Evidence map›Paper›PMID 31725789›Full record

ArticlePloS one2019

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

Catharine Prussing, Kevin J Emerson, Sara A Bickersmith, Maria Anice Mureb Sallum, Jan E Conn

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Catharine PrussingUniversity at Albany, State University of New York, School of Public Health, Department of Biomedical Sciences, Albany, NY, United States of America.ORCID 0000-0001-6425-6839
Kevin J EmersonDepartment of Biology, St. Mary's College of Maryland, St. Mary's City, MD, United States of America.
Sara A BickersmithWadsworth Center, New York State Department of Health, Albany, NY, United States of America.
Maria Anice Mureb SallumDepartamento de Epidemiologia, Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, SP, Brasil.
Jan E ConnUniversity at Albany, State University of New York, School of Public Health, Department of Biomedical Sciences, Albany, NY, United States of America.

Funding

Latitudinal Landscape Genomics and Ecology of Anopheles DarlingiR01AI110112 · NIAID · WADSWORTH CENTER · PI CONN, JAN E · 2014 to 2024
$3.7M
NIAID NIH HHS R01 AI110112
6 · The paper itself

Abstract

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.

Indexed as

Disease VectorsForestsAnimalsAnophelesBrazilDiscriminant AnalysisFemaleGene OntologyGeographyMalariaPolymorphism, Single NucleotidePrincipal Component Analysis

Identifiers

PMID31725789
PMCPMC6855485

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.