the nucleotide diversity (\u0026#120579;\u0026#120587;), Watterson's theta (θW) and Tajima's D. Chromosome arm 3L is typically favoured for the genetic diversity study due to the absence of large polymorphic inversions in any of the \u003cem\u003eAn. gambiae\u003c/em\u003e subspecies. The genome region spanning from 15,000,000 to 41,000,000 was used for the diversity calculations. Furthermore, we opted to use sites that are less influenced by selection, employing 4-fold degenerate sites in our analysis. In addition, an investigation was conducted into the heterozygosity and homozygosity across the genome. These analyses have the potential to facilitate a more in-depth understanding of the demographic history of the mosquito population. We used a window size of 10,000 sites.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMosquito populations\u003c/h2\u003e \u003cp\u003eThe entire genome of 631 \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e collected in 2022 and 34 samples collected in 2020\u0026ndash;2021 was sequenced using the Illumina HiSeq platform (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A species assignment analysis of the genomic data based on ancestry informative markers and principal components analysis revealed that most of the samples (477) were assigned to \u003cem\u003eAn. coluzzii\u003c/em\u003e, 114 samples were assigned to \u003cem\u003eAn. gambiae s.s\u003c/em\u003e., and 72 samples were assigned to \u003cem\u003eAn. arabiensis\u003c/em\u003e. Two samples were not assigned to a taxon due to an insufficient level of confidence in the assignment. In addition to the collected samples, data from 418 \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e from across Burkina Faso included in the MalariaGEN dataset were analysed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The cryptic species subgroup \u003cem\u003eAn. tengrela\u003c/em\u003e and \u003cem\u003eAn. goundry\u003c/em\u003e are named gcx4 in the MalariaGen dataset.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMosquito species distribution per region (new collection)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. arabiensis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAn. coluzzii\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAn. gambiae\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003egcx4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunassigned\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoucle du Mouhoun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCascades\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentre-Sud\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHauts-Bassins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSahel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMosquito species distribution per region (MalariaGen dataset)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. arabiensis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAn. coluzzii\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAn. gambiae\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003egcx4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCascades\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentre-Ouest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlateau Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHauts-Bassins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoucle de Mouhoun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGeographical population structure\u003c/h2\u003e \u003cp\u003eTo visualise the impact of geography on population structure, a principal component analysis was performed on chromosome 3L, which is used due to the low impact of chromosomal inversions and rearrangements. The analysis showed that there is a clear structure between species of the \u003cem\u003eAn. gambiae\u003c/em\u003e complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Hybrid individuals, labelled as \u0026ldquo;unassigned\u0026rdquo; taxa, were seen between \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e, and \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. arabiensis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The cryptic species (gcx4) included samples form a cluster without other samples very closed or mixed with them (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.a).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo investigate the impact of fine geographical scale on population structure, a principal component analysis was conducted per species and per region on chromosome 3L. The analysis of \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e samples from Burkina Faso reveal no evidence of a geographical structure in the population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u0026amp; b, S Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests a lack of a substantial geographical isolation barrier within the country, and no impact of the different climate zones on mosquito population structure. We performed a PCA per year to check whether there were any discrepancies in the samples collected each year in the country. To this end, mosquito samples previously collected in Burkina Faso were analysed. Principal component analysis (PCA) revealed the absence of any distinct structure in the populations of \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. gambiae s.s\u003c/em\u003e per year (S Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u0026amp; b).\u003c/p\u003e \u003cp\u003ePrincipal component analysis on the 3L chromosome arm show a weak structure in \u003cem\u003eAn. arabiensis\u003c/em\u003e population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The Neighbours joining tree (NJT) was performed to ensure the structure of \u003cem\u003eAn. arabiensis\u003c/em\u003e population. We found a slight difference between \u003cem\u003eAn. arabiensis\u003c/em\u003e samples from the Hauts-Bassins region and \u003cem\u003eAn. arabiensis\u003c/em\u003e samples from other regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, S Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We performed a PCA per year to check whether the weak structure observed in \u003cem\u003eAn. arabiensis\u003c/em\u003e population is due to the year of collection. PCA revealed the absence of any distinct structure in the populations of \u003cem\u003eAn. arabiensis\u003c/em\u003e per year (S Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe X chromosome plays an important role in mosquito speciation and reproductive isolation. The 3L chromosome gives the general geographical population structure while the X chromosome offers specific population genetic. The X chromosome may show clear structure even when autosomes show weak differentiation. PCA and NJT were performed on the X chromosome to ensure the population structure. A close affinity is observed between the gcx4 samples and \u003cem\u003eAn. coluzzii\u003c/em\u003e samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The analysis of the \u003cem\u003eAn. arabiensis\u003c/em\u003e samples revealthe presence of two distinct clusters on chromosome X separated Hauts-Bassins samples from other regions samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb \u0026amp; c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDemographic story of mosquito population\u003c/h2\u003e \u003cp\u003eWe conducted the analysis to determine the effects of malaria control intervention in \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e population\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity\u003c/h2\u003e \u003cp\u003eThe nucleotide diversity refers to a molecular genomic notion which serves to estimate the level of polymorphism present in a genomic region within a given population. It quantifies the number of nucleotide differences per site between two DNA sequences, selected at random, from the same population. The genetic diversity analysis of the \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e species showed that the cryptic species have demonstrated the lowest nucleotide diversity, at approximately 0.01, followed by the \u003cem\u003eAn. arabiensis\u003c/em\u003e populations, at 0.015, while the nucleotide diversity of the \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e population was approximately 0.023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The analysis revealed that all \u003cem\u003eAn. arabiensis\u003c/em\u003e populations showed similar levels of nucleotide diversity, except for the population from Hauts-Bassins region, which demonstrated the lowest diversity of approximately 0.012. This finding suggests that the demographic histories of all \u003cem\u003eAn. arabiensis\u003c/em\u003e populations within these regions may be substantially similar, apart from the Hauts-Bassins population. All the \u003cem\u003eAn. coluzzii\u003c/em\u003e populations exhibited a comparable level of nucleotide diversity, with a slight increase observed in \u003cem\u003eAn. coluzzii\u003c/em\u003e mosquitoes collected in cascades region. Within the \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e populations, mosquitoes collected in Hauts-Bassins exhibited the highest observed nucleotide diversity. The diversity estimators have grouped the \u003cem\u003eAn. arabiensis\u003c/em\u003e in single cluster except the \u003cem\u003eAn. arabiensis\u003c/em\u003e population from Hauts-Bassins. The \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e have been grouped also into single cluster except \u003cem\u003eAn\u003c/em\u003e. \u003cem\u003ecoluzzii\u003c/em\u003e in from Cascades, as well as the \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e from Hauts-Bassins. An investigation of the nucleotide diversity across region and years within each \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e population revealed a near-smooth distribution, except for the \u003cem\u003eAn. coluzzii\u003c/em\u003e population at Hauts-Bassins in 2017 (S Fig.\u0026nbsp;7). In general, the \u003cem\u003eAn. arabiensis\u003c/em\u003e population in Hauts-Bassins region exhibits the lowest diversity, while the \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e populations in the Hauts-Bassins region demonstrate the highest diversity.\u003c/p\u003e \u003cp\u003eThe taxon gcx4 has exhibited low nucleotide diversity 0.01 with a positive Tajima D value would be consistent with a reduction in population size, whereas all \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e have demonstrated a negative Tajima D value which is consistent with an excess of rare variants and historical population expansion. The confidence interval of gcx4 goes into negatives, it is quite possible that the population is still expanding slowly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMosquito heterozygosity\u003c/h2\u003e \u003cp\u003eA genome-wide measure of heterozygosity was defined as the ratio of heterozygous single-nucleotide polymorphisms (SNPs) to non-reference homozygous SNPs (Wang et al. 2015). The majority of \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e samples exhibited a heterozygosity per window of 0.015 (1.5%), with a decrease towards chromosome ends as is expected due to reduced rates of recombination (S Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLower heterozygosity and the presence of blocks of run of homozygosity were observed at specific loci, including insecticide resistance loci such as the glutathione S-transferase epsilon (GSTe) locus in the \u003cem\u003eAn. coluzzii\u003c/em\u003e population indicative selective pressure on this locus within the population (S Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eContrastingly, the majority of \u003cem\u003eAn. arabiensis\u003c/em\u003e samples exhibited heterozygosity levels of approximately 0.01 (1%) (S Fig.\u0026nbsp;6). This could be attributed to the comparatively smaller population size of \u003cem\u003eAn. arabiensis\u003c/em\u003e in the country when compared to \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImpact of vector control\u003c/h2\u003e \u003cp\u003eThe impact of the vector interventions is not always easy to measure. The use of innovative bednets should improve entomological intervention effectiveness, but their epidemiological impact on malaria control requires further evaluations including those based on genomics to track the potential link between the interventions and mosquito demography and evolution. Our study based on genomic analysis showed the different demographic changes in the major malaria vectors of the \u003cem\u003eAn. gambiae\u003c/em\u003e complex in Burkina Faso.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eAn. arabiensis\u003c/em\u003e population showed a clear distinction from the other members of the \u003cem\u003eAn. gambiae\u003c/em\u003e complex as \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. coluzzii\u003c/em\u003e characterized by lower levels of heterozygosity and nucleotide diversity. This contrasts with findings from Tanzania, where a previous study reported high levels of genetic diversity in \u003cem\u003eAn. arabiensis\u003c/em\u003e (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This might suggest that inbreeding is more prevalent in \u003cem\u003eAn. arabiensis\u003c/em\u003e populations in Burkina. \u003cem\u003eAnopheles arabiensis\u003c/em\u003e populations show evidence of a smaller population size or a bottleneck as demonstrated by their genetic diversity, heterozygosity and ROH. However, Tajima's D remains negative, suggesting that the population may still be expanding, although at a reduced rate. These findings indicate that \u003cem\u003eAn. arabiensis\u003c/em\u003e is not expanding as fast as \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. coluzzii\u003c/em\u003e which could be caused by vector control interventions especially for \u003cem\u003eAn. arabiensis\u003c/em\u003e from Hauts-Bassins.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnopheles gambiae s.s\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e populations showed significant nucleotide diversity with a normal heterozygosity level and negative Tajima\u0026rsquo;s D. This is often indicative of either a large effective population size, which serves to maintain polymorphism, or a history of population admixture or gene flow, which in turn introduces novel alleles. The observation that mosquitoes exhibit normal heterozygosity suggests that there were extensive panmixia and gene flow among them. In such large, interconnected populations, any local reduction in heterozygosity is rapidly compensated for by migration or mutation. Indeed, low heterozygosity and the presence of blocks of run of homozygosity were observed at specific loci, for example, in the GSTE locus in the \u003cem\u003eAn. coluzzii\u003c/em\u003e population. This could be indicative of selective pressure on this locus within the population. The selection across the population facilitates the dissemination of advantageous variants, including resistance alleles, for adaptation and surviving purposes. The absence of extensive runs of homozygosity further suggests that breeding is predominantly outbred, and that populations across diverse environments remain genetically well-mixed. The combination of significant nucleotide diversity and negative Tajima's D in \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e indicates a recent expansion in its population and /or pervasive selection sweep (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The ecological context, characterised by intense insecticide pressure (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), ongoing malaria transmission, and extensive habitat variability, suggests that these genomic patterns emerge from the interplay of rapid population growth and continual adaptive bouts. The mosquito\u0026rsquo;s capacity for rapid adaptation to control measures could be an indicator of its significant standing diversity. The large effective population of these mosquitoes serves to buffer genetic diversity against even strong directional forces, while facilitating sustained evolution in response to environmental and anthropogenic pressures. These results suggest that the implementation of vector control measures in the country is not an effective method for preventing the expansion of \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e populations.\u003c/p\u003e \u003cp\u003eThe taxon gcx4 has exhibited low nucleotide diversity with a positive Tajima D value, whereas all other \u003cem\u003eAn. gambiae s.l\u003c/em\u003e. have demonstrated a negative Tajima D value. The absence of novel samples may be attributable to the collection methodology used. It is noteworthy that gcx4 does not appear to be a significant vector, and they were detected at larval state. A positive Tajima D value indicates that gcx4 is experiencing a crash, but the Tajima\u0026rsquo;s D, whilst slightly positive, is not statistically significant. It is quite possible that the population is still expanding slowly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGene drive implementation\u003c/h2\u003e \u003cp\u003eGene drive technology is currently under development to strengthen vector control tools. Recently developed CRISPR\u0026ndash;Cas9-based gene-drive systems are highly efficient in laboratory settings, offering the potential to reduce the prevalence of vector-borne diseases (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Gene drives are designed to reduce insect vectors target population or to render them unable to transmit pathogen, in the face of the emergence of insecticide resistance, the novel technologies as gene drive could be worth for vector control. However, few countries have initiated the process of developing or adapting regulatory frameworks to oversee gene drive research and potential future applications. The scientific advances, combined with ethical and social considerations, will facilitate the transparent and responsible advancement of gene drive technology towards field implementation (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Beyond communities or society acceptance, vector genomics need to be studied to understand how the gene drive will spread and better predict or attenuate the rise of potential resistance in the field.\u003c/p\u003e \u003cp\u003ePopulation structure significantly affects the gene drive as the genetic barriers could lead to spread failure or altered dynamics. Our study showed a slight difference between \u003cem\u003eAn. arabiensis\u003c/em\u003e samples from the Hauts-Bassins region and \u003cem\u003eAn. arabiensis\u003c/em\u003e samples from other regions. The presence of geographical structure in \u003cem\u003eAn. arabiensis\u003c/em\u003e populations could impede the spread of the gene drive. This could be a potential initial target for the gene drive as it would limit its spread while allow scientists to evaluate the outcome in a more control fashion. The presence of population structure would need the development of a population specific gene drive (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Geographical structure was not detected in \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e in Burkina Faso. The genetic connectivity of mosquito populations has the potential to facilitate the rapid dissemination of a successful gene drive construct across extensive geographical areas. This is a key objective for population suppression drives that aim to have a substantial impact on malaria transmission. The capacity of propagation could represent a significant advantage in achieving malaria vector suppression or replacement. It has been demonstrated that even self-limiting gene drive systems, which are designed to spread for a limited number of generations, might achieve population elimination with repeated releases in the presence of high gene flow that helps to distribute the gene drive allele or construct (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).The interconnectedness of \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e populations therefore present a potential pathway for a gene drive to exert a country-wide effect on malaria transmission. The absence of geographical structure ought to facilitate the spread of the gene drive construct in the mosquito population within the country.\u003c/p\u003e \u003cp\u003eTherefore, it is crucial to evaluate gene drive constructs carefully in the context of the genetic diversity present in the target populations to accurately predict their impact and ensure their effectiveness across different ecological zones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure connected to climatic conditions\u003c/h2\u003e \u003cp\u003eNo geographical structure was identified in \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e collected in the three climatic zones in Burkina Faso implicating a high gene flow of these vectors in the country. In the Sahelian zone, dryest and hottest, no \u003cem\u003eAn. gambiae s.s\u003c/em\u003e. were found which could indicate that it is less adapted to the climate. The absence of geographical structure suggests that the presence of a substantial geographical isolation barrier in the country is unlikely, and that mosquito migration may potentially occur in the absence of such barriers. A study in Tanzania showed a geographical structure of \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e within the north-western and north- eastern population while structure was not found in \u003cem\u003eAn. arabiensis\u003c/em\u003e (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The Burkina Faso ecological factors in the three zones do not impact much the genetic diversity or heterogeneity of the mosquitoes. The absence of geographical population structure within \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e populations provides the evidence for spatial gene flow between these locations. This is an indication that the malaria control strategies must be monitored to avoid resistance that can spread quickly due to high gene flow. This is consistent with a study previously conducted by Kientega et al (2024) which showed no geographical structure within \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e collected in the Hauts-Bassins region (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnopheles arabiensis\u003c/em\u003e showed a structure distinguishing the Hauts-Bassins individuals in the Soudanian zone from other individuals which is clearly visible on chromosome X. This may be due to the role of the X chromosome on the postzygotic isolation between species. The X chromosome plays a major role in postzygotic isolation between \u003cem\u003eAn. gambiae s.s\u003c/em\u003e. and \u003cem\u003eAn. arabiensis\u003c/em\u003e (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Chromosome X is more divergent between these taxa than in the autosomes (\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The geographical structure observed in \u003cem\u003eAn. arabiensis\u003c/em\u003e may be due to the geographical factors since Hauts-Bassins region is in the Sudanian zone (humid zone). The Cascades region does not have many \u003cem\u003eAn. arabiensis\u003c/em\u003e, it might be due to the environment condition (wettest zone in the country).\u003c/p\u003e \u003cp\u003eThe cryptic taxa \u003cem\u003eAn. tengrela\u003c/em\u003e and \u003cem\u003eAn. goundry\u003c/em\u003e, previously identified in Burkina Faso, were not found in this study, despite the fact that samples were collected in areas including the two regions, Cascades (Sid\u0026eacute;radougou near Tengrela) and Plateau-Central (Po Dongo near Goundry Village), where the cryptic species had previously been identified (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This suggests that these cryptic species are not distributed throughout the country or the collection methods introduced a bias given that almost all known \u003cem\u003eAn. goundry\u003c/em\u003e and \u003cem\u003eAn. tengrela\u003c/em\u003e were collected as larvae. Further studies could be necessary to characterize the extent of the species distributions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings indicate that \u003cem\u003eAn. arabiensis\u003c/em\u003e is not expanding as fast as \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. coluzzii\u003c/em\u003e which could be caused by vector control interventions. The cryptic taxa \u003cem\u003eAn. tengrela\u003c/em\u003e and \u003cem\u003eAn. goundry\u003c/em\u003e, previously identified in Burkina Faso, were not found in this study, this suggests that the cryptic species are not distributed throughout the country or the collection methods introduced a bias given that almost all known \u003cem\u003eAn. goundry\u003c/em\u003e and \u003cem\u003eAn. tengrela\u003c/em\u003e were collected as larvae. Our study showed a geographical differentiation in \u003cem\u003eAn. arabiensis\u003c/em\u003e. Geographical population structure was not detected in \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e collected in the three climatic zones in Burkina Faso implicating a high gene flow of these vectors in the country. These findings have important implications for both current vector control strategies and the potential implementation of gene drive technologies. The presence of geographical structure in \u003cem\u003eAn. arabiensis\u003c/em\u003e populations could impede the spread of the gene drive. This could be a potential initial target for the gene drive as it would limit its spread while allow scientists to evaluate the outcome in a more control fashion. The lack of geographical population structure in \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e offers the potential for rapid spread of gene drive construct. It demands also a coordinated and a regional approach to manage the insecticide resistance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003es.s:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSensu stricto\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003es.l:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSensu lato\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAn:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eAnopheles\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSTe:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlutathione S-transferase epsilon\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNJT:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeighbour joining tree\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026#120579;\u0026#120587;:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNucleotide diversity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eθW:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWatterson's theta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQC:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIM:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAncestry-informative marker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAg1000G:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eAnopheles gambiae\u003c/em\u003e 1000 genome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBWA:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBurrows-Wheeler aligner\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGS:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNext generation sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGATK:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome analysis toolkit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNA:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeoxyribonucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLLIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong-lasting insecticidal net\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emm:\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMillimetre\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePair base\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe protocol of the sampling was approved by the Institutional Ethics Committee of the Institut de Recherche en Sciences de la Sant\u0026eacute; (32-2022/CEIRES). All the activities related to this paper were not required any other ethics approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work is funded by the Gates Foundation [INV-037164] and the Wellcome [224487/Z/21/Z] which the Institut de Recherche en Sciences de la Sant\u0026eacute; received to carried out mosquito sampling and data analyses. Also, the Pan-African Mosquito Control Association\u0026rsquo;s was funded by the Bill and Melinda Gates Foundation (INV-031595). The MalariaGEN Vector Observatory is supported by multiple institutes and funders. The Wellcome Sanger Institute\u0026rsquo;s participation was supported by funding from Wellcome (220540/Z/20/A, \u0026apos;Wellcome Sanger Institute Quinquennial Review 2021\u0026ndash;2026\u0026apos;) and the Bill \u0026amp; Melinda Gates Foundation (INV-001927 and INV-068808).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eHK, JB and MK conceived the study. HK, MK, HM, NT, GS performed samples collection. PP, AHK and AL provided edition. AM and CSC produced the genomic data. AD, AM and TN provided funding and resources. AD, HM, MN and CSC supervised the study. HK, JN and MK carried out data analysis and visualization. HK, JN and MK drafted the manuscript. All authors have read and approved this version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to acknowledge the international collaboration and funded as Bill Gate foundation and Wellcome Trust, which are supporting the safe and sustainable implementation of gene drive technology for malaria vector control in Africa. We greatly thank the Institut de Recherche en Sciences de la Sant\u0026eacute; and the Ministry of Health for the implementation of the nationwide sampling of malaria mosquitoes. Ministry of Health of Burkina Faso, the community health workers. We address our gratitude to the MalariaGEN Vector Observatory which is an international collaboration working to build capacity for malaria vector genomic research and surveillance. We would like to thank Liverpool School of Tropical Medicine, Broad Institute of Harvard and MIT. The authors would like to acknowledge the staff of the Wellcome Sanger Genomic Surveillance unit and the Wellcome Sanger Institute Sample Logistics, Sequencing and Informatics facilities for their contributions. Thank you to the health workers and the populations of the sampling sites for their cooperation during the sample collection.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eJupyter Notebooks and scripts to reproduce all the analyses are available in this link: https://drive.google.com/drive/folders/1I-gPhfqEY9Vn0pprrhgetc8z-41-3Ply?usp=sharing. The SNPs and haplotypes data are available on the homepage of MalariaGEN and can be accessed using the malariagen_data package. The raw sequences in FASTQ format and the aligned sequences in BAM format were stored in the European Nucleotide Archive (ENA, Study Accession n\u0026deg; ERR12776294-ERR12871281).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. 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Available from: www.pnas.org.\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Population structure, malaria vectors, gene drive, population expansion, vector control","lastPublishedDoi":"10.21203/rs.3.rs-9271625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9271625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMalaria control tools are nowadays challenged by the expansion and persistence of insecticide resistance in the malaria vector along with the changes in vector behaviour. To complement and reinforce the existing control tools, innovative vector control methods, such as gene drives, are currently under development. Understanding the \u003cem\u003eAnopheles gambiae s.l.\u003c/em\u003e population structure is crucial for the implementation of genetic control tools and improvement of existing control strategies. Here we investigated the genetic diversity and population structure of \u003cem\u003eAnopheles gambiae s.l.\u003c/em\u003e populations from three climatic zones in Burkina Faso.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results revealed the absence of geographical population structure in \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. gambiae\u003c/em\u003e s.s collected in the three climatic zones, implicating high gene flow within the country. \u003cem\u003eAnopheles arabiensis\u003c/em\u003e showed a structure that separates Hauts-Bassins samples in the Soudanian zone from others \u003cem\u003eAn. arabiensis\u003c/em\u003e samples. The cryptic species (\u003cem\u003eAn. goundry\u003c/em\u003e and \u003cem\u003eAn. tengrela\u003c/em\u003e) previously identified in the country were not detected in this study. Our findings indicate also population expansion in \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e however \u003cem\u003eAn. arabiensis\u003c/em\u003e populations show evidence of a smaller population size or a bottleneck.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings indicate that \u003cem\u003eAn. arabiensis\u003c/em\u003e is not expanding as fast as \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. coluzzii\u003c/em\u003e which could be caused by vector control interventions. The population structure of \u003cem\u003eAn. gambiae s.s\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e across the country is characterized by a lack of geographical differentiation. These findings have important implications for both current vector control strategies and the potential implementation of gene drive technologies. Indeed, the lack of geographical population structure offers the potential for rapid spread of gene drive construct. It demands also a coordinated and a regional approach to manage the insecticide resistance.\u003c/p\u003e","manuscriptTitle":"Non-geographical population structure of malaria vector Anopheles gambiae and Anopheles coluzzii but weak structure in Anopheles arabiensis within Burkina Faso: Implications for vector control and gene drive implementation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 06:36:32","doi":"10.21203/rs.3.rs-9271625/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"74249758994196530255411600898208024077","date":"2026-05-13T11:23:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T16:50:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84456608786934013633571580737691583710","date":"2026-05-06T02:38:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100974817687142321623724508729571954735","date":"2026-04-29T09:38:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40423813681110537140617316059374004718","date":"2026-04-26T08:39:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T12:22:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-02T19:45:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T23:15:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T23:14:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2026-03-30T19:43:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7f2d82a-dc66-44ff-af45-51f6491b194f","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"74249758994196530255411600898208024077","date":"2026-05-13T11:23:18+00:00","index":68,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T16:50:40+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"84456608786934013633571580737691583710","date":"2026-05-06T02:38:45+00:00","index":63,"fulltext":""},{"type":"reviewerAgreed","content":"100974817687142321623724508729571954735","date":"2026-04-29T09:38:37+00:00","index":41,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T06:36:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 06:36:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9271625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9271625","identity":"rs-9271625","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}