Scaphoideus titanus, a leafhopper native to North America and invasive in Europe, is the vector of the Flavescence dorée phytoplasma, the causal agent of the most important form of grapevine yellows in European vineyards. We studied 10 polymorphic microsatellite loci and a 623 bp fragment of the mitochondrial cytochrome oxidase II gene in native S. titanus from north-eastern America and introduced European populations, to elucidate the colonization scenario. Consistent with their recent history, invasive European populations were less genetically diverse than American populations for both types of markers, suggesting a recent bottleneck. Significant isolation by distance was detected between American populations but not between European populations. None of the European mitochondrial haplotypes was found in the American vineyards, from which they are assumed to have originated. The precise source of the invasive S. titanus populations therefore remains unclear. Nevertheless, the high heterozygosity of North-East American populations  suggests that this region is part of the native range of S. titanus. Clustering population genetics analyses with microsatellite and mitochondrial data suggested that European populations originated from a single introduction event. Most of the introduced populations clustered with populations from Long Island, the Atlantic Coast winegrowing region in which Vitis aestivalis occurs. International trade has facilitated the movement of species, both in general and for particular taxonomic groups, but only a small fraction of the species transported become established, and only about 1% of the established species become pests Molecular genetics provides powerful tools for invasion studies, making it possible to link introduced populations to their potential sources, to identify genetic changes associated with shifts between areas and to infer the occurrence of evolutionary processes Scaphoideus titanus Ball , is the main vector of Flavescence dorée phytoplasma, a wall-less intracellular bacterium restricted to phloem sieve tubes that is highly pathogenic to several major grapevine cultivars, rapidly leading to the death of the vine Vitis vinifera), a newly introduced insect vector (S. titanus) and a local native phytopathogen (the Flavescence dorée phytoplasma) The Nearctic leafhopper, S. titanus was first observed in 1932 by Ball S. titanus was first captured in open woodlands and herbaceous vegetation, but Vitis species were subsequently identified as a host plant for this insect S. titanus has been reported to be limited to a more restricted area, encompassing New York State Vitis riparia stands than in commercial vineyards S. titanus was first reported in 1958, in the vineyards of south-western France S. titanus was also reported in south-eastern France S. titanus continued to spread, to Spain in 1996 In Europe, S. titanus colonization and expansion were based on the use of random amplified polymorphic DNA (RAPD) markers. European S. titanus populations were found to be less diverse and structured than American populations. The low level of genetic variability in Europe was interpreted as a consequence of the recent introduction of S. titanus, and the lack of genetic structure was considered to result from the transport of grapevine canes, leading to human-mediated dispersal S. titanus samples collected in north-eastern America from four different Vitis spp.  and in Europe  from V. vinifera. We investigated the invasion history of S. titanus, by addressing the following questions: 1) Is there any difference in genetic diversity between invasive and native populations? 2) Do nuclear microsatellites and mitochondrial sequences provide concordant information about the invasion scenario? 3) Did European populations originate from a single introduction or multiple introductions from North America?Early efforts to trace the geographic origins of introductions and to investigate the patterns of S. titanus individuals from 2004 to 2006, at 10 sites in north-eastern America (NE America hereafter) and 22 sites in Western Europe (S. titanus populations were collected in 2004 from unmanaged vines growing wild in four viticulture areas: Long Island (Hither Hills-1 and Hither Hills-2) and Outer Coast Plain (Rio Grande) for V. aestivalis, Niagara Peninsula (Niagara-1 from V. labrusca and Niagara-2) for V. vinifera and Finger Lakes  for V. riparia , from cultivated V. vinifera. In France, 15 populations were collected from the main viticulture areas: Cognac (Boutiers and Lignières-Sonneville), Bordeaux , Midi-Pyrénées (Connac), Languedoc-Roussillon , Provence  and Corsica (Casamozza)  . Four poThe sampled individuals were immediately placed in 96% ethanol, in which they were stored until DNA extraction for genotyping and sequencing. Total DNA was extracted by the salting-out method S. titanus individuals. The technical protocol for nine of these loci  has been described elsewhere et al. We used 10 microsatellite loci to genotype a subsample of 610 H) EH) between the NE American and European populations, for each of the 10 microsatellite loci studied. These statistical analyses were carried out with R 2.8.1 Genetic variation within populations was quantified by estimating allelic frequencies, the mean number of alleles per locus (A) and unbiased heterozygosity, (FIShttp://www1.montpellier.inra.fr/URLB/), according to the expectation maximization (EM) algorithm of Dempster et al. Deviations from Hardy-Weinberg equilibrium and unbiased estimates of FST between populations, with excluding null alleles (ENA) correction, implemented in FreeNA, to correct for the positive bias induced by the presence of null alleles FST values were obtained by bootstrapping 2000 times over loci. As described by Rousset FST/(1-FST)) and the logarithm of geographic distance for pairs of populations were correlated, in Mantel test using GENEPOP. We used 1500 permutations to assess significance.Population structure was assessed by calculating the pairwise http://ppa.launchpad.net/olivier-langella/) and the Cavalli-Sforza and Edwards chord distance. Bootstrap values were calculated by resampling loci and are given as a percentage of 2000 replicates.Neighbor-joining trees were constructed for populations S. titanus populations considered together and for American and European populations considered separately. The software was run with the admixture option, allowing for some mixed ancestry within individuals. Ten independent runs were carried out for each value of K (K = 1–8), with a burn-in period of 100,000 iterations and 500,000 replications. We used the method described by Evanno et al. The Bayesian clustering approach implemented in STRUCTURE version 2.3.3 i) to each possible American or European source population (s) by GENECLASS2 Finally, we used the method of Rannala and Mountain LEU-COII), previously studied by Moran et al. CCA CAA ATT TCT GAA CAT TGA CCA 3′). Primers were synthesized with an additional forward/reverse M13/pUC sequence at the 5′ end of the mtd13 and mtd18 primers, respectively. PCR was performed in a final volume of 25 µl containing 0.75 U Taq polymerase and 1 x buffer (Invitrogen), 0.2 µM of each primer, 0.15 mM of each dNTP, 2 mM MgCl2, and 2.5 µg bovine serum albumin. The mixture was heated at 94°C for 4 minutes and then subjected to 30 cycles of 45 s at 94°C, 30 s at 50°C, 1 min at 72°C and a final extension step of 10 min at 72°C.We analyzed a 623 bp mtDNA fragment containing a portion of the cytochrome oxidase subunit II and tRNAleu genes (referred to hereafter as tRNAPCR products were purified with the Qiaquick PCR purification kit (Qiagen). Sequencing reactions were performed with the SequiTherm EXCEL™ II DNA Sequencing Kit (Epicentre Biotechnologies), with the M13/pUC forward and reverse labeled primers. Sequencing was carried out with a 4000 L automatic DNA sequencer (Li-Cor) and 6% Long Ranger (TEBU) gels.GST is significantly smaller than NST, which takes into account the genetic distances between haplotypes. Diversity indices and pairwise differentiation were assessed with ARLEQUIN 3.11 FST/(1-FST)) and the logarithm of geographic distance for all pairs of populations in a Mantel test MtDNA data analyses were performed for all populations for which at least three individuals had been sequenced: 22 European and six American populations . The hapHO) ranged from 0.398 to 0.581 for NE American populations and from 0.369 to 0.535 for European populations. Expected heterozygosities (HE) were between 0.692 and 0.771 for NE American populations and from 0.543 to 0.675 for European populations .The 10 microsatellite loci displayed various degrees of polymorphism, each having five (Sti15 and Sti80) to 39 (Sti36) alleles, with a mean of 21 alleles per locus for all samples. In total, 217 alleles observed, 115 of which were found in both NE American and European individuals, 84 of which were found only in NE American populations and 18 of which were specific to Europe. Observed heterozygosities .Significant heterozygote deficiencies and significant positive ulations . MoreoveSignificant global linkage disequilibrium was found only in three of the 1080 pairwise tests carried out, for Sti15 & Sti80 (Hither Hills-2), Sti6 & Sti64 (Assas) and Sti15 & Sti46 (Anierès). The 10 microsatellite markers were thus considered independent.P<0.05): 130 of the 137 pairwise FST values were significantly different from zero  to 0.082 , with a mean pairwise FST value of 0.042  to 0.109  and the mean pairwise FST was 0.046 .Most pairwise sample comparisons with ENA correction for the presence of null alleles showed significant genetic differentiation after correction for multiple comparisons (rom zero . Within FST/1-FST) between pairs of populations were weakly but significantly correlated with geographic distance for NE American sample sites , whereas no pattern of IbD was detected for European populations .Genetic distances (S. titanus populations clearly clustered into two groups: 1) the European populations, which grouped together with poor clade resolution and 2) the American populations, which clustered into four subgroups (with more than 50% bootstrap support): the first subgroup consisted of the Geneva-1, Geneva-2, Geneva-3, Cornell and Dresden populations, the second subgroup consisted of the Niagara-1 and Niagara-2 populations, the third subgroup consisted of the Hither Hills-1 and Hither Hills-2 populations and the last subgroup consisted of the Rio Grande population alone  with no more than 10% null alleles had no significant effect on the results obtained (data not shown).In the neighbor-joining tree , the on alone . This toS. titanus samples were analyzed separately, no particular structure could be identified, consistent with the results of neighbor-joining tree analysis (We used a Bayesian clustering method to detect possible cryptic substructure in the whole dataset (NE America and Europe). The ▵K statistic analysis . RestricSti6, Sti15, Sti31-07, Sti34, Sti46, Sti64 and Sti80). We first checked the efficacy of this technique by assigning NE American individuals to NE American populations: 96% of the individuals were assigned to the correct NE American population (P<0.05). By contrast, control assignment tests placed European leafhoppers in their correct European populations for only 76.3% of the individuals, consistent with the low level of population differentiation observed in Europe. The most probable source of each European population was determined by analyzing the mean likelihood of assigning an individual from sample i to sample s . The deduced most probable source for each European site was identified as the sample with the lowest –log(Li→s) values  as the most plausible source for 12 of the 14 European populations  was identified as the most probable source and for one population from Southern Italy (Tramonti), a Niagara Peninsula population (where V. vinifera grows) was identified as the most probable source. No European population was found to have a most probable source population in the Finger Lakes region (where V. riparia grows). Differences in –log(Li→s) between the various putative sources were small. We were unable to assess the significance of these differences because there is currently no statistical procedure available for doing so Null alleles cause a bias in assignment tests, decreasing the power to assign individuals to their source populations correctly ) values . These culations . For oneLEU-COII MtDNA fragments from one to 11 individuals from 30 populations were successfully sequenced  was widely distributed throughout Europe and was present in each of the populations sampled, whereas two rarer haplotypes (g and j) were found across the South of France (both the south-western and south-eastern regions) but were apparently absent from Switzerland and Italy. One haplotype (h) was restricted to Switzerland .In North America, the most distant haplotypes found in different populations were separated by 11 mutation steps, whereas haplotypes present in populations from the same site differed by no more than seven mutations. Haplotype distribution was independent of host plant. Two closely related haplotypes were found in the Niagara Peninsula and Finger Lakes regions. The major haplotype (b) was also present in the Long Island region, on the Atlantic Coast, where the two other haplotypes (e and f) genetically close to those in Europe were found. Haplotype f occupies a central position in the unrooted network, but was found in only two American individuals. The southernmost population from Rio Grande had only private haplotypes in our sampling c and d, .GST = 0.056) than in Europe (GST = 0.012). Moreover, NST reached 0.077 and was significantly higher than GST in America, whereas the difference between NST and GST was not significant in Europe , whereas no such isolation was detected in Europe . Both these explanations are possible for S. titanus populations. Furthermore, within this native group, both mitochondrial and nuclear markers showed there to be a significant genetic structure and IbD. The level of heterozygosity was high and almost all the alleles observed (92%) were found in NE America, consistent with the location of this region within the native range of S. titanus. The American populations were geographically structured. This phylogeographic pattern was confirmed by mitochondrial data, as the differentiation index NST (taking into account similarity between haplotypes) was significantly higher than GST (based solely on haplotype identity). The concordance of mitochondrial and nuclear results suggests that the main patterns of dispersion are similar for males and females in S. titanus.A clustering analysis of nuclear markers showed that the NE American populations formed a well defined group that was differentiated from the introduced European Vitis sp. predominated. It is therefore difficult to disentangle the effects of host plant and geographic location. However, in the Niagara region, where insects were sampled from two different host plants (Vitis vinifera and V. labrusca), no differentiation as a function of host plant was found. We therefore hypothesize that geographic location may be the main factor underlying the differentiation between populations in the native area.In three of the four vine-growing areas sampled in NE America, a single cultivated Levels of genetic diversity were significantly lower in European than in American populations, in analyses of both the mitochondrial and nuclear genomes. The founder effect resulting from the recent introduction of this insect in Europe S. titanus populations in Europe. No significant phylogeographic pattern or IbD was found, with concordant results obtained for nuclear and mitochondrial markers. Weak genetic structure in European populations was also reported by Bertin et al.et al.S. titanus populations with seven microsatellite loci. The lack of genetic differentiation in European S. titanus populations may be indicative of long-distance gene flow . Such long-distance movements of grapevines occurred frequently in the past S. titanus adults (in the framework of compulsory protection regulations) reduces the risk of egg infestations on the material used for plantation, but cannot entirely eliminate this risk.By contrast to our findings for the native range, we found no evidence of genetic structure for V. aestivalis grows. The mitochondrial data were partly consistent with this hypothesis, although the European haplotypes were not found in the American samples. Mitochondrial data showed that NE American populations were phylogeographically structured, the haplotypes found in Long Island being very closely related to the European populations. This suggests that, even if we did not sample the exact source population, it is probably located in the vicinity of the Long Island region. Consequently, of the four NE American wine-growing areas sampled here, Long Island appears to be the most plausible area of origin for the introduced populations of S. titanus.Most of the European individuals were preferentially assigned to source populations from the Atlantic Coast (Long Island region) in which S. titanus into Europe thus involves the establishment of intermediate invasive populations elsewhere. These intermediate populations would have originated from the native area and served as the immediate source of the invasive European populations. In serial invasion processes of this type, founder events in the intermediate populations may lead to additional genetic bottlenecks. The observed moderate genetic variability reduction in the European populations compared to North American populations of S. titanus is consistent with the occurrence of a series of genetic bottlenecks. However, historical data and the known distribution of S. titanus worldwide indicate that there are unlikely to be unsampled intermediate populations as this species has only ever been recorded in North America and Europe. The “invasive bridgehead” hypothesis and serial invasion scenarios in general are therefore unlikely for this particular species. Nevertheless, we cannot exclude the possibility that unstudied or even undetected populations from North America (‘ghost populations’) constitute the actual source of the European S. titanus populations S. titanus also occurs in woodland stands S. titanus populations associated with different natural hosts may therefore be genetically different from those sampled from Vitis species and these populations associated with other hosts may be the true source of European populations.Some recent studies of worldwide introductions have suggested that many widespread invasions may have stemmed from a particularly successful invasive population rather than from the native range. This phenomenon is known as the “invasive bridgehead effect” th century, when large amounts of grapevine material were brought into France from various North American regions for use in breeding programs, initially to combat powdery mildew PhylloxeraS. titanus eggs may therefore have been introduced on several occasions during this period, but probably from the same region of North America. The populations of the first insects introduced may have remained small for a number of years before expanding. Long-distance gene flow within Europe, due to natural migration and the transportation by humans of grapevine canes and grafts carrying eggs, would then account for the genetic homogeneity subsequently observed over a large geographic scale. Future studies based on the use of recently developed methods, such as ABC S. titanus and more recently colonized countries in Central and Eastern Europe should improve our understanding of the colonization history of this pest species. One of the advantages of ABC analysis is that it can handle unsampled populations. This makes it possible to consider scenarios involving a bridgehead invasive population that has not been sampled.The existence in Europe of two main mitochondrial haplotypes – (i), which is common to most of the individuals sampled, and (g), which is present in seven of the 14 French populations – the inference from microsatellite data that there was only one main source for the introduction, and the detection of a single genetic cluster for European populations by both Bayesian analysis and a distance-based Neighbor-joining method suggest that a single introduction event accounts for most, if not all of the individuals in Europe. The high frequency of the rare haplotype (h) in Switzerland may result from genetic drift, which may have favoured a rare haplotype locally that was lost or remained undetected elsewhere. The hypothesis of a second introduction event cannot be completely excluded but is not supported by microsatellite results. More detailed investigation of the distribution of haplotype (h) in Central and Eastern European vineyards is warranted, but it is not currently possible to determine with confidence how many introduction events have occurred. According to historical reports, this insect was introduced into several geographic regions at about the same time: south-western and south-eastern France, and north-western Italy


Triatoma infestans, the major vector of Chagas disease south of the Amazon in South America, has a large distribution of wild populations, contrary to what has previously been stated. These populations have been suspected of being the source of reinfestation of human habitats and could impede the full success of vector control campaigns. This study examined gene flow between intra-peridomestic populations and wild populations collected in the surround areas in three Andean localities in Bolivia. The populations were defined according to temporal, ecological, and spatial criteria. After DNA extraction from the legs of each insect, the samples were analyzed using seven microsatellite markers. First, the analysis of molecular variance (AMOVA) detected an absence of differentiation between wild and intra-peridomestic populations, although strong structuring was observed between the populations within each environment. Then for some populations, the Bayesian method of assignment to inferred populations showed very similar assignment patterns of the members of wild or intra-peridomestic populations in each locality. Finally, the detection of the first-generation migrants within the different populations provided evidence of insect displacement from the wild to the intra-peridomestic environment. This result indicates that, after control campaigns in the Andes, controlling this new paradigm of vector transmission risk stemming from the invasion of human habitats by wild populations of T. infestans requires long-term maintenance of public monitoring to keep the risk at a minimal level. Since wild populations of T. infestans have also been detected elsewhere in Argentina, Paraguay, and Chile, there is an urgent need to take these populations into account in future monitoring of Chagas disease transmission. Triatoma infestans  remains the main and most widespread vector of Trypanosoma cruzi, the causative agent of Chagas disease. This species has long been considered almost exclusively domestic, but the increase in wild population discoveries in the Andes, in the Gran Chaco lowland region, and in Chile shows that wild T. infestans populations are much more widespread than previously thought . A Bayesian approach was implemented to determine the number of genetic clusters within the entire data set using STRUCTURE v. 2.2 ,33. RunsGENECLASS2 software  was usedT. infestans specimens collected in three localities in wild and domestic environments . The overall amplification success of the loci was 97.7%, ranging from 92.8% (locus TiD09) to 99.3% . All loci were polymorphic. No significant association between the pairs of locus genotypes within the overall sample and within each sampled locality was found at the nominal 1% level, a result supporting the statistical independence of loci. The number of alleles per locus in the entire sample ranged from three (TiF11) to 19 (TiF03), and the mean allelic richness was 8.0 ± 3.9. Genetic variability indices in the three samples composed of all analyzed individuals in each locality are presented in FIS values significantly different from random predictions) were observed in Quillacollo and Thago Thago but not in Sapini (FIS) from Quillacollo and Thago Thago (P < 0.02), while only the FIS value was significantly different between Quillacollo and Thago Thago (P < 0.01). Samples from each locality were divided into subsamples according to space and ecotopes (intra-peridomestic or wild) and the summary of genetic variability, per locus and per subsample, is presented in FIS). Data from seven previously described microsatellite loci were analyzed for a total of 277 n Sapini . The locP < 10−4, see P < 10−4). The first hierarchical AMOVA, grouping subsamples according to their geographic origin , showed a higher variance component among localities (18%) than among subsamples within localities (6.7%), and in both cases the FCT  and FSC  indices were significant (P < 10−4), showing geographical structuring between localities and also some possible structuring within localities. This geographical structure was fully confirmed by the topology of the neighbor-joining tree built from Cavalli Sforza’s genetic distances between wild and intra-peridomestic populations from the three localities, where the bootstrap values at the nodes grouping each locality were highly significant . The genetic structure of the subsamples clustered according to different spatial levels was explored with the analysis of molecular variance (AMOVA) using all loci for distance computation and all data, with 0.12% threshold for missing data . Among tnificant . To furtnificant . HoweverThe Bayesian method of the assignment implemented in STRUCTURE detected two clusters in the overall sample  whether 2 > 0.70, In each locality, the putative first-generation migrants were detected on the basis of the highest likelihood ratio calculated between the subsample from which the individual was sampled and among all other subsamples in the locality including its own subsample . PutativT. infestans observed in Quillacollo and Thago Thago is high and similar to that previously observed in the same area analyzing sylvatic populations with the same set of microsatellite loci [The overall genetic diversity of ite loci . Strong ite loci  and evenite loci . Therefoite loci . This grite loci . The lowT. infestans members within a village evolve in agreement with the assumption of panmixia [T. infestans [Triatoma longipennis for which the panmictic unit corresponds to a collection of insects captured in a single peridomestic structure [Triatoma dimidiata from Guatemalan populations, the results suggest that the size of the panmictic unit is not the same in different regions, probably due to variable migration rates between populations [The search for the size of the natural panmictic unit contributes to determining the geographical scale where random mating among members of a species is potentially possible. The putative panmictic unit is the most suitable to define populations for population genetics analysis to derive the relationships that exist between the members of these populations at different geographical scales, which is needed for control strategies. The allozyme polymorphism analyses first showed that the panmixia ; then itpanmixia . Other snfestans ,39,41 astructure . Howeverulations . Here, HFST value is 0.179, P < 10-5), up to 242 km away (Sapini to Thago Thago) as observed between populations collected in different localities in Argentina over long distances [FST value comparisons showed a significant differentiation . These results also support restricted gene flow among wild populations at a small geographic scale that can be explained by the conjuncture of two phenomena: an intrinsically low ability of T. infestans to move actively by walking or flying in the case of adults and the foundation of colonies mostly created by few founder specimens (founder effect).In the present case, a strong geographic structure was observed between the three localities  the sampling strategy that grouped the bugs in potentially panmictic populations using the spatial criterion and (2) the use of microsatellites, which are highly polymorphic markers. The absence of significant differentiation between the insects collected in intra-peridomestic and nearby wild environments favors the hypothesis of bug dispersion between ecotopes and the putative sylvatic origin of the reinfesting bugs. The analyses conducted with the STRUCTURE program confirmed this result, showing that close intra-peridomestic and wild populations may be composed of members that are genetically very similar.T. infestans can reach intra-peridomestic areas. For T. infestans, it seems that this flow of insects between the two environments has previously existed in different areas because a higher level of genetic relatedness exists between wild and intra-peridomestic in each geographical area than between areas [T. infestans represent a health danger, but few data are available on the kinetics of the recolonization process of the human habitat by these populations.The first study by  and the en areas . This pren areas ,52,53. Ten areas . To dateen areas . More geen areas ,57. It nhttp://www1.paho.org/english/hcp/hct/dch/incosur.htm. Accessed 2013 Oct 21), launched in 1991 to control T. infestans, has met extensive success in Brazil, Chile, Paraguay, and Uruguay, but control failures have been declared in the Gran Chaco of Argentina, Bolivia, and Paraguay [http://www.paho.org/, Southern Cone Initiative to Control/Eliminate Chagas Disease (INCOSUR), Annual meeting 2011). The certification process is a tool that validates the efforts made and the effectiveness of the eradication actions, but monitoring by health authorities often breaks down thereafter, and these attitudes should be precisely examined to prevent transmission risk from persisting especially in areas (Bolivian Andes) where population flows between wild and domestic environments are strongly suspected. This issue was recently fully addressed by emphasizing the role of native vector populations, which occur in natural ecotopes and reinvade/reinfest human habitats [T. infestans populations than initially described, requires further research to understand the dynamics of wild population movements and their ability to adapt to new ecotopes. In monitoring, emphasis on inhabitant-based participation rather than the systematic search for triatomines by professional teams, which is very costly, could be considered by the health authorities, as well as prioritizing awareness and education campaigns for the human populations at risk [The Southern Cone Initiative to Control/Eliminate Chagas (INCOSUR, Paraguay ,58,59. Ihabitats . Native  at risk ,61. SustT. infestans in the Gran Chaco, is more likely related to other phenomena than reinfestation by wild populations; one of the involved mechanisms is the development of a pyrethroid resistance [It would be incomplete not to mention that the importance of the persistence of dwelling infestation by sistance -64; howesistance . In fact


FST that apportions the standardized variance in allele frequencies within and between population groups. Because local adaptations increase population differentiation, high-FST may be found at closely linked loci under selection and used to identify genes undergoing directional or heterotic selection. We re-examined these processes using HapMap data. We analyzed 3 million SNPs on 602 samples from eight worldwide populations and a consensus subset of 1 million SNPs found in all populations. We identified four major features of the data: First, a hierarchically FST analysis showed that only a paucity (12%) of the total genetic variation is distributed between continental populations and even a lesser genetic variation (1%) is found between intra-continental populations. Second, the global FST distribution closely follows an exponential distribution. Third, although the overall FST distribution is similarly shaped (inverse J), FST distributions varies markedly by allele frequency when divided into non-overlapping groups by allele frequency range. Because the mean allele frequency is a crude indicator of allele age, these distributions mark the time-dependent change in genetic differentiation. Finally, the change in mean-FST of these groups is linear in allele frequency. These results suggest that investigating the extremes of the FST distribution for each allele frequency group is more efficient for detecting selection. Consequently, we demonstrate that such extreme SNPs are more clustered along the chromosomes than expected from linkage disequilibrium for each allele frequency group. These genomic regions are therefore likely candidates for natural selection.Studies of the apportionment of human genetic variation have long established that most human variation is within population groups and that the additional variation between population groups is small but greatest when comparing different continental populations. These studies often used Wright’s  Knowledge about population genetic structure is central to the study of human origins, DNA forensics, and complex diseases. The present-day genetic diversity observed among human populations was shaped by biological and demographic events that marked their signatures in the genome. Processes such as selection and genetic drift increased the frequency of rare alleles and the genetic diversity among populations FST statistics It is well established that the genetic diversity among human populations is low FST between populations , an approach that suffers from several caveats FST upward. Rare alleles were shown to have a major impact on population structure and must be considered when studying the global genetic diversity However, these interpretations should be treated with caution for several reasons: first, many studies used a small number of polymorphic SNPs  – reflecting a limited genetic diversity – or are based on a small number of samples from few populations that do not capture the genetic diversity of the global human population. Second, even for larger datasets  the usefulness for learning about history and natural selection has been limited due to biases in the ways polymorphisms were chosen F-statistics describe the level of heterozygosity in each level of a hierarchically subdivided population F-statistics relate the departure from panmixia in the total population and within subpopulations to the total homozygosity due to the Wahlund effect between subpopulations. For a population with a hierarchical structure of three levels – individuals (I), subpopulations (S), and total population (T) – F-statistics quantify the genetic differentiation at each level using three indices: FIT, FIS, and FST (see supplementary text F-statistics for measuring population differentiation). The most commonly reported statistic, FST, measures the differentiation of a subpopulation relative to the total population and is directly related to the variance in allele frequency between subpopulations FST depend on several factors such as allele frequencies, population subdivisions, and demographic processes and are difficult to be predicted analytically in the absence of the complete genomewide FST distribution FST calculated from a subset of the FST distribution is often used to quantify the overall genetic divergence between human populations .Wright’s FST to the genomewide background FST . The rational is pan-selectionist; if natural selection favors one allele over others at a particular locus in some populations, the FST at that locus would be larger than FST at other loci in which most differences between populations are due to random genetic drift. However, this approach is not straightforward because extreme population differentiation by itself cannot be assumed to be indicative of a recent population-specific positive selection. In constructing the F-statistics model, Wright assumed an infinite number of populations F-statistics are strongly subjected to random genetic drift FST values over short distances may be due to strong linkage disequilibrium (LD) not selection FST values driven by each process remains a challenging task that requires a sufficiently large SNP catalog. Such a comprehensive SNP catalog became recently available in the third HapMap phase A widely used approach to detect regions under selective pressure is to compare single-locus FST distribution, its mean, and its variance FST distribution to that obtained from SNPs grouped by minor allele frequency  and derive a linear equation to describe the relationship between FST and the mean minor allele frequency. We also compare the clustering of high-FST SNPs along chromosomes in each allele frequency group to the clustering expected from linkage disequilibrium. Last, we devise a strategy to detect genomic regions candidate for natural selection.Here, we study the extent of genetic differentiation in eight human populations ascribed to three continental populations and their intra-continental populations . We estiAssessing Data Quality) and used for subsequent analyses is shown in HapMap phase 3 (second draft) includes new populations and additional samples to existing populations genotyped in previous phases In the continental dataset, over 82% of the SNPs are common  ≥0.05) and less than 5% are considered rare (MAF <0.01). A comparison of the MAF distributions between the continental and the intra-continental datasets reveals gross differences in allele frequencies : for exaF-statistics  regions of the X chromosome  that sharply declines. These results are contrary to previous descriptions of a slowly declining FST distribution with high SNP densities at the tail; for example, Akey et al. FST distribution . These reports relied on relatively small samples of common SNPs from admixed populations that do not represent the worldwide distribution of genetic variation.For autosomal SNPs, the  of 0.08 . The bioFST distribution closely follows an exponential distribution with λ = 12.5, even though the Kolmogorov-Smirnov test rejected that hypothesis  to 1.17 (chromosome 6) SNPs every 1,000 nucleotides, the distributions of FST and MAF have a similar mean and standard deviation for all autosomes  and the highly diverged SNPs (0.6% of SNPs have FST ≥0.4). The FST distribution for the X-chromosome (γ = 1.7) and enriched in highly diverged SNPs (5% of SNPs have FST ≥0.4). The distribution follows a near-exponential distribution (λ = 8.15) with a moderate decline, compared to the autosomal FST distribution.As expected, the  = 0.09)  is more ribution  than theribution  in shaperomosome  is also FST distribution is substantially higher than that of autosomal SNPs, consistent with the smaller effective population size or selection on X-linked loci Q statistic to calculate the FST ratio of autosomes to X chromosomes (Eq. 2) and tested for deviations from an expected Q of 0.75 (Eq. 3). We found a significantly lower genetic differentiation between continental populations of Q = 0.63±0.01 , indicating that these populations exhibit a smaller genetic differentiation in their X chromosome than expected by chance. This low ratio could be the result of long-range male-migration from Africa that was maintained due to continuous expansions through the time period of when non-African populations formed. Alternative explanations can be a stronger selection on X-linked loci or an accelerated genetic drift assumed to occur in non-African history after the split from Africans.The mean X-chromosomal F-statistics depends on the assumption of infinite number of subpopulations FST in a subdivided population of finite size FST distribution, whereas for ten or more populations the FST distribution resembles bell-shape. The reason for the inverse J-shaped distribution for fewer populations is due to the high likelihood that all populations will have similar allele frequencies and that in the later generations the same alleles may be fixed in all subpopulations. By contrast, a bell-shaped distribution appears because the chance of the same allele being fixed in many subpopulations is extremely small eN = 10,000 FST distributions, respectively, but instead, both datasets exhibit a similar inverse J-shaped FST distribution , 0.1–0.2 (n = 607 K), 0.2–0.3 (n = 516 K), 0.3–0.4 (n = 440 K), and 0.4–0.5 (n = 407 K) – shows distinct shapes for each distribution. The majority of the SNPs (52%) were concentrated in the low-frequency allele groups (0–0.2), whereas only 14% of the SNPs were ascribed to the most common allele frequency group (0.4–0.5). Each FST distribution appears to follow an exponential distribution, even though the Kolmogorov-Smirnov test rejected that hypothesis.Because nearly all the 3 million SNPs in our continental dataset are non-coding, it is reasonable to assume neutrality. Under neutrality, newly introduced variants require a long time to reach high frequencies. During this time, recombination will tend to break down the linkage disequilibrium (LD) between neighboring variants. Consequently, common variants tend to be older ribution  thus increquency  is thereFST distribution of each MAF group is very narrow, we used its mean values to study the relationship with the mean MAF. We found a linear relationship between MAF (To study the relationships between ribution . Because and MAF :(4)FST SNPs within each allele frequency group are likely to share similar evolutionary history, we hypothesized that they would be more clustered along chromosomes than other SNPs. To test that hypothesis, we picked SNPs with extreme high-FST values from the top 0.005 percentile of each FST distribution  (Because adjacent high-ribution . These S<0.0001) . SimilarFST>threshold SNPs clustering along chromosomes is demonstrated in FST>threshold SNPs reside in a very close proximities: 29–42% of the FST>threshold SNP pairs from all allele frequency groups are located within less than 10 kilobases (kb) from each other and 17–25% of them are within 10 kb to 100 kb from each other. Although FST>threshold SNPs from the common allele frequency group (0.4–0.5) accounted for a small fraction of FST>threshold SNPs (14%), the short distances between adjacent SNP pairs suggest high clumping as well.The extent within FST>threshold SNPs could have been formed by either the hitchhiking effect of SNPs surrounding a region under natural selection or genetic drift. To test which of these forces shaped the observed clusters, we calculated the LD between adjacent FST>threshold and FST<threshold SNPs for Africans, Europeans, and Asians (r2) between adjacent FST>threshold SNPs is biphasic: initially high (0–10 kb) and later decays. As expected, we found low LD (r2<0.3) when the inter-SNP distances were larger than 100 kb. Non-African populations exhibited a slower decay than African populations over all physical distances. The decay is moderate for common alleles and sharper for low-frequency allele groups.The observed clusters of d Asians , S6–S7. FST>threshold SNPs exhibit significantly higher r2 values (bootstrap test p<0.0001) than FST<threshold up to distance of 1 Mb . This disparity can be explained by the clustering of FST<threshold SNPs in LD blocks of different sizes. Eberle et al. The decline in LD was similar between old SNPs  regardleFST<threshold SNPs was also affected by demographic processes. The variability in the extent of LD between continental populations clearly marks their population history. Africans have the shortest LD  SNP catalog genotyped over eight worldwide populations is the best approximation to the global genetic diversity available. We therefore used the HapMap catalog to quantify the amount of genetic diversity between and within eight human populations more accurately than previously done . The genetic variation in the population structure was measured using hierarchical panmixia  and thatOur findings suggest that the high migratory rates within continents and the relative ineffectiveness of geographical and socio-economical barriers maintained our shared genetic history and prevented the genetic isolation of the studied populations FSTFST distribution is approximately exponentially distributed  and generation time (t), not on the minor allele frequency range. Thus FST is expected to exhibit a similarly-shaped distribution for different minor allele frequency groups. The variation in FST distributions for different MAF groups is explained by the neutral theory. Under neutrality, most of the evolutionary changes are the result of genetic drift acting on neutral alleles, thus the time until a mutation event can be modeled as a Poisson process. This process if memoryless; that is, if an allele did not mutate in time period 0t, it has the same probability to mutate in time period 1t as it had in time period 0t. As expected, this probability is higher for common alleles and lower for rare alleles. We have shown that the measure of genetic differentiation, FST, is a random variable that approximately follows an exponential distribution with a mean λ  it behaves as a random exponential variable with a mean and standard deviation fλ. Because common alleles are more likely to mutate in any time period than are rare allele, they will exhibit higher fλ than rare allele in a linear relationship . SNPs and samples that passed our filtering criteria were termed “QC++”  at d “QC++” .Because many SNPs were not genotyped in all eight populations, we created two datasets: “continental” with ∼3 million SNPs that were genotyped in at least one population of each continent and “intra-continental” a common subset of ∼1 million SNPs that were genotyped in all eight populations. Y-linked and mitochondrial SNPs were not included in the study due to their small number. Analyses were carried out on the continental dataset, unless stated otherwise.F-statistics required six indices: FIS that measures the correlation between alleles of individuals relative to the intra-continental population, FSC that measures the correlation between alleles of intra-continental population relative to the continental population, and FCT that measures the correlation between alleles of continental population relative to the total population. The remaining indices – FIC, FIT, and FST – were similarly defined . Hierarchical F-statistics were calculated for all autosomal SNPs and separately for males and females X-chromosomal SNPs from the non-recombining regions. The significance of the variation between regions within continents was tested by bootstrap analysis of randomizing individuals between regions of the same continent and repeating the process 10,000 times. Hierarchical F-statistics were calculated with the HierFstat package version 0.04–4 To study the distribution of genetic diversity between distinct populations, we considered a hierarchical population structure of three levels: individuals (I), intra-continental populations (S), and continental populations (C) . Using tFST. For each SNP, we calculated the frequencies of both alleles in each population. We then identified the allele with the smallest global frequency (P) when calculated as a weighted average over all populations so that  for a distribution with unknown mean Although the dynamics of FST for autosomes and X chromosome provides further insights into the demographic history of populations. If the difference Q between FST values of autosomes and X chromosome Comparing estimates of Q is approximately:Q was estimated by a bootstrap analysis preformed with 10,000 selecting random datasets of FST values to calculate Q.Deviation from this expectation may indicate different demographic histories for autosomes and X-linked SNPs. The significance of FST distribution, SNPs were divided into five allele frequency groups according to their MAF . The FST distribution was then calculated for each allele frequency group. The Kolmogorov-Smirnov test (α = 0.01) for a distribution with unknown mean FST distribution follows an exponential distribution.To study the effect of minor allele frequency (MAF) on the shape of the FST values, the top 0.005 percentile of each FST distribution was set as a threshold. SNPs with FST values above the threshold were considered FST>threshold SNPs and all other SNPs were considered FST<threshold.To study the difference between SNPs with high- and low-FST>threshold SNPs are more clustered than FST<threshold SNPs by comparing the distances between adjacent SNPs of each allele frequency group. Because there are fewer FST>threshold SNPs, we used a random subset of FST<threshold SNPs of equal size. Distances were calculated separately for each allele frequency group and the dispersal of the distance distributions was assessed using three measures: coefficient of variation FST<threshold SNPs.We tested whether FST>threshold SNPs and FST<threshold SNPs using the squared correlation coefficient (2r). The LD was calculated separately for each continental population and allele frequency group. We used a bootstrap approach to estimate the significance of the results with 10,000 random subsets of FST<threshold SNPs.Similarly, we compared the linkage disequilibrium (LD) between adjacent Figure S1Distribution of genetic variation per HapMap population and phase. SNPs were classified in ten minor allele groups based on their frequency in each population and further subdivided by HapMap phases: 1 (blue), 2 (green), and 3 (red). The number of SNPs genotyped in each phase (1..3n) and the total number of SNPs (totn) are marked.(TIF)Click here for additional data file.Figure S2Fitting the expected cumulative distribution function of an exponential distribution to the FST distribution. The two distributions largely overlap.(TIF)Click here for additional data file.Figure S3FST values of SNPs from the continental dataset versus their expected exponential values.FST values were calculated for all SNPs (red), excluding rare ones (MAF <0.05) (blue) for autosomal (a) and X-chromosomal (b) SNPs.(TIF)Click here for additional data file.Figure S4Distribution of locus-specific FST in eight populations .FST values were obtained for a. 1,100,484 autosomal SNPs, and b. 32,650 SNPs on the non-recombining region of the X chromosome. The histograms show bin distribution as indicated on the x-axis and the cumulative distribution (line).(TIF)Click here for additional data file.Figure S5A histogram of the distances between adjacent FST>threshold SNPs for five allele frequency groups.(TIF)Click here for additional data file.Figure S6LD for five allele frequency groups as a function of physical distance in Europeans. LD (2r) in European populations is plotted as a function of physical distance on a log-scale for five allele frequency groups (a–e). To simplify the presentation, the mean and standard error of the mean 2r for the FST >threshold SNPs (blue) and FST<threshold (red) are presented for different between-SNP distances . FST>threshold SNPs are marked as green dots.(TIF)Click here for additional data file.Figure S7LD for five allele frequency groups as a function of physical distance in Asians. LD (2r) in Asian populations is plotted as a function of physical distance on a log-scale for five allele frequency groups (a–e). To simplify the presentation, the mean and standard error of the mean 2r for the FST >threshold SNPs (blue) and FST<threshold (red) are presented for different between-SNP distances . FST>threshold SNPs are marked as green dots.(TIF)Click here for additional data file.Table S1Summary of HapMap phase 3 (second draft) data used in our analyses. The number of SNPs that passed or failed QC++ (top) and the number of unrelated samples that passed or failed QC++ (bottom).(DOC)Click here for additional data file.Table S2Summary of SNP statistics per chromosome. Number of SNPs segregating in all samples within the continental dataset, SNPs density, mean and standard deviation of MAF, and mean and standard deviation of FST for each chromosome.(DOC)Click here for additional data file.Text S1Assessing data quality, F-statistics for measuring population differentiation, and Supporting Information References.(DOC)Click here for additional data file.


North East Europe harbors a high diversity of cultures and languages, suggesting a complex genetic history. Archaeological, anthropological, and genetic research has revealed a series of influences from Western and Eastern Eurasia in the past. While genetic data from modern-day populations is commonly used to make inferences about their origins and past migrations, ancient DNA provides a powerful test of such hypotheses by giving a snapshot of the past genetic diversity. In order to better understand the dynamics that have shaped the gene pool of North East Europeans, we generated and analyzed 34 mitochondrial genotypes from the skeletal remains of three archaeological sites in northwest Russia. These sites were dated to the Mesolithic and the Early Metal Age . We applied a suite of population genetic analyses  and compared past demographic models through coalescent simulations using Bayesian Serial SimCoal and Approximate Bayesian Computation. Comparisons of genetic data from ancient and modern-day populations revealed significant changes in the mitochondrial makeup of North East Europeans through time. Mesolithic foragers showed high frequencies and diversity of haplogroups U , a pattern observed previously in European hunter-gatherers from Iberia to Scandinavia. In contrast, the presence of mitochondrial DNA haplogroups C, D, and Z in Early Metal Age individuals suggested discontinuity with Mesolithic hunter-gatherers and genetic influx from central/eastern Siberia. We identified remarkable genetic dissimilarities between prehistoric and modern-day North East Europeans/Saami, which suggests an important role of post-Mesolithic migrations from Western Europe and subsequent population replacement/extinctions. This work demonstrates how ancient DNA can improve our understanding of human population movements across Eurasia. It contributes to the description of the spatio-temporal distribution of mitochondrial diversity and will be of significance for future reconstructions of the history of Europeans. The history of human populations can be retraced by studying the archaeological and anthropological record, but also by examining the current distribution of genetic markers, such as the maternally inherited mitochondrial DNA. Ancient DNA research allows the retrieval of DNA from ancient skeletal remains and contributes to the reconstruction of the human population history through the comparison of ancient and present-day genetic data. Here, we analysed the mitochondrial DNA of prehistoric remains from archaeological sites dated to 7,500 and 3,500 years Before Present. These sites are located in North East Europe, a region that displays a significant cultural and linguistic diversity today but for which no ancient human DNA was available before. We show that prehistoric hunter-gatherers of North East Europe were genetically similar to other European foragers. We also detected a prehistoric genetic input from Siberia, followed by migrations from Western Europe into North East Europe. Our research contributes to the understanding of the origins and past dynamics of human population in Europe. Our current knowledge of the origins of human populations and their migratory history relies on archaeological, anthropological, linguistic and genetic research. The study of genetic markers, especially the maternally inherited mitochondrial DNA (mtDNA), has allowed important events in the genetic history of humans to be reconstructed The mtDNA diversity of prehistoric populations has been previously described for Palaeolithic/Mesolithic hunter-gatherers from Central, Eastern and Scandinavian Europe During the Upper Palaeolithic , the northernmost latitudes of Europe were covered by an ice sheet that prevented settlement by anatomically modern humans. With the glacial retreat at the end of the Ice Age  The geographical position of NEE makes it subject to influences from both Western and Eastern Eurasia, which could explain the linguistic and cultural diversity, observed in the area today. Two different linguistic families are spoken: Indo-European languages  and Finno-Ugric languages  data from prehistoric human remains collected in northwest Russia . In parthttp://www.ncbi.nlm.nih.gov/genbank/; accession numbers KC414891-KC414924).The skeletal remains from aUz, aPo, and aBOO were genetically analysed by i) direct sequencing of the mtDNA hyper-variable region I  and ii) typing of 22 haplogroup-diagnostic single nucleotide polymorphisms (SNPs) in the coding-region using the GenoCore22 reaction The success of DNA amplification reactions varied among archaeological sites as follows: 9/42 individuals (21.5%) for aUz, 2/3 (66.7%) for aPo, and 23/23 (100.0%) for aBOO. The higher success rates (100%) observed for samples from aBOO were consistent with their younger age and excellent macroscopic preservation, probably due to the cold climatic conditions of the Kola Peninsula . The preIn order to identify the genetic affinities of the two ancient populations with other ancient and present-day Eurasian populations, mtDNA hg distributions were compared by Principal Component Analysis (PCA). The PCA plot of the first two components , C (27%), U2e (18%), U5a (9%), and H (9%), indicated an ‘admixed’ composition of ‘European’  and ‘Central/East Siberian’  hgs, based on the PCA plot . InteresIn order to test the potential population affinities formulated on the basis of the hg-frequency PCA and the distance map, we examined the present-day geographical distribution of the haplotypes found in aUzPo via haplotype sharing analyses . These aTo further evaluate the apparent significant genetic discontinuity between aUzPo and modern extant populations of NEE and Saami, we analyzed Bayesian Serial SimCoal (BayeSSC) coalescent simulations At the 3,500 uncal. yBP site of aBOO, we observed 39% ‘European’ hgs: U5a (26%), U4 (9%), T (4%), and 61% ‘Central/East Siberian’ hgs: C (35%), Z (13%), D (13%). Concordant with this admixed hg make-up, PCA indicated a position close to present-day Siberians . This poHaplotype sharing analyses for aBOO confirmed the genetic affinity with modern-day West and Central/East Siberians inferred from the PCA , but alsAlthough haplotype sharing analyses revealed genetic links between aBOO and extant populations of NEE, a strong genetic differentiation was obvious between aBOO, modern-day NEE and Saami. This genetic discontinuity was further supported by BayeSSC analyses ; Table 3Previously described populations of hunter-gatherers of Central/East Europe aHG  and ScanAlthough the aBOO individuals were also characterized by high frequencies of hg U, the group appeared less close to the Palaeolithic/Mesolithic hunter-gatherers aHG and aPWC on the PCA plot than aUzPo. Haplotype sharing analyses  also shoTo date, all studies on ancient Mesolithic/Palaeolithic hunter-gatherers from Europe have reported large proportions of hg U: 64% in aUzPo, 73% in aHG, 74% in aPWC; and hg U was also found in three out of five Mesolithic individuals of Spain The detection of haplogroup H in the Mesolithic site of aUz (one haplotype) is noteworthy. To date, haplogroup H has either been rare or absent in groups of hunter-gatherers previously described. It has not been found in hunter-gatherer mtDNA datasets of eastern Europe While the Mesolithic aUzPo site showed genetic affinities with extant populations of West Siberia in hg-based analyses, the precise genetic origins of aUzPo individuals was more difficult to identify from haplotypic data due to the high number of basal haplotypes. At the archaeological level also, the Siberian connection with aUzPo is less clear. The material culture present in the burials of aUz links these populations with the neighboring regions in the West but also in the East and South-East Interestingly, samples from aBOO, which are 4,000 years younger and located further North-West than aUzPo, were characterized by a large proportion and elevated diversity of mtDNA lineages showing a clear ‘Central/East Siberian’ origin . Haplogroups C and D are the most common hgs in northern, central and eastern Asia. They are thought to have originated in eastern Asia and expanded through multiple migrations after the Late Glacial Maximum  The genetic links between the sample populations of aUzPo/aBOO and the extant populations of Siberia follow a general pattern discussed for the early and mid-Holocene . Facilitated by the East-West extension of vegetation zones between the Russian Far East and Eastern Europe The apparent genetic discontinuity between aUzPo and aBOO is consistent with craniometrical analysis that have proposed a genetic discontinuity between the two groups despite the finding of ‘caucasoid’ and unusual ‘mongoloid’ cranial features at both sites The present-day Saami populations display clear haplotypic differences from all the ancient populations sampled for DNA so far  where none of the hg V and U5b1b1a lineages distinctive of the Saami could be detected. We show here that the mitochondrial ancestors of the Saami could not be identified in the ancient NEE populations of aUzPo or aBOO, despite the latter site being within the area occupied by Saami today. The widespread modern-day distribution of U5b1 and V lineages makes it difficult to identify the origins of the Saami Individuals from 7,500 year-old aUzPo and 3,500 year-old aBOO show remarkable genetic dissimilarities with present-day North East Europeans: high frequencies of hg U, the presence of mtDNA lineages of ‘Central/East Siberian’ origin, and near absence (one out of 34 samples) of hg H which comprises up to ∼50% in extant European populations Overall, our results illustrate the power of aDNA to reconstruct the complex genetic history of NEE, which is made of past migrations from both Siberia and Europe. Ancient DNA also reveals the plasticity of demographic events in human populations at both the scale of NEE and Eurasia. Future accumulation of genetic data from ancient populations will make it possible to establish more genetic relationships between past human populations in space and time.A total of 146 human teeth—representing 74 individuals—were collected from three archaeological sites in northwestern Russia: Yuzhnyy Oleni Ostrov, Popovo, and Bolshoy Oleni Ostrov  and Popovo (aPo). Ninety-six teeth representing 48 individuals were obtained from the Yuzhnyy Oleni Ostrov archaeological site, which is located on Yuzhnyy Oleni Island, Onega Lake, Karelia (61°30′N 35°45′E). The site was first discovered in the 1920s during quarrying excavations, which led to the subsequent destruction of most parts of the graveyard. Scientific excavation of the site by Soviet archaeologists in the 1930s and the 1950s eventually unearthed a total of 177 individuals in 141 different mortuary features We also analyzed human remains from the Early Metal Age archaeological site of Bol'shoy Oleni Ostrov  in the Kola Peninsula. This site is located within the area currently inhabited by Saami individuals. Fourty-five teeth representing 23 individuals were obtained from this archaeological site, located in the Murmansk region, Kola Peninsula (68°58′N 33°05′E). Several excavation campaigns have been undertaken between 1927 and 2006. Radiocarbon dates for two graves were obtained from the Oxford Radiocarbon Accelerator Unit, United Kingdom, and revealed an age of around 3237±32 yBP  and 3610–3420 BC (95.4%)) and 3195±39 yBP; calibrated dates, 3500–3430 BC (68.2%) and 3530–3390 BC (95.4%)) for grave 12 and grave 13, respectively, corresponding to the Early Metal Age. The organic preservation of artifacts made of bone, antlers and wood in this site is exceptional for this time period and geographical location DNA isolation, amplification and quantitation were performed at the aDNA laboratory of the Australian Centre for Ancient DNA (ACAD), University of Adelaide. Whenever possible, two distinct teeth were analyzed for each ancient individual. The outer surface of each tooth was decontaminated, first, through exposure to ultra-violet (UV) light for 20 min on each side, then, through gentle wiping using a paper towel soaked in sodium hypochlorite (bleach). The protocol described in The mtDNA HVR-I was amplified and sequenced between np 16056 and 16410 as described in The copy-number of two HVR-I fragments - L16209/H16303 (133 bp) and L16209/H16348 (179 bp) - was estimated in selected aDNA extracts  by quantitative real-time PCR following the protocol detailed in Six individuals were randomly selected , for which the second sample was sent to G.B. at the Johannes Gutenberg University of Mainz for independent replication of DNA extraction, HVR-I amplification and direct sequencing. PCR products were cloned and sequenced. Ancient DNA work at the Johannes Gutenberg University was carried out according to protocols described in Strict precautions were taken in order to minimize the risk of contamination by modern DNA and detect artefactual mutations arising from contamination and aDNA degradation. Seven criteria support the authenticity of the mtDNA data presented here.Pre-PCR DNA work was carried out at the ACAD, a purpose-built laboratory dedicated to aDNA studies. The laboratory is under positive air-pressure and physically isolated from any molecular biology laboratory amplifying DNA. Routine decontamination of the laboratory surfaces and instruments involves exposure to UV radiation and thorough cleaning using bleach, decon90 (decon) and ethanol. In order to protect the laboratory environment from modern human DNA, researchers are required to wear protective clothes consisting of a whole body suit, a face-mask, a face-shield, gumboots, and three pairs of surgical gloves that are changed between individual working steps.Blank controls (one extraction blank for every five ancient samples and two PCR/GenoCoRe22 blank controls for every six reactions) allowed monitoring and controlling large-scale and systematic contamination within the laboratory or in the reagents. In addition, haplotypes similar to those of the users of the laboratory could not be observed from aDNA extracts. Mitochondrial DNA data from the archaeologists and anthropologists involved in the collection of the samples was not available. However, we estimate as rather low the probability that contamination by a few modern-day individuals would generate the diversity and specific patterns of mtDNA lineage distribution observed in the ancient populations under investigation.Multiple replications of HVR-I amplification and direct sequencing were performed in order to detect artefactual sequences due to contamination, DNA degradation or jumping PCR events. When possible, two teeth were collected for each individual and DNA was extracted independently from each sample . For each extract, each PCR fragment and each GenoCore22 SNP position was genotyped from at least two independent PCR products . This strategy was chosen over cloning for most of the individuals examined here. In low-template conditions, clone sequences can represent the small population of highly degraded starting DNA templates that were exponentially amplified by the one single PCR. In our opinion, a hierarchical replication strategy based on multiple independent amplifications is a powerful alternative to cloning in order to detect artefactual mutations and provides confidence about the authenticity of our DNA sequences.The independent replications of DNA extraction/amplification/direct sequencing carried out at the Johannes Gutenberg University confirmed the diagnostic mutations initially identified at the ACAD in the six selected individuals: UZOO-77, BOO57-1, BOO72-1, BOO72-4, BOO72-7, and BOO72-15 .Sequencing of cloned PCR products for six individuals  allowed the corresponding haplotypes to be verified. The sequences showed nucleotide positions modified by post-mortem damage as inconsistent cytosine to thymine or guanine to adenosine base changes . For onep = 0.2215 for the 133 bp short fragment and p = 0.5381 for the 179 bp long fragment). A significantly larger number of copies for the shorter (133 bp) compared to the larger (179 bp) fragment was statistically confirmed by a one-tailed paired t-test (p = 0.04337) in R version 2.12 . Quantitative PCR results suggest a low level of contaminating DNA molecules, the presence of which would have been detected by higher copy-number of longer (less fragmented) DNA molecules.The amount of template mtDNA molecules for two fragments of different sizes (133 bp and 179 bp) was estimated and compared in order to test whether they were consistent with low concentrations of recent human mtDNA contaminants in six selected aDNA extracts . The size distribution of endogenous aDNA molecules was previously shown to be skewed towards smaller fragment sizes due to post-mortem damage, i.e. DNA fragmentation The phylogenetic consistency of the haplotypes and matching hgs assignments of both HVR-I data and coding region SNPs, were indicative of the robustness of the mtDNA typing approach presented here.Mitochondrial DNA data from aUzPo and aBOO were compared to data obtained from other ancient and present-day populations. Data for extant populations were compiled in the MURKA mtDNA database and integrated software, which currently contains 168,000 HVR-I records from published studies and is curated by co-authors V. Z., O.B. and E.B. of the Russian Academy of Medical Sciences, Moscow. A sub-sample of 91 ancient and modern Eurasian populations  was used for comparative analysis. Names of modern-day populations were abbreviated using ISO codes in capital letters, and in small letters when ISO codes were not available. Unless specified otherwise, the same population labels were used for all the maps and analyses in this study, i.e., PCA, haplotype sharing and analysis of coalescent simulations .prcomp in R version 2.12 .PCA was performed using the hg frequency database for ancient and modern-day populations described in http://genofond.ru, also see The genetic distances between 144 pools of extant Eurasian populations and each of the aUzPo and aBOO populations were calculated using the software DJ  and previously described ancient populations. Percentages of shared haplotypes between ancient and present-day populations were represented in a bar plot. Percentages of shared haplotypes among ancient populations were represented in a table, the cells of which were colored according to a gradient reflecting the haplotypic similarities between the populations compared.ST) for the ancient and extant populations were calculated in Arlequin version 3.11  of a single population was allowed to grow exponentially. The values of the growth rate were drawn from a uniform prior distribution, such that the population has evolved from a Palaeolithic population of Ne 5,000 that lived 1,500 generations ago. The values for the modern-day (NEE or saa) Ne were drawn from a uniform distribution: we explored present-day Ne between 100,000 and 30,000,000 for NEE and 1,000 to 500,000 for saa. Population statistics were estimated at various points in time, corresponding to the age of the ancient populations considered in models H0a to H0e .Under the models of genetic continuity H0, the effective population size (Ne and being the recipient (sink population) of a migration from CE (source population). Population sizes of each of the present-day sink population (NEE) and source population (CE) were drawn from a uniform distribution of Ne varying from 100,000 to 15,000,000 individuals. Migration and divergence times were estimated from uniform distributions . Three different percentages of the source population size were tested for the value of percentages of migrants: 10%, 50% and 75%.Under the models of migration H1, we assumed a single NEE population undergoing an exponential growth in Nhttp://www.stanford.edu/group/hadlylab/ssc/index.html). The distribution of six selected population statistics (haplotype diversity and fixation indices FST) were drawn from the simulations and compared to the corresponding observed population statistics in an ABC framework http://www.stanford.edu/group/hadlylab/ssc/index.html. Goodness of fit of the different models tested was compared using AICs Population statistics were calculated for 100,000 genealogies simulated using BayeSSC Click here for additional data file.Figure S2Principal Component Analysis of mtDNA haplogroup frequencies, non-redundant ancient haplotypes only. A. Recalculated frequencies. B. PCA plots. The first two dimensions account for 42.4% of the total variance. Grey arrows represent hg loading vectors, i.e., the contribution of each hg. Red dots represent ancient populations described in this study (non-redundant haplotypes only): aUzPo2, Yuzhnyy Oleni Ostrov/Popovo ; aBOO2, Bol'shoy Oleni Ostrov . Other ancient populations were labelled as follows: aEG, confederated nomads of the Xiongnu ; aHG, Palaeolithic/Mesolithic hunter-gatherers of Central/East Europe ; aKAZ, Nomads from Kazakhstan ; aKUR, Siberian Kurgans ; aLBK, Neolithic individuals from Germany ; aLOK, Lokomotiv Kitoi Neolithic individuals ; aSP, Neolithic individuals from Spain ; aPWC, Scandinavian Pitted-Ware Culture foragers ; aUST, Ust'Ida Neolithic population . Extant populations were abbreviated as follows: ALB, Albanians; ale, Aleuts; alt, Altaians; ARM, Armenians; aro, Arorums; AUT, Austrians; AZE, Azerbaijani; BA, Bashkirs; bas, Basques; BEL, Belarusians; BGR, Bulgarians; BIH, Bosnians; BU, Buryats; CHE, Swiss; CHU, Chukchi; CU, Chuvashes; CYP, Cypriots; CZE, Czechs; DEU, Germans; esk, Eskimos; ESP, Spanish; EST, Estonians; eve, Evenks; evn, Evens; FIN, Finns; FRA, French; GBR, British; GEO, Georgians; GRC, Greeks; HRV, Croatians; HUN, Hungarians; ing, Ingrians; IRL, Irish; IRN, Iranians; IRQ, Iraqi; ISL, Icelanders; IT-88, Sardinians; ITA, Italians; JOR, Jordanians; kab, Kabardians; ket, Kets; kham, Khamnigans; khan, Khants; KK, Khakhassians; KO, Komi; kor, Koryaks; KR, Karelians; kur, Kurds; LTU, Lithuanians; LVA, Latvians; man, Mansi; ME, Mari; MNG, Mongolians; MO, Mordvinians; NEN_A, eastern Nenets; NEN_E, western Nenets; nga, Nganasans; niv, Nivkhs; nog, Nogays; NOR, Norwegians; POL, Poles; PRT, Portuguese; PSE, Palestinans; ROU, Romanians; RUS, Russians; SA, Yakuts; saa, Saami; SAU, Saudi Arabians; SE, Ossets; sel, Selkups; sho, Shors; SVK, Slovakians; SVN, Slovenians; SWE, Swedes; SYR, Syrians; TA, Tatars; tel, Telenghits; tof, Tofalars; tub, Tubalars; TUR, Turks; tuv, Tuvinians; UD, Udmurts; UKR, Ukrainians; ulc, Ulchi; vep, Vepses; yuk, Yukaghirs.(PDF)Click here for additional data file.Figure S3Direct and clone sequences for six selected samples. A. UZOO77. B. BOO57-1. C. BOO72-1. D. BOO72-4. E. BOO72-7. F. BOO72-15. “_1” after the individual identification number signifies that the sequences have been obtained after DNA extraction and sequencing from a first sample at the Australian Centre for Ancient DNA. “_2” after the individual identification number signifies that the sequences have been obtained after DNA extraction, cloning and sequencing from a second sample at the Institute of Anthropology, Johannes Gutenberg University of Mainz.(PDF)Click here for additional data file.Table S1Description and references for hg C1 HVR-I sequences found in modern-day populations of Eurasia.(PDF)Click here for additional data file.Table S2Grave and museum collection number for Yuzhnyy Oleni Ostrov, Popovo and Bol'shoy Oleni Ostrov specimens.(PDF)Click here for additional data file.Table S3Results of SNP typing in the mtDNA coding region using the GenoCore22 SNaPshot assay. SNPs typed on the L-strand are reported in capital letters in the reference rCRS profile, whereas SNPs typed on the H-strand are reported in small letters. Missing data signifies allelic dropout or fluorescence signal below the background threshold . ‘g/a’ indicates the presence of a mixed signal for the position interrogated. A mixed signal was repeatedly obtained at position 8994 (haplogroup W) with the detection of an additional G base. However, the rest of the profile never could support phylogenetically the presence of the G base at this particular position. For each individual, profiles were obtained from two independent extracts, except for individual BOO72-9 for which a second samples was not available and for UZOO-77, BOO57-1, BOO72-10, BOO72-4, BOO72-7, BOO72-15, and BOO72-1, for which the second individual was extracted in an independent laboratory. rCRS, revised Cambridge Reference Sequence; hg, haplogroup.(PDF)Click here for additional data file.Table S4Results of quantitative PCR.(PDF)Click here for additional data file.Table S5Details of ancient and modern-day populations used in comparative analyses.(PDF)Click here for additional data file.


Wolbachia endosymbiotic bacteria though other secondary endosymbionts have not been reported in ants. It has been suggested that the ability of Wolbachia to invade and remain in an ant population depends on the number of coexisting queens in a colony. We study the genetic and social structure of populations in the ant Formica cinerea which is known to have populations with either monogynous or polygynous colonies. We screen populations for several endosymbiotic bacteria to evaluate the presence of different endosymbionts, possible association between their prevalence and the social structure, and the association between endosymbiont prevalence and genetic differentiation of ant populations.Many insects, including ants, are infected by maternally inherited Wolbachia, 3.8% by Cardinium and 33% by Serratia. There was significant variation among the populations regarding the proportion of nests infected by Serratia, Wolbachia and the pooled set of all the endosymbionts. Some individuals and colonies carried two of the bacteria, the frequency of double infections agreeing with the random expectation. The proportion of infected ants  did not correlate significantly with the population level relatedness values. The difference in the prevalence of Wolbachia between population pairs correlated significantly with the genetic distance (microsatellites) of the populations.We found three endosymbiotic bacteria; 19% of the nests were infected by Wolbachia and Cardinium demonstrate the importance of screening several endosymbionts when evaluating their possible effects on social life and queen-worker conflicts over sex allocation. The low prevalence of Wolbachia in F. cinerea departs from the pattern observed in many other Formica ants in which all workers have been infected. It is likely that the strain of Wolbachia in F. cinerea differs from those in other Formica species. The correlation between the difference in Wolbachia prevalence and the pair-wise genetic distance of populations suggests that spreading of the bacteria is restricted by the isolation of the host populations.The discovery of several endosymbionts and co-infections by  Buchnera in aphids, Blochmannia in Camponotus carpenter ants, Wigglesworthia glossinidium in tsetse flies and Baumannia cicadellinicola in leafhoppers . Haplotype E was prevailing and included most samples from all the populations. Haplotype A was found in four individuals from two colonies of population 4 (Jääli) and had a single transition in the tRNA-Ser part of the amplified region. Haplotypes B and C had single nucleotide changes, both leading to an amino acid change (I to V and L to I). Haplotype B was detected in individuals from four colonies of population 5 (Koppana sand pit). Haplotype C occurred in two populations, in individuals from four colonies of population 1 (Juurussuo) and from three colonies of population 4 (Jääli). Haplotype D could be derived from the haplotype C by a synonymous transition and was observed in only a single colony in population 3 (Haukipudas). In the populations that had two haplotypes, only one haplotype was found in each nest.The mitochondrial sequences revealed five haplotypes A-E  produced a strong band that was confirmed to belong to Wolbachia by sequencing. Different concentrations (20-60 ng/ul) of genomic template were tested for a subset of samples, as well as different annealing temperatures (50-60°C) and several MgCl2 concentrations (1-3 mM) but PCR produced either almost invisibly faint bands that had a correct fragment size or mostly no bands at all for F. cinerea wsp. When hemi-nested PCR with several bacteria-specific primer pairs was used to amplify 16SrDNA, we successfully amplified Wolbachia, Serratia and Cardinium from F. cinerea. The other bacteria  were not detected.No amplification was initially detected when the Wolbachia was found in eight out of ten populations and the proportion of infected individuals in those ranged from 1.2% (population 5) to 33% (population 7) (Table Wolbachia. Some singleton sites of the obtained sequence (896 bp) can represent PCR errors, but there were 49 parsimony informative sites. Among the haplotypes, four from Liminka (population 6) differed from all the others at 21-27 sites (mean difference p = 0.025). Among the other haplotypes, the largest number of pair-wise nucleotide differences was 13 (p = 0.014) and the largest differences were between sequences from Haukipudas (population 3) and the island of Hailuoto . In a neighbour-joining tree, the sequences from these two areas tended to form groups of their own, otherwise there were no clear geographical groupings  that were known to carry different wFex-strains of Wolbachia determined by the wsp sequences [[F. aquilonia]. The 16SrDNA sequences obtained from these hosts were either identical to one of the sequences found in the major group from F. cinerea or differed by only one nucleotide , and the proportion of infected individuals ranged from 1.3% (population 5) to 5% (population 10) . The parsimony informative sites separated three haplotypes in the Juurussuo population (population 1) from the others by five specific differences. The main group of sequences was 98-99% similar to s Figure .GU592756-GU592768] gave 98% similarities with symbiont S from Acyrthosiphon pea aphid (M27040), secondary symbiont type-R of Aphis craccivora , secondary symbiont of Acyrthosiphon pisum , and Candidatus Serratia symbiotica   .Serratia symbiotica is known as a secondary endosymbiont of aphids  done in the total volume of 15 μl. The PCR program included the steps of denaturation at 94°C for 5 min, 35 cycles at 94°C for 1 min, at 46°C for 50 sec, at 72°C for 1 min and final extension at 72°C for 10 min. Three microlitres of the product was run on 1.5% agarose gel to verify the success of cloning and the correct size of the band by using M13 forward and reverse primers flanking the insertion site in the cloning vector (pCR 2.1-TOPO). The rest of the reaction was purified with the MinElute 96 UF PCR Purification Kit (Qiagen). Both strands were sequenced from PCR products from one to five clones per individual by a vector-specific primer pair (included in the TOPO TA-cloning kit) and sequencing was done with the Big Dye Ready Reaction Kit as 1/8 part reaction according to the instructions of the manufacturer (Applied Biosystems). Sequences were inspected by the program Sequencher v. 4.7 and the alignments were done in MEGA v 3.1 [Hemi-nested PCR  was usedTTTC-3') , SpiroplTAA -3') , BuchnerCGAA-3') , PASS=CaACAT-3') , CardiniACG -3') , EPS (EPATTC-3')  and WolbTGT -3') . A PCR rGA v 3.1 .ST [ST estimates between populations were tested by comparing the initial estimates against the distribution obtained by permutation of the nests (100 times). The genetic distances and phylogenetic analyses were conducted using MEGA version 3.1 [The genetic relatedness (r) and inbreeding (F) coefficients for ant nests and populations were calculated using the algorithms of Queller and Goodnight  and PamiST  by samplsion 3.1 . The hapThe authors declare that they have no competing interests.AS was responsible for the laboratory work, bacteria identifications, analysis of the microsatellites, constructed the phylogenies and mt haplotype network and drafted the manuscript. PP coordinated the study, carried out the population genetic studies and helped to draft the manuscript. The final version of the manuscript was read and approved by both authors.ST estimatesUPGMA clustering of populations based on the F. Relatedness values are shown for the populations.Click here for fileF. cinerea individualsScheme of mitochondrial haplotype network for . The lines separating the haplotypes involve one nucleotide change.Click here for fileWolbachia 16 S rDNA sequences from F. cinereaThe neighbour-joining tree of . The tree has sequences (896 bp) amplified from F. cinerea in this study and three sequences from GenBank. The tree is based on distances estimated by using the Kimura's 2-parameter method with gaps and missing data excluded from pair-wise comparisons. The numbers on the nodes represent the bootstrap percentages from 1,000 replications.Click here for fileWolbachia from F. cinereaBest matches among GenBank sequences for . The matches are based on results from a BLAST search.Click here for fileWolbachia 16SrDNA sequences from Formica ants and other arthropodsThe neighbour-joining tree of . The tree compares haplotypes obtained from F. cinerea and other Formica ants in this study and 16 sequences taken from GenBank.Click here for fileCardinium sequence from F. cinereaThe Genbank matches for . The matches are based on results from a BLAST search.Click here for fileCandidatus Serratia symbiotica from F. cinereaThe best matches among GenBank sequences for . The matches are based on results from a BLAST search.Click here for fileCandidatus S. symbiotica sequencesThe neighbour-joining tree of . The tree has sequences (471 bp) amplified from F. cinerea in this study and one sequence from GenBank. The tree is based on distances estimated by using the Kimura's 2-parameter method with gaps and missing data excluded from pair-wise comparisons. The numbers on the nodes represent the bootstrap percentages from 1,000 replications.Click here for file