Resistance to insecticides in Anopheles mosquitoes threatens the effectiveness of the most widespread tools currently used to control malaria. The genetic underpinnings of resistance are still only partially understood, with much of the variance in resistance phenotype left unexplained. We performed a multi-country large scale genome-wide association study of resistance to two insecticides widely used in malaria control: deltamethrin and pirimiphos-methyl. Using a bioassay methodology designed to maximise the phenotypic difference between resistant and susceptible samples, we sequenced 969 phenotyped female An. gambiae and An. coluzzii from ten locations across four countries in West Africa (Benin, Côte d'Ivoire, Ghana and Togo), identifying single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) segregating in the populations. The patterns of resistance association were highly multiallelic and variable between populations, with different genomic regions contributing to resistance, as well as different mutations within a given region. While the strongest and most consistent association with deltamethrin resistance came from the region around Cyp6aa1 , this resistance was based on a combination of several independent CNVs in An. coluzzii , and on a non-CNV bearing haplotype in An. gambiae . Further signals involved a range of cytochrome P450, mitochondrial, and immunity genes. Similarly, for pirimiphos-methyl, while the strongest signal came from the region of Ace1 , more widespread signals included cytochrome P450s, glutathione S-transferases, and a subunit of the nAChR target site of neonicotinoid insecticides. The regions around Cyp9k1 and the Tep family of immune genes were associated with resistance to both insecticide classes, suggesting possible cross-resistance mechanisms. These locally-varying, multigenic and multiallelic patterns highlight the challenges involved in genomic monitoring and surveillance of resistance, and form the basis for improvement of methods used to detect and predict resistance. Based on simulations of resistance variants, we recommend that yet larger scale studies, exceeding 500 phenotyped samples per population, are required to better identify associated genomic regions.

Big Data Institute Li Ka Shing Centre for Health Information and Discovery University of Oxford Oxford United Kingdom

Biology Centre of the Czech Academy of Sciences Institute of Entomology Branišovská 31 370 05 České Budějovice Czech Republic

Centre Suisse de Recherches Scientifiques en Côte d'Ivoire 01 BP 1303 Abidjan 01 Côte d'Ivoire

Department of Biomedical Sciences University of Cape Coast Cape Coast Ghana

Department of Parasitology Noguchi Memorial Institute for Medical Research University of Ghana Accra Ghana

Department of Vector Biology Liverpool School of Tropical Medicine Pembroke Place Liverpool L3 5QA UK

Laboratory of Ecology and Ecotoxicology Department of Zoology Faculty of Sciences Université de Lomé 01 B P 1515 Lomé 01 Togo

Tropical Infectious Diseases Research Centre 01 B P 526 Cotonou Benin

Wellcome Sanger Institute Hinxton Cambridge CB10 1SA United Kingdom

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Nat Commun. 2023 Aug 16;14(1):4946. doi: 10.1038/s41467-023-40693-0. PubMed

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