Genome-wide analysis of genetic diversity in Anopheles darlingi from Rondônia State, Brazil
Year: 2025
Type: article
Abstract: Anopheles darlingi is the primary malaria vector in Central and South America and is responsible for most malaria transmission in the Amazon region. In this study, we perform whole-genome analysis of individual An. darlingi mosquitoes to explore genomic diversity, signatures of selection, and insecticide resistance markers. We analysed wild-caught (n = 20) and colony-maintained (n = 8) mosquitoes from the State of Rondônia, Brazil. In total, 1.54 million high-quality single-nucleotide polymorphisms (SNPs) were identified. Population genomic analysis revealed genetic differentiation between the colony and wild populations. No SNPs previously associated with insecticide resistance were detected. However, several SNPs were observed in four genes commonly associated with insecticide resistance: ace1, rdl, gste2, and vgsc. Genes under directional selection were identified, but no clear selective sweeps were found using genome-wide selection scans. Gene duplications were identified in cytochrome P450 genes, which are known to metabolise pyrethroids. This study provides a detailed genetic profile of An. darlingi, highlighting genes potentially involved in insecticide resistance, and presents an analysis of signatures of selection based on WGS data for this species. Our findings identify markers in insecticide resistance-associated genes that warrant further investigation through phenotypic-genotypic assays.
Source: Communications Biology
Language: English
Topic: Malaria Research and Control
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,190
Funders: Bill and Melinda Gates Foundation, UK Research and Innovation, Research Councils UK, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Ministério da Saúde, Medical Research Council, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council
Awards: G1131867873, AI assisted genomic profiling for the personalisation of treatment and control of infections, FIOCRUZ - to develop innovative tools that will help diagnose malaria cases, control malaria transmission and improve effectiveness of malaria treatment in t..., G60822148, Infection-AID: AI assisted genomic profiling to inform the Diagnosis, personalised treatment and control of infections, G6879302011, G7455513328, G8372498244, G8670990856, GenES_VBD network (Genomic Epidemiology tools for the Surveillance of Vector Borne Diseases: applied to tick species, reservoirs, and pathogens, G9000242701
OpenAlex ID: W4416982649
