• View in gallery

    Map of collection sites across the Yucatan State.

  • View in gallery

    Analysis of the proportion of significant linkage disequilibrium (N = 78 pairwise comparisons) for each community and collection site. Bars represent Bayesian 95% highest density intervals (95% HDI). Proportions with HDI that overlap 0.05 were not considered to have a credible excess of linkage disequilibrium.

  • View in gallery

    Regression analysis of allele frequencies of two para mutations: I1,016 and C1,534 in 27 collections from Yucatan.

  • View in gallery

    (A) Regression analysis of pairwise FST/(1 − FST) for all 13 loci against ln(geographic distances[km]). (B) pairwise FST/(1 − FST) for the putatively neutral loci against ln(geographic distances [km]), and (C) pairwise FST/(1 − FST) for insecticide resistance loci against ln(geographic distances [km]). Regression analysis equation and correlation coefficient are also shown.

  • 1.

    Gubler DJ, 2004. The changing epidemiology of yellow fever and dengue, 1900 to 2003: full circle? Comp Immunol Microbiol Infect Dis 27: 319330.

    • Search Google Scholar
    • Export Citation
  • 2.

    García-Rejón JE, López-Uribe MP, Loroño-Pino MA, Arana-Guardia R, Puc-Tinal M, López-Uribe GM, Coba-Tún C, Baak-Baak CM, Machain-Williams C, Reyes-Solis GC, Lozano-Fuentes S, Saavedra-Rodriguez K, Black WC, Beaty BJ, Eisen L, 2012. Aedes (Stegomyia) aegypti and Aedes (Howardina) cozumelensis in Yucatán State, México, with a summary of published collection records for Ae. cozumelensis. J Vector Ecol 37: 365372.

    • Search Google Scholar
    • Export Citation
  • 3.

    Loroño-Pino MA, Farfán-Ale JA, Zapata-Peraza AL, Rosado-Paredes EP, Flores-Flores LF, García-Rejón JE, Díaz FJ, Blitvich BJ, Andrade-Narváez M, Jiménez-Ríos E, Blair CD, Olson KE, Black W 4th, Beaty BJ, 2004. Introduction of the American/Asian genotype of dengue 2 virus into the Yucatan State of Mexico. Am J Trop Med Hyg 71: 485492.

    • Search Google Scholar
    • Export Citation
  • 4.

    Loroño-Pino MA, García-Rejón JE, Machain-Williams C, Gomez-Carro S, Nuñez-Ayala G, Nájera-Vázquez M del R, Losoya A, Aguilar L, Saavedra-Rodriguez K, Lozano-Fuentes S, Beaty MK, Black WC 4th, Keefe TJ, Eisen L, Beaty BJ, 2013. Towards a Casa Segura: a consumer product study of the effect of insecticide-treated curtains on Aedes aegypti and dengue virus infections in the home. Am J Trop Med Hyg 89: 385397.

    • Search Google Scholar
    • Export Citation
  • 5.

    World Health Organization, 2009. Dengue and Dengue Hemorrhagic Fever. 117 WFSN, ed.

  • 6.

    Norma Oficial Mexicana, 2010. NOM-EM-003-SSA2-2010 para la Vigilancia Epidemiologica. Prevencion y Control de Enfermedades Transmitidas por Vector. Diario Oficial de la Federacion, Junio 2010, Mexico.

    • Search Google Scholar
    • Export Citation
  • 7.

    Gorrochotegui-Escalante N, Gomez-Machorro C, Lozano-Fuentes S, Fernandez-Salas I, Munoz MD, Farfan-Ale JA, Garcia-Rejon J, Beaty BJ, Black WC, 2002. Breeding structure of Aedes aegypti populations in Mexico varies by region. Am J Trop Med Hyg 66: 213222.

    • Search Google Scholar
    • Export Citation
  • 8.

    Gorrochotegui-Escalante N, Munoz MD, Fernandez-Salas I, Beaty BJ, Black WC, 2000. Genetic isolation by distance among Aedes aegypti populations along the northeastern coast of Mexico. Am J Trop Med Hyg 62: 200209.

    • Search Google Scholar
    • Export Citation
  • 9.

    García GP, Flores AE, Fernández-Salas I, Saavedra-Rodríguez K, Reyes-Solis G, Lozano-Fuentes S, Bond JG, Casas-Martínez M, Ramsey JM, García-Rejón J, Domínguez-Galera M, Ranson H, Hemingway J, Eisen L, Black WC IV, 2009. Recent rapid rise of a permethrin knock down resistance allele in Aedes aegypti in Mexico. PLoS Negl Trop Dis 3: e531.

    • Search Google Scholar
    • Export Citation
  • 10.

    Harris AF, Rajatileka S, Ranson H, 2010. Pyrethroid resistance in Aedes aegypti from Grand Cayman. Am J Trop Med Hyg 83: 277284.

  • 11.

    Yanola J, Somboon P, Walton C, Nachaiwieng W, Somwang P, Prapanthadara L-a, 2011. High-throughput assays for detection of the F1534C mutation in the voltage-gated sodium channel gene in permethrin-resistant Aedes aegypti and the distribution of this mutation throughout Thailand: high-throughput assays to detect the F1534C mutation in sodium channel gene of the Aedes aegypti. Trop Med Int Health 16: 501509.

    • Search Google Scholar
    • Export Citation
  • 12.

    Saavedra-Rodriguez K, Strode C, Suarez AF, Salas IF, Ranson H, Hemingway J, Black WC, 2008. Quantitative trait loci mapping of genome regions controlling permethrin resistance in the mosquito Aedes aegypti. Genetics 180: 11371152.

    • Search Google Scholar
    • Export Citation
  • 13.

    Saavedra-Rodriguez K, Suarez AF, Salas IF, Strode C, Ranson H, Hemingway J, Black WC IV, 2012. Transcription of detoxification genes after permethrin selection in the mosquito Aedes aegypti. Insect Mol Biol 21: 6177.

    • Search Google Scholar
    • Export Citation
  • 14.

    Stevenson BJ, Pignatelli P, Nikou D, Paine MJ, 2012. Pinpointing P450s associated with pyrethroid metabolism in the dengue vector, Aedes aegypti: developing new tools to combat insecticide resistance. PLoS Negl Trop Dis 6: e1595.

    • Search Google Scholar
    • Export Citation
  • 15.

    Saavedra-Rodriguez K, Strode C, Flores AE, Garcia-Luna S, Reyes-Solis G, Ranson H, Hemingway J, Black WC, 2014. Differential transcription profiles in Aedes aegypti detoxification genes after temephos selection. Insect Mol Biol 23: 199215.

    • Search Google Scholar
    • Export Citation
  • 16.

    Fulton RE, Salasek ML, DuTeau NM, Black WC 4th, 2001. SSCP analysis of cDNA markers provides a dense linkage map of the Aedes aegypti genome. Genetics 158: 715726.

    • Search Google Scholar
    • Export Citation
  • 17.

    Gomez-Machorro C, Bennett KE, Munoz MD, Black WC, 2004. Quantitative trait loci affecting dengue midgut infection barriers in an advanced intercross line of Aedes aegypti. Insect Mol Biol 13: 637648.

    • Search Google Scholar
    • Export Citation
  • 18.

    Timoshevskiy VA, Severson DW, deBruyn BS, Black WC, Sharakhov IV, Sharakhova MV, 2013. An integrated linkage, chromosome, and genome map for the yellow fever mosquito Aedes aegypti. PLoS Negl Trop Dis 7: e2052.

    • Search Google Scholar
    • Export Citation
  • 19.

    Reyes-Solis GC, Saavedra-Rodriguez K, Suarez AF, Black WC, 2014. QTL mapping of genome regions controlling temephos resistance in larvae of the mosquito Aedes aegypti. PLoS Negl Trop Dis 8: e3177. doi:10.1371/journal.pntd.0003177.

    • Search Google Scholar
    • Export Citation
  • 20.

    Black WC, DuTeau NM, 1997. RAPD-PCR and SSCP analysis for insect population genetic studies. Crampton J, Beard CB, Louis C, eds. The Molecular Biology of Insect Disease Vectors: A Methods Manual. New York: Chapman and Hall, 361373.

    • Search Google Scholar
    • Export Citation
  • 21.

    Saavedra-Rodriguez K, Urdaneta-Marquez L, Rajatileka S, Moulton M, Flores AE, Fernandez-Salas I, Bisset J, Rodriguez M, McCall PJ, Donnelly MJ, Ranson H, Hemingway J, Black WC 4th, 2007. A mutation in the voltage-gated sodium channel gene associated with pyrethroid resistance in Latin American Aedes aegypti. Insect Mol Biol 16: 785798.

    • Search Google Scholar
    • Export Citation
  • 22.

    Urdaneta-Marquez L, Bosio C, Herrera F, Rubio-Palis Y, Salasek M, Black WC, 2008. Genetic relationships among Aedes aegypti collections in Venezuela as determined by mitochondrial DNA variation and nuclear single nucleotide polymorphisms. Am J Trop Med Hyg 78: 479491.

    • Search Google Scholar
    • Export Citation
  • 23.

    Glaubitz JC, 2004. CONVERT: a user-friendly program to reformat diploid genotypic data for commonly used population genetic software packages. Mol Ecol Notes 4: 309310.

    • Search Google Scholar
    • Export Citation
  • 24.

    Lewis PO, Zaykin DV, 2001. Genetic Data Analysis: Computer Program for the Analysis of Allelic Data. Free program distributed by the authors over the internet. Available at: http://lewis.eeb.uconn.edu/lewishome/software.html. Accessed August 2013.

    • Search Google Scholar
    • Export Citation
  • 25.

    Excoffier L, Lischer HE, 2010. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 10: 564567.

    • Search Google Scholar
    • Export Citation
  • 26.

    Lunn DJ, Thomas A, Best N, Spiegelhalter D, 2000. WinBUGS - A Bayesian modeling framework: concepts, structure, and extensibility. Stat Comput 10: 325337.

    • Search Google Scholar
    • Export Citation
  • 27.

    Rosenberg MS, Anderson CD, 2011. PASSaGE: pattern analysis, spatial statistics and geographic exegesis. Version 2. Methods in Ecology and Evolution 2: 229232.

    • Search Google Scholar
    • Export Citation
  • 28.

    Slatkin M, 1993. Isolation by distance in equilibrium and nonequilibrium populations. Evolution 47: 264279.

  • 29.

    Bosio CF, Harrington LC, Jones JW, Sithiprasasna R, Norris DE, Scott TW, 2005. Genetic structure of Aedes aegypti populations in Thailand using mitochondrial DNA. Am J Trop Med Hyg 72: 434442.

    • Search Google Scholar
    • Export Citation
  • 30.

    Huber K, Le Loan L, Hoang TH, Ravel S, Rodhain F, Failloux AB, 2002. Genetic differentiation of the dengue vector, Aedes aegypti (Ho Chi Minh City, Vietnam) using microsatellite markers. Mol Ecol 11: 16291635.

    • Search Google Scholar
    • Export Citation
  • 31.

    Flores AE, Ponce G, Silva BG, Gutierrez SM, Bobadilla C, Lopez B, Mercado R, Black WC, 2013. Wide spread cross resistance to pyrethroids in Aedes aegypti (Diptera: Culicidae) from Veracruz State Mexico. J Econ Entomol 106: 959969.

    • Search Google Scholar
    • Export Citation
  • 32.

    Aponte HA, Penilla RP, Dzul-Manzanilla F, Che-Mendoza A, López AD, Solis F, Manrique-Saide P, Ranson H, Lenhart A, McCall PJ, Rodríguez AD, 2013. The pyrethroid resistance status and mechanisms in Aedes aegypti from the Guerrero state, Mexico. Pestic Biochem Physiol 107: 226234.

    • Search Google Scholar
    • Export Citation
 
 
 
 

 

 

 

 

 

 

 

 

 

Local Evolution of Pyrethroid Resistance Offsets Gene Flow Among Aedes aegypti Collections in Yucatan State, Mexico

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  • Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado; Laboratorio de Arbovirología, Centro de Investigaciones Regionales Dr. Hideyo Noguchi, Universidad Autónoma de Yucatán, Mérida, México; Laboratorio de Entomología Médica, Facultad de Ciencias Biológicas, Universidad Autónoma de Nuevo León, Monterrey, México

The mosquito Aedes aegypti is the major vector of the four serotypes of dengue virus (DENV1–4). Previous studies have shown that Ae. aegypti in Mexico have a high effective migration rate and that gene flow occurs among populations that are up to 150 km apart. Since 2000, pyrethroids have been widely used for suppression of Ae. aegypti in cities in Mexico. In Yucatan State in particular, pyrethroids have been applied in and around dengue case households creating an opportunity for local selection and evolution of resistance. Herein, we test for evidence of local adaptation by comparing patterns of variation among 27 Ae. aegypti collections at 13 single nucleotide polymorphisms (SNPs): two in the voltage-gated sodium channel gene para known to confer knockdown resistance, three in detoxification genes previously associated with pyrethroid resistance, and eight in putatively neutral loci. The SNPs in para varied greatly in frequency among collections, whereas SNPs at the remaining 11 loci showed little variation supporting previous evidence for extensive local gene flow. Among Ae. aegypti in Yucatan State, Mexico, local adaptation to pyrethroids appears to offset the homogenizing effects of gene flow.

Introduction

Aedes aegypti is the principal urban vector of dengue, chikungunya, and yellow fever viruses.1 The mosquito is closely associated with human habitation: the immature stages are found in water-holding containers in the peridomestic environment and adult females commonly feed and rest indoors. In Yucatan State in Southern Mexico, Ae. aegypti is ubiquitous in large urban centers and in smaller communities2 and dengue is hyperendemic.3,4 The mosquito is controlled through a combination of physical source reduction (removal or alteration of potential water-holding containers) and use of insecticides including the organophosphate temephos to eliminate larvae from water-holding containers and since 2000 pyrethroids have been used to control adults in and around homes.5,6

Although insecticide pressure is high in large urban centers in Yucatan State, fine-scale refugia from insecticides are present. Refugia from temephos include atypical development sites (e.g., vacant lots or storm water drains) for larvae that may not be included in the control effort and residential premises that go untreated as a result of a lack of access by mosquito control teams. Indoor environments provide refugia against vehicle-based spraying of pyrethroids caused by limited spray penetration of the relatively closed concrete homes typical of Yucatan State. Moreover, the intensity of insecticide-based mosquito control to a large extent follows the occurrence of dengue disease cases, and therefore is variable both in space and over time.

This complex and dynamic spatiotemporal mosaic of insecticide pressure, together with mosquito migration through flight or human transport of containers infested with eggs or larvae, shape the rate and patterns of gene flow among Ae. aegypti populations in Yucatan State. Earlier reports on the genetic structure of Ae. aegypti collections based on determination of mitochondrial ND4 haplotypes, indicated that among and within three regions of Mexico—Northeastern, Pacific, and Yucatan Peninsula—collections can be expected to remain genetically uniform within distances up to 150 km.7,8 Moreover, recent data have been gathered from the Yucatan Peninsula on the emergence and rapid rise to near fixation in large urban centers of a knockdown resistance (kdr)-conferring allele (I1,016) in the voltage-gated sodium channel gene (para) of Ae. aegypti.4,9 Another para mutation that is also strongly implicated in pyrethroid resistance involves a cysteine replacement (C1,534).10,11

Our goal in this study is to test two alternative hypotheses concerning the evolution and spread of pyrethroid resistance in Yucatan State, Mexico. One hypothesis is that pyrethroid resistance is uniformly spread throughout Yucatan State by high rates of gene flow. If this hypothesis is valid then we would expect genes that confer pyrethroid resistance to follow the same patterns of variation as neutral genes distributed throughout the genome. An alternative hypothesis is that, despite high rates of gene flow, pyrethroid evolution occurs locally. Specifically, we would expect the frequencies of I1,016 and C1,534 to increase in the presence of pyrethroids and, possibly when pyrethroids are removed, to decline in frequency if these alleles have a low fitness in the absence of insecticides. If this hypothesis is correct we would then expect genes that confer pyrethroid resistance to exhibit greater spatial variation than detected among neutral genes.

Herein, we examine variation at 13 single nucleotide polymorphisms (SNPs). These include I1,016 and C1,534 in para and two cytochrome P450 genes, CYP9J32 and CYP9J29 previously associated with permethrin resistance in a QTL mapping study12 and in a microarray analysis.13 Moreover, CYP9J32 has exhibited pyrethroid metabolizing activity14 and was also overexpressed in Mexican mosquito lines selected with temephos alongside a carboxyl/esterase gene CCE1C.15 The other eight SNPs are putatively insecticide-neutral loci that have been examined in previous population genetic and mapping studies.12,1619 Alleles at these 13 loci were compared among 27 collections of Ae. aegypti originating from eight different communities in Yucatan State. This allowed us to quantify gene flow and genetic structuring among and within communities and collections, and to determine whether patterns of genetic differentiation between putatively neutral genes are similar to patterns observed among insecticide resistance-associated genes.

Materials and Methods

Mosquito collections and rearing.

Aedes aegypti mosquitoes used in this study originated from previously described collections of larvae in Yucatan State over a 12-month period from March 2010 to February 2011.2 Fourth-instar larvae and pupae were retrieved from a variety of containers found on residential premises or in cemeteries. We typically sampled ≥ 20 containers per collection site. After eclosion, adults were identified to species and Ae. aegypti females were artificially blood fed to generate F1 eggs. Only egg papers that subsequently resulted in > 500 adults were used for the study. We randomly selected from 29 to 60 F1 adults per collection to minimize the likelihood of oversampling families in single containers. This was particularly important in the current study because family biased sampling could generate genetic clusters that could be misinterpreted as local adaptation.

This study includes 27 collections from eight different communities in Yucatan State, totaling 1,301 mosquitoes (Table 1, Figure 1). Yucatan has a subtropical climate with seasonal heavy rainfall from June to October and sporadic rain during the remaining year. Mosquito collections were made in eight communities with different levels of urbanization. Seven communities surround the main urban city Merida (population = 777,615), which is centrally located and connected by roads among all communities by distances ranging from 5 to 150 km (Figure 1). Altitude ranges from 0 to 20 meters above sea level and human population size in our collection communities varied from 2,111 to 39,611.2 For small-scale analysis, we obtained nested collections within Merida, Uman, and Caucel (Supplemental Figure 1). Five of these collections (comprising 232 mosquitoes) originated from the city of Merida (the largest urban center in the state), and were located within 3 km of each other (Supplemental Figure 1). Other local collections were made in the satellite communities of Caucel (549 mosquitoes from 11 collections located within 3 km of each other—Supplemental Figure 2) and Uman (285 mosquitoes in five collections located within 5 km of each other—Supplemental Figure 3) or from other single locations in communities > 25 km distant from Merida (234 mosquitoes in five collections Table 1, Figure 1). Supplemental figures are included to show the sizes of the collection areas in Merida, Uman, and Caucel. The largest collection site was Centro in the center of Merida, whereas the smallest was Siglo XXI in Uman. Because > 20 containers were sampled from each collection site, Centro would have had the lowest density of samples, whereas Siglo XXI would have had the greatest sampling density.

Table 1

Collection sites, geographical coordinates, and number of mosquitoes used for analysis*

CommunityCollectionsnLongitudeLatitude
MeridaCentro46−89.6174520.95163
San Jose Vergel46−89.5884820.95532
Xoclan Canto47−89.6649220.96700
Vergel I47−89.5786520.94833
Chuburna46−89.6328221.01425
CaucelBalcones I29−89.7027021.00083
Arboleda34−89.7064521.00285
Herradura31−89.6893520.99722
Sol Caucel IV50−89.7135221.00682
Pedregales Caucel45−89.6906820.99987
Los Almendros II60−89.7019520.99543
Los Almendros Caucel59−89.7003320.99758
Hacienda Caucel61−89.7125721.00577
Los Cocos Caucel60−89.7130020.99593
Torres I60−89.6942521.00487
Viva Caucel60−89.7182720.99827
UmanItzincab Palomeque48−89.6943220.91413
Paseos de Itzincab48−89.6960320.91733
San Lorenzo47−89.7314020.89287
Acim 147−89.7057820.91895
Arcos I48−89.7388520.88988
Siglos XX147−89.7345220.88532
Progreso 47−89.6778321.28067
Ticul 47−89.5452220.39727
Hunucma 46−89.8834321.02115
Maxcanu 47−90.0025020.58583
Tinum-Piste 47−88.3920020.76650

Cities Merida, Uman, and Caucel are composed of several nested collections. The remaining sites are composed of a single collection.

Figure 1.
Figure 1.

Map of collection sites across the Yucatan State.

Citation: The American Society of Tropical Medicine and Hygiene 92, 1; 10.4269/ajtmh.14-0277

DNA isolation and SNP genotyping.

The DNA was isolated from individual mosquitoes by the salt extraction method20 and suspended in 150 μL of TE buffer (10 mM Tris-HCl, 1 mM EDTA pH 8.0). The SNP identification, allele-specific polymerase chain reaction (PCR), melting curve conditions, and genotype readings followed published procedures.11,12,21,22 Table 2 lists the names of the SNP markers, their VectorBase identification numbers, physical or genetic location in the genome (if known) and the primer sequences used for allele-specific PCR. We obtained SNP genotypes at 13 SNP loci in each mosquito. Table 3 describes the salient features of each of the 13 SNPs including: chromosome and linkage location, gene name, the position of the SNP in the gene and in the codon, whether the SNP codes for a replacement substitution, and the alternate nucleotides at the site.

Table 2

Single nucleotide polymorphic markers, gene vector base ID, genetic or physical location, and allele-specific oligonucleotide sequences*

SNP markersGene vector base IDPhysical/genetic locationAllele-specific oligonucleotide sequence (5′–3′)
Putative neutral loci
Amy 447Amylase II (AAEL013421)1-32 cM165LT-ACCGAACGACTTCAATGCG
5ST-ACCGAACGACTTCAATACT
CCAGCAGTTACGCACCTGATAG
Amy 450Amylase II (AAEL013421)1-32 cM165LT-TAGTCGTAGATTTCAGAA
5ST-TAGTCGTAGATTTCAAAC
AACTTCCCTGCAGTCCCC
TrypEEarly Trypsin (AAEL007818)2-19 cM165LT-GGCTACCGCATAACCCTGAACCACA
5ST-CTACCGCATAACCATGAACC
TGGCTGAGTCCCAGAAGG
VCPVitellogenic carboxypeptidase precursor (AAEL15312)2-23 cM175LT-GTTGACACCCGAKCGGTAACCTTCC
5ST-GTTGACACCCGAKCGGTAACCTCCT
CAGCCACTTGACGAGAACAT
ChymoChymotrypsin (AAEL014188)2q4.4185LT-CGTCGTTTGGTTGCGGARTTCAGCG
5ST-CGTCGTTTGGTTGCGGARTTCAACA
CGGGACTGACTCCYCCTTGATAG
MaltMaltase (AAEL009524)3p3.1185LT-ACCGTCCARATCCCCGATAGCG
5ST-ACCGTCCARATCCCCGATAACA
GAAAYTTCTACCAAGTTTACCCAA
GPIGlucose-6-phosphate isomerase (AAEL012994)3q5LT-GCTGATTGCCATGTACGAACACCAG
5ST-GCTGATTGCCATGTACGAACACTAA
CGTCCCAGATGACACCCT
ApyrApyrase 2 (AAEL006347)3-68 cM125LT-ATTTCCAGTTTGAATCTGA
5ST-ATTTCCAGTTTGAATCAGT
GCTTTTAAGTCTCGTTTTCG
Insecticide resistance loci
CCE1CCarboxyl/cholinesterase 1C (AEL003201)1q2.1195LT-TACTCCTATTGCAGATTTTATTCAC
5ST-TACTCCTATTGCAGATTTTATTTAT
TCCACGAGGAATCGGKAT
CYP9J32Cytochrome P450 oxidase 9J32 (AAEL008846)2q1.1195LT-ACTGCTTCCTTGATGATTGTG
5ST-ACTGCTTCCTTGATGATTATT
AAGTTTGATGATTAAGATGGG
I1,016Voltage gated sodium channel (AAEL006019)3q2.5185LT-ACAAATTGTTTCCCACCCGCACCGG
5ST-ACAAATTGTTTCCCACCCGCACTGA
TGATGAACCSGAATTGGACAAAAGC
C1,534Voltage gated sodium channel (AAEL006019)3q2.5185LT-TCTACTTTGTGTTCTTCATCATGTG
5ST-TCTACTTTGTGTTCTTCATCATATT
TCTGCTCGTTGAAGTTGTCGAT
CYP9J29Cytochrome P450 oxidase 9J29 (AAEL014610)3-50cM125LT-CATCGGGTCACGGTTTCCG
5ST-CATCGGGTCACGGTTTTCA
GAACGAAAATCTACGCAGCAT

Locations were previously determined.12,1619 5LT corresponds to the sequence 5′-GCGGGCAGGGCGGCG GGGGCGGGGCC-3′ and 5ST to the sequence 5′-GCGGGC-3′.

Table 3

Physical position of single nucleotide polymorphisms (SNPs)*

MarkerAnnotationAmino acid/SNPCodonSilent/replaceNucleotideNucleotides from ATG
Chrom 1
Amy447α-amylasePro149CCTSilentT/G447
Amy450α-amylasePro150CCTSilentT/G450
CCE1Cα-esteraseAsp276GATSilentT/C828
Chrom 2
TrypEtrypsin 3A1 Precursor5′UTRIndelCACAGCC/−−4
VCPcathepsin BPro95CCASilentA/G285
Chymoserine-type endopeptidaseVal110GTCIleG/A328
CYP9J32cytochrome P450Ser260TCASilentA/C780
Chrom 3
Maltmaltase precursorAsp40GATSilentT/C123
I1,016paraVal776GTAIleA/G2328
C1,534paraPhe1269TTCCysT/G3807
GPIglucose-6-phosphate isomeraseLys500AAGSilentG/A1500
CYP9J29cytochrome P450Ala487GCASilentA/G1461
Apyrapyrase precursor5′UTR A/T−260

Gene annotation, residue position if SNP belongs to coding region; type of replacement and SNP position in coding sequence starting from ATG.

Statistical analysis of allele frequencies.

Individual diploid genotypic data at the 13 SNP loci was reformatted with the program CONVERT23 for subsequent use in the programs GDA24 and ARLEQUINv.3.5.25 Pairwise linkage disequilibrium analyses were also conducted in ARLEQUINv.3.5 and tested for significance using the expectation-maximization (EM) algorithm with 10,000 permutations. Proportions of significant linkage disequilibrium among 78 pairwise comparisons were compared by Bayesian 95% Highest Density Intervals (95% HDI) using WinBUGS.26 All additional analyses were performed using three different sets of data that included: 1) all 13 SNP loci, 2) only the eight neutral SNP loci and, 3) only the five insecticide resistance SNP loci (Table 2). Variation in genotype frequencies among communities and within collections was determined by analysis of molecular variance (AMOVA) using ARLEQUINv.3.5. This program also estimated fixation indexes within (FIS) and among (FST) collections and computed the significance of the variance components associated with each level of genetic structure by a nonparametric permutation test with 10,000 pseudoreplicates. Population genetic analyses were carried out using the Wright's FST estimate assuming the island model. We performed a Mantel analysis to test the relationship between geographic and genetic distances using the software PASSaGE.27 A normalized Mantel correlation coefficient (r) was obtained from matrices of pairwise Slatkin's linearized FST [FST/(1 − FST)]28 and pairwise geographic distances. Significance was tested by randomly permuting the order of the elements within the distance matrices.

Results

SNP allele and genotype frequencies within collections.

A total of 1,301 Ae. aegypti individuals, from 27 collections in eight different communities, were genotyped at 13 SNP marker loci. Allele frequencies are shown in Supplemental Table 1. Genotype proportions at each of the 13 loci were tested for Hardy Weinberg (HW) equilibrium. Table 4 lists the fixation index (FIS = 1 − [Hobs/Hexp]) where Hobs is the observed number of heterozygotes and Hexp is the expected number of heterozygotes obtained for each locus in each collection. Seven out of 351 collection-by-locus tests could not be performed as one of the alleles was fixed. From the remaining 344 collection-by-locus tests, 31 (9.0%) did not fit HW proportions at an α = 0.05. Lack of fit to HW proportions occurred eight times in 26 tests at locus CCE1C and this was significantly more than expected (χ2[1 d.f.], P = 0.024). Lack of fit to HW proportions occurred at 4 of 12 tests in the Tinum-Piste collection but this was not significantly more than expected (χ2[1 d.f.], P = 0.316). Overall, there was an excess of heterozygotes at locus CCE1C but otherwise genotypes conformed to HW expectations. The consistent heterozygote excess at locus CCE1C suggests the genotype assay for this SNP may be inaccurate or that there is a heterozygote advantage conferred by the SNP. The fact that it encodes a transition and is synonymous makes heterozygote advantage unlikely. The excess of heterozygotes at CCE1C might also be associated with duplication. However, in earlier studies we found that the segregation of alleles at the para locus12 and three different CCE-C genes fit a single gene model.19

Table 4

Fixation index (FIS) for each collection site at each SNP marker*

CommunityFIS coefficient and Hardy Weinberg equilibrium
   CollectionPutative neutral lociInsecticide resistance loci
Amy447Amy450TrypEVCPChymoMaltGPIApyrCCE1CCYP9J32I1,016C1,534CYP9J29
Merida
 Centro0.06−0.080.230.040.110.200.12−0.14−0.02−0.24−0.080.09
 San Jose Vergel−0.070.03−0.12−0.170.15−0.050.33−0.18−0.03−0.30−0.130.15
 Xoclan Canto−0.100.00−0.11−0.16−0.02−0.11−0.01−0.11−0.010.20−0.23−0.05−0.05
 Vergel I0.000.01−0.08−0.05−0.330.02−0.16−0.11−0.06−0.280.240.190.11
 Chuburna0.48−0.380.32−0.170.24−0.070.23−0.10−0.050.310.020.160.26
Caucel
 Balcones−0.100.350.04−0.08−0.170.270.10−0.06−0.440.05−0.190.260.38
 Arboleda−0.02−0.06−0.16−0.29−0.100.50−0.16−0.260.01−0.320.000.36
 Herradura−0.130.28−0.05−0.13−0.20−0.13−0.25−0.18−0.180.01−0.220.21
 Sol Caucel IV−0.050.07−0.33−0.09−0.05−0.11−0.13−0.02−0.290.110.110.390.31
 Pedregales Caucel−0.140.37−0.09−0.070.130.170.30−0.08−0.24−0.040.250.67−0.04
 Los Almendros II−0.030.000.17−0.17−0.050.04−0.06−0.18−0.340.100.19−0.070.21
 Los Almendros Caucel−0.100.020.07−0.20−0.020.15−0.15−0.05−0.140.11−0.060.000.67
 Hacienda Caucel−0.060.20−0.09−0.14−0.19−0.13−0.08−0.12−0.240.160.100.100.45
 Los Cocos Caucel−0.070.32−0.28−0.240.06−0.090.17−0.04−0.360.080.140.490.06
 Torres I−0.11−0.04−0.07−0.07−0.08−0.08−0.09−0.11−0.200.200.230.370.23
 Viva Caucel−0.060.110.03−0.180.27−0.05−0.10−0.05−0.26−0.020.120.170.12
Uman
 Itzincab Palomeque−0.150.78−0.08−0.29−0.02−0.06−0.12−0.07−0.08−0.100.140.00−0.18
 Paseos de Itzincab−0.150.370.12−0.21−0.130.170.01−0.13−0.31−0.02−0.15−0.03−0.05
 San Lorenzo0.080.34−0.19−0.12−0.060.120.65−0.06−0.14−0.23−0.050.60
 Acim 1−0.07−0.07−0.20−0.19−0.11−0.15−0.03−0.12−0.33−0.19−0.11−0.090.11
 Arcos I−0.12−0.09−0.24−0.19−0.02−0.19−0.15−0.11−0.34−0.24−0.17−0.210.09
 Siglos XXI−0.26−0.24−0.24−0.190.260.26−0.08−0.16−0.230.20−0.21−0.110.02
Progreso−0.170.110.180.300.03−0.390.08−0.02−0.18−0.02−0.01−0.160.27
Ticul−0.11−0.07−0.11−0.07−0.08−0.12−0.07−0.04−0.040.05−0.21−0.17
Hunucma−0.110.15−0.08−0.07−0.140.240.15−0.16−0.20−0.19−0.080.000.27
Maxcanu−0.030.17−0.210.000.240.12−0.08−0.04−0.23−0.040.12−0.210.23
Tinum-Piste−0.010.40−0.12−0.260.120.15−0.12−0.330.321.001.000.28

Bold numbers show markers that did not fit Hardy-Weinberg equilibrium (P < 0.05). Positive values show an excess of homozygotes. Negative value is caused by an excess of heterozygotes. (–) indicates fixation.

Linkage disequilibrium among loci.

An analysis of linkage disequilibrium was performed to determine whether alleles at the 13 SNP loci segregated independently from one another. There were 2,106 ([13 × 12/2] × 27) potentially pairwise linkage disequilibrium comparisons except that alleles were fixed at certain loci in specific collections. From a total of 2,034 dilocus-by collection tests, 225 (11.1%) exhibited significant linkage disequilibrium. The Bayesian HDI around this rate extended from 9.8% to 12.5% for all sites and these proportions changed only slightly when removing the para SNPs or the other putative resistance markers (Figure 2). Thus, there were an excess proportion of loci in disequilibrium. Figure 2 indicates that this was not associated with one community or collection site in a community. In Merida, the HDI did not cross the α = 0.05 threshold in three of five collection sites. In Caucel, the HDI did not contain the 0.05 threshold in eight of eleven collection sites. In Uman, HDI did not contain the 0.05 threshold in three of six collection sites and only crossed the threshold in one of five remaining sites. Table 5 lists for each of 78 di-locus pairs the number of collections exhibiting disequilibrium. Only two of these, (TrypE/Amy450 and I1,016/C1,534) exceed the α = 0.05 threshold. Of possible di-locus comparisons, I1,016 and C1,534 were in disequilibrium in 20 of 27 collections. Figure 3 indicates that frequencies of resistant alleles I1,016 and C1,534 co-occur in 81.2% of the individuals within collections.

Figure 2.
Figure 2.

Analysis of the proportion of significant linkage disequilibrium (N = 78 pairwise comparisons) for each community and collection site. Bars represent Bayesian 95% highest density intervals (95% HDI). Proportions with HDI that overlap 0.05 were not considered to have a credible excess of linkage disequilibrium.

Citation: The American Society of Tropical Medicine and Hygiene 92, 1; 10.4269/ajtmh.14-0277

Table 5

Number of collection sites that showed significant linkage disequilibrium at a pairwise di-locus comparison

ChromosomeNumber of collections with significant disequilibrium at each dilocus comparison
 LociAmy447Amy450CCE1CTrypEVCPChymoCYP9J32MaltI1,016C1,534GPICYP9J29Apyr
1Amy447521313104210
1Amy450 57*163322415
1CCE1c  1120332042
2TrypE   134463213
2VCP    41444233
2Chymo     6333122
2CYP9J32      553240
3Malt       31122
3I1,016        20251
3C1,534         253
3GPI          32
3CYP9J29           2
3Apyr            

P ≤ 0.05.

P ≤ 0.001.

Figure 3.
Figure 3.

Regression analysis of allele frequencies of two para mutations: I1,016 and C1,534 in 27 collections from Yucatan.

Citation: The American Society of Tropical Medicine and Hygiene 92, 1; 10.4269/ajtmh.14-0277

SNP allele frequencies among collections.

We performed an AMOVA assuming a hierarchical structure of eight communities. Three of these communities—Merida, Uman, and Caucel—contained five, six, and 11 collections, respectively, whereas the remaining five communities (Hunucma, Maxcanu, Ticul, Progreso, and Tinum-Piste) were represented by single collections. An AMOVA including all 13 loci indicated that 3.2% of the total variation was attributable to difference among communities, whereas 93.8% arose within collections (P < 0.05) Table 6. However, AMOVAs performed for each locus separately (Table 7) revealed that the percentage of total variation among communities ranged widely from 0% to 23.5%. Putatively neutral loci showed < 1.2% variation among communities but C1,534, I1,016, and CCE1C varied significantly among communities accounting for 23.5%, 13.2%, and 3.2% of the overall variance, respectively.

Table 6

Analysis of molecular variance (AMOVA) for three sets of data*

Data setSource of variationSum of squaresF% Variance
All loci
 Among communities169.90.032*3.2
 Among collections within communities139.80.030*3.9
 Among mosquitoes within collections4650.40.062*93.8
 Total4960.2  
Putative neutral loci
 Among communities24.20.000−0.03
 Among collections within communities68.50.027*2.7
 Among mosquitoes within collections2513.40.027*97.3
 Total2606  
Insecticide resistance loci
 Among communities145.80.068*6.8
 Among collections within communities71.30.035*3.3
 Among mosquitoes within collections2136.90.100*90
 Total2354.1  
Table 7

Analysis of molecular variance (AMOVA) by locus*

LociAmong communitiesAmong collections within communities
SSDd.f.%SSDd.f.%
Putative neutral
 Amy447371.23190.9
 Amy450670.612193.2
 TrypE47−0.110192.6
 VCP27−0.16191.4
 Chymo27−0.914194.1
 Malt37−0.310192.9
 GPI37−0.610194.3
 Apyr270.23191.0
Insecticide resistance
 CCE1C1273.37191.7
 CYP9J3257−0.623194.0
 I1,01657713.216193.4
 C1,53467723.513194.2
 CYP9J29570.013192.8

Sum of squares (SSD), degrees of freedom (d.f.), and % of variation obtained among communities (8) and among collection within communities.

Two additional AMOVAs were performed to compare variation among collections in the frequency of putatively neutral markers with variation in the frequency of the five resistance markers (Table 6). For the putatively neutral markers, variation among communities was non-significant and accounted for only 2.7% of the variation among collections within communities. For insecticide resistance loci, variation among communities accounted for 6.8% and 3.3% among collections within communities.

Isolation by distance and genetic structure.

A Mantel analysis was performed to test for isolation by distance. Figure 4 shows the relationship between Slatkin's linear pairwise FST (FST/[1 − FST]) and the natural logarithm of the pairwise geographic distances for the three groups of markers. Populations were isolated by distance when analyzing all 13 markers (r2 = 0.22, Mantel Prob = 0.0001). However, this pattern was inconsistent when considering only neutral markers (r2 = 0.0004, Mantel Prob = 0.1669) for which no isolation by distance was evident and insecticide resistance markers, which exhibited isolation by distance (r2 = 0.22, Mantel Prob = 0.0001), but only among collections that were farther apart. Collections on the left side of the graph were obtained in the same city, whereas collections on the right side came from small towns (Maxcanu, Progreso, Ticul) located further away from the larger cities.

Figure 4.
Figure 4.

(A) Regression analysis of pairwise FST/(1 − FST) for all 13 loci against ln(geographic distances[km]). (B) pairwise FST/(1 − FST) for the putatively neutral loci against ln(geographic distances [km]), and (C) pairwise FST/(1 − FST) for insecticide resistance loci against ln(geographic distances [km]). Regression analysis equation and correlation coefficient are also shown.

Citation: The American Society of Tropical Medicine and Hygiene 92, 1; 10.4269/ajtmh.14-0277

Discussion

The AMOVA and Mantel analyses of putatively neutral SNPs indicated high rates of gene flow among all collections in Yucatan State. However, allele frequencies at putative pyrethroid resistance loci were highly heterogeneous. In particular, C1,534 and I1,016 varied greatly in frequency among communities. These results suggest that these mutations are under strong selection pressure in Yucatan Ae. aegypti populations. We propose that this spatial variability is driven by local selection pressure generated by the use of pyrethroids in and around houses of dengue patients. Our indirect estimation of the intensity of insecticide use for vector control based in the reported dengue cases for each community from 2006 to 2010 (Supplemental Figure 4) suggests that insecticide is spatially and temporally heterogeneous across Yucatan communities. For example, a high number of dengue cases was reported for Merida from 2006 to 2010, suggesting high use of insecticides during this time span. Meanwhile, low to moderate intensity was estimated for the remaining examined communities, except for high use in Ticul and Progreso in 2007, and in Uman and Ticul in 2009, where dengue outbreaks occurred. As a common trend the use of insecticides increased in 2010, when most of the communities experienced dengue outbreaks resulting in moderate to high insecticide use by vector control campaigns.

This scenario is supported by our Mantel analysis for insecticide resistance loci, which identified a significant positive correlation between genetic and geographic distances. Our mosquito collections are connected to Merida through single roads. Although some collections are separated by large geographic distances (> 25 km) from Merida, we did not obtained significant variability at putative neutral loci, suggesting that there is abundant mosquito migration through eggs, larvae, and adults among collections that maintains high rates of gene flow. This was not surprising given our earlier studies with mitochondrial ND4 haplotypes that showed free gene flow among Yucatan collections within 150 km of distance.7,8 Previous studies in Asia using either ND4 or microsatellite markers showed that Ae. aegypti populations were genetically different with a pattern of panmictic populations occurring in and near urban centers, but more genetically differentiated with increasing geographical distance from the urban centers.29,30

We expected to detect excess linkage disequilibrium among our SNP markers because Ae. aegypti in these communities are frequently subject to population suppression that may generate strong population bottlenecks that can, in turn, generate disequilibrium. Furthermore, a selective sweep might cause alleles linked to the para locus on chromosome 3 to be in linkage disequilibrium. As shown in Figure 2, there was an overall excess of analyses in which significant disequilibrium was detected. Furthermore, we found strong linkage disequilibrium between the C1,534 and I1,016 loci in the para gene. This suggests that either there has been a local selective sweep of both mutations or that the mutations arose independently and have been selected for in parallel. To test genetic sweep we would have to include SNPs present at introns in the para gene or surrounding genes in chromosome 3. However, this analysis was done in a prior study21 and only very low levels of disequilibrium were discovered.

We wished to test two alternative hypotheses concerning the evolution and spread of pyrethroid resistance in Yucatan State, Mexico. Our first hypothesis was that pyrethroid resistance will be uniformly spread throughout Yucatan State by high rates of gene flow. If so, then we expected genes that confer pyrethroid resistance to follow the same patterns of variation as neutral genes distributed throughout the genome. This pattern was not seen. Even though the frequencies of all neutral markers were uniformly spread throughout all of our 27 collection sites, markers C1,534, I1,016, and to a lesser extent CCE1C varied greatly in frequency among sites.

The alternative hypothesis was that, despite high rates of gene flow, pyrethroid evolution occurs locally. We expected the frequencies of I1,016, C1,534, CCE1C, CYP9J32, and CYP9J29 to increase in the presence of pyrethroids and, possibly when pyrethroids are removed, to decline in frequency if these alleles have low fitness in the absence of insecticides. If so, we would then expect I1,016, C1,534, CCE1C, CYP9J32, and CYP9J29 to exhibit greater spatial variation than detected among neutral genes. This is the pattern observed with I1,016, C1,534, and CCE1C.

These results suggest a general model for the movement of insecticide-resistant genes in the Yucatan. High rates of gene flow occur among Ae. aegypti populations within 150 km of one another. This gene flow spreads alleles at all loci, including resistance loci, across the State. In areas where insecticides are applied, the frequencies of resistance alleles increase. Conversely, in areas where insecticides are no longer applied, and assuming negative fitness associated with resistance alleles in the absence of insecticides, the frequencies of resistance alleles will decline. In an operational mosquito control context, our findings indicate that pyrethroid susceptibility can persist locally even with high gene flow and high widespread frequencies of pyrethroid resistance alleles. This situation is beneficial for insecticide resistance management; providing critical “islands” of pyrethroid susceptibility that provide a source for susceptibility alleles. Because wide cross resistance to pyrethroids has been reported among mosquito populations across Mexico,31,32 the use of non-pyrethroid molecules listed in the new Official Mexican policy for the surveillance and control of vectors6 will help to maintain the sink of pyrethroid-susceptible Ae. aegypti in Yucatan State.

  • 1.

    Gubler DJ, 2004. The changing epidemiology of yellow fever and dengue, 1900 to 2003: full circle? Comp Immunol Microbiol Infect Dis 27: 319330.

    • Search Google Scholar
    • Export Citation
  • 2.

    García-Rejón JE, López-Uribe MP, Loroño-Pino MA, Arana-Guardia R, Puc-Tinal M, López-Uribe GM, Coba-Tún C, Baak-Baak CM, Machain-Williams C, Reyes-Solis GC, Lozano-Fuentes S, Saavedra-Rodriguez K, Black WC, Beaty BJ, Eisen L, 2012. Aedes (Stegomyia) aegypti and Aedes (Howardina) cozumelensis in Yucatán State, México, with a summary of published collection records for Ae. cozumelensis. J Vector Ecol 37: 365372.

    • Search Google Scholar
    • Export Citation
  • 3.

    Loroño-Pino MA, Farfán-Ale JA, Zapata-Peraza AL, Rosado-Paredes EP, Flores-Flores LF, García-Rejón JE, Díaz FJ, Blitvich BJ, Andrade-Narváez M, Jiménez-Ríos E, Blair CD, Olson KE, Black W 4th, Beaty BJ, 2004. Introduction of the American/Asian genotype of dengue 2 virus into the Yucatan State of Mexico. Am J Trop Med Hyg 71: 485492.

    • Search Google Scholar
    • Export Citation
  • 4.

    Loroño-Pino MA, García-Rejón JE, Machain-Williams C, Gomez-Carro S, Nuñez-Ayala G, Nájera-Vázquez M del R, Losoya A, Aguilar L, Saavedra-Rodriguez K, Lozano-Fuentes S, Beaty MK, Black WC 4th, Keefe TJ, Eisen L, Beaty BJ, 2013. Towards a Casa Segura: a consumer product study of the effect of insecticide-treated curtains on Aedes aegypti and dengue virus infections in the home. Am J Trop Med Hyg 89: 385397.

    • Search Google Scholar
    • Export Citation
  • 5.

    World Health Organization, 2009. Dengue and Dengue Hemorrhagic Fever. 117 WFSN, ed.

  • 6.

    Norma Oficial Mexicana, 2010. NOM-EM-003-SSA2-2010 para la Vigilancia Epidemiologica. Prevencion y Control de Enfermedades Transmitidas por Vector. Diario Oficial de la Federacion, Junio 2010, Mexico.

    • Search Google Scholar
    • Export Citation
  • 7.

    Gorrochotegui-Escalante N, Gomez-Machorro C, Lozano-Fuentes S, Fernandez-Salas I, Munoz MD, Farfan-Ale JA, Garcia-Rejon J, Beaty BJ, Black WC, 2002. Breeding structure of Aedes aegypti populations in Mexico varies by region. Am J Trop Med Hyg 66: 213222.

    • Search Google Scholar
    • Export Citation
  • 8.

    Gorrochotegui-Escalante N, Munoz MD, Fernandez-Salas I, Beaty BJ, Black WC, 2000. Genetic isolation by distance among Aedes aegypti populations along the northeastern coast of Mexico. Am J Trop Med Hyg 62: 200209.

    • Search Google Scholar
    • Export Citation
  • 9.

    García GP, Flores AE, Fernández-Salas I, Saavedra-Rodríguez K, Reyes-Solis G, Lozano-Fuentes S, Bond JG, Casas-Martínez M, Ramsey JM, García-Rejón J, Domínguez-Galera M, Ranson H, Hemingway J, Eisen L, Black WC IV, 2009. Recent rapid rise of a permethrin knock down resistance allele in Aedes aegypti in Mexico. PLoS Negl Trop Dis 3: e531.

    • Search Google Scholar
    • Export Citation
  • 10.

    Harris AF, Rajatileka S, Ranson H, 2010. Pyrethroid resistance in Aedes aegypti from Grand Cayman. Am J Trop Med Hyg 83: 277284.

  • 11.

    Yanola J, Somboon P, Walton C, Nachaiwieng W, Somwang P, Prapanthadara L-a, 2011. High-throughput assays for detection of the F1534C mutation in the voltage-gated sodium channel gene in permethrin-resistant Aedes aegypti and the distribution of this mutation throughout Thailand: high-throughput assays to detect the F1534C mutation in sodium channel gene of the Aedes aegypti. Trop Med Int Health 16: 501509.

    • Search Google Scholar
    • Export Citation
  • 12.

    Saavedra-Rodriguez K, Strode C, Suarez AF, Salas IF, Ranson H, Hemingway J, Black WC, 2008. Quantitative trait loci mapping of genome regions controlling permethrin resistance in the mosquito Aedes aegypti. Genetics 180: 11371152.

    • Search Google Scholar
    • Export Citation
  • 13.

    Saavedra-Rodriguez K, Suarez AF, Salas IF, Strode C, Ranson H, Hemingway J, Black WC IV, 2012. Transcription of detoxification genes after permethrin selection in the mosquito Aedes aegypti. Insect Mol Biol 21: 6177.

    • Search Google Scholar
    • Export Citation
  • 14.

    Stevenson BJ, Pignatelli P, Nikou D, Paine MJ, 2012. Pinpointing P450s associated with pyrethroid metabolism in the dengue vector, Aedes aegypti: developing new tools to combat insecticide resistance. PLoS Negl Trop Dis 6: e1595.

    • Search Google Scholar
    • Export Citation
  • 15.

    Saavedra-Rodriguez K, Strode C, Flores AE, Garcia-Luna S, Reyes-Solis G, Ranson H, Hemingway J, Black WC, 2014. Differential transcription profiles in Aedes aegypti detoxification genes after temephos selection. Insect Mol Biol 23: 199215.

    • Search Google Scholar
    • Export Citation
  • 16.

    Fulton RE, Salasek ML, DuTeau NM, Black WC 4th, 2001. SSCP analysis of cDNA markers provides a dense linkage map of the Aedes aegypti genome. Genetics 158: 715726.

    • Search Google Scholar
    • Export Citation
  • 17.

    Gomez-Machorro C, Bennett KE, Munoz MD, Black WC, 2004. Quantitative trait loci affecting dengue midgut infection barriers in an advanced intercross line of Aedes aegypti. Insect Mol Biol 13: 637648.

    • Search Google Scholar
    • Export Citation
  • 18.

    Timoshevskiy VA, Severson DW, deBruyn BS, Black WC, Sharakhov IV, Sharakhova MV, 2013. An integrated linkage, chromosome, and genome map for the yellow fever mosquito Aedes aegypti. PLoS Negl Trop Dis 7: e2052.

    • Search Google Scholar
    • Export Citation
  • 19.

    Reyes-Solis GC, Saavedra-Rodriguez K, Suarez AF, Black WC, 2014. QTL mapping of genome regions controlling temephos resistance in larvae of the mosquito Aedes aegypti. PLoS Negl Trop Dis 8: e3177. doi:10.1371/journal.pntd.0003177.

    • Search Google Scholar
    • Export Citation
  • 20.

    Black WC, DuTeau NM, 1997. RAPD-PCR and SSCP analysis for insect population genetic studies. Crampton J, Beard CB, Louis C, eds. The Molecular Biology of Insect Disease Vectors: A Methods Manual. New York: Chapman and Hall, 361373.

    • Search Google Scholar
    • Export Citation
  • 21.

    Saavedra-Rodriguez K, Urdaneta-Marquez L, Rajatileka S, Moulton M, Flores AE, Fernandez-Salas I, Bisset J, Rodriguez M, McCall PJ, Donnelly MJ, Ranson H, Hemingway J, Black WC 4th, 2007. A mutation in the voltage-gated sodium channel gene associated with pyrethroid resistance in Latin American Aedes aegypti. Insect Mol Biol 16: 785798.

    • Search Google Scholar
    • Export Citation
  • 22.

    Urdaneta-Marquez L, Bosio C, Herrera F, Rubio-Palis Y, Salasek M, Black WC, 2008. Genetic relationships among Aedes aegypti collections in Venezuela as determined by mitochondrial DNA variation and nuclear single nucleotide polymorphisms. Am J Trop Med Hyg 78: 479491.

    • Search Google Scholar
    • Export Citation
  • 23.

    Glaubitz JC, 2004. CONVERT: a user-friendly program to reformat diploid genotypic data for commonly used population genetic software packages. Mol Ecol Notes 4: 309310.

    • Search Google Scholar
    • Export Citation
  • 24.

    Lewis PO, Zaykin DV, 2001. Genetic Data Analysis: Computer Program for the Analysis of Allelic Data. Free program distributed by the authors over the internet. Available at: http://lewis.eeb.uconn.edu/lewishome/software.html. Accessed August 2013.

    • Search Google Scholar
    • Export Citation
  • 25.

    Excoffier L, Lischer HE, 2010. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 10: 564567.

    • Search Google Scholar
    • Export Citation
  • 26.

    Lunn DJ, Thomas A, Best N, Spiegelhalter D, 2000. WinBUGS - A Bayesian modeling framework: concepts, structure, and extensibility. Stat Comput 10: 325337.

    • Search Google Scholar
    • Export Citation
  • 27.

    Rosenberg MS, Anderson CD, 2011. PASSaGE: pattern analysis, spatial statistics and geographic exegesis. Version 2. Methods in Ecology and Evolution 2: 229232.

    • Search Google Scholar
    • Export Citation
  • 28.

    Slatkin M, 1993. Isolation by distance in equilibrium and nonequilibrium populations. Evolution 47: 264279.

  • 29.

    Bosio CF, Harrington LC, Jones JW, Sithiprasasna R, Norris DE, Scott TW, 2005. Genetic structure of Aedes aegypti populations in Thailand using mitochondrial DNA. Am J Trop Med Hyg 72: 434442.

    • Search Google Scholar
    • Export Citation
  • 30.

    Huber K, Le Loan L, Hoang TH, Ravel S, Rodhain F, Failloux AB, 2002. Genetic differentiation of the dengue vector, Aedes aegypti (Ho Chi Minh City, Vietnam) using microsatellite markers. Mol Ecol 11: 16291635.

    • Search Google Scholar
    • Export Citation
  • 31.

    Flores AE, Ponce G, Silva BG, Gutierrez SM, Bobadilla C, Lopez B, Mercado R, Black WC, 2013. Wide spread cross resistance to pyrethroids in Aedes aegypti (Diptera: Culicidae) from Veracruz State Mexico. J Econ Entomol 106: 959969.

    • Search Google Scholar
    • Export Citation
  • 32.

    Aponte HA, Penilla RP, Dzul-Manzanilla F, Che-Mendoza A, López AD, Solis F, Manrique-Saide P, Ranson H, Lenhart A, McCall PJ, Rodríguez AD, 2013. The pyrethroid resistance status and mechanisms in Aedes aegypti from the Guerrero state, Mexico. Pestic Biochem Physiol 107: 226234.

    • Search Google Scholar
    • Export Citation

Author Notes

* Address correspondence to Karla Saavedra-Rodriguez, Department of Microbiology, Immunology and Pathology, Colorado State University, Ft. Collins, CO 80523-1690. E-mail: ksaavedr@colostate.edu

Financial support: The study was supported by the Innovative Vector Control Consortium and the National Institutes of Health through a Fogarty International Training Grant (2D43TW001130) and an International Collaborations in Infectious Disease Research Program (U01-AI-088647).

Authors' addresses: Karla Saavedra-Rodriguez, Meaghan Beaty, Saul Lozano-Fuentes, Steven Denham, Barry Beaty, Lars Eisen, and William C. Black IV, Colorado State University, Microbiology, Immunology and Pathology, Fort Collins, CO, E-mails: ksaavedr@colostate.edu, meaghanb8y@gmail.com, slozano@colostate.edu, Steven.Denham@rams.colostate.edu, barry.beaty@colostate.edu, lars.eisen@colostate.edu, and wcb4@lamar.colostate.edu. Julian Garcia-Rejon, Guadalupe Reyes-Solis, Carlos Machain-Williams, and Maria Alba Loroño-Pino, Universidad Autonoma de Yucatan, Instituto Hideyo Noguchi, Merida, Yucatan, Mexico, E-mails: grejon@uady.mx, carmenclh@gmail.com, carlos.machain@uady.mx, and maria.lorono@gmail.com. Adriana Flores-Suarez and Gustavo Ponce-Garcia, Universidad Autonoma de Nuevo Leon, Biologia de Invertebrados, San Nicolas de los Garza, Nuevo Leon, Mexico, E-mails: adrflores@gmail.com and gponcealfa@gmail.com.

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