Targeted genomic surveillance of insecticide resistance in African malaria vectors

• DOI: 10.1101/2025.02.14.637727
• Publication Date: 2025-02-17T05:30:17Z
• Authors: Sanjay C Nagi; Eric R Lucas; Faisal Ashraf; Trevor Mugoya; Edward Lukyamuzi; Shannan Summers; Calvin Yee; Christopher G Jacob; Harun Njoroge; Thomas Pemberton; John Essandoh; Martin Lukindu; Honorine Kaboré; Grégoire Sawadogo; Jessica Williams; Arjen E. Van‘t Hof; Anastasia Hernand...; Christina Hubbart; Kate Rowlands; Anna Jeffreys; Scott Goodwin; Naomi Park; Cristina Ariani; Alexander Egyir-Y...; Sonia Goncalves; Shavanthi Rajatileka; Kirk Rockett; Victoria J. Simpson; Alistair Miles; David Weetman; Jonathan Kayondo; Tony Nolan; Martin J Donnelly
• Keywords: Anopheles gambiae; Insecticide Resistance; Population genomics

The emergence of insecticide resistance is threatening the efforts malaria control programmes, which rely heavily on a limited arsenal insecticidal tools, such as insecticide-treated bed nets. Importantly, genomic surveillance vectors can provide critical, policy-relevant insights into presence and evolution resistance, allowing us to maintain extend shelf life these interventions. Yet complex genetic architecture combined with resource constraints in malaria-endemic settings, have thus far precluded widespread use genomics routine surveillance. Meanwhile, stakeholders sub-Saharan Africa are moving towards locally driven, decentralised generation data, underscoring need for standardised robust workflows. To address this need, we demonstrate an approach targeted Anopheles gambiae s.l Illumina sequencing. We target 90 loci genome, including 55 resistance-associated mutations 35 ancestry informative markers. This protocol coupled advanced, automated software accurate reproducible variant analysis. able elucidate population structure our cohorts accurately identify most species An. complex. report frequencies variants at insecticide-resistance explore continued pyrethroid site, Voltage-gated sodium channel. Applying platform recently established colony field-caught resistant mosquitoes (Siaya, Kenya), identified seven independent contributing reduced efficacy nets East Africa. Additionally, leverage machine learning algorithm (XGBoost) possibility predicting bioassay mortality using genotypes alone. achieved very high accuracy (73%), demonstrating potential predictively monitor resistance. Together tools practical, scalable solution monitoring while advancing goal building local capacity