Coalescent simulations were consistent with genetic continuity between present day individuals and individuals dating to 7,000 BP, 4,800 BP or 3,000 BP. Meanwhile, genetic differences observed between 7,000 BP and 3,000 BP as well as between 4,800 BP and 3,000 BP were inconsistent with genetic drift alone, suggesting gene flow into the region from distant gene pools or structure within the population. These results indicate that despite some level of continuity between ancient groups and present-day populations, the region exhibits a complex demographic history during the Holocene.Archaeogenomic studies have largely elucidated human population history in West Eurasia during the Stone Age. However, despite being a broad geographical region of significant cultural and linguistic diversity, little is known about the population history in North Asia. We present complete mitochondrial genome sequences together with stable isotope data for 41 serially sampled ancient individuals from North Asia, dated between c.13,790 BP and c.1,380 BP extending from the Palaeolithic to the Iron Age. Analyses of mitochondrial DNA sequences and haplogroup data of these individuals revealed the highest genetic affinity to present-day North Asian populations of the same geographical region suggesting a possible long-term maternal genetic continuity in the region. We observed a decrease in genetic diversity over time and a reduction of maternal effective population size (N Genetic continuity between pre-Neolithic and Neolithic populations of Near East10, iii. Increased mobility in West Eurasia during Bronze Age12, and iv. Genetic continuity in East Asia during the Holocene13. In contrast, the population history in North Asia has remained largely unknown with a limited number of published ancient genomes15. Here we fill this archaeogenetic gap by examining complete mitochondrial genome sequences and presenting radiocarbon dates of 41 serially sampled ancient individuals from North Asia, corresponding to the three major administrative regions of the Russian Federation including Cis-Baikal (Irkutsk Oblast), Trans-Baikal  and Yakutia (Sakha Republic)  Fig. .Figure 119. The first humans populating the region had West Eurasian origin21. The Baikal region of Siberia has been occupied by humans since Middle Palaeolithic23. The areas west  and east  of the Lake Baikal have been inhabited since the Palaeolithic and together with Yakutia exhibit a vast variety of prehistoric cultures, including the Neolithic and Bronze Age Kitoi and Glazkovo cultural entities26. Both archaeological and genetic data have shown similarities between the cultures of Cis-/Trans-Baikal regions and Yakutia31. The Neolithic in North Asia is not associated with sedentism and agriculture, but characterized by the appearance of characteristic stone production techniques (i.e. polishing) and presence of pottery of eastern origin26. Until the Iron Age, the region was inhabited by foraging groups. The most important cultural shift in the region might be associated with the arrival of metal in the Bronze Age as well as beginning of pastoralism in the Iron Age32.Archaeological data indicates an intensive and complex prehistory in North Asia. East Siberia has been inhabited since the early Paleolithic, as evidenced by sites with pebble industries7. All libraries showed elevated frequencies of cytosine deamination at 5′ read termini33  excavated from the Baikal and Yakutia regions in North Asia Fig. . 14 indi13C and δ15N)  placed the material between c.13,790 BP and c.1,380 BP , and ii. comprising full mitochondrial sequences, mitochondrial HVRI (16059–16365) sequences and haplogroup data   and ii. with HVRI sequences  both merged with presented ancient individuals from North Asia   Fig. . MDS anaups Fig. . We obseups Fig. .Figure 342. The CISB group shared most lineages with Tubalar42, KA43 and Early Bronze Age groups of Russia (BO)12, which might reflect the Siberian roots of BO, consistent with MDS based on Fst . Since the individuals were sampled from a large region and a long time period, we further evaluated the haplotype diversity in the spatial  and in the temporal  groups. The range of haplotype diversity in the spatial groups  was similar to that in the temporal groups  (Mann-Whitney U-test p > 0.05). Although temporal grouping revealed that genetic variation decreased over time in the Lake Baikal and Yakutia regions , diversity estimates for both spatial and temporal groups were not significantly lower than for the present-day populations in the region   . The 121,000 simulations conducted for each comparison frequently (>95%) yielded FST values equal to or higher than those observed  yielded as large FST values across all 11 × 11 demographic parameter combinations studied in each comparison (Although was not significant after multiple testing correction)  analysis using BEASTure Fig. . When weved Fig. . We theron) Fig. . Hence, 32. In this work, we present the stable isotopes of carbon and nitrogen as well as mitochondrial genomes of 41 ancient individuals from the Lake Baikal and Yakutia belonging to the general chronological frame of the North Asian archaeological cultural complexes47.Lake Baikal and Yakutia have a rich Holocene archaeological record allowing the investigation of population history, demographic events, and adaptation to the environment in North Asia48. The variation in both the δ13C and δ15N values in the present study showed that there were differences in the amount of consumed aquatic resources, and probably also differences in the consumed fish species between the individuals and groups, especially between different geographical regions. For example, Yakutia individuals had the lightest δ13C values  in Mesolithic and Early Neolithic Cis-Baikal individuals  and Proteinase K (10 mg/ml)56, and after full disolution the DNA extract was concentrated using Amicon filters (Millipore) and purified with silica-based MinElute spin columns (Qiagen)57. A blank negative control was added for every 8–10 samples in the isolation step. The DNA was eluted in 110 ul of Elution Buffer (EB) (Qiagen). Double-stranded blunt-end Illumina genomic libraries were prepared from 20 µl DNA following Meyer & Kircher protocol58 with omission of sonication step unnecessary when handling aDNA. Each library was amplified in five replicates, using 0.5 ul of 10 µM index primer per library, following the thermocycling conditions as in59 with the number of cycles estimated individually for each library based on quantitative PCR (qPCR). For sample N4b2DR, three damage-repaired libraries were prepared with USER enzyme (NEB/BioNordika). Libraries were sequenced using Illumina HiSeq X platform at the SciLife sequencing center at Stockholm.Sample preparation, extraction of DNA from bone and teeth samples and library preparation were carried out in ancient DNA facilities at the Archaeological Research Laboratory (AFL), Stockholm University. The samples were cleaned from earth and potential contaminants and the teeth were wiped with 1–3% NaOCl solution and ddH60. In brief, paired-end reads with a minimum 11 bp overlap were merged and residual adapter sequences were trimmed61. Merged reads were mapped to the human reference genome (version hs37d5) using BWA62 version 0.7.13 in single end mode with parameters “−n 0.01 −o 2 −l 16500”6. PCR duplicates were filtered using FilterUniqSAMCons_cc.py61. Reads mapped to the reference genome with more than 10% mismatches and shorter than 35 bp were filtered. To prepare comparative dataset, we remapped published ancient genome sequence data for individuals labeled as “samples published fullMT seq” in Supplementary Tables Sequence data was analysed as in33 were examined using PMDtools6. Mitochondrial DNA based contamination estimation was performed following a method developed in34 and a Bayesian method developed in35 as in60. In the method based on Green et al., alleles present less than 5% in 311 modern mtDNAs  with depth of minimum 10× and base quality of minimum 30 were identified. After filtering transition type mutations, point estimate of contamination was obtained by summing the counts of consensus and alternative alleles across all sites. In the method based on Fu et al., mitochondrial consensus sequences were called per sample using samtools mpileup63 and vcftools64. Using “contamMix” library in R, probabilities of authenticity were calculated.Ancient DNA specific nucleotide misincorporation patterns (increased frequency of cytosine to thymine transitions at 5′ end of DNA)7. Analysis was restricted with reads with minimum mapping quality of 30. Ratio of reads mapping to Y chromosome to the reads mapping to both X and Y chromosome was calculated.Biological sex of the individuals was determined by Ry method65 was used with parameters “-doFasta 2 -doCounts 1 -minQ 30 -minMapQ 30 -setMinDepth 3”. For a total of 115 present-day individuals 68. For 667 ancient individuals, mitochondrial HVRI region sequences (16059–16365) were obtained from GenBank. Mitochondrial haplogroups of these individuals were assigned comparing informative SNPs in HVRI region which are reported against revised Cambridge Reference Sequence (CRS)70 to PhyloTree mtDNA tree (build 17)68  and of the published individuals for which full genome sequences were available  (Supplementary Tables http://www.causascientia.org/math_stat/ProportionCI.html). Principal component analysis (PCA) was performed using “PCA” function of “FactoMineR” library in R. “factoextra” library in R was used to extract and visualize the eigenvalues and variances of the dimensions. To measure genetic differentiation among populations, Slatkin’s linearized FST between pairs of populations71 was calculated and the significance of the FST was assessed by 10,000 random permutations of population labels using Arlequin v.3.5.272. FST was applied to two datasets including samples with full and partial  mtDNA sequences  on FST values was performed using the “metaMDS” function in the “vegan” library and plotted using “ggplot2” library in R. Mitochondrial haplotype diversity, the probability that two randomly chosen haplotypes are different in the sample74, was calculated for all population samples using DnaSP75 v.6 by “excluding sites with missing data” option. Haplotype sharing was calculated using Arlequin v.3.5.272. The normalized haplotype sharing was estimated by dividing the number of shared haplotypes between two groups divided by the maximum number of possible comparisons (Nmax = Ntot*(Ntot − 1)/2).Mitochondrial haplogroup frequencies and 95% confidence intervals were calculated on two different datasets  were generated by coalescent simulations using the fastsimcoal2 software78. Here, we tested the null hypothesis that the observed FST value could arise by genetic drift (i.e. population continuity). We sampled simulated populations through time, with a demographic scenario of exponential population growth, employing a range of parameter values such as different effective population sizes (Supplementary Table −7 per site per generation for the full human mitochondrial genome sequence77. We performed 1000 simulations for each combination of population sizes, computed FST using Arlequin v.3.5.272 and calculated the proportion of simulated FST values that are greater than the observed FST. Details about the simulation parameters, population datasets, FST values and each pairs of population comparisons are given in Supplementary Table Population continuity was tested by calculating the observed The new generated full mitochondrial genome data have been deposited in GenBank under ID code: MH359189 - MH359229.Supplementary InformationDataset 1Dataset 2


SNPs) and microsatellites (MS) of the malaria parasite, Plasmodium falciparum. This is particularly striking in South America where two major founder populations of P. falciparum have been identified that are presumed to have arisen from the transatlantic slave trade. Given the importance of the major variant surface antigen of the blood stages of P. falciparum as both a virulence factor and target of immunity, we decided to investigate the population genetics of the genes encoding “Plasmodium falciparum Erythrocyte Membrane Protein 1” (PfEMP1) among several countries in South America, in order to evaluate the transmission patterns of malaria in this continent. Deep sequencing of the DBLα domain of var genes from 128 P. falciparum isolates from five locations in South America was completed using a 454 high throughput sequencing protocol. Striking geographic variation in var DBLα sequences, similar to that seen for SNPs and MS markers, was observed. Colombia and French Guiana had distinct var DBLα sequences, whereas Peru and Venezuela showed an admixture. The importance of such geographic variation to herd immunity and malaria vaccination is discussed.Strong founder effects resulting from human migration out of Africa have led to geographic variation in single nucleotide polymorphisms ( This spatial variation is particularly striking in South America where two main genetic clusters, previously shown through SNPs and MS variation, suggest independent introductions of P. falciparum from Africa through the transatlantic slave trade about 500 years ago , among several countries in South America to evaluate the population structure of these genes at the scale of a continent, and thus describe the transmission patterns of malaria. The P. falciparum genome is composed of up to 60 var genes with each representing a different antigenic form. To achieve clonal antigenic variation within the host, PfEMP1 is expressed sequentially in a mutually exclusive manner  domains. Among the different DBL domains characterized, DBLα is the most conserved and is involved in the adherence of infected red blood cells (RBCs) to uninfected RBCs  What are the limits of var gene diversity in these South American populations? (ii) Does population structuring at var loci exist between/among the South American populations?, and (iii) Are var gene population genetics a reflection of geographic population structure on the South American continent when compared to SNPs and MS markers? The answers to these molecular epidemiological questions would allow us to predict the potential for epidemic transmission of P. falciparum clones not previously observed across South America.To date, limited population sampling of 22.1Ethical clearance was obtained from the local ethics committees in each country sampled. The informed consent procedure for the study consisted of a presentation of the aims of the study to the community followed by invitation of individuals (or their parents/guardians) for enrollment. At the time of sample collection, the purpose and design of the study was explained to each individual and verbal informed consent was collected by a minimum of two people. The verbal consent process was consistent with the ethical expectations for each country at the time of enrollment, and the ethics committees approved these procedures.All the samples collected from French Guiana and analyzed in this study were from blood collections that were required as standard medical care for any patient presenting with a fever on admission to the hospital. According to French legislation , biobanking and the secondary use of remaining human clinical samples for scientific purposes is possible if the corresponding patient is informed and has not objected to such use. This requirement was fulfilled for the present study; each patient was informed via the hospital brochure entitled “Information for Patients,” and no immediate or delayed patient opposition was reported to the Malaria NRC by the clinicians.For samples collected in Colombia, each patient (or their parents/guardians) gave informed written consent. Ethical clearance was granted by the Ethics Committee of the Centro de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia .http://www.clinicaltrials.gov.Concerning the Peruvian samples, the study protocol was approved by both the Ethical Review Committee of the Universidad Peruana Cayetano Heredia and the Institute of Tropical Medicine, Antwerp, Belgium. The research was performed in accordance with the ethical standards of the Peruvian Ministry of Health. The trial has been registered as an International Standard Randomised Controlled Trial, number NCT00373607 at For samples collected in Venezuela, each patient gave written informed consent and ethical clearance was obtained from the Comité Ético Científico del Instituto de Medicina Tropical de la Universidad Central de Venezuela.2.2P. falciparum isolates typed in this study  or by finger prick (~50 μl) after obtaining informed consent.The 128 dy Table . These i2.3Single nucleotide polymorphisms (SNPs) and microsatellite (MS) genotyping were previously performed in the study of Yalcindag et al. . These d2.4var DBLα domain has previously been used as a marker for var gene diversity in other global studies  , 0.075 units of GoTaq DNA Polymerase (Promega), 2 μl of purified genomic DNA template, and 27 μl of water. Each isolate was amplified using forward and reverse primers containing the same MID tag combination. Amplifications were carried out on an Eppendorf EP Gradient Mastercycler using the following reaction conditions: 95°C for 2 min, followed by 30 cycles of 95°C for 40 s, 49°C for 1 min 30 s, 65°C for 1 min 30 s, and a final extension step of 65°C for 10 min. PCR amplification was confirmed visually by nucleic acid staining  followed by gel electrophoresis (1.5% agarose in 0.5× TBE buffer) demonstrating a band of the appropriate size (~550–700 bp). Positive controls  and negative control (no template) were included for quality assurance.DNA from blood samples was extracted using the DNeasy Blood and Tissue Kit  according to the manufacturer's recommendations and eluted in 100 μl of elution buffer per 200 μl of whole blood or per dried filter blot. The conserved var DBLα amplicons of 550‐ to 700‐bp lengths using the forward and reverse sequence reads from each direction. Sequencing was performed by Seqwright Genomics .The PCR products were purified using the SPRI method (solid‐phase reversible immobilization) , and PCR amplicon concentrations were measured using the Quant‐iT PicoGreen dsDNA Kit per the manufacturer's instructions (Invitrogen). Known concentrations of control DNA were prepared as directed by the Roche Technical Bulletin . We assayed fluorescence intensity using a PerkinElmer VICTOR X3 multilabel plate reader, with fluorescein excitation wavelength of ~480 nm and emission of ~520 nm wavelength. We prepared four PCR amplicon library pools, each containing equimolar amounts of up to 60 PCR amplicons all with unique MID tags. These four pools were sequenced in the forward and reverse directions on segregated regions of one full 454 plate using GS FLX Titanium chemistry (Roche). This 454 high throughput sequencing approach provides average read lengths of 400 bp, therefore lending itself to the assembly of the individual 2.5http://www.cbs.dtu.dk/services/MultiPass-1.0, and the following cleaning steps described below are implemented in a python script available here: https://github.com/454data/postprocess. The sff‐files obtained from each region on the 454‐plate were divided into smaller isolate‐specific sff‐files by identification of reads with exact matching MID sequences in both ends using BioPython v1.57  = .9979 as described in Rask et al.  using the blastn feature of BLAST+ 2.2.25 (NCBI), with expectation value criteria of 1e−50 (90 sequences removed). Sequences were also BLASTed against the remaining (non‐DBLα) 3D7 genome and searched using a DBLβ HMM with HMMer v3.1 (http://hmmer.org); however, no hits were found. After the human and nontarget P. falciparum check, a small number of sequences remained that had no similarity to a DBLα tag HMM and these were removed. The pipeline was validated and optimized on experimental sequence data generated on the laboratory clones  for which published genome sequences are available. More than 90% of the sequences obtained from the control samples had no errors when compared to the known references, and the deviating sequences had only one to three errors. To define distinct DBLα types shared within and between populations, we clustered nonredundant sequences from all isolates from the South American populations by average linkage using a sequence identity threshold of 96%. This 96% cut off was chosen to define unique DBLα types as it has been previously shown to be robust at defining DBLα types with identical sequences (excluding minor sequence errors)  is defined as: PTSAB = 2nAB/nA + nB. PTS values range from 0 to 1, where a PTS score of 0 signifies no DBLα type sharing and 1 signifies complete sharing of all DBLα types.Pairwise type sharing (PTS), analogous to Sørensen's Index , is a useful statistic to analyze diversity and determine the proportion of DBLα types shared between isolate repertoires and among the South American populations . PTD was calculated as follows: PTD2.6.5Chao–Sørensen's Index provides another estimate of similarity among the South American populations. This index adjusts for the abundance of each DBLα type (not just presence or absence of the DBLα type) in the population, as well as adjusting for the effect of unseen shared DBLα types in conditions of under sampling AB.Similar to PTS (or QS), the Chao–Sørensen's Index was also used to determine genetic distance and the level of genetic differentiation between the South American isolates/populations using DBLα types. This measure was defined as Chao–Sørensen's Quotient of Distance (Chao–Sørensen's QD) and was calculated as: 2.7The distribution of genetic diversity of DBLα types among the South American populations was investigated using the same methods and tools previously described in Yalcindag et al. . For the2.7.1FST estimates  between the various indices of genetic distance  for SNPs, MS, and DBLα types, and geographical distance between each South American population pair. Pairwise geographic distances were computed using MapInfo . The significance of the relationship was assessed with a Mantel test using 10,000 permutations. In addition, the genetic distance (FST) of the SNP markers among the South American populations was compared with the other indices of genetic distances for the MS (FST) and the DBLα types  for the South American populations. All Pearson's correlation coefficients (r) were calculated in Microsoft Excel.Pairwise Weir and Cockerham's 2.7.2Principal component analyses (PCA) were performed on the matrix of binary allele profiles using the R‐package “Adegenet” Jombart, . These a2.7.3K), ranging from K = 1 to the number of South American populations included (K = 5)  among the 128 isolates from the five South American populations (Table N = 10) available for comparison  had repertoires composed of ≥20 DBLα types  was 42 (range = 13–92); however, the majority of isolates  for SNPs, MS, and DBLα types. This analysis showed a positive correlation between geographic distance and the indices of genetic distance for the DBLα types , and that these patterns were comparable to the other neutral markers (SNPs and MS) previously examined  based on the SNPs and MS markers were compared to different indices of genetic distance  for the DBLα types, positive correlations were observed . Therefore, increasing either the genetic distance (FST) for the SNPs or MS markers resulted in an increased genetic distance  for the DBLα types  between the South American populations. Figure pes Fig. . This reFinally, the PCA based on the SNPs, MS, and the DBLα types Figure a clearly4P. falciparum that are constantly evolving is critical to achieve control. In this context, detection of genetic variation in the genes encoding the major surface antigens is important for disease surveillance as immunity to such antigens drives the dynamics of the transmission system and allows for epidemics to be antigenically characterized. For example, the single copy hemagglutinin gene of the influenza virus is used to predict spatial and temporal patterns of disease  have been utilized as diagnostic markers of diversity  existing in the South American human population. This would lead to less frequent outcrossing  during meiotic recombination in the mosquito phase of the parasite life cycle. The observed limited local diversity of DBLα types in South America raises the possibility that the highly immunogenic PfEMP1 could be a vaccine target in this location.In contrast to var DBLα types mirrors the population structure reported previously using SNPs and MS markers in the same populations  and/or mixing among the South American countries suggests the necessity to develop local strategies for vaccination vs. a pan‐continental approach. To conclude, the use of var population genomics in South America allowed for the description of the genetic complexity in the reservoir of infection as well as a better understanding of P. falciparum epidemiology in relation to differences in parasite antigenic variation.Whether the underlying driver of geospatial structuring of the None declared.KX845707–KX851405.All DBLα sequences analyzed in this study are publicly available for download in GenBank:  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


The Coloured individuals show evidence of varying proportions of admixture with Khoesan, Bantu-speakers, Europeans, and populations from the Indian sub-continent. Whole-genome sequencing data reveal extensive genomic diversity, increasing our understanding of the complex and region-specific history of African populations and highlighting its potential impact on biomedical research and genetic susceptibility to disease.The Southern African Human Genome Programme is a national initiative that aspires to unlock the unique genetic character of southern African populations for a better understanding of human genetic diversity. In this pilot study the Southern African Human Genome Programme characterizes the genomes of 24 individuals (8 Coloured and 16 black southeastern Bantu-speakers) using deep whole-genome sequencing. A total of ~16 million unique variants are identified. Despite the shallow time depth since divergence between the two main southeastern Bantu-speaking groups (Nguni and Sotho-Tswana), principal component analysis and structure analysis reveal significant ( African populations show a high level of genetic diversity and extensive regional admixture. Here, the authors sequence the whole genomes of 24 South African individuals of different ethnolinguistic origin and find substantive genomic divergence between two southeastern Bantu-speaking groups. The complex history of the people of sub-Saharan Africa is reflected in the diversity of extant populations and recent migrations that have led to extensive regional admixture11. This diversity provides both a challenge and an opportunity for biomedical research and the hope that Africans will one day benefit from genomic medicine.African populations harbor the greatest genetic diversity14 (8.9%), whites of European origin (8.9%), an Indian population originating from the Indian sub-continent (2.5%), and a small proportion of additional ethnolinguistic affiliations not broadly covered in the aforementioned (http://www.statssa.gov.za/). The focus of this pilot study from the Southern African Human Genome Programme (SAHGP) is on the southeastern Bantu-speaker and COL populations.Present day South Africans include a major ethnolinguistic group of black southeastern Bantu-speakers (79.2% of the population), an admixed population  referred to as Coloured (COL)17. It further supports two different migration paths, one in the east and one in the west of Africa, giving rise to southeastern Bantu-speaker (SEBs) and southwestern Bantu-speakers (SWB)18. Migration of SEB is estimated to have occurred in multiple distinct waves  along the eastern coast23. The patterns of distribution of artifacts and rock art from different iron-age sites indicate the complex nature of the interactions between the Bantu-speaking immigrants and the Khoesan (KS) inhabitants24. These involved long phases of coexistence, trade, assimilation of hunter–gatherer peoples into agro-pastoralist communities, and in some cases the displacement of KS groups28. Such interactions have not only involved linguistic and cultural exchange but also admixture at the genetic level30. It can be postulated that the migration of each Bantu-speaking group into a new territory likely involved an independent set of interactions and admixture events with the resident agro-pastoralist Bantu-speaker and hunter–gatherer populations.Archeological evidence suggests that the migration of groups of Bantu-speaking agro-pastoralists into southern Africa was initiated about 2000 years ago21. The Nguni-speakers expanded to occupy the coastal areas extending down the east coast of South Africa, whereas Sotho-Tswana-speakers expanded across the highland plateau between the eastern escarpment and the more arid regions in the west21. Although the details of the arrival of these populations are unclear, it is proposed that the Nguni- and Sotho-Tswana-speakers or their antecedents migrated to southern Africa and started occupying vast territories by the fifteenth century31. Some of the boundaries between these populations have, however, been obscured by more recent migrations, conquests, admixture, and, in some cases, rapid language adaptations, especially over the last two centuries32. This makes the consideration of geography and language important when assessing the divergence of these groups and begs the question as to whether genetic studies would be sufficiently powered to detect population differences.These migrations and interactions have led to the formation of ethnolinguistic divisions within the SEB of present day South Africa, of which the two major groups are the Nguni-speakers and Sotho-Tswana-speakers who are estimated to have diverged geographically over the past 500 years or so38. However, the focus of most of these studies has been to analyze the genomic diversity among hunter–gatherers and the extent of their admixture in the present day SEB38. An early study, based on mtDNA, Y-chromosome, and a limited number of autosomal markers, suggested that the ethnolinguistic divisions between the major SEB groups were reflected by observed genetic divergence, although the clustering was not consistent for the three data types39. More recent genome-scale studies have not replicated the substructure within the SEB38 and in some cases the authors concluded that the SEB is genetically a relatively homogenous group. This assumption needs more thorough investigation.Southern African populations have recently been investigated using a number of genomic approaches including genotyping array and, more rarely, whole-genome sequencing (WGS) technologieshttp://www.statssa.gov.za/). When recruiting study participants from urban settings, the ethnolinguistic boundaries become blurred and the distinctions are no longer evident. We have therefore purposely recruited the SEB for this study from rural and semi-urban regions where we anticipated little or no ethnolinguistic admixture.Over the past century there has been extensive urbanization of SEB in South Africa and the migration to economic hubs has resulted in a confluence of multiple ethnolinguistically diverse groups  and 8 COL individuals. The first major aim was to study possible correlations between language groups and genetic clustering. The results suggest that, at least for individuals sampled on the basis of both language and geographic location, there is a discernable genetic separation between the two major SEB ethnolinguistic groups. The second major aim was to investigate the ancestral composition of the COL individuals based on novel WGS data and a comprehensive assortment of potential ancestral populations. As a result of the inclusion of additional representative populations our study demonstrates a much stronger South Asian ancestry in the COL when compared to previous studies. We document significant novel SNV discovery from the 24 WGS and highlight the potential implications for disease susceptibility in Africans.44 9, resulting in 1703 variants. Their gene locations were determined and 146 genes had at least two LOF variants in the data set, of which 22 genes showed a potential knockout configuration  in at least one individual. Six of the genes were excluded because they are listed in the false discovery panel47 and the LOF variants in the remaining 16 genes are shown in Table http://www.omim.org/), with the exception of SLC17A9, which has variants segregating with autosomal dominant disseminated superficial actinic porokeratosis-8 in two unrelated Chinese families (http://wwwgenecards.org).SNVs and indels were annotated according to genic locations using ANNOVAR48, KGP46, and the AGVP study9  showed that the COL individuals form a dispersed cluster linked to African and non-African populations including European, South-Asian (Indian sub-continent), and Austronesian populationsunts Fig.. Howeverp < 10−6; Fig. Despite the recent linguistic and geographic divergence between the XHS and SOT groups, genetic data using PCA showed them to be significantly different p < 10−; Fig. 6.9. The ZUS individual was recruited in Soweto, Johannesburg, which is a cosmopolitan area having attracted migrants from across southern Africa, with different ethnic backgrounds. Soweto has a complex history, including people who were forcibly relocated there under apartheid legislation from other urbanized areas in the 1950s53. Thus, Soweto has an effective 120-year history of people from different backgrounds living together in an urbanized setting. Although there is only one ZUS individual in the study and so comment risks being anecdotal, the fact that the ZUS individual does not cluster in our sample with the ZUL individuals (from the AGVP data set) indicates that care needs to be taken in interpreting ethnic origin when recruiting from urbanized areas in African countries. Language and self-identity may not be good markers for genetic background.One individual who self-identified as XHS was found to have recent non-African admixture of European origin  scores (in 25 kb windows) across the genome . The gene SEMA4F encodes a transmembrane class IV semaphorin family protein, which plays a role in neural development. This gene has been suggested to be involved in neurogenesis related to prostate cancer, the development of neurofibromas, and breast cancer tumorigenesis. In addition to cancers, the SEMA4F gene has also been suggested to be involved in pulmonary tuberculosis and dyslexia. The EREG (epiregulin) gene can function as a ligand of EGFR , as well as a ligand of most members of the ERBB (v-erb-b2 oncogene homolog) family of tyrosine-kinase receptors and is known to be associated to diseases like colorectal cancer, and hypopharynx cancer. Similarly, the PTF1A gene is a transcription factor involved in pancreatic development. Diseases associated with PTF1A include pancreatic cancer and cerebellar agenesis. In the absence of data for variation in phenotype or disease incidence or prevalence in these groups, it is not possible to infer whether the highlighted genes have any medical or evolutionary significance for these populations. Nevertheless, the genetic differences in these regions might flag some of these diseases/traits for epidemiological investigation among the southern African populations.To investigate the differentiation of SOT and XHS further, we studied the distribution of average fixation index  and low sharing with non-African populations. This trend was also observed when the analysis was extended to examining SNVs, irrespective of MAC, shared between the SEB and a single KGP46 population  ancestry and repeated the analysis58  segments in the SAHGP populations was compared to various populations from Africa and other continents 9 clear differences in the nature, source, and timing of admixture in the Sotho and Zulu are evident.Although hinted at in an earlier study23. These include migrations, displacements, admixture, and adoption of new languages, that might have rendered language alone an inadequate proxy for capturing underlying genetic differences, especially in urban centers. According to oral history, linguistic and archaeological evidence, a common ancestry is likely to be as recent as 1000–1200 years for the SOT and XHS66. Therefore, the differences in their genetic structure, in addition to differential admixture, could represent the consequences of very recent geographic, linguistic, and cultural separation with concomitant genetic drift effects, given the small effective population sizes67. The small sample sizes for this pilot study, as well as the lack of population-scale WGS data from the KS populations, restricted our ability to investigate the role of genetic drift and selection in the genetic differentiation. Studies based on larger sample sizes will be necessary to assess the extent to which these factors have influenced the genomic differences. Given that differences were observed, this provides a compelling argument for investigating population substructure in South African studies as this may affect the outcomes and interpretation of biomedical genetic association and pharmacogenomics studies in the region.A failure to detect genetic differences between SEB groups who speak different but related languages in some of the previous studies is likely due to large-scale demographic changes that have occurred over the last two centuries51. In most of these studies, the Chinese and the Gujrati populations from the HapMap data set68 have been used to represent East/Southeast Asian and South Asian ancestries, respectively. A survey of the seventeenth century slave-trade routes, however, suggests these to be unsuitable proxies for the populations that might have contributed the East Asian and South Asian ancestry in COL individuals. Based on data from the KGP46 and Malay genome studies50, we were able to identify the Malay as a better proxy for the Southeast Asian and the Bengali (BEB) for the South Asian ancestry. Moreover, the geographic locations of these populations were found to be much closer to the seventeenth century Dutch trading posts69 and historical accounts of the presence of these groups in the Cape during that time is also well documented70. Based on the use of the more appropriate comparative populations, we were able demonstrate that the South Asian contribution was higher in comparison to the East Asian contribution. This was corroborated in an independent study30 of COL individuals in South Africa.Turning our attention to the admixed COL populations of South Africa, several studies have detected up to five distinct ancestry components, arising from KS, Bantu-speaker, East Asian/Southeast Asian, South Asian, and European admixtureIn conclusion, the SAHGP pilot study emphasizes the high discovery rate of novel variants in African populations. Despite previous reports of relatively low genetic divergence among SEBs, we detected significant population differentiation between two SEB groups in South Africa, highlighting the need to consider population structure in disease-association studies involving southern African populations. Our study is limited by the small number of participants and lack of representation of additional ethnolinguistic groups in the region. In particular, the absence of population-scale WGS data for KS groups restricted our ability to fully utilize our WGS data in analyses such as admixture mapping and local ancestry detection. The availability of such data would enable a more comprehensive analysis and is expected to provide novel insights.71. The DNA was normalized and sent to the service provider (Illumina Fast Track) as a single batch at the same time, and all the data were returned in one batch.The study was approved by the Human Research Ethics Committee  of the University of the Witwatersrand, Johannesburg (Protocol number: M120223). Three groups of participants were enrolled and venous blood was collected into tubes containing EDTA anticoagulant. Inclusion criteria were as follows: male, over the age of 18 years, four grandparents who speak the same language as the participant , not known to be related to the other participants in the study, and willing to provide broad informed consent ). Where feasible, community engagement preceded enrollment. Three main ethnolinguistic groups were included in this SAHGP pilot study. Individuals self-identified in terms of the ethnolinguistic group as part of the recruitment process. Group 1: individuals of mixed ancestry (referred to as COL in the South African context) were recruited through the Western Province Blood Transfusion Service by Sister Debbie Joubert under the guidance of Professor Soraya Bardien. Group 2: Sotho (Sotho-Tswana-speakers): seven individuals in this group were recruited from in and around the town of Ventersburg in the Free State Province, following community engagement done by Professor Michèle Ramsay and recruitment by Mr. and Mrs. Botha and Mrs. van den Berg. Group 3: Xhosa-speakers (Nguni language): eight individuals were recruited by Dr Nomlindo Makubalo from her medical clinic in the Eastern Cape Province. One individual was a Zulu-speaker (Nguni language) from Johannesburg. All DNA samples were extracted in the same laboratory using a modification of the salting out procedureThe DNA samples were normalized to ~60 ng/µl and ~5 µg DNA was submitted to the Illumina Service Centre for sequencing on the Illumina HiSeq 2000 instrument  with a minimum of ×30 coverage. Initial analysis of the raw read data was conducted by Illumina FastTrack Sequencing Services using their in-house-developed Isaac analysis pipeline.Each sample was also genotyped using the IlluminaOmni2.5 genotyping array.72. During the mapping selection phase, low-quality 3′ ends and adaptor sequences were trimmed. Following the alignment-phase PCR duplicates were marked and indels realigned by the Isaac Alignment Software. Finally, the base-quality scores were recalibrated using GATK73 to generate the final sorted, duplicate marked, indel-realigned BAM files that were used for variant calling  of the human genome reference sequence using the Isaac Alignment Software9 of the 74. The variant calling was conducted independently at the University of the Witwatersrand (Wits) and the University of Pretoria (UP) using the same GATK pipeline with varying parameters . Variant type counts for SNVs, indels and CNVs within each population was calculated.The annotation was performed with the ANNOVAR softwarehttp://www.ensembl.org/biomart/). The description of genes and their potential functions was inferred using GeneCards (http://www.genecards.org/).The identification of genes in genomic regions of interests was performed using BioMart  for each pair of individuals, based on the average proportion of alleles shared in common at the genotyped SNPs. The genotype data set was used for the IBD analysis and revealed no level of relatedness based on the π_hat values generated, where values of greater than 0.1875 are indicative of closely related individuals.As several of the analysis methods used in this study assume the use of unrelated samples for accurate results, we assessed the data set for relatedness using an identity-by-descent (IBD) approach in PLINK v1.946 and the AGVP9 (ZUL) data sets. As the main application of this analysis was to compare the SFS within each data set, it needs to be noted that variation in sequencing depths among data sets might introduce some biases in cross data set comparisons.To avoid bias due to possible incorrect assignment of ancestral alleles, a folded site frequency spectrum (SFS) based on MACs was calculated using a custom perl script. The script was used to study SFS in the three SAHGP populations along with eight randomly selected samples from representative African  and non-African populations (CEU) from the KGP76 was used to identify mitochondrial haplotypes for each individual. For this, all reads were aligned using BWA-mem to the RSRS sequence. The BAM files produced were then uploaded to mtDNA-server service as suggested by the webserver documentation. This service performs QC filtering  and annotates regions of low complexity and NUMTS and finally assigns the most likely haplogroups.Haplogrep277. For each person, the variants detected from the WGS were extracted, and converted into the correct format before being input into the AMY-tree program.Y-chromosome haplogroup analysis was done using the AMY-tree algorithm and tool45. The SNVs showing MAF > 0.05 were excluded as they were assumed to be mutations of lower impact. The distribution of the LOF variants in each individual was analyzed and if an individual was found to contain two different heterozygous LOF mutations, one in each chromosome, as inferred from phased whole-genome sequence data, in the same gene, the individual was characterized as a potential “complete knockout” with respect to that gene. Not all SNVs could be phased accurately because they were novel and therefore, when in doubt, we made the assumption that they were in a trans-configuration.The LOF mutations in our data set include Stop Gain, Stop Loss, Frameshifts (defined as indel in exon which in not a multiple of 3), and Splice Variants . The above-mentioned categories of mutations in the whole-genome sequence data were identified using ANNOVAR46 data for Yoruba in Ibadan (YRI), Luhya in Webuye, Kenya (LWK), Utah residents with Northern and Western European ancestry (CEU), and Bengali in Bangladesh (BEB) were used. In addition, Malays from the Singapore Sequencing Malay Project50; Black South Africans from Soweto (SEB2)34; several populations from the study by Schlebusch et al.30, namely several Khoe-San (KS), and COL groups (COLC and COLW), South-east Bantu-speakers (SEB1) and SWB were included in the comparisons. Moreover, Baganda (BAG) and Zulu (ZUL) whole-genome sequences from the AGVP data set9 were also included in the analyses. The data were merged using PLINK v1.975, and filtered to exclude SNVs and/or individuals with poor quality. For both PCA and ADMIXTURE the SNVs were pruned to select sample SNVs not in LD with each other, leaving ~197 K SNVs for analysis.We investigated population structure using both PCA and structure analysis. In choosing comparative populations we used prior work and historical knowledge. After some preliminary experimentation we chose specific data sets  was used to visualize the results from the PCA and population structure analyses.Structure analysis was done using ADMIXTUREST) at the whole-genome level provides an estimate of the genetic distance between two populations and has been used extensively in inferring relationships between a set of populations81. We investigated the relationship between the southern African populations in our data sets and two distinct sets of populations known to be related to them; the Bantu-speaking groups  and the KS populations from southern Africa. For this a merged data set consisting of the SAHGP data, Schlebusch et al.30 and AGVP9 was generated. The Weir and Cockerham’s (WC) FST estimate82 was computed between the SAHGP and other groups using PLINK v1.975.The analysis of the fixation index  as the three ancestral populations and the SEB2 from Schlebusch et al.30 as the target population). Based on the ascertainment of ancestry of all the 20 SNP windows, the windows showing <20% of YRI ancestry were masked out to generate a minimal non-admixed SEB data set.Three data sets—the SAHGP, KGP46 data set, we merged the 15 SEB individuals  with the KGP46 data sets and identified those variants that occur precisely twice in the merged data set (f2 variants)84. As the sample sizes in the two data sets were not uniform and an unbiased estimate of f2 sharing was difficult, instead of performing a complete f2 analysis we focused on those f2 variants that occur at least once in one of the 15 SEB and once in the KGP46 data set. A similar analysis was performed using SNVs shared between only two populations irrespective of the minor allele frequency. This was mainly done to compensate for the small sample size in our study, which might have considered some SNVs to be singletons that could have been present multiple times if we had included more samples. Both these analyses were performed for the COL individuals. We also identified SNPs that occur in only one of the five continental population sets in the KGP46 data and studied their distribution in the 15 SEB and the 8 COL individuals.To compare rare allele sharing between the SAHGP and the KGP46 data set to Zulu from the AGVP9 and SEB from the SAHGP data set. For this, we scanned the genome using 1 Mb sliding windows (with no overlap) and computed the number of SNVs occurring in that region in each population. The empirical distribution of SNV densities thus obtained for each population was used to assign a rank score and p-value to the density level observed for each window in that population. A similar scan was conducted using 25 kb windows. We noted that there are marked differences in sample size and coverage between data sets such as KGP46, AGVP9, and SAHGP, and these factors could also result in differences in estimation of SNV densities. Therefore, we considered only the regions for which both Zulu and SEB were found to show similar SNVs densities and vary strongly with both of the other African populations.To study the variation in SNV enrichment patterns within Africa, we compared SNV densities in the YRI and LWK from the KGP9, and Schlebusch et al.30, all genotyped on Illumina Omni 2.5 M SNP chip, were merged using PLINK v1.975  from the data. The populations were merged according to linguistic and geographic affinities into superpopulations . Access to data is determined by a Data Access Committee (DAC: EGAC00001000734). Data access decisions can be passed to the EGA by emailing ega-helpdesk@ebi.ac.uk with the email address of each applicant and confirmation of the dataset(s) to provide access. The EGA will then create an EGA account with the relevant access permissions.The WGS and the SNP-array data that form the basis of the findings reported in the study have been deposited in the in the European Genome-phenome Archive (EGA; Supplementary InformationSupplementary Data 1Peer Review File


Phascolarctos cinereus), one of Australia’s most iconic species, was historically widely distributed throughout much of eastern Australia but currently represents a complex conservation challenge. To better understand the challenges to koala genetic health, we assessed the phylogeographic history of the koala. Variation in the maternally inherited mitochondrial DNA (mtDNA) Control Region (CR) was examined in 662 koalas sampled throughout their distribution. In addition, koala CR haplotypes accessioned to Genbank were evaluated and consolidated. A total of 53 unique CR haplotypes have been isolated from koalas to date (including 15 haplotypes novel to this study). The relationships among koala CR haplotypes were indicative of a single Evolutionary Significant Unit and do not support the recognition of subspecies, but were separated into four weakly differentiated lineages which correspond to three geographic clusters: a central lineage, a southern lineage and two northern lineages co-occurring north of Brisbane. The three geographic clusters were separated by known Pleistocene biogeographic barriers: the Brisbane River Valley and Clarence River Valley, although there was evidence of mixing amongst clusters. While there is evidence for historical connectivity, current koala populations exhibit greater structure, suggesting habitat fragmentation may have restricted female-mediated gene flow. Since mtDNA data informs conservation planning, we provide a summary of existing CR haplotypes, standardise nomenclature and make recommendations for future studies to harmonise existing datasets. This holistic approach is critical to ensuring management is effective and small scale local population studies can be integrated into a wider species context.The Australian continent exhibits complex biogeographic patterns but studies of the impacts of Pleistocene climatic oscillation on the mesic environments of the Southern Hemisphere are limited. The koala ( The Australian continent exhibits complex biogeographic patterns as a consequence of the interplay between continental drift, topography and climatic change –3. In coPhascolarctos cinereus) is currently widely distributed throughout eastern Australia, from north Queensland to South Australia, including several insular populations . undaries . Althougundaries ,14,16,18Environmental Protection and Biodiversity Conservation Act 1999 [As an internationally recognised wildlife icon, the koala presents complex and challenging management issues. The conundrum arises due to the differing impacts of threatening processes and previous management across their range . ThroughAct 1999 . In contAct 1999  requirinAct 1999 ,31–33. KControl Region (CR) haplotypes resulted in difficulty placing subsequent smaller-scale studies into an overall national framework: a context which could have been used to effectively inform koala conservation and management.A series of studies have previously assessed the population genetics of koalas at a range of scales ,34–49. TCR sequence data to investigate relationships among koala populations nationally, the impact of known biogeographic barriers, the extent of female mediated gene flow and signatures of demographic changes across the range. In addition, we aim to consolidate existing CR data to establish a coherent dataset which will enhance future studies and management decisions by providing a framework to enable local information to be considered in the context of the entire koala distribution.This study revisits the question of koala phylogeography, examining the largest dataset gathered to date, including samples from a range of time periods (1870s to 2015) to provide information on the biogeographic patterns in eastern Australia and provide a robust framework for koala management and conservation. Specifically we aim to use mtDNA Tissue samples were obtained from 662 wild koalas from throughout the distribution , Table 1CR  was amplified using the marsupial-specific primers L15999M and H16498M [© . Sequencing was resolved on an AB 3730xl Sequencer at AGRF Sydney.Genomic DNA was extracted using Qiagen DNeasy Blood and Tissue kit  following standard protocols or according to a high salt method . An ~850 H16498M . PCRs weCR was obtained from five museum specimens . Subsequent library and bait preparation, and hybridisation capture are described in [Additionally, sequence data for the mtDNA J530556; ,52). TheJ530556;  and TsanSequences were checked and edited with reference to chromatograms using Sequencher v 5.3.2 . Any individuals with ambiguous sequence data or unique singleton haplotypes  were re-amplified and sequenced for verification. Unique haplotype sequences were lodged with GenBank under accession numbers KX618862 –KX618876).In addition to the unique sequences generated in this study, we obtained existing published koala haplotypes available on GenBank . Since a number of koala mtDNA sequences of varying sizes exist, we compared all the existing haplotypes, identifying potential duplicates . All haplotypes were then assigned standardised names as described in 7 iterations, sampling every 1000th. The most appropriate model of evolution available was selected, the Hasegawa–Kishino–Yano model , the closest relative of the koala [A) was calculated between all identified lineages within the tree using MEGA.Phylogenetic relationships amongst all 53 unique haplotypes (i.e. novel haplotypes reported here and those from previous studies) were estimated using Maximum Likelihood (ML) and Bayesian Inference (BI). ML analyses were implemented in GARLI 2.1 Web service . The besel , based he koala , was useCR haplotypes using the TCS procedure [http://popart.otago.ac.nz). Information on the frequency of haplotypes was only obtained from the sampling in the present study. Indels were coded as a single mutation. Two networks were generated: one including all 53 sequences with missing data masked and another including only sequences with >850 bp. To combine all the available information we manually compared and combined the two networks. We took a conservative approach to the number of mutations in short sequences, assuming they were identical to longer sequences where data was missing.A haplotype network was created including all 53 unique koala mtDNA rocedure , which urocedure , implemeCR diversity and differentiation were assessed. Haplotypic diversity (h), nucleotide diversity (π) and pairwise differentiation (ΦST) amongst populations were estimated using ARLEQUIN v3.5.2.2 [S [For sites sampled in this study where at least 7 individuals were sampled, the levels of mtDNA v3.5.2.2 . Deviati5.2.2 [S  and Taji5.2.2 [S .CT). The analysis was run with and without location data. To determine the most appropriate grouping for our dataset we estimated FCT for up to 20 populations (i.e. the number of sampled locations).Partitioning of mtDNA diversity was assessed using an Analysis of Molecular Variance (AMOVA), implemented in ARLEQUIN. Partitioning was examined (i) within vs between populations across the range to determine general patterns of differentiation, and (ii) between populations from previously described subspecies  to determine if subspecies correspond to genetic partitioning and (iii) the same as (ii) but with all known reintroduced populations removed from the analysis, as translocated populations may disrupt natural levels of differentiation. A Spatial Analysis of Molecular Variance (SAMOVA), implemented in SAMOVA v2 was used to define homogenous groups . This anST) was also assessed using Isolation By Distance, Web Service (IBDWS) version 3.23 [ST/(1- ΦST) [The influence of geographic distance on the extent of differentiation (Φion 3.23 . The nat(1- ΦST)  and geogCR lineages and the overall dataset. The HKY model of substitution (the most appropriate available based on jMODELTEST) was selected and a substitution rate of 15% per million years was selected, which has previously been applied to several macropod marsupials [7 generations, sampling every 10000th generation. The results were visualised in TRACER and calibrated for generation time .CR haplotypes were identified in the 662 contemporary koalas sampled in this study. When compared to haplotypes previously accessioned to GenBank, 15 haplotypes were found to be novel and 21 had previously been reported  but varied substantially from 0.12 to 0.75 across the sites sampled. Nucleotide diversity was 0.73% across all koalas sampled, and again ranged widely among the sampling sites from no variation (i.e. a single haplotype) to 0.60% within-sites . The resThe results of the AMOVA revealed that genetic diversity was partitioned between sampling sites (88.84% of the variation) rather than within sites (11.16%). There was evidence of significant partitioning of genetic diversity between the previously described subspecies (both including and excluding introduced populations) but only moderate amounts of genetic diversity were partitioned between the ‘subspecies’ (35–43%), relative to between sites within (50–54%). The SAMOVA analysis identified three as the optimal number of groups. Regardless of incorporating geographic data the SAMOVA separated populations between Maryborough (site 4) and Redlands (site 5), and between Iluka (site 9) and Pine Creek (site 10).Pairwise comparisons of population differentiation ranged from 0 to 1, and significant differences occurred between most sites . The exca priori groups tested, although the grouping of NSW and Qld haplotypes and the southern lineage failed to converge after 2000 steps. Models of demographic expansion could not be rejected as the non-linear least squares algorithm failed to converge after 2000 steps for all a priori groupings. BSP showed each lineage appeared relatively stable over time, except for the southern lineage, which exhibited an increase in population size  with noPotorous tridactylus [Petrogale pencillata [Pseudomys oralis [Two biogeographic barriers, the BVB and CRB, previously identified in other eastern Australian species, have impacted the movement of koalas. The BVB appears to separate the two northern lineages (Lineages 1 and 2) from the central lineage (Lineage 3). This biogeographic barrier has previously been reported as impacting the phylogeography of a range of species including mammals , amphibidactylus ), brush-ncillata ) and Hass oralis ) as wells oralis . Finallys oralis ,82,83).There is no evidence of barriers further south, such as the HVB impacting on phylogeographic patterns in koalas, reflecting the idiosyncratic nature of many Pleistocene barriers ,79. It iCR diversity among sites is indicative of limited female movement on shorter timescales . These results are consistent with previous regional and local population genetic studies of koalas and suggests that differentiation of koala populations is associated with contemporary (i.e. Post-European) barriers to dispersal [Overall, the spread of haplotypes and lack of substructure within lineages suggests koalas have maintained effective  genetic connectivity historically (i.e. over evolutionary time). However, significant differentiation and partitioning of mtDNA ispersal ,44,45,49ispersal ,85. In cispersal ,39. TogeCR data; rather our BSP indicated koala populations have remained relatively stable or increased during the past 1000 years. Tsangaras et al. [CR haplotypes when comparing contemporary samples to those from the late 1800s and early 1900s. Furthermore, our extensive sampling of contemporary koalas revealed that only one of these haplotypes appears restricted to historic specimens.The levels of mtDNA diversity detected in koalas in this study are typical of arboreal species and those with specific habitat requirements . The coms et al.  found liCR diversity loss in koalas.The BSP does however suggest a post-LGM expansion, particularly into the southern regions of the range . This paCR has been widely employed to assess genetic diversity and phylogeography, and still forms the basis of management programmes for many species [CR still forms the basis of many management strategies. We suggest researchers undertake simple measures such as ensuring only new unique sequences are named. For complex systems, it may be necessary for researchers to adopt strict nomenclature systems, such as those used in human genetics. One option is adopting the revised Cambridge Naming System (rCNS), where haplotype names are based on the SNPs which distinguish that haplotype from a reference sequence [Sequence variation in mtDNA  species . In koal species ,38,41,45sequence ,90. ThisCR, providing a standardised nomenclature and framework for management and future work S1 File(ZIP)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


Symphonia globulifera L. f. (Clusiaceae) to discriminate the influence of drift-dispersal vs. landscape/ecological features and historical processes on FSGS. We used spatial principal component analysis and Bayesian clustering to assess spatial genetic heterogeneity at SSRs and examined its association with plastid DNA and habitat features. African populations (from Cameroon and São Tomé) displayed a stronger FSGS than Neotropical populations at both marker types (mean Sp = 0.025 vs. Sp = 0.008 at SSRs) and had a stronger spatial genetic heterogeneity. All three African populations occurred in pronounced altitudinal gradients, possibly restricting animal-mediated seed dispersal. Cyto-nuclear disequilibria in Cameroonian populations also suggested a legacy of biogeographic history to explain these genetic patterns. Conversely, Neotropical populations exhibited a weaker FSGS, which may reflect more efficient wide-ranging seed dispersal by Neotropical bats and other dispersers. The population from French Guiana displayed an association of plastid haplotypes with two morphotypes characterized by differential habitat preferences. Our results highlight the importance of the microenvironment for eco-evolutionary processes within persistent tropical tree populations.The analysis of fine-scale spatial genetic structure (FSGS) within populations can provide insights into eco-evolutionary processes. Restricted dispersal and locally occurring genetic drift are the primary causes for FSGS at equilibrium, as described in the isolation by distance (IBD) model. Beyond IBD expectations, spatial, environmental or historical factors can affect FSGS. We examined FSGS in seven African and Neotropical populations of the late-successional rain forest tree  Fine-scale spatial genetic structure (FSGS), the non-random spatial distribution of genotypes within populations, is shaped by microevolutionary processes such as dispersal, local genetic drift and selection . FSGS stThe strength of FSGS depends primarily on an organism’s life history traits, of which life form and breeding system are the most relevant in plants. Indeed, stronger FSGS is found in herbaceous plants than in trees, as well as in partially or completely selfing than in outcrossing species . Populatet al. ) for each individual in each GP. STRUCTURE was chosen because it is particularly efficient at detecting GPs that co-occur in the same geographical site when spatial structure is weak  for each individual on the first global or local sPCA axes, respectively ; 2) the  is weak  and becad et al. , and usio et al.  (see S2 S 2.3.1.  using eiS 2.3.1. .Q) computed above. First, to examine the strength of SGH, we assessed Q values and genetic differentiation (FST) among co-occurring GPs, considering higher Q and higher FST as indicators of stronger divergence. FST among GPs within populations was estimated and tested with permutation tests using SPAGeDi, assigning individuals to GPs based on Q≥50% or Q≥87.5%, the latter category susceptible to include genetically pure, first and later-generation backcrosses [Q≥87.5%, e.g. representing putative distinct reproductive demes, we assessed the presence of null alleles, deviation from Hardy-Weinberg proportions and strength of FSGS as above. Second, to test for historically diverged lineages, we assessed cyto-nuclear disequilibria [R to detect if groups of individuals carrying the same haplotype differed in their mean individual sPCA scores. We also tested for association of haplotypes and GPs (Q≥50%) using Fisher tests that ignored spatial autocorrelation in the data and we performed partial Mantel tests based on similarity matrices  in which we controlled for spatial autocorrelation through a spatial distance matrix using the Ecodist package [R. In Paracou, we further tested the association between GPs or haplotypes and morphotypes, similarly using Fisher and partial Mantel tests. Third, we examined altitudinal stratification of genetic variation in each population as would be expected, for example, in the case of restricted mobility of dispersers due to slopes [ad hoc defined altitudinal classes differed in their mean individual sPCA scores, and in their mean Q value for each GP. Association with altitudinal classes was preferred over a regression analysis because the relationship to test is not necessarily linear.If within-population SGH occurs, its strength, its association with plastid DNA and its spatial arrangement, e.g. in the context of habitat variation, can provide further information on its ecological and evolutionary determinants. As measures of SGH, we used the individual sPCA scores and GP ancestry proportions . Sp and eig.sPCA were both positively correlated with altitudinal sampling range .Significant FSGS was observed in all populations except Yasuní, with an estimated strength of FSGS from São Tomé , Fig 1. n et al.  in ParacSão Tomé . FSGS waSp ranging from -0.0032 to 0.4883. The signal was an order of magnitude greater than at nuclear markers, with stronger structure in African (Sp ≥ 0.277) than in Neotropical populations  was highest for K = 3 in BCI and for K = 1 in Paracou, the solutions we eventually retained  in the African populations reflecting putative coexisting demes, while individuals in American populations were more homogeneous or admixed on average , but the L-test for local structure was not significant in any population, suggesting that neighbouring individuals were not strongly differentiated. STRUCTURE analysis for codominant markers across populations revealed that each population segregated into its own GP except the two Cameroonian populations, which shared the same two GPs. Within populations, the number of GPs that best explained the data was ons Figs , S2 FileAmerican  vs. AfriAmerican , S2 FileFIS within GPs (Q≥0.875) was generally non-significant . In Paracou, individuals from the same morphotype were genetically more related at plastid DNA than expected at random .Cyto-nuclear disequilibria based on ANOVA were detected in the three African populations only: individuals carrying different haplotypes differed in their mean sPCA score . Haplotyad hoc altitude classes differed in their mean sPCA scores  contributed to deviations from Hardy-Weinberg genotypic proportions within populations. There was evidence for cyto-nuclear disequilibria and historical gene pool differentiation in the two Cameroonian populations, while the population from French Guiana displayed an association of plastid haplotypes with two morphotypes characterized by differential habitat preferences.Six out of seven Sp statistic, which is robust towards differences in sampling scheme [S. globulifera. This can affect FSGS, which is expected to increase in low-density populations because of a reduced overlap of seed shadows [a priori expectations, however, weak FSGS was observed in the low-density Neotropical populations BCI and Yasuni, where sampling included also younger individuals (>1cm dbh) potentially representing cohorts of related individuals. The weaker than expected FSGS in these populations could have been caused by confounding factors, e.g. increased animal-mediated dispersal distance in low-density populations [Some methodological issues are worth discussing with regard to our results. First, samples were collected either randomly or following approximate transects in different populations Figs  and 3. Tg scheme ,101. Secg scheme . Third,  shadows . Againstulations . In any Sp, respectively. This indicates a sufficient power for the purpose of the study. In fact, a dataset of 18 genic SSRs on ca. 30 individuals [et al. 2016 are reported in Q should complement the interpretation of K because K alone does not characterize population substructure well. Examining both statistics in the populations where K = 3, we can interpret GPs in São Tomé as putative distinct demes with some degree of reproductive isolation, whereas in BCI, we conclude that GPs are mostly a result of allele frequency gradients (Although the number of SSRs used in our study was low (3–5), these highly polymorphic markers were able to detect significant FSGS in all and in six out of seven populations, by means of the G-test and ividuals  had a loradients .The observed FSGS patterns in our study can be explained through a series of factors, including topographic complexity, seed and pollen dispersal features, biogeographic history and, potentially, microenvironmental adaptation.Sp from 0.0126 to 0.0372) and animal- or gravity-mediated seed dispersal , in agry traits ,73. Sp alow (see ), a typi1, , in alow (see ).S. globulifera. In addition, the complexity of habitats and the vegetation associated with such gradients could have favoured microenvironmental adaptation of S. globulifera (see below) and/or specialization of its dispersers, in terms of behaviour or community composition [S. globulifera dispersers are available for our study populations. On the other hand, large-scale differences in disperser communities between continents can contribute to explaining the observed pattern. Bats (Artibeus spp.) or tapirs (Tapirus terrestris and Tapirus bairdii), endemic to the Neotropics, can disperse propagules from hundreds of meters to several kilometres . Steep topography is known to restrict the mobility of animal species ,100,106,position ,108, resometres . Such cyto-nuclear associations reflect the sympatric occurrence of differentiated lineages. An allopatric differentiation of such lineages is most commonly proposed, for instance in distinct refugia where rainforest species persisted during the dry and cold periods of the Pleistocene . Cameroouge area –114. A cbulifera . The cytbulifera ,115). Rabulifera ,116.S. globulifera morphotype is associated with moist valley bottoms whereas the alternative morphotype preferentially grows in the upper part of slopes. The morphotype—haplotype association in our data and the morphotype—GP association based on genic SSRs ,125124,1uspected ,72,74. Fd Clusia –129) andS. globulifera populations, from non-significant or weak FSGS in Neotropical populations to pronounced structure in African ones. The strength of FSGS correlated with both disperser communities and altitudinal sampling range, while our data also contained evidence for co-occurrence of differentiated lineages and GP aggregation following habitat features. These results highlight the importance of spatially explicit eco-evolutionary processes in the local habitat exploitation of an ancient tropical tree species. The microenvironmental scale thus seems crucial for evolutionary processes in persistent populations of tree species, as has recently been shown in reports on microenvironmental adaptation in forest trees [We detected a wide diversity of FSGS patterns within st trees –120,122.S1 File(XLSX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(DOCX)Click here for additional data file.S4 File(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Fascioloides magna (Trematoda: Fasciolidae) is an important liver parasite of a wide range of free-living and domestic ruminants; it represents a remarkable species due to its large spatial distribution, invasive character, and potential to colonize new territories. The present study provides patterns of population genetic structure and admixture in F. magna across all enzootic regions in North America and natural foci in Europe, and infers migratory routes of the parasite on both continents.F. magna was inferred using program STRUCTURE; (ii) the genetic interrelationships between populations were analysed by PRINCIPAL COORDINATES ANALYSIS; and (iii) historical dispersal routes in North America and recent invasion routes in Europe were explored using MIGRATE.In total, 432 individuals from five North American enzootic regions and three European foci were analysed by 11 microsatellite loci. Genetic data were evaluated by several statistical approaches: (i) the population genetic structure of F. magna and their potential translocation routes were determined. Flukes from the first European focus, Italy, were related to F. magna from northern Pacific coast, while parasites from the Czech focus originated from south-eastern USA, particularly South Carolina. The Danube floodplain forests (third and still expanding focus) did not display relationship with any North American population; instead the Czech origin of the Danube population was indicated. A serial dilution of genetic diversity along the dispersion route across central and eastern Europe was observed. The results of microsatellite analyses were compared to previously acquired outputs from mitochondrial haplotype data and correlated with past human-directed translocations and natural migration of the final cervid hosts of F. magna.The analysis of dispersal routes of the parasite in North America revealed west-east and south-north lineages that partially overlapped in the central part of the continent, where different host populations historically met. The exact origin of European populations of F. magna and an origin of European foci.The present study revealed a complex picture of the population genetic structure and interrelationships of North American and European populations, global distribution and migratory routes of The online version of this article (doi:10.1186/s13071-016-1811-z) contains supplementary material, which is available to authorized users. Invasions and introductions of non-native  species into novel localities are processes that have dramatically affected ecosystems worldwide. Human-caused biological introductions are a major force in shaping global biodiversity , 2 and oThe introduction of invasive parasites is usually an unintentional result of introduction of their final or intermediate hosts through international trade, global transportation, markets or tourism , 6. ParaFascioloides magna  (Trematoda: Fasciolidae), which was transferred along with its cervid hosts from the Nearctic zone to the Palaearctic, where it established local populations [F. magna infections depend mainly on the type of its final host. Red deer (Cervus elaphus), white-tailed deer (Odocoileus virginianus), caribou (Rangifer tarandus) and wapiti (Cervus elaphus canadensis) (definitive hosts) tolerate giant liver fluke very well and contribute significantly to maintaining its populations. Fascioloidosis in domestic ruminants and majority of free-living ruminants  may have a lethal effect [A suitable model for above mentioned studies is the veterinary important parasite giant liver fluke, ulations . Giant ll effect .F. magna is of North American (NA) origin where it co-evolved with an ancestral cervid host, Odocoileus spp. Currently, the parasite occurs in five major enzootic areas across the United States and Canada: (i) northern Pacific coast (NPC); (ii) Rocky Mountain trench (RMT); (iii) northern Quebec and Labrador (NQL); (iv) Great Lakes region (GLR); and (v) Gulf coast, lower Mississippi, and southern Atlantic seaboard (SAS) .F. magna in the Lower Silesian Wilderness in the south-western Poland, close to the Czech border [F. magna is considered to be an invasive species with high potential to spread and colonize new geographic territories and establish local populations. Therefore, the population genetics of F. magna has emerged as an interesting topic for investigation.After its introduction to Europe, the giant liver fluke established three natural foci of infection: (i) north-western Italy (IT); (ii) Czech Republic and south-western Poland (CZ-PL); and (iii) Danube floodplain forests (DFF) . Thh border . Since mF. magna [F. magna populations revealed that: (i) IT population was specific and distinct from two other EU populations; (ii) flukes from IT clustered with parasites from RMT and NPC; western NA origin of the IT population was confirmed; an exact origin of the IT focus was not specified; (iii) flukes from DFF were closely related to CZ parasites and Czech origin of the Danube population was suggested; (iv) F. magna from CZ and DFF clustered with parasites from SAS; south-eastern US origin of the CZ population was implied; an exact origin of the CZ focus was not specified; (v) multiple introductions of F. magna from NA to EU were confirmed [F. magna clades, western (NPC and RMT) and eastern . This genetic makeup correlated with data on historical distribution of white-tailed deer in the eastern and wapiti in the western part of NA [Non-recombining, maternally inherited mitochondrial DNA (mtDNA) was applied in our previous population studies on F. magna , 11 as art of NA .F. magna. Mitochondrial DNA as a single locus molecular marker with specific structure and mode of inheritance has its limitations and reliance only on mtDNA markers in population genetic studies has been criticized, e.g. [F. magna population studies [Although the analyses of mt haplotypes provided useful pilot information about the interrelationships of giant liver fluke on both continents, they did not reveal detailed population history and migratory routes of the parasite within and between the continents, as well as an exact origin of the European populations of ed, e.g. . Besidesed, e.g. . Microsaed, e.g. . Recentl studies .F. magna across all enzootic regions in North America and natural foci in Europe. We were interested in particular topics: (i) Would microsatellite data confirm the differentiation of North American F. magna populations into western and eastern lineages, as previously determined by mt haplotypes? (ii) Compared with mtDNA markers, microsatellites possess different genetic characteristics , hence, would they provide more detailed structuring of NA populations of the giant liver fluke? (iii) Since previous studies indicated that migration pattern of F. magna in NA correlates with the distribution and movements of cervids, we tested alternative hypotheses of historical population migration in NA allowing several competing colonization scenarios and compared the results with data on migration routes of definitive hosts; (iv) Would microsatellites confirm the population structure of EU F. magna populations as determined by mtDNA? (v) While mt haplotypes specified the origin of EU populations of F. magna only at the level of enzootic regions, do microsatellites promise a determination of the exact origin (particular US state/Canadian province) of European populations? (vi) Does the dispersal history of introduced EU populations correlate with chronological findings of the parasite according to the literature data? The results on the patterns of population structure derived from an extensive dataset of alleles for 11 microsatellite loci were discussed with outputs recovered previously from mt haplotypes. Also, the data on migration pattern of the giant liver fluke were compared with data on human-directed translocation and natural migration of cervid hosts of F. magna.The aim of the present study was to apply a multilocus approach to improve understanding of the patterns of population genetic structure and admixture in F. magna were isolated from fibrous pseudocysts in the liver parenchyma of final hosts, rinsed in PBS buffer and preserved in 96 % ethanol. We analysed 432 individuals from 38 final hosts from all North American (NA) enzootic regions  and 10 pmol of each of the primers  and effective (Ne) alleles, their frequencies in populations, observed heterozygosity (Ho), expected heterozygosity (He) and deviation from Hardy-Weinberg equilibrium (HWE) were calculated using GENALEX 6.5 , 22. Lining, FIS . The sigER 2.2.3 .F. magna  underwent bottlenecks. PL population was not included in the analysis due to the low number of sampled specimens, thus CZ-PL focus was represented only by Czech samples. In addition, we assessed NA populations which were closely related to the European populations (RMT and SAS). RMT was used for a comparison with IT due to a low number of samples from the NPC population. Coalescent simulations were applied to generate gene diversities for each population and locus expected from the observed number of alleles given the sample sizes and assuming mutation-drift equilibrium. Expected and observed gene diversities were compared to assess whether gene diversity excess or deficit at each locus was present. The stepwise mutation model (SMM) and the two-phase model (TPM) of microsatellite evolution were employed in making calculations using Wilcoxon signed-rank tests [The programme BOTTLENECK  was applnk tests  with 2,0nk tests .F. magna was inferred based on a Bayesian clustering approach implemented in STRUCTURE 2.3.4 [ad hoc statistic ΔK [6 MCMC repetitions, following a burn-in period of 100,000 iterations was conducted to test the accuracy of results [The population genetic structure of RE 2.3.4 , a modelistic ΔK  were est results . For imp results  was applF. magna populations using GENALEX 6.5 [In addition to Bayesian clustering, PRINCIPAL COORDINATES ANALYSIS (PCoA) was used to analyse the genetic interrelationships between global ALEX 6.5 , 22. PCoALEX 6.5 . A phyloALEX 6.5 . BootstrHistorical dispersal routes in NA and recent invasion routes in EU were explored using a Bayes factor approach  in MIGRAF. magna. Due to low number of samples, the PL population was not included in the analysis. We simulated several models of gene flow: (i) following the chronology of literature records on the spread of flukes, we modelled an invasion from the Czech Republic to Slovakia and then from Slovakia along the Danube to Hungary and Croatia; (ii) alternatively, we modelled Slovakia as the source and the Czech Republic, Hungary and Croatia as the recipients; and (iii) we reversed the direction of the invasion starting from Croatia towards Hungary, Slovakia and Czech Republic. The full model allowing migration between all population pairs also was tested. All models were compared and ranked using Bayes Factor analysis based on marginal likelihoods obtained in the MIGRATE analysis. MIGRATE was run using Bayesian inference and Brownian motion approximations of the stepwise microsatellite mutation model. Each run comprised 5 parallel chains with temperatures 1.0, 1.5, 3.0, 100 and 1,000,000 and each run was replicated 10 times. Run parameters were: 300,000 MCMC steps burn-in, sampling 3,000 parameters every 1,000 steps for the NA dataset and 200,000 MCMC steps burn-in, sampling 2,000 parameters every 1,000 steps for the European dataset. Prior distributions were uniform with a range of 0.00 to 60.00 for Theta (Θ) and 0.00 to 1,000.00 for migration rate (M).Since MIGRATE was successfully applied to identify the source of a freshwater tapeworm recently introduced to northern Africa , we usedΘ) of EU and NA populations identified as their potential source  to quantify the impact of bottlenecks. PL was not included due to the small sample size. We also assessed the possibility of capturing recent bottleneck events using the skyline plot option for Θ. MIGRATE was run using the same settings as above, using the winning model of migration direction for the EU dataset.Complementary to the BOTTLENECK analysis, in MIGRATE we also estimated relative population sizes  per locus, ranging from 2.636 (NPC) to 6.818 (SAS) . HW and LD patterns of disequilibria in RMT, SAS and CZ-PL pointed to possible population substructuring or recent admixture, while in the other NA regions and EU foci inner population substructuring is probably absent. Potential sutures in population structure, independent of a priori population determination, were further explored using Bayesian assignment in STRUCTURE and clustering analysis in PCoA.HW disequilibrium was found for multiple loci in RMT, SAS and CZ-PL populations  according to the ΔK statistics  and eastern (NQL) NA regions  for more detailed determination of genetic relationships. In STRUCTURE, the second distinct genetic cluster was formed by EU populations CZ-PL and DFF and eastern NA populations SAS and GLR Fig. ; K = 2. F. magna from DFF did not cluster with any NA population , which confirmed western NA origin of nes Fig. . On the nes Fig. . Within nes Fig. . Individnes Fig. . BootstrF. magna in NA using MIGRATE revealed the most probable scenario  deviation from equilibrium expectations  were subjected to a recent bottleneck under the SMM and TPM mutation models using the BOTTLENECK software Table . All popns Table .Table 4AΘ compared to CZ.Comparison of the mean number of effective (Ne) and different (Na) alleles per population between native and invasive populations suggests a similar pattern Table . In compΘ for IT was approximately one tenth of that for NPC  , for whiF. magna  [F. magna from the westernmost NPC region. This finding strongly opposes the results based on mtDNA, where the western and eastern populations were clearly separated with no overlaps. It is difficult to explain the genetic relationship of flukes coming from geographically isolated and remote localities at opposite sides of the continent, with no commercial or conservation reasons to translocate their cervid hosts. Hypothetically, these two populations may be the only remnants of the original ancient lineage of F. magna unadulterated by translocation or recent natural overlap. There could have been an ancient overlap with subsequent extirpation of host populations on the shared range and complete isolation of flukes in NQL in the far north-east and NPC in the far west. This could give rise to shared genetic makeup of F. magna in west (Oregon and British Columbia) and north-east (Quebec and Labrador), resulting in isolated remnant fluke populations with a shared ancestry.Differences were also apparent within the eastern NA clade of and NQL) , where mF. magna, such as white-tailed deer, wapiti and caribou. While F. magna co-evolved with ancestral white-tailed deer Odocoileus spp. and was widespread in major wetland habitats throughout NA, wapiti or caribou sympatric with white-tails encountered giant liver fluke in overlapping contaminated wetlands [F. magna migration correlates with the distribution of white-tails in south-eastern USA and their northern migratory routes.Results of the MIGRATE analysis indicated two most probable paths of dispersal in NA, the west-east and the south-north NA lineages, which correlate with historical movement and current distribution of definitive hosts of wetlands . Althougwetlands , unrestrwetlands . Implemewetlands . Currentwetlands . Thus, tF. magna were translocated from NPC in the early 1900s to Wainwright in Alberta (RMT); three decades later wapiti were relocated from Wainwright to GLR region [F. magna from west into the eastern fluke population around the Great Lakes.The second west-east NA lineage might be related to historical distribution of wapiti, which were established across NA south of the forested boreal zone from the Atlantic to the Pacific oceans. Wapiti occurred from northern British Columbia east to New York, and south to South Carolina . They weR region . This maF. magna in the overlapping GLR region and introduced it into the northernmost NQL. The GLR was apparently the region of intersections of final host’s dispersal routes where F. magna could have retained some ancestral genetic structure. At the same time, the GLR population probably has intermingled with flukes after translocation of infected white-tailed deer within eastern NA, and limited introduction of infected wapiti from the west.The admixture of parasite’s gene pool in the central GLR region, reflected also by positive HW and LD tests, is very probably related also to historical spatial distribution of white-tailed deer and caribou. Previous more northern geographic range of white-tailed deer and more southern of caribou resulted in shared pastures of both cervids in overlapping regions around Great Lakes. In the 19th century, peripheral populations of caribou within the USA were eradicated and populations that occurred from Minnesota to Maine, in New York, Wisconsin and Michigan were presumed to be extinct or disappeared . The curF. magna, the structure of introduced populations provides a somewhat different picture.Populations introduced from native areas and isolated in novel territories are in general characterized by reduced allelic richness, lower genetic diversity, significantly stronger influence of random genetic drift and bottleneck effect . In F. mThe discrepancy between results obtained from different methods  could suggest that bottlenecks associated with founder effect were not severe enough to drastically reduce genetic variability in the introduced populations. Alternatively, the failure of BOTTLENECK to uncover population contractions could be affected by the occurrence of null alleles in some populations . In parview see ). Recentview see . Whilst ns (e.g. ) are canF. magna infrapopulations (a set of parasite species coming from single host) [F. magna (mainly in SAS) even represented an overall level of variation detected in the respective region. Thus, cervids imported from NA harboured highly polymorphic F. magna infrapopulations, which established a genetically rich fluke population in the CZ focus.In BOTTLENECK analysis, the comparison between EU and NA populations revealed that the bottleneck effect was neither evident between IT/NPC nor between the CZ-PL focus and SAS region. Smaller, but statistically significant difference in population size was found between SC and CZ-PL populations, with probable continuation of the dilution of population diversity towards the Danube area. The pattern of serially pronounced bottlenecks on the trajectory from NA to CZ-PL and DFF corroborates the proposed scenario of fluke invasion in the central European area. The CZ population was established by direct import of  multiple infected host individuals and created a genetically relatively rich fluke population with high potential for further spread. Another important fact is that a high genetic diversity was observed both in mt haplotypes of le host)  and in aF. magna from CZ-PL to DFF European foci. Outputs of population statistics , microsatellites contributed to revealing population genetic patterns of F. magna that could not be achieved using mtDNA based methods. The employment of analytical methods based on different statistical presumptions  allowed relatively robust interpretation of the data, which generally agreed with the expectations based on host phylogeography and historical records on host movements [In conclusion, current results achieved the initial expectations of our study. Microsatellites, as multi-locus markers characterized by Mendelian inheritance and higher level polymorphisms, provided more detailed population structuring and novel insights on North American and European populations of ovements , 39.F. magna in Europe is a dynamic and ongoing process. Therefore, future monitoring of the parasite distribution is necessary as a basis to prevent transmission to farmed and domestic ruminants. In addition to the veterinary importance, constant tracing of fascioloidosis will help us to maintain up-to-date status of the parasite occurrence.The giant liver fluke is a parasite with “never-ending story”; some facts of its ancestral history were known, while some remained unrevealed until recently. The very recent findings of the parasite in novel territories (e.g. Poland , 56 and