Apis mellifera unicolor is indubitably an African subspecies endemic to Madagascar, its relationship with honey bees from three archipelagos in the southwest Indian Ocean (SWIO) hotspot of biodiversity is misunderstood. We compared recent mtDNA diversity data to an original characterization of the nuclear diversity from honey bees in the Mascarenes and Comoros archipelagos, using 14 microsatellites, but also additional mtDNA tRNALeu-cox2 analysis. Our sampling offers the most comprehensive dataset for the SWIO populations with a total of 3,270 colonies from 10 islands compared with 855 samples from Madagascar, 113 from Africa, and 138 from Europe. Comprehensive mitochondrial screening confirmed that honey bees from La Réunion, Mauritius, and Comoros archipelagos are mainly of African origin  and that coexistence with European lineages occurs only in the Mascarenes. PCA, Bayesian, and genetic differentiation analysis showed that African colonies are not significantly distinct on each island, but have diversified among islands and archipelagos. FST levels progressively decreased in significance from European and African continental populations, to SWIO insular and continental populations, and finally among islands from the same archipelago. Among African populations, Madagascar shared a nuclear background with and was most closely related to SWIO island populations (except Rodrigues). Only Mauritius Island presented clear cytoplasmic disequilibrium and genetic structure characteristic of an admixed population undergoing hybridization, in this case, between A. m. unicolor and A. m. ligustica, A. m. carnica and A. m. mellifera-like individuals. Finally, global genetic clustering analysis helped to better depict the colonization and introduction pattern of honey bee populations in these archipelagos.With globalization the Western honey bee has become a nearly cosmopolitan species, but it was originally restricted to the Old World. This renowned model of biodiversity has diverged into five evolutionary lineages and several geographic “subspecies.” If  Islands are rich reservoirs of biodiversity with high endemism across diverse taxonomic groups –3. OftenApis mellifera, a generalist pollinator, deserve particular attention. The honey bee is established in all three SWIO archipelagos and, has the particularity to occur both in wild and domesticated states. If multiple livestock species are known to be exotic and deliberately brought to all these islands by human [A. mellifera is not as obvious. Genomic analysis suggested that A. mellifera originated and colonized its native geographic range—Africa, Europe, the Middle East, and some regions in Asia—at least 300,000 years ago [A. mellifera diverged into five evolutionary lineages [I, AII, AIII and Z sub-lineages [A. mellifera for beeswax for almost 9,000 years [Apis literature [A. m. unicolor has been described as endemic to Madagascar [A. m. unicolor colonized Madagascar well before human arrival. Nevertheless, the hypothesis of natural colonization by A. mellifera into the nearby SWIO archipelagos is still questioned, especially regarding to botanical studies that consider it to be introduced from Madagascar into the nearby SWIO archipelagos  suggest records –36).Leu-cox2 intergenic region from mtDNA supported an insular African sub-group in the SWIO islands, distinct from continental sub-lineages [A. m. unicolor in the Mascarenes (except Rodrigues), Comoros, and Seychelles archipelagos was shown by shared haplotypes with Madagascar [Leu-cox2 diversity on each island. Despite mtDNA similarities, using microsatellite markers, honey bee populations between the Seychelles Archipelago and Madagascar  were found to be genetically differentiated [Recent combined analysis of both the ND2 gene and the tRNAlineages . The predagascar . Hints oentiated . In the entiated  reachingentiated . These fA. mellifera in the SWIO, we propose an interpretation of the intraspecific phylogeographic patterns in the three archipelagos.The present study characterizes for the first time, the nuclear genetic diversity of honey bee populations within La Réunion and Mauritius (Mascarenes Archipelago) and populations found in Grande Comore, Mohéli, Anjouan, and Mayotte Islands (Comoros Archipelago). We compared the mtDNA polymorphism with nuclear diversity and structuration help in the detection of ongoing hybridization between subspecies on La Réunion and Mauritius. Since multiples lineages coexist in these islands, processes shaping the genetic diversity are hard to disentangle without a large and diverse dataset, capable of discerning between African and European populations. For that reason, we implemented additional sampling from continental native populations and previous datasets from Madagascar , SeychelA. mellifera populations from the SWIO islands was assessed by de novo genotyping of worker honey bees from 2,860 colonies from both insular and continental areas. In order to have a comprehensive understanding of SWIO honey bee phylogeography, a dataset containing 1,528 individuals formerly described in Madagascar, Seychelles, Rodrigues, South Africa, and Italy was also employed [N = 4,388) covered honey bee populations in the SWIO islands and different habitats throughout Africa and Europe  and 13.6% of those in Mauritius . 15.8% of the known managed honey bee populations in La Réunion  were saA. m. adansoni, A. m. lamarckii, A. m. scutellata, A. m. capensis, A. m. monticola, and A. m. litorea. In Europe, 138 colonies were sampled at 22 locations covering known habitats of the two M lineage subspecies (A. m. iberiensis and A. m. mellifera) and three C lineage subspecies . Finally, 12 colonies to a strictly known introduced insular population from Tahiti were collected.Sampling of honey bee colonies in continental Africa and Europe provided reference native populations for comparison. A total of 113 colonies were sampled at 28 locations in the known ranges of continental African subspecies, https://github.com/petewarden/openheatmap/tree/master/mapfileprocess/test_data/TM_WORLD_BORDERS-0.3.All maps depicting sampling lovations were generated and using QGIS software  and deriLeu-cox2 intergenic region [DraI was performed following manufacturer recommendations (Promega). tRNALeu-cox2 amplified, and restriction fragments were visualized on 4% agarose gels and recorded. All restriction profiles detected were already described in A. mellifera populations, easing their identification.Total DNA was extracted from legs of one honey bee per colony as described in . Ancestrc region . Amplific region . Then enAll DNA samples were amplified using multiplex PCR reactions with 14 microsatellite loci: A113, A24, AP55, A88, A28, A29, AP289, AP273, (A)B124, A8, A35, AP33, AP66, and AP43 –47. Multnull) was estimated for each population unit with FREENA [all), observed heterozygosity (Hobs), unbiased expected heterozygosity (Hnb), and FIS per population unit were estimated using GENETIX 4.05 [FSTs were estimated using FSTAT 2.9.3.2 software [FST values.Genetic diversity was analyzed at both fine and coarse scales by considering different population levels with at least five individuals per apiary. Intra-island/country genetic variation was estimated using each site as a population unit (398 sites) while inter-insular/continental analysis considered all sites from the same island/country (11 islands and 21 countries). Null allele frequency per locus  steps were run for all samples  simultaneously, considering a K interval [N = 2,050), ii) Mauritius (N = 367), iii) islands of the Comoros Archipelago (N = 120) and iv) continental populations (N = 263). STRUCTURE HARVESTER [Principal Component Analysis (PCA) was used to further explore genetic differentiation between populations, using R 3.0.2 software  and adeginterval  with teninterval . In parainterval , which tinterval . SubsequARVESTER , CLUMPP ARVESTER  and DISTons N = 2. STRUCTULeu-cox2 intergenic region, and 14 loci microsatellites are available in Full sample details including sampling geo-coordinates, multilocus genotypes using the mtDNA tRNALeu-cox2 intergenic region identified 19 restriction profiles in 4,252 colonies from SWIO, Africa, Europe, and Tahiti honey bee populations. The African lineage was characterized by A1, A4, A6, A11, A14, A16, A48, A49, A50, A51, Z2 and Z7 profiles while the European M lineage was distinguished by M3, M4, M6, M7, M7’ and M65. On gels, C1 and C2 profiles were difficult to discriminate (1bp difference), so those were coded as C1/C2, but both indicated the European C lineage.Successful analysis of the tRNALeu-cox2 DraI profiles characteristic of the African lineage. The Mascarenes Archipelago was the only region in the SWIO to exhibit three different lineages: i) in La Réunion, 95.5% lineage A, 4.3% C, and 0.3% M; ii) in Mauritius, 54.2% A, 44.7% C and 1.1% M; iii) in Rodrigues, 100% C. Proportions of evolutionary lineages formerly reported for the Mascarenes using tRNALeu-cox2 sequencing . As for continental populations, distribution of tRNALeu-cox2 restriction profiles shifted from A maternal lineages in African colonies to M and C maternal lineages in European colonies  was larcolonies .N = 4,388) were considered successfully genotyped (with less than 40% missing data). Preliminary analysis showed negligible low null allele frequencies for all insular and continental populations listed in loci were captured in the La Réunion and Mauritius populations (n ≥ 5) showed that African and Mascarenes honey bees have the highest genetic diversity of the populations tested , Mauritius (FIS = 0.067), Madagascar (FIS = 0.055), Zimbabwe (FIS = 0.056), and South Africa (FIS = 0.015). Significant departures from Hardy-Weinberg equilibrium were detected in France (FIS = 0.126) and Germany (FIS = 0.107)  were detected, but only 48 pairs out of 3,655 (considering n ≥ 5) were significantly different (P < 0.00037). Among these significant pairs, 25 involved two sites from remote environments located in a geological cirque (REU126 and 124). Bayesian clustering (STRUCTURE and INSTRUCT) and multivariate methods detected no genetic structuration among honey bee samples from La Réunion   . Indeed = 2,050) .FST values reached much higher levels in Mauritius (-0.022 to 0.201) and 32 pairs out of the 120 were significant, including 19 involving MUS20 and 21 (same beekeeper) paired with other apiaries (P < 0.00048) (more details in Leu-cox2 characteristic of the European C lineage and ii) 83 of these colonies belonged to sites MUS20 and 21  associated with the highest null allele frequency (Anull = 8.3%). Genetic differentiation among the Comoros islands was even visible with the distinction of four groups on PCA  54.8% of Mauritius in green cluster 5 and iii) 99.6% of Rodrigues colonies assigned to blue cluster 1. As SWIO reference, 99.4% of Madagascar colonies clustered to yellow cluster 2. Mauritius shared a nuclear genetic diversity  with Seychelles colonies mostly assigned to the same cluster . Compared to all other populations, Mauritius presented the most mixed population with four clusters (pink) derived mainly from continental Africa and cluster 2 mainly found in Madagascar. A geographic paradox appeared as 100% of the colonies from the most distant island to Madagascar, Grande Comore, was assigned to the same cluster. This SWIO island structure was also supported by significant differentiation estimated by pairwise significant FST values, but still lower than those of the continents  Madagascar honey bees were genetically distinct from continental African colonies, ii) La Réunion bees differentiated early from Madagascar genetic group, iii) Mauritius and Seychelles bees share a genetic background with La Réunion, but were still differentiated, iv) the second intra-cluster found on Mauritius was associated to one European cluster, v) Comoros Archipelago bees share a genetic background with continental African populations and Madagascar, but still formed a different population, and vi) Rodrigues shares a genetic background with European populations, yet formed a new population, unlike Tahiti.A. mellifera) populations established in the Mascarenes and Comoros archipelagos present high nuclear microsatellite polymorphism for insular populations. Despite having generally similar African mtDNA backgrounds, different levels of nuclear genetic differentiation were apparent within and between archipelagos. No genetic structure was found at La Réunion, while in Mauritius, genetic data indicated the coexistence of two clusters. Both populations have a close relationship to Madagascar honey bees rather than to native African continental populations. Ongoing hybridization between African and European lineages was evident in Mauritius.This study shows that honey bee , but mtDNA sequences characteristic of A. m. mellifera, A. m. carnica, and A. m. ligustica were also found. However, if multivariate analysis indicated that this population was similar to, but distinct from Madagascar, it also showed a certain proximity to European colonies. The presence of European honey bee colonies confirmed previous reports stating that introduction of exotic subspecies occurred before import prohibitions established in 1982 [La Réunion was dominated by  in 1982 . Unlike  in 1982 –76. Diff in 1982 .Despite the topology of La Réunion, reaching an elevation of 3069 m , no geneA. m. unicolor, more than to temperate, introduced subspecies. However, it would be interesting to investigate this in wider genomic and coding regions to determine whether this adaptation is due only to the African genome. As an example, it has recently been shown that a positive selection signal appeared in Chromosome 11 with an excess of European over African ancestry which may confer an adaptive advantage to Africanized honey bee populations found in Brazil [Interestingly, La Réunion has the highest proportions of African colonies and undegraded native forest habitats, estimated at 25% of the original cover . This din Brazil .A. m. ligustica-like queens [A. m. unicolor mtDNA haplotypes were poorly differentiated and were assigned to the same genetic cluster as native European colonies, and the reverse occurred. In both mtDNA African and European colonies found in Mauritius, a continuum in nuclear assignment toward the opposite cluster suggested ongoing introgression in both directions. Such phenomena have already been reported on other European islands and between the African and European C lineage in A. m. iberiensis [A. m. siciliana [For Mauritius, the genetic composition of honey bee populations was the most complex case among all the SWIO islands, highlighting the impact of human honey bee importation. In our sampling, similar proportions of local and exotic lineages were detected. However, one apiary highly contributed to the European frequency as a beekeeper imported a huge batch of e queens . As in Le queens , 80. Nuceriensis  and A. miciliana .A. m. ligustica and carnica. In Mauritius, two apiaries (MUS20 and 21) showed “pure” European colonies due to relatively recent introductions confirmed by field investigation and government reports [As at Rodrigues, C and M colonies were genetically similar to continental populations of  reports . Whether reports , 83.A. mellifera) into five lineages with up to 31 subspecies [A. m. iberiensis [A. m. unicolor from Madagascar [A. mellifera population histories may be difficult to develop due to the long relationship with humans and modern global transportation of honey bee colonies [Leu-cox2 intergenic region done here in the SWIO populations confirmed that these insular populations are mainly of African origin. Since mtDNA indicates only maternal lineage, the nuclear genetic diversity observed confirmed that the SWIO islands (excepted Rodrigues) are more closely related to Madagascar than to any African populations. Nevertheless, the differentiation index (FST), Bayesian, and multivariate analyses showed that SWIO populations present nuclear genetic diversity distinct from Madagascar. Yet this observed divergence between Madagascar and others SWIO islands was still lower than that observed between continental African A and European M and C populations.Geographic and climatic barriers have played an important role in the evolution of the Western honey bee  or the Comoros (FST = 0.14 to 0.29). Evidence of gene flow between La Réunion, Mauritius, and the Seychelles Archipelago is highlighted by global Bayesian clustering analysis. This may have resulted from past connections via the exchange of colonies, possibly during the colonial period through the route to India in the XVIII century.Nuclear genetic structuration among SWIO populations could be explained by several non-exclusive hypotheses. First, to obtain such genetic differentiation levels among islands, geographic isolation by ocean barriers should have been maintained long enough to restrict the gene flow, resulting in homogenization. By comparison, the local honey bee from the Balearic Archipelago experienced introduction probably in the XVIII and XIX centuries, and the .04–0.27 ) were ofFST values among populations were ≥2x higher than among SWIO islands with different evolutionary lineages coexistence. In comparison, Rodrigues, which is likely from a European genetic background (mtDNA and microsatellites), showed differentiation values higher than the admixed population of Mauritius, La Réunion, or any other islands from the Seychelles or Comoros archipelagos. This could mean that the genetic diversity pool created by European colonies brought to La Réunion and Mauritius might not fully explain their differentiation from other local populations. If not European, the genetic differentiation may have resulted from colonization by or introduction of African lineages. However, Madagascar was the closest native African population the SWIO islands and none of the continental African populations showed similar proximity .Secondly, genetic differentiation among islands could be due only to divergent lineages assembly. Nevertheless, in native continental areas, A. mellifera started to diverge throughout its native range, all islands of the SWIO were completely formed, and the Mascarenes were already colonized by angiosperms. For example, the Dombeyoideae family  and the4 Ma ago  from Haw4 Ma ago –93 or fa4 Ma ago –97. The 4 Ma ago . A simil4 Ma ago . A secon4 Ma ago  and has 4 Ma ago . In all ychelles , and AfrA. m. unicolor from Madagascar to the Mascarenes and Seychelles archipelagos, old enough to detect population differentiation within the sub-lineage. The use of nuclear and mitochondrial markers uncovered the presence of exotic subspecies and different levels of hybridization with indigenous populations in the archipelagos. The numerous interactions recorded between A. mellifera and endemic species [Genetic diversity and structure of honey bee populations of SWIO islands suggests ancient colonization events of  species , 101–105 species , stress Apart from its ecological role, these populations with singular genetic diversity deserve particular attention, especially against the global loss of honey bee colonies –109. NowS1 FigFirst line and from left to right: Geographic positions of 127 sampling sites from La Réunion, 31 from Mauritius, and 20 from Rodrigues in the Mascarenes Archipelago. Second line and from left to right: Geographic positions of the 81 sampling sites from Madagascar, 43 sites in the Seychelles Archipelago with 22 sites from Mahé, 16 from Praslin, and 5 sites from La Digue. N = Number of honey bee colonies sampled by island. Layer used for QGIS map is Open Street Map. Sampling † from (39), * (40), ˟° (38).(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(A) Overall sampling size scale and (B) comparative lower scale. Only the three largest continental populations of Italy, France, and South Africa are represented to increase readability.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigA) STRUCTURE bar plots (K = 2 to 5) for 263 honey bee colonies sampled in Africa and Europe, inferred from 14 microsatellite loci. Each vertical line represents the posterior assignment probability of a single individual to one or more genetic clusters (one color). Sites are separated by black lines. Maternal origin for each individual  defined by the DraI test on the COI-COII intergenic region is presented at the top. B) Average likelihood of runs in STRUCTURE L(K) along with number of K clusters for African and European sites. C) ΔK, estimator of the optimal number of clusters (K) according to Evanno et al. (58). The two graphs were created using Structure Harvester (61).(TIF)Click here for additional data file.S6 FigA) STRUCTURE bar plots at K = 2, B) Average likelihood of runs in STRUCTURE L(K) along with number of clusters (K) for La Réunion. C) ΔK, estimator of the optimal number of clusters (K) according to Evanno et al. (58).(TIF)Click here for additional data file.S7 FigDraI test on the COI-COII intergenic region. B) Average likelihood of runs in STRUCTURE L(K) with the number of K clusters for Mauritius. C) ΔK, estimator of the optimal number of clusters (K) according to Evanno et al. (58).A) STRUCTURE bar plots at K = 2 and 3. Sites are separated by black lines and are ordered from MUS01 to 31. Maternal origin (top) for each individual  defined by the (TIF)Click here for additional data file.S8 FigA) STRUCTURE bar plots from K = 2 to 5. All colonies had haplotypes from the COI-COII intergenic region characteristic of the African evolutionary lineage. B) Average likelihood of runs in STRUCTURE L(K) along with number of K clusters for Comoros Archipelago. C) ΔK, estimator of the optimal number of clusters (K) according to Evanno et al. (58).(TIF)Click here for additional data file.S9 FigA) Average likelihood of runs in STRUCTURE L(K) along with number of K clusters for global STRUCTURE based on 4,388 honey bees Comoros Archipelago . B) ΔK, (TIF)Click here for additional data file.S10 FigDAPC bar plots are presented for K = 3 to 8, based on 4,388 honey bees.(TIF)Click here for additional data file.S11 FigColors of the different clusters correspond to the (TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableCOI-COII: number of individuals with missing COI-COII data, Nall: mean number of alleles; Hnb and Hobs: unbiased expected and observed heterozygosity, respectively; FIS (* significant at P < 0.05) and Anull: mean allele null frequency.N: number of colonies per site; NA(DOCX)Click here for additional data file.S3 TableAfter Bonferroni corrections, permutations tests were only significant among French sites (in bold P < 0.000549). Colors as in (DOCX)Click here for additional data file.S4 TableStatistical significance for the permutation tests after Bonferroni corrections is indicated in bold (P < 0.000476). Colors as in (DOCX)Click here for additional data file.


Galeorhinus galeus Linnaeus, 1758) is a temperate, coastal hound shark found in the Atlantic and Indo-Pacific oceans. In this study, the population structure of Galeorhinus galeus was determined across the entire Southern Hemisphere, where the species is heavily targeted by commercial fisheries, as well as locally, along the South African coastline. Analysis was conducted on a total of 185 samples using 19 microsatellite markers and a 671 bp fragment of the NADH dehydrogenase subunit 2 (ND2) gene. Across the Southern Hemisphere, three geographically distinct clades were recovered, including one from South America , one from Africa  and an Australia-New Zealand clade. Nuclear data revealed significant population subdivisions  indicating limited gene flow for tope sharks across ocean basins. Marked population connectivity was however evident across the Indian Ocean based on Bayesian clustering analysis. More locally in South Africa, F-statistics and multivariate analysis supported moderate to high gene flow across the Atlantic/Indian Ocean boundary , with exception of samples from Struisbaai and Port Elizabeth which differed significantly from the rest. Discriminant and Bayesian clustering analysis indicated admixture in all sampling populations, decreasing from west to east, corroborating possible restriction to gene flow across regional oceanographic barriers. Mitochondrial sequence data recovered seven haplotypes  for South Africa, with one major haplotype shared by 87% of the individuals and at least one private haplotype for each sampling location except Port Elizabeth. As with many other coastal shark species with cosmopolitan distribution, this study confirms the lack of both historical dispersal and inter-oceanic gene flow while also implicating contemporary factors such as oceanic currents and thermal fronts to drive local genetic structure of G. galeus on a smaller spatial scale.The tope shark ( K-selected traits they exhibit such as low fecundity, late sexual maturity and a long lifespan with slow growth rates [Elasmobranchs are currently regarded as one of the most vulnerable extant vertebrate groups and many of the species are threatened with extinction . The expth rates . Furtherth rates . In ordeth rates . This coth rates ,7.Galeorhinus galeus Linnaeus 1758) is a commercially important shark species distributed in temperate waters around the world [G. galeus, Mustelus mento, M. whitney and Squalus acanthias, are lumped under the generic local name “tollo” [G. galeus is ranked as critically endangered and was subject to intensive fishing throughout its distribution. Though drastic declines have occurred, the population continues to be fished without restraint in Argentina and Uruguay [G. galeus comprises only one population extending across Brazil, Uruguay and Argentina with large aggregations of sharks moving in to closed bays of northern Argentina during spring for parturition [Galeorhinus galeus also has an Indo-South Pacific distribution in the Southern Hemisphere wherein it occupies the temperate waters of Australia and New Zealand [G. galeus and as of 1986, implemented eight quota management areas (QMAs). Despite this and genetic evidence for one panmictic population [G. galeus in Australia and New Zealand is currently managed as separate stocks [The tope shark  genetic discontinuity exists across the Southern Hemisphere oceans including the south Pacific, south Atlantic and south Indian Oceans and (2) genetic discontinuity exists across the Indian/Atlantic Ocean boundary with differentiation found between Atlantic and Indian G. galeus. A dual-marker approach was applied where variation in the mitochondrial ND2 gene and 19 microsatellite markers were used to assess genetic diversity and population connectivity of G. galeus across the Southern Hemisphere and the South African coastline.Previous population genetic studies have supported distinct continental populations of e waters ,33. HoweG. galeus [Across the Southern Hemisphere, 185 fin clips or muscle biopsies were collected including 22 from Chile, 10 from Argentina, 124 from South Africa, nine from Australia and 20 from New Zealand . Genetic. galeus . Genomic. galeus .ND2) were analysed for a total of 81 samples of G. galeus using the species-specific primers of Farrell et al., 2009 [2  and 0.5 U GoTaq DNA polymerase . PCR amplifications were performed in an Applied Biosystems (ABI)  thermal cycler version 2.09 using cycling conditions as described by [® Terminator 3.1 Cycle Sequencing Kit  and a ABI 3730xl Genetic Analyser. All mtDNA sequences were manually edited and aligned using the MUSCLE alignment algorithm available in MEGA 6 [Sequences of the mitochondrial gene NADH dehydrogenase subunit 2 . Particular care was taken with allele scoring and control samples were added with each independent capillary electrophoresis run.A total of 185 l., 2011  and nineM. canis ,46. Sout. galeus . The PCRK), number of haplotypes (H), haplotype diversity (h) and nucleotide diversity (π). Genetic structure across sampling sites was investigated using two different approaches. Firstly, an analysis of molecular variance (AMOVA) [CT), among populations (ФSC), and within populations (ФSC). The Kimura-2 (K2) model selected according to the Bayesian Information Criterion (BIC) generated in MEGA [ST) based on haplotype frequencies were estimated across the Atlantic, South Indian and South Pacific oceans. Pairwise ΦST values were computed in ARLEQUIN using 20,000 permutations for both Southern Hemisphere and South African datasets. Sequential false discovery rate (FDR) corrections of the significant values were performed following the Benjamini and Hochberg (B–H) method [The software DNASP and ARLEQUIN 3.5  were use (AMOVA)  was cond in MEGA  was empl) method . The rec) method . A phylo) method  based on) method  to creatD [S [R) and sum of squared deviations (SSD) indices [To assess the demographic history of the populations, past demographic and population expansions were evaluated using two methods. Firstly, using the neutrality test, computation of Tajima’s D  and Fu’sD [S  statistiD [S . In addi indices  were calПs) was computed in HP-RARE 1.1 [a priori defined grouping of Atlantic-  versus South Indian Ocean sampling populations . The genetic distance matrix for all AMOVAs was estimated by pairwise differences and the significance levels of the variance components and F-statistics values were tested by 20,000 nonparametric permutations. Pairwise FST was estimated for all pair of samples and significance was tested using 20,000 permutations in ARLEQUIN. A false discovery rate was determined for multiple tests using the B–H method and applied to minimise type I errors. Tests for isolation-by-distance (IBD) were performed for the South African samples using the web interface isolation by distance web service (IBDWS) 3.23 [ST values against corresponding minimum geographical distances. Geographic distances were measured with the path tool option in GoogleEarth 6.2.2 (Google Inc.) using the shortest path, via sea, between any two sampling locations and assuming that G. galeus travels along the coast. Significance was tested by 30,000 randomisations of the data.Departure from the expectations of Hardy-Weinberg Equilibrium (HWE) was examined locus by locus and across each geographic sample in ARLEQUIN. Linkage disequilibrium (LD) between all pairs of loci was also tested in ARLEQUIN, followed by correction for multiple comparisons. Microsatellite scoring errors due to stuttering, large allele dropouts and null alleles were assessed in MICROCHECKER 2.2.3 . IndicesRARE 1.1  using thWS) 3.23  by plottfind.clusters function, which runs successive K-means clustering with increasing number of clusters (k), was used to assess the number of clusters which maximizes between group variance and minimizes within group variance [k, we applied the Bayesian Information Criterion (BIC) for assessing the best supported model. Then, DAPC was performed on the pre-defined clusters based on geographical sampling location (i.e. k = K) using the dapc function. Finally, a Bayesian clustering analysis was performed in STRUCTURE 2.3.4 [6 iterations were made with 105 burn-in periods using an admixture model with correlated allele frequencies. The web-based STRUCTURE HARVESTER 0.6.93 software [Discriminant analysis of principal components (DAPC), a non-model-based (multivariate) clustering method, was implemented in the R package ADEGENET . The DAPvariance . For selRE 2.3.4  to detecsoftware  was usedsoftware . The prosoftware  was usedND2 gene was sequenced and analysed for a total of 96 G. galeus samples. Across the Southern Hemisphere, this resulted in a total of 15 haplotypes ranging from one (NZ) to six (SA) per geographical location. The overall haplotype diversity (h) was 0.626 ± 0.057 with a nucleotide diversity (π) of 0.004 ± 0.001 (h = 0.822 ± 0.097). The haplotype network indicated that haplotypes were almost exclusively associated with one of three ND2 lineages linked to geographical origin, one including all South African samples, one including all Australian and New Zealand samples and a lineage of South American origin . None of the loci showed evidence of scoring errors due to stuttering, large allele dropouts or the presence of null alleles in MICRO-CHECKER. All diversity estimates for each location are presented in A) and unbiased expected heterozygosity (uHE) ranged from 3(ARG) to 11(SA) and 0.373(ARG) to 0.807(CHI) respectively. Unbiased expected heterozygosity (uHE) and the effective number of alleles (NE) shows nuclear genetic diversity to be higher in the Pacific Ocean  relative to the rest of the Southern Hemisphere locations. Mean rarefied private allelic richness per locus and per location averaged 1.21  and nucleotide (π = 0.001 ± 0.000) diversity overall. One major haplotype was shared amongst 87% of individuals and all sampling sites except Port Elizabeth exhibited at least one unique haplotype (P < 0.05) at seven  of the 19 loci. No LD was present for any of the loci pairwise comparisons. MICROCHECKER indicated no scoring errors due to stuttering, large allele dropout or the presence of null alleles. Nuclear genotypic diversity such as unbiased expected heterozygosity and allelic richness were comparable for G. galeus across the Atlantic and Indian Ocean. The overall number of alleles ranged from NA = 5 to 6 in the Indian Ocean samples, and from NA = 4 to 8 in the Atlantic Ocean samples. Expected heterozygosity was highest for Robben Island (uHE = 0.707) and lowest for Struisbaai (uHE = 0.600)  .ND2 gene, genetic differentiation was evident among geographic sampling populations since based on AMOVA, only a small percentage of variation was explained by the within-population level of subdivision while a significant level of variation amongst the geographic populations existed . Further grouping hypotheses to test for structuring between ocean basins were not significant , irrespective of South Africa being grouped with the Atlantic- or Indian Ocean  except for between New Zealand and Australia . Substantial population isolation was evident within the Atlantic , Indian  and Pacific Ocean  samples.Based on the an Ocean . All of ustralia . OverallCT = 0.137, P < 0.05) while none of the a priori grouping hypotheses tested was significant . For the K-means method, a k value of nine  represented the best summary of the data that maximized the variance between groups while minimizing the variance within groups. When using the pre-defined clusters based on geographical sampling location, the DAPC plot confirmed strong separation between the five Southern Hemisphere populations of  overlap . Finally overlap . The ass overlap . For thi overlap . The ass overlap .ND2 sequence variation across six local populations resulted in seven haplotypes, with one common haplotype shared among all the sampling populations. The AMOVA analysis showed no significant molecular variation amongst the sampling populations  with most of the variation attributed to amongst individual variation within populations. Also, no significant variation was detected between Indian and Atlantic Ocean samples  . For the nuclear data, the single level AMOVA testing for population differentiation, was significant  while the hierarchical AMOVA showed no differentiation between oceanic basins tested for a priori     . The pai= 0.273) . Pairwis and SB) . Low but 0.1478) . We therK = 3–7 while Delta K supported three clusters  for all of the geographic sampling populations (P = 0.652) was also not evident. Since the haplotype genealogies depict three clades most likely linked to continental shelves, the analysis of demographic history was also presented by clade rather than by sampling location. Tests for neutrality indicated a departure from mutation-drift equilibrium for all three clades and the unimodal curves detected for the mismatch distribution was also indicative of populations having passed through a recent demographic expansion. Although the observed mismatch distribution was compared to two models of population development, the expansion and decline model versus the constant model, these observations were carefully interpreted as observed mismatch distributions may be a consequence of several demographic processes.Overall the parameters of neutrality for ulations , suggestP = 0.040) reflecting an excess of rare polymorphisms and population expansion in the past (P = 0.010). This was further supported by the non-significance for the sum of squares distribution (SSD) and relatively low levels of Harpending’s raggedness index obtained for all sampling populations. For the entire South African dataset, a process of expansion is suggested by the unimodal curve of mismatch distributions but was not statistically supported . The latter observation of deviation from neutrality for the pooled South African dataset could well be an artefact of sampling in that the South African samples do not necesarrily represent a panmictic population assumed not to be affected by local, rapid demographic processes [On a local scale, only the collection of Robben Island showed significant Tajima’s D value  is comparable to those reported for other commercially exploited shark species such as Mustelus mustelus, Sphyrna lewini, Carcharhinus brachyurus and C. falciformis [e.g. Prionace glauca and Carcharhinus falciformis), recent studies have reported low levels of genetic diversity also for highly migratory pelagic species including Pseudocarcharias kamoharai [Carcharhinus longimanus [Patterns of contemporary and historical gene flow were determined for ciformis ,26,27,66amoharai  and Carcngimanus .ND2 haplotypes, this study confirms historical dispersal for G. galeus along continental shelves and over short geographic distances with CHI and ARG as well as NZ and AUS sharing a single haplotype. This is supported by pairwise ФST values and haplotype genealogy showing association with geographical distance. These results are in agreement with previous findings for this species, suggesting a lack of historical gene flow across the large open expanses of the South Atlantic, South Pacific and South Indian oceans [e.g. one sample from Argentina pooled with 11 samples from Peru. Divergent lineages with geographic correspondence, as was seen with G. galeus, can result from two alternative scenarios; vicariance or lineage sorting [S. lewini [Carcharhinus limbatus [C. brachyurus [C. leucas [G. galeus in southern Australia and New Zealand [P. glauca in the Pacific Ocean [G. galeus across the Southern Hemisphere.Based on mtDNA n oceans . The stun oceans  also pos sorting . Since i sorting  and the  sorting . The clo sorting . The net sorting ,33 sugge sorting  reported. lewini , Carcharlimbatus , C. bracachyurus  and C. l. leucas , which s Zealand  and P. gic Ocean . This syic Ocean , this stG. galeus over smaller and larger spatial scales. For example, the Mid-Atlantic and Benguela barriers together with the presence of gyres and straits possibly restricts gene flow between sampling populations of Argentina and South Africa while the Tasman Sea and the Great Australian Bight (GAB) are likely barriers between Australian samples and that of New Zealand. It should be noted that a panmictic population of G. galeus was previously found to exist between Australia and New Zealand [Carcharodon carcharias) [Galeocerdo cuvier) [G. galeus with migration around the tip of South Africa, most likely associated with the Agulhas leakage [G. galeus exhibits extensive migratory patterns within the Indian and Pacific ocean basins [G. galeus aggregates during autumn (March to May) and spring (September to November) when water temperatures are slightly cooler. Across the Tasman Sea, Hernández et al., 2015 [G. galeus migrates between New Zealand and southern Australia and that these migrations occur more often over time. Similar to aggregation patterns noted in South Africa, G. galeus tends to aggregate in large numbers during spring and summer within the South West Atlantic (SWA), in closed gulfs and bays of northern Patagonia, and are believed to be the primary nursery grounds for the species [G. galeus in the main nursery ground for the SWA and showed that the species prefers cool temperate waters ranging from temperatures between 17°C and 19°C and exhibits a yo-yo oscillatary movement within the water column. The seasonal migratory patterns exhibited by the aforementioned studies seem to indicate that G. galeus habours nursery grounds within coastal bay areas and seasonally aggregates towards these sites. Further understanding these migratory patterns will play an important role in the development of informed fishing and regulatory policies, particularly regarding protection measures for critical habitats such as G. galeus nursery grounds.The microsatellite data did not support contemporary gene flow between all geographic sampling sites implying that known biogeographic barriers in the Southern Hemisphere can hinder gene flow for  Zealand  and did charias)  and the  cuvier)  and high leakage . Previoun basins ,21. On al., 2015  showed t species . Also, C species  studied ND2 divergence was observed across the Atlantic/ Indian Ocean boundary, illustrating high levels of connectivity across the South African coastline. A single genealogical clade was detected indicating historical admixture between the Indian and Atlantic Ocean or quite possibly incomplete lineage sorting due to recent co-ancestry. The overall low haplotypic diversity in combination with a single haplotype shared by most of the individuals is similar to what was found for Mustelus mustelus [Carcharodon carcharias [G. galeus.On a local scale, no inter-oceanic mustelus  and Carcrcharias  assessedrcharias ,33 and rrcharias ,79. The rcharias . Bigger ST and standardized G”ST values in particular, confirmed low but significant levels of genetic differentiation amongst local populations. Very similar levels of heterogeneity in microsatellite allelic distributions has recently been reported for smaller coastal shark species over range-wide and more restricted distributions including M. mustelus, M. henlei and Carcharhinus isodon [G. galeus were predicted to occur as a result of habitat preference [G. galeus. Most noteworthy in the aforementioned study is that the coalescent analysis of migration supported assymetric gene flow of G. galeus from the Indian to the Atlantic Ocean, concordant with the Atlantic Ocean–Indian Ocean connection of G. galeus via Agulhas leakage proposed in the current study.The microsatellite dataset, and pairwise Fs isodon ,81,82. Ss isodon  and thateference  and couleference  also impGaleorhinus galeus. On a Southern Hemisphere scale, all sampled populations comprise distinct genetic groups and therefore management units in fisheries terms. This implies that any form of replenishment in the Pacific, Atlantic and Indian oceans will have to be done locally, without any genetic input from geographically distant populations. For local samples of G. galeus, the genetic data suggests that there could be more than one contemporary barrier affecting gene flow along the South African coastline. Although this do not result in fully differentiated ‘stocks’ in the classic fisheries sense, local managers should recognise the existence of a highly admixed population along the south-west coast and possibly more discrete populations on the east coast. We therefore suggest that locally, G. galeus should be managed, not just on an ecosystems-based approach in line with the marine bioregions of South Africa, but it should be taken into account that since most of the fishery efforts are centered on the southwestern coast, G. galeus of Atlantic origin might be most vulnerable. Furthermore, differences exhibited in mitochondrial haplotypes and microsatellite genotypes, between these and other populations included from the Southern Hemisphere, could facilitate trade-monitoring efforts for internationally traded products such as fins and meat which are known to be exported from South Africa to other countries.The outcomes of this study could have immediate implications for the local and more global management of S1 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(DOCX)Click here for additional data file.


Xenopsylla cheopis), which infests several mammals, primarily rats (Rattus spp.), is the most notorious vector of human plague. In this study, we measured the genetic differentiation among populations of fleas from the Asian house rat (Rattus tanezumi) and the brown rat (R. norvegicus) using microsatellite markers in order to investigate the extent of host-switching in this parasite.The oriental rat flea (P < 0.001) of the variation was derived from within populations, followed by variation among groups ; in contrast, variation within groups of populations was nearly absent . Analyses of the pairwise fixation index revealed that most of the ten allopatric population pairs but none of the five sympatric population pairs were significantly differentiated. Moreover, based on genetic structure clustering analysis, there was obvious differentiation between allopatric populations but not between sympatric population pairs.We developed 11 polymorphic microsatellite loci for our study, nine of which showed high potential for inbreeding. AMOVA showed that the majority (84.07%, These results indicate the presence of frequent migrations of the oriental rat flea between the sympatric Asian house rat and brown rat, causing a high rate of gene flow and limited genetic differentiation. We suggest that there is no clear boundary limiting the migration of oriental rat fleas between the two hosts, and thus both rat species should be monitored equally for the purposes of plague prevention and control. Yersinia pestis, is the underlying cause of several of the worst pandemics in human history. There have been three major historical plague pandemics that caused a total of 200 million deaths: the first occurred in the area around the Mediterranean Sea in the 6th century, the second occurred in Europe in the 14th century, and the third started in China during the middle of the 19th century and from there spread worldwide  have been recorded as infected by Y. pestis, the oriental rat fleas contributing over 90% of the total number of infected flea samples [Rattus tanezumi) and brown rat (R. norvegicus) [China has one of the highest rates of plague incidences. There are 12 natural plague epidemic foci, involving 19 provinces and covering an area of 143.45 km2 . In Yunn samples . The maivegicus)  and the Many plague outbreaks have involved fleas that are able to infect multiple host species. Although it is widely accepted that flea switching among different sympatric hosts increases plague prevalence , few stuMicrosatellites are highly polymorphic molecular markers that are frequently used in population genetic analyses to assess levels of relatedness among populations . In thisWe trapped live rats using trap cages in the houses of local residents from five sites in Yunnan province, China Table , Fig. 1.EcoRI and HindIII. Adaptors containing EcoRI and MseI sites were ligated onto the fragments using T4 ligase . Enrichment for the motifs (AG)15 and (GT)15 was performed using Streptavidin MagneSphere Paramagnetic Particles . Enriched DNA was PCR-amplified and cloned using the pMD18-T Vector System Kit . Following β-galactosidase screening, ~100 plasmids were isolated, and cloned inserts were amplified and sequenced using M13 universal primers on an automated ABI 3730xl DNA Analyzer . Primer sequences were designed by Primers 5  for 40 loci and were tested for their ability to produce a readable pattern using genomic DNA samples from 10 randomly selected fleas. Finally, 11 primer pairs were selected as working primers. The primer sequences and repeat motif information for all 11 microsatellite loci are listed in Table Microsatellites were developed using a traditional isolation protocol . Briefly2O, 0.4 μl of dNTPs, 2 μl of buffer, 0.3 μl of each F/R primer, 2 μl of template DNA, and 0.2 μl of Taq DNA polymerase. Reaction mixtures were denatured at 94 °C for 5 min and subjected to 35 cycles of 94 °C for 30 s, 54 °C for 35 s, and 72 °C for 40 s, followed by a final extension step of 72 °C for 3 min. PCR products were separated using a non-denaturing polyacrylamide (8%) gel. GeneScan was used for polymorphism detection on an ABI 3730xl DNA Analyzer system . Null alleles, scoring errors, and large allelic drop-outs in the microsatellite data were checked using GeneMarker v.1.9.1 [Total DNA from each flea was extracted using the DNeasy tissue kit. The 5' end of each forward primer was tagged with a FAM fluorophore. All 20 μl PCR reactions were performed using an ABI Veriti96 PCR kit  and contained 14.8 μl of ddHHo) and expected heterozygosity (He) of each microsatellite locus were determined for all samples, and tests of Hardy-Weinberg equilibrium (HWE) were carried out using Arlequin v.3.11 [FIS) of the 11 loci were analyzed using POPGene v.1.32 [FST) calculations were also conducted in Arlequin. For AMOVA, we defined three levels of genetic structure: among groups, namely among the five allopatric sites; within groups of populations, namely between sympatric populations of the five allopatric sites; and within populations, namely within each of the ten populations. For FST analysis, we only calculated ten allopatric and five sympatric population pairs. We also calculated the geographical distances between each pair of allopatric sampling sites using ArcGIS v.10.5  and tested for correlations between geographical distances and allopatric FST values using SPSS v.25.0 .The observed heterozygosity (n v.3.11 . Tests te v.1.32 . AMOVA (K) with 20,000 iterations used for burn-in. Probability estimates were determined using 100,000 Markov chain Monte Carlo (MCMC) iterations. Values of K were set from 1 to 10 with five runs for each value under an admixture model. In addition, the software programs Structure harvester [The software program STRUCTURE v.2.3.4  was usedarvester , CLUMPP arvester , and DisP < 0.01)  were positive. The observed frequency (Fo) of each locus was distributed within the range of the 95% confidence interval of the mean.All 11 of the newly developed microsatellite loci exhibited polymorphisms among the flea samples. A total of 61 alleles were detected at the 11 loci, among which the p19 locus exhibited the highest allelic richness with 12 alleles. All loci except for p29 significantly deviated from Hardy-Weinberg equilibrium (P < 0.001). Variation among groups, namely among the five geographical groups, accounted for 14.96% of the total variation (P < 0.001). However, variation within groups of populations, namely within five geographical groups of two sympatric hosts, accounted for only 0.97% of the total variation and was not a significant source of variation (P > 0.05)  of variation was derived from within populations, namely within the ten flea populations (5) Table .Table 3RFST values of fleas from Asian house rats  and brown rats  were significantly correlated with the geographical distance between populations.Analysis of the pairwise fixation indexes of the 11 loci revealed that, for the fleas from the Asian house rat, seven out of 10 allopatric population pairs were significantly differentiated, while for the fleas from the brown rat, eight out of 10 allopatric population pairs were significantly differentiated Table . InteresK = 2. Clustering results showed that there were considerable structural differences among the five allopatric populations. Specifically, the MG population pairs were more distinct than the other four population pairs  [Cynomys ludovicianus) may play an important role in dispersing O. hirsuta. According to this hypothesis, the oriental rat flea could be considered to have considerable a close relationship with its Rattus hosts. One may note that, since the oriental rat flea could also infest more than one host species, one rat might also influence the spread of fleas from the other rat species (and vice versa). We suggest that it will not happen in our oriental rat flea case, because the two house rats have rather similar biological and ecological habits [In the current study, we used 11 newly developed microsatellite loci to assess genetic variation among flea populations. First, neutrality tests showed that the hirsuta) , in whicl habits .Rattus spp. [Ho < He) supports to some degree the possibility of inbreeding among the fleas [It should be emphasized that, although there was significant differentiation among the allopatric flea populations, none of the five sympatric population pairs were significantly differentiated. The oriental rat flea parasitizes many genera of rodents inside/outside houses but prefers tus spp. , of whictus spp. , 25. Thetus spp. , 27. Thutus spp. . Moreovehe fleas . As a reAs mentioned above, the oriental rat flea is the predominant vector of bubonic plague. Although the study area, Yunnan, is the likely site for the origin of the third plague pandemic , the epiWhile it is widely accepted that flea switching among different sympatric hosts increases plague prevalence, little is known about the extent of switching among hosts. In this study, we measured the genetic differentiation among populations of fleas from two sympatric rat hosts using microsatellite markers. The AMOVA results showed that the majority of the variation was derived from within populations while variation between sympatric populations within the five allopatric sites was nearly absent. Analyses of the pairwise fixation index revealed that most of the ten allopatric population pairs but none of the five sympatric population pairs were significantly differentiated. Moreover, based on genetic structure clustering analysis, there was obvious differentiation between allopatric populations but not between sympatric population pairs. These results indicate the presence of frequent migrations of the oriental rat flea between the sympatric Asian house rat and brown rat, causing a high rate of gene flow and limited genetic differentiation. We suggest that there is no clear boundary limiting the migration of oriental rat fleas between the two hosts, and thus both rat species should be monitored equally for the purposes of plague prevention and control.


Glycyrrhiza inflata B. (Fabaceae) is restricted to the fragmented desert habitat in China and has undergone a dramatic decline due to long-term over-excavation. Determining the genetic structure of the G. inflata population and identifying a core collection could help with the development of strategies to conserve this species.Habitat fragmentation, water resources and biological characteristics are important factors that shape the genetic structure and geographical distribution of desert plants. Analysis of the relationships between these factors and population genetic variation should help to determine the evolutionary potential and conservation strategies for genetic resources for desert plant populations. As a traditional Chinese herb, G. inflata populations based on microsatellite markers. A high level of population genetic divergence (FST = 0.257), population bottlenecks, reduced gene flow and moderate genetic variation (HE = 0.383) were detected. The genetic distances between the populations significantly correlated with the geographical distances, and this suggests that habitat fragmentation has driven a special genetic structure of G. inflata in China through isolation by distance. STRUCTURE analysis showed that G. inflata populations were structured into three clusters and that the populations belonged to multiple water systems, which suggests that water resources were related to the genetic structure of G. inflata. In addition, the biological characteristics of the perennial species G. inflata, such as its long-lived seeds, asexual reproduction, and oasis ecology, may be related to its resistance to habitat fragmentation. A core collection of G. inflata, that included 57 accessions was further identified, which captured the main allelic diversity of G. inflata.We investigated the genetic variation of 25 G. inflata has been shaped by habitat fragmentation, water resources and biological characteristics. This genetic information and core collection will facilitate the conservation of wild germplasm and breeding of this Chinese medicinal plant.Recent habitat fragmentation has accelerated genetic divergence. The population genetic structure of  Habitat fragmentation, water resources and biological characteristics are important factors that shape the genetic structure and geographical distribution of desert plants. Analyses of the relationships between these factors and variations in population genetics should help determine the evolutionary potential and conservation strategies of genetic resources for desert plant populations –5. As inGlycyrrhiza inflata Batal. (Fabaceae) is one of the most important medicinal plant species that is threatened by habitat fragmentation. G. inflata has been officially recorded as the source plant of Radix Glycyrrhizae (liquorice) in Chinese pharmacopoeia [G. inflata have been used as Chinese medicines, flavouring agents, tobacco additives [G. inflata flowers from May to July. The flower size variation ranges from 6 to 12 mm, with purple or light purple corolla [G. inflata are small and hard, and their germination is difficult in extremely dry and cold climates. G. inflata is a perennial herb that possesses strong underground rhizomes and reproduces both sexually by seeds and asexually by below-ground rhizomes. As a drought-tolerant and deep-rooted plant, G. inflata has a fully developed rhizome system, which is very important for sand fixing in arid and semi-arid ecosystems [Distributed in the northwestern desert areas, including the Taklamakan Desert, the Hami basin and the Hexi Corridor of Gansu province in China, acopoeia . The drydditives  and cosmdditives . Liquoridditives . Liquoridditives –20. G. i corolla . The seeosystems ,22.G. inflata populations. Phylogeographical studies have found that desertification in the Tarim Basin has been present since the early Pleistocene [G. inflata [G. inflata have not received effective protection due to the lack of rational use and conservation strategies. The low quality of cultivated products does not meet the requirements of Chinese pharmacopoeia, resulting in the over-excavation of wild resources. Data on the genetic diversity of G. inflata and the factors that shape this structure are essential for formulating appropriate conservation strategies and are also key factors for improving cultivated germplasms [G. inflata have mainly focused on its phytochemistry and pharmacology and have paid less attention to the distribution of genetic variation across geographical ranges.The Taklamakan Desert is the largest desert of China, and it is the largest habitat of istocene . Becauseistocene . Plant pistocene . In addi inflata ,25. Althrmplasms ,27. PrevG. inflata have been developed or reported for the conservation and breeding of this important medicinal plant species.In addition, the development of core collections, which consist of subsets of an entire collection with minimal redundancy, maximizes the possible genetic diversity of a species; thus, this is one of the most effective approaches for conserving and exploring the genetic variation of a species ,29. At tSSRs (simple sequence repeats) are comprehensively distributed throughout the plant genome and are very suitable for examining the genetic diversity of plants to identify abundant polymorphisms, co-dominant inheritance, and transferability by employing simple technology (PCR-based) . With thG. inflata in China, using 20 EST-SSR markers. Furthermore, based on this genetic information, a core collection of G. inflata was identified to maximize the representativeness of the genetic diversity. Our results lay an important foundation for the conservation of wild resources and the breeding of G. inflata.In the present study, we aimed to estimate the genetic diversity, population genetic structure patterns, and the impact of habitat fragmentation, water resources and the biological characteristics on the genetic diversity of G. inflata were sampled from 25 natural populations, which covered almost the entire geographical range of this species in China. Leaf samples from 15 to 20 individuals were randomly sampled from each population and dried using silica gel. Because G. inflata has transverse rhizomes that are approximately 3–5 m in length, samples were collected at 5 m intervals to avoid collection from the same clone. Among these 25 populations, three populations  were collected from the Hexi Corridor of Gansu province, four populations  were collected from the Hami Basin of Xinjiang province, and the remaining 18 populations  were distributed in the Tarim Basin of Xinjiang province. In addition, the spatial coordinates  of each population were recorded using a handheld GPS . The representative specimens of plants from each population were preserved in the herbarium of the South China Botanical Garden, Chinese Academy of Sciences .Young leaves of 446 individuals of G. inflata was not an officially endangered or endemic plant species in 2011 (http://www.plant.csdb.cn/protectlist and http://rep.iplant.cn/protlist/2?page=42), and our study was not related to any commercial harvest of wild materials. Therefore, no specific permissions were required for collecting young leaves. Additionally, only small amounts of young leaves were collected to extract DNA in this study, and the natural conditions of wild plants were not affected. Moreover, none of the populations were located in protected areas or private land, and thus no specific permission was required to work with these populations.Glycyrrhiza species were downloaded from NCBI. EST-SSR loci were identified using MISA [A total of 50,000 unigenes of ing MISA , and pri-1.Genomic DNA was extracted from dried leaves using a modified cetyltrimethyl ammonium bromide (CTAB) method . The DNA2, 0.2 mM of dNTP mix, 0.5 μM of 5′-tagged forward primers (FAM and HEX) and 3′ general primers, 0.1 U of Taq polymerase  and 10–50 ng of template DNA. The PCR thermal profile was as follows: denaturation at 94°C for 4 min, followed by 30 cycles at 94°C for 30 s, 51–57°C for 30 s, 72°C for 45 s, and a final 10-min extension step at 72°C. The fragments were genotyped using an ABI 3730XL capillary sequencer  with LIZ-500 as an internal size standard label (Applied Biosystems). The resulting chromatograms were visualized and analysed using GeneMarker V1.5 .All of the samples were genotyped with 20 EST-SSRs. The PCR reaction volume was 10 μL, which contained 1 × PCR reaction buffer, 1.5 mM of MgClG. inflata can use their rhizomes for asexual reproduction, the individuals sampled from the same population could be same-clone individuals even though we sampled the plants at 5 m intervals. The same clone individuals were tested using GenAlEx 6.1 [Because AlEx 6.1 ,53. The AlEx 6.1 . DeviatiAlEx 6.1 ,56.FST values. This approach is based on the theory that the outlier loci, which are candidates for being subject to natural selection, have low or high levels of genetic differentiation compared to neutral expectations [FST values were estimated for comparison with the FST values of all loci at the 95% confidence intervals. LOSITAN starts by running 100,000 simulations, and then the distribution of FST can be computed using putatively neutral loci that are derived from the simulations. Second, BayeScan v2.1 (http://cmpg.unibe.ch/software/bayescan/) was used to identified the candidate outlier loci based on the hierarchical Bayesian method [Two methods for neutrality tests were employed for all of the microsatellite loci. First, LOSITAN 1.0.0  softwarectations . In thisn method . The parNE), allelic richness (AR) and inbreeding coefficient (FIS) were analysed with FSTAT 2.9.3.2 [s) was estimated to be s = 2 FIS / (1+ FIS) [HO) and expected (HE) heterozygosities [Within each population, the number of effective alleles  . The obsgosities  were estgosities ,53. The FST statistic, G’ST, G”ST and Dest, which were implemented in FSTAT2.9.3.2 and GENALEX 6.3 [K) was set from 1 to 10, and each value of K was determined over 10 runs with an admixture model that correlated allele frequencies using a burn-in length of 50,000 iterations and 100,000 MCMC iterations. Structure Harvester (http://taylor0.biology.ucla.edu/structureHarvester/), a web-based programme for collating the results generated by the programme STRUCTURE, was used to define the most reasonable K value [The genetic differentiation between populations was evaluated by the ALEX 6.3 ,53. STRUALEX 6.3 ,65. The  K value . Analysi K value .FST / (1-FST)) and geographic distance was assessed using Mantel’s test [The association between genetic distance , between pairs of populations over a long period of time (~4Ne generations) using nuclear microsatellite data under the suggested Brownian microsatellite mutation model. The ML mode was used to analyse these datasets. An adaptive heating scheme was used, and the model was run for 10 short chains with 100,000 visited and 5000 stored genealogies and two long chains with 10,000,000 visited and 50,000 sampled genealogies [mc values, which represent the gene flow between pairs of populations over the past few generations. The two software systems were used to estimate the migration rates based on the neutral loci identified in outlier tests.Based on the coalescent  theory, ealogies . The conealogies , which uI) was used as the second criterion of maximization, and the core collection was constructed using 70 maximum iterations and 10 replications.The M strategy (Maximization strategy) was proposed by Schoen and Brown  and implFST values from the initial data and the revised data, we did not find any significant biases . Linkage disequilibrium analyses for 20 EST-SSR markers indicated that 228 of 6,460 (3.5%) comparisons were significant after the Bonferroni correction, but they did not centre on a specific population or locus. Therefore, the linkage disequilibrium of these 20 EST-SSR markers was not significant. The test for Hardy-Weinberg equilibrium found that out of 680 locus-population combinations in G. inflata, 177, 79 and 38  showed significant deviation at P = 0.05, 0.01 and 0.001, respectively. Twelve populations  showed significant deviations from HWE due to the excess of heterozygotes, whereas four populations  showed significant deviations from HWE due to the deficiencies in heterozygotes. Among these 20 loci, a total of three  and one (C464) loci were detected as outlier loci by LOSITAN and BAYESCAN, respectively. The number of effective alleles (NE), allelic richness (AR), observed (HO) and expected (HE) heterozygosity, inbreeding coefficient (FIS) and selfing rate (s) are listed in HO within a population was 0.460, and it ranged from 0.252 to 0.723, which was slightly higher than the HE (averaged 0.383). The average of the inbreeding coefficient (FIS) values was -0.182 (P = 0.0001), and the coefficient value ranged from -0.88 to 0.107. The average number of different alleles (np) within every population was 55, and the average ratio of the number of different alleles (np) within one population to the number of different alleles within the species  (ns) was 31.39%. Compared with the species level, a small number of alleles (19.89–39.20%) were present in every population, suggesting a low level of genetic variation in some populations and a high level of differentiation among the populations . The analysis of molecular variance (AMOVA) revealed that the majority of genetic variation occurred within populations  and that genetic variation among populations constituted 22.94%  between the genetic distance (FST / (1-FST)) and geographical distance of G. inflata populations in China  had experienced population bottlenecks under the assumption of the IPM and IAM models. In addition, eight of the 15 populations  displayed a shifted distribution of allele frequencies , but sigG. inflata, and the average of mc was 0.0128, with values that ranged from 0.0078 to 0.1518 , which was used as the second criteria of maximization. The Shannon indexes (I) obtained from the 10 best core collections ranged from 0.395 to 0.517. Each of the 10 cores consisted of accessions from 3 clusters, which provided 17.6%, 14.5% and 7.1% of the samples to the core, respectively , Minfeng (MF) and Luopu (LP) populations, is more fragile than the northern margin and more sensitive to human activities [The significant population bottlenecks might be related to a recent decline in population size because of habitat destruction. Fourteen populations that experienced bottlenecks exhibit an excess of heterozygosity. During a population decline, the number of alleles decreases more rapidly than the heterozygosity; thus, when a population that recently underwent a population decline exhibits an excess of heterozygosity . This fitivities . Agricultivities . In additivities .G. inflata are small and germinate with difficulty in natural habitats [Contemporary gene flow was found to be lower than historical gene flow, which suggests that gene flow has been further reduced during the recent habitat fragmentation caused by human factors. It has been known that the gene flow of seed plants is associated with pollen or seeds . The seehabitats , althoughabitats ,82.FST = 0.257, G’ST = 0.244, G”ST = 0.414 and Dest = 0.226, P < 0.001) [Lycium ruthenicum (GST = 0.2155) and lower than that of the endangered species Gymnocarpos przewalskii (GST = 0.588) [G. inflata). The Mantel test revealed that the genetic distances (FST / (1-FST)) between populations were significantly correlated with the geographical distances. In addition, the populations distributed in the junctions between the clusters have mixed structure types   [Medicago ruthenica  [Rheum tanguticum  [G. inflata has a high level of genetic divergence.Fragmentation usually decreases the size of habitats and increases the spatial distances between habitats or populations, thus leading to ‘isolation by distance’ (IBD), which is considered to be the primary factor that leads to a high divergence between populations and a special genetic structure (SGS) . Our stu< 0.001)  that was= 0.588) ,84 (the  and ZP) , and ZP, and ZP)  and thusulations . Therefo, 5.21%)  and xero 10.81%)  and simip/ns = 31.39%) was revealed in the G. inflata species. Due to the explosive increase in the human population, the natural habitat of G. inflata has been gradually encroached upon by agricultural, industrial and urban development, resulting in a dramatic decline of the number of populations of this species. These isolated or small populations of G. inflata are more vulnerable to the impact of genetic drift, which may have caused the reduced numbers of alleles and polymorphism within the populations as well as the random fixation of differentiating alleles in different populations.A low level of polymorphism within populations . When genomic and EST-SSRs were compared using the same set of genotypes, the number of polymorphisms detected with EST-SSRs was lower than that of genomic SSRs [HE = 0.32) was significantly reduced in the expressed sequence tag EST-SSRs compared with anonymous SSRs (HE = 0.59) in lettuce [Populus euphratica has a higher genetic variation  [G. inflata may have be due to its biological characteristics as a desert plant. First, as a perennial species, G. inflata may be expected to be less vulnerable than annual species to habitat fragmentation [G. inflata  are annual, but the underground parts are perennial [G. inflata plants die in the winter, these parts turn green again during the following spring. Compared to short-lived species, individual plants or sub-populations of long-lived species such as G. inflata are more likely to persist, easing the pressure from habitat fragmentation. Second, asexual reproduction using underground roots or rhizomes can produce individuals that are genetically identical to their parents, which is an effective mechanism for the protection of genetic diversity. In desert plants, asexual reproduction is one of the main adaptive traits [G. inflata germinate with difficulty in extremely dry and cold climates [G. inflata can remain active during long periods of dormancy. Seeds that fall into the soil can form a seed bank and germinate gradually according to the degree of damage to the seed coat. The seed bank provides the plants with the opportunity to re-establish a population following destruction. Fourth, the Taklamakan Desert shows typical oasis ecology in which oases are distributed near the rivers. The desert provides a natural buffer zone between the oases. In other words, the distribution of G. inflata in the desert may have already experienced habitat fragmentation for a substantial amount of time. Thus, G. inflata may have acquired adaptability and resistance to the habitat fragmentation due to the removal of maladapted genotype or biological characteristics. For example, some researchers have found that, compared to Glycyrrhiza uralensis Fisch, which is distributed in the prairie, there are more adventitious buds on the underground stems of G. inflata and marginal, somewhat undulate G. inflata leaflets, which are beneficial for the survival and expansion of the G. inflata populations in the desert [Here, the mic SSRs ,95. Simk lettuce . Nevo an lettuce  have pro3–0.878) . By comperennial . Althougclimates ,98. Howee desert ,21.G. inflata with 57 accessions was identified, and this collection successfully covered the allelic diversity of the G. inflata species. This collection is the first core collection of Glycyrrhiza Linn. The core collection of G. inflata that was developed will facilitate the breeding of new cultivars of G. inflata to meet market demand as well as meeting the demands of genetic and phytochemical studies. In addition, we observed that nine populations  provided more than 50% of the samples to the core Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOC)Click here for additional data file.S5 Table(DOC)Click here for additional data file.S6 Table(DOC)Click here for additional data file.S7 Table(DOC)Click here for additional data file.


Coupling dense genotype data with new computational methods offers unprecedented opportunities for individual-level ancestry estimation once geographically precisely defined reference data sets become available. We study such a reference data set for Finland containing 2376 such individuals from the FINRISK Study survey of 1997 both of whose parents were born close to each other. This sampling strategy focuses on the population structure present in Finland before the 1950s. By using the recent haplotype-based methods ChromoPainter (CP) and FineSTRUCTURE (FS) we reveal a highly geographically clustered genetic structure in Finland and report its connections to the settlement history as well as to the current dialectal regions of the Finnish language. The main genetic division within Finland shows striking concordance with the 1323 borderline of the treaty of Nöteborg. In general, we detect genetic substructure throughout the country, which reflects stronger regional genetic differences in Finland compared to, for example, the UK, which in a similar analysis was dominated by a single unstructured population. We expect that similar population genetic reference data sets will become available for many more populations in the near future with important applications, for example, in forensic genetics and in genetic association studies. With this in mind, we report those extensions of the CP + FS approach that we found most useful in our analyses of the Finnish data. Second, as the well-established methods to control for genetic ancestry in common variant association studies do not necessarily work well for rare variants  and eastern (E) parts of the country .Second, we catalog the Finnish population structure at a finer scale, identifying nearly 20 geographically clustered populations that overlap minimally with each other in general and cover approximately similar surface areas of the country. We find striking concordance between many of the genetic populations and the dialectal regions of the Finnish language. We validate the robustness of the fine-scale structure to characteristics of the data such as the sample size and sampling density of the individuals and study the relationships of these populations by comparing two approaches for building a hierarchical tree.Results, we report the fine-scale analysis of the Finnish population structure and assess its robustness. Connections to the earlier work on the Finnish population structure are given in Discussion. We have made our results available through a website (see Data availability).In The FINRISK Study is a representative, cross-sectional survey of the Finnish working age population (age range 25–74) that, since 1972, has collected a random sample of 6000–8000 individuals every 5 yr to study risk factors of chronic diseases in Finland. Our data were from the FINRISK Study survey of 1997  and inclP-value below 10−6, or call rate below 99.9%. This resulted in 238,438 SNPs. For the CP analysis with rare variants we ignored the MAF filter and included all SNPs with minor allele count above 1 resulting in 303,221 SNPs. All MAFs, HWE values, and call rates were calculated using PLINK version 1.07  below 5%, Hardy–Weinberg equilibrium ion 1.07 .We excluded the individuals that stood out from the other samples with average heterozygosity |F| > 0.025 or variant missingness rate >0.003. We also excluded individuals on two genotyping plates with poor quality. We calculated the relatedness for each pair of individuals using both PLINK 1.07  and GCTAAs the genotyping of the individuals from the FINRISK Study survey of 1997 upweighted Eastern Finland in its sampling, about half of the full data set of 2376 individuals were located in the provinces of Northern Karelia and Northern Savonia . To studhttp://www.shapeit.fr/files/genetic_map_b37.tar.gz, downloaded 25.6.2014).The genotype data (after QC) were phased jointly for all individuals with SHAPEIT2  programs  homogenous groups using a nonparametric Bayesian mixture model implemented through a Markov Chain Monte Carlo (MCMC) algorithm [more details in After the FS analysis, we performed an additional step to improve the population assignment by maximizing the overall posterior probability. This was done as in i, we calculated a logarithm of ratio of average E vs. W chunks asen is the number of eastern and wn the number of western reference individuals and iec and iwc are the number of chunks in coancestry matrix that individual i copied from an eastern individual e or a W individual w. The eastern reference individuals included those individuals from the province of Northern Karelia (NKA) who belonged to the E population in FS results with two populations .To assess the probability of each individual belonging to either W or E population, we applied a semisupervised Gaussian mixture model (GMM) on CP’s coancestry matrix. For each individual ulations  and, accr2 < 0.2 within 1 cM windows and excluded the long-range linkage disequilibrium (LD) regions according to To compare the chromosome painting method with standard methods that use only unlinked markers, we performed PCA with SmartPCA of EIGENSOFT package  plot, which made it possible to compare the two PCA plots. We performed 100,000 random samplings for each group and show their distribution using violin plots in The comparison  was perfFile S1).To study the effect of including rare variants in CP analyses, we did a similar comparison between our main data set and the data set that also included the rare variants  is population a’s (b’s) copying proportion from population i. We verified that the choice of the number of reference populations has only a minor effect on TVD . We built TVD-tree in K-1 steps by, at each step, calculating TVD for each pair of the current populations and merging those two populations whose TVD was the smallest. In particular, TVD was at each step computed with respect to the K dimensional copying vectors even though the number of current populations decreased by one at each step. We visualized the tree by scaling the branch lengths proportional to the TVD value of the corresponding pair of populations. The code is publicly available (see URLs).To calculate a genetic distance between populations that takes into account the haplotype information but does not depend on the sample sizes of the populations, we calculated TVD as described in ST measures genetic differentiation by comparing allele frequencies between two populations. We estimated pairwise FST values between the main W and E division . The official Finnish names of these provinces are LAP: Lapin lääni, NOS: Oulun lääni, OST: Vaasan lääni, CNF: Keski-Suomen lääni, NSA: Kuopion lääni, SSA: Mikkelin lääni, NKA: Pohjois-Karjalan lääni, SKA: Kymen lääni, TAV: Hämeen lääni, SWF: Turun ja Porin lääni, SOF: Uudenmaan lääni, ÅLA: Ahvenanmaan lääni.http://www.helsinki.fi/∼sinikerm/; PLINK, http://pngu.mgh.harvard.edu/∼purcell/plink/; GCTA, http://cnsgenomics.com/software/gcta/; SHAPEIT2, https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html; ChromoPainter and FineSTRUCTURE, http://paintmychromosomes.com/; EIGENSOFT, https://www.hsph.harvard.edu/alkes-price/software/.Scripts for estimating population assignment probabilities and TVD are available at https://thl-biobank.elixir-finland.org/. Results for fine-scale structure at different levels are available at https://www.fimm.fi/en/research/projects/finnpopgen. File S1 includes Figures S1–S10 and File S2 includes Tables S1 and S2.The genotype data used in this study are available through the National Institute for Health and Welfare Biobank Materials and Methods). This sample covered 10 out of 12 provinces of Finland well (1996 definition of provinces) with the exceptions of Lapland  and Åland . There were large differences in the sampling density also across the other provinces and therefore our main analysis used a subset of 1042 individuals with a more uniform spatial distribution .All our samples were genotyped using Illumina HumanCoreExome-12 BeadChip. Our main analysis used 238,438 directly genotyped SNPs with MAF > 5% that passed the quality control metrics  the individuals from the 11 provinces are in these two PC plots compared to a random set of individuals. CP clearly clusters the individuals from five provinces  tighter than SmartPCA whereas the opposite is true only for the province of TAV. In the remaining five provinces, we see little difference between the two methods. These results illustrate CP’s overall tendency to cluster individuals who live geographically closer more tightly together than SmartPCA, especially in the northern and eastern parts of the country, which we expect to be the most genetically isolated due to their later permanent inhabitation by a relatively small set of individuals starting from the 1500s (Discussion).Generating a haplotype-based coancestry matrix using CP requires considerably more computational resources than calculation of the empirical correlation matrix across a set of independent variants. Therefore, we started by evaluating whether the higher computational cost of CP is compensated by CP capturing more information than the standard relatedness matrix based on independent SNPs. File S1). This indicates that in our data, the high-quality common variants sufficiently capture the haplotype structure compared to all available variants.We also assessed whether an addition of 64,783 low-frequency and rare variants (MAF < 5%) available on the genotyping chip affected the CP results but did not observe any noticeable difference compared to the common variant analysis . The clustering model of FS did not report almost any uncertainty for this binary population assignment . To reveal more detailed differences between individuals in the proportions of genome related to the two populations, we used a GMM to assess how certain each individual was to belong to the W or E population based on the CP coancestry matrix and FS output . In To establish the high-level genetic structure in Finland, we applied FS to the output of CP by allowing exactly two populations. As expected, the main genetic division was between W and E parts of the country . The paiDiscussion.Next, we compared this genetic border to historical records and dialectal patterns, both showing features of W and E differentiation. The first and the most densely inhabited regions concentrated on Southern Finland and the coastal regions up to the Bothnia bay dividing Finland into the southwestern early-settlement region (ESR) and the northeastern late-settlement region (LSR), which became permanently inhabited from the 1500s  Jutikka. While iMaterials and Methods) on the coast of Ostrobothnia , and it joined the southwestern part of Finland to the Kingdom of Sweden and Eastern Finland to Novgorod . In obothnia . The genobothnia .http://www.kotus.fi/kielitieto/murteet/suomen_murteet, 2015 and The primary dialectal division of the Finnish language is into E and W dialects (File S1). As an example of fine-scale genetic structure in Finland, i.e., < 25 individuals) and because we have verified its robustness to sample size included in the analysis by a comparison with another subset of the data . Discussion for more information about this population). The pairwise FST values corresponding to these 17 populations (Table S1 and Table S2 in File S2) show that overall P6 has relatively small FST values with all other populations indicating approximately equal relatedness to both E and W populations.When FS was run without a preassigned number of populations, it divided our sample of 1042 individuals into 52 populations  that does not depend on the sizes of the populations. In Sample size and sample density). File S1), these four regions  split into 11 , 18 (427), 8 (123), and 15 (314) populations, respectively. Hence, we observe fine-scale population structure across the whole of Finland, which is in contrast to an FS analysis of the UK of the late 1800s (File S1).To visualize the hierarchical structure of these populations, we compared two agglomerative clustering algorithms to build a hierarchical tree for the populations. FS provides an algorithm (here: FS-tree) that at each level of the tree building merges the two populations resulting in the highest posterior probability among all possible merges. te 1800s , is the In addition to the primary E–W dialectal division , the FinMaterials and Methods) while the full data set contained considerably more individuals in the regions of NSA and NKA  In the more uniformly distributed data sets (File S1), these are the nine populations first to split in FS-tree but with the data set of 2376 individuals  we observed that the first split divides all the data sets into E and W but in the two largest data sets , the division made by FS-tree assigned essentially all individuals above the treaty of Nöteborg to the E population, which did not match with the main genetic E–W split identified earlier by explicitly modeling two populations  and FineSTRUCTURE (FS) in revealing fine-scale genetic structure in Finland. First, we defined geographically the main genetic division between Eastern and Western Finland using precisely located samples based on parents’ birthplaces. Second, we characterized the fine-scale genetic population structure present before the 1950s. Our results serve as a population genetic reference for future design and interpretation of genetic association studies and individual-level ancestry estimation in Finland. We validated CP + FS results by comparing them to a standard PCA, incorporating a more sensitive uncertainty measure, comparing different ways of building the hierarchical structure among the populations, and studying the effect of sample size and sampling density on our results. In general, we found CP + FS to be a useful and robust method for fine-scale population structure analysis with a few caveats that will be important to take into account in future applications in other populations. We note that our analyses do not explicitly model migrations and admixture events and future studies with complementary approaches and complementary data from the neighboring countries of Finland are required to study these topics.Finland has, for the last 10,000 yr, been a border region between W populations of Scandinavia, southern populations of the Baltics, and E populations of European Russia as summarized by Consistent with Southern Savonian settlers inhabiting Eastern and Northern Finland, we indeed observe that SSA, and its neighboring province SKA, are the only areas of the early-settlement region that are primarily covered by the E population that extends from SSA to the LSR . On the Our analysis established that the 1323 borderline of the treaty of Nöteborg is a very accurate description of the main genetic split within Finland . This maAn interesting detail of the split between the W and E populations is a group of individuals in Torne Valley (fin. Tornionlaakso) in Southwestern Lapland who are assigned to the W population even though geographically they are separated from the rest of the W individuals by E individuals near Oulu . While iFile S1). There are no large differences in the area covered or number of individuals included in each population . When we further split P6 into its three subpopulations , we noticed that one of these remained stable across the birth years of our samples  while the other two spread out from Karelia to Savonia (green population) and from the city of Vyborg (fin. Viipuri) throughout the country (yellow population) within this timeframe (1922–1972). During and after the Second World War (1939–1945), ∼400,000 Finns from the larger Karelia region southeast of the current borders of Finland were relocated throughout the other parts of Finland. Hence, the youngest third of our sample born after 1957 could, in principle, have such relocated Karelians among their grandparents. However, Figure S9 in File S1 also shows that the dispersal of P6 has started already among individuals born in 1941–1957, and therefore we cannot entirely explain the dispersal by relocations during and after the war .In our analysis, the exceptionally dispersed P6 dominating the Southern Karelia region also spread out widely across Savonia, near the large southern cities of Helsinki and Turku as well as along the genetic East–West borderline of File S1). We note that the two approaches are not estimating the same quantity: Our GMM estimates mixture proportions of an individual originating from each predefined source population, while FS carries out an unsupervised clustering without admixture modeling.In order to reveal details of the Finnish population structure, we extended CP + FS output in two ways. First, we devised a GMM to estimate uncertainty of population assignment because FS does not report any uncertainty estimate by default. With GMM we can stratify individuals based on the proportion of their genome related to each source population, which information is not available in the FS output. In our analysis, we clustered individuals across Finland based on their genetic makeup with respect to canonical northeastern (Northern Karelia) and southwestern (Southwest Finland) reference samples . Quantite.g., 17 in Results.Second, we introduced TVD-tree to complement the default FS-tree for building a hierarchical tree structure for the populations. Traversing an FS-tree from its leaves toward its root, small populations quickly merge to larger ones since small changes to the population assignment cause a relatively small decrease in the model probability. Instead, TVD-tree uses a difference in average ancestries between two populations and hence is less dependent on the sample sizes of the populations. To combine the complementary properties of FS-tree and TVD-tree, we used FS-tree to choose the level of populations (While a discrete population assignment remains an approximation to the underlying genetic relationships between individuals, it provides valuable information for individual-level ancestry estimation and design and analysis of rare variant association studies. We anticipate that our experiences and tools reported here in the context of Finland will be useful for fine-structure analyses of other populations.www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.300217/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.