Evidence of two lineages of the dengue vector Aedes aegypti in the Brazilian Amazon, based on mitochondrial DNA ND4 gene sequences

Genetic variation was estimated in ten samples populations of Aedes aegypti from the Brazilian Amazon, by using a 380 bp fragment of the mitochocondrial NADH dehydrogenase subunit 4 (ND4) gene. A total of 123 individuals were analyzed, whereby 13 haplotypes were found. Mean genetic diversity was slightly high (h = 0.666 ± 0.029; π = 0.0115 ± 0.0010). Two AMOVA analyses indicated that most of the variation (~70%-72%) occurred within populations. The variation found among and between populations within the groups disclosed lower, but even so, highly significant values. FST values were not significant in most of the comparisons, except for the samples from Pacaraima and Rio Branco. The isolation by distance (IBD) model was not significant (r = 0.2880; p = 0.097) when the samples from Pacaraima and Rio Branco were excluded from the analyses, this indicating that genetic distance is not related to geographic distance. This result may be explained either by passive dispersal patterns (via human migrations and commercial exchange) or be due to the recent expansion of this mosquito in the Brazilian Amazon. Phylogenetic relationship analysis showed two genetically distinct groups (lineages) within the Brazilian Amazon, each sharing haplotypes with populations from West Africa and Asia

Challenges in Estimating Insecticide Selection Pressures from Mosquito Field Data

Insecticide resistance has the potential to compromise the enormous effort put into the control of dengue and malaria vector populations. It is therefore important to quantify the amount of selection acting on resistance alleles, their contributions to fitness in heterozygotes (dominance) and their initial frequencies, as a means to predict the rate of spread of resistance in natural populations. We investigate practical problems of obtaining such estimates, with particular emphasis on Mexican populations of the dengue vector Aedes aegypti. Selection and dominance coefficients can be estimated by fitting genetic models to field data using maximum likelihood (ML) methodology. This methodology, although widely used, makes many assumptions so we investigated how well such models perform when data are sparse or when spatial and temporal heterogeneity occur. As expected, ML methodologies reliably estimated selection and dominance coefficients under idealised conditions but it was difficult to recover the true values when datasets were sparse during the time that resistance alleles increased in frequency, or when spatial and temporal heterogeneity occurred. We analysed published data on pyrethroid resistance in Mexico that consists of the frequency of a Ile1,016 mutation. The estimates for selection coefficient and initial allele frequency on the field dataset were in the expected range, dominance coefficient points to incomplete dominance as observed in the laboratory, although these estimates are accompanied by strong caveats about possible impact of spatial and temporal heterogeneity in selection