Nyssorhynchus darlingi genome-wide studies related to microgeographic dispersion and blood-seeking behavior


Background In Brazil, malaria is concentrated in the Amazon Basin, where more than 99% of the annual cases are reported. The main goal of this study was to investigate the population structure and genetic association of the biting behavior of Nyssorhynchus (also known as Anopheles) darlingi, the major malaria vector in the Amazon region of Brazil, using low-coverage genomic sequencing data. Methods Samples were collected in the municipality of Mâncio Lima, Acre state, Brazil between 2016 and 2017. Different approaches using genotype imputation and no gene imputation for data treatment and low-coverage sequencing genotyping were performed. After the samples were genotyped, population stratification analysis was performed. Results Weak but statistically significant stratification signatures were identified between subpopulations separated by distances of approximately 2–3 km. Genome-wide association studies (GWAS) were performed to compare indoor/outdoor biting behavior and blood-seeking at dusk/dawn. A statistically significant association was observed between biting behavior and single nucleotide polymorphism (SNP) markers adjacent to the gene associated with cytochrome P450 (CYP) 4H14, which is associated with insecticide resistance. A statistically significant association between blood-seeking periodicity and SNP markers adjacent to genes associated with the circadian cycle was also observed. Conclusion The data presented here suggest that low-coverage whole-genome sequencing with adequate processing is a powerful tool to genetically characterize vector populations at a microgeographic scale in malaria transmission areas, as well as for use in GWAS. Female mosquitoes entering houses to take a blood meal may be related to a specific CYP4H14 allele, and female timing of blood-seeking is related to circadian rhythm genes.

Parasites & Vectors
Gabriel Carrasco-Escobar
Gabriel Carrasco-Escobar
Assistant Professor

My research interests include infectious diseases epidemiology, causal inference, global health, Climate Change, Data Science, Urban Health, and Geospatial modeling & viz.