Spatial and temporal signatures of genomic insecticide resistance in the Anopheles arabiensis mosquito malaria vector from Ethiopia

Spatial and temporal signatures of genomic insecticide resistance in the Anopheles arabiensis mosquito malaria vector from Ethiopia | Research Square 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatial and temporal signatures of genomic insecticide resistance in the Anopheles arabiensis mosquito malaria vector from Ethiopia Araya Eukubay, Kelly L. Bennett, Habte Tekie, Anastasia Hernandez-Koutoucheva, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8686648/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jun, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Insecticide resistance in Anopheles mosquitoes threatens the effectiveness of key malaria control tools such as insecticide-treated nets (ITNs) and indoor residual spraying (IRS) in Ethiopia. Genomic analysis is essential to model known and novel molecular markers of insecticide resistance for effective resistance management. This study investigated insecticide resistance genes using whole-genome sequencing in a major malaria vector, Anopheles arabiensis sampled across the whole regions of Ethiopia and found high geographic and temporal variability in genes associated with insecticide resistance. The Vgsc-L995F target-site substitution in the voltage-gated sodium channel gene was highly prevalent in northern Ethiopia but less common at other sites. Metabolic modes of resistance in western Ethiopia were indicated by the high frequencies of copy number variants observed at the cytochrome P450 cluster Cyp6aa/p and the carboxylesterase Coeae2-7g . Frequencies of genetic markers associated with molecular target sites and metabolic resistance were generally lower in the Central Rift Valley. However, copy number variants (CNVs) at Gste2 and Cyp9k1 were observed at high frequency. We observed seasonal shifts in both target-site and metabolic marker frequencies, including increasing frequencies of Vgsc-L995F and several cytochrome P450 variants during the major transmission season. These patterns were specific to each location. Findings indicate that molecular insecticide resistance arises from a complex interplay of factors, including malaria control interventions, agricultural practices, human behavior, and possibly vector behavior. Selection scans revealed signals of selection on chromosome 2L, centered on the Coejhe1-5e genes in Werkamba, northernmost Ethiopia. Additional signals were detected on chromosome 3L (~ 20 Mb), near genes that may regulate detoxification pathways, including those associated with the ubiquitin–proteasome system in Asossa, western Ethiopia. Findings highlight the importance of integrating genomic surveillance of resistance markers into entomological monitoring to strengthen insecticide resistance management. They also underscore the need to investigate lesser-known sources of adaptive change that may have significant consequences for vector control. Biological sciences/Genetics Biological sciences/Molecular biology Biological sciences/Zoology Anopheles arabiensis target site resistance metabolic resistance insecticide resistance malaria mosquito vector Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Insecticides have been crucial in preventing and controlling vector-borne diseases. However, the emergence and widespread distribution of insecticide resistance is jeopardizing their effective implementation 1 . For example, the rapid emergence of insecticide resistance in Anopheles mosquito vectors has contributed to the resurgence of malaria, accounting for about 19% of resurgence events according to a systematic review by Cohen et al. 2 . Within Africa, the intensity of insecticide resistance and its underlying molecular basis varies greatly between mosquito vectors, across geography and over time 3 , 4 . These variations are often influenced by the strength and type of insecticide applied in major malaria interventions such as insecticide-treated nets (ITNs) and indoor residual spraying (IRS) 5 – 7 . In addition, the distribution of insecticide resistance is also influenced by human behaviors, including personal insecticide use 8 , 9 , and environmental factors such as anthropogenic pollution 10 , 11 . Monitoring such variation in insecticide resistance is vital to the application of appropriate resistance management strategies 12 , 13 , but it is largely based on laboratory-intensive phenotype bioassays often coupled with assays of only a few known molecular markers 14 – 16 . Genomic surveillance provides a complementary or alternative approach, allowing for the rapid and efficient monitoring of known molecular markers but also the identification of novel variation under selection 17 – 20 . This approach is not yet routinely applied to Anopheles vectors in Sub-Saharan Africa 21 , 22 and information on the geographical and seasonal presence of insecticide resistance is often lacking, including within the East African country of Ethiopia. Resistance to insecticides in Anopheles mosquitoes primarily evolves through target-site insensitivity and changes to metabolic detoxification, although there is growing evidence that cuticular changes and behavioral adaptations also contribute 23 , 24 .Target-site resistance involves alterations in the genes that encode for the protein targets of insecticides. For example, amino acid substitutions in the voltage-gated sodium-channel ( Vgsc ) gene, like L995S or the linked substitutions L995F and N1570Y, confer resistance to pyrethroids and DDT 19 , 25 . Additionally, whole genome sequence analysis recently revealed another two linked substitutions, Vgsc -V402L and Vgsc -I1527T, which synergistically enhance An. coluzzii resistance to pyrethroids 17 , 19 , 26 . The primary mutations conferring resistance to dieldrin are the amino acid substitutions A296G and A296S in the gamma-aminobutyric (GABA) gene Rdl 27 , 28 . Furthermore, the substitution Ace1 -A280S (also known as G119S) in the acetylcholinesterase-1 ( Ace-1 ) gene is known to confer resistance to carbamates and organophosphates 29 , often co-occurring with gene duplications that mitigate its associated fitness cost 26 , 30 . Metabolic insecticide resistance mechanisms can arise from overproduction, repetition, or enhancement of detoxification enzyme genes, leading to the enzymatic breakdown or sequestering of insecticides 31 . Copy number variants (CNVs) in detoxification gene clusters, such as cytochrome P450s ( Cyp6aa/p , Cyp9k1 ) 18 , glutathione S-transferase ( Gste2 ) 32 , and carboxylesterases ( Coeae2g-6g , Coeae1f-2f ) 33 , 34 are key drivers of resistance to pyrethroids, organophosphates, and DDT. For example, studies of Anopheles across West and East Africa have revealed high frequencies of Cyp6aa/p and Coeae2-7g CNVs associated with deltamethrin and pirimiphos-methyl resistance 18 , 33 , 35 , 36 , while duplications of the Gste2 gene contribute to resistance to multiple classes of insecticides 26 , 35 , 37 . The wide range of insecticide resistance mechanisms present within Anopheles vectors highlights the dynamic nature of genomic adaptation to insecticide pressure, emphasizing that it requires regular whole-genome monitoring to inform vector control strategies. Anopheles arabiensis is the primary malaria vector in Ethiopia and has a wide geographical distribution across the country 38 . This vector is found across lowland, highland, irrigated, and non-irrigated settings 39 – 42 , where it uses a wide range of habitats, including clear sunlit waters in permanent and temporary streams, hoofprints, artificial dams, roadside puddles, borrow pits, and rain pools 39 , 43 – 45 . The vector exhibits opportunistic, anthropozoophilic biting behavior, occurring throughout the night 46 , 47 . Although An. arabiensis shows a preference for human hosts indoors, it has a stronger inclination toward bovine hosts outdoors 47 – 49 . Its resting behavior is predominantly exophilic, favoring outdoor sites such as cattle sheds, pit shelters, ground holes, and vegetation over indoor resting spots. Bioassays of insecticide resistance in An. arabiensis have shown high resistance phenotypes to pyrethroids such as deltamethrin and permethrin, organophosphates including malathion, and the organochlorine DDT, with the levels differing across Ethiopia 14 , 50 – 53 . However, within Ethiopia, the molecular basis of insecticide resistance has been primarily investigated using targeted molecular assays of Vgsc knockdown down resistance mutations 14 , 50 – 53 , and gene expression studies of metabolic markers including cytochrome P450s, glutathione S-transferases, and carboxylesterases confined to a single location in the central region of the country 54 , 55 . For the first time, we used whole-genome sequencing to explore the genomic landscape of the An. arabiensis malaria vector across the whole of Ethiopia. Moreover, we present the first comprehensive genomic analysis of this species over both the major and minor malaria transmission seasons and across multiple years. Using longitudinal data, we analysed both SNPs and CNVs to investigate the spatial pattern of known target-site and metabolic resistance markers in the Anopheles genome. We also applied genome-wide selection scans to identify novel markers with the potential to be under local selection pressure from insecticides. Methods Mosquito Sampling Mosquitoes were collected from fifteen sites (Fig. 1 ) across the northern, central Great Rift Valley, southern, southwestern, and western Ethiopia. The sites are malarious with different ecoepidemiology, where both the ITN and IRS malaria vector control have been implemented for decades. Malaria transmission in most regions of Ethiopia follows a biannual seasonal pattern, with a major peak from September to December after the main rainy season (June-September) and a minor peak from April to July following the short rainy season (March and May) 56 – 58 . Mosquitoes were sampled during both major (September-December) and minor (April-July) transmission seasons between 2020 and 2023. Anopheles mosquitoes were sampled from selected households using a CDC light trap, mouth aspiration, and prokopack aspiration, both from indoor and outdoor locations, following the standard procedures described 59 . The CDC light traps were installed from 18:00 pm to 6:00 am. Battery-assisted Prokopack and manual aspirations were of a 15-minute duration per household from 6:00 am to 8:00 am, targeting indoor walls, under furniture, and animal shelters. Larval collections were performed using a standard dipper. Larvae were reared to adults for morphological identification, and all mosquito samples were identified morphologically using the standard identification keys 60 before individual preservation in 0.3 mL volume of PCR plates using 150 µL of 80% ethanol. Whole genome sequencing and processing Mosquitoes were whole-genome sequenced as part of the Malaria Vector Genome Observatory following the Anopheles gambiae 1000 Genomes Project ( https://malariagen.github.io/vector-data/ag3/methods.html ) protocols. DNA of individual mosquito specimens was extracted using the Qiagen DNeasy Blood and Tissue kit (Qiagen Sciences, MD, USA) according to the manufacturer's protocol. DNA was fragmented using Covaris Adaptive Focused Acoustics, and paired-end multiplex libraries were prepared as per Illumina’s instructions. All mosquito specimens were sequenced using the Illumina HiSeq X platform. Bioinformatic analysis, including raw sequence processing, quality filtering, read alignment, and variant calling, was performed using pipelines designed by the Anopheles gambiae 1000 Genomes project ( https://malariagen.github.io/vector-data/ag3/methods.html ). Reads were aligned to the AgamP4 reference genome using BWA version 0.7.15 and single nucleotide polymorphism (SNP) data generated with GATK version 3.7.0. Samples with a median coverage less than 10x, with less than 50% genome coverage, or with a high contamination threshold (> 4.5%) were excluded. CNV calling was based on copy number state generated across windows of the genome using normalized coverage data and a Gaussian Hidden Markov Model (HMM) implemented in hmmlearn ( GitHub - hmmlearn/hmmlearn: Hidden Markov Models in Python, with scikit-learn like API ) as previously described by Lucas et al. (2019) 18 . CNV calls were filtered such that only those with a high likelihood > 1000 predicted by the HMM model were retained. To increase CNV prediction accuracy, individuals with a high coverage variance (> 0.35) were removed. Genotypes at biallelic SNPs that met site filtering criteria were phased into haplotypes using a combination of read-backed and statistical phasing methods using WhatsHap V1.0 61 and SHAPEIT V4.2 62 , respectively, following protocols of the Anopheles gambiae 1000 Genome Project https://malariagen.github.io/vector-data/ag3/methods.html ). Insecticide Resistance Frequencies for population cohorts organised by the location, year, and month of collection were calculated for non-synonymous amino acid substitutions using the transcript for genes associated with pyrethroid, DDT, and organophosphate resistance. These included the gene encoding for the voltage-gated sodium channel ( Vgsc ; AGAP004707), which is the target site for pyrethroids, the GABA-gated chloride channel (resistance to dieldrin) gene, which is the target site for dieldrin ( Rdl ; AGAP006028), the acetylcholinesterase gene ( Ace1 ; AGAP001356), which codes for the target of organophosphates and a glutathione S-transferase gene, for which substitutions confer resistance to DDT and permethrin ( Gste2 ; AGAP009194). To account for sequencing and alignment error, only substitutions at a frequency > 5% were considered. We also generated the frequencies of copy number variants (CNVs) present at greater than 5% for cytochrome P450 genes such as Cyp6aa/p ( AGAP002862-AGAP013128) 63 and Cyp9k1 (AGAP000818), Cyp9m1 (AGAP009363) 64 , Cyp6z2 (AGAP008218) 65 , the carboxylesterases Coeae1f/2f (AGAP006227-AGAP006228) 34 and Coeae2-7g (AGAP006723-AGAP006727) 33 , the acetylcholinesterase Ace1 (AGAP001356) 66 , and glutathione S-transferase Gste2 (AGAP009194) 26 , 30 . All the analyses were conducted in the malariagen_data Python package. A chi-square contingency test was applied to assess the statistical significance of regional patterns in either amino acid substitutions or CNV frequencies at known resistance markers using the SciPy Python package 67 . For statistical analysis, the study sites were categorized by regions according to their geographical proximity: Northern (Werkamba, Raya-Azebo, Harbu), Central (Melka-Werer, Metehara, Wonji, Koka, Batu, Edo Gojola), Southern (Dilla, Arbaminch), Southwestern (Asendabo), and Western (Agnuak, Asossa). Genome-wide selection scans A genome-wide selection scan (GWSS) was performed using Garud’s H12 homozygosity statistic 68 across windows of the genome to detect signals of recent positive selection using the malariagen_data Python package ( https://malariagen.github.io/vector-data/ag3/api.html ). A window size calibration was performed for each chromosome arm before H12 analysis to determine the distribution of H12 values below 0.1 for the 95th percentile. The analysis was restricted only to chromosomes two and three since the X chromosome in the An. arabiensis is highly divergent from the An. gambiae reference genome. Results Population sampling A total of 1272 An. gambiae s.l. collected from fifteen sites were whole-genome sequenced at an average median sequence coverage of 34X. Specimens were identified as An. arabiensis based on Ancestry Informative Markers (AIM) and Principal Components Analysis (PCA) with known taxa during the standard data processing protocols of the Anopheles gambiae 1000G Project ( https://malariagen.github.io/vector-data/ag3/api.html ). A total of 69,636,442 SNPs passing site quality filters were segregated in the dataset, of which 33,534,459 were biallelic (Table 1 ). Table 1 Summary of Anopheles arabiensis samples whole-genome sequences available for analysis. Region Sampling site Latitude Longitude Year Month Anopheles arabiensis (N) Gambella Agnuak 7.504 34.293 2023 July 25 November 27 B/Gumuz Asossa 10.062 34.539 2023 June 23 October 13 South Ethiopia Arbaminch 6.033 37.583 2023 May 49 November 49 Dilla 6.254 38.173 2023 May 19 October 42 Oromia Asendabo 7.462 37.152 2023 July 50 November 45 Edo Gojola 7.975 38.722 2020 September 85 Batu 7.933 38.717 2021 September 7 2021 October 150 2023 May 49 2023 October 49 Koka 8.411 39.021 2021 October 110 Sodere 8.404 39.388 2021 September 6 Wonji 8.376 39.311 2023 May 46 October 6 November 30 Metehara 8.551 39.522 2023 June 50 October 9 November 40 Amhara Harbu 10.562 39.465 2023 May 49 November 49 Tigray Raya-Azebo 12.667 39.75 2023 July 50 November 28 Werkamba 13.667 38.917 2023 November 17 Afar Melka-Werer 9.19 40.105 2023 June 50 October 50 Table S1 Frequency of Vgs c-L995F resistance allele in Anopheles arabiensis from Ethiopia by geographical regions. Statistical significance was determined using a chi-square test (df = 1). A p-value ≤ 0.05 was considered significant. Location χ 2 df p -value Northern Central 129.11 1 < 0.0001 Southern 65.80 1 < 0.0001 Southwestern 27.73 1 < 0.0001 Western 33.70 1 < 0.0001 Central Southern 1.68 1 0.1956 Southwestern 0.59 1 0.4435 Western 0.01 1 0.9301 Southern Southwestern 2.55 1 0.1106 Western 0.37 1 0.5451 Western Southwestern 0.41 1 0.5245 Table S2 Frequency of Cyp6aa/p, Coeae2-7g , Cyp9k1, Gste2 , and Cyp6z2 copy number variation (CNVs) in Anopheles arabiensis across geographical regions in Ethiopia. Statistical significance was determined using a chi-square test (df = 1). A p-value ≤ 0.05 was considered significant. Location Genes Cyp6aa/p Coeae2-7g Cyp9k1 Gste2 Cyp6z2 χ 2 p-value χ 2 p-value χ 2 p-value χ 2 p-value χ 2 p-value Northern Central 1.442 0.2298 0.666 0.4144 27.380 < 0.0001 1.298 0.2546 0.006 0.9375 Southern 8.693 0.0032 2.551 0.1102 1.051 0.3052 4.541 0.0331 0.000 1.0000 Southwestern 0.000 0.9914 0.621 0.4307 1.940 0.1637 0.000 1.0000 0.621 0.4307 Western 39.773 < 0.0000 0.611 0.4343 0.012 0.9113 33.795 < 0.0000 0.023 0.8791 Central Southern 32.352 < 0.0000 1.289 0.2562 36.386 < 0.0001 2.295 0.1298 0.000 1.0000 Southwestern 0.202 0.6531 0.085 0.7707 4.304 0.0380 0.242 0.6225 1.169 0.2797 Western 109.873 < 0.0000 3.289 0.0697 11.098 0.0009 42.437 < 0.0000 0.159 0.6904 Southern Southwestern 5.909 < 0.0151 0.021 0.8836 5.104 0.0239 1.931 0.1646 0.817 0.3660 Western 10.908 < 0.0010 5.302 0.0213 1.008 0.3153 12.088 0.0005 0.085 0.7713 Western Southwestern 25.734 < 0.0000 2.172 0.1406 0.800 0.3711 18.550 < 0.0000 0.031 0.8601 Table S3 Putative genes showing increased H12 values (peaks) in the Asossa Anopheles arabiensis cohorts. Gene ID Conting Start End Name Description AGAP011227 3L 19635709 19644016 NaN Tartan AGAP011229 3L 19810767 19826552 NaN Tartan AGAP011231 3L 19867709 19868872 NaN fibrinogen AGAP011242 3L 19935159 19978825 NaN E3 ubiquitin ligase SMURF1/2 AGAP011243 3L 19979246 19979846 NaN Ribonuclease H2 subunit AGAP011244 3L 19979876 19981896 NaN rRNA 2'-O-methyltransferase fibrillarin AGAP011245 3L 19982535 19983733 NaN chloride channel AGAP011246 3L 19984046 19985106 NaN origin recognition complex subunit 6 AGAP011247 3L 19986323 19995087 NaN Longitudinals lacking protein-like AGAP011249 3L 19999770 20002401 NaN pre-mRNA-splicing factor CWC26 AGAP011251 3L 20072720 20077159 NaN ubiquitin-like protein 5 AGAP011254 3L 20102345 20111903 Arl5 ADP-ribosylation factor-like protein 5 Table S4 List of sample sets and accession numbers of the sequence samples. Samples with multiple run accessions are given within their respective sample sets. Sample sets Accession number 1270-VO-MULTI-PAMGEN-VMF00162 ERR6045394-ERR6045422,ERR6045425-ERR6045453,ERR6045456-ERR6045484 1270-VO-MULTI-PAMGEN-VMF00162 ERR5987917-ERR5987948,ERR5987949-ERR5987980,ERR5987981-ERR5988012 1270-VO-MULTI-PAMGEN-VMF00162 ERR5967771-ERR5967798,ERR5967799-ERR5967826,ERR5967827-ERR5967854 1270-VO-MULTI-PAMGEN-VMF00218 ERR10419623-ERR10419646 1270-VO-MULTI-PAMGEN-VMF00218 ERR10419672-ERR10419694 1270-VO-MULTI-PAMGEN-VMF00218 ERR10490762-ERR10490785 1270-VO-MULTI-PAMGEN-VMF00218 ERR10490810-ERR10490977 1270-VO-MULTI-PAMGEN-VMF00218 ERR10970454-ERR10970494, ERR11041589-ERR11041629 1324-VO-ET-GOLASSA-VMF00257 ERR12262329-ERR12262396 1324-VO-ET-GOLASSA-VMF00257 ERR12314213-ERR12314278 1324-VO-ET-GOLASSA-VMF00257 ERR12325996-ERR12326278 1324-VO-ET-GOLASSA-VMF00257 ERR12547479-ERR12547495 and ERR12767674 1324-VO-ET-GOLASSA-VMF00275 ERR12871282-ERR12871307 1324-VO-ET-GOLASSA-VMF00275 ERR12948031-ERR12948246 1324-VO-ET-GOLASSA-VMF00275 ERR12983126-ERR12983194 1324-VO-ET-GOLASSA-VMF00275 ERR13101382-ERR13101590 1324-VO-ET-GOLASSA-VMF00275 ERR13146891-ERR13146976 Supplementary Figures Target-site insecticide resistance variants To investigate the presence of target site resistance, we calculated the frequencies of non-synonymous (amino acid altering) substitutions at the voltage-gated sodium channel ( Vgsc : gene id AGAP004707-RD), acetylcholinesterase ( Ace-1 : gene id AGAP001356-RA), and GABA-gated channel gene ( Rdl : gene id AGAP006028-RA). The Vgsc -L995F associated with knockdown resistance to pyrethroids 19 , 69 was detected at a high frequency in the Northern Ethiopia Tigray region, with a frequency between 38% and 94% (Fig. 2 ). Lower but appreciable frequencies of 13–19% were also detected in the south-central regions of Amhara and South Ethiopia, as well as the western regions of Gambella and Benshangul Gumuz. We used a chi-squared contingency test to explore whether there was a significant difference in the frequency of Vgsc -L995F across the different regions of Ethiopia. We found a significant difference in the Vgsc -L995F frequency in the northern region when compared to frequencies in the central, southern, southwestern, and western regions of Ethiopia (P < 0.05, Supplementary Table S1). All other comparisons were non-significant. In some locations, we also observed a marked difference in frequencies moving into the major transmission season, but these were location-specific. For example, in Agnuak in western Gambella and Dilla in South Ethiopia, frequencies of L955F increased by 11% and 14%, respectively. In contrast, frequencies in northern Raya-Azebo and Asendabo in the southwest decreased by 8%. Substitutions associated with insecticide resistance, including Ace1 -G280S and Rdl -A296S, were not observed at > 5% frequency in all the sites except in the southern Ethiopian, Dilla town where the frequency of A296S was 6%, similar to previous observations for An. arabiensis from East Africa 36 , 70 . Copy number variation at known metabolic insecticide resistance genes We calculated the proportion of individuals with any number of CNV amplifications for each gene across study sites and seasons. The proportion of individuals with at least one CNV amplification at the Cyp6aa/p gene cluster, associated with pyrethroids and organophosphate insecticides 18 , 26 , was particularly high (65%) at the western site of Gambella. Furthermore, a chi-squared contingency test revealed the western region to have higher frequencies of CNVs compared to other regions in Ethiopia (P < 0.05, Supplementary Table 2, Fig. 3 ). In contrast, CNV frequencies at Cyp6aa/p were moderate or low but statistically similar (P > 0.05, Supplementary Table 2) in cohorts from Northern Ethiopia (0–29%), the Central Ethiopian Great Rift Valley (6–12%), and southwestern Ethiopia (6–7%). Moderate and statistically different frequencies were observed in Southern Ethiopia (6–29%) (P < 0.05, Supplementary Table 2). Frequencies of amplifications at the carboxylesterase Coeae2-7g were also comparatively high in western Gambella, with a frequency of 26% compared to less than 13% at other sites (Fig. 3 ). However, a chi-squared contingency test revealed that the CNV frequencies at the Coeae2-7g gene cluster were not significantly different for most regional comparisons, although frequencies in the western region were significantly different from that of southern Ethiopia (P < 0.05, Supplementary Table 2). CNV frequencies at other cytochrome P450s previously associated with resistance, including Cyp9k1 and Cyp6z2 , were generally comparable across sites (Fig. 3 ). Cyp9k1 amplification was consistently high (21–65%) and, in general, statistically similar across northern, western, southern, and southwestern Ethiopia (P > 0.05, Supplementary Table 2). However, frequencies were significantly higher, reaching up to 78% in the Central Great Rift Valley (P < 0.05, Supplementary Table 2) The frequency of Cyp6z2 CNVs remained low (0–18%) and was not statistically different across the different regions of Ethiopia (P > 0.05, Supplementary Table 2). We observed that frequencies in both Cyp9k1 and Cyp6z2 showed a frequency increase during the major transmission season in certain locations only, revealing that there is variation in the seasonal dynamics of metabolic resistance markers. For example, Cyp9k1 amplifications reached 78% compared to a lower frequency of 54% during the minor transmission season in Afar (Melka-Werer). Furthermore, Cyp6z2 CNV frequencies reached 15% in Tigray (Raya-Azebo) compared to 0% during the minor transmission season. In contrast to observations for cytochrome P450s and carboxylesterases, frequencies of Gste2 amplifications associated with pyrethroid and organophosphate resistance (Mitchell et al., 2014) 32 were highest in An. arabiensis from the northern region (35–98%), southwestern Ethiopia (79–91%), southern Ethiopia (70–78%) and the Central Ethiopian Great Rift Valley sites (58–88%), which were statistically similar to one another for most comparisons (P > 0.05, Supplementary Table 2). Conversely, frequencies of Gste2 were significantly lower in the western Ethiopian sites of Gambella and Benishangul-Gumuz (23–35%) (Fig. 3 ). Interestingly, the known SNPs of Gste2 (L119V, I114T) associated with resistance to DDT and pyrethroids 32 , 71 were absent in An. arabiensis from Ethiopia. Genome-wide selection scans A genome-wide scan for recent selection was performed using the H12 statistic, to investigate the presence of known and novel selection signals 68 . We identified a novel signal of selection on chromosome 2L, spanning 22.27–22.33 Mb, which encompasses the genes Coejhe1-5e . This selection signal was restricted to the cohorts from Werkamba only (Fig. 4 ). We checked for CNV amplifications at these genes and found a moderate to high (33–70%) frequency at Coejhe3e in all cohorts but a particularly high frequency of 82% in Werkamba. Although we found a 0–17% CNV frequency at Coejhe4e across all cohorts, CNVs were absent in Werkamba. We did not observe CNVs at the Coejhe1 , 2 , and 5e genes (Supplementary Figure S1). Similarly, we identified a signal of selection close to the Vgsc gene in the Werkamba site only (Fig. 4 ), where we found a high frequency of the L995F substitution. H12 analysis also revealed a novel signal of selection centred on chromosome 3L spanning 19.9–20.3 Mb, which is approximately 400 kb. The sweep signal encompasses 12 protein-coding genes (Supplementary Table S3), including chloride channel, fibrinogen, E3 ubiquitin ligase SMURF1/2, ADP-ribosylation factor-like protein 5, and ubiquitin factor-like protein 5. Notably, these selective signatures were only found in the Asossa site in Western Ethiopia (Fig. 4 ), and they were absent in any of the other study locations. Similarly, we found a signal of positive selection on the 3R chromosome around 31.84 Mb, encompassing the Cyp9m1, Cyp9m2, Gstu3 , odorant-receptor, nuclear hormone receptor FTZ-F1 beta , and cellular retinaldehyde-binding protein genes, in Northern Ethiopian cohorts from Raya-Azebo and Harbu and in the Wonji site located in the Central Rift Valley (Supplementary Figure S2). We screened for CNVs amplification in this region and found low frequencies at Gstu3 ranging from 0–14% across all cohorts, including those without a selection signal. Furthermore, we did not find amplifications at the other genes (Supplementary Figure S1), suggesting that the minor peak we observed could result from other structural variants such as SNPs, insertions, or indels. We also observed a more pronounced selection signal in the 2R chromosome centered on the Cyp6aa/p gene cluster (Supplementary Figure S3) in all populations of An. arabiensis , despite low frequencies of CNVs in cohorts from the Central Ethiopian Great Rift Valley. The clear signal of selection in the absence of a high CNV frequency suggests that an amino acid substitution (Supplementary Figure S4) or another structural variant may drive selection at this cytochrome P450. In support of the variable frequencies of Gste2 amplifications we observed, a signal of selection was detected at the gene on 3R in An. arabiensis from the Central Ethiopian Great Rift Valley and northern Amhara region (Supplementary Figure S2), but not the western region. Only a weak signal was observed in the northern sites. We did not observe a difference in selection signals between the major and minor malaria transmission seasons. Discussion In this study, we used whole-genome sequence analysis of 1272 An. arabiensis from Ethiopia to investigate molecular insecticide resistance mechanisms. We found marked geographical and temporal heterogeneity in resistance profiles across Ethiopia, pointing to potential influences of environmental and behavioral factors that warrant further investigation. Alongside established resistance markers, analysis of whole genomes allowed the identification of novel selection signals, highlighting new candidates for drivers of insecticide resistance, suggesting the need for targeted and routine monitoring to inform vector control strategies. We found considerable geographical variation in the molecular markers of insecticide resistance across Ethiopia. The historical use of DDT-based IRS since the 1960s 72 , followed by the scale-up of pyrethroid-based LLINs since 2005 73 , has been similarly applied across all malarious areas in Ethiopia. Despite the uniform application of vector control strategies across the country, the observed geographical differences in resistance profile could be impacted by differences in agricultural pesticide runoff. For example, the high frequency of Vgsc -L995F and selection signal observed in northern Ethiopia is likely driven by the region's intensive agricultural use of pyrethroids (lambda-cyhalothrin) and persistent DDT applications in horticultural crops, compounded by historical DDT exposure 74 . The western Ethiopia lowland also supports intensive large- and small-scale farming, mainly cotton and sesame 75 , and uses broad-spectrum pesticides 76 – 78 , which could explain the observed higher frequencies of cytochrome P450 and carboxylesterase CNVs. We also observed a comparatively lower frequency of Gste2 CNVs in the western region may reflect differing insecticide selection pressure. Although Gste2 amplifications are strongly associated with DDT metabolism 71 , their current distribution is more likely shaped by fitness costs 79 , cross-resistance to pyrethroids 71 , 80 – 82 , and other ecological factors such as agricultural chemical exposure and oxidative stress environments 83 , 84 . Furthermore, the comparable frequency of Cyp6aa/p CNVs we observed in the northern, southwestern and central Great Rift Valley regions may be influenced by shared agricultural practices, as farmers in both regions cultivate vegetables and cereals and apply similar classes of pesticides 74 , 85 . Additionally, the implementation of comparable vector control interventions such as ITNs and IRS may contribute to uniform selection pressure. However, there are also differences in intervention coverage and the type of insecticides used at the different locations that may contribute to variation in resistance patterns. For example, in western Ethiopia, where malaria transmission is high and year-round 57 , IRS with carbamate, organophosphate, and neonicotinoid (clothianidin-based) insecticides has been implemented 41 , 86 , 87 , alternating every two years in addition to the standard LLINs intervention in selected villages. This approach may increase metabolic-based resistance frequency while reducing pyrethroid target-site resistance-conferring markers due to potential fitness costs 88 , 89 . Additionally, it is possible that Anopheles behavior contributes to differences in the frequency of insecticide resistance-conferring markers. For example, in the cooler highlands of northern Ethiopia, lower outdoor temperatures may drive higher indoor biting rates among vectors such as An. arabiensis 42 , 90 , potentially exposing An. arabiensis to greater selection pressure from pyrethroid-based indoor interventions such as ITNs. However, this hypothesis is not fully supported due to a lack of species-specific comparative studies determining An. arabiensis behavior across the sites. However, it highlights that we know little of how this behavioral trait contributes to selection for insecticide resistance. Finally, we expect geography itself to impact the presence of genomic markers of insecticide resistance. For example, although not fully supported by the statistical analysis, we observed lower frequencies of both Vgsc -L995F and cytochrome P450s in the Great Rift Valley compared to elsewhere, despite high frequencies of Cyp9k1 , indicating the presence of selection for pyrethroid resistance 26 , 91 , 92 . Therefore, additionally or alternatively, it may be that restricted gene flow between the Great Rift Valley and other Ethiopian An. arabiensis has historically limited the spread of adaptive variation across the country. For example, genetic differentiation between the northwestern highlands and the Great Rift Valley of Ethiopia has been previously observed based on measures of F ST using microsatellite markers (Nyanjom et al., 2003) 93 . The high mountains surrounding the central Rift Valley populations may restrict mosquito gene flow, as evidenced by prior studies 70 , 94 , underscoring the necessity for further investigation into population connectivity in An. arabiensis . We observed marked differences in genomic insecticide resistance markers across the minor and major transmission seasons, demonstrating for the first time the potential of genomic data to track localised selection pressure from insecticides across a fine temporal scale. We found that seasonal differences were location-specific, and we did not have multi-year comparisons across seasons for statistical confirmation. However, we found that resistance-conferring variants (L995F and Cyp6aa/p CNV) increased during the major malaria transmission (September to December) in western (Gambella) and southern (Dilla, Arbaminch) cohorts. The increase was potentially driven by intense insecticide pressure from agricultural runoff 78 and/or LLIN utilization favoring individuals carrying resistant alleles over susceptible alleles, since IRS was not applied at these locations in 2023. In contrast, in northern (Raya-Azebo) and southwestern (Asendabo) Ethiopia, resistance variants unexpectedly increased in cohorts collected in July, which is typically the onset of the major rainy season rather than during the peak transmission season (September-December). This increase is possibly driven by year-round irrigation, sustained breeding habitats, and high intervention (ITN) utilization 95 , thereby maintaining selection pressure even before the onset of major transmission. Additionally, it may be coupled with climate variability, such as erratic rainfall, that supports emerging mosquito populations exposed to residual agricultural pesticides 74 , 96 . Our genome-wide selection scan revealed a novel signal of selection on the 2L chromosome in cohorts from northern Ethiopia (Werkamba). This selective sweep was centered on a gene cluster encoding five carboxylesterases: Coejhe1e , Coejhe2e , Coejhe3e , Coejhe4e , and Coejhe5e . While the primary function of these genes is in juvenile hormone regulation, their involvement in insecticide resistance is increasingly recognized. Indeed, there is a growing body of evidence demonstrating that members of these clusters, particularly Coejhe2e , are found consistently upregulated in organophosphate- and pyrethroid-resistant An. arabiensis populations 64 , 97 , suggesting their potential involvement in the metabolic insecticide resistance mechanism. We also identified a small selection peak on the 3L chromosome arm around 19.9–20.3 Mb in cohorts from Asossa in western Ethiopia. This region encompasses genes like E3 ubiquitin ligase SMURF1/2, chloride channel, ADP-ribosylation factor-like protein 5 (Arl5), ubiquitin factor-like protein 5, and fibrinogen. While these genes themselves have not yet been directly linked to insecticide resistance in Anopheles species, related E3 ubiquitin-protein ligase classes (such as SMURF1/2 like genes) have been identified as hub genes in resistance to organophosphate and pyrethroids in An. arabiensis and An. gambiae from Kenya and Benin 99 . Moreover, studies in yeast and Drosophila suggest that Arl5 and ubiquitin factor-like protein 5 support resistance by regulating stress response 100 – 102 . The ubiquitin factor-like protein 5 regulates mRNA splicing to assure that detoxification or antioxidant genes are expressed correctly. The Arl5 helps proteins move around the cell to keep it stable when oxidative stress is present 101 , 102 . Furthermore, the chloride channel ortholog, pICln-like protein, is a conserved regulator of spliceosomal snRNP assembly and cellular volume control in Drosophila with potential implications for gene expression under stress or developmental conditions 103 . Variants that enhance the efficiency of splicing or stress response may amplify the production of detoxification enzymes 104 , 105 . It is unknown whether these genes perform analogous functions in An. arabiensis and contribute to increased insecticide resistance, but they highlight the need for further functional investigation. In addition, we detected a small peak on the 3R chromosome centered around the Cyp9m1, Cyp9m2 , and Gstu3 genes previously reported to be overexpressed in pyrethroid-resistant An. arabiensis and An. funestus 64 , 106 , 107 . This peak was identified particularly in northern sites (Raya-Azebo and Harbu) and the central Great Rift Valley site of Wonji, but not in other surveyed sites. Cyp9m1 and Cyp9m2 encode Cyp450s commonly associated with metabolic insecticide detoxifications, while Gstu3 belongs to the Gste class, implicated in conjugation-based detoxification of pyrethroid and DDT. Although these genes are functionally linked to resistance, we did not detect CNVs at the Cyp9m1 and Cyp9m2 loci, which are typical of cytochrome P450 metabolic resistance 26 , 33 , 34 . Although CNVs in Gstu3 were identified at a low frequency, this was true of all study sites including those where no selection peak was observed. Our findings indicate that structural variation at this locus might be widespread but not necessarily under strong directional selection. Besides, the selection signal also comprises other genes with a potential role in insecticide resistance, including odorant receptors, the nuclear hormone receptor FTZ-F1 beta , and cellular retinaldehyde-binding protein 108 , 109 . Odorant receptors, while primarily involved in olfactory signalling, may contribute to behavioral resistance by modulating host-seeking and avoidance of insecticide-treated surfaces 108 , 110 . FTZ-F1 beta, a nuclear hormone receptor, regulates cuticular protein expression and has been shown to confer pyrethroid resistance in Culex pipiens pallens by modulating cuticular permeability 109 . The role of other genes in the mosquitoes is not studied. The selection signal for all aforementioned genes was minimal, but the strength of the peak could be affected by sampling effort. Further longitudinal sampling is needed to monitor the persistence of selection and confirm its potential significance in insecticide resistance. However, findings demonstrate how genomic surveillance is instrumental in identifying novel selection signals 20 , 22 , 36 , 70 , enabling the detection of genetic adaptations for further study that may contribute to insecticide resistance. Conclusion This study presents the first whole-genome sequencing data for An. arabiensis from Ethiopia using trans-seasonal collections across the major and minor malaria transmission seasons and multiple years from sites representing the whole of Ethiopia. This novel effort reveals, for the first time, the importance of capturing both spatial and seasonal patterns in the genomic landscape of insecticide resistance to uncover the full extent of its variation. An understanding of fine-scale temporal dynamics is critical in offering further resolution for the tailoring of malaria vector control strategies at the right time and with targeted interventions. In particular, our findings highlight the complexity of insecticide resistance in An. arabiensis across Ethiopia, driven by spatially heterogeneous mechanisms hypothesized to be shaped by vector control pressures, agricultural pesticide runoff, and interactions between human and vector behavior. In the Tigray region of northern Ethiopia, a high prevalence of Vgsc- L995F is likely to reduce the efficacy of pyrethroid-based interventions, potentially elevating vector survival. Conversely, in the western region, elevated Cyp6aa/p CNV amplification and moderate L995F frequencies may undermine LLIN and IRS efficacy while possibly promoting cross-resistance to organophosphates. In the central Rift Valley, high amplification of Cyp9k1 and Gste2 may circumvent the efficacy of conventional ITNs and IRS despite lower Vgsc-L995F frequencies. Further study is required to directly link the agricultural use of pesticides and human and vector behavior to both geographical and temporal genomic resistance patterns, as well as resistance phenotypes. Such studies will be crucial to comprehensively recognise the complexity of the selection landscape and to inform evidence-based control strategies. Data availability The sequences of the samples identified in this study were submitted to the European Nucleotide Archive (ENA; accession numbers are given in Supplementary Table S4). Declarations Competing interests The authors declare that they have no competing interests. Funding 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 Gates Foundation (INV-001927 and INV-068808). The Liverpool School of Tropical Medicine's participation was supported by and the Gates Foundation (INV-068808), the National Institute of Allergy and Infectious Diseases ([NIAID] R01-AI116811), with additional support from the Medical Research Council (MR/P02520X/1). The latter grant is a UK-funded award and is part of the EDCTP2 programme supported by the European Union. Lemu Golassa of Addis Ababa University was funded by Gates Foundation Grant no. INV-050277. Author Contribution AE and KLB conducted the data analysis, interpretation and wrote the manuscript. AE and FG conducted sample collection, processing, and data collection. HT facilitated field sample collection and assisted data interpretation. AHK and FG conducted data analysis. AM, DA, CCC and LG conceptualised and designed the study, interpreted the data and assisted in drafting the manuscript. All the authors revised the manuscript. Acknowledgement This study was supported by the MalariaGEN Vector Observatory, which is an international collaboration working to build capacity for malaria vector genomic research and surveillance, and involves contributions by the following institutions and teams. Wellcome Sanger Institute: Paballo Chauke, Katherine Figueroa, Kevin Howe, Mara Lawniczak; Liverpool School of Tropical Medicine: Julia Jeans, Lee Hart, Jon Brenas, Victoria Simpson, Eric Lucas, Sanjay Nagi, Martin Donnelly; Broad Institute of Harvard and MIT: Jessica Way, George Grant; Pan-African Mosquito Control Association: Jane Mwangi, Edward Lukyamuzi, Sonia Barasa, Ibra Lujumba, Elijah Juma. The authors would like to thank the staff of the Wellcome Sanger Genomic Surveillance unit and the Wellcome Sanger Institute Sample Logistics, Sequencing and Informatics facilities for their contributions. Data Availability The sequences of the samples identified in this study were submitted to the European Nucleotide Archive (ENA; accession numbers are given in Supplementary Table S4). References Van Den Berg, H. et al. Recent trends in global insecticide use for disease vector control and potential implications for resistance management. Sci. Rep. 11 , 23867 (2021). Cohen, J. M. et al. Malaria resurgence: A systematic review and assessment of its causes. Malar J 11 , (2012). Ochomo, E. O. et al. The efficacy of long-lasting nets with declining physical integrity may be compromised in areas with high levels of pyrethroid resistance. Malar. J. 12 , 1–10 (2013). Coleman, M. et al. Developing global maps of insecticide resistance risk to improve vector control. Malar. J. 16 , 1–9 (2017). Collins, E. et al. The relationship between insecticide resistance, mosquito age and malaria prevalence in Anopheles gambiae s.l. from Guinea. Sci. Rep. 9 , 1–12 (2019). Metelo-Matubi, E. et al. The effect of a mass distribution of insecticide-treated nets on insecticide resistance and entomological inoculation rates of Anopheles gambiae s.l. in Bandundu City, Democratic Republic of Congo. Pan Afr. Med. J. 40 , 1–18 (2021). Teshome, A. et al. Resistance of Anopheles stephensi to selected insecticides used for indoor residual spraying and long-lasting insecticidal nets in Ethiopia. Malar. J. 22 , 1–8 (2023). Boakye, A. D. A., Adasi, K., Appawu, M., Brown, C. A. & Wilson, M. D. Patterns of Household Insecticide Use and Pyrethroid Resistance in Anopheles Gambiae Sensu Stricto (Diptera: Culicidae) within the Accra Metropolis of Ghana Published By : Entomological Society of Southern Africa. Afr. Entomol. 17 , 125–130 (2009). Chiana, N. S. et al. Rapid evolution of insecticide resistance and patterns of pesticides usage in agriculture in the city of Yaoundé, Cameroon. Parasit. Vectors . 15 , 186 (2022). Nkya, T. E., Akhouayri, I., Kisinza, W. & David, J. P. Impact of environment on mosquito response to pyrethroid insecticides: Facts, evidences and prospects. Insect Biochem. Mol. Biol. 43 , 407–416 (2013). Reid, M. C. & McKenzie, F. E. The contribution of agricultural insecticide use to increasing insecticide resistance in African malaria vectors. Malar. J. 15 , 1–8 (2016). Moyes, C. L. et al. Evaluating insecticide resistance across african districts to aid malaria control decisions. Proc. Natl. Acad. Sci. U. S. A. 117, 22042–22050 (2020). Hancock, P. A. et al. Mapping trends in insecticide resistance phenotypes in African malaria vectors. PLoS Biol. 18 , 1–23 (2020). Messenger, L. A. et al. Insecticide resistance in Anopheles arabiensis from Ethiopia (2012–2016): A nationwide study for insecticide resistance monitoring. Malar. J. 16 , 1–14 (2017). Riveron, J. M. et al. Insecticide Resistance in Malaria Vectors: An Update at a Global Scale. in IntechOpen (2018). 10.5772/intechopen.78375 Soumaila, H. et al. Countrywide insecticide resistance monitoring and first report of the presence of the L1014S knock down resistance in Niger, West Africa. Malar. J. 21 , 1–14 (2022). The Anopheles gambiae 1000 Genomes Consortium. Genetic diversity of the African malaria vector Anopheles gambiae. Nature 552 , 96–100 (2017). Lucas, E. R. et al. Whole-genome sequencing reveals high complexity of copy number variation at insecticide resistance loci in malaria mosquitoes. Genome Res. 29 , 1250–1261 (2019). Clarkson, C. S. et al. The genetic architecture of target-site resistance to pyrethroid insecticides in the African malaria vectors Anopheles gambiae and Anopheles coluzzii. Mol. Ecol. 30 , 5303–5317 (2021). Kientega, M. et al. Whole-genome sequencing of major malaria vectors reveals the evolution of new insecticide resistance variants in a longitudinal study in Burkina Faso. Malar. J. 23 , 280 (2024). Hancock, P. A., Ochomo, E. & Messenger, L. A. Genetic surveillance of insecticide resistance in African Anopheles populations to inform malaria vector control. Trends Parasitol. 40 , 604–618 (2024). Nagi, S. C. et al. Targeted genomic surveillance of insecticide resistance in African malaria vectors. Biorxiv http://biorxiv.org/lookup/doi/ (2025). 10.1101/2025.02.14.637727 Carrasco, D. et al. Behavioural adaptations of mosquito vectors to insecticide control. Curr. Opin. Insect Sci. 34 , 48–54 (2019). Dortey, M. D. A. et al. Exploring the metabolic and cuticular mechanisms of increased pyrethroid resistance in Anopheles gambiae S.l populations from Ghana. Sci. Rep. 15 , 1–10 (2025). Diabate, A. et al. The spread of the Leu-Phe kdr mutation through Anopheles gambiae complex in Burkina Faso: Genetic introgression and de novo phenomena. Trop. Med. Int. Health . 9 , 1267–1273 (2004). Lucas, R. E. et al. Genome-wide association studies reveal novel loci associated with pyrethroid and organophosphate resistance in Anopheles gambiae and Anopheles coluzzii. Nat. Commun. 14 , 4946 (2023). Grau-Bové, X. et al. Evolution of the insecticide target Rdl in african anopheles is driven by interspecific and interkaryotypic introgression. Mol. Biol. Evol. 37 , 2900–2917 (2020). Du, W. et al. Independent mutations in the Rdl locus confer dieldrin resistance to Anopheles gambiae and An. arabiensis. Insect Mol. Biol. 14 , 179–183 (2005). Weill, M. et al. The unique mutation in ace-1 giving high insecticide resistance is easily detectable in mosquito vectors. Insect Mol. Biol. 13 , 1–7 (2004). Lucas, E. R. et al. A high throughput multi-locus insecticide resistance marker panel for tracking resistance emergence and spread in Anopheles gambiae. Sci. Rep. 9 , 1–10 (2019). Ffrench-Constant, R. H. The molecular genetics of insecticide resistance. Genetics 194 , 807–815 (2013). Mitchell, S. N. et al. Metabolic and target-site mechanisms combine to confer strong DDT resistance in Anopheles gambiae. PLoS ONE 9 , (2014). Lucas, E. R. et al. Copy number variants underlie the major selective sweeps in insecticide resistance genes in Anopheles arabiensis from Tanzania. PLoS Biol. 22 , e3002898 (2024). Nagi, S. C. et al. Parallel Evolution in Mosquito Vectors-A Duplicated Esterase Locus is Associated With Resistance to Pirimiphos-methyl in Anopheles gambiae. Mol. Biol. Evol. 41 , 1–11 (2024). Kouamé, R. M. A. et al. Widespread occurrence of copy number variants and fixation of pyrethroid target site resistance in Anopheles gambiae (s.l.) from southern Côte d’Ivoire. Curr Res. Parasitol. Vector-Borne Dis 3 , (2023). Polo, B. et al. Genomic surveillance reveals geographical heterogeneity and differences in known and novel insecticide resistance mechanisms in Anopheles arabiensis across Kenya. BMC Genom. 26 , 599 (2025). Nagi, S. C., Oruni, A., Weetman, D. & Donnelly, M. J. RNA-Seq-Pop: Exploiting the sequence in RNA sequencing—A Snakemake workflow reveals patterns of insecticide resistance in the malaria vector Anopheles gambiae. Mol. Ecol. Resour. 23 , 946–961 (2023). Eligo, N. et al. Anopheles arabiensis continues to be the primary vector of Plasmodium falciparum after decades of malaria control in southwestern Ethiopia. Malar. J. 23 , 1–9 (2024). Gone, T., Balkew, M. & Gebre-Michael, T. Comparative entomological study on ecology and behaviour of Anopheles mosquitoes in highland and lowland localities of Derashe District, southern Ethiopia. Parasit. Vectors . 7 , 1–10 (2014). Demissew, A. et al. Impact of sugarcane irrigation on malaria vector Anopheles mosquito fauna, abundance and seasonality in Arjo-Didessa, Ethiopia. Malar. J. 19 , 1–8 (2020). Haileselassie, W. et al. The effect of irrigation on malaria vector bionomics and transmission intensity in western Ethiopia. Parasit. Vectors . 14 , 1–11 (2021). Esayas, E. et al. Bionomic characterization of Anopheles mosquitoes in the Ethiopian highlands and lowlands. Parasit. Vectors . 17 , 1–15 (2024). Getachew, D., Balkew, M. & Tekie, H. Anopheles larval species composition and characterization of breeding habitats in two localities in the Ghibe River Basin, southwestern Ethiopia. Malar. J. 19 , 1–13 (2020). Tsegaye, A. et al. Anopheles larval habitats seasonality and environmental factors affecting larval abundance and distribution in Arjo-Didessa sugar cane plantation, Ethiopia. Malar. J. 22 , 1–12 (2023). Tarekegn, M., Tekie, H., Wolde-hawariat, Y. & Dugassa, S. Habitat characteristics and spatial distribution of Anopheles mosquito larvae in malaria elimination settings in Dembiya District, Northwestern Ethiopia. Int. J. Trop. Insect Sci. 42 , 2937–2947 (2022). Bedasso, A. H. et al. Malaria vector feeding, peak biting time and resting place preference behaviors in line with Indoor based intervention tools and its implication: scenario from selected sentinel sites of Ethiopia. Heliyon 8 , (2022). Ashine, T. et al. Plasticity of blood feeding behavior of Anopheles mosquitoes in Ethiopia: a systematic review. Parasit. Vectors . 17 , 1–13 (2024). Tarekegn, M., Dugassa, S., Negash, Y., Tekie, H. & Woldehawariat, Y. Host feeding preferences of malaria vectors in Dembia district, Northwestern Ethiopia. Int. J. Trop. Insect Sci. https://doi.org/10.1007/s42690-025-01585-6 (2025). Abossie, A. et al. Higher outdoor mosquito density and Plasmodium infection rates in and around malaria index case households in low transmission settings of Ethiopia: Implications for vector control. Parasit. Vectors . 17 , 1–14 (2024). First evidence of. high knockdown resistance frequency in Anopheles arabiensis (Diptera: Culicidae) from Ethiopia. Am. J. Trop. Med. Hyg. 83 , 122–125 (2010). Balkew, M. et al. Insecticide resistance in Anopheles arabiensis (Diptera: Culicidae) from villages in central, northern and south west Ethiopia and detection of kdr mutation. Parasit. Vectors . 3 , 40 (2010). Asale, A. et al. Evaluation of the efficacy of DDT indoor residual spraying and long-lasting insecticidal nets against insecticide resistant populations of Anopheles arabiensis Patton (Diptera: Culicidae) from Ethiopia using experimental huts. Parasit. Vectors . 7 , 1–9 (2014). Alemayehu, E. et al. Mapping insecticide resistance and characterization of resistance mechanisms in Anopheles arabiensis (Diptera: Culicidae) in Ethiopia. Parasit. Vectors . 10 , 1–11 (2017). Simma, E. A. et al. Genome-wide gene expression profiling reveals that cuticle alterations and P450 detoxification are associated with deltamethrin and DDT resistance in Anopheles arabiensis populations from Ethiopia. Pest Manag Sci. 75 , 1808–1818 (2019). Messenger, L. A. et al. A whole transcriptomic approach provides novel insights into the molecular basis of organophosphate and pyrethroid resistance in Anopheles arabiensis from Ethiopia. Insect Biochem. Mol. Biol 139 , (2021). Midekisa, A., Beyene, B., Mihretie, A., Bayabil, E. & Wimberly, M. C. Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia. Parasit. Vectors . 8 , 1–11 (2015). Taffese, H. S. et al. Malaria epidemiology and interventions in Ethiopia from 2001 to 2016. 7 , 1–9 (2018). Kitawa, Y. S. & Asfaw, Z. G. Space-time modelling of monthly malaria incidence for seasonal associated drivers and early epidemic detection in Southern Ethiopia. Malar. J. 22 , 301 (2023). WHO. Malaria Entomology and Vector Control: Participants Guide. 94–97. (2013). Coetzee, M. Key to the females of Afrotropical Anopheles mosquitoes (Diptera: Culicidae). Malar. J. 19 , 70 (2020). Martin, M. et al. WhatsHap: fast and accurate read-based phasing. 1–18 (2016). https://doi.org/10.1101/085050 Delaneau, O., Zagury, J. F., Robinson, M. R., Marchini, J. L. & Dermitzakis, E. T. Accurate, scalable and integrative haplotype estimation. Nat. Commun. 10 , 24–29 (2019). Njoroge, H. et al. Identification of a rapidly-spreading triple mutant for high-level metabolic insecticide resistance in Anopheles gambiae provides a real-time molecular diagnostic for antimalarial intervention deployment. Mol. Ecol. 31 , 4307–4318 (2022). Omoke, D. et al. Whole transcriptomic analysis reveals overexpression of salivary gland and cuticular proteins genes in insecticide-resistant Anopheles arabiensis from Western Kenya. BMC Genomics 25 , (2024). Ibrahim, S. S. et al. Molecular drivers of insecticide resistance in the Sahelo-Sudanian populations of a major malaria vector Anopheles coluzzii. BMC Biol. 21 , 125 (2023). Edi, C. V. et al. CYP6 P450 Enzymes and ACE-1 Duplication Produce Extreme and Multiple Insecticide Resistance in the Malaria Mosquito Anopheles gambiae. PLoS Genet 10 , (2014). Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods . 17 , 261–272 (2020). Garud, N. R., Messer, P. W., Buzbas, E. O. & Petrov, D. A. Recent Selective Sweeps in North American Drosophila melanogaster Show Signatures of Soft Sweeps. PLoS Genet. 11 , 1–32 (2015). Martinez-Torres, D. et al. Molecular characterization of pyrethroid knockdown resistance (kdr) in the major malaria vector Anopheles gambiae s.s. Insect Mol. Biol. 7 , 179–184 (1998). Mwinyi, S. H. et al. Genomic Analysis Reveals a New Cryptic Taxon Within the Anopheles gambiae Complex With a Distinct Insecticide Resistance Profile in the Coast of East Africa. Mol. Ecol. https://doi.org/10.1111/mec.17762 (2025). Riveron, J. M. et al. A single mutation in the GSTe2 gene allows tracking of metabolically based insecticide resistance in a major malaria vector. Genome Biol. 15 , 1–20 (2014). WHO. Implementation of Indoor Residual Spraying of Insecticides for Malaria Control in the WHO African Region Report. 1–65. (2007). Gari, T. & Lindtjørn, B. Reshaping the vector control strategy for malaria elimination in Ethiopia in the context of current evidence and new tools: Opportunities and challenges 11 Medical and Health Sciences 1108 Medical Microbiology 11 Medical and Health Sciences 1117 Public He. Malar. J. 17 , 1–8 (2018). Gebretsadkan, A. et al. Knowledge and practice of farmers on safe use and handling of chemical pesticides: in the case of Tigray, Northern Ethiopia. J Integr. Pest Manag 16 , (2025). Degife, A. W., Zabel, F. & Mauser, W. Assessing land use and land cover changes and agricultural farmland expansions in Gambella Region, Ethiopia, using Landsat 5 and Sentinel 2a multispectral data. Heliyon 4 , e00919 (2018). Deressa, A. D. & Alemu, K. Assessment of Pesticide Use by Farmers in Assosa District, Benishagul Gumuz National Regional State of Ethiopia. J. Health Environ. Res. 8 , 37 (2022). Afata, T. N., Mekonen, S., Shekelifa, M. & Tucho, G. T. Prevalence of Pesticide Use and Occupational Exposure Among Small-Scale Farmers in Western Ethiopia. Environ. Health Insights . 16 , 1–11 (2022). Keru, I. U., Geletu, A. K. & Wondimu, K. T. Multi residue analysis of persistent organochlorine pesticides in fish from Baro River, Gambella, Southwest Ethiopia. Reg. Stud. Mar. Sci. 66 , 103126 (2023). Tchouakui, M. et al. Fitness Costs of the Glutathione S-Transferase Epsilon 2 (L119F-GSTe2) Mediated Metabolic Resistance to Insecticides in the Major African Malaria Vector Anopheles Funestus. Genes 9 , 645 (2018). Kouamo, M. F. M. et al. Allelic variation in a cluster of epsilon glutathione S-transferase genes contributes to DDT and pyrethroid resistance in the major African malaria vector Anopheles funestus. BMC Genom. 26 , 452 (2025). Assatse, T. et al. Anopheles funestus Populations across Africa Are Broadly Susceptible to Neonicotinoids but with Signals of Possible Cross-Resistance from the GSTe2 Gene. Trop. Med. Infect. Dis. 8 , 244 (2023). Tchigossou, G. M. et al. Investigation of DDT resistance mechanisms in Anopheles funestus populations from northern and southern Benin reveals a key role of the GSTe2 gene. Malar J 19 , (2020). Messenger, L. A. et al. Insecticide resistance in Anopheles arabiensis from Ethiopia (2012–2016): a nationwide study for insecticide resistance monitoring. Malar. J. 16 , 469 (2017). Demissew, A. et al. Evidence of pyrethroid resistance in Anopheles amharicus and Anopheles arabiensis from Arjo-Didessa irrigation scheme, Ethiopia. PLOS ONE . 17 , e0261713 (2022). Jirata, U., Asere, T. G., Balcha, Y. B. & Gure, A. Levels of organochlorine pesticides in onion and tomato samples from selected towns of Jimma Zone. Ethiopia Heliyon . 10 , e35033 (2024). MIS. Ethiopian National Malaria Indicator Survey. (2015). PMI Vectorlink Project. The PMI VectorLink Project Annual Report:October 1, 2021–September 30,.. (2022). https://stacks.cdc.gov/view/cdc/146398 (2022). Medjigbodo, A. A. et al. Putative pleiotropic effects of the knockdown resistance (L1014F) allele on the life-history traits of Anopheles gambiae. Malar. J. 20 , 480 (2021). Fitness cost of target-site and metabolic resistance to pyrethroids drives restoration of susceptibility in a highly resistant Anopheles gambiae population from Uganda |. PLOS One https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271347 Animut, A., Balkew, M. & Lindtjørn, B. Impact of housing condition on indoor-biting and indoor-resting Anopheles arabiensis density in a highland area, central Ethiopia. Malar. J. 12 , 1–8 (2013). Hearn, J. et al. Multi-omics analysis identifies a CYP9K1 haplotype conferring pyrethroid resistance in the malaria vector Anopheles funestus in East Africa. Mol. Ecol. 31 , 3642–3657 (2022). Djoko Tagne, C. S. et al. A single mutation G454A in the P450 CYP9K1 drives pyrethroid resistance in the major malaria vector Anopheles funestus reducing bed net efficacy. Genetics 229 , 1–40 (2025). Nyanjom, S. R. G. et al. Population Genetic Structure of Anopheles arabiensis Mosquitoes in Ethiopia and Eritrea. J. Hered . 94 , 457–463 (2003). Odero, J. O. et al. Genomic evidence of spatially structured gene flow and divergent insecticide resistance backgrounds of the malaria vector Anopheles funestus in Tanzania. Genetics 230 , iyaf117 (2025). Tadesse, E. M., Baruda, Y. S. & Tadesse, T. M. Assessment of household insecticide-treated bed net ownership, utilization, and associated factors in Kersa Woreda, Jimma Zone, Southwest Ethiopia. J. Health Popul. Nutr. 43 , 214 (2024). Jirata, U., Asere, T. G., Balcha, Y. B. & Gure, A. Levels of organochlorine pesticides in onion and tomato samples from selected towns of Jimma Zone. Ethiopia Heliyon . 10 , e35033 (2024). Abdalla, H. et al. Insecticide resistance in Anopheles arabiensis in Sudan: temporal trends and underlying mechanisms. Parasit. Vectors . 7 , 213 (2014). Nagi, S. C., Harding, N. J., Lawniczak, M. K. N., Donnelly, M. J. & Miles, A. An atlas of positive selection in the genomes of major malaria vectors. 07.16.664900 Preprint at (2025). https://doi.org/10.1101/2025.07.16.664900 (2025). Odhiambo, C. A. et al. Key gene modules and hub genes associated with pyrethroid and organophosphate resistance in Anopheles mosquitoes: a systems biology approach. BMC Genom. 25 , 1–16 (2024). Rosenwald, A. G. et al. ARL1 and membrane traffic in Saccharomyces cerevisiae. Yeast 19 , 1039–1056 (2002). Hofmann, I. & Munro, S. An N-terminally acetylated Arf-like GTPase is localised to lysosomes and affects their motility. J. Cell. Sci. 119 , 1494–1503 (2006). Wilkinson, C. R. M. et al. Ubiquitin-like Protein Hub1 Is Required for Pre-mRNA Splicing and Localization of an Essential Splicing Factor in Fission Yeast. Curr. Biol. 14 , 2283–2288 (2004). Methylosome subunit pICln. (icln) – Drosophila melanogaster (Fruit fly). UniProtKB – A1ZAW5. UniProt Consortium (2025). https://www.uniprot.org/uniprotkb/A1ZAW5/entry Wei, P. et al. Alternative splicing of a carboxyl/choline esterase gene enhances the fenpropathrin tolerance of Tetranychus cinnabarinus. Insect Sci. 30 , 1255–1266 (2023). Díaz-Terenti, B., Ruiz, J. L. & Gómez-Díaz, E. Alternative splicing and its regulation in the malaria vector Anopheles gambiae. Front Malar 2 , (2024). Sandeu, M. M., Mulamba, C., Weedall, G. D. & Wondji, C. S. A differential expression of pyrethroid resistance genes in the malaria vector Anopheles funestus across Uganda is associated with patterns of gene flow. PLoS ONE . 15 , 1–21 (2020). Kouamo, M. F. M. et al. Allelic variation in a cluster of epsilon glutathione S-transferase genes contributes to DDT and pyrethroid resistance in the major African malaria vector Anopheles funestus. BMC Genom. 26 , 452 (2025). Wang, Q., Shentu, X., Yu, X. & Liu, Y. Insect Odorant-Binding Proteins (OBPs) and Chemosensory Proteins (CSPs): Mechanisms and Research Perspectives in Mediating Insecticide Resistance. Biology 14 , 1452 (2025). Xu, Y. et al. Transcription factor FTZ-F1 regulates mosquito cuticular protein CPLCG5 conferring resistance to pyrethroids in Culex pipiens pallens. Parasit. Vectors . 13 , 514 (2020). He, Z. et al. Genome-wide identification and expression profiling of odorant receptor genes in the malaria vector Anopheles sinensis. Parasit. Vectors . 15 , 143 (2022). Supplementary & Tables. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesFigures.docx Cite Share Download PDF Status: Published Journal Publication published 25 Jun, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 05 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor invited by journal 02 Feb, 2026 Editor assigned by journal 25 Jan, 2026 Submission checks completed at journal 25 Jan, 2026 First submitted to journal 24 Jan, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information

Related documents

• Genomic analysis reveals the emergence of molecular insecticide resistance in the malaria vector, Anopheles gambiae, from Western Ethiopia#4942
• 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#182032
• Genomic insights into the spread and evolution of insecticide resistance variants in Anopheles gambiae s.l. from Burkina Faso.#25701
• Emergence of Genetic Mutations associated with Malaria Diagnostic and Artemisinin Partial Resistance in Somalia: A Genomic Surveillance Study#467678
• Genome-wide analysis of selection on the malaria parasite Plasmodium falciparum in West African populations of differing infection endemicity: Supplementary data#304839