Genetic diversity and population structure of Glossina pallidipes in Uganda and western Kenya
© Ouma et al; licensee BioMed Central Ltd. 2011
Received: 18 April 2011
Accepted: 28 June 2011
Published: 28 June 2011
Glossina pallidipes has been implicated in the spread of sleeping sickness from southeastern Uganda into Kenya. Recent studies indicated resurgence of G. pallidipes in Lambwe Valley and southeastern Uganda after what were deemed to be effective control efforts. It is unknown whether the G. pallidipes belt in southeastern Uganda extends into western Kenya. We investigated the genetic diversity and population structure of G. pallidipes in Uganda and western Kenya.
AMOVA indicated that differences among sampling sites explained a significant proportion of the genetic variation. Principal component analysis and Bayesian assignment of microsatellite genotypes identified three distinct clusters: western Uganda, southeastern Uganda/Lambwe Valley, and Nguruman in central-southern Kenya. Analyses of mtDNA confirmed the results of microsatellite analysis, except in western Uganda, where Kabunkanga and Murchison Falls populations exhibited haplotypes that differed despite homogeneous microsatellite signatures. To better understand possible causes of the contrast between mitochondrial and nuclear markers we tested for sex-biased dispersal. Mean pairwise relatedness was significantly higher in females than in males within populations, while mean genetic distance was lower and relatedness higher in males than females in between-population comparisons. Two populations sampled on the Kenya/Uganda border, exhibited the lowest levels of genetic diversity. Microsatellite alleles and mtDNA haplotypes in these two populations were a subset of those found in neighboring Lambwe Valley, suggesting that Lambwe was the source population for flies in southeastern Uganda. The relatively high genetic diversity of G. pallidipes in Lambwe Valley suggest large relict populations remained even after repeated control efforts.
Our research demonstrated that G. pallidipes populations in Kenya and Uganda do not form a contiguous tsetse belt. While Lambwe Valley appears to be a source population for flies colonizing southeastern Uganda, this dispersal does not extend to western Uganda. The complicated phylogeography of G. pallidipes warrants further efforts to distinguish the role of historical and modern gene flow and possible sex-biased dispersal in structuring populations.
Glossina pallidipes is a major vector of animal African trypanosomiasis. The vector has also been implicated in the transmission of Human African Trypanosomiasis (HAT). For example, the expansion of T. b. rhodesiense (Tbr) sleeping sickness beyond its traditional focus in southeastern Uganda to western Kenya in the 1950s was attributed to G. pallidipes. The first confirmed case of Tbr in Kenya was reported in 1942, having spread from southeastern Uganda along the Sio River. The spread was attributed to Glossina pallidipes infestation in the Busia district on the Kenya-Uganda border . Further evidence for involvement of G. pallidipes in transmission of HAT was obtained from the isolation of the T. b. rhodesiense parasite from G. pallidipes
Despite its role as a vector of trypanosomiasis, the dynamics of G. pallidipes populations in Uganda and the extent to which these populations are linked by dispersal to western Kenya populations were hitherto unknown. Department of Lands and Survey maps produced in the late 1960s  indicate that G. pallidipes is contiguously distributed across west-central Uganda occurring in one main belt. However, GIS prediction maps show the existence of two belts in the western and southeastern part of the country . The southeastern belt extends into western Kenya and is believed to have been responsible for extending the focus of T. rhodesiense HAT into western Kenya . The western belt occupies areas around Murchison Falls in Uganda, and regions south of Lake Albert. In the first half of the 20th century, G. pallidipes was the most abundant species in southeastern Uganda followed by G. brevipalpis and G. f. fuscipes respectively . Although all three species were thought to have dispersed into the area from other places , no records exist on the source populations. Fluctuation of G. pallidipes trap densities in Uganda in the 1970s and early 1980s ,, possibly due to competition between G. pallidipes and G. fuscipes, led some authors to conclude that G. pallidipes could have disappeared from southeastern Uganda . Recently it was claimed that G. pallidipes had re-invaded southeastern Uganda leading to significant increases in prevalence of trypanosome infections in cattle . The re-invasion hypothesis demands a deeper understanding of the dynamics of G. pallidipes populations in southeastern Uganda and the adjoining western Kenya fly belt.
Sampling locations and tsetse trapping
Sample sizes and genetic diversity indices for mitochondrial COI and seven microsatellite loci in six populations of G. pallidipes
A R a
0.7319 S; 34.31608 E
-0.6118 S; 34.30044E
0.9800 N; 30.5500 E
2.2800 N; 31.6000 E
0.5221 N; 34.1149 E
DNA extraction, amplification, sequencing and genotyping
Genomic DNA was extracted using the DNeasy blood and tissue kit (Qiagen) as per the manufacturer's instructions. For each population, 16 to 21 of the microsatellite-genotyped individuals (see below) were randomly selected for mtDNA typing. In the case of samples from Kabunkanga (KB), we typed all of the individuals in the sample. We amplified a 473 bp fragment of cytochrome c oxidase subunit I (COI) using the primers GpCOI_F1 (5'-GAGCCTTAATTGGAGATGATC-3') and GpCOI_R1 (5'- GATGTGCTCATACAATAAATCC-3'). Fragments were amplified in a 30 μl reaction employing 1X buffer (Applied Biosystems), 0.2 mM each dNTP (New England Biolabs), 0.6 μM primers, 2 mM MgCl2, 0.4 mg/mL BSA (New England Biolabs) and 0.6 units AmpliTaq Gold DNA Polymerase (Applied Biosystems) using 50 cycles and an annealing temperature of 50°C. Sequencing was performed on a 3730 × l DNA Analyzer (Applied Biosystems). Sequences were deposited in GenBank (Additional file 1, Table S1).
We also typed between 16 and 48 individuals per population at 7 polymorphic microsatellite loci: GpA19a, GpB20b, GpC26b , GpB115 , GpCAG133 , GmC17 and GmK06 . Primer sequences for the first five loci were modified with the addition of a 4 base "pigtail" sequence (GTTT) to the 5' end of the reverse primer . We performed PCR using a touchdown protocol employing decreasing annealing temperatures of 61 to 51°C over 11 cycles, followed by annealing at 50°C for 35 cycles. The reactions were performed in 12.5 μl volumes using 1× PCR Buffer and 2 mM MgCl2, 0.2 mM each dNTP and 3 μg BSA, 5 pmol fluorescently-labeled forward primer and 5 pmol reverse primer, and 0.5 units AmpliTaq Gold.
Descriptive statistics and marker validation
For mtDNA, we calculated haplotype diversity (H d ) and nucleotide diversity (π) using the program DnaSP v5 . For microsatellites, we calculated allelic richness, as well as observed (H o ) and expected heterozygosities (H e ), using the program GenAlex v. 6.41 . Loci were tested for deviations from Hardy Weinberg equilibrium (HWE) and for linkage disequilibrium (LD) using the program Genepop v4.0 . Markov chain parameters were set to 10,000 dememorizations, 1000 batches, and 10,000 iterations per batch for both tests.
Population differentiation and structure
For both mtDNA and microsatellite data, we calculated estimates of pairwise differentiation between populations using the program Arlequin v3.1  and tested for significant differentiation using 1000 permutations. We employed measures of differentiation based on haplotype or allelic frequencies (FST) and measures that accounted for the evolutionary distance between haplotypes or alleles (ΦST, RST). Unlike FST, ΦST and RST take into account the evolutionary distance among alleles rather than only their frequencies. We also performed an analysis of molecular variance (AMOVA) on both mtDNA and microsatellite data to evaluate the extent to which genetic variation was explained by differences among and within populations.
We evaluated evolutionary relationships among maternal (mtDNA) tsetse lineages using a parsimony network generated by the program TCS v1.21 . Finer scale structuring at microsatellite loci was assessed using the Bayesian model-based clustering algorithm implemented in STRUCTURE 2.2 . STRUCTURE assigns individuals to K populations based on their multilocus genotypes. We conducted five independent Markov chain Monte Carlo (MCMC) assignment runs for each K from K = 1 to K = 6 assuming an admixture model with correlated allele frequencies. We conducted the MCMC runs using 250,000 steps after throwing away the first 50,000. Evanno's criterion  and the method of Pritchard et al.  were used to identify the likeliest number of clusters. For final assignment of individuals to clusters, we used 500,000 MCMC steps. As an alternative approach for summarizing microsatellite variation across populations, we performed principle components analysis (PCA) using the "adegenet" package in R . In contrast to the Bayesian assignment algorithm above, the PCA approach does not make any assumptions about HWE or LD and allows for a visual assessment of the degree to which populations differ from each other.
Microsatellite data were used to obtain pairwise genetic distance and relatedness values between individuals for all flies collected in 2008 in Uganda (KB: 4 males, 12 females; MF: 9 males, 39 females; and OK: 4 males, 26 females). Genetic distances were calculated based on  by using the program Alleles in Space 1.0 , both within and between populations, while pairwise relatedness was computed in Kingroup v2  via maximum-likelihood estimation . Means, standard errors, and 95% confidence intervals were determined for males and females separately. The significance of within- and between-population differences between the two sexes was tested using one-sided t-tests. Only the 2008 samples were considered in order to avoid temporal fluctuations and afford a snapshot of sex-specific dispersal patterns.
Following sequential Bonferroni correction, we detected no significant linkage between any pair of loci in any of the six populations. We detected a significant departure from HWE in locus GpB20b in three populations (Additional file 2, Table S2). Only Kapesur exhibited a significant departure after Bonferroni correction and the absolute value of FIS in this population was close to zero. Therefore, all further analyses included GpB20b.
Among 113 Glossina pallidipes, twenty-two mitochondrial haplotypes were detected of which only two (# 7, 17) were shared among sampling sites, Okame, Kapesur, and Lambwe (Table 1 and Additional file 1, Table S1). Twenty mitochondrial haplotypes were 'private' (i.e., confined to a single sampling site). Nine haplotypes were singletons. Mitochondrial diversity, the probability that two randomly chosen haplotypes differ, varied from 0.43 in Okame to 0.86 in Lambwe. The overall mean was 0.65 ± 0.15. Nucleotide diversities π (the average number of nucleotide differences per site) varied nearly ten-fold, from only c. 0.0015 in Nguruman to 0.014 at Kapesur and an overall mean of 0.008 ± 0.005.
Among 237 flies (474 genomes), seven microsatellite loci afforded 51 alleles (Table 1). The number of alleles per locus ranged from 2 at GmC17 to 18 at GpB20b. Average allelic richness (AR, allelic diversity corrected for variations in sample size) ranged from 3.2 to 5.6, and was least in Okame and Kapesur; these locations also exhibited the least heterozygosities although He values did not differ significantly from the four other estimates. Microsatellite diversity measures for Lambwe were not significantly greater than in Okame and Kapesur (H(df = 2) = 0.27, P = 0.87 ). The foregoing locations share the same fly belt but Lambwe G. pallidipes has been subjected to repeated, and unsuccessful eradication attempts in the past 30 years .
Estimates of the expected (He) and observed (Ho) microsatellite diversities were closely similar, thereby roughly indicating random matings within populations. Formal tests of hypothesis are provided by F IS = 0 and indicated a significant difference at only one locus in only one sample (GpB20b at KP, Additional file 2, Table S2). Population F IS , averaged over loci, indicated no departures from random mating within populations (Table 3).
Genetic differentiation and population structure
Results of an AMOVA testing for genetic structure in G. pallidipes sampled in Kenya and Uganda
Sum of squares
mtDNA ( F ST )
mtDNA ( Φ ST )
microsatellites ( F ST )
microsatellites ( R ST )
Estimates of FST at the mitochondrial locus COI for populations of G. pallidipes
As evident in a haplotype network (Figure 1 and additional file 1, Table S1), only two of the 23 haplotypes were shared between any populations. These two haplotypes were the only sequences recovered from flies in Okame (OK) and Kapesur (KP) and represented a subset of the haplotypes found in flies from Lambwe Valley (LV). One of these haplotypes is found within a clade of widespread maternal lineages (Clade C), while the other is the only representative of Clade B. Clades A and B are quite distant from each other and from Clade A (2.4% and 3.3% of constituent nucleotides, respectively), although both clades include haplotypes from sampling sites that are geographically close to sites where only clade C haplotypes are recovered. Clades A and B topologies are also very different from the one for clade C. Clades A and B include either only one (Clade B) or a few (Clade A) recently diverged haplotypes, as suggested by the small number of mutational steps that separate them. Clade C not only comprises more than twice as many haplotypes than clade A, it includes haplotypes found in the same population which are separated by more mutational steps from haplotypes found in the same populations than haplotypes only found in other populations.
Estimates of FST (below diagonal) and RST (above diagonal) and FIS on diagonal in G. pallidipes measured across seven microsatellite loci
Relatedness and genetic distance values were obtained from microsatellite data and computed both within and between samples (KB - Kabunkanga, MF - Murchison Falls, OK - Okame)
Genetic Distance Between Individuals
Relatedness Between Individuals
P -value (t-test)
P -value (t-test)
0 km (within: KB, MF, OK)
dF < dM
rF > rM
186 km (between: KB, MF)
dF > dM
rF < rM
341 km (between: MF, OK)
dF > dM
rF < rM
400 km (between: KB, OK)
dF > dM
rF < rM
186-400 km (between: KB, MF, OK)
dF > dM
rF < rM
All studies to date of breeding structure in the Morsitans group tsetse flies have indicated highly structured populations among which there has been little detectable gene flow . Our results in G. pallidipes are in agreement with the earlier findings. Populations in western Uganda were significantly differentiated from flies in the northeastern corner of Lake Victoria, and these populations were further differentiated from the population in Nguruman in south-central Kenya. Furthermore, tsetse populations were not homogeneous within the three regions.
Indices of differentiation inferred from mtDNA and microsatellites indicated that populations at Okame, Kapesur and Lambwe Valley form a genetically homogeneous group relative to the populations lying approximately 400 km to the east or west. Within this group, however, genetic diversity was less in Okame and Kapesur than in Lambwe Valley. In fact, mtDNA haplotypes recovered from Okame and Kapesur formed a subset of those found in the Lambwe Valley. Similarly, with the exception of one allele at one locus, microsatellite alleles in Okame and Kapesur were also a subset of those found in the Lambwe Valley (data not shown). Past control operations under the Farming in Tsetse Controlled Areas (FITCA) project http://www.au-ibar.org/index.php/en/projects/completed-projects/fitca/achievements, are likely to be responsible for the genetic structuring. Historically, the three populations may have been part of a large, panmictic, and genetically diverse population, and control activities may have severely reduced population sizes in Okame and Kapesur leading to the observed reduction in genetic diversity. Once the FITCA project ended in the early 2000s, gene flow from Lambwe Valley could have led to increased genetic diversity and allelic homogenization. Alternatively, the Okame and Kapesur populations are not relicts of a larger population but originated from two recent colonizations from the Lambwe Valley. A priori, both scenarios are equally likely. However, since earlier genetic studies indicated that the Lambwe Valley tsetse population is large and has been in residence for a long time , it is most likely that the low genetic diversity observed in Okame and Kapesur flies is due to recent colonization rather than a past bottleneck.
As in the Lake Victoria region, populations in western Uganda differed significantly over the approximately 190 km separating Kabunkanga and Murchison Falls. Populations of G. pallidipes at Kabunkanga and Murchison Falls exhibited similar microsatellite frequencies, but extremely divergent mtDNA haplotypes.
Because of differences in evolutionary rates and inheritance patterns between bi-parentally inherited microsatellite loci and maternally-inherited mtDNA, direct comparisons between the results of these two types of molecular marker might be misleading. To investigate the possibility of sex-biased dispersal, in addition to comparing microsatellite and mtDNA results, we carried out an individual-based sex-specific analysis of the level of genetic differentiation and relatedness using only microsatellite data. If dispersal is sex-biased, we expect to encounter higher genetic differentiation within populations and more genetically similar individuals across populations in the better-dispersing sex, while the more philopatric sex will exhibit higher relatedness values between individuals within populations and increased genetic dissimilarity and lesser relatedness between populations relative to the more mobile sex .
Our data can suggest that males disperse over longer distances than females (Table 5 and Figure 4). Despite the fact that females are believed to be highly mobile  due to their relatively larger body size, males are active for longer periods of time  and devote blood-meals exclusively to the production of fat, which is used as an energy reserve for flight . Additionally, the asymmetry in male versus female dispersal could be attributed to flight constraints imposed on females by carrying a larva, which can double the weight of a female at the peak of pregnancy . The male-biased dispersal recovered from microsatellite data needs further scrutiny as the small male sample sizes in this study did not allow for rigorous testing of hypothesis, as the study was not designed for this purpose.
The low level of microsatellite differentiation between tsetse at Kabunkanga and Murchison Falls is also hard to reconcile with the absolute divergence in COI sequences observed between tsetse flies from these two sites. The net average nucleotide divergence was 2.7%, consistent with a divergence time of 1.8 million years, assuming a molecular clock ticking at 1.5% divergence per million years . Therefore, unequal dispersal rates would have to have been maintained for an extremely long period in order to generate the conflicting signals in microsatellites and mtDNA.
A mitochondrial sweep, due perhaps to Wolbachia infection favoring the amplification of a particular mitochondrial lineage in one population, could have shortened the time frame over which this apparent divergence accumulated. Even in this case, though, sufficient time has passed to allow the accumulation of mtDNA diversity in both populations without any concomitant exchange of haplotypes. Owing to the possibility of past bottlenecks and rare long-distance colonizations, as well as sex-biased dispersal, the phylogeographic history of G. pallidipes appears to be complex.
Aside from the seemingly contradictory signals from microsatellites and mtDNA in western Uganda, we also observed neighboring populations in the Lake Victoria region that shared two mtDNA lineages differing by about 2% without observing any of the intervening haplotypes. This would suggest that, G. pallidipes colonized the Lake Victoria region independently at least twice or a very large and diverse population of G. pallidipes underwent a severe bottleneck or series of lesser bottlenecks, leaving only remnants of the past diversity. A deeper understanding of the phylogeography of G. pallidipes will require greater context and range-wide relationships should be explored more thoroughly in the future.
The current study greatly enhances our understanding of G. pallidipes population dynamics especially in Uganda, which has been a missing link in previous samplings. To the best of our knowledge, this is the first report on the population structure of this species in Uganda based on natural samples. In an earlier paper that described the population structure of G. pallidipes at a macrogeographic scale covering almost its entire range, only a single sample from a laboratory colony of G. pallidipes originating from Uganda nearly three decades ago was analyzed . In another study Ouma et al.  discussed the relict G. pallidipes populations in Lambwe and Nguruman, and demonstrated temporal and seasonal stability of G. pallidipes populations in these areas. Such temporal stability has also been reported in G. fuscipes fuscipes. These previous studies were reviewed  and suggested significant differentiation among natural populations of G. pallidipes in eastern and southern Africa. However, in the absence of samples from Uganda, it was always difficult to put the data into perspective and understand the re-infestation of western Kenya including Lambwe Valley and Busia-Teso regions by G. pallidipes.
The findings of this study have reaffirmed the importance of gathering genetic data prior to implementing area-wide tsetse vector control operations as recommended for creation of G.p. gambiensis free zones in the Niayes region of Senegal . Genetic data should be generated as part of baseline data collection to provide the much needed scientific evidence upon which control measures can be effectively implemented.
This study underscores the importance of tailoring both monitoring and control measures to the population-specific circumstances and history, and the importance of understanding the evolutionary dynamics likely to have shaped the breeding structure of each population. This is exemplified by our findings at different levels:
1- On a broad spatial scale our results point to the presence of at least three genetically discrete fly belts among which there has been little detectable gene flow in the region extending from western Uganda to Nguruman in southwestern Kenya. Such strong geographic structuring of G. pallidipes should limit the geographic scale on which area wide vector control needs to be implemented.
2- On a local scale our data point to specific populations where control and detection methods need improvement. In keeping with earlier studies , , we have identified the Lambwe Valley as a region where such revision is needed for two reasons. First, despite years of intensive control efforts and very low fly densities detected by current trapping methods, the population is still highly variable genetically and thus probably quite large. Second, this population has served as a source for seeding neighboring regions.
3- Our data suggest the existence of both historical (mtDNA-microsatellite comparion) and current (microsatellite-inferred) male-biased dispersal. This contradicts the general idea that females are better dispersers than males and because of its relevance for control and eventual sterile insect release activities should be further explored to understand its genetic, ecological and physiological underpinnings. Further research is needed to clarify sex-biased dispersal in this species and to demonstrate it in other morsitans group flies.
4- Finally, control efforts on small populations may vary in efficacy and can be optimized if coupled with inferences from genetic data, as exemplified by Okame/Kapesur. If flies from these sites originated as rare immigrations from neighboring sites, as we suggest, control efforts can be long-lasting, even if control measures and monitoring activities are lessened over time, as the probability of re-infestation is low, as shown by extensive studies on breeding structure in morsitans group flies. On the other hand, if increases in tsetse densities in a given area are due to expansion of relict populations rather than re-infestation, then efforts can be ineffective, if local control is lessened before the population is completely extirpated. This task is rather difficult not only to achieve but also to evaluate by using traditional sampling methods.
This investigation received financial support from National Institutes of Health-Fogarty International Collaboration Award no. 1R03 TW008413 - 01A1, Fogarty Global Infectious Diseases Training Grant, D43 TW 007391, the UNICEF/UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases (TDR) project ID A80132, and Biosciences eastern and central Africa (BeCA) (project no. 2/2007). The research was accomplished while JOO was a Fogarty Research Fellow at Yale University. We thank the technical staff of KARI-TRC and NaLIRRI for excellent assistance with field sampling, and the Centre Director KARI-TRC and Director NaLIRRI for facilitation. We also would like to thank two anonymous reviewers for their helpful comments.
- Onyango RJ, Van Hoeve K, De Raadt P: The epidemiology of Trypanosoma rhodesiense sleeping sickness in Alego location, Central Nyanza, Kenya. I. Evidence that cattle may act as reservoir hosts of trypanosomes infective to man. Trans R Soc Trop Med Hyg. 1966, 60 (2): 175-82. 10.1016/0035-9203(66)90024-1.View ArticlePubMedGoogle Scholar
- Willett KC: Some Observations on the Recent Epidemiology of Sleeping Sickness in Nyanza Region, Kenya, and Its Relation to the General Epidemiology of Gambian and Rhodesian Sleeping Sickness in Africa. Trans R Soc Trop Med Hyg. 1965, 59: 374-94. 10.1016/0035-9203(65)90055-6.View ArticlePubMedGoogle Scholar
- Willett KC: Trypanosomes Isolated from Glossina palpalis and G. pallidipes in Sakwa, Kenya. Trans R Soc Trop Med Hyg. 1964, 58: 391-6. 10.1016/0035-9203(64)90083-5.View ArticlePubMedGoogle Scholar
- Uganda, D.o.L.a.S: Tsetse distributions, 1969. 1971, Atlas of Uganda. Entebbe, 1969-Google Scholar
- Guiliano C: Predicted areas of suitability for tsetse flies: Uganda. 2009, FAO GeoNetworkGoogle Scholar
- Okoth JO: Tsetse and trypanosomiasis control problems in South-East Uganda: past, present and alternative strategies. Schweiz Med Wochenschr. 1999, 129 (31-32): 1091-1098.PubMedGoogle Scholar
- Harley JMB: Activity cycle of Glossina pallidipes Austen, G. palpalis fuscipes Newstead, and G. brevipalpis Newstead. Bull Entomol Res. 1965, 56: 141-160. 10.1017/S0007485300057102.View ArticleGoogle Scholar
- Okoth JO: Observation on the composition of Glossina population at Lugala, south Busoga district, Uganda. East Afr Med J. 1980, 57 (5): 332-6.PubMedGoogle Scholar
- Okoth JO: Further observations on the composition of Glossina population at Lugala, South Busoga, Uganda. East Afr Med J. 1982, 59 (9): 582-4.PubMedGoogle Scholar
- Waiswa C: Glossina fuscipes fuscipes in the trypanosomiasis endemic areas of south eastern Uganda: apparent density, trypanosome infection rates and host feeding preferences. Acta Trop. 2006, 99 (1): 23-9. 10.1016/j.actatropica.2006.06.005.View ArticlePubMedGoogle Scholar
- Magona JW: Implications of the re-invasion of Southeast Uganda by Glossina pallidipes on the epidemiology of bovine trypanosomosis. Vet Parasitol. 2005, 128 (1-2): 1-9. 10.1016/j.vetpar.2004.10.020.View ArticlePubMedGoogle Scholar
- Krafsur ES, Griffiths N: Genetic variation at structural loci in the Glossina morsitans species group. Biochem Genet. 1997, 35 (1-2): 1-11.View ArticlePubMedGoogle Scholar
- Krafsur ES, Endsley MA: Microsatellite diversities and gene flow in the tsetse fly, Glossina morsitans s.l. Med Vet Entomol. 2002, 16 (3): 292-300. 10.1046/j.1365-2915.2002.00378.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Ouma JO, Marquez JG, Krafsur ES: Microgeographical breeding structure of the tsetse fly, Glossina pallidipes in south-western Kenya. Med Vet Entomol. 2006, 20 (1): 138-49. 10.1111/j.1365-2915.2006.00609.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Ouma JO, Marquez JG, Krafsur ES: Macrogeographic population structure of the tsetse fly, Glossina pallidipes (Diptera: Glossinidae). Bull Entomol Res. 2005, 95 (5): 437-47.PubMed CentralView ArticlePubMedGoogle Scholar
- Krafsur ES: Tsetse flies: genetics, evolution, and role as vectors. Infect Genet Evol. 2009, 9 (1): 124-41. 10.1016/j.meegid.2008.09.010.PubMed CentralView ArticlePubMedGoogle Scholar
- Baldry DA: A history of Rhodesian sleeping sickness in the Lambwe Valley. Bull World Health Organ. 1972, 47 (6): 699-718.PubMed CentralPubMedGoogle Scholar
- Baylis M, Nambiro CO: The effect of cattle infection by Trypanosoma congolense on the attraction, and feeding success, of the tsetse fly Glossina pallidipes. Parasitology. 1993, 106 (Pt 4): 357-61.View ArticlePubMedGoogle Scholar
- Ouma JO: Characterization of microsatellite markers in the tsetse fly, Glossina pallidipes (Diptera: Glossinidae). Mol Ecol Notes. 2003, 3 (3): 450-453. 10.1046/j.1471-8286.2003.00480.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Ouma JO, Marquez JG, Krafsur ES: New polymorphic microsatellites in Glossina pallidipes (Diptera: Glossinidae) and their cross-amplification in other tsetse fly taxa. Biochem Genet. 2006, 44 (9-10): 471-7.PubMed CentralView ArticlePubMedGoogle Scholar
- Baker MD, Krafsur ES: Identification and properties of microsatellite markers in tsetse flies Glossina morsitans sensu lato (Diptera: Glossinidae). Mol Ecol Notes. 2001, 1 (4): 234-236. 10.1046/j.1471-8278.2001.00087.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Hyseni C, B J, Gomez-Ocampo Z, Ouma JO, Okedi L, Gaunt M, Caccone A: The Glossina morsitans morsitans (Diptera: Glossinidae) genome as a source of microsatellite markers for other tsetse fly (Glossina) species. Mol Ecol Resour. 2011, 11: 3-Google Scholar
- Brownstein MJ, Carpten JD, JR S: Modulation of non-templated nucleotide addition by Taq DNA Polymerase: Primer modifications that facilitate genotyping. Biotechniques. 1996, 20: 1004-1010.PubMedGoogle Scholar
- Librado P, Rozas J: DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics. 2009, 25 (11): 1451-2. 10.1093/bioinformatics/btp187.View ArticlePubMedGoogle Scholar
- Peakall R, Smouse PE: GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes. 2006, 6 (1): 286-298.View ArticleGoogle Scholar
- Rousset F: Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour. 2008, 8: 103-106. 10.1111/j.1471-8286.2007.01931.x.View ArticlePubMedGoogle Scholar
- Excoffier L, Laval G, Schneider S: Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform Online. 2005, 1: 47-50.PubMed CentralGoogle Scholar
- Clement M, Posada D, Crandall KA: TCS: a computer program to estimate gene genealogies. Mol Ecol. 2000, 9 (10): 1657-9. 10.1046/j.1365-294x.2000.01020.x.View ArticlePubMedGoogle Scholar
- Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155 (2): 945-59.PubMed CentralPubMedGoogle Scholar
- Evanno G, Regnaut S, Goudet J: Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005, 14 (8): 2611-20. 10.1111/j.1365-294X.2005.02553.x.View ArticlePubMedGoogle Scholar
- Jombart T: adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008, 24 (11): 1403-5. 10.1093/bioinformatics/btn129.View ArticlePubMedGoogle Scholar
- Nei M, Tajima F, Tateno Y: Accuracy of estimated phylogenetic trees from molecular data. II. Gene frequency data. J Mol Evol. 1983, 19 (2): 153-70. 10.1007/BF02300753.View ArticlePubMedGoogle Scholar
- Miller MP: Alleles in space (AIS): computer software for the joint analysis of interindividual spatial and genetic information. J Hered. 2005, 96 (6): 722-4. 10.1093/jhered/esi119.View ArticlePubMedGoogle Scholar
- Konovalov DA, Manning C, Henshaw MT: KINGROUP: a program for pedigree relationship reconstruction and kin group assignments using genetic markers. Mol Ecol Notes. 2004, 4 (4): 779-782. 10.1111/j.1471-8286.2004.00796.x.View ArticleGoogle Scholar
- Konovalov DA, Heg DIK: TECHNICAL ADVANCES: A maximum-likelihood relatedness estimator allowing for negative relatedness values. Mol Ecol Res. 2008, 8 (2): 256-263. 10.1111/j.1471-8286.2007.01940.x.View ArticleGoogle Scholar
- Brightwell R, Dransfield R, Maudlin I, Stevenson P, Shaw A: Reality Vs. Rhetoric - a survey and evaluation of tsetse control in East Africa. Agric Hum Values. 2001, 18: 219-233. 10.1023/A:1011131826919.View ArticleGoogle Scholar
- Prugnolle F, de Meeus T: Inferring sex-biased dispersal from population genetic tools: a review. Heredity. 2002, 88 (3): 161-5. 10.1038/sj.hdy.6800060.View ArticlePubMedGoogle Scholar
- Cuisance D: Dispersion linéaire de Glossina palpalis gambiensis et G. tachinoides dans une galerie forestière en zone soudano-guinéenne (Burkina Faso). Revue d'Elevage et de Médecine Vétérinaire des Pays Tropicaux. 1985, 38: 153-172.Google Scholar
- Colvin J, Gibson G: Host-seeking behavior and management of tsetse. Annu Rev Entomol. 1992, 37: 21-40. 10.1146/annurev.en.37.010192.000321.View ArticlePubMedGoogle Scholar
- Rogers DJ, Randolph SE: Metabolic strategies of male and female tsetse (Diptera: Glossinidae) in the field. Bull Entomol Res. 1978, 68 (04): 639-654. 10.1017/S0007485300009615.View ArticleGoogle Scholar
- Hargrove JW: Reproductive abnormalities in tsetse flies in Zimbabwe. Entomologia Experimentalis et Applicata. 1999, 92 (1): 89-99. 10.1046/j.1570-7458.1999.00528.x.View ArticleGoogle Scholar
- Echodu R: Temporal stability of Glossina fuscipes fuscipes populations in Uganda. Parasit Vectors. 2011, 4 (1): 19-10.1186/1756-3305-4-19.PubMed CentralView ArticlePubMedGoogle Scholar
- Solano P: Population genetics as a tool to select tsetse control strategies: suppression or eradication of Glossina palpalis gambiensis in the Niayes of Senegal. PLoS Negl Trop Dis. 2010, 4 (5): e692-10.1371/journal.pntd.0000692.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.