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    Sample localities of Anopheles darlingi in eastern Brazil. GA = Granja Alves; LI = Lagoa dos Indios; STN = Santana; ARA = Aracanga; BEL = Belem; MOJ = Moju.; PEX = Peixoto de Azevedo.

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POPULATION STRUCTURE OF THE MALARIA VECTOR ANOPHELES DARLINGI IN A MALARIA-ENDEMIC REGION OF EASTERN AMAZONIAN BRAZIL

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  • 1 Department of Biology, University of Vermont, Burlington, Vermont; Wadsworth Center, New York State Department of Health, Albany, New York; Center for Bioinformatics, University of Copenhagen, Copenhagen, Denmark; Aeras Global Tuberculosis Vaccine Foundation, Rockville, Maryland; Walter Reed Army Institute for Research, Museum Support Center, Smithsonian Institution, Suitland, Maryland; Instituto Evandro Chagas, Ananindeua, Para, Brazil

Anopheles darlingi is the primary malaria vector in Latin America, and is especially important in Amazonian Brazil. Historically, control efforts have been focused on indoor house spraying using a variety of insecticides, but since the mid-1990s there has been a shift to patient treatment and focal insecticide fogging. Anopheles darlingi was believed to have been significantly reduced in a gold-mining community, Peixoto de Azevedo (in Mato Grosso State), in the early 1990s by insecticide use during a severe malaria epidemic. In contrast, although An. darlingi was eradicated from some districts of the city of Belem (the capital of Para State) in 1968 to reduce malaria, populations around the water protection area in the eastern district were treated only briefly. To investigate the population structure of An. darlingi including evidence for a population bottleneck in Peixoto, we analyzed eight microsatellite loci of 256 individuals from seven locations in Brazil: three in Amapa State, three in Para State, and one in Mato Grosso State. Allelic diversity and mean expected heterozygosity were high for all populations (mean number alleles/locus and HE were 13.5 and 0.834, respectively) and did not differ significantly between locations. Significant heterozygote deficits were associated with linkage disequilibrium, most likely due to either the Wahlund effect or selection. We found no evidence for a population bottleneck in Peixoto, possibly because the reduction was not extreme enough to be detected. Overall estimates of long-term Ne varied from 92.4 individuals under the linkage disequilibrium model to ∞ under the heterozygote excess model. Fixation indices and analysis of molecular variance demonstrated significant differentiation between locations north and south of the Amazon River, suggesting a degree of genetic isolation between them, attributed to isolation by distance.

INTRODUCTION

The distribution of Anopheles darlingi within the Amazon Basin, where its primary importance in malaria transmission is well-documented,13 appears to be continuous, with no obvious barriers that can isolate populations. A similar situation is found in An. gambiae in sub-Saharan Africa, except for the Rift Valley complex.4 For both species, the isolation by distance (IBD) model,5 which could promote genetic divergence6 and ultimately speciation, seems a reasonable one (however, see Donnelly and others7). The IBD has also been proposed to explain genetic differentiation in the African malaria vector An. funestus.8 However, a study comparing mitochondrial DNA (mtDNA) sequences and microsatellite loci of samples of another malaria vector, An. albimanus, from throughout Latin America found a weak IBD only in the microsatellite loci.9 Support for this model for An. darlingi is mixed. The IBD was detected in samples from Venezuela, Brazil, and Bolivia using mtDNA restriction fragment length polymorphism (RFLP) data10 and could be a factor influencing the 4–5% sequence divergence noted in the ribosomal DNA (rDNA) internal transcribed spacer 2 (ITS2) in a sample from southeastern Brazil compared with four samples from Amazonian and northeastern Brazil.11 Conversely, a recent analysis of 19 localities spanning much of the range of An. darlingi (southern Mexico to southern Brazil) using partial sequence from the mtDNA cytochrome oxidase subunit I (COI) gene, detected only a weak association between geographic and genetic distances, and did not fit a simple model of IBD.12

Intensive use of insecticides, most commonly on the inside walls of houses in the Amazon Basin to control An. darlingi,13 may have resulted in a population bottleneck, and is believed to have caused behavioral changes from endophagy (indoor feeding)1,14 to exophagy (outdoor feeding) at least in some Amazonian regions.2,1518 Of additional interest is the fact that An. darlingi was reported to have been eradicated from several districts of the Amazon city of Belem in 1968 after an intensive campaign, although it was detected there again in some districts in the mid 1990s.19 The water protection area in the district of Entrocamento, Belem, to the east, which consists of forest surrounding two large lakes, was treated with insecticide during the 1990s only briefly and sparingly when malaria cases were detected in an illegal settlement that was soon dismantled.

Mato Grosso State has had a history of gold mining since the colonial period.20 Typically, manual gold miners in Amazonian Brazil are vulnerable migrants who work in inaccessible, underdeveloped areas under poor conditions with little or no protection from vectors that may transmit parasitic diseases, particularly malaria.21 Interestingly, such alluvial gold deposits are often located in habitats preferred by the primary malaria vector An. darlingi, and intensive malaria transmission is frequently associated with small-scale gold-mining.21 Peixoto de Azevedo, one of our sample localities, was founded in 1986 because gold was discovered in the area,18 and malaria prevalence as high as 20.5% was recorded.22 Because insecticides were used extensively to control An. darlingi during a malaria epidemic in Peixoto de Azevedo that peaked in 1992,20 we anticipated the possibility of detecting the signature of a bottleneck in this sample.

The focus of the present study was to use microsatellite markers23 to assess population structure in An. darlingi and to look for the signature of a population bottleneck in an area of Brazil known to have had a history of insecticide use.13,24 We used markers that are particularly sensitive to population differentiation.4

METHODS

Collections.

Localities (Figure 1) from Amapa State included Granja Alves (GA; 0°02′S, 51°05′W) and Lagoa dos Indios (LI; 0°02′S, 51°11′W), both on the outskirts of the capital city of Macapa, and Santana (STN; 0°01′S, 51°09′W), slightly southwest of Macapa. Localities in Para State included the district of Entrocamento in the capital city of Belem (BEL; 1°41′S, 48°40′W), Aracanga (ARA; 1°37′S, 48°36′W), approximately 12 km east of Belem, and Moju (MOJ; 1°52′S, 48°45′W), southwest of Belem. Peixoto de Azevedo (PEX; 10°23′S, 54°54′W) is in northern Mato Grosso State, just south of the border with Para State. In all sites, adult female An. darlingi were collected outdoors in 1997–1998 either resting in vegetation near houses or on the outer walls of houses, at cattle or water buffalo corrals, or by landing catches between approximately 6:30 pm and 8:30 pm, identified morphologically within 24 hours,25 and stored in 95% ethanol until use. The landing catch protocol was reviewed and approved by the Institutional Review Boards at the University of Vermont and The New York State Department of Health and the Biosafety Committee of the Instituto Evandro Chagas in Belem, Brazil. The Walter Reed Army Institute for Research Human Use Review Committee reviewed and approved it as a minimal risk protocol. Six of seven of the collection sites are in the lowland tropical rain forest ecoregion of the Amazon region,26 the heart of the range of An. darlingi.2,3 Peixoto is in the savannah ecoregion and along with BEL, MOJ, and ARA are in areas where An. darlingi is considered the main regional vector.26 In contrast, in GA, LI, and STN, An. darlingi abundance is relatively low compared with An. marajoara, which is the local primary vector.27 The number of mosquitoes analyzed ranged from 25 in PEX to 44 in BEL.

Extraction of DNA and microsatellite genotyping.

Procedures and loci for the eight microsatellite markers used for this study are as previously described.23 Repeat motifs included four GA dinucleotides, two AC dinucleotides, and two GT dinucleotides.23 Microsatellite alleles were amplified by polymerase chain reaction with one fluorescent-labeled primer in a PTC 100 (MJR Research, Waltham, MA) thermal cycler using the following conditions: a 5-minute denaturation at 95°C; 30 cycles for 20 seconds at 95°C, 30 seconds at 60°C, and 20 seconds at 72°C; and a final extension for 5 minutes at 72°C.23 Data were then analyzed using GENOTYPER software (Applied Biosystems, Foster City, CA). An ABI 373 automated sequencer (Applied Biosystems) with an internal size standard was used for allele size determination.

Data analysis.

Unbiased estimates of expected and observed heterozygosities were calculated using genetic data analysis.28 The inbreeding coefficient FIS was obtained using GENEPOP.29 Deviations from Hardy-Weinberg (HW) equilibrium were estimated using exact tests30 and global tests in GENEPOP version 3.4 (updated version).31 Tests for linkage disequilibrium between pairs of loci were performed in ARLEQUIN version 2.0.32

We estimated genetic differentiation by calculating FST, 29 and RST 33 using Fstat version 2.9.3.234 and RSTCalc version 2.235 with significance determined using permutation tests. Although FST estimates under the infinite alleles model (IAM) are considered more reliable when fewer than 20 microsatellite loci are used36 compared with RST under the step-wise mutation model (SMM),33 we used both estimates.37 To examine the distribution of genetic variation among individuals, among sample locations, and between samples from Amapa and Para States, we performed an analysis of molecular variance in ARLEQUIN version 2 using FST (IAM values) and RST (SMM values). 32 We used the hierarchical model for genotypic data with groups of populations and no within-individual level. We grouped PEX first with the Para samples (because it is geographically closer to them) and compared this grouping with the placement of PEX with the Amapa samples (because the fixation indices demonstrated smaller distance values between PEX and these samples). We explored isolation by distance as a possible explanation for population differentiation by the regressions of FST/(1 − FST) and RST/(1 − RST) against the ln of straight line geographic distance between sample pairs.38 To test the significance of the regression, we used a Mantel test39 with 10,000 permutations as implemented in ARLEQUIN version 2.32 We also performed separate Mantel tests for populations less than 500 km apart (i.e., all except PEX), as recommended.40

To investigate loci exhibiting heterozygote excess (HE), we used the heterozygosity tests in BOTTLENECK,41 which compare two estimates of expected heterozygosity, He, based on allele frequencies and Heq based on the number of alleles and sample size. At mutation-drift equilibrium (MDE), both estimates should be equal (He = Heq) in a population, whereas after a bottleneck He should be > Heq, and the opposite, He < Heq, may signal an expansion. Long-term effective population size (Ne) was obtained using NeEstimator version 1.3.42 Estimates were based on two mutation models, IAM and SMM, which are at the extreme ends of mutation models and should therefore provide good range estimates of long-term Ne.43 In all analyses, when multiple tests were used, a sequential Bonferroni procedure was applied.44

RESULTS

Genetic diversity.

All eight microsatellites were polymorphic in the seven populations analyzed, and the highest numbers of alleles per locus were observed in the sample sites in Amapa State (5–26) compared with those in Para and Mato Grosso States (3–22) (Table 1). The mean expected heterozygosity (HE) value was high, 0.834, compared with the mean observed heterozygosity (HO) of 0.500. The lowest HE value (0.215) was in MOJ in Para State, and the highest HE value (0.966) was in PEX in Mato Grosso State.

Hardy-Weinberg deviation and linkage disequilibrium.

Significant deviation from HW equilibrium was associated with heterozygote deficits in 26 of 56 possible tests after sequential Bonferroni correction (corrected α = 0.0009), and inbreeding coefficients were positive in all cases of significant heterozygote deficits (Table 1). Heterozygote deficits are usually the result of null alleles, inbreeding, or a Wahlund effect (samples consisting of several pooled subpopulations).45,46

However, we also observed linkage disequilibrium across all populations for variable loci in 64 of 156 tests (Table 1). After Bonferroni correction, nine pairs of loci in PEX and one pair of loci in LI (corrected α = 0.0002) had significant linkage disequilibrium. In every case where significant linkage disequilibrium was observed, significant heterozygote deficits were also seen.

Population differentiation.

The FST and RST values ranged from 0.0 to 0.1841 and from 0.0 to 0.297, respectively, and both values indicated that the same populations were significantly differentiated in 17 of 21 possible tests after the Bonferroni correction (Table 2). Significant differentiation was observed between the three localities in Amapa State (LI, GA, and STN) and the three localities in Para State (BEL, ARA, and MOJ, Figure 1). Within Amapa, there was little to no differentiation among the three localities. Significant differentiation was found between PEX and all populations surrounding the Amazon River where FST and RST ranged from 0.0880 to 0.1204 and from 0.0821 to 0.1404, respectively (Table 2).

A significant positive correlation was found using both FST estimates under IAM and RST estimates under SMM with the Mantel test when populations < 500 km apart were tested (R2 = 0.9791, P = 0.014 for FST and R2 = 0.9276, P = 0.007 for RST; Table 3) and also when all populations were included in the analysis (Table 3).

Analysis of molecular variance showed that most variation (84%) was within sample locations, but there was significant variation (12–13%) between sample sites in Amapa and Para states, under both IAM and SMM models, irrespective of whether PEX was included with samples from Amapa or Para (Table 4).

Effective population size and mutation drift equilibrium.

Estimates of Ne differed considerably depending on the model used. Under linkage disequilibrium, the overall Ne was 92.4, with 95% confidence intervals (CIs) of 87.8–97.4 (Table 5). Population Ne for PEX (54 individuals, 95% CI = 39.4–83.0) and for BEL (∞, 95% CI = 242.3–∞) were congruent with the known history of each population (intensive insecticide use in PEX versus little to no use in BEL). However, under heterozygote excess overall Ne values for both populations were ∞. Overlapping confidence intervals for populations using each model suggest that populations are subjected to similar amounts of genetic drift.47

In our populations of An. darlingi, results from the heterozygosity tests under SMM and IAM show contrasting results (Table 6). The stepwise mutation model indicates a heterozygote deficit at more loci in four of seven populations but none of these were significant at the 0.05 level. The infinite alleles model indicates that all populations have more loci demonstrating heterozygote excess. Two populations, GA and PEX, were significant for a heterozygote excess (P = 0.0091 and 0.0184, respectively). However, tests under IAM can wrongly detect heterozygote excess in populations that have not undergone a bottleneck.41 Use of the strict SMM is recommended,41 and under this model, no significant portion of loci with heterozygote excess was detected in any sample (Table 6). We also examined microsatellite allele frequency distributions in each population, grouping alleles from all loci into allele frequency bins as indicated.48 However, all seven graphs were virtually identical, showing the typical L-shape pattern consistent with populations at MDE, and a mode shift, as is predicted for a population having recently undergone a bottleneck, was not detected in any population.

DISCUSSION

The mean number of alleles in An. darlingi (13.5) and mean HE (0.834) are in the same range as values for An. albimanus (average = 10.9 alleles and average unbiased heterozygosity = 0.78), the only other primary neotropical malaria vector assessed with microsatellite markers.9 All studies of An. darlingi regardless of marker type (polytene chromosomes, allozymes, mtDNA RFLPs and sequences, ITS2 sequences) have detected moderate levels of genetic variability,3 and the present study is congruent with earlier work. We detected moderate levels of variability in both the populations where intensive insecticide treatment was carried out (PEX: mean HE = 0.871, A = 13.0) and where it was not carried out (BEL: mean HE = 0.703, A = 7.1) that were not significantly different from the five remaining sampling sites where levels of insecticide or frequency of application were known to be modest or irregular.

In two studies of allozymes in An. darlingi,49,50 significant deviations from HW equilibrium were not detected, but more recently a comparison of two Amazonian populations (one in each of Amazonas and Amapa states) detected significant deviations in seven of eight loci examined.51 Heterozygote deficits in relation to HW equilibrium are commonly detected at microsatellite loci in anophelines, e.g., An. dirus,52 An. gambiae,4,53 An. funestus,54 and An. albimanus.9 In our study, deficits were recorded for seven of eight loci and across all populations, with most significant values for ADC01, ADC138, ADC29, and ADC02 (21 of 26 estimates that were significant after Bonferroni correction). A significant departure from HW equilibrium combined with linakge disequilibrium usually suggests inbreeding, population subdivision, more formally known as the Wahlund effect, or selection. Another possible explanation is that loci could be located on the same chromosome.

If one considers inbreeding, all loci should be affected equally by this phenomenon, with uniform heterozygote deficiencies. Our data demonstrate that deficits (HO values in Table 1) are heterogeneous among loci. However, when related individuals mate in species such as mosquitoes with separate sexes, partial inbreeding can take place, creating a heterozygote deficit. In the life history of An. darlingi both sexes actually emerge the same day (an unusual phenomenon in mosquitoes55), but males cannot mate until their genitalia have rotated,56 a mechanism that probably reduces the likelihood of inbreeding. Anopheles darlingi possess high fecundity and external fertilization, but females mate before questing for a blood meal,57 and dispersal as far as 7.2 km has been measured by mark-release-recapture.58 These life history factors taken together mean that inbreeding is a highly unlikely cause of deficits in the present study of An. darlingi.

The Wahlund effect is seen when different gene pools have inadvertently been sampled as though they were one.8 Because we collected adults that are highly mobile, it is possible that a mixture of An. darlingi from different locations that differ in allele frequencies at a locus was sampled. The most likely sampling sites for this would be the three in Amapa State, which are 4–11 km apart, and BEL and ARA in Para State, which are 12 km apart.

Another possibility is selection, when microsatellite loci (themselves considered to be neutral) are linked to loci that are under selection. Positional effects of microsatellite markers on FST values (inside or outside inversions) are well-known in A. gambiae, 4,53 but FST estimates were not affected when four microsatellite markers inside and four outside chromosomal inversions were compared in An. funestus.8 Fourteen inversions in An. darlingi have been identified5961 (Conn JE, unpublished data), and mapping of An. darlingi microsatellite markers23 onto the polytene chromosomes by in situ hybridization is currently under way (Conn JE, unpublished data). Only additional studies can determine whether any of the microsatellite loci in An. darlingi are linked to loci under selection (i.e., inside inversions) and whether this has an influence on estimates of population differentiation.

Null alleles are also commonly cited as causing heterozygote deficits 4,45,62 but in the present study we detected considerable linkage disequilibrium across all populations for several loci in 64 of 156 tests. Linkage disequilibrium is not an expected outcome if the deficit is a result of null alleles because all individuals have the same probability of carrying a null allele.4 Furthermore, in our study, the deficits were not clustered around one locus but were genome wide and across all populations.

The Mantel tests excluding PEX and for all populations were significant for FST and RST values (Table 3). Because the IAM-based FST estimates are considered to be sensitive to greater geographic distances where there is an overlap of the effect of mutation and the effect of isolation-by-distance,40 the expected result was that the analysis including the sample site > 500 km distant (PEX) would not be significant. Our results were not consistent with this expectation, and suggest that the effect of geographic distances on genetic differentiation is very important, at least in this region of the range of An. darlingi.

Although the level of differentiation among Brazilian populations of An. darlingi located on either side of the Amazon River (Figure 1) is comparable to that detected using microsatellites in An. albimanus populations from Central and South America,9 and also to An. gambiae populations in both west and east Africa,4,63 the result was somewhat surprising, considering that all An. darlingi populations are from the center of its range.2 It is possible that the substantial differences in Ne among populations under linkage disequilibrium (range = 54–∞) may have contributed to the genetic differentiation.7,64 However, there are limits to detecting and interpreting heterogeneities in population structure in large-scale geographic studies,7 such as those that found moderate gene flow and no differentiation among several populations of An. darlingi in South America.12,50 Similarly, a study of An. funestus comparing east (Kenya) and west (Burkina Faso and Senegal) African samples, using mtDNA and rDNA ITS2 sequences did not detect significant population structure,65 whereas a study based on microsatellite loci identified high genetic differentiation.66 In An. dirus D a mitochondrial study detected no differentiation over a large area (> 1,200 km) in Thailand,52 but a study using microsatellite markers found significant structure over approximately 400 km.67 Our finding of population differentiation in An. darlingi in this region of Amazonian Brazil may be important for malaria control because it suggests that genetically modified mosquitoes released in this area would experience a substantial barrier to gene flow. We think it unlikely that the Amazon River per se is a barrier, since this eastern part of the Amazon Delta has many small islands as well as the large Ilha de Marajó (Figure 1), with suitable An. darlingi habitat.68 A more likely explanation for the differentiation is a combination of IBD and the heterogeneity of the Ne. We also note that the properties of markers used in previous studies of An. darlingi (i.e., less polymorphic, lower mutation rate69,70) were probably not sensitive enough to detect this level of differentiation, and most of them were assessing samples at much greater geographic scales.

The overall Ne of 92.4 individuals under linkage equilibrium is similar to that estimated for the malaria vector An. albimanus (Ne = 96), but under heterozygote excess all values of An. darlingi are ∞. If we consider that these two models provide range estimates,43 An. darlingi has a very broad range with the upper bound greater than that found in several estimates for An. gambiae.43,47,71 Estimates of Ne are based on expected heterozygosity assuming equilibrium,43 but the populations of An. darlingi under study deviated significantly from HW equilibrium and also displayed linkage disequilibrium. Such a violation may have contributed to the higher than expected values detected under heterozygote excess.

If we focus on the lower bound provided by the IAM estimates, the small Ne of An. darlingi could be the result of an overall bottleneck due to insecticide use or seasonal fluctuations as proposed for An. albimanus.9 Seasonal fluctuations in An. darlingi are quite pronounced2,72 and there is ample evidence for insecticide use over many years.13 The large Ne and allele distribution indicating MDE of An. darlingi in Belem could reflect the very low use of insecticide in this eastern district of Entrocamento, the source of the municipal water supply for the city of Belem. We think it likely that the small Ne of 54 individuals in PEX, a site of high malaria prevalence in the early 1990s22 where insecticides were used repeatedly to reduce An. darlingi,24 could be attributed to a bottleneck that took place within the 4Ne generation time necessary for detection.41 In theory, populations that have undergone a recent bottleneck, such as PEX in the early 1990s, should have lost rare alleles even if each maintained substantial heterozygosity. We found some evidence for a bottleneck in PEX (a significant heterozygote excess under IAM, Table 6) but this was not supported under the SMM model. Unexpectedly, we also detected a significant heterozygote excess in GA, Amapa State, where there is no record of substantial insecticide treatment until 2001,73 which is after our samples had been collected. Both the number of polymorphic microsatellite loci we sampled (n = 8) and our mean sample size (n = 36.6 individuals) are within the range where bottlenecks will most likely be detected (8–10 loci and 30 individuals per locus48). However, the power of the bottleneck test is such that approximately 20% of the time an actual bottleneck will remain undetected.41,48 Perhaps, as was suggested for Aphidius ervi,74 the reduction in population size of An. darlingi in PEX was not extreme enough to be detected by BOTTLENECK. The Nes of the three sampling sites in Amapa (GA, LI, and STN), which ranged from 133.6 to 351.7 under IAM, were also of interest because in these sites An. marajoara is presently the primary malaria vector and the abundance of An. darlingi is relatively low,27 although it is common in a nearby riverine habitat (Sao Raimundo do Pirativa) where it is considered to be the principal vector.72

Many analyses of population structure assume MDE, but this is violated (at least using the mtDNA marker) in studies of several malaria vectors, including An. dirus,52 An. gambiae and An. arabiensis,75 An. marajoara76 and An. darlingi.12 Subsequent studies of some of the same species7,67 confirmed the violation of MDE using multi-locus tests. The demographics of these species are thus unstable, usually in the form of bottlenecks and (or) expansions, and make accurate estimates of gene flow from FST estimates particularly difficult.4 Our assessment of An. darlingi in the present study suggests that in this eastern area of Amazonian Brazil these populations are in MDE, whereas among South American samples a Pleistocene expansion was detected by mtDNA sequences.12

The study of population structure in An. darlingi also has important implications, particularly in relation to its role in the transmission of Plasmodium falciparum in the Amazon.77 A study of genetic structure in P. falciparum in five locations in Amazonian Brazil including Serra do Navio in Amapá State, which is near the three study sites GA, LI, and STN in the present study of An. darlingi and another, Tailândia, which is near MOJ in Para State in the present study, found that despite the increase in the number of cases of infection with P. falciparum in Brazil from the 1970s to the 1990s,13 there was no evidence of a recent epidemic expansion of any particular clone.78 This study also detected substantial divergence and low levels of gene flow between populations of P. falciparum, which did not conform to IBD. Studies of P. falciparum and An. darlingi, its primary Amazonian host, based on sample sites in Amapa and Para States detected similar aspects of population structure: significant linkage disequilibrium and genetic divergence, although the effect of IBD was not the same. Although recognizing that differences in Plasmodium79 and Anopheles56 mating systems will influence each organism’s population structure, we advocate studies of population structure in both P. falciparum and An. darlingi from the same localities because it seems likely they would provide insights that could result in more effective control measures that are specific to particular malaria-endemic regions. This is particularly important in Amazonian Brazil where P. falciparum resistance to several commonly used drugs, e.g., chloroquine,80 sulfadoxine-pyrimethamine,81 and mefloquine82 has been detected, and where a recent study determined that there has been significant under-reporting of cases of infection with P. falciparum, a trend that appears to be worldwide and is especially notable outside Africa.83

Table 1

Sample size (N), number of alleles (A), expected (HE), and observed (HO) heterozygosities, as well as deviations from Hardy-Weinberg equilibrium (FIS)*

LocusLI N = 41STN N = 40GA N = 40ARA N = 37BEL N = 39MOJ N = 21PEX N = 21All populations
* LI = Lagoa dos Indios; STN = Santana; GA = Granja Alves; ARA = Aracanga; BEL = Belem; MOJ = Moju; PEX = Peixoto de Azevedo.
P < 0.001 by sequential Bonferroni procedure.
P < 0.05 by sequential Bonferroni procedure.
ADC01A20192288152626
HE0.9300.9180.9070.6450.7300.8830.9660.935
HO0.615†0.575†0.6†0.395†0.614†0.519†0.696†0.57†
FIS0.3510.3770.3420.3910.1610.4170.250
ADC02A1114104441014
HE0.7960.8290.7990.4930.5490.2150.8580.834
HO0.537‡0.475†0.65‡0.316†0.442‡0.1540.375†0.405†
FIS0.3290.4300.1890.3620.1980.2880.586
ADC107A7684491313
HE0.8010.7540.8000.6230.6210.8680.9370.849
HO0.667‡0.6‡0.675‡0.184†0.545‡0.7600.111†0.529†
FIS0.1690.2070.1580.7080.1240.1260.884
ADC110A1099810111212
HE0.8360.8240.8630.8340.8000.8650.8970.866
HO0.683†0.8500.7‡0.7630.7270.692‡0.9170.755†
FIS0.185−0.0320.1910.0860.0910.203−0.022
ADC137A81077791212
HE0.7360.7740.7070.8010.7650.8710.8420.838
HO0.675‡0.6410.6000.737‡0.7210.667‡0.625‡0.669†
FIS0.0840.1740.1530.0810.0590.2380.262
ADC138A7696581212
HE0.8010.7530.8170.7910.6520.7410.8420.815
HO0.244†0.275†0.25†0.421†0.19†0.231†0.2†0.262†
FIS0.6980.6380.6970.4710.7110.6930.766
ADC28A44344455
HE0.3870.4530.3710.6940.7120.6100.7220.625
HO0.2750.3330.3660.368†0.512‡0.6300.304†0.394†
FIS0.2920.2670.0130.4730.284−0.0330.584
ADC29A1112111115121415
HE0.7570.7870.7870.8440.7960.9000.9060.908
HO0.439‡0.525‡0.525‡0.378†0.231†0.429†0.333†0.414†
FIS0.4230.3360.3360.5550.7130.5300.638
All loci (mean)A9.810.09.96.57.19.013.013.5
HE0.7560.7620.7560.7160.7030.7440.8710.834
HO0.517†0.534†0.546†0.445†0.498†0.51†0.398†0.5†
Table 2

Pairwise estimates of genetic differentiation (FST below diagonal and RST above diagonal) between samples of Anopheles darlingi Brazil*

LISTNGAARABELMOJPEX
* For definitions of abbreviations, see Table 1.
P = 0.05 by Bonferroni correction (1,000 permutation).
P < 0.05.
LI0.01420.0030.2529†0.1807†0.2522†0.0821†
STN−0.00390.00130.2970†0.2094†0.2937†0.1351†
GA−0.0044−0.00080.2569†0.1839†0.2494†0.1024†
ARA0.1833†0.1710†0.1841†0.0265†0.1060†0.1596†
BEL0.1798†0.1674†0.1736†0.0114‡0.1015†0.1334†
MOJ0.1579†0.1469†0.1564†0.0461†0.0549†0.1404†
PEX0.0955†0.0880†0.0938†0.1102†0.1204†0.0875†
Table 3

Correlation between geographic distances and genetic differentiation (FST, RST) among populations of Anopheles darlingi from Brazil

Mantel test
GroupingFixation indexR2P
AllFST0.676060.0016
AllRST0.5410770.0189
< 500 kmFST0.9791630.0138
< 500 kmRST0.9275640.0073
Table 4

Results of analyses of molecular variance (AMOVA) with eight microsatellite loci based on FST and RST values*

FSTRST
Grouping of samples sitesSource of variationdfVariance componentPVariance componentP
* Significance levels were based on 1,023 permutations. PEX = Peixoto de Azevedo.
PEX with Pará sitesBetween groups112.25%< 0.00136.630.028
Among populations within groups53.65%< 0.0014.47< 0.001
Within populations50584.09%< 0.00158.63< 0.001
PEX with Amapá sitesBetween groups112.87%< 0.00137.03< 0.001
Among populations within groups53.39%< 0.0014.75< 0.001
Within populations50583.73%< 0.00158.22< 0.001
Table 5

Estimated Ne based on the linkage disequilibrium (LD) and heterozygote excess (HE) models*

LD95% CIHE95% CI
* CI = confidence interval; NA = not applicable. For definitions of other abbreviations, see Table 1.
ARA780.4120.5–∞NA
BE242.3–∞NA
GA152.489.6–430.3NA
LI133.681.7–321.5NA
MOJ123.561.0–1945.5NA
PXT5439.4–83.0NA
STN351.7139–∞NA
All populations92.487.8–97.4NA
Table 6

Number of loci exhibiting heterozygote excess (He) and heterozygosity expected from the observed number of alleles (Heq) under both SMM and IAM models*

LISTNGAARABEMOJPEX
* SMM = stepwise mutation model; IAM = infinite alleles model. For definitions of other abbreviations, see Table 1.
SMMHe< Heq6673644
He> Heq2215244
P (He> Heq)0.05830.0570.09960.5810.05160.44390.4312
IAMHe< Heq2211110
He> Heq6677778
P (He> Heq)0.30340.2980.00910.09070.08950.10430.0184
Figure 1.
Figure 1.

Sample localities of Anopheles darlingi in eastern Brazil. GA = Granja Alves; LI = Lagoa dos Indios; STN = Santana; ARA = Aracanga; BEL = Belem; MOJ = Moju.; PEX = Peixoto de Azevedo.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 74, 5; 10.4269/ajtmh.2006.74.798

*

Address correspondence to Jan E. Conn, Wadsworth Center, New York State Department of Health, Griffin Laboratory, 5668 State Farm Road, Slingerlands, NY 12159. E-mail: jconn@wadsworth.org

Authors’ addresses: Jan E. Conn and Joseph Vineis, Wadsworth Center, New York State Department of Health, Griffin Laboratory, 5668 State Farm Road, Slingerlands, NY 12159, Telephone: 518-869-4575, Fax: 518-869-6487, E-mail: jconn@wadsworth.org. Jonathan P. Bollback, Center for Bioinformatics, University of Copenhagen, Copenhagen Ø, Denmark. David Y. Onyabe, Aeras Global Tuberculosis Vaccine Foundation, Rockville, MD 20850. Richard C. Wilkerson, Walter Reed Army Institute for Research, Museum Support Center, Smithsonian Institution, 4210 Silverhill Road, Suitland MD 20746. Marinete M. Póvoa, Instituto Evandro Chagas, Programa de Pesquisas em Malaria, Ananindeua, Para, Brazil.

Acknowledgments: We are particularly grateful to the field team of M. M. Póvoa (Instituto Evandro Chagas, Belem, Para, Brazil) for logistical support in collecting mosquito samples. We thank T. N. Robinson and T. Hunter (University of Vermont) and L. Meehan (Griffin Laboratory, Wadsworth Center) for technical help and advice.

Financial support: This study was supported by National Institutes of Health grants AI R2940116 and AI R0154139 to Jan E. Conn.

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