Eyprepocnemis plorans subsp. plorans harbors a very widespread polymorphism for supernumerary (B) chromosomes which appear to have arisen recently. These chromosomes behave as genomic parasites because they are harmful for the individuals carrying them and show meiotic drive in the initial stages of population invasion. The rapid increase in B chromosome frequency at intrapopulation level is thus granted by meiotic drive, but its spread among populations most likely depends on interpopulation gene flow. We analyze here the population genetic structure in 10 natural populations from two regions (in the south and east) of the Iberian Peninsula. The southern populations were coastal whereas the eastern ones were inland populations located at 260–655 m altitude. The analysis of 97 ISSR markers revealed significant genetic differentiation among populations (average GST =  0.129), and the Structure software and AMOVA indicated a significant genetic differentiation between southern and eastern populations. There was also significant isolation by distance (IBD) between populations. Remarkably, these results were roughly similar to those found when only the markers showing low or no dropout were included, suggesting that allelic dropout had negligible effects on population genetic analysis. We conclude that high gene flow helped this parasitic B chromosome to spread through most of the geographical range of the subspecies E. plorans plorans.The grasshopper  Eyprepocnemis plorans subsp. plorans lives in moist habitats near river courses and coastal areas throughout the circum-Mediterranean region, also reaching the Caucasus and the Arabian Peninsula E. plorans plorans are rather young Supernumerary chromosomes, also called accessory or B chromosomes, are additional chromosomes found in some individuals at some populations of about 15% of eukaryotic species ISF, STF and ITF) from observed intrapopulation heterozygosities as well as expected population and metapopulation heterozygosities, is the most commonly used tool IS and FIT), they have certain advantages: the ease of generating a high number of markers, because no previous genomic information is necessary; relatively high reproducibility; and the possibility of visualizing them on agarose gels Gene flow may be directly analyzed by demographic studies, e.g. capture-mark-recapture methods, but it can also be indirectly estimated through population genetic analysis by molecular markers. In this latter case, the calculation of Wright's indices to estimate inbreeding at several hierarchical levels  is different from those found in the southern region (B2 and B24), but all of them share the same kinds of repetitive DNAs In this paper, we analyze several parameters of population genetics and structure in 10 natural populations of the grasshopper The six primers employed yielded a total of 97 ISSR markers ranging in size from 180 to >2000 bp in the 10 populations as a whole, with 99% of the loci being polymorphic. Per region, however, the percentage of polymorphic loci fell to 77.9% (SD =  3.13) in the southern populations and 76.7% (SD =  1.78) in the eastern ones .full and f = 0 as the best models, as indicated by the DIC parameters full model indicated an inbreeding value (0.96) being highly unlikely for a polygynandric organism like E. ploransf = 0 (no inbreeding) for all subsequent calculations.Data analysis with Hickory v1.1 (II), an analogous of Nei's GST, was 0.129, i.e. very similar to the Bayesian estimate of this latter parameter (Gst-B = 0.123), and 95% confidence intervals indicated that both were significantly higher than zero. These low GST values indicate that genetic diversification of these populations by drift is impeded by high gene flow. An AMOVA showed the existence of significant population structure, with 9.61% variation among populations within regions (φSC) (P<0.0001), 15.05% between the southern and eastern regions (φCT) (P<0.001) and the remaining 75.34% found within populations (φST) (ns (φST) .et al. The Structure software, which analyzes genetic structure through a Bayesian approach, showed that the number of genetic groups  best fitting our data, inferred following Evanno The Mantel test revealed significant isolation by distance (IBD) when we used pairwise-Fst values  or Dice's dissimilarity indices  for genetic distance , showed the longest distance between the southern and eastern regions  displayed consistent patterns under the two controls performed, but 3 markers failed to show repeatability under control 1 only, 40 failed for control 2 only, and 4 markers failed for both controls . Only siA total of 101 progeny analyses (PAs) were performed to test inheritance of 23 ISSR markers . As a whTo analyze the possible incidence of allelic dropout on population genetics and structure results, we repeated calculations with Hickory and Structure after discarding the 10 markers showing high incidence of dropout detected by the PCR repeatability analysis  or inheritance analysis . The population parameters estimated with the 87 remaining markers, listed in The analysis of population structure with the 87 ISSR markers showing slight dropout or none at all, with the Structure software, gave very similar results to those found when including all the markers, with K =  2 best fitting our data, and high proportions of individuals from the southern populations being included in one group and those from the eastern populations in the other group see  and S2.Even with only 46 markers, Structure showed that K =  2 best fitted our data, and the genetic differentiation between the southern and eastern regions was clearly evident, with high proportions of individual assignment to the correct region see  and S4.E. plorans, with 99% of the loci showing variation in the 10 populations as a whole, and more than 75% of loci being variable in each population analyzed. Since ISSR markers are dominant, only expected heterozygosity (hs) can be calculated (assuming Hardy-Weinberg equilibrium), and it ranged between 0.205 in Socovos and 0.235 in Mundo. These values are similar to those described for ISSR markers in other insects such as, for instance, Antheraea mylittaPlutella xylostellaGonatocerus ashmeadiSinipta dalmani (0.274–0.326) Anopheles darlingi (0.27–0.32) As in other organisms previously analyzed E. plorans is a polygynandric species Hs  was lower than Ht , suggesting a possible heterozygous deficiency which could be explained by allelic dropout, inbreeding or population subdivision. As explained above, dropout effects are negligible in this case, and inbreeding is unlikely since (II) and Gst-B, and the highest variance between regions, compared to within regions, revealed the existence of population subdivision for the analyzed areas. Population structure was also shown by the Structure software. A K of 2 best explained the data, with population groups being well defined in coincidence with the southern and eastern regions, so that more than 90% of individuals were properly assigned to them. Remarkably, our analysis of population genetics and structure parameters, after discarding those markers showing allelic dropout, showed almost identical results to those performed with the complete data set, suggesting that allelic dropout has a minor effect on these estimations. Therefore, although ISSR, and other markers being affected by allelic dropout, become less relevant with the advent of new sequencing technologies and genotyping methods, they still constitute an efficient and low-cost method of revealing population structure, especially in non-model organisms.The significant values found for θE. plorans analyzed here, the low (but significant) values of GST found suggest that gene flow is high in these populations, but not high enough to avoid a certain genetic differentiation between southern and eastern populations. The grasshopper E. plorans is more a jumper than a flyer, but it has a high dispersal ability facilitated by its polyphagy with low dietary demand In the 10 populations of the grasshopper ST and IBD in a grasshopper species, performed in the South American grasshopper Sinipta dalmani (using RAPD markers), revealed extensive variation for Fst , i.e. slightly higher than those detected by us for the ISSR markers in E. plorans, but no significant IBD was found among the 8 populations analyzed in S. dalmaniStenopelmatus are other orthopterans in which significant IBD has been revealed with ISSR and mtDNA markers E. plorans for the ISSR markers is consistent with previous IBD shown, in this same species, for B-chromosome frequency The only previous analysis of FST values observed for these ten populations of E. plorans plorans from the south and east of the Iberian Peninsula, contributes to explain the rapid spread of parasitic B chromosomes throughout most of its geographical range. B chromosomes are genomic parasites of strictly vertical transmission E. plorans plorans, in accord with the high dispersal ability reported for this species The high gene flow inferred from the low GWhereas the five populations from the southern region  all carried B chromosomes, only two  from the eastern region carried them. In this latter region, the observed pairwise Fst values see  indicateEyprepocnemis plorans were collected at 10 natural populations in the south and east of the Iberian Peninsula (Specimens of the grasshopper eninsula . No specISSR markers were amplified by polymerase chain reaction (PCR) using the primers shown in To test the incidence of PCR errors generating the absence of bands suggesting false recessive homozygotes , we performed two types of replicates for the same individual, one where the two replicates shared the same master mix and were amplified at the same time (control 1), and another where the replicates were made from different master mixes and were amplified on different days (control 2). The frequency of allelic dropout was quantified as a percentage of individuals showing inconsistent genotypes in the two replicates.full model, where the values of population differentiation  and inbreeding  were different from zero, ii) the f = 0 model, which assumes no inbreeding within populations, iii) the theta = 0 model, based on the absence of population differentiation, and iv) the f free model, or free model, where values of f were chosen randomly for the analysis of the data regardless of prior information. The choice of the model was based on the DIC parameter, analogous to AIC in Bayesian model selection ISSR data were analyzed by Bayesian methods, using the Hickory v1.1 software The existence of isolation by distance (IBD) was analyzed by means of a Mantel Test comparing the matrix of pairwise-Fst values with the matrix of geographical distances between the sampled populations, with the Zt-win software Also, we investigated IBD by using Dice's dissimilarity index E. plorans, we analyzed molecular variance (AMOVA) with the Arlequin v3.5 software To elucidate the population structure in With the genetic matrix distance (pairwise-Fst), estimated using the program AFLP-surv from the ISSR marker data, we inferred a phylogenetic tree under the Neighbor-Joining method To identify genetically homogeneous groups within our samples, we used a Bayesian algorithm implemented in the Structure v2.3.1 software To analyze inheritance of ISSR markers, we performed 10 controlled crosses analyzing the markers derived from one or more of the ISSR6, ISSR7, and ISSR14 primers . The remInheritance expectations for autosomal markers, shown in Figure S1Delta K values with respect to K, according to the calculation method by Evanno et al. . These results were found using the 87 ISSR markers showing no (or low level of) allelic dropout.(TIF)Click here for additional data file.Figure S2Ancestry of every individual in either of the two groups, using the 87 ISSR markers showing no allelic dropout (or low level thereof), yielded by the Structure software. Each vertical bar represents one of the 255 individuals analyzed. Group 1 (southern region) is represented in red color, and Group 2 (eastern region) is shown in green color. Bar length is proportional to the inferred ancestry values into each group for each individual.(TIF)Click here for additional data file.Figure S3Delta K values with respect to K, according to the calculation method by Evanno et al. . These results were found using the 46 ISSR markers showing no allelic dropout.(TIF)Click here for additional data file.Figure S4Ancestry of each individual to either of the two groups, using the 46 ISSR markers showing no allelic dropout, yielded by the Structure software. Each vertical bar represents one of the 255 individuals analyzed. Group 1 is represented in red, and group 2 in green color. Bar length is proportional to the inferred ancestry values into each group for each individual.(TIF)Click here for additional data file.Table S1Statistical parameters for the different models after Bayesian inference.(DOC)Click here for additional data file.Table S2Analysis of Molecular Variance (AMOVA).(DOC)Click here for additional data file.Table S3Likelihood values obtained for calculation of the number of groups (K) best fitting the data through the Evanno method. Reps =  replicates.(DOC)Click here for additional data file.Table S4Pairwise-Fst genetic distances (bottom) and geographical distances in Km (top).(DOC)Click here for additional data file.Table S5Dice's dissimilarity coefficients between populations (bottom) and standard errors (top).(DOC)Click here for additional data file.Table S6Failure rate of PCR amplification for every marker (%) under the two controls performed. Dropout values above 30% are marked in bold. N1 and N2 indicate the number of individuals where a given allele was tested for PCR repeatability under controls 1 and 2, respectively.(DOC)Click here for additional data file.Table S7Inheritance analysis of ISSR markers by means of the goodness-of-fit chi square test. 0 =  Absence of the allele, 1 =  Presence of the allele, Obs.1 =  Observed frequency of 1, exp.1 =  Expected frequency of 1. Progeny analyses showing significance after sequential Bonferroni are indicated by an asterisk.(DOC)Click here for additional data file.Table S8Population genetic parameters found with Hickory (f = 0 model) with 87 loci, i.e. after removing the 10 ISSR markers showing the highest dropout values. Note that hs values are estimates of genetic diversity (panmictic or expected heterozygosity) for each population sample, whereas Hs is the average of hs values for all population samples. Ht is the heterozygosity that would be observed if all population samples would come from a single population.(DOC)Click here for additional data file.Table S9Difference (%) in the population-genetics parameters estimated with 97 or 46 ISSR markers. Note that hs values are estimates of genetic diversity (panmictic or expected heterozygosity) for each population sample, whereas Hs is the average of hs values for all population samples. Ht is the heterozygosity that would be observed if all population samples would come from a single population.(DOC)Click here for additional data file.Table S10Proportion of individuals from each population included into each group by Structure for 87 loci and K = 2.(DOC)Click here for additional data file.Table S11Proportion of individuals from each population included into each group by Structure for 46 loci with K = 2.(DOC)Click here for additional data file.Table S12Primers employed and number of markers obtained.(DOC)Click here for additional data file.Table S13Summary of crosses and primers tested.(DOC)Click here for additional data file.Table S14Expected proportion of marker-carrying progeny for autosomal markers. 1 =  marker presence; 0 =  marker absence.(DOC)Click here for additional data file.Table S15Expected proportion of marker-carrying progeny for sex-linked markers. 1 =  marker presence; 0 =  marker absence.(DOC)Click here for additional data file.


Glossina spp.) are the sole vectors of Trypanosoma brucei—the agent of human (HAT) and animal (AAT) trypanosomiasis. Glossina fuscipes fuscipes (Gff) is the main vector species in Uganda—the only country where the two forms of HAT disease (rhodesiense and gambiense) occur, with gambiense limited to the northwest. Gff populations cluster in three genetically distinct groups in northern, southern, and western Uganda, respectively, with a contact zone present in central Uganda. Understanding the dynamics of this contact zone is epidemiologically important as the merger of the two diseases is a major health concern. We used mitochondrial and microsatellite DNA data from Gff samples in the contact zone to understand its spatial extent and temporal stability. We show that this zone is relatively narrow, extending through central Uganda along major rivers with south to north introgression but displaying no sex-biased dispersal. Lack of obvious vicariant barriers suggests that either environmental conditions or reciprocal competitive exclusion could explain the patterns of genetic differentiation observed. Lack of admixture between northern and southern populations may prevent the sympatry of the two forms of HAT disease, although continued control efforts are needed to prevent the recolonization of tsetse-free regions by neighboring populations.Tsetse flies ( Glossina spp. (Diptera: Glossinidae), are the sole vectors of the trypanosomes causing human (HAT) and animal (AAT) African trypanosomiasis [Tsetse flies, somiasis . Currentsomiasis , 3. As vsomiasis , 5, in 2somiasis . Controlsomiasis –13.The efficacy of the methods that target vector population reduction can be improved by understanding the population dynamics of the vector species. Genetic tools can be very helpful in this regard, as they allow for identifying barriers to gene flow, predicting fly movements, and assessing reinvasion risks from neighboring sites, where control is not implemented . PopulatG. palpalis gambiensis in Guinea and Senegal have identified populations that are sufficiently isolated to warrant attempts at complete eradication [G. p. gambiensis and G. p. palpalis in Senegal, Burkina Faso, and Equatorial Guinea show patterns of high gene flow characterized by spatial and temporal heterogeneity influenced by landscape fragmentation [Population genetic studies of riverine dication , 21. Popentation , 22, 23.entation , 23, andentation .Gff, a riverine subspecies in the palpalis group, is the major HAT transmission vector. Except for a disjunct region in Ethiopia/Sudan, Uganda and western parts of Kenya represent the eastern edge of its range [Gff densities are strongly influenced by ecological and climatic features, since temperature and precipitation may change the vegetation landscape and thus density and size of tsetse populations [In Uganda, ts range , where iulations –31.Gff has great capacity for dispersal and recolonization of suitable habitats [Gff populations across Uganda showed that this taxon is structured into three major genetic population clusters, with a southern and northern cluster separated by Lake Kyoga [Gff populations in Uganda, including those from the contact zone [ Ecological data suggest that habitats . Such rake Kyoga  and a thke Kyoga . Althougke Kyoga . Geneticact zone , 26, 33.Gff populations might also be impacted by the symbiotic bacteria they carry. All tsetse species harbor a vertically transmitted mutualistic symbiont, Wigglesworthia glossinidia, which is necessary for host physiology. As expected, the genetic structure of Wigglesworthia reflected the Gff host mtDNA patterns of genetic differentiation [Wolbachia [Glossina morsitans morsitans, causing cytoplasmic incompatibility (CI) in the laboratory; crosses between Wolbachia infected females and uninfected males result in embryonic lethality, while the reciprocal cross are fertile [Wolbachia in Gff in Uganda has shown that infection prevalence and density in different populations vary, and that individual flies can carry more than one Wolbachia strain [Wolbachia mediated incompatibilities between populations can contribute to the genetic disjunction we observe in Gff as a result of CI mediated effects.The patterns of genetic differentiation between ntiation . On the olbachia –36. This fertile . Our stua strain . Thus, WGff together with its tendency to cluster in discrete habitats  and its strong association with density-independent factors suggest that local adaptation to environmental parameters may also contribute to the maintenance of population divergence. This implies that habitat availability will largely control densities of populations and their connectivity. Due to lack of obvious vicariant barriers, the contact zone was speculated to result from secondary contact of flies following allopatric divergence and expansion . HoweverGff populations in northwestern Uganda transmit only the gambiense form of HAT caused by Trypanosoma brucei gambiense, while Gff populations in the southern genetic clusters transmit the rhodesiense form of HAT caused by T. b. rhodesiense. In recent years, the rhodesiense disease has been expanding its range from the historical loci in the southeast into new foci in central Uganda north of Lake Kyoga [The ke Kyoga . In factke Kyoga . Since tke Kyoga . Thus, uke Kyoga , 38–42.Gff are in contact in central Uganda and used multilocus genetic data to examine population structure and dynamics and to evaluate if environmental differences might be involved in maintaining the genetic difference between the two genetic clusters north and south of Lake Kyoga. We discuss our data on the stability of the different genetic populations around Lake Kyoga in the context of the potential merger of the two disease belts and the ongoing vector control programs.In the current study, we comprehensively sampled the area where the genetically differentiated populations of 2. Each fly was stored individually in 90% ethanol. Localities generally reflect the riverine/woodland habitat preferred by Gff.Tsetse flies were collected using biconical traps  during fDNA was extracted from tsetse legs using PrepGEM Insect DNA extraction kit  or DNeasy kits . PCR was used to amplify a 530 base pair (bp) fragment of the mitochondrial gene cytochrome C oxidase II (COII) from a subset of flies from each locality following Beadell et al. . IndividA) and observed (HO) and expected (HE) heterozygosity of populations. DnaSP v5.0 [π) diversity.We tested microsatellite loci for within site deviations from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) using Genepop v4.0 . Markov aSP v5.0  was usedFST using Genepop v4.0 [For all sites from which we had temporal samplings, we characterized the proportion of the variance attributable to differences in sampling dates using the analysis of molecular variance (AMOVA) as implemented in Arlequin v3.5  on both pop v4.0  and genepop v4.0  for the FST values [Overall genetic differentiation among localities was assessed by estimating pairwise T values , and sigT values . For botT values , to analT values .K = 1–15 were carried out. For all runs, an admixture model and independent allele frequencies were used with a burn-in value of 250,000 steps followed by 1,000,000 iterations. The optimal value of K was determined using STRUCTURE HARVESTER v0.6 [ad hoc statistic “ΔK” [Population structure was inferred from microsatellite data using the Bayesian clustering method implemented in STRUCTURE 2.3 . Three iTER v0.6  to calcutic “ΔK” .To determine whether the patterns of genetic structure were a result of sampling related individuals, we estimated relatedness and examined relationships for all individual pairs using the program ML-Relate . Each stK populations. Samples were placed into the respective population based upon the highest percentage of membership (q) using a threshold value of q ≥ 0.90 [Lh), the greatest likelihood among all sampled localities (Lmax), and their ratio (Lh/Lmax). Because migrants from unsampled populations can be misclassified as residents, we selected the Rannala and Mountain [Lh and Lh/Lmax, using 1,000 simulated individuals and the default 0.01 Type I error (α).To identify first generation migrants, we used the Bayesian approaches implemented in STRUCTURE and GeneClass 2.0 , 56. Preq ≥ 0.90 . We usedMountain  criterioMountain  to deterFST), mean assignment indices (mAIc), the variance in assignment indices (vAIc), and mean pairwise relatedness (mPr) [We used Fstat v2.9.3.2  to test ss (mPr) , 59, 60.Gff distribution but could trap flies in only 23 sites despite similar environmental conditions and equivalent collection efforts. We collected 2918 tsetse flies with an average of 127 per locality (n < 2) capture rates occurred at sites near cattle corridors, where farmers apply synthetic pyrethroid acaricides for tick and tsetse control on cattle weekly, and in districts subjected to control efforts for tsetse flies using insecticide treated traps [Gff are particularly low [ We visited 49 sites spanning the known arly low .In all analyses for both microsatellite and mtDNA markers, we included published data from seven sites , 26. TheAR ranged from 2.900 to 7.677, HO ranged from 0.487 to 0.604, HE ranged from 0.46 to 0.652, and FIS ranged from −0.015 to 0.126  and were excluded from further analysis. Pgp17 was also excluded because of scoring inaccuracies likely due to the large range of allele sizes (70 bp to >200 bp). We detected no significant linkage among the 15 remaining loci. Overall, all sites showed moderate to high levels of genetic variability; to 0.126 . HE andπ) ranged from 0 to 0.00664 ( The mtDNA dataset consisted of 489 COII sequences (530 bp), including 244 new and 245 published sequences , 26, and 0.00664 . In 12% FST values between samples were also low (Table  S6). MtDNA haplotype frequencies were not different in three of the four sites between the two time points for each site , although some change was observed in BN, likely due to the loss of the two least common haplotypes in the 2011 samples.We tested for differences in genetic diversity between samples collected in 2008 and 2011 at 4 sites ((5) of the Appendix). AMOVA using both microsatellite allele frequencies and mtDNA haplotype frequencies suggested that differences between temporally-spaced samples were not significant; however, differences among localities were significant . The variation explained by site was greater for mtDNA than for microsatellite data. Pairwise  The phylogenetic relationships among the 57 mtDNA haplotypes are shown in  Bayesian clustering analyses performed in STRUCTURE identified 3 genetic clusters including multiple sampling sites—northern , southern , and western . The analysis also showed admixture across genetic clusters with flies from Junda (JN) showing significant ancestry with flies from both southern and western genetic clusters . FST values between sites based on microsatellites ranged from 0.06 to 0. . FST values between clusters detected by STRUCTURE ranged from 0.14 to 0.21 . AMOVA results using microsatellite allele frequencies (P ≥ 0.01) and between  sites were significant, though most of the variation was between individuals within sites.quencies  showed tMeasures of relatedness based on microsatellite variation using both likelihood and Bayesian methods showed that the majority (85.8%) of the individuals are unrelated . MigrantThe four statistical methods implemented did not detect significant differences in sex-biased dispersal in both one-sided and two-sided tests . Gff flies collected in central Uganda where multiple genotypes from genetically distinct population clusters were shown to exist in a zone of contact. Our analyses included multiple sampling sites and identified three main genetic clusters with northern, southern, and western distributions in concordance with previous studies [In this study, we undertook an expanded temporal and spatial analysis of population genetic structures of  studies , 26. We Increased sampling in the Lake Kyoga region revealed a much higher spatial resolution of the contact between these three clusters and the associated admixture zone. However, our additional mtDNA sampling recovered intermediate haplotypes between the northern and southern haplogroups, shown in yellow in Gff [Wolbachia-induced mating incompatibility is driving the differentiation between the population structure inferred from mitochondrial and nuclear DNA. Wolbachia mediated effects on host fitness and host population genetic can drive patterns of mtDNA variation regardless of the nuclear DNA background [Wolbachia infection prevalence and strain types present in flies in the contact zone is necessary to further clarify the role of Wolbachia mediated reproductive effects.There was a substantial mismatch in the assignment of mitochondrial haploytpes and ancestry based on microsatellite data. This mismatch could be explained by sex bias in dispersal, as has been detected in other population studies of Gff , 25, butckground , 61, 62.Migration of flies between sites from different genetic clusters is extremely low, particularly in relation to north-south migration. We detected only a single migrant from a southern locality to a northern locality (BN to NAM) and no migrants in the other direction. Despite this, it does appear that mitochondrial introgression is occurring in a south to north direction , especiaThe contrast between the occurrence of intracluster migration and the low level of interacluster migration could be caused by two mechanisms—restriction of movement between clusters due to vicariant barriers or through reciprocal competitive exclusion of flies originating from adjacent clusters following local adaptation. As the contact zone lies around Lake Kyoga and major rivers and flies can cross riverine barriers separating sites within clusters, there is a lack of obvious physical barriers to gene flow that would explain the observed genetic differentiation among the genetic clusters seen in this study. Gff sites, north and south of Lake Kyoga, suggesting congruence between the genetic differentiation of Gff and the differentiation of its environment which we are exploring further. Competitive exclusion could also be driving the observed pattern, and experimental mating studies could test this hypothesis. This is a compelling biological hypothesis and one that has significant implications for vector control, thus representing an important direction for future study. Environmental barriers could also be important in maintaining the genetic distinctiveness between clusters, and we have preliminary data that show a substantial gradient in climatic conditions between The observation of high gene flow amongst localities within the three genetic clusters indicates that control efforts, undertaken solely at local scales, are unlikely to produce long-lasting results due to reinvasion from adjacent areas and that, in the absence of continued control, areas presently free of tsetse, such as the ones in south central Uganda where control is currently enforced and that yielded low tsetse captures, are likely to become recolonized, especially given the habitat suitability of many of these regions. Gff could suggest that the two regions they occupy could be managed as separate entities, this interpretation bears caution, as elimination of one population could result in rapid population expansion of the other. Experimental control with strict monitoring at sites in the contact zone could assist in understanding how the dynamics between these two populations change in response to control measures. In addition, this study highlights the importance of ongoing monitoring of Gff in Uganda to provide quantitative information on population densities and dispersal rates to inform efficient control strategies into the future.Whilst the low genetic admixture between northern and southern populations of Wolbachia mediated bidirectional CI in determining such patterns, and looking at the association between genetic and environmental variables and the impact of climatic change in shaping the distribution of Gff and thus disease risk. Future work will be directed at exploring further the underlying causes of the genetic differentiation between the three genetic clusters by performing mating experiments to look at mating compatibility, exploring further the role of Appendix 1: Information on the study site, time of collection and number of flies caught in each site.Appendix 2: Details of microsatellite markers used in the study.Appendix 3: Per locus estimates of FIS at 18 microsatellite loci for each sampling locality.Appendix 4: Mitochondrial haplotype information, including frequencies observed across studied populations. New haplotypes recovered from this study are indicated by ∗.Appendix 5: Results of an AMOVA testing for temporal genetic structure in four populations of G. fuscipes sampled in 2008 and also in 2011.Appendix 6: Fst values for temporal samples calculated on microsatellite data. Non-significant values are in bold.Appendix 7: Microsatellite-based FST values pairwise comparison between sampling localities of G. f. fuscipes in Uganda. Appendix 8: Microsatellite-based FST values for pairwise comparisons among the three populations detected using Bayesian clustering.Appendix 9: Details of all first generation migrants detected by Geneclass 2.0, using Lh, Lh/Lmax and STRUCTURE. Appendix 10: Comparison of mtDNA clade and microsatellite assignment for each individual where both data types were collected.Figure 1: Microsatellite allele frequencies observed in seven populations of G. f. 52 fuscipessampled at different time points. Numbers after location code indicate the time interval 53 (in generations) since the first sampling.Click here for additional data file.


Solanum rostratum, an annual, self-compatible plant that has been introduced around the globe. This study is the first to compare the genetic diversity of Solanum rostratum between native and introduced populations. We aim to (1) determine the level of genetic diversity across the studied regions; (2) explore the likely origins of invasive populations in China; and (3) investigate whether there is the evidence of multiple introductions into China.We investigate native and introduced populations of S. rostratum. We studied five populations in each of three regions across two continents: Mexico, the U.S.A. and China.We genotyped 329 individuals at 10 microsatellite loci to determine the levels of genetic diversity and to investigate population structure of native and introduced populations of S. rostratum. Genetic diversity was significantly lower in Chinese and U.S.A. populations, but we found no regional difference in inbreeding coefficients (FIS) or population differentiation (FST). Population structure analyses indicate that Chinese and U.S.A. populations are more closely related to each other than to sampled Mexican populations, revealing that introduced populations in China share an origin with the sampled U.S.A. populations. The distinctiveness between some introduced populations indicates multiple introductions of S. rostratum into China.We found the highest genetic diversity among Mexican populations of  Studying the genetic diversity and structure of introduced populations is a key component to understand the potential of introduced species to establish and spread in the novel range ,2. For eSolanum rostratum is a self-compatible species that produces nectarless hermaphroditic flowers with dimorphic anthers, and is pollinated by bees [Solanum rostratum has spread to the U.S.A., Canada [S. rostratum is treated as a noxious weed as it grows aggressively following habitat disturbance [ by bees . Individ by bees . Thought by bees ,11, Sola, Canada , Europe,, Canada , the for, Canada , South K, Canada ,15. In mturbance , and livturbance .S. rostratum has occurred in China during the last 30 years, with the first record made in ChaoYang city of the Liaoning province in 1981 [S. rostratum has spread across a large area in northern China, namely in the provinces of Beijing, Hebei, Jilin, Liaoning, Shanxi provinces and Xinjiang Uygur Autonomous Region [S. rostratum usually grow in open, disturbed habitats, such as roadsides, fallow fields and along train tracks. Previous studies have shown on-going dispersal towards the north of China, and indicate that S. rostratum is at potential risk of an outbreak [One of the most recent invasions of  in 1981 . Despites Region -20. As ioutbreak .S. rostratum. We studied five populations in each of three regions across two continents: Mexico, the U.S.A. and China. We genotyped individuals using 10 microsatellite loci to address the following questions: (1) What is the level of genetic diversity across the studied regions and to what extent is genetic diversity reduced in introduced populations? (2) What are the likely sources of origin of invasive populations in China inferred from the genetic relationships among samples? (3) Is there evidence of multiple introductions into China? Our study represents the first attempt to characterise the genetic diversity and population structure of native and introduced populations of S. rostratum and offers a unique insight into the historical pathways of dispersal of this invasive weed.We use recently developed genetic tools  to invesSolanum rostratum. The sampling sites were located in road sides, banks of rivers, waste land. These sites didn't belong to a national park or other protected area of land and the relevant regulatory body concerned with protection of wildlife, and they also didn't belong to private land. We confirm that the field studies did not involve endangered or protected species. A total of 15 populations were sampled from July 2010 to June 2011. Five populations were sampled from each of the following three regions: central Mexico, the Kansas-Oklahoma region in the U.S.A. where records date back at least to the 1880's (Kansas State University Herbarium) and northern China  following manufacturer’s instructions. Individuals were genotyped at ten microsatellite loci previously developed for ostratum . Estimatostratum . All locNa); number of effective alleles (Ne); expected heterozygosity (HE); observed heterozygosity (HO); inbreeding coefficient (FIS). In addition, for each population we also calculated the proportion of polymorphic loci (P), allelic richness averaged across loci (RS), and the number of private alleles. The significance of FIS was calculated using 150000 randomizations, and adjusting the significance threshold for multiple comparisons using a Bonferroni correction in FSTAT 2.9.3.2 [For each population-locus combination, the following indices were calculated using GENALEX 6.4 : number  2.9.3.2 .HO, HE, FIS and FST, and relatedness using 1000 permutations in FSTAT [To test for overall differences in genetic variation between the three geographic regions , we compared allelic richness, in FSTAT . We alsoFST using FSTAT, and used these statistics to compare the average levels of between-population differentiation in the three studied regions. In addition, we calculated pairwise genetic distances between populations using Nei’s (1978) standardized genetic distance Ds. We used the resulting distance matrix to generate an UPGMA tree using TFPGA1.3 [To assess the level of population genetic structure we used a series of complementary approaches. We calculated pairwise TFPGA1.3 , and assTFPGA1.3 .ST-like option in Arlequin) and 1000 permutations to test for statistical significance.We also used an analysis of molecular variance  implemeK groups that minimize the amount of linkage-disequilibrium [S. rostratum has a mixed mating system in both native and introduced populations ([unpublished data). The InStruct analysis was conducted in a nested fashion. We first analysed the entire data set to determine the number of clusters across the three geographic regions. We ran InStruct with K-values between 1 and 15 (the number of studied populations), using a 100000 burn-in and 100000 MCMC iterations with trimming every 100 generations, and estimating both admixture and cluster's selfing rate. Each MCMC chain was re-run three times. Because accurately determining the optimal number of clusters can be difficult when regions display strong genetic structure, we then ran InStruct separately for the populations in the two identified clusters (see Results): Mexico  and USA/China , using the same parameter values. The optimal number of clusters (K*) for each analysis was assessed using ∆K statistic [In addition, we used InStruct  to jointilibrium . Becauselations  but also in the U.S.A. (FIS = 0.104—0.433) and to a lesser extent Mexico (FIS = 0.124—0.371)  .HO), and gene diversity (HE), were significantly higher in Mexico than in the U.S.A. and China (FIS) nor population differentiation (FST) was statistically different between geographic regions (FST = 0.047), while Chinese populations had higher average genetic differentiation (FST = 0.267). Calculation of FST in China without the BC population reduced this value to FST =0.100, intermediate between the values observed in the U.S.A. and Mexico  or population differentiation (FST)  .FST values, representing the degree of genetic differentiation between populations, ranged from 0.018 (CHE-HAY) to 0.399 (BC-TEM) in individual population pairs , while Chinese populations had the highest average value . Mexican populations had an intermediate average FST . The high genetic differentiation observed among Chinese populations is due in part to the distinctiveness of one population, BC. This population has an average pairwise FST = 0.260 (0.224–0.282) with other Chinese populations and FST=0.274 (0.224–0.399) when compared to all other 14 populations. Calculating pairwise FST without the BC population gives a Chinese average of FST = 0.059 ± 0.010, similar to the value observed for U.S.A. populations .Pairwise on pairs . Among rulations . In pairDs; The UPGMA analysis calculated from Nei's pairwise genetic distance Ds;  showed aS. rostratum is contained within populations (FST= 0.299). The proportion of genetic variance partitioned among populations within regions was 14% (FSC= 0.164), while differences among geographic regions explained 16% of the variation (FCT= 0.162). All variance components were statistically significant  of genetic variation in K* = 2) that corresponded to either Mexican or U.S.A./Chinese populations  among Mexican populations of ulations . Whalen iversity . The redS. rostratum might rely on other aspects of its reproductive biology to maintain similar inbreeding levels in native and introduced populations. For example, the reproductive morphology of S. rostratum, which combines within-flower herkogamy, non-spontaneous pollen release requiring buzz-pollination, and enantiostyly may facilitate maintaining a relatively high outcrossing rate in introduced populations [ Although among populations we observed a wide range of variability in inbreeding rates , we deteulations .S. rostratum in the U.S.A. (>130 years), there has been ample opportunities for the U.S.A. to act as the source material for the Chinese populations. All of our analyses showed a stronger genetic affinity of Chinese populations with the U.S.A. than with Mexico, which demonstrates that invasive Chinese populations share a common origin with U.S.A. material. This common origin could be explained in at least two ways. First, Chinese populations could be directly derived from genetic stock in the U.S.A. region. Second, both U.S.A. and Chinese populations could have been independently derived from populations located elsewhere (e.g. unsampled populations in northern Mexico). Distinguishing between these two possibilities would require more extensive sampling throughout North America. However, given the residence time of S. rostratum in the former Soviet Union [The genetic differentiation between the Chinese populations at Baicheng in Jilin province (BC) raises the possibility that the invasion to China has occurred through multiple introductions. The BC population had the lowest genetic diversity and highest inbreeding coefficient of all populations, so part of this measured differentiation may be due to an extreme founding event . Howeveret Union , and is S. rostratum in northeast China raises the question as to how is it being dispersed. Using Chaoyang city, the first record of S. rostratum in China in 1981, as the focus for the spread of invasive populations and the location of the extant populations sampled here (at an approximate distance of 500km in both NE and SW directions), we can calculate that this species has dispersed at an average rate of 16km/year and probably much faster. This rate of dispersal is probably much too fast to be accounted naturally, especially given that this species lacks means for long-distance dispersal [S. rostratum is buffalo-bur—are incorrect. In reality, the fruits remain attached to the plant even after maturation and thus cannot be dispersed in isolation [S. rostratum plants in the Great Plains could help them travel longer distances as tumble weeds. The relative rapid colonization of ispersal . Earlierispersal ) that thsolation . Insteadsolation . In addisolation ; cited isolation ) also suS. rostratum in China has been facilitated by both massive change in land use and accidental human transport. As a weed of disturbed habitats and roadsides, S. rostratum may have taken advantage of the alteration of the landscape by people and cattle movement, and expanded its range by spreading through newly opened habitats. It is also likely that dispersal of S. rostratum is further facilitated by accidental transport in contaminated grain or forage as it often grows on the margins of crop fields, as well as by transfer of plant and soil waste. In China, some populations may have been also dispersed by seasonally flooded streams and transported as stowaways along train tracks and roads allowing them to quickly travel long distances. We believe that the rapid dispersal of S. rostratum is in Mexico, and shows that the colonization of this species to other areas has been accompanied by a loss of genetic diversity without an increase in the level of inbreeding. The invasion of S. rostratum to China has probably originated in sampled populations of the U.S.A. or closely related to them. The distinctiveness of the BC population from other Chinese populations indicates multiple introductions of S. rostratum into China.The genetic data presented here demonstrates that the centre of diversity of Table S1Data per locus per population. Obtained with Genalex.(DOCX)Click here for additional data file.Table S2FST values between 15 sampled populations of Solanum rostratum.Pairwise (DOCX)Click here for additional data file.Table S3Ds; below diagonal) among 15 sampled populations of Solanum rostratum derived from GenALEx.Nei’s Unbiased Identity  and Distance ((DOCX)Click here for additional data file.


Circus pygargus), using mitochondrial DNA data from 13 breeding populations and two wintering populations.Environmental preferences and past climatic changes may determine the length of time during which a species range has contracted or expanded from refugia, thereby influencing levels of genetic diversification. Connectivity among populations of steppe-associated taxa might have been maximal during the long glacial periods, and interrupted only during the shorter interglacial phases, potentially resulting in low levels of genetic differentiation among populations. We investigated this hypothesis by exploring patterns of genetic diversity, past demography and gene flow in a raptor species characteristic of steppes, the Montagu's harrier (Consistent with our hypothesis, Montagu's harrier has relatively low genetic variation at the mitochondrial DNA. The highest levels of genetic diversity were found in coastal Spain, France and central Asia. These areas, which were open landscapes during the Holocene, may have acted as refugia when most of the European continent was covered by forests. We found significant genetic differentiation between two population groups, at the SW and NE parts of the species' range. Two events of past population growth were detected, and occurred ca. 7500-5500 and ca. 3500-1000 years BP in the SW and NE part of the range respectively. These events were likely associated with vegetation shifts caused by climate and human-induced changes during the Holocene.The relative genetic homogeneity observed across populations of this steppe raptor may be explained by a short isolation time, relatively recent population expansions and a relaxed philopatry. We highlight the importance of considering the consequence of isolation and colonization processes in order to better understand the evolutionary history of steppe species. Contemporary patterns of genetic diversity and population structure reflect not only current patterns of genetic exchange but also past dispersal processes and levels of gene flow among populations during historical climatic events ,2. In maCircus pygargus is perhaps the best example of raptor species specialized in such steppe habitats, which has also adapted well to farmland habitats [The Montagu's harrier habitats ,24. Thishabitats  , migrathabitats ). Europehabitats -30. To dWe used molecular analyses to evaluate levels of genetic diversity and connectivity among Montagu's harrier breeding populations. We collected genetic material from across the whole breeding range (Spain to Kazakhstan) and from two overwintering populations (located in western Africa and in the Indian sub-continent). With this information, we analyzed historical demographic patterns, in order to better understand how past climatic dynamics may have affected the species and shaped its current distribution and population genetic structuring. Specifically, our aims were: first, to evaluate the genetic diversity of Montagu's harrier populations and analyze different scenarios that may explain the observed genetic structure ; second, to investigate past demographic changes in the species, and patterns of gene flow between genetically structured populations, looking for evidence of asymmetrical exchanges between these. Finally, we discuss the implications of these results for the understanding of the patterns shaping the phylogeographic structure of the fauna associated with steppe ecosystems.We sequenced a total of 1063 bp of t-RNA Trp and ND2 and 714 bp of COI genes from all samples (n = 284), which were collapsed into 36 and 13 haplotypes, respectively . The concatenated data set (1777 bp) yielded 51 different haplotypes defined by 35 polymorphic sites. Overall, gene and nucleotide diversities were Hd = 0.663  and π = 0.0008 , respectively  or the concatenated data set Table . HaplotyThe two phylogenetic methods used (Maximum likelihood and Bayesian inference) were largely consistent in the (lack of) relationships recovered. Despite the broad geographic sampling Figure , there wCT estimate obtained for scenario 3 was similar to that obtained for scenario 1, and close to significance  and largest between Pakistan and western Spain (PAK-SpW: ΦST = 0.324). In contrast, comparisons involving Senegal were significant only with central France (PLO) and Pakistan . Considering only breeding populations, eastern Spain (SpE) showed the highest number of significant pairwise comparisons (seven out of 12), while the largest differentiation among populations was found between western Spain (SpW) and central France (PLO) (SpW-PLO: ΦST = 0.216). Finally, the correlation between population pairwise ΦST values and their geographical distances was not significant .Overall, 27 out of 105 pairwise Φes Table . DiffereD  and Fu's S F, suggesting that the overall population size has fluctuated in the past. The majority of D and S Fvalues were negative for most of the studied populations, but Fu's S Frejected neutrality (p < 0.02) in only two breeding populations, whereas Tajima's D rejected neutrality (p < 0.05) in five populations , which peaked at 10.28 (90% HPD = 2.82 - 17.57). Based on these values of θ, both populations appeared to have grown substantially following divergence. The average estimate for the scaled splitting time was t = 0.37 , and close to null from NE into SW . Conversion of these values of m resulted in an estimated number of migrants of approximately 0.004 females per generation from NE to SW populations , and 22 females per generation from SW to NE populations. However, the associated error to these estimates was large .The effective population sizes and demographic trends estimated by the Bayesian Skyline Plot (BSP) analysis indicated recent population size increases in both regions (SW and NE). However, the overall increase was much less marked for the SW populations, which showed a roughly linear increase during the second part of the Holocene. Based on a range of mutation rates from 0.02 to 0.055 s/s/Myr, population growth started at approximately 7500 years before present (BP) in SW and 4000 years BP in NE Figure . The timAquila adalberti [Milvus milvus [Hieraaetus fasciatus [Haliaeetus albicilla [Gypaetus barbatus [Overall, we found little mitochondrial DNA variation among populations of Montagu's harrier throughout the breeding range. Although variability was low, we nevertheless found significant differentiation between southwestern and northeastern populations. In contrast to the low among-population variability, our study revealed a relatively high degree of genetic diversity at the mtDNA for this species as compared with those found in other sympatric raptor species. These other raptors often show much less polymorphism, even when using genetic markers that are more variable than the mitochondrial control region (examples from vultures in ). Some odalberti , Red kits milvus , Bonelliasciatus , White-tlbicilla , and Beabarbatus . The mtDbarbatus . Despitebarbatus -39 suggebarbatus . Fossil barbatus . HoweverThe haplotype network consisted of two distinct, but closely related, lineages Figure  that coua priori assigned to different overwintering areas. This hypothesis of different migration routes between spring and autumn is supported by ring recoveries [In many animal species, the patterns of genetic differentiation and gene flow are highly influenced by the geographical characteristics of the places they inhabit and by their migratory behavior. However, in the Montagu's harrier, neither the geographical barriers (mountain ranges) nor the intercontinental migratory divide (populations wintering in Africa or in the Indian sub-continent) represented significant barriers against gene flow. Interestingly, the levels of genetic differentiation between populations were unrelated to the geographical distances separating these populations. This could be explained by the long-distance migration behavior of the species. Western European breeding birds  winter primarily in west Africa , whicoveries  and councoveries , althougcoveries . While tcoveries .Our results strongly support a recent population expansion as an important cause of the relative homogeneity across populations. Such expansion is indicated by the shallow phylogenetic tree and by the star-like haplotype network Figure . The genAlthough this particular issue has received little attention so far (phylogeographic studies of steppe species are still scarce), our data are consistent with the recent idea of interglacial refugia, which proposes that, in addition to the traditional high-latitude refugia of boreal species, cryptic refugia might have existed in other areas in the south during the interglacials ,9. In faOur results point to a short isolation time, relatively recent population expansions and relaxed philopatry as the main factors determining the relative genetic homogeneity observed across populations of a steppe-associated raptor species. In contrast to the traditional view ,63, our a priori according to sampling locality, and these groups were considered as populations for genetic analyses   and a portion of the cytochrome oxidase subunit I (COI) via polymerase chain reaction (PCR). Primers L5216-H5766 and L5758-H6313 were used for the amplification of ND2 gene [2, and 0.5 U of Taq DNA polymerase (Biotools) in a total volume of 10 µL.Blood samples were digested (8 h) in 250 µL SET buffer in the presence of SDS (2%) and proteinase K (10 ng/µL). Feathers were processed like blood samples but increasing proteinase K (20 ng/µL) and time of digestion (16 h). Total genomic DNA was extracted using standard NHND2 gene , and BirPCR-products were purified with Exonuclease I and Shrimp Alkaline Phosphatase enzymatic reactions . Purified reactions were sequenced in an ABI 3130 automated sequencer (Applied Biosystems) using dye-terminator chemistry  with the same primers used for PCR. All sequences are accessible at GenBank .ST comparisons for each mtDNA region and for the concatenated data set, using a Tamura-Nei evolutionary model [ST comparisons was given by a P value calculated using 10,000 random permutation tests; p-values were further adjusted according to sequential Bonferroni corrections for multiple tests [We edited and aligned DNA sequences using Bioedit  and Clusry model , as thisry model . The sigle tests . Evidencle tests ; see FigSince all Montagu's harrier overwintering in the Indian subcontinent come from Asian breeding populations , winter ST /(1-ΦST) in Arlequin 3.5.1.2. The statistical significance of correlations between distance matrices was obtained from 5,000 random permutations of matrix elements.A pattern of isolation-by-distance was explicitly tested using Mantel tests to compare pairwise geographic and genetic distances between populations. These were statistically tested using linearized pairwise differentiation indexes .Phylogenetic relationships of Montagu's harrier were reconstructed by examining mtDNA sequence variation in all samples. The best-fit evolutionary model for the concatenated data set was determined using the Akaike information criterion implemented in Modeltest 3.7. A Maximum-Likelihood (ML) tree was built using a heuristic search starting from a neighbour-joining tree and a tree bisection reconnection (TBR) algorithm for branch swapping, with random addition of sequences in PAUP* 4.0 . The staC. pygargus with a haplotype network calculated using the median-joining algorithm [http://www.fluxus-engineering.com.We also represented the genealogical relationships of all the analyzed samples of lgorithm  in NETWOC. pygargus, considering a model of mutation-drift equilibrium, were addressed using analyses based on different coalescent approaches. Firstly, we tested the data against a neutral Wright-Fisher model using Fu's Fs [D [P must be less than 0.02 to be significant due to the non-normal distribution of the Fs statistic [eN) [7 generations with a sampling frequency of 1000 steps. Convergence was assessed using Tracer v1.5 [Signatures of demographic changes or selection in the recent history of  Fu's Fs  and Taji's Fs [D , which atatistic . Secondltic [Ne) . We usedtic [Ne) ,80,81. Ftic [Ne)  and ran cer v1.5  and unceA separated into two populations (SW and NE) at time T, to simultaneously determine (1) time since divergence (t), (2) effective population sizes of each population (θSW and θNE) and the ancestral population (θA) at time of split, and (3) immigration rates (SW mand NEm). We ran three replicate runs with a random seed to initiate each run. In all analyses, we used at least 20 Markov-coupled chains with a geometric heating scheme, a burn-in of 200,000 steps, and run until the effective sample sizes . We also used a lower (µ = 2 × 10-8 s/s/y) and an upper (µ = 5.5 × 10-8 s/s/y) limit for this conversion, as indicated for the BSP analysis. To estimate generation time (g) we used the equation g = α + (s/(1- s)), where α is the age of first reproduction in females and s is the expected adult survival rate [g) of 4 years to express the output parameters  in demographic units as follows: effective population size: N = θ/4µg; number of migrants per generation: M = θ × m/2g; divergence time in years: T = tg/2µ.Thirdly, because AMOVA tests revealed genetic breaks between geographical regions , we used the program IMa  to test ESS; see ) for eacval rate . Age at val rate ,85. TherJTG conceived and designed the study, collected samples, helped in molecular genetic work, performed analysis and drafted the manuscript. FA participated in the study design, carried out molecular genetic work, performed analysis and helped to draft the manuscript. FM, JT, AS, VB participated in the study design, collected samples and helped to draft the manuscript. BA conceived and designed the study, collected samples and helped to draft the manuscript. All authors read and approved the final manuscript.


Polygonum cespitosum, a highly selfing, tetraploid Asian annual introduced to North America. We used nine diploidized polymorphic microsatellite markers to study 16 populations in the introduced range (northeastern North America), via the analyses of 516 individuals, and asked the following questions: 1) Do populations have differing levels of within-population genetic diversity? 2) Do populations form distinct genetic clusters? 3) Does population structure reflect either geographic distances or habitat similarities? We found low heterozygosity in all populations, consistent with the selfing mating system of P. cespitosum. Despite the high selfing levels, we found substantial genetic variation within and among P. cespitosum populations, based on the percentage of polymorphic loci, allelic richness, and expected heterozygosity. Inferences from individual assignment tests (Bayesian clustering) and pairwise FST values indicated high among-population differentiation, which indicates that the effects of gene flow are limited relative to those of genetic drift, probably due to the high selfing rates and the limited seed dispersal ability of P. cespitosum. Population structure did not reflect a pattern of isolation by distance nor was it related to habitat similarities. Rather, population structure appears to be the result of the random movement of propagules across the introduced range, possibly associated with human dispersal. Furthermore, the high population differentiation, genetic diversity, and fine-scale genetic structure  in the introduced range suggest that multiple introductions to this region may have occurred. High genetic diversity may further contribute to the invasive success of P. cespitosum in its introduced range.Molecular markers can help elucidate how neutral evolutionary forces and introduction history contribute to genetic variation in invaders. We examined genetic diversity, population structure and colonization patterns in the invasive  Genetic variation can be substantially altered when species are introduced into new ranges. The amount of genetic variation and its distribution within and among populations in the new range is determined by the number of introductions, the diversity of the founders, mating system and other life-history traits, and post-introduction processes such as genetic drift, gene flow, and selection As a result of the introduction and invasion processes, genetic variation is often dramatically reduced, since populations in the introduced range are usually established by a small number of founders representing only a fraction of the genetic diversity present in the native range Life history traits such as mating system can also be a strong determinant of both within-population variation and population structure. Self-compatibility and/or apomictic reproduction have long been recognized as a key characteristic of ideal weeds Polygonum (s.l.) cespitosum Blume  were selected  with 0.1 μL of AmpliTaq Gold DNA polymerase , 0.5 μL of dye-labeled forward primer (10 μM), 0.5 μL of reverse primer (10 μM), 0.5 μL of premixed dNTP  and 1.5 μL of DNA. Thermocycling consisted of a touchdown thermal cycling program http://cores.lifesciences.cornell.edu/brcinfo/).Total genomic DNA was extracted from 100 mg of frozen leaf tissue using the DNeasy Plant Mini kit , and its concentration and purity was quantified on a NanoDrop spectrophotometer . DNA concentration ranged from 20 to 100 ng/μl. We genotyped each sample at seven microsatellite loci known to be polymorphic across the species . In order to confirm the assignment of alleles for marker Poce26 and to provide additional insight into allelic relations at Poce28, PCR products of several individuals were cloned using the TOPO TA Cloning kit  and sequenced with the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems). For marker Poce26, we identified a six bp indel in the flanking region surrounding the tetramer repeat, whose presence or absence corresponded to the assignment of alleles described above. For Poce28, all size differences were attributable to differences in repeat number. Apparently, the homeologous copies have not yet diverged. We present results using the first coding scheme for this locus because it minimizes the amount of missing data. Analyses using the second coding scheme (not presented) produced very similar results. Furthermore, analyses leaving out data from both Poce26 and Poce28 also produced very similar results.In summary, we scored all individuals for nine diploidized loci. Fewer than 1% of all individuals were missing data. Because a moderate number of individuals had fragments that were inconsistent with whole repeat numbers, we scored microsatellite alleles as Mendelian alleles, not as repeat counts.P, proportion of polymorphic loci; A, mean number of alleles per locus ; eA, mean number of effective alleles , oH, observed heterozygosity ; eH, unbiased expected heterozygosity ((2N/(2N-1)) * (1-Σpi2)); FIS, inbreeding coefficient (1-(oH/eH)); the number of private alleles and the number of multilocus genotypes. To obtain a conservative estimate of the number of multilocus genotypes, we ignored the loci with missing data.We calculated the following genetic diversity indices for each population using Arlequin v. 3.11 rareA, was inferred using the rarefaction method implemented in Hp-Rare Allelic richness after correcting for unequal sample sizes (rarefaction), FSTP-values for each pair of populations (90000 permutations) using Arlequin. We used a conservative Bonferroni correction to account for multiple comparisons. To test for isolation by distance (IBD), a Mantel test FST), and the matrix of geographical (Euclidean) distances between populations was performed with 9999 permutations using Arlequin. The analysis was repeated using the matrix of logarithm of the distance between populations To determine population differentiation we computed pairwise FST), and the matrix of differentiation in light availability (Euclidean distance in GSF) and soil moisture (Euclidean distance in percentage of field capacity) between populations. Finding a significant correlation between the matrix of genetic differentiation and those of differentiation in light and soil moisture availability would be interpreted as non-random establishment of genotypes in different habitats (i.e. new habitats are colonized by a subset of genotypes instead of a random sample). These tests were performed for the 14 populations for which environmental data were available  between the matrix of pairwise genetic differentiation between populations (vailable .STRUCTURE v. 2.3 STRUCTURE assumes a model in which there are K populations (where K is unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals are then probabilistically assigned to one or more clusters. The membership of each individual in a cluster is estimated as a coefficient that ranges from zero to one, with one indicating full membership in a cluster. We performed 10 independent runs for each value of K ranging from one to 16 using a burn-in period of 105 iterations followed by a sample of 106 iterations. We used the default parameters of the program to allow population admixture and correlated allele frequency across populations HARVESTERSTRUCTURE results files and to generate CLUMPP input files. We then used CLUMPP v. 1.2.2 K., using the Greedy option for K values of three to five and the LargeKGreedy option for K values greater than five. Membership in clusters was visualized using the program DISTRUCT v. 1.1 We used a model-based Bayesian clustering method implemented in the program K, and determined the value of K for which this probability was the highest. Second, we calculated ΔK following the method described in K is a quantity based on the rate of change in the log probability of the data between successive K values.To determine the number of clusters most appropriate for the interpretation of our data, we first calculated the mean log probability of the data for each In the 516 individuals analyzed, a total of 88 alleles were identified for the nine microsatellite loci, an average of 9.8 alleles per locus. The average number of alleles per locus ranged from three (Poce28–1) to 23 (Poce20).A, ranged from one to four. Rarefaction of the number of alleles per locus to a standardized sample rendered almost identical results (range 1–3.96). The number of multilocus genotypes varied across populations from one to 17, but was ≤10 for most populations (12 out of 16). We found a total of 27 private alleles, present in 10 of the 16 populations. The number of private alleles per population ranged from one to five , with the exception of WEI, where all loci were monomorphic. The average number of alleles observed per locus,  to five .FIS) varied among populations from 0.756 to 1 (P<0.001 across loci and populations). Genetic diversity within populations was not related to sample size .Expected heterozygosity ranged from zero (WEI) to 0.646 MIA, . Observe756 to 1 ; all polUnder the infinite allele model, four populations deviated significantly from mutation-drift equilibrium , but onlFST values were highly significant and generally very high  there is a secondary large peak in ΔK at K = 5, 2) the rate at which the mean estimated log probability of the data [LnP(D)] increases slows markedly at K = 5  at higher values of K, no additional genetic clusters characteristic of individual sampling locations are identified. We present results for K = 2–5 to provide a comprehensive understanding of the structure in our data (K = 2–16).A strict interpretation of our results using the method of Evanno et al. at K = 5  K = 5 isK = 5 solution, most populations were composed of individuals belonging to a single genetic cluster . In some instances, individuals from multiple populations were assigned to the same genetic cluster: one cluster included ARM, NYE and WAD  or the log of the distances . Similarly, we did not find any evidence that populations more similar to one another along either environmental axis (light availability and soil moisture) were also more genetically similar .We detected no significant isolation by distance (IBD) between populations, either using the matrix of linear Euclidean distances . The lack of a detectable association between genetic and environmental factors suggests that recent colonization of open sites is occurring in the form of multiple independent events, as opposed to the spread of a similarly adapted subset of genotypes  genetic variation  than the studied introduced-range populations  in 16 populations of Polygonum cespitosum under the infinite allele (IAM), the two-phase (TPM) and stepwise mutation models (SMM).(DOCX)Click here for additional data file.Appendix S2Pairwise FST values for all population comparisons. All values are significant  after Bonferroni correction for multiple comparisons .(DOCX)Click here for additional data file.Appendix S3Population structure inferred by Bayesian cluster analyses for 516 Polygonum cespitosum individuals from 16 populations (K = 2–16).(DOCX)Click here for additional data file.Appendix S4Genetic diversity and population structure of 4 Polygonum cespitosum populations from the native range.(DOCX)Click here for additional data file.


PSORS4 locus at chromosome 1q21 that harbours the LCE3C and LCE3B genes. This variant allele (LCE3C_LCE3B-del) is common in patients with psoriasis and other autoimmune disorders from certain ethnic groups.There is increasing evidence of the importance of copy number variants (CNV) in genetic diversity among individuals and populations, as well as in some common genetic diseases. We previously characterized a common 32-kb insertion/deletion variant of the PSORS4 locus. By a PCR-based genotyping assay we characterised the profiles of the LCE3C_LCE3B-del and the linkage disequilibrium (LD) pattern between the variant allele and the tag SNP rs4112788. Our results show that most populations tend to have a higher frequency of the deleted allele than Sub-Saharan Africans. Furthermore, we found strong LD between rs4112788G and LCE3C_LCE3B-del in most non-African populations (r2 >0.8), in contrast to the low concordance between loci (r2 <0.3) in the African populations.Using array-CGH (Agilent 244 K) in samples from the HapMap and Human Genome Diversity Panel (HGDP) collections, we identified 54 regions showing population differences in comparison to Africans. We provided here a comprehensive population-genetic analysis of one of these regions, which involves the 32-kb deletion of the LCE3C_LCE3B-del allele and the LD pattern across populations suggest that the differences between ethnic groups might not be due to natural selection, but the consequence of genetic drift caused by the strong bottleneck that occurred during “out of Africa” expansion.These results are another example of population variability in terms of biomedical interesting CNV. The frequency distribution of the  Structural variants, largely represented by copy number variants (CNV), are a rich source of genetic polymorphism and they may potentially have a strong impact on genetic diversity among individuals -3. The bHuman population genetics studies allow major population branches and subpopulation groups to be defined , for whiPSORS4 locus on chromosome 1q21 that harbours the LCE3C and LCE3B genes. The deleted allele (LCE3C_LCE3B-del) is common in patients with psoriasis among populations of European ancestry ) and metabolism (carboxylase activity [adjP = 0.007]). Relaxing the p-values to 0.05, we also found enrichment in keratinisation (adjP = 0.0352), the category represented by LCE3C and LCE3B genes.We first carried out a Gene Ontology (GO) analysis , Brahui (BRA), Mozabite (ALG), Maya (MAYA) and Chinese Han (CHB)) presented log2 ratios ≤0.25, limit considered significantly low for a copy-number loss by our algorithm  consistently and reliably detected the LCE3C_LCE3B-del . The highest frequency of the deletion was detected in the PIMA population, with an allele deletion frequency of 75% (log2 ratio in the sample pool of −0.67). The ALG population also had a high frequency of the LCE3C_LCE3B-del (67% with an intensity log2 ratio of −0.30). As expected from the aCGH data, Sub-Saharan populations have low frequencies of the deleted allele (28% in Bantu (BAN), 34% in YRI, and 35% in Pygmies (PYG)). However, two Asian populations (Hazara (HAZ) and Yakut (YAK)) have a LCE3C_LCE3B-del frequency lower than that of Sub-Saharan Africans (21% in HAZ and 23% in YAK), although the small number of samples for these two population groups (17 and 11 samples) made the estimation of their low frequency unreliable. The frequency of the LCE3C_LCE3B-del in the other populations varied between 50% and 62%. We estimated the Hardy-Weinberg equilibrium for each of the 13 populations and no significant deviations were observed, suggesting that genotyping errors were scarce . LCE3C_LCE3B-del was found to be common in most populations even though in a heterozygous state  was found in the Karitiana (KAR) population from the Amazon region of Brazil.We then evaluated the frequency of e Figure . Hence, LCE3C_LCE3B-del between populations, such as the Surui (SUR), KAR and YAK. However, these ethnic groups are geographically isolated and they are less likely to have suffered genetic mixture from other populations. It is known that specific demographic histories may be key factors to explain allele frequencies among human ethnic groups [The small sample sets of some of the populations studied here may be responsible for dramatic differences observed in the frequencies of the c groups ,52 and tLCE3C_LCE3B 32-kb deletion [A is associated with the non-deleted CNV allele in all cases, while rs4112788G can be associated with both CNV non-deleted and deleted alleles. While the first case is valid for all the populations analysed, the latter association differed between groups.We evaluated the linkage disequilibrium (LD) between the tag SNP rs4112788 and the deletion  in all tG in sub-Saharan African individuals but not for the LCE3C_LCE3B-del . We found a high concordance between the frequency of rs4112788G and the LCE3C_LCE3B-del in most of the other populations, with r2 values >0.8 in most of them. Some exceptions with r2 values around 0.5 were the CHB (0.47) and South East Asians (0.54), YAK, Makrani (MAK) and HAZ populations (Table There is a high frequency of homozygosity for rs41127882 ratios above 0.25 (both direct and dye-swap labels) for 3 consecutive probes in at least one of the comparison experiments, and concordance with the two CNV algorithms. We focussed on CNV of at least 30-kb in order to identify relative large polymorphisms that could present a specific pattern of variation among human populations.In this study, we first documented the distribution of regions with variable copy number between twelve populations, comparing them with a Sub-Saharan African population (YRI) by aCGH, and using two independent CNV algorithms. We used stringent criteria to define a CNV based on the logPrevious studies of samples in the HapMap collection and the HGDP panel highlighted a gradual decrease in genetic diversity in function of the distance from Sub-Saharan Africa ,36-40, aInterestingly, we found an enrichment of segmental duplications (SD) in the loci detected and 58 known genes totally or partially overlapping some CNV regions . In such scenario, however, the fitness advantage would have to outweigh the loss of the LCE3C_LCE3B genes and the potential regulatory changes of the LCE cluster incurred by disruption to the surrounding genomic region. A similar scenario has been put forward for rearrangements associated with the alpha-globin gene family, where recurrent deletions of HBA1 and HBA2 associated with alpha-thalassemia have reached a high frequency in Mediterranean and Pacific populations [LCE3C_LCE3B-del are also thought to be associated to other genetic variants [LCE3C_LCE3B-del only represents another genetic factor involved in susceptibility to these diseases, together with environmental factors like infections, drugs, stress, smoking and climate.We found most populations to have a high percentage of the deleted allele, mostly in the heterozygous state, with the exception of some isolated instances and Karitiana population, which present a high number of relatives pairs that could reduce the representativeness of allele and genotype frequencies in this population . The aCGulations . Moreovevariants -57, and variants -61. ThusMCPH1 and ASPM), and high LDs [Populations in the HGDP have different sample sizes and, in some of them, first and/or second degree relatives pairs have been detected , which chigh LDs ,64. It whigh LDs .LCE3C and LCE3B genes is induced upon epidermal activation as a consequence of inflammation or skin disease [LCE genes in this cluster. It is possible that other genes fulfil the function of LCE3C and LCE3B, although imperfectly, contributing to the abnormal differentiation and epidermal hyperproliferation characteristic of psoriatic lesions. Thus, when other susceptibility components are not present, the deletion is insufficient to produce the abnormal phenotype but when several susceptibility components concur, the LCE3C_LCE3B-del could lead to disease development.It is clear that the deleted allele has been established in most world populations, which probably has some kind of functional consequences. Expression of the  disease . HoweverLCE3C_LCE3B-del, with allele G at rs4112788 being the only one in strong LD [a priori, the possibility that the association between these SNPs and the LCE3C_LCE3B-del could vary among populations, a strong LD between a given SNP and CNV suggest a single origin of both variants. For this reason we did not expect to find strong association between other SNPs and the LCE3C_LCE3B-del in other populations.In the previous study identifying the 32-kb deletion associated with psoriasis in several populations of European ancestry, 14 SNPs were found related with trong LD . Despiter2 variance could be attributed to differences between continental regions [A strong LD might be also a common feature of a biallelic CNV, which is particularly useful in association studies for complex disorders in which the redundancy of information implied by LD can be used to optimize genotyping. Nevertheless, this might not be useful in association studies of all populations, since LD patterns vary among populations of different geographic origin and a much higher proportion of  regions , with si regions ,67.LCE3C_LCE3B-del are consistent with previous studies showing that the extent of LD in non-Africans is higher than in Africans [r2 values >0.8 in all non-African populations with the exception of two Chinese groups (CHB and population grouped as SEA) and the Makrani population. Although these exceptions are not defined as “isolated” in the HGDP, they may reflect a particular demographic and genetic history of these populations or alternatively, a bias due to the small number of individuals from these populations in the study. However, the LD pattern between rs4112788G and LCE3C_LCE3B-del found for all populations differs from other studies. While the trend observed for general LD consists of a successive increase in the LD in Middle East-North Africa, Central South Asia, Europe, East Asia, Oceania and America with respect to Sub-Saharan Africa, we essentially detect low r2 values in African populations and similar high values for the rest of the world.Our results for Africans , reflectLCE3C_LCE3B-del found in most of non-Sub-Saharan African populations suggests the fixation of the deleted allele as a consequence of the genetic drift that occurred during the exit from Africa. The CNV deleted allele has been associated with susceptibility to autoimmune diseases, not only implying that natural selection but that neutral demographic processes can define the differences in disease frequencies and phenotypic diversity among human populations.Our results show a rise in the number of CNV as the geographic distance from Africa increases, reflecting the influence of geography on human genetics. The higher frequency of Population genetics has the power to provide insights into the demographic history of populations, the selective pressures acting on genetic variation and the mutational processes generating diversity. With NGS able to precisely define the many types and forms of CNV, and other structural variations, we would expect a rapid advance in the discovery and characterisation of novel variants over the next few years. The analysis in patient samples and in subjects of different populations will define their evolutionary history and/or their role in human adaptation and disease..To investigate the variability of CNV in human populations we used the Human Genome CGH Microarray Kit 244 k , which contains over 244,000 probes and covers the entire genome at 10 kb resolution. The initial detection of CNV was performed using a DNA-pooling approach. Experiments were performed in duplicate with DNA labelling colour reversal (dye-swap). The specific experimental protocol that we used was based on the Agilent Oligonucleotide Array-Based CGH for Genomic DNA Analysis.We selected 86 DNA samples from two populations of the HapMap Collection and 343 DNA samples from 21 ethnic groups of the Human Genome Diversity Panel-Centre d’Etude du Polymorphisme Humain (HGDP-CEPH) grouped into 11 populations. Each population group contained samples from 20 to 50 individuals .CNV gene content was determined with the RefSeq gene annotation from the UCSC Genome Browser. To measure the enrichment of genes located within CNV that show population differences in comparison with the rest of genes in the human genome, we analysed the enrichment of GO categories using the Gene Set Analysis Toolkit V2  that were replaced by samples of the same ethnic group but from HGDP-CEPH (Yoruba from Nigeria and Han Chinese). In addition, we included 25 new populations for this analysis, also from HGDP-CEPH, grouped into 18 ethnic groups  was genotyped by multiplex PCR with 5′FAM modification, subsequent capillary electrophoresis and Gene Mapper analysis , as described previously [The The rs4112788 SNP was genotyped using a C_31910050_10 TaqMan® Pre-Designed SNP Genotyping Assay . PCR amplification was performed according to the product specifications and alleles were discriminated on a 7900HT Fast Real-Time PCR system, analysing the data with SDS 2.3 package (Applied Biosystems). When genotyping the SNP, a total of 56 samples from different populations were no longer available due to the poor quality of the DNA and therefore, they were not analysed  in the populations analysed by aCGH against the Yoruba population. Information about the genes and SD overlapping these loci.Click here for fileSupplementary Table 1. Population used by aCGH analysis.Click here for fileSupplementary Table 2. Population used in LCE3C_LCE3B-del genotyping.Click here for file