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      Macrogeographic genetic structure of Lutzomyia longipalpis complex populations using Next Generation Sequencing

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          Abstract

          Lutzomyia longipalpis is the main vector of Leishmania infantum, the causative agent of visceral leishmaniasis in the Neotropical realm. Its taxonomic status has been widely discussed once it encompasses a complex of species. The knowledge about the genetic structure of insect vector populations helps the elucidation of components and interactions of the disease ecoepidemiology. Thus, the objective of this study was to genotypically analyze populations of the Lu. longipalpis complex from a macrogeographic perspective using Next Generation Sequencing. Polymorphism analysis of three molecular markers was used to access the levels of population genetic structure among nine different populations of sand flies. Illumina Amplicon Sequencing Protocol ® was used to identify possible polymorphic sites. The library was sequenced on paired-end Illumina MiSeq platform. Significant macrogeographical population differentiation was observed among Lu. longipalpis populations via PCA and DAPC analyses. Our results revealed that populations of Lu. longipalpis from the nine municipalities were grouped into three clusters. In addition, it was observed that the levels of Lu. longipalpis population structure could be associated with distance isolation. This new sequencing method allowed us to study different molecular markers after a single sequencing run, and to evaluate population and inter-species differences on a macrogeographic scale.

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          Most cited references54

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          Applications of next generation sequencing in molecular ecology of non-model organisms.

          As most biologists are probably aware, technological advances in molecular biology during the last few years have opened up possibilities to rapidly generate large-scale sequencing data from non-model organisms at a reasonable cost. In an era when virtually any study organism can 'go genomic', it is worthwhile to review how this may impact molecular ecology. The first studies to put the next generation sequencing (NGS) to the test in ecologically well-characterized species without previous genome information were published in 2007 and the beginning of 2008. Since then several studies have followed in their footsteps, and a large number are undoubtedly under way. This review focuses on how NGS has been, and can be, applied to ecological, population genetic and conservation genetic studies of non-model species, in which there is no (or very limited) genomic resources. Our aim is to draw attention to the various possibilities that are opening up using the new technologies, but we also highlight some of the pitfalls and drawbacks with these methods. We will try to provide a snapshot of the current state of the art for this rapidly advancing and expanding field of research and give some likely directions for future developments.
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            Gene flow and the geographic structure of natural populations

            M Slatkin (1987)
            There is abundant geographic variation in both morphology and gene frequency in most species. The extent of geographic variation results from a balance of forces tending to produce local genetic differentiation and forces tending to produce genetic homogeneity. Mutation, genetic drift due to finite population size, and natural selection favoring adaptations to local environmental conditions will all lead to the genetic differentiation of local populations, and the movement of gametes, individuals, and even entire populations--collectively called gene flow--will oppose that differentiation. Gene flow may either constrain evolution by preventing adaptation to local conditions or promote evolution by spreading new genes and combinations of genes throughout a species' range. Several methods are available for estimating the amount of gene flow. Direct methods monitor ongoing gene flow, and indirect methods use spatial distributions of gene frequencies to infer past gene flow. Applications of these methods show that species differ widely in the gene flow that they experience. Of particular interest are those species for which direct methods indicate little current gene flow but indirect methods indicate much higher levels of gene flow in the recent past. Such species probably have undergone large-scale demographic changes relatively frequently.
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              Interpreting principal component analyses of spatial population genetic variation.

              Nearly 30 years ago, Cavalli-Sforza et al. pioneered the use of principal component analysis (PCA) in population genetics and used PCA to produce maps summarizing human genetic variation across continental regions. They interpreted gradient and wave patterns in these maps as signatures of specific migration events. These interpretations have been controversial, but influential, and the use of PCA has become widespread in analysis of population genetics data. However, the behavior of PCA for genetic data showing continuous spatial variation, such as might exist within human continental groups, has been less well characterized. Here, we find that gradients and waves observed in Cavalli-Sforza et al.'s maps resemble sinusoidal mathematical artifacts that arise generally when PCA is applied to spatial data, implying that the patterns do not necessarily reflect specific migration events. Our findings aid interpretation of PCA results and suggest how PCA can help correct for continuous population structure in association studies.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: Methodology
                Role: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                3 October 2019
                2019
                : 14
                : 10
                : e0223277
                Affiliations
                [1 ] Programa de Pós-Graduação em Doenças Infecciosas e Parasitárias, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brasil
                [2 ] Laboratório de Parasitologia Humana, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brasil
                [3 ] Laboratório de Pesquisas e Análises Genéticas, Departamento de Parasitologia, Universidade Estadual Paulista, Botucatu, São Paulo, Brasil
                [4 ] Laboratório de Doenças Parasitárias, Universidade Estadual do Ceará, Fortaleza, Ceará
                Instituto Oswaldo Cruz, BRAZIL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-7677-9351
                Article
                PONE-D-19-12263
                10.1371/journal.pone.0223277
                6776309
                31581227
                5317fcc2-58a6-49cd-8551-1a64c096d3c4
                © 2019 Casaril et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 April 2019
                : 17 September 2019
                Page count
                Figures: 3, Tables: 3, Pages: 15
                Funding
                This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001 ( https://www.capes.gov.br/) to AEC. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Biology and Life Sciences
                Genetics
                Population Genetics
                Biology and Life Sciences
                Population Biology
                Population Genetics
                People and places
                Geographical locations
                South America
                Brazil
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Sequence Assembly Tools
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Sequence Assembly Tools
                Medicine and Health Sciences
                Infectious Diseases
                Disease Vectors
                Insect Vectors
                Sand Flies
                Biology and Life Sciences
                Species Interactions
                Disease Vectors
                Insect Vectors
                Sand Flies
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Research and Analysis Methods
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Next-Generation Sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Next-Generation Sequencing
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Next-Generation Sequencing
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Next-Generation Sequencing
                Medicine and Health Sciences
                Infectious Diseases
                Disease Vectors
                Insect Vectors
                Biology and Life Sciences
                Species Interactions
                Disease Vectors
                Insect Vectors
                Custom metadata
                All relevant data are available from Gene bank, BioProject ID: PRJNA555630 ( https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA555630).

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