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Am. J. Trop. Med. Hyg., 73(4), 2005, pp. 726-733
Copyright © 2005 by The American Society of Tropical Medicine and Hygiene

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MICROSATELLITE AND MITOCHONDRIAL GENETIC DIFFERENTIATION OF ANOPHELES ARABIENSIS (DIPTERA: CULICIDAE) FROM WESTERN KENYA, THE GREAT RIFT VALLEY, AND COASTAL KENYA

EMMANUEL A. TEMU* AND GUIYUN YAN
Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York


ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The population genetic structure of the African malaria vector Anopheles arabiensis from western Kenya, the Great Rift Valley, and coastal Kenya was investigated using 12 microsatellite loci and a partial sequence of mtDNA dehydrogenase gene subunit 5 (ND5). The mean number of alleles and the observed heterozygosity were similar for the mosquito populations from the three regions as revealed by the microsatellite data. A total of 30 polymorphic sites in the ND5 gene defined 39 haplotypes. Six haplotypes were shared among four populations from the three distinct ecological conditions, and they constituted 92% of the total number of individuals sequenced. Mitochondrial haplotype and nucleotide diversity were high. Microsatellite markers within polymorphic inversions revealed a level of genetic differentiation (FST = 0.116) four to seven times higher than markers outside inversions (FST = 0.016) or inside fixed inversions (FST = 0.027). Mitochondrial ND5 gene sequences did not reveal significant genetic differentiation for the same four populations ({Phi}ST = –0.008). The contrasts in the level of genetic differentiation between microsatellite markers inside polymorphic inversions, the mitochondrial ND5 gene, and microsatellite markers outside inversions suggest that the level of genetic differentiation in An. arabiensis populations across the Great Rift Valley varies significantly among different areas of the genome. Variations in the degree of genetic differentiation with respect to the chromosomal location of microsatellite markers may result from intrinsic characteristics of the markers, demographic or historic factors affecting these populations, and the possible adaptive significance of chromosomal inversions to climatic conditions.


INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Anopheles arabiensis is an important malaria vector that widely coexists with other malaria vector species throughout Africa. This species shows a greater tendency to feed and rest outdoors and is generally less susceptible than Anopheles gambiae to the effects of indoor insecticide spraying or insecticide-treated nets—methods commonly used for mosquito control in malaria-endemic areas. The efficacy of existing control measures is threatened by increasing parasite resistance to antimalarial drugs1 and the spread of insecticide resistance in vector mosquitoes.2 These problems have prompted a search for alternative, sustainable methods to control malaria, such as genetic manipulation of vectors by introducing genes that confer refractoriness to the parasite.3 The management of insecticide resistance in vector mosquitoes and the implementation of new strategies such as genetic control require knowledge of the mosquitoes’ population genetic structure and the extent of gene flow between mosquito populations.

Using microsatellite markers and mitochondrial DNA sequences, it has been demonstrated that the Great Rift Valley in East Africa is an important gene flow barrier for An. gambiae.4,5 The role of the Great Rift Valley in the population genetic structure of An. arabiensis is less certain.6,7 Unlike An. gambiae, An. arabiensis populations are found inside the Great Rift Valley and occur throughout a series of high, dry, relatively uninhabited plateaus in Kenya.8 The extensive distribution of these populations could facilitate the exchange of genes across the Great Rift Valley. Previous studies on the genetic structure of An. arabiensis have arrived at conflicting conclusions. Analyses of microsatellites in samples of An. arabiensis collected in localities across a 700-km area in Kenya did not detect any significant genetic differentiation.6 In contrast, microsatellites revealed strong genetic differentiation between An. arabiensis populations in East Africa that were separated by 200 km.9,10 Petrarca and others7 demonstrated that An. arabiensis populations from West Africa exhibited higher inversion polymorphism than those from East Africa. Interestingly, they found that Sudan populations exhibited more evident inversion similarities to populations from west of the Great Rift Valley than to those from east of the Great Rift Valley, suggesting that this Valley plays an important role in the genome evolution of An. arabiensis. Furthermore, the distribution of inversion frequencies may reflect mosquito adaptation to different climate conditions.11

Studies of the Anopheles arabiensis population genetic structure from western and coastal Kenya using microsatellite markers were performed by Kamau and others.6 The study presented here used microsatellite loci and sequences of a mitochondrial gene to investigate genetic structure of An. arabiensis populations from not only western and coastal Kenya, but also from the Great Rift Valley, thus providing more comprehensive data with which to investigate the effect of the Great Rift Valley on the population structure of An. arabiensis. To assess the effect of nuclear molecular marker location in relation to chromosomal inversions on the ability to detect population genetic structure, we used loci located both outside and inside inversions. Our rationale was that inversions might act as a recombination suppressor, thus protecting coadapted genes from recombination.11 We showed that nuclear markers outside chromosome inversions revealed low level of genetic differentiation consistent with mtDNA data that did not detect any genetic structure; however, a high level of genetic structure was detected using the markers inside polymorphic inversions.


MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Study sites and mosquito collection and processing. The mosquitoes were collected from four localities in Kenya: Ahero (34°57.2'E, 0°11.5'S) close to Lake Victoria in the Western Province; Majimoto (36°03.08'E, 0°17.1'N) and Kaptombes (36°01.0'E, 0°23.7'N) in the Great Rift Valley; and Paziani (39°50.4'E, 3°36.0'S) in the Coastal Province (Figure 1Go). The coastal area and the Victoria Lake basin are more than 700 km apart.



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  		    FIGURE 1. Map of Kenya showing sampling site distribution.

 
The climate differs in these three regions. The lake basin has an inland equatorial climate modified by the effects of altitude (1,140–1,300 m in elevation) and lake water. The coastal climate is hotter and more humid, with little variation in annual temperature. The pattern of rainfall is bimodal in both areas, with a long period of rain between March and June and a short period of rain between October and November. There is far less rainfall in the Great Rift Valley than in coastal or western Kenya. The average minimum and maximum temperatures from 1970 to 2000 were 15.0°C and 28.4°C in Kisumu, western Kenya; 22.5°C and 30.1°C in Kilifi, coastal Kenya; and 19.9°C and 32.7°C in Nguruman in the Rift Valley.12 The relative humidity is highest in the coastal region, while the Great Rift Valley is the driest and has the lowest relative humidity.

Mosquitoes were collected from houses using the pyre-thrum spray collection method.13 All mosquitoes were sampled from April to May 1999. Specimens were identified as A. gambiae s.l. in the field according to the morphologic key14 and then preserved in 100% ethanol. All mosquitoes used in this study were females. DNA from mosquito carcasses were extracted individually and resuspended in 100 µL of 1 x TE buffer (10 mM Tris, 1.0 mM EDTA). Anopheles mosquitoes were identified according to species using the PCR method.15 Only An. arabiensis was selected for micro-satellite and mtDNA sequence analysis.

Microsatellite genotyping. Twelve microsatellite markers isolated from An. gambiae were used for genotyping. These markers were selected based on general genome coverage and reliability in amplification with An. arabiensis. The primer sequences of marker AG2H46 were based on the alternative primer sequences of An. gambiae.16 The microsatellite genotyping method on the LI-COR automated DNA analyzer (Model 4200, Li-Cor Inc., Lincoln, NE) was used as previously described.17 A total of 229 individuals (N = 62, 55, 52, and 60 for Ahero, Majimoto, Kaptombes, and Paziani, respectively) were genotyped.

Sequencing of mitochondrial DNA. A 968-bp segment of mtDNA dehydrogenase gene subunit 5 (ND5)18 was amplified by PCR. The ND5 gene was chosen because prior studies involving Kenyan populations of both An. arabiensis and An. gambiae demonstrated that this region was polymorphic.5,19 The PCR primers were 19CL: 5'-CTT CCA CCA ATT ACT GCT ATA ACA G-3', corresponding to positions 6731 to 6755 of An. gambiae mtDNA genome, and DMT3A: 5'-AGG ATG AGA TGG CTT AGG TT-3', corresponding to positions 7680 to 7699. The PCR was conducted in a 50 µL reaction containing 1 µL of a 1:50 DNA dilution, 25 pmol primers, 5 µL 10 x reaction buffer, 25 mM MgCl2, 200 µM each dNTP, and 1 U Taq polymerase (Fermentas, Hanover, MD). The cycling condition was 5 minutes denaturation at 94°C, followed by 35 cycles of 15 seconds denaturation at 94°C, 15 seconds annealing at 50°C, and 1 minute extension at 72°C, ending with a final extension at 72°C for 5 minutes. The PCR products were purified using the Wizard Spin columns kit (Promega, Madison, WI) following the protocol recommended by the manufacturer. The purified PCR products were sequenced uni-directionally using the DMP3A primer with Big Dye terminator fluorescent chemistry (Applied Bio-systems, Warrington, UK) on an Applied Biosystems Model 377 DNA sequencer. However, for the samples that showed ambiguous readings, the second strands of ND5 gene were sequenced using the 19CL primer. All samples used for microsatellite genotyping were sequenced, and 196 individuals (N = 56, 45, 43, and 52 for Ahero, Majimoto, Kaptombes, and Paziani, respectively) yielded sequences without ambiguous readings. All haplotype sequences were submitted to GenBank (accession numbers: AY597211–AY597249).

Data analyses. Microsatellite data. The genetic diversity of microsatellite markers was quantified using heterozygosity, number of alleles, and allele frequencies observed in each population. Observed (HO) and expected heterozygosity (HE) were calculated using GENEPOP 3.0. 20 Analysis of variance (ANOVA) was used to determine whether the number of alleles varied among populations and locations of the microsatellite markers with respect to chromosomal inversions and on autosomal or sex chromosomes. The differences in observed heterozygosity among the populations were examined using analysis of variance with populations and loci as factors.21 Conformance of genotype frequencies to the Hardy-Weinberg equilibrium (HWE) was tested using Fisher exact test for each population and each locus, and the statistical significance of multiple testing was adjusted using the Bonferroni correction procedure. Population differentiation was examined with Wright F-statistics.22 The RST statistic,23 an FST-analogous estimator for microsatellite markers, was not calculated because the RST parameter has been highly criticized in recent literature.24,25 Estimates of FST were computed using FSTAT computer software,26 and the statistical significance was adjusted for multiple comparisons using the Bonferroni correction procedure.

Mitochondrial data. The ND5 gene sequences from 196 An. arabiensis individuals were aligned using BioEdit soft-ware.27 We used 595-bp sequences (equivalent to position 7055 to 7650 region of An. gambiae mtDNA genome18) in the following analyses because not all 968-bp nucleotide sequences were complete for some specimens. Haplotype diversity, its variance, and nucleotide diversity were calculated using DnaSP version 4.28 We used the Tajima and the Fu and Li tests29,30 to examine the hypothesis of selective neutrality of nucleotide substitutions.31 Tajima’s32 D test examines whether the average number of pairwise nucleotide differences (K) between sequences is larger or smaller than expected from the observed number of polymorphic sites (S). Under the assumption of a random mating population at equilibrium, the difference (i.e., D) between K and S is expected to be zero. The Fu and Li30 D* and F* tests take a genealogical approach; they are based on the principle of comparing the number of mutations on internal branches with those on external branches. Compared with a neutral mode of evolution, balancing selection would result in an excess of internal mutations, and directional selection would result in an excess of external mutations. The Fu and Li30 D* test statistic calculates the difference between the number of singletons (mutations appearing only once among the sequences) and the total number of mutations, whereas the F* test statistic calculates the difference between the number of singletons and the average number of nucleotide differences between pairs of sequences. The out group used for calculation of D* and F* was an ND5 gene sequence of A. gambiae (accession number NC002084). Significant D or D* statistics indicate possible purifying selection, population expansion, or selective sweeps. Negative values of the test indicate an excess of rare variants that could result from a recent population expansion or a selective sweep.33

Analysis of molecular variance (AMOVA) was used to test the hypothesis of random distribution of haplotype frequencies among the four populations, based on the Kimura 2P genetic distance, using Arlequin software.34 Kimura 2P genetic distance was calculated between all pairs of mtDNA haplotypes based on the number of shared or different mutations for each population. Hierarchical AMOVA was used, and the two populations from the Great Rift Valley were grouped. Thus, there were a total of three groups (western Kenya, the Great Rift Valley, and coastal Kenya) in our AMOVA. Based on the information of haplotypes and their frequencies, fixation indices were calculated, and their significance was tested by permutation tests (10,000 replicates).


RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Microsatellite DNA polymorphism. A similar level of genetic diversity was detected with the 12 microsatellite loci for populations from western Kenya, the Great Rift Valley, and coastal Kenya (Table 1Go). The average number of alleles over all loci ranged from 4.9 to 5.6 and did not vary significantly among the four populations (ANOVA, F = 0.11, df = 3, 44, P > 0.05). The average observed heterozygosity per locus ranged from 0.334 to 0.441 for the four populations (F = 0.56, df = 3, 44, P > 0.05). The loci inside fixed (AG2H143, AG2H603, AG2H637, AGXH131, and 1D1) and polymorphic (AG2H79 and 33C) inversions showed a higher but insignificant observed heterozygosity than those outside inversions (AG2H46, 29C, AG3H93, AG3H158, and AGXH678) (F = 1.92, df = 1, 46, P > 0.05).


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TABLE 1
Genetic diversity of four Anopheles arabiensis populations from Kenya
 
Deviations from the Hardy-Weinberg expectation were found in 14 out of 48 tests in the four populations, all due to heterozygosity deficiency with the exception of marker 1D1 in the Paziani population (Table 1Go). Loci AG2H46 and AG2H637 showed large, positive FIS values for all four populations, suggesting that null alleles may be present in these markers. Null alleles arise when mutations in the primer annealing sites prevent primers from annealing, and they generally result in a heterozygosity deficiency. The maximum likelihood method estimated that the null allele frequencies ranged from 0.22 to 0.33 at locus AG2H46 and from 0.69 to 0.92 at locus AG2H637.

Overall, the genetic differentiation among the four populations was relatively high, with an average FST of 0.099 (P < 0.001) (Table 2Go). Markers outside chromosomal inversions appear to show a smaller FST value than those inside fixed or polymorphic inversions. We recalculated the population genetic differentiation statistics for loci outside and inside inversions. We found that in the four populations studied here, the mean FST was 0.011 (P > 0.05) for markers outside inversions, 0.163 (P < 0.001) for markers inside fixed inversions, and 0.116 (P < 0.001) for markers inside polymorphic inversions (Table 2Go).


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TABLE 2
The FST statistics of Amopheles arabiensis populations from western Kenya, the Great Rift Valley, and coastal Kenya
 
We noted that locus 1D1 showed an extremely large FST value. The high frequencies of null alleles for markers AG2H46 and G2H637 may also cause biased estimation of population genetic differentiation statistics. When these three markers (1D1, AG2H46 and G2H637) were excluded from the analysis, the mean FST was 0.016 (P > 0.05) for markers outside inversions, 0.027 (P < 0.01) for markers inside fixed inversions, and 0.116 (P < 0.001) for markers inside polymorphic inversions (Table 2Go). Therefore, population genetic structure detected by the loci located inside polymorphic inversions was about seven times greater than for markers outside inversions.

Population pairwise comparisons of microsatellite allele frequencies used nine markers and excluded markers 1D1, AG2H46, and G2H637. We found that the two populations within the Great Rift Valley, located 35 km apart, were not significantly differentiated (FST = 0.002, P > 0.05; Table 3Go). However, the population from western Kenya exhibited a large genetic differentiation from the populations in the Great Rift Valley (FST = 0.064; P < 0.001; Table 3Go). Similarly, the FST estimate between coastal Kenya and the Great Rift Valley was large (0.071, P < 0.001) On the other hand, genetic differentiation between western Kenya and coastal Kenya was slightly smaller (FST = 0.023; P < 0.001). Interestingly, the markers outside inversions detected much smaller genetic difference among the pairs of populations (FST = 0.017 for western Kenya/Rift Valley pair; 0.026 for coastal Kenya/Rift Valley pair; and 0.008 for western Kenya/coastal Kenya pair; Table 3Go). Similarly, the markers inside fixed inversions revealed a low degree of genetic differentiation (FST = 0.038 for western Kenya/Rift Valley pair; 0.041 for coastal Kenya/Rift Valley pair; and –0.001 for western Kenya/coastal Kenya pair). However, markers inside polymorphic inversions detected much large genetic differentiation (FST = 0.146 for western Kenya/Rift Valley pair; 0.160 for coastal Kenya/Rift Valley pair; and 0.066 for western Kenya/coastal Kenya pair (P < 0.001 for all FST estimates, Table 3Go). Thus, this analysis demonstrated that the magnitude of genetic differentiation varied dramatically with respect to marker locations inside or outside polymorphic chromosomal inversions.


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TABLE 3
Pairwise genetic differentiation (FST) of Anopheles arabiensis populations from western Kenya, the Great Rift Valley, and coastal Kenya
 
Mitochondrial DNA polymorphism. Moderate to high polymorphism was found in the ND5 gene in the four An. arabiensis populations studied. Among the 196 individuals in which the 595-bp sequences of ND5 gene were analyzed, a total of 39 haplotypes were defined by 30 polymorphic sites (Figure 2Go). All four populations shared six haplotypes (number 1, 2, 4, 6, 13, and 15 in Figure 2Go), and the frequencies of these shared haplotypes were 76.7%, 71.6%, and 75.0% in the western Kenya (Ahero), the Great Rift Valley (Majimoto and Kaptombes), and the coastal Kenya (Paziani) populations, respectively. Twenty-nine out of a total of 39 haplotypes (74.4%) were unique to the populations from one of the three regions (Figure 2Go). Specifically, nine haplotypes, representing 16.1% of the total sequences, were from the western Kenya population, 16 (18.2%) were from the Great Rift Valley, and four (7.7%) were from coastal Kenya. Haplotype and nucleotide diversities were similar for the four populations. Haplotype diversity ranged from 0.83 to 0.86, and nucleotide diversity ranged from 0.47% to 0.57% (Table 4Go). The majority (76.5% to 85.6%) of the nucleotide substitutions in the ND5 gene were synonymous (Table 4Go). There were 13 non-synonymous substitutions out of the 39 haplotypes. Non-synonymous substitutions (T {leftrightarrow} A at position 7432 and G {leftrightarrow} A at position 7493) occurred in three populations and in multiple individuals. Non-synonymous substitutions (C {leftrightarrow} A) at position 7096 were found in two individuals from Ahero and Kaptombes.



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  		    FIGURE 2. Distribution of ND5 gene haplotypes in Anopheles arabiensis populations from western Kenya (West), the Great Rift Valley (RVal), and coastal Kenya (East). Polymorphic nucleotide positions correspond to the Anopheles gambiae mitochondrial genome sequence. The number of occurrences of a haplotype in each of the three regions is shown.

 

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TABLE 4
Summary statistics of ND5 gene polymorphism of Anopheles arabiensis from western Kenya, the Great Rift Valley, and coastal Kenya
 
Tajima’s test of departures from the neutral expectations did not detect a significant deviation from neutrality (Table 4Go). However, Fu and Li’s D* and F* statistics detected a significant deviation from neutrality for one population from western Kenya (Ahero), suggesting the existence of excessive rare nucleotide polymorphisms with respect to predictions of the neutral theory, with the possible effects of purifying, background selection, and/or population expansion.29,35 For the other three populations (Majimoto, Kaptombes, and Paziani), the D, D* and F* statistics were not significant, suggesting that the nucleotide substitutions of the ND5 gene are consistent with the neutral evolution theory.

AMOVA did not detect significant variations for the two populations in the Great Rift Valley or among the three regions (western Kenya, the Great Rift Valley, and coastal Kenya). The three regions contributed to only 0.88% of the variance in sequence variation. The fixation index ({Phi}ST) for the three regions was estimated at –0.008 (P = 0.867), suggesting a lack of significant genetic structure at the ND5 locus for An. arabiensis populations in Kenya.


DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
We found that the extent of genetic differentiation in An. arabiensis populations from three ecological zones in Kenya varied significantly with respect to microsatellite markers’ location relative to chromosomal inversions. Microsatellite markers outside inversions and inside fixed inversions revealed little genetic differentiation between western Kenya An. arabiensis populations and those from the Great Rift Valley, or between coastal Kenya and the Great Rift Valley populations (Table 3Go). However, the microsatellite markers inside polymorphic inversions detected population genetic structure differentiation that is four times greater. Low level of genetic structure detected by microsatellite markers outside inversion was consistent with the mitochondrial ND5 gene sequences, which did not reveal any significant structure for the same populations. Therefore, the contrasting differences in the level of genetic differentiation among a) micro-satellite markers inside polymorphic inversions, b) the mitochondrial ND5 gene, and c) microsatellite markers outside inversions suggest that the level of genetic differentiation in An. arabiensis populations from distinct ecological conditions varies significantly among different regions within the genome.

This phenomenon, gene flow variation among different regions within a genome, was observed in the chromosomal forms of An. gambiae in Mali, West Africa.36 In particular, microsatellite markers on chromosome 2 exhibited the highest genetic differentiation between the Bamako and Mopti forms of An. gambiae. Generally, inversion polymorphism is lower in An. arabiensis populations from East Africa than in populations from West Africa, and the majority of polymorphic inversions found in West Africa, except 2Rb and 3Ra inversions, appear to be absent in East Africa populations.7,37 Other polymorphic inversions may show adaptive significance. For example, the frequency changes of inversion 2Ra in An. arabiensis correlate with climatic variations in West Africa,11 whereas 2Rb was associated with a preference for indoor resting behaviors in coastal Tanzania.38

The genetic differentiation for microsatellite markers inside or outside inversions between the population pair’s western Kenya and the Great Rift Valley or coastal Kenya and the Great Rift Valley was two to four times greater than for that between western and coastal Kenya. The three microsatellite markers inside inversion 2La (AG2H143, AG2H603, and AG2H637) exhibited insignificant genetic differentiation among the four populations, as opposed to high differentiation revealed by loci inside inversion Xbcd (AGXH131 and 1D1) or in polymorphic inversions 2Rb and 3Ra (AG2H79 and 33C). Inversions 2La and Xbcd are fixed, and they are characteristics of An. arabiensis species7 across the African continent. Inversion 2La is also found in An. gambiae but is highly polymorphic. Inversions 2La and 2Rb, which conferred adaptive fitness of An. arabiensis to dry savanna, may have been introgressed to An. gambiae.39 The frequencies of inverted arrangements of 2La and 2Rb in An. gambiae increase with aridity11 and are therefore a likely cause of the spread of anthropophilic An. gambiae from the rain forest into savanna areas.40 A nonrandom pattern of inversion distribution suggests that these rearrangements are the product of selection.40 In our work, the contrasting level of genetic differentiation revealed by microsatellite loci found outside and inside polymorphic inversions could be due to factors such as the intrinsic characteristics of the markers and the demographic history of these populations.

Western Kenya, the Great Rift Valley, and coastal Kenya differ greatly in ecological and climatic conditions. In particular, the Great Rift Valley is dry and hot all year, whereas western and coastal Kenya areas are more humid and exhibit clear seasonality. These climatic conditions could impose selection on An. arabiensis populations, and therefore lead to non-neutrality of some chromosomal inversions and the microsatellite markers inside those inversions. Chromosomal inversions in An. gambiae were found associated with adaptation to environmental conditions, such as aridity7 and insecticide resistance.37 The observed variation in the magnitude of genetic differentiation revealed by microsatellite markers inside and outside chromosomal inversions in An. arabiensis also raises the possibility that chromosomal inversions may have adaptive significance to climatic conditions in this species.

The ND5 gene haplotype and nucleotide diversity reported here are comparable to the previous published results for An. gambiae populations from West and East Africa19 and in An. arabiensis populations from Senegal, western Kenya, South Africa,19 and Malawi.41 In the current study, six haplotypes were shared among four populations from three distinct ecological conditions, and these haplotypes constituted 92% of the total number of individuals sequenced (Figure 2Go). Although moderate to high-level polymorphism was found in the ND5 gene of four An. arabiensis populations across Kenya (Table 4Go), the magnitude of genetic differentiation was not significant ({Phi} ST = –0.008). Similar to the findings of Besansky and others,19 Tajima’s D test of the ND5 nucleotide polymorphism did not detect deviation from neutrality in our populations. These results support the neutral theory of mitochondrial gene evolution and suggest that mitochondrial gene flow is extensive in An. arabiensis across the Great Rift Valley in East Africa.

Our results have implications for the interpretation of population genetic structure results in An. arabiensis. Specifically, the physical location of molecular markers with respect to chromosomal inversions could have a profound effect on the extent to which genetic structure is detected. Thus, meta-population analysis results of anopheline population genetic structure using data from different populations and different genetic markers should be interpreted with caution. For example, Donnelly and others9,10 reported significant genetic differentiation in An. arabiensis populations that were separated by more than 200 km in East Africa (FST = 0.026, P < 0.001), and within samples from Mozambique that were only 25 km apart (P < 0.0001). Similarly, a high level of genetic differentiation was found in An. arabiensis populations from the Reunion and Mauritius Islands (FST ranged from 0.080 to 0.215; P < 0.0001), located 240 km apart in East Africa.42 On the other hand, An. arabiensis populations occurring 700 km apart across the Great Rift Valley in Kenya6 were found not to be genetically differentiated (FST ranged from 0.003 to 0.027, P > 0.05). The conflicting results concerning genetic diversity and the population structure of An. arabiensis, as detected by different microsatellite markers, may be the partial consequence of choosing different markers. Our results suggest that it is particularly important to know the physical locations of these markers on the chromosomes as well as the occurrence of chromosomal inversions in the study populations.

In summary, the level of genetic differentiation revealed by microsatellite loci varied significantly with respect to the chromosomal location of the markers. Loci located inside polymorphic chromosomal inversions showed that populations of An. arabiensis from western Kenya, the Great Rift Valley, and coastal Kenya are highly structured, and the magnitude of genetic differentiation was 6 to 8 larger than what was revealed by loci located outside inversions for the same populations. Despite the high haplotype diversity in the mtDNA ND5 gene, this marker did not detect any significant genetic structure ({Phi}ST = –0.008). These results suggest that the level of genetic differentiation in An. arabiensis populations varies significantly among different regions of the genome. Such variability in the degree of genetic differentiation with respect to the chromosomal location of the markers may result from intrinsic characteristics of microsatellite markers, demographic or historic factors affecting these populations, and the possible adaptive significance that chromosomal inversions have for climatic conditions.

Received April 26, 2004. Accepted for publication June 3, 2005.

Acknowledgments: The authors thank C. N. Mbogo and N. Minakawa for their assistance with specimen collection. Three anonymous reviewers provided critical comments.

Financial support: The study is supported by the National Institutes of Health (NIH) grants D43 TW01505 and R01 AI 50243.

* Address correspondence to Emmanuel A. Temu, Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY 14260. E-mail: emmatemu{at}yahoo.com Back

Authors’ addresses: Emmanuel Temu and Guiyun Yan, Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY 14260, Telephone: (716) 645-2363, Fax: (716) 645-2975.

Reprint requests: Guiyun Yan, Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY 14260, Telephone: (716) 645-2363 ext 121, Fax: (716) 645-2975, E-mail: gyan{at}buffalo.edu.


REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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