Minimal genetic differentiation of the malaria vector Nyssorhynchus darlingi associated with forest cover level in Amazonian Brazil

Autoři: Catharine Prussing aff001;  Kevin J. Emerson aff002;  Sara A. Bickersmith aff003;  Maria Anice Mureb Sallum aff004;  Jan E. Conn aff001
Působiště autorů: University at Albany, State University of New York, School of Public Health, Department of Biomedical Sciences, Albany, NY, United States of America aff001;  Department of Biology, St. Mary’s College of Maryland, St. Mary’s City, MD, United States of America aff002;  Wadsworth Center, New York State Department of Health, Albany, NY, United States of America aff003;  Departamento de Epidemiologia, Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, SP, Brasil aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225005


The relationship between deforestation and malaria in Amazonian Brazil is complex, and a deeper understanding of this relationship is required to inform effective control measures in this region. Here, we are particularly interested in characterizing the impact of land use and land cover change on the genetics of the major regional vector of malaria, Nyssorhynchus darlingi (Root). We used nextera-tagmented, Reductively Amplified DNA (nextRAD) genotyping-by-sequencing to genotype 164 Ny. darlingi collected from 16 collection sites with divergent forest cover levels in seven municipalities in four municipality groups that span the state of Amazonas in northwestern Amazonian Brazil: São Gabriel da Cachoeira, Presidente Figueiredo, four municipalities in the area around Cruzeiro do Sul, and Lábrea. Using a dataset of 5,561 Single Nucleotide Polymorphisms (SNPs), we investigated the genetic structure of these Ny. darlingi populations with a combination of model- and non-model-based analyses. We identified weak to moderate genetic differentiation among the four municipality groups. There was no evidence for microgeographic genetic structure of Ny. darlingi among forest cover levels within the municipality groups, indicating that there may be gene flow across areas of these municipalities with different degrees of deforestation. Additionally, we conducted an environmental association analysis using two outlier detection methods to determine whether individual SNPs were associated with forest cover level without affecting overall population genetic structure. We identified 14 outlier SNPs, and investigated functions associated with their proximal genes, which could be further characterized in future studies.

Klíčová slova:

Brazil – Deforestation – Forests – Genetic loci – Malaria – Molecular genetics – Population genetics – principal component analysis


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