Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do INPA |
Texto Completo: | https://repositorio.inpa.gov.br/handle/1/16332 |
Resumo: | Background. Anopheles darlingi is the most important malaria vector in the Neotropics. An understanding of A. darlingi's population structure and contemporary gene flow patterns is necessary if vector populations are to be successfully controlled. We assessed population genetic structure and levels of differentiation based on 1,376 samples from 31 localities throughout the Peruvian and Brazilian Amazon and Central America using 5-8 microsatellite loci. Results. We found high levels of polymorphism for all of the Amazonian populations (mean RS = 7.62, mean HO = 0.742), and low levels for the Belize and Guatemalan populations (mean RS = 4.3, mean HO = 0.457). The Bayesian clustering analysis revealed five population clusters: northeastern Amazonian Brazil, southeastern and central Amazonian Brazil, western and central Amazonian Brazil, Peruvian Amazon, and the Central American populations. Within Central America there was low non-significant differentiation, except for between the populations separated by the Maya Mountains. Within Amazonia there was a moderate level of significant differentiation attributed to isolation by distance. Within Peru there was no significant population structure and low differentiation, and some evidence of a population expansion. The pairwise estimates of genetic differentiation between Central America and Amazonian populations were all very high and highly significant (FST = 0.1859 - 0.3901, P < 0.05). Both the DA and FST distance-based trees illustrated the main division to be between Central America and Amazonia. Conclusion. We detected a large amount of population structure in Amazonia, with three population clusters within Brazil and one including the Peru populations. The considerable differences in Ne among the populations may have contributed to the observed genetic differentiation. All of the data suggest that the primary division within A. darlingi corresponds to two white gene genotypes between Amazonia (genotype 1) and Central America, parts of Colombia and Venezuela (genotype 2), and are in agreement with previously published mitochondrial COI gene sequences interpreted as incipient species. Overall, it appears that two main factors have contributed to the genetic differentiation between the population clusters: physical distance between the populations and the differences in effective population sizes among the subpopulations. © 2008 Mirabello et al; licensee BioMed Central Ltd. |
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Mirabello, LisaVineis, Joseph H.Yanoviak, Stephen P.Scarpassa, Vera MargaretePôvoa, Marinete MarinsPadilla, Norma R.Acheé, Nicole L.Conn, Jan E.2020-06-03T20:53:08Z2020-06-03T20:53:08Z2008https://repositorio.inpa.gov.br/handle/1/1633210.1186/1472-6785-8-3Background. Anopheles darlingi is the most important malaria vector in the Neotropics. An understanding of A. darlingi's population structure and contemporary gene flow patterns is necessary if vector populations are to be successfully controlled. We assessed population genetic structure and levels of differentiation based on 1,376 samples from 31 localities throughout the Peruvian and Brazilian Amazon and Central America using 5-8 microsatellite loci. Results. We found high levels of polymorphism for all of the Amazonian populations (mean RS = 7.62, mean HO = 0.742), and low levels for the Belize and Guatemalan populations (mean RS = 4.3, mean HO = 0.457). The Bayesian clustering analysis revealed five population clusters: northeastern Amazonian Brazil, southeastern and central Amazonian Brazil, western and central Amazonian Brazil, Peruvian Amazon, and the Central American populations. Within Central America there was low non-significant differentiation, except for between the populations separated by the Maya Mountains. Within Amazonia there was a moderate level of significant differentiation attributed to isolation by distance. Within Peru there was no significant population structure and low differentiation, and some evidence of a population expansion. The pairwise estimates of genetic differentiation between Central America and Amazonian populations were all very high and highly significant (FST = 0.1859 - 0.3901, P < 0.05). Both the DA and FST distance-based trees illustrated the main division to be between Central America and Amazonia. Conclusion. We detected a large amount of population structure in Amazonia, with three population clusters within Brazil and one including the Peru populations. The considerable differences in Ne among the populations may have contributed to the observed genetic differentiation. All of the data suggest that the primary division within A. darlingi corresponds to two white gene genotypes between Amazonia (genotype 1) and Central America, parts of Colombia and Venezuela (genotype 2), and are in agreement with previously published mitochondrial COI gene sequences interpreted as incipient species. Overall, it appears that two main factors have contributed to the genetic differentiation between the population clusters: physical distance between the populations and the differences in effective population sizes among the subpopulations. © 2008 Mirabello et al; licensee BioMed Central Ltd.Volume 8Attribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessAnopheles DarlingiMicrosatellite DnaAnimalsAnophelesBayes TheoremCentral AmericaClassificationDisease CarrierDisease TransmissionGenetic VariabilityGeneticsGenotypeMalariaPopulation DensitySouth AmericaAnimalAnophelesBayes TheoremCentral AmericaGenotypeInsect VectorsMalariaMicrosatellite RepeatsPopulation DensitySouth AmericaVariation (genetics)Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South Americainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleBMC Ecologyengreponame:Repositório Institucional do INPAinstname:Instituto Nacional de Pesquisas da Amazônia (INPA)instacron:INPAORIGINALartigo-inpa.pdfartigo-inpa.pdfapplication/pdf2464397https://repositorio.inpa.gov.br/bitstream/1/16332/1/artigo-inpa.pdfbe223b3f91e0befa2f36685aac6977c1MD511/163322020-06-03 17:04:41.875oai:repositorio:1/16332Repositório de PublicaçõesPUBhttps://repositorio.inpa.gov.br/oai/requestopendoar:2020-06-03T21:04:41Repositório Institucional do INPA - Instituto Nacional de Pesquisas da Amazônia (INPA)false |
dc.title.en.fl_str_mv |
Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America |
title |
Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America |
spellingShingle |
Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America Mirabello, Lisa Anopheles Darlingi Microsatellite Dna Animals Anopheles Bayes Theorem Central America Classification Disease Carrier Disease Transmission Genetic Variability Genetics Genotype Malaria Population Density South America Animal Anopheles Bayes Theorem Central America Genotype Insect Vectors Malaria Microsatellite Repeats Population Density South America Variation (genetics) |
title_short |
Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America |
title_full |
Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America |
title_fullStr |
Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America |
title_full_unstemmed |
Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America |
title_sort |
Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America |
author |
Mirabello, Lisa |
author_facet |
Mirabello, Lisa Vineis, Joseph H. Yanoviak, Stephen P. Scarpassa, Vera Margarete Pôvoa, Marinete Marins Padilla, Norma R. Acheé, Nicole L. Conn, Jan E. |
author_role |
author |
author2 |
Vineis, Joseph H. Yanoviak, Stephen P. Scarpassa, Vera Margarete Pôvoa, Marinete Marins Padilla, Norma R. Acheé, Nicole L. Conn, Jan E. |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Mirabello, Lisa Vineis, Joseph H. Yanoviak, Stephen P. Scarpassa, Vera Margarete Pôvoa, Marinete Marins Padilla, Norma R. Acheé, Nicole L. Conn, Jan E. |
dc.subject.eng.fl_str_mv |
Anopheles Darlingi Microsatellite Dna Animals Anopheles Bayes Theorem Central America Classification Disease Carrier Disease Transmission Genetic Variability Genetics Genotype Malaria Population Density South America Animal Anopheles Bayes Theorem Central America Genotype Insect Vectors Malaria Microsatellite Repeats Population Density South America Variation (genetics) |
topic |
Anopheles Darlingi Microsatellite Dna Animals Anopheles Bayes Theorem Central America Classification Disease Carrier Disease Transmission Genetic Variability Genetics Genotype Malaria Population Density South America Animal Anopheles Bayes Theorem Central America Genotype Insect Vectors Malaria Microsatellite Repeats Population Density South America Variation (genetics) |
description |
Background. Anopheles darlingi is the most important malaria vector in the Neotropics. An understanding of A. darlingi's population structure and contemporary gene flow patterns is necessary if vector populations are to be successfully controlled. We assessed population genetic structure and levels of differentiation based on 1,376 samples from 31 localities throughout the Peruvian and Brazilian Amazon and Central America using 5-8 microsatellite loci. Results. We found high levels of polymorphism for all of the Amazonian populations (mean RS = 7.62, mean HO = 0.742), and low levels for the Belize and Guatemalan populations (mean RS = 4.3, mean HO = 0.457). The Bayesian clustering analysis revealed five population clusters: northeastern Amazonian Brazil, southeastern and central Amazonian Brazil, western and central Amazonian Brazil, Peruvian Amazon, and the Central American populations. Within Central America there was low non-significant differentiation, except for between the populations separated by the Maya Mountains. Within Amazonia there was a moderate level of significant differentiation attributed to isolation by distance. Within Peru there was no significant population structure and low differentiation, and some evidence of a population expansion. The pairwise estimates of genetic differentiation between Central America and Amazonian populations were all very high and highly significant (FST = 0.1859 - 0.3901, P < 0.05). Both the DA and FST distance-based trees illustrated the main division to be between Central America and Amazonia. Conclusion. We detected a large amount of population structure in Amazonia, with three population clusters within Brazil and one including the Peru populations. The considerable differences in Ne among the populations may have contributed to the observed genetic differentiation. All of the data suggest that the primary division within A. darlingi corresponds to two white gene genotypes between Amazonia (genotype 1) and Central America, parts of Colombia and Venezuela (genotype 2), and are in agreement with previously published mitochondrial COI gene sequences interpreted as incipient species. Overall, it appears that two main factors have contributed to the genetic differentiation between the population clusters: physical distance between the populations and the differences in effective population sizes among the subpopulations. © 2008 Mirabello et al; licensee BioMed Central Ltd. |
publishDate |
2008 |
dc.date.issued.fl_str_mv |
2008 |
dc.date.accessioned.fl_str_mv |
2020-06-03T20:53:08Z |
dc.date.available.fl_str_mv |
2020-06-03T20:53:08Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio.inpa.gov.br/handle/1/16332 |
dc.identifier.doi.none.fl_str_mv |
10.1186/1472-6785-8-3 |
url |
https://repositorio.inpa.gov.br/handle/1/16332 |
identifier_str_mv |
10.1186/1472-6785-8-3 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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Volume 8 |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
BMC Ecology |
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BMC Ecology |
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