• Loading metrics

Population Genetics and Reproductive Strategies of African Trypanosomes: Revisiting Available Published Data

  • Mathurin Koffi ,

    Contributed equally to this work with: Mathurin Koffi, Thierry De Meeûs (MK); (TDM)

    Affiliation Université Jean Lorougnon GUEDE, UFR Environnement-Santé, Laboratoire des Interactions Hôte-Microorganismes-Environnement et Evolution (LIHME), Daloa, Côte d'Ivoire

  • Thierry De Meeûs ,

    Contributed equally to this work with: Mathurin Koffi, Thierry De Meeûs (MK); (TDM)

    Affiliations IRD, UMR 177 IRD-CIRAD INTERTRYP, Centre International de Recherche-Développement sur l’Elevage en zone Subhumide (CIRDES), Bobo-Dioulasso, Burkina-Faso, IRD, UMR177 IRD-CIRAD INTERTRYP, Campus International de Baillarguet, TA A-17/G, Montpellier, France

  • Modou Séré,

    Affiliation IRD, UMR 177 IRD-CIRAD INTERTRYP, Centre International de Recherche-Développement sur l’Elevage en zone Subhumide (CIRDES), Bobo-Dioulasso, Burkina-Faso

  • Bruno Bucheton,

    Affiliations IRD, UMR177 IRD-CIRAD INTERTRYP, Campus International de Baillarguet, TA A-17/G, Montpellier, France, Programme National de Lutte contre la Trypanosomiase Humaine Africaine, Conakry, Guinée

  • Gustave Simo,

    Affiliation University of Dschang, Faculty of Sciences, Department of Biochemistry, Dschang, Cameroon

  • Flobert Njiokou,

    Affiliation University of Yaoundé 1, Faculty of Sciences, Department of Animal Biology and Physiology, Yaoundé, Cameroon

  • Bashir Salim,

    Affiliations University of Khartoum, Department of Parasitology, Faculty of Veterinary Medicine, Khartoum North, Sudan, Parasites, Vectors and Vector-borne Diseases, Agricultural Research Council-Onderstepoort Veterinary Institute, Onderstepoort, South Africa

  • Jacques Kaboré,

    Affiliations IRD, UMR 177 IRD-CIRAD INTERTRYP, Centre International de Recherche-Développement sur l’Elevage en zone Subhumide (CIRDES), Bobo-Dioulasso, Burkina-Faso, Université Polytechnique de Bobo-Dioulasso, UFR Sciences et Techniques, Bobo-Dioulasso, Burkina Faso

  • Annette MacLeod,

    Affiliation University of Glasgow, Wellcome Centre for Molecular Parasitology, Henry Wellcome Building of Comparative Medicine, Glasgow, United Kingdom

  • Mamadou Camara,

    Affiliation Programme National de Lutte contre la Trypanosomiase Humaine Africaine, Conakry, Guinée

  • Philippe Solano,

    Affiliation IRD, UMR177 IRD-CIRAD INTERTRYP, Campus International de Baillarguet, TA A-17/G, Montpellier, France

  • Adrien Marie Gaston Belem,

    Affiliation Université Polytechnique de Bobo-Dioulasso, UFR Sciences et Techniques, Bobo-Dioulasso, Burkina Faso

  • Vincent Jamonneau

    Affiliation IRD, UMR 177 IRD-CIRAD INTERTRYP, Centre International de Recherche-Développement sur l’Elevage en zone Subhumide (CIRDES), Bobo-Dioulasso, Burkina-Faso

Population Genetics and Reproductive Strategies of African Trypanosomes: Revisiting Available Published Data

  • Mathurin Koffi, 
  • Thierry De Meeûs, 
  • Modou Séré, 
  • Bruno Bucheton, 
  • Gustave Simo, 
  • Flobert Njiokou, 
  • Bashir Salim, 
  • Jacques Kaboré, 
  • Annette MacLeod, 
  • Mamadou Camara


Trypanosomatidae are a dangerous family of Euglenobionta parasites that threaten the health and economy of millions of people around the world. More precisely describing the population biology and reproductive mode of such pests is not only a matter of pure science, but can also be useful for understanding parasite adaptation, as well as how parasitism, specialization (parasite specificity), and complex life cycles evolve over time. Studying this parasite’s reproductive strategies and population structure can also contribute key information to the understanding of the epidemiology of associated diseases; it can also provide clues for elaborating control programs and predicting the probability of success for control campaigns (such as vaccines and drug therapies), along with emergence or re-emergence risks. Population genetics tools, if appropriately used, can provide precise and useful information in these investigations. In this paper, we revisit recent data collected during population genetics surveys of different Trypanosoma species in sub-Saharan Africa. Reproductive modes and population structure depend not only on the taxon but also on the geographical location and data quality (absence or presence of DNA amplification failures). We conclude on issues regarding future directions of research, in particular vis-à-vis genotyping and sampling strategies, which are still relevant yet, too often, neglected issues.


African trypanosomes are parasites with a complex life cycle. Apart from Trypanosoma equiperdum, which is transmitted sexually [1], they necessarily involve two host species: a vertebrate (mammals, e.g., humans, for the taxa involved in the present paper) and a vector, primarily a tsetse fly, in which sexual recombination events may occur in the salivary glands [24]. Some species (Trypanosoma congolense and Trypanosoma vivax) can alternatively be mechanically transmitted by biting insects (e.g., Tabanidae, Stomoxys), while Trypanosoma evansi has lost its ability for tsetse transmission and is only transmitted mechanically [57].

Because of their small size and the difficulty (or impossibility) of using mark-release recapture techniques, population biology of most parasites can only be studied through the analysis of the spatiotemporal distribution of polymorphic genetic markers [8]; trypanosomes are not an exception. These studies are made more difficult by the existence of two relevant compartments, vertebrate and vector. The former offers more opportunities to access population genetics data, whereas, to our knowledge, few or no robust population genetics studies of trypanosomes isolated from tsetse flies have provided interpretable results in terms of demography (but see [9] and our comments on this survey below).

Regarding strains circulating in vertebrates, sound studies of population genetics, mostly based on microsatellite markers, have recently begun to emerge. We hereby propose to revisit all the available published data on trypanosomes isolated in vertebrates and discuss them in light of the known or assumed reproductive system, taking into account the spatiotemporal structure. As much as possible, the technical problems related to the frequently encountered DNA amplification errors will be taken into account. Such problems arise most particularly when parasites are directly amplified from biological fluids without the costly and inconvenient isolation step using, for example, the kit for in vitro isolation of trypanosomes (KIVI) [10] or rodent inoculation (RI) [11]. Note that in [11], based on allozyme profiles, these authors concluded that isolation introduced a strong selection bias because particular profiles appeared to be associated with specific isolation methods. It appeared that this was more the result of a difference in gene expression of trypanosomes inoculated in a mammal (RI) or in an axenic medium (KIVI). Indeed, strains isolated with RI, KIVI, or directly amplified from blood were demonstrated to belong to the same population (no selection bias) [12] and only one strain (probably the major one in multiple infections) was isolated.

Clonal propagation is widespread in parasites and particularly microbes [13,14], making the study of the consequences of clonality on population genetics parameters highly relevant. After an overview of basic concepts in the genetics of clonal populations, this paper revisits the available data on the following: (i) human African trypanosomes responsible for human African trypanosomiasis (HAT, or sleeping sickness) in West and Central Africa (Trypanosoma brucei gambiense type 1) and East Africa (Trypanosoma brucei rhodesiense) with data from cattle and tsetse flies, and (ii) trypanosomes isolated from domestic ungulates from The Gambia responsible for nagana (T. vivax and T. congolense “savannah” type) and trypanosomes isolated from camels in Sudan, outside the tsetse areas, responsible for surra (T. evansi).

Overview of Basic Concepts in the Genetics of the Clonal Population

The most important concept to remember is that clones accumulate random mutations, especially on noncoding portions of their genome [8,15,16]. This results in an accumulation of heterozygosity at all loci, because once a homozygous site has experienced mutation, it becomes heterozygous and has very little chance of becoming homozygous again (reverse mutation is unlikely). A homoplasy event (identity between two alleles by random convergent mutation) is therefore the only process that can limit such accumulation; depending on the number of available K alleles, more or fewer homozygous genotypes (by state and not by descent) are expected (i.e., 1/K) [17]. For example, for SNPs, which generally exhibit two alleles (K = 2) [1820], we expect 50% apparent homozygosity in clones; whereas for reasonably polymorphic microsatellite loci, this proportion will fall (e.g., to 3.3% with K = 30) [17]. Relative inbreeding of individuals as compared to subsamples is measured using Wright's [21] FIS. This FIS is equal to zero when reproduction locally follows the panmictic model (fully sexual with a random union of gametes to form zygotes) and becomes negative in highly clonal organisms.

In totally clonal populations, FIS directly reflects the size of the population and the mutation rate of genetic markers used [12,16,22,23]. Strongly negative in small populations (-1 being the lowest limit), it will reach less extreme values in larger populations, especially with markers with a high mutation rate and a large number of possible alleles [16]. It has been shown that a low rate of sexual recombination (i.e., the proportion of individuals that are sexually produced at each generation) suffices to significantly change these findings. For a low sex rate (e.g., around 0.1), the proportion of heterozygosity is expected to approach that of panmictic populations. Very low sex rates (0.15 to 0.001) generate negative but also highly variable FIS from one locus to another, with some rare loci showing high homozygosity (FIS>>0) [15,16]. Variance of FIS across loci is also expected to occur in case of locus-specific technical problems (e.g., null alleles). This explains why it is important to detect the presence of technical problems during a PCR at one or a few loci. Because it leads affected loci to display a false homozygous profile. It will thus increase the FIS estimated at that locus over the value observed at unaffected loci and lead to erroneous conclusions on the reproductive mode [19].

If mutation within the region flanking the targeted locus affects the sector where the PCR primer should hybridize, it can generate null alleles (alleles that are not amplified by the PCR reaction). This generates fictitious homozygous genotypes (genotypes heterozygous for the null allele appear homozygous for the amplified allele) [8].

Limited DNA quantities can also lead to amplification failures of one or both alleles as a result of competition for the Taq polymerase. This is known as allelic dropout, which can also generate substantial numbers of false homozygous phenotypes [24].

In any case, the loci involved will present an increase in their FIS, which may lead to a profile similar to an almost entirely clonal population with very rare events of sexual recombination. Null homozygotes will be seen as missing genotypes (blanks). Allelic dropout should generally not generate missing data, but it can when primers are not perfect and DNA concentrations are very small. Nevertheless, in clonal diploids, the consequences of null alleles and/or allelic dropout cannot be distinguished (e.g., [19]). This is why we prefer the term "amplification problems," because most of the time the true mechanism cannot be precisely identified, the limit between the two is sometimes fuzzy, and the consequences on FIS are undistinguishable.

Homoplasy has an insignificant impact on FIS (e.g. [25], pages 62–63), even in clonal organisms [17], and can never produce a pattern similar to rare sex or amplification problems.

Sampling design remains a critical issue [8]. This appears even more critical for small organisms that can be subdivided at extremely small scales, such as the individual host [26], and which often display short generation times, like all microbes. Gathering entities from too-distant sampling sites and/or dates will mix individuals that belong to genetically differentiated subpopulations or cohorts into a single and genetically heterogeneous subsample. This phenomenon is called the Wahlund effect, which can significantly alter population genetics estimates and, when combined with clonal propagation, can drive some estimators in unpredictable directions [27]. This is especially true for FIS, which asymptotically tends toward its panmictic expectation (FIS = 0) when there is a strong Wahlund effect in clones [22]. This is also the case for linkage disequilibrium, which can increase with a moderate Wahlund effect or decrease when there is a strong Wahlund effect in clonal populations [27]. For T. b. gambiense, for instance, much less than one year suffices for significant differentiation to appear between two subsamples from the same focus [28]. Hence, pooling strains that were sampled several years apart will necessarily generate strong Wahlund effects with unpredictable, though important, consequences on the behavior of population genetics parameters.

Another signature of clonality is the presence of identical genotypes at several loci, meaning that several individuals display exactly the same genotype at all loci, which are usually called multilocus genotypes (MLGs). If polymorphic enough, six loci should be enough to ascertain this [29,30]. Using this criterion, however, is not without problems. The presence of repeated genotypes may also be generated in strongly subdivided populations with closed reproductive systems such as selfing, or can be hindered by Wahlund effects [31,32]. Random amplification problems (dropout) and multiple infections can also considerably alter the pattern of MLGs.

When a population is clonal, it was shown that estimating FIS from a sufficient number of loci (say, six to seven) and out of sufficient sample sizes (say, 10 to 20) can lead to an accurate estimate of immigration and/or clonal sizes [12,23,26]. Of course it is undoubtedly better to gather results from more loci (the more the better). Nevertheless, it is known that FIS estimates display little variance across loci in pure clones. This is why this statistic is preferable to FST for estimating clonal sizes; it provides more accurate and less variable inferences [22] and is totally independent of homoplasy (at least close to equilibrium or in reasonably small populations of ≤1,000 individuals) [17]. Clonal size (NCl) is the number of propagating clones. It is a clonal equivalent to the effective population size defined for sexual populations. Note that effective population sizes can be computed in clonal populations, but provide contradictory results depending on what definition of effective population size is used. Inbreeding effective population size will be increased in clones (infinity in full clones), while variance effective population size will be reduced. When clonal populations are strongly subdivided into numerous subpopulations and the mutation rate u<<m (migration rate), the number of immigrant clones can accurately be estimated as [22]: (Eq. 1)

When u cannot be neglected, then this quantity measures the product NCl (m+u).

When working with an isolated population, then clonal size can be estimated as [23]: (Eq. 2)

Another useful case is when the system is composed of two subpopulations, because then NCl and m can be estimated separately as [12]: (Eq. 3) (Eq. 4) where FST is Wright's fixation index [21] of subpopulations as compared to the total population, and measures inbreeding of subsamples as compared to total inbreeding. This last parameter is less accurately estimated than FIS in most situations, especially in clonal populations [22].

These methods require almost perfect genotyping (no amplification problems) and, for most of them, a clear idea of the mutation rate of the genetic markers used. Nevertheless, most of these equations will provide useful results, at least for comparison purposes. For instance, with T. b. gambiense in western Africa [12], this has provided strong evidence that substantially more clones were circulating than what could be speculated from human prevalence. This was still true even when using the highest mutation rates usually given for microsatellites (10−3). Since lower mutation rates provide a larger estimation of NCl, this was strong evidence for the existence of numerous invisible trypanosome clones circulating in the foci. Later, it was found that many seropositive patients without visible trypanosomes appeared as trypanotolerant subjects acting as reservoirs for the parasite [28,33,34]. This contributed to at least partly explaining the genetic results. We will discuss this issue further at the end of the paper.

Comparison across studies is difficult if the markers used are different, because different mutation rates will produce different estimates (see Eqs 14). This is why it is better to use as many loci of the same type (e.g., microsatellite loci) as possible, so that the mean across loci should provide results that may be compared across loci. Sharing similar loci across studies is, of course, even better. This is often the case for T. brucei population genetics studies, because few markers are available. This is also true for T. b. gambiense in Guinea, Ivory Coast [12], and the Central African Republic [23], countries that used the same seven loci (see below). When the results from different kinds of markers are compared, the relevance of the comparison will strongly depend on reasonable knowledge (order of magnitude) of the average mutation rate of each type of marker (see below for a comparison between mini- and microsatellite markers and discussion). Nevertheless, when too many amplification problems obscure the results, no inference is possible and, hence, neither is a comparison (see below). It must be added that very small amounts of sexual recombination or unresolved problems of DNA amplifications will eliminate any reasonable inference using either clonal or sexual models.

Population Genetic Studies of African Trypanosomes

There are few studies on population genetics of African trypanosomes, and even fewer for which the data are available online. Here, we will discuss the published data based on the genotyping of microsatellite and minisatellite markers of different species of trypanosomes from different geographic zones that are presented in Fig 1. We revisited three datasets in which most subsamples were subjected to isolating/amplification techniques (KIVI or RI) before molecular analyses: seven subsamples of T. b. gambiense from humans (Ivory Coast, Guinea, Cameroon, Equatorial Guinea, Central African Republic, Congo, and Uganda) and eight subsamples of T. b. rhodesiense from humans (five from Uganda, Kenya, and Zambia), cattle (two from Uganda) and tsetse flies (one from Uganda) (Fig 1). The trypanosome's DNA from isolated/amplified parasites did not present amplification problems. Nevertheless, strain isolation techniques are very costly in terms of labor and logistics, and all suffer from low success rates, with long adaptation periods needed to propagate the parasite in sufficient numbers [35]. This is why recent surveys use direct DNA PCR amplification from body fluid samples.

Fig 1. Location of different samples of trypanosomes reanalyzed with population genetics tools for estimating population parameters.

1: Boffa and Dubréka (Guinea) HAT foci. Strains were isolated (I) [12] or directly amplified (D) [28]. 2: Bonon HAT focus (I) (Ivory Coast) [12]. 3: Campo, Bipindi, and Fontem HAT foci (Cameroon), (I) [23], (D) [36], 4: Mbini and Kogo HAT foci (Equatorial Guinea), (I) [23]. 5: Batangafo and Obo HAT foci (Central African Republic), (I) [23]. 6: Omougou HAT foci (Uganda), (I) [23]. 7: Nyanza HAT focus (Kenya), (I) [22,37]. 8: Luangwa HAT focus (Zambia), (I) [22,37]. 9: Busoga HAT focus (Uganda), (I) [22,37], (D) [38]. 10: Soroti HAT focus (Uganda), (D) [38]; 11. Maluku HAT foci (DRC), (D) [36]. 12: Nagana area of Central River District (The Gambia), (D) [5,7]. 13: Surra area of Darfour, Kurdofan, Kassala, Halfa and Showak (Sudan), (D) [39]. μ: Microsatellite genotyping; mini: minisatellite genotyping.

We also revisited several sets of data from which trypanosome DNA underwent direct amplification from biological fluids (blood, lymph or cerebrospinal fluid) with no isolation step: one dataset of T. b. gambiense from Guinea, one of T. vivax from The Gambia, one of T. congolense ("savannah" type) from The Gambia, and one of T. evansi from Sudan. One supplementary dataset examined blood sampled from people infected with T. b. rhodesiense from Uganda and Malawi studied at seven microsatellite markers [38]. The results obtained on "forest" type T. congolense were also introduced for discussion. Another dataset on T. b. gambiense [36], using blood-amplified microsatellite loci, with many obvious amplification problems, was also included.

Datasets were all recoded into appropriate formats with CREATE V 1.1 [40]. Wright's FIS and FST analyses were performed with Fstat V 2.9.4 [41] (updated from [42]). The significance of differentiation was tested using Fstat 2.9.4 with the G-based test [43] after 10,000 permutations of individuals across subsamples. To compute 95% confidence intervals, we used bootstrap over loci or Jackknife over subsample with Fstat 2.9.4, as described in [8]. When there were fewer than five loci and subsamples, we used bootstrap over alleles with Genetix 4.05 [44]. Regression and testing were undertaken with R 2.14.0 [45]. To compare FIS values between two subsamples, we used the exact Wilcoxon signed-rank test for paired data, the pairing unit being each locus. This test was undertaken with R 2.14.0. To test if FIS decreases with time in the Bonon focus (Ivory Coast), we used the Page test for ordered alternatives in which matching items are, again, the loci. These two tests are described in Siegel and Castellan's book [46]. Genetic distances were computed with MSA 4.05 [47], and Neighbor Joining Trees built with MEGA 5.2.2 [48]. The robustness of nodes in trees was assessed with 1,000 bootstraps undertaken with PHYLIP V 3.68 [49] with Cavalli-Sforza and Edward's chord distance [50]. In this last case the dataset was converted to PHYLIP format with Convert 1.31 [51].

Trypanosomes isolated by KIVI or RI

The published results of these analyses concern T. b. gambiense type 1 in West Africa and Central Africa from humans [12, 23] [23] and T. b. rhodesiense in East Africa from humans, cattle, and tsetse flies (Fig 1) [22, 37]. On these isolated stocks of T. b. gambiense type 1, few or no amplification problems occurred at microsatellite loci, and nearly all individuals were heterozygous. In West Africa, out of seven loci (one problematic locus, Trbpa1/2, was removed), three individuals were found to be homozygous at locus M6C8 [12]. In Central Africa [23], no homozygotes were found except for 13 at locus M6C8: one in a very small subsample (not used), and 12 in Bipindi as a fixed (no polymorphism) allele that therefore did not influence the FIS estimate. Moreover, the variation of the FIS from one locus to another was clearly explained by the genetic diversity observed at each locus, and therefore related to the mutation rate specific to each of these loci (Fig 2), as is expected in pure clones [12, 19]. More homozygotes were observed in T. b. rhodesiense minisatellites from humans, especially in the less polymorphic minisatellite (292), as expected [17]. No homozygotes were observed in trypanosome minisatellite loci in tsetse flies, while many appeared in cattle (see below).

Fig 2. Regression between FIS, inbreeding index of individuals relative to subpopulations per locus, and Nei's unbiased estimator of genetic diversity Hs [52] in Trypanosoma brucei gambiense 1 [19] in West Africa [12] and Central Africa [23].

The proportion of variance explained by the model (R2) and the corresponding p-values are indicated.

Across these studies, the demographic parameters of trypanosomes were estimated by assuming that HAT foci are reasonably well isolated from each other. This is true almost everywhere, except possibly in Guinea, where clonal size will appear overestimated by an order of approximately two [12]. Therefore, if we assume isolation between foci, clonal size can be estimated using the following formula [23]: (Eq. 5)

These clonal sizes were estimated in HAT foci that contained at least five genotyped stocks. The average rates of mutations were assumed to be u = 10−3 for microsatellite markers [53] (for u = 10−4 or u = 10−5 one must multiply these numbers by 10 or 100, respectively) and a rate of u = 0.03 for minisatellite markers (see [54], Table 7.5, page 393).

The results based on these calculations are shown in Fig 3. In this figure, the number of parasites in Dubreka (Guinea) seems lower in 2002 than in 1998; however, the difference is not significant (bilateral Wilcoxon signed rank test, p-value = 0.687). Meanwhile, the number of parasites continuously increases in Bonon (Ivory Coast) from 2002 to 2007 (Page test for ordered alternatives, p-value < 0.001), despite treatment campaigns conducted for patients before and during this period [55,56]. This result coincides with the beginning of the civil unrest in 2002 in Ivory Coast, with a decrease in the participation rate of the population at risk during medical surveys [56]. In Central Africa, the very low clonal population sizes confirm the low risk of infection in the foci studied as compared to West African foci [57]. Obviously, at least in Cameroon, the prevalence of HAT is very low in most foci, although T. b. gambiense is still found in animals [58]. For T. b. rhodesiense foci, we also noted very low numbers of circulating parasites (NCl ≈ 5 ± 4), but this probably stems from an overestimation of the mutation rate. Indeed, u = 0.03 does not match the weak genetic diversity found in the available subsamples, especially for locus 292 (Hs = 0.47). If locus 292 is removed, or the mean mutation rate divided by 10, NCl of the human T. b. rhodesiense foci fits in the range (14–24) and (12–105), respectively, which seems to more accurately reflect the real situation in terms of human infections [59]. In any case, whether human T. b. rhodesiense or T. b. gambiense 1 is taken into consideration, West African foci seem more dynamical than Central and East African foci, even if the mutation rate of minisatellite loci is substantially decreased (for comparisons across countries and studies within T. b. gambiense 1, the loci are the same). Interestingly, in the cattle subsample of T. b. rhodesiense in the Busoga focus (Uganda), substantial variation of FIS was observed across loci (from -0.01 to 0.34 in 1988 and -0.27 to 0.18 in 1990) and across years (from 0.27 in 1988 to -0.02 in 1990), which is the signature of sexual recombination occurring at different rates, depending on the subsample. It is also worth noting that when comparisons were possible (similar dates and sites), there was a strong and significant differentiation between strains sampled from different host species, with a much stronger signal than the differentiation observed between different sampling dates (Table 1). This suggests that trypanosomes circulating in humans and those circulating in animals come from different and genetically distant populations (or even species). Finally, the tsetse fly subsample from Busoga 1969 behaved as a purely clonal population (no homozygotes). This allowed for estimating the clonal population size at NCl = 45, with a 95% confidence interval of [447]. Nevertheless, the very small sample size (five) makes this purely anecdotal at best.

Fig 3. Estimates of clonal population sizes, NCl = -(1+FIS)/(4uFIS) for an isolated clonal population, of Trypanosoma brucei gambiense type 1 (Tbg1) and T. b. rhodesiense (Tbr) infecting humans with a mutation rate u = 10−3 for microsatellite markers (μ) and u = 0.03 for minisatellite markers (mini).

The name of the focus, the year of sampling, and the sample sizes are shown on the abscissa. Labels of sampling zones are the same as in Fig 1. For higher mutation rates (e.g., u = 0.0001 or 0.00001 for microsatellites), NCl values must be multiplied by 10 and 100, respectively.

Table 1. Genetic differentiation (FST) between Trypanosoma brucei rhodesiense subsamples from the Busoga focus (Uganda) according to hosts (human or cattle) and/or year of sampling and significance testing (p-value).

Data were computed out of three minisatellite loci [37]. When host species is different, only subsamples not separated by more than three years were compared.

Non-isolated T. b. gambiense

The study conducted by [60] was a preliminary study carried out using microsatellite DNA amplified directly from biological fluids (i.e., blood, lymph of cervical lymph nodes, and cerebrospinal fluid) to compare the genetic diversity of trypanosomes encountered in these body fluids, as well as to circumvent the in vivo and/or in vitro isolation of trypanosomes.

In this study, an increase in homozygous profiles was observed as compared to previous studies (see above), together with greater heterogeneity of FIS between loci. This observation could be diagnostic of recent sexual recombination that may have occurred in the Guinea HAT foci. Nevertheless, a significant number of amplification failures were also observed during this study. Studying the relationship between the proportion of observed heterozygous profiles and these amplification failures, a highly significant negative relationship was revealed (Fig 4), indicating that the higher the number of failures in a complete genotype, the smaller the observed number of heterozygous profiles among expressed loci. This result strongly suggests that a large proportion of "homozygous" profiles observed in this study are in fact heterozygous (one of the two alleles was not amplified as a result of amplification problems) [60]. Moreover, amplifications from lymph nodes in Guinea produced significantly fewer failures than the other fluids (p-value < 0.001) [60], because lymph nodes present higher parasitemia in Guinea [61]. Other subsequent experiments demonstrated that most homozygous and many missing genotypes observed by [60] were in fact true heterozygotes [62]. A more recent theoretical approach [19] confirmed that null alleles and/or allelic dropouts can reasonably explain the frequency of homozygous profiles observed in this dataset.

Fig 4. Results of the logistic regression between the proportions of heterozygous genotypes observed in Trypanosoma brucei gambiense from Guinea amplified from biological fluids and the number of amplification failures [28].

The relationship has been tested with the chi2 test, which proved highly significant (p-value < 0.001). 95% confidence intervals are presented with dotted lines.

For another dataset on T. b. gambiense [36], loci display a strange behavior with huge variance of FIS (from -1 to +0.79). It was shown that under pure clonality, we expect FIS_exp = -(1-HS)/HS, where HS is the unbiased estimator of genetic diversity [19,63]. According to this work, the criterion for significant departure of observed FISFIS_obs from the expected FIS_exp at one locus in one subsample is when ΔFIS = |FIS_obs-FIS_exp| ≤ 0.05 × |FIS_obs-FIS_exp|. In the other cases, the two values are considered as superimposed. In case of null alleles or rare sex, superimposition decreases. However, it decreases much faster with sex. Here, the proportion of superimposed FIS is 50%. This is either compatible with approximately 1% of sex or with 100% clonality and approximately 50% of null alleles (or allelic dropouts) if we refer to the graphic method of [19]. This result is in variance with other observations in similar zones [23]. This should encourage re-genotyping of all homozygous profiles before any useful inference can be made from this dataset.

Non-isolated T. b. rhodesiense

In Uganda (two sub-samples) and Malawi (one sub-sample), T. b. rhodesiense were sampled at large spatial (69–150 km between the most distant sites) and temporal (two years) scales [38,64]. This was likely to generate spatiotemporal Wahlund effects and, as expected, produced unreliable results. While reanalyzing these data, strong variance in F statistics was observed, with a clear positive correlation of FIS with the largest distance in the zone considered as a subpopulation by the authors (R2 = 0.225, p-value = 0.03). This strongly suggests a Wahlund effect. Using the isolates from the smallest area (i.e., Soroti in Uganda, the least affected by Wahlund effects), Sere et al.'s criterion [19] (see above) was compatible with either 99.9% clonality or 100% clonality, and with 20%–50% allelic dropouts or null alleles. Using the same equation as above (Eq 5), with FIS = -0.8 in that subsample, we computed NCl = 57 (u = 10−3) with a 95% bootstrap confidence interval of (9–187), which is not far from the number of patients seen (158) [64]. Nevertheless, the high variance across loci and the unicity of the subsample used that extended over one year may prevent further comparisons with other studies. For the other subsamples, some sexual recombination might be occurring, but the data are so heterogeneous that very little can be definitely concluded. Indeed, it is known that the Wahlund effect in clonal organisms can have unpredictable and important effects on population genetic parameters, such as F-statistics and linkage disequilibrium [32]. Multiple infections were also observed during this survey. Multiple infections can alter heterozygosity and linkage disequilibrium estimates, and hence have the potential to alter the genetic picture obtained from the population studied. Nonetheless, multiple infections seemed very rare in [38] and probably altered the results very little, if at all.

Animal trypanosomes

Here, three studies based on direct amplifications from the blood of infected animals gave different results, but all suggested the existence of amplification problems.

For T. vivax amplified from cattle, donkeys, and horses in The Gambia [5], reanalysis of the data revealed negative FIS that were highly variable across loci [19]. This variability appeared independent from genetic diversity (Fig 5). This is difficult to interpret by the occurrence of rare sex events because no locus showed any FIS > 0 (highest FIS = -0.4). This can be explained by the existence of amplification failures (allele dropout) that may be more or less frequent depending on the locus considered. Only two loci appeared to have the expected pure clonality profiles and no amplification problems (Fig 5). This would need further analysis to be confirmed, but a more accurate estimate of FIS would allow for making demographic inferences. Several multiple infections noted during this survey may add additional problems. Another recent paper [65] on American strains (thus exclusively mechanically transmitted), based on relatively small samples, also found odd results, with FIS varying from approximately -1 to +1. This probably also comes from considerable amplification problems.

Fig 5. No relationship (R2 = 0) between FIS per locus and Hs for T. vivax from The Gambia [5].

The only two loci that seem to behave as expected in a clonal population are connected with a dotted line. These two loci are therefore probably free of amplification problems.

For T. congolense (“savannah” type) [7], the samples came from the same host and the same site as in [5]. What is striking is the tremendous homozygosity observed for all loci, but with spectacular variances between loci or between subsamples (host species and year), and a dearth of repeated multilocus genotypes. The authors interpreted these findings as resulting from a combination of the Wahlund effect (mixture of differentiated subpopulations) and frequent sex between related stocks. When reanalyzing the data, this interpretation is difficult to reconcile with the variances observed across loci (Fig 6). Indeed, the Wahlund effect, which is a factor that affects the whole genome homogeneously, and frequent sex (whether inbred or not) cannot generate such variances [8,16]. The second remarkable observation is the great genetic divergences that exist between individual genotypes, as shown in Fig 7. Many genotypes found are indeed almost 100% divergent (according to the seven microsatellite markers used), which is unexpected for individuals of the same species sampled in a relatively narrow space and time. Such distances can be seen between T. b. gambiense and T. b. rhodesiense strains, if we use the shared allelic distance for the data presented in Fig 8. It is worth noting that when analyzing the most homogeneous group in the tree structure shown in Fig 7 (indicated with a bracket), one locus has shown a substantial excess of heterozygotes (FIS = -0.273) while all the others nearly exhibited FIS = 1. Consequently, this "homogeneous" group can hardly be interpreted as a true entity, because no known reproductive system can generate such a pattern. Finally, the high rate of amplification failures (31%), in addition to all previous observations, and the probable multiple infections, add up to cast doubt on the validity of the genotypes observed. New markers need to be designed and very cautious sampling strategies should be used (at the narrowest spatiotemporal and host species scales). Meanwhile, it seems premature to formulate any inferences on the reproductive system or the population structure of this problematic "species." More recent studies were conducted on strains of the "forest" type from the Fontem focus in Cameroon amplified from mammal blood [66] and tsetse flies [9]. "Savannah" types, although present, apparently could not be genotyped (no data provided). We tried to reanalyze the "forest"-type data. Analysis of FIS on contemporaneous subsamples provided results compatible with a substantial amount of sex or clonality with a large proportion of amplification problems (approximately 50%). Frequent missing data, added to obvious cases of multiple strain infections, easily explain the problems encountered, making the data difficult to interpret clearly.

Fig 6. Variation of FIS between loci and between subsamples (host species and year) for Trypanosoma congolense (“savannah” type) from The Gambia [7].

95% confidence intervals were obtained by jackknife on subsamples (three host species and two years), except for the average across all loci where the interval was obtained by bootstrapping over loci.

Fig 7. Neighbor-joining dendrogram based on a shared allele distance matrix [67] among pairs of individuals of Trypanosoma congolense ("savannah") from The Gambia [7].

The first letter represents the host species (C for cattle, H for horse, and D for donkey) and is followed by the year and finally by the individual numbers. The bracket indicates the most homogeneous group. The genotypes that are identical at all seven loci are shown in bold.

Fig 8. Neighbor-joining dendrogram based on a Cavalli-Sforza and Edwards [80] distance matrix of different samples of Trypanosoma brucei gambiense type 1 in Western and Central Africa and computed out of eight microsatellite loci [12].

Red, T. b. gambiense reference strains; gold, T. b. gambiense type 2; green, T. b. brucei; blue, T. b. rhodesiense. Isolates suspected of deriving from immigrants are in italics. Major (>50%) bootstrap values are also indicated. Bootstraps were undertaken with the isolate Stib215 as the root (T. b. brucei).

Regarding the samples of T. evansi isolates from camels in Sudan, an average profile consistent with a panmictic model (FIS ≈ 0) was observed [39]. Sexual recombination occurs in tsetse flies’ salivary glands [68,69]. Thus, recombination is highly unlikely for a trypanosome away from any tsetse area where only mechanical transmission occurs [39]. Moreover, FIS variance from one locus to another, together with an absence of any repeated genotype, has made it very difficult to interpret such data. The absence of non-amplified genotypes (missing data) dismissed null alleles as a possible explanation. Moreover, random amplification failures (allelic dropout) alone did not seem to explain the data sufficiently. Simulations were then used by the authors to validate the hypothesis that the presence of allele dropout and Wahlund effects (sites contain a mixture of individuals that belong to genetically differentiated populations) may explain the patterns observed. These problems, however, did not prevent the detection of a very significant isolation by distance. It is worth noting that a more recent study that used other loci in Asian T. evansi samples found data compatible with total clonality in that species (high and invariable heterozygote excesses) [70]. A more recent theoretical study [19] confirmed that allele dropouts can alone explain T. evansi data from Sudan.


The first, and somewhat frustrating, observation that comes from the overview presented here is the heterogeneity of the datasets in terms of the technique and marker used, with few comparisons possible. Researchers should develop and focus on more robust (in terms of amplification failures) and numerous markers. Highly polymorphic markers should be preferred, because homoplasy will tend to slow the speed at which equilibrium is reached [17]. Ideally, from a logistic point of view, markers that can be amplified directly from body fluids should be preferred. This seems to exclude SNPs for these approaches, but genomic approaches currently being developed will probably provide another perspective regarding reproductive strategies in the long run.

Trypanosome strain isolation techniques are tedious and costly, and present very low success rates [35]. Revisiting many of the available datasets emphasizes that avoiding trypanosome strain isolation steps is possible. However, amplifying parasite DNA directly from the host's body fluids can generate numerous technical problems, probably for the most part due to the small amount of trypanosome DNA available in biological fluids. It will be essential to improve amplification techniques, as proposed in a recent article [62]. Additionally, sampling strategies should target the narrowest possible spatial and temporal scales for each subsample before estimates of ecological parameters can be planned. Multiple infections, when present, will add to the difficulty of interpretation, especially if combined with amplification problems. Indeed, in this case, spurious segregation and recombination can be expected to occur, leading to erroneous interpretations. Multiple infections are encountered in severe infections, together with direct DNA amplification from body fluids. Since only the major circulating strain is amplified through isolation techniques (e.g., RI) [11,12], strain isolation might be a costly cure to this problem. Nevertheless, a less costly technique, yet to be designed, would be welcome, since it is to be expected that many more problematic datasets containing such problems will be gathered in the near future, in particular with genomic approaches.

The clonality of T. b. gambiense 1 is now a well-established fact [12,19], at least in a mid- to short-term perspective. For T. vivax and T. evansi, this also seems to be the case, despite imperfect data; but further studies with better genetic markers will be required for confirmation. For other taxa, taxonomic heterogeneity, together with DNA amplification problems and sampling difficulties, or even absence of reliable data, include enough confounding factors that prevent definitive conclusions from being drawn for any of them. We know that sexual recombination is possible within and between different laboratory strains of T. b. brucei, T. b. gambiense 2, and T. b. rhodesiense (and never T. b. gambiense 1) in tsetse fly salivary glands [2,71]. We also know that these three taxa are composed of different and divergent entities ([72,73]; see also below). Some distant members of these lineages are also suspected of having undergone hybridization events in the past [74]. The importance of recombination in wild conditions and in these different lineages thus remains an important route for further investigation. T. congolense, T. vivax, and T. evansi do not colonize tsetse flies’ salivary glands [57]. Although absence of sexual recombination could be predicted for these taxa, this remains to be determined with appropriate sampling and tools. Escaping the tsetse belt means that new environments and new hosts can be colonized. This provides an advantage that might have occurred several times independently in different lineages of T. b. brucei [75]. In the absence of possible sexual recombination with the tsetse fly salivary gland, this naturally would have led to the propagation of different asexual lineages of T. evansi and T. equiperdum. If these different lineages can coexist in the same environments, an interesting track of investigation remains to be undertaken.

When estimates are possible, clonal population sizes appear relatively consistent or slightly higher than what medical surveys suggested. Nevertheless, for microsatellite markers, such estimates were undertaken with the assumption of a very important averaged mutation rate for microsatellite loci (u = 0.001). There is evidence, however, that mitosis generates less frequent mutations and that the mutation rate could be as low as 0.00001 for microsatellite loci in clonally propagating populations [19]. This would provide a pessimistic picture of the current epidemiological state of sleeping sickness, especially in West African foci where tens of thousands clones would be expected to circulate in hidden human and/or animal reservoirs. In a context in which the elimination of the disease is considered as a reasonable target by the WHO, which has projected fewer than 2,000 reported cases per year and more than 90% foci with less than one case per 10,000 inhabitants [76], clarifying this issue is an important goal. More analyses on more samples are therefore needed.

Another emerging feature of these studies is the tremendous genetic heterogeneity within a taxon that can be observed in many studies, especially (but not only) in T. congolense. These heterogeneities suggest the existence of subdivisions into very small clusters (subsets) or even the existence of different species that remain to be characterized. As already mentioned in a previous article [77], the T. brucei complex should inspire us to rethink the taxonomy of these parasites of medical and veterinary importance. According to Fig 8, two clusters can be distinguished (though with a low level of bootstrap support) in T. b. gambiense: one from Guinea, transmitted by Glossina palpalis gambiensis in mangrove areas with a high lymphatic tropism, and one from Ivory Coast and Central Africa, transmitted by G. p. palpalis in forest areas with high blood tropism. Some more or less recent immigration signatures can be suspected based on Fig 8. A Dubreka strain (isolate 70 2ms Guinea Dubreka 1998) has been observed within the West and Central Africa "clade," and a Bonon strain has been observed within the Guinean "clade" (TT2 4ms CI Bonon 2002). Furthermore, a possible hybrid between a T. b. gambiense strain and a non-gambiense T. brucei (B4E427 msCI Bonon 2004) has also been observed. The possible existence of hybrids will need to be investigated further, because they could provide evidence of the (very) rare occurrence of sexual recombination in T. b. gambiense. Using the divergence measured between the two alleles of each hemigenome (Meselson effect [78,79]), genomic studies should confirm long-term clonality and also provide a tool to estimate when T. b. gambiense 1 became totally clonal.

We will not dwell on the cases of T. b. rhodesiense (see also [81]), T. b. gambiense group 2, or T. b. brucei whose heterogeneity (Fig 8) barely hides the probable existence of species complexes that remain to be deciphered and are likely related to ecological differences (host and/or landscape). This is not a purely academic consideration, given the economic and public health significance of these human and animal parasites from sub-Saharan Africa, and will require further scientific investigations.

Finally, some of the issues discussed above will find new answers with the emergent use of genomic approaches and analyses of SNPs on a large scale. Nevertheless, to date, these approaches still need tedious and costly isolation of parasite isolates. SNPs display maximum homoplasy and will thus reach expected equilibrium values with a much higher number of generations (more than 20,000) [17]. Because of the huge number of markers involved, wide-scale genomic studies will meet the difficulty of handling markers with a heterogeneous determinism, from purely neutral to highly selected, with little opportunity for screening the most neutral markers. Because SNP mutation rates are also expected to be extremely low in clones, the signature of selective events can never be wiped out before a prohibitive number of generations. For these reasons, microsatellite markers (especially dinucleotide markers, because of their non-coding nature) will remain the markers of choice for studying the (short-term) population genetics of trypanosomes, owing to high polymorphism and high mutation rates, and because they also offer the best opportunity for direct amplification from body fluids.

Box 1. Key Learning Points

  1. Direct amplification of parasite DNA from body fluids (blood, lymph, or cerebrospinal fluid) allows for study of the population genetics of African trypanosomes, by bypassing the costly and tedious strain isolation steps. Nevertheless, two problems remain to be solved: the existence of amplification problems, mainly due to small concentrations of trypanosome DNA, and multiple infections. This may blur the true genotypes of individuals and even generate spurious recombinations. In some cases, this jeopardizes the population genetics inferences that can be extracted from available current data.
  2. Except for the monophyletic Trypanosoma brucei gambiense type 1, taxa belonging to the subgenus Trypanozoon (T. b. gambiense type 2, T. b. brucei, T. b. rhodesiense, T. evansi, and T. equiperdum) are composed of divergent lineages. Some of these lineages probably display different ecological needs that remain to be described. This might unveil particular features that might prove useful in some instances (e.g., control).
  3. Sexual recombination can only occur in tsetse salivary glands, and should therefore be absent from trypanosomes that avoid this step, such as T. evansi, T. congolense, T. vivax, and T. equiperdum. This awaits confirmation based on relevant sampling with flawless markers. Sex seems absent from T. gambiense type 1, even if some old and rare hybridization events might have occurred in the past, which remains to be confirmed. For T. b. gambiense type 2, T. b. brucei, and T. b. rhodesiense, appropriate sampling with flawless markers and homogeneous subsamples in time and space will teach us more about the biosytematics of these complex lineages and about the frequency with which sex occurs in the different lineages that compose these three taxa.
  4. In lineages known to be fully clonal, estimating clonal size of subpopulations and immigration from neighboring sites (foci) is possible. For lineages displaying some recombination, unless if frequent enough (e.g., approximately 50%), inferences might prove problematic.

Box 2. Top Five Papers

  1. Balloux, F., Lehmann, L., De Meeûs, T. 2003. The population genetics of clonal and partially clonal diploids. Genetics 164, 1635–1644.
  2. De Meeûs, T., Lehmann, L., Balloux, F. 2006.Molecular epidemiology of clonal diploids: A quick overview and a short DIY (do it yourself) notice. Infect. Genet.Evol. 6, 163–170.
  3. Gibson, W.C. 2007. Resolution of the species problem in African trypanosomes. Int. J. Parasitol. 37, 829–838.
  4. Koffi, M., De Meeûs, T., Bucheton, B., Solano, P., Camara, M., Kaba, D., Cuny, G., Ayala, F.J., Jamonneau, V. 2009. Population genetics of Trypanosoma brucei gambiense, the agent of sleeping sickness in Western Africa. Proc. Natl. Acad. Sci. U. S. A. 106, 209–214.
  5. Balmer, O., Beadell, J.S., Gibson, W., Caccone, A. 2011. Phylogeography and Taxonomy of Trypanosoma brucei. PLoSNegl. Trop. Dis. 5, e961.


  1. 1. Claes F, Buscher P, Touratier L, Goddeeris BM. Trypanosoma equiperdum: master of disguise or historical mistake? Trends Parasitol. 2005;21(7):316–21. pmid:15923142
  2. 2. MacLeod A, Tweedie A, McLellan S, Taylor S, Cooper A, Sweeney L, et al. Allelic segregation and independent assortment in T. brucei crosses: Proof that the genetic system is Mendelian and involves meiosis. Mol Biochem Parasitol. 2005;143(1):12–9. pmid:15941603
  3. 3. Tait A, MacLeod A, Tweedie A, Masiga D, Turner CMR. Genetic exchange in Trypanosoma brucei: Evidence for mating prior to metacyclic stage development. Mol Biochem Parasitol. 2007;151(1):133–6. pmid:17134768
  4. 4. Tait A, Morrison LJ, Duffy CW, Cooper A, Turner CM, Macleod A. Trypanosome genetics: populations, phenotypes and diversity. Vet Parasitol. 2011;181:61–8. pmid:21570772
  5. 5. Duffy CW, Morrison LJ, Black A, Pinchbeck GL, Christley RM, Schoenefeld A, et al. Trypanosoma vivax displays a clonal population structure. Int J Parasitol. 2009;39(13):1475–83. pmid:19520081
  6. 6. Gibson WC. Resolution of the species problem in African trypanosomes. Int J Parasitol. 2007;37(8–9):829–38. pmid:17451719
  7. 7. Morrison LJ, Tweedie A, Black A, Pinchbeck GL, Christley RM, Schoenefeld A, et al. Discovery of mating in the major African livestock pathogen Trypanosoma congolense. Plos One. 2009;4(5):e557.
  8. 8. De Meeûs T, McCoy KD, Prugnolle F, Chevillon C, Durand P, Hurtrez-Boussès S, et al. Population genetics and molecular epidemiology or how to "débusquer la bête". Infect Genet Evol. 2007;7(2):308–32. pmid:16949350
  9. 9. Simo G, Fogue PS, Melachio TTT, Njiokou F, Kuiate JR, Asonganyi T. Population genetics of forest type of Trypanosoma congolense circulating in Glossina palpalis palpalis of Fontem in the South-West region of Cameroon. Parasites & Vectors. 2014;7:85.
  10. 10. Aerts D, Truc P, Penchenier L, Claes Y, Leray D. A Kit for in vitro isolation of yrypanosomes in the field: first trial with sleeping sickness patients in the Congo Republic. Trans R Soc Trop Med Hyg. 1992;86(4):394–5. pmid:1440814
  11. 11. Jamonneau V, Barnabé C, Koffi M, Sané B, Cuny G, Solano P. Identification of Trypanosoma brucei circulating in a sleeping sickness focus in Côte d’Ivoire: assessment of genotype selection by the isolation method. Infect Genet Evol. 2003;3:143–9. pmid:12809809
  12. 12. Koffi M, De Meeûs T, Bucheton B, Solano P, Camara M, Kaba D, et al. Population genetics of Trypanosoma brucei gambiense, the agent of sleeping sickness in Western Africa. Proc Natl Acad Sci U S A. 2009;106(1):209–14. pmid:19106297
  13. 13. Prugnolle F, De Meeûs T. The impact of clonality on parasite population genetic structure. Parasite. 2008;15(3):455–7. pmid:18814722
  14. 14. De Meeûs T, Prugnolle F, Agnew P. Asexual reproduction in infectious diseases. In: Schön I, Martens K, van Dijk P, editors. Lost Sex: The Evolutionary Biology of Parthenogenesis. NY: Springer; 2009. p. 517–33.
  15. 15. Balloux F, Lehmann L, De Meeûs T. The population genetics of clonal and partially clonal diploids. Genetics. 2003;164(4):1635–44. pmid:12930767
  16. 16. De Meeûs T, Lehmann L, Balloux F. Molecular epidemiology of clonal diploids: A quick overview and a short DIY (do it yourself) notice. Infect Genet Evol. 2006;6(2):163–70. pmid:16290062
  17. 17. De Meeûs T. Genetic identities and local inbreeding in pure diploid clones with homoplasic markers: SNPs may be misleading. Infect Genet Evol. 2015;33:227–32. pmid:25960105
  18. 18. Rosenberg NA, Huang L, Jewett EM, Szpiech ZA, Jankovic I, Boehnke M. Genome-wide association studies in diverse populations. Nat Rev Genet. 2010;11:356–66. pmid:20395969
  19. 19. Sere M, Kaboré J, Jamonneau V, Belem AMG, Ayala FJ, De Meeûs T. Null allele, allelic dropouts or rare sex detection in clonal organisms: simulations and application to real data sets of pathogenic microbes. Parasites & Vectors. 2014;7:331.
  20. 20. Vignal A, Milan D, SanCristobal M, Eggen A. A review on SNP and other types of molecular markers and their use in animal genetics. Genetics Selection Evolution. 2002;34(3):275–305.
  21. 21. Wright S. The interpretation of population structure by F-statistics with special regard to system of mating. Evolution. 1965;19:395–420.
  22. 22. De Meeûs T, Balloux F. F-statistics of clonal diploids structured in numerous demes. Mol Ecol. 2005;14(9):2695–702. pmid:16029471
  23. 23. Simo G, Njiokou F, Tume C, Lueong S, De Meeûs T, Cuny G, et al. Population genetic structure of Central African Trypanosoma brucei gambiense isolates using microsatellite DNA markers. Infect Genet Evol. 2010;10(1):68–76. pmid:19819349
  24. 24. Miller CR, Joyce P, Waits LP. Assessing allelic dropout and genotype reliability using maximum likelihood. Genetics. 2002;160(1):357–66. pmid:11805071
  25. 25. De Meeûs T. Initiation à la génétique des populations naturelles: Applications aux parasites et à leurs vecteurs. Marseille: IRD Editions; 2012. p. 356
  26. 26. Nébavi F, Ayala FJ, Renaud F, Bertout S, Eholié S, Moussa K, et al. Clonal population structure and genetic diversity of Candida albicans in AIDS patients from Abidjan (Côte d'Ivoire). Proc Natl Acad Sci U S A. 2006;103(10):3663–8. pmid:16501044
  27. 27. Prugnolle F, De Meeûs T. Apparent high recombination rates in clonal parasitic organisms due to inappropriate sampling design. Heredity. 2010;104(2):135–40. pmid:19812614
  28. 28. Kaboré J, MacLeod A, Jamonneau V, Ilboudo H, Duffy C, Camara M, et al. Population genetic structure of Guinea Trypanosoma brucei gambiense isolates according to host factors. Infect Genet Evol. 2011;11:1129–35. pmid:21515408
  29. 29. Arnaud-Haond S, Duarte CM, Alberto F, Serrão EA. Standardizing methods to address clonality in population studies. Mol Ecol. 2007;16:5115–39. pmid:17944846
  30. 30. Delmotte F, Leterme N, Gauthier JP, Rispe C, Simon JC. Genetic architecture of sexual and asexual populations of the aphid Rhopalosiphum padi based on allozyme and microsatellite markers. Mol Ecol. 2002;11:711–23. pmid:11972759
  31. 31. Razakandrainibe FG, Durand P, Koella JC, De Meeus T, Rousset F, Ayala FJ, et al. "Clonal" population structure of the malaria agent Plasmodium falciparum in high-infection regions. Proc Natl Acad Sci U S A. 2005;102(48):17388–93. Epub 2005/11/23. pmid:16301534
  32. 32. Prugnolle F, De Meeus T. Apparent high recombination rates in clonal parasitic organisms due to inappropriate sampling design. Heredity (Edinb). 2010;104(2):135–40. Epub 2009/10/09.
  33. 33. Bucheton B, MacLeod A, Jamonneau V. Human host determinants influencing Trypanosoma brucei gambiense infections outcomes. Parasite Immunol (Oxf). 2011;33:438–47.
  34. 34. Jamonneau V, Ilboudo H, Kabore J, Kaba D, Koffi M, Solano P, et al. Untreated human infections by Trypanosoma brucei gambiense are not 100% Fatal. PLoS Negl Trop Dis. 2012;6(6):e1691. pmid:22720107
  35. 35. Büscher P, Bin Shamamba SK, Ngoyi DM, Pyana P, Baelmans R, Magnus E, et al. Susceptibility of Grammomys surdaster thicket rats to Trypanosoma brucei gambiense infection. Trop Med Int Health. 2005;10(9):850–5. pmid:16135191
  36. 36. Morrison LJ, Tait A, McCormack G, Sweeney L, Black A, Truc P, et al. Trypanosoma brucei gambiense Type 1 populations from human patients are clonal and display geographical genetic differentiation. Infect Genet Evol. 2008;8(6):847–54. pmid:18790085
  37. 37. MacLeod A, Tweedie A, Welburn SC, Maudlin I, Turner CMR, Tait A. Minisatellite marker analysis of Trypanosoma brucei: Reconciliation of clonal, panmictic, and epidemic population genetic structures. Proc Natl Acad Sci U S A. 2000;97(24):13442–7. pmid:11078512
  38. 38. Duffy CW, MacLean L, Sweeney L, Cooper A, Turner CMR, Tait A, et al. Population genetics of Trypanosoma brucei rhodesiense: clonality and diversity within and between foci. PLoS Negl Trop Dis. 2013;7(11):e2526. pmid:24244771
  39. 39. Salim B, De Meeûs T, Bakheit MA, Kamau J, Nakamura I, Sugimoto C. Population genetics of Trypanosoma evansi from Camel in Sudan. PLoS Negl Trop Dis. 2011;5:e1196. pmid:21666799
  40. 40. Coombs JA, Letcher BH, Nislow KH. CREATE: a software to create input files from diploid genotypic data for 52 genetic software programs. Mol Ecol Res. 2008;8:578–80.
  41. 41. Goudet J. Fstat (ver. 2.9.4), a program to estimate and test population genetics parameters. Updated from Goudet (1995). 2003.
  42. 42. Goudet J. FSTAT (Version 1.2): A computer program to calculate F-statistics. J Hered. 1995;86(6):485–6.
  43. 43. Goudet J, Raymond M, De Meeûs T, Rousset F. Testing differentiation in diploid populations. Genetics. 1996;144(4):1933–40. pmid:8978076
  44. 44. Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F. GENETIX 4.05, logiciel sous Windows TM pour la génétique des populations. Laboratoire Génome, Populations, Interactions, CNRS UMR 5000, Université de Montpellier II, Montpellier (France). 2004.
  45. 45. R-Development-core-team. R: A Language and Environment for Statistical Computing. 2.14.0 ed: R Foundation for Statistical Computing, Vienna, Austria,, ISBN 3-900051-07-0; 2014.
  46. 46. Siegel S, Castellan NJ JR. Nonparametric Statistics for the Behavioral Sciences, Second Edition. New-York: McGraw-Hill Inc.; 1988. p. 410
  47. 47. Dieringer D, Schlötterer C. Microsatellite analyser (MSA): a platform independent analysis tool for large microsatellite data sets. Mol Ecol Notes. 2003; 3(1):167–9.
  48. 48. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods. Mol Biol Evol. 2011;28(10):2731–9. pmid:21546353
  49. 49. Felsenstein J. PHYLIP: Phylogeny Inference Package. Seattle, WA 98195–5065, USA Department of Genome Sciences and Department of Biology, University of Washington, Box 355065,; 2008.
  50. 50. Cavalli-Sforza LL, Edwards AWF. Phylogenetic analysis: model and estimation procedures. Am J Hum Genet. 1967;19:233–57. pmid:6026583
  51. 51. Glaubitz JC. CONVERT: A user-friendly program to reformat diploid genotypic data for commonly used population genetic software packages. Mol Ecol Notes. 2004;4(2):309–10.
  52. 52. Nei M, Chesser RK. Estimation of fixation indices and gene diversities. Ann Hum Genet. 1983;47(Pt 3):253–9. Epub 1983/07/01.
  53. 53. Ellegren H. Microsatellites: simple sequences with complex evolution. Nat Rev Genet. 2004;5(6):435–45. Epub 2004/05/22. pmid:15153996
  54. 54. Hedrick PW. Genetics of Populations, Third Edition. Sudbury, Massachusetts: Jones and Bartlett Publishers; 2005. p. 737
  55. 55. Dje NN, Miezan TW, N'Guessan P, Brika P, Doua F, Boa F. Geographic distribution of trypanosomiasis treated in Ivory Coast from 1993 to 2000. Bull Soc Path Ex. 2002;95:359–61.
  56. 56. Kaba D, Dje NN, Courtin F, Oke E, Koffi M, Garcia A, et al. The impact of war on the evolution of sleeping sickness in west-central Cote d'Ivoire. Trop Med Int Health. 2006;11(2):136–43. pmid:16451337
  57. 57. Simarro PP, Cecchi G, Franco JR, Paone M, Diarra A, Ruiz-Postigo JA, et al. Estimating and mapping the population at risk of sleeping sickness. PLoS Negl Trop Dis. 2012;6(10):e1859. pmid:23145192
  58. 58. Njiokou F, Nimpaye H, Simo G, Njitchouang GR, Asonganyi T, Cuny G, et al. Domestic animals as potential reservoir hosts of Trypanosoma brucei gambiense in sleeping sickness foci in Cameroon. Parasite. 2010;17(1):61–6. Epub 2010/04/15. pmid:20387740
  59. 59. Berrang-Ford L, Odiit M, Faustin M, Waltner-Toews D, McDermott J. Sleeping sickness in Uganda: revisiting current and historical distributions. Afr Health Sci. 2006;6(4):223–31. pmid:17604511
  60. 60. Kabore J, Macleod A, Jamonneau V, Ilboudo H, Duffy C, Camara M, et al. Population genetic structure of Guinea Trypanosoma brucei gambiense isolates according to host factors. Infect Genet Evol. 2011;11(5):1129–35. Epub 2011/04/26. pmid:21515408
  61. 61. Camara M, Kaba D, KagbaDouno M, Sanon JR, Ouendeno FF, Solano P. [Human African trypanosomiasis in the mangrove forest in Guinea: epidemiological and clinical features in two adjacent outbreak areas]. Med Trop (Mars). 2005;65(2):155–61. Epub 2005/07/26.
  62. 62. Kabore J, De Meeus T, Macleod A, Ilboudo H, Capewell P, Camara M, et al. A protocol to improve genotyping of problematic microsatellite loci of Trypanosoma brucei gambiense from body fluids. Infect Genet Evol. 2013;20:171–6. Epub 2013/08/21. pmid:23954418
  63. 63. Nei M, Chesser RK. Estimation of fixation indices and gene diversities. Ann Hum Genet. 1983;47(Pt 3):253–9.
  64. 64. MacLean LM, Odiit M, Chisi JE, Kennedy PG, Sternberg JM. Focus-specific clinical profiles in human African Trypanosomiasis caused by Trypanosoma brucei rhodesiense. PLoS Negl Trop Dis. 2010;4(12):e906. Epub 2010/12/15. pmid:21151878
  65. 65. Garcia HA, Rodrigues AC, Rodrigues CMF, Bengaly Z, Minervino AHH, Riet-Correa F, et al. Microsatellite analysis supports clonal propagation and reduced divergence of Trypanosoma vivax from asymptomatic to fatally infected livestock in South America compared to West Africa. Parasites & Vectors. 2014;7:210.
  66. 66. Simo G, Sobgwi PF, Njitchouang GR, Njiokou F, Kuiate JR, Cuny G, et al. Identification and genetic characterization of Trypanosoma congolense in domestic animals of Fontem in the South-West region of Cameroon. Infect Genet Evol. 2013;18:66–73. pmid:23624186
  67. 67. Bowcock AM, Ruiz-Linares A, Tomfohrde J, Minch E, Kidd JR, Cavalli-Sforza LL. High resolution of human evolutionary trees with polymorphic microsatellites. Nature. 1994;368(6470):455–7. Epub 1994/03/31. pmid:7510853
  68. 68. Gibson W, Peacock L, Ferris V, Williams K, Bailey M. The use of yellow fluorescent hybrids to indicate mating in Trypanosoma brucei. Parasit Vectors. 2008;1(1):4. Epub 2008/02/27. pmid:18298832
  69. 69. Tait A, Macleod A, Tweedie A, Masiga D, Turner CM. Genetic exchange in Trypanosoma brucei: evidence for mating prior to metacyclic stage development. Mol Biochem Parasitol. 2007;151(1):133–6. Epub 2006/12/01. pmid:17134768
  70. 70. McInnes LM, Dargantes AP, Ryan UM, Reid SA. Microsatellite typing and population structuring of Trypanosoma evansi in Mindanao, Philippines. Vet Parasitol. 2012;187(1–2):129–39. Epub 2012/01/11. pmid:22230026
  71. 71. Gibson WC. The significance of genetic exchange in trypanosomes. Parasitol Today. 1995;11(12):465–8.
  72. 72. Adams ER, Hamilton PB, Gibson WC. African trypanosomes: celebrating diversity. Trends Parasitol. 2010;26: 324–8. pmid:20382076
  73. 73. Balmer O, Beadell JS, Gibson W, Caccone A. Phylogeography and Taxonomy of Trypanosoma brucei. PLoS Negl Trop Dis. 2011;5(2):e961. pmid:21347445
  74. 74. Goodhead I, Capewell P, Bailey JW, Beament T, Chance M, Kay S, et al. Whole-genome sequencing of Trypanosoma brucei reveals introgression between subspecies that is associated with virulence. mBio. 2013;4(4):e00197–13. pmid:23963174
  75. 75. Carnes J, Anupama A, Balmer O, Jackson A, Lewis M, Brown R, et al. Genome and phylogenetic analyses of Trypanosoma evansi reveal extensive similarity to T. brucei and multiple independent origins for dyskinetoplasty. PLoS Negl Trop Dis. 2015;9(1):e3404. pmid:25568942
  76. 76. Franco JR, Simarro PP, Diarra A, Ruiz-Postigo JA, Jannin JG. The journey towards elimination of gambiense human African trypanosomiasis: not far, nor easy. Parasitology. 2014;141(6):748–60. pmid:24709291
  77. 77. Koffi M, De Meeûs T, Bucheton B, Solano P, Camara M, Kaba D, et al. Population genetics of Trypanosoma brucei gambiense: a clone in the dark, Proceedings of the 30th biennial conference of the International Scientific Council for Trypanosomiasis Research and Control. (ISCTRC), Kampala, Uganda 2009, AU-IBAR, Nairobi, Kenya 125: 137–158. 2011.
  78. 78. Judson OP, Normark BB. Ancient asexual scandals. Trends Ecol Evol. 1996;11(2):41–6. pmid:21237759
  79. 79. Mark Welch DB, Meselson M. Evidence for the evolution of bdelloid rotifers without sexual reproduction or genetic exchange. Science. 2000;288(5469):1211–5. pmid:10817991
  80. 80. Cavalli-Sforza LL, Edwards AWF. Phylogenetic analysis: model and estimation procedures. Am J Hum Genet 1967;19:233–57. pmid:6026583
  81. 81. Balmer O, Beadell JS, Gibson W, Caccone A. Phylogeography and taxonomy of Trypanosoma brucei. PLoS Negl Trop Dis. 2011;5(2):e961. Epub 2011/02/25. pmid:21347445