A Genotyping Array for the Globally Invasive Vector Mosquito, Aedes albopictus

BACKGROUND: Although whole-genome sequencing (WGS) is the preferred genotyping method for most genomic analyses, limitations are often experienced when studying genomes characterized by a high percentage of repetitive elements, high linkage, and recombination deserts. The Asian tiger mosquito (Aedes albopictus), for example, has a genome comprising up to 72% repetitive elements, and therefore we set out to develop a single-nucleotide polymorphism (SNP) chip to be more cost-effective. Aedes albopictus is an invasive species originating from Southeast Asia that has recently spread around the world and is a vector for many human diseases. Developing an accessible genotyping platform is essential in advancing biological control methods and understanding the population dynamics of this pest species, with significant implications for public health. METHODS: We designed a SNP chip for Ae. albopictus (Aealbo chip) based on approximately 2.7 million SNPs identified using WGS data from 819 worldwide samples. We validated the chip using laboratory single-pair crosses, comparing technical replicates, and comparing genotypes of samples genotyped by WGS and the SNP chip. We then used the chip for a population genomic analysis of 237 samples from 28 sites in the native range to evaluate its usefulness in describing patterns of genomic variation and tracing the origins of invasions. RESULTS: Probes on the Aealbo chip targeted 175,396 SNPs in coding and non-coding regions across all three chromosomes, with a density of 102 SNPs per 1 Mb window, and at least one SNP in each of the 17,461 protein-coding genes. Overall, 70% of the probes captured the genetic variation. Segregation analysis found that 98% of the SNPs followed expectations of single-copy Mendelian genes. Comparisons with WGS indicated that sites with genotype disagreements were mostly heterozygotes at loci with WGS read depth \u3c 20, while there was near complete agreement with WGS read depths \u3e 20, indicating that the chip more accurately detects heterozygotes than low-coverage WGS. Sample sizes did not affect the accuracy of the SNP chip genotype calls. Ancestry analyses identified four to five genetic clusters in the native range with various levels of admixture. CONCLUSIONS: The Aealbo chip is highly accurate, is concordant with genotypes from WGS with high sequence coverage, and may be more accurate than low-coverage WGS

Tick-, mosquito-, and rodent-borne parasite sampling designs for the National Ecological Observatory Network

Parasites and pathogens are increasingly recognized as significant drivers of ecological and evolutionary change in natural ecosystems. Concurrently, transmission of infectious agents among human, livestock, and wildlife populations represents a growing threat to veterinary and human health. In light of these trends and the scarcity of long-term time series data on infection rates among vectors and reservoirs, the National Ecological Observatory Network (NEON) will collect measurements and samples of a suite of tick-, mosquito-, and rodent-borne parasites through a continental-scale surveillance program. Here, we describe the sampling designs for these efforts, highlighting sampling priorities, field and analytical methods, and the data as well as archived samples to be made available to the research community. Insights generated by this sampling will advance current understanding of and ability to predict changes in infection and disease dynamics in novel, interdisciplinary, and collaborative ways. (Résumé d'auteur

Tick-, Mosquito-, and Rodent-Borne Parasite Sampling Designs for the National Ecological Observatory Network [Special Feature: NEON Design]

Parasites and pathogens are increasingly recognized as significant drivers of ecological and evolutionary change in natural ecosystems. Concurrently, transmission of infectious agents among human, livestock, and wildlife populations represents a growing threat to veterinary and human health. In light of these trends and the scarcity of long-term time series data on infection rates among vectors and reservoirs, the National Ecological Observatory Network (NEON) will collect measurements and samples of a suite of tick-, mosquito-, and rodent-borne parasites through a continental-scale surveillance program. Here, we describe the sampling designs for these efforts, highlighting sampling priorities, field and analytical methods, and the data as well as archived samples to be made available to the research community. Insights generated by this sampling will advance current understanding of and ability to predict changes in infection and disease dynamics in novel, interdisciplinary, and collaborative ways

Evaluation of species distribution model algorithms for fine-scale container-breeding mosquito risk prediction

The present work evaluates the use of species distribution model (SDM) algorithms to classify high densities of small container-breeding Aedes mosquitoes (Diptera: Culicidae) on a fine scale in the Bermuda Islands. Weekly ovitrap data collected by the Department of Health, Bermuda for the years 2006 and 2007 were used for the models. The models evaluated included the algorithms Bioclim, Domain, GARP (genetic algorithm for rule-set prediction), logistic regression and MaxEnt (maximum entropy). Models were evaluated according to performance and robustness. The area under the receiver operating characteristic curve was used to evaluate each model\u27s performance, and robustness was assessed according to the spatial correlation between classification risks for the two datasets. Relative to the other algorithms, logistic regression was the best and MaxEnt the second best model for classifying high-risk areas. We describe the importance of covariables for these two models and discuss the utility of SDMs in vector control efforts and the potential for the development of scripts that automate the task of creating risk assessment maps. © 2010 The Authors. Medical and Veterinary Entomology © 2010 The Royal Entomological Society

Modelling the distribution of the vector Aedes aegypti in a central Argentine city

Aedes aegypti (Diptera: Culicidae) is an urban mosquito involved in the transmission of numerous viruses, including dengue, chikungunya and Zika. In Argentina, Ae. aegypti is the main vector of dengue virus and has been involved in several outbreaks in regions ranging from northern to central Argentina since 2009. In order to evaluate areas of potential vector-borne disease transmission in the city of Córdoba, Argentina, the present study aimed to identify the environmental, socioeconomic and demographic factors driving the distribution of Ae. aegypti larvae through spatial analysis in the form of species distribution models (SDMs). These models elucidate relationships between known occurrences of a species and environmental data in order to identify areas with suitable habitats for that species and the consequent risk for disease transmission. The maximum entropy species distribution model was able to fit the training data well, with an average area under the receiver operating characteristic curve (AUC) of \u3e 0.8, and produced models with fair extrapolation capacity (average test AUC: \u3e 0.75). Human population density, distance to vegetation and water channels were the main variables predictive of the vector suitability of an area. The results of this work will be used to target surveillance and prevention measures, as well as in mosquito management

Design for mosquito abundance, diversity, and phenology sampling within the National Ecological Observatory Network

The National Ecological Observatory Network (NEON) intends to monitor mosquito populations across its broad geographical range of sites because of their prevalence in food webs, sensitivity to abiotic factors, and relevance for human health. We describe the design of mosquito population sampling in the context of NEON\u27s long‐term continental scale monitoring program, emphasizing the sampling design schedule, priorities, and collection methods. Freely available NEON data and associated field and laboratory samples, will increase our understanding of how mosquito abundance, demography, diversity, and phenology are responding to land use and climate change