Argentina hosts more than 30 Native American groups, who are widely distributed throughout the country. Mataco-Guaycurú speakers settled in the ecoregion of Gran Chaco and represent 26.7% of the extant aboriginal population of the country. To further investigate the genetic attributes of these speakers, we focused our attention on four aboriginal groups, namely, Wichí, Toba, Pilagá and Mocoví, belonging to the Mataco-Guaycurú linguistic group. Our main goal was to evaluate the interrelationships among the groups and the relationships of these groups with admixed urban populations and to assess correspondences between molecular analysis and historical information. A total of 890 samples (282 Native Americans and 608 inhabitants of admixed urban areas) were analysed. Genetic information was gathered from 15 autosomal STRs, 17 Y-STRs, entire mtDNA control region sequences, 24 AIM-SNPs and 46 AIM-DIPs. Native American signatures were detected in 97.9% of mtDNA lineages, 89.1% of Y-haplotypes and 90.3% to 96.9% of autosomal markers. Wichí exhibited the genetic composition with the largest Native American contribution among the groups and a weak signal of gene flow. This work provides extended genetic information of potential interest in the fields of molecular anthropology and forensic genetics. Subsequently, admixture between Native Americans and European conquerors began, followed by Native American admixture with the colonists, resulting in new demographic categories including mestizos (admixed between Native Americans and Europeans), criollos (European descendants born in the Americas), mulatos , sambos (admixed between Native Americans and Africans) and many others resulting from the admixture between these new categories. Progressively, a new dominant population (consisting mainly of criollos) displaced the autochthonous populations (indigenous inhabitants) to outlying areas. The conquest followed by colonial expansion displaced aboriginals and reduced the geographical areas occupied by them, who consequently experienced a strong reduction in number due to persecution, death and subjugation.The demographic history of Argentina is the result of multiple migration events. Initially, Native American settlers, migrating southward, arrived at the region presently occupied by Argentina over 12,000 years ago. During this period, the migrating tribal groups differentiated by developing unique cultures and languages. The indigenous population settled in this region is estimated to have consisted of 300,000–500,000 people at the time when the first large group of European conquerors arrived. The overall distribution was uneven, with a higher population density in the west, flanking the Andes, and a lower density in the “pampas” and Patagonian regionsQuechua language) is a South American eco-geographical region that includes North-Central Argentina, Bolivia, Paraguay and southern Brazil. This extended area is located between latitudes 16°50′S and 33°50′S and between longitudes 67°50′W and 57°50′W, covering an area of approximately 1,391,000 km2. Aboriginal populations inhabiting the Argentinean part of the Chaco region were the last to submit to the power of the Spaniards and then to the national government.The Gran Chaco ; the group in the jungle, immigrating from Amazonas (Tupí-Guaraní and Arawak); and the group in the mountains or Andean region (Lule-Vilela)3.When the Spaniards arrived in Argentinean Chaco in the 16th and 17th centuries. This novelty, adopted primarily by Toba and Mocoví, facilitated their expansion to the south. Mataco (Wichí) did not incorporate the horse and remained pedestrian in the northwestern region of the Argentinean Gran Chaco.The tribes of Guaycurú speakers, including Toba, Pilagá, Mocoví and Abipón (extinct), became equestrian after the introduction of horses by the Europeans between the 164, while others related them to Macro-Panoan5.The Mataco-Guaycurú linguistic branch included twelve languages spoken in Argentina, Bolivia, Brazil and Paraguay. There are no clear relationships between Mataco-Guaycurú languages and other aboriginal languages; some authors related them to Macro-Ge6, there are 40,036 Wichí (90.3% of the Wichí who inhabit the country) inhabiting Chaco, Formosa and Salta Provinces; 69,452 individuals of Toba ethnicity (68.5%) inhabiting Chaco, Formosa and Santa Fe Provinces; 4,465 Pilagá (88.4%) living in Formosa Province and 15,837 Mocoví (76.7%) living in Chaco and Santa Fe Provinces. In total, these ethnicities constitute approximately 26% of the extant Native American populations inhabiting Argentina.Currently, according to the last Argentinean Supplementary Survey of Indigenous PeoplesIn this work, we analysed the genetic relationships between different groups of Mataco-Guaycurú-speaking tribesmen. We used a large set of polymorphic genetic markers transmitted either bi- or uniparentally. The marker set included autosomal short tandem repeats (STRs), Y-chromosome STRs, the entire control region of the mitochondrial DNA (mtDNA) D-loop sequence and ancestry-informative markers (AIMs) that included deletion-insertion polymorphisms (AIM-DIPs) and single nucleotide polymorphisms (AIM-SNPs), which were used to obtain the genetic landscape of these tribal groups. To evaluate the strength of the genetic relationships between isolated tribal groups and admixed urban populations, a sample set from Formosa, Chaco, Santa Fe Provinces and the cosmopolitan population of Buenos Aires Province were also analysed. We assessed the extent of gene flow between the populations and the three major parental lineages, namely, the Sub-Saharan African, European and Native American lineages, based on the Centre d’Etude du Polymorphisme Humain (CEPH) Panel datasets.7, HLA markers8, mitochondrial Native American haplogroup (hg) frequencies by PCR/RFLP assays9, mtDNA hypervariable region I sequences10, a set of autosomal STRs11 and Y-STRs12. Demarchi and García Ministro13 summarized previous analyses12 and concluded that the Argentine population of Gran Chaco was genetically homogeneous, with intense gene flow between the Mataco and Guaycurú groups.Previous studies focusing on tribal groups inhabiting Gran Chaco analysed serological polymorphismsAiming to broaden the scope of available genetic data, we expanded the analysed groups, the number and type of polymorphic genetic markers used and the extent of mtDNA sequences. The data obtained allowed us to analyse genetic structure and gene flow by STRs and AIMs and provided novel information concerning Y-chromosomal and mtDNA haplotypes. This work provides genetic information that can be used to better characterize the descendants of Native Americans. The large dataset provided here significantly expands existing genetic databases about the region and might be of interest to molecular anthropologists and human population and forensic geneticists.Aiming to investigate the genetic composition of Mataco-Guaycurú speakers as a consequence of internal migrations and after contact with European descendants, we compared genetic information across 15 autosomal STRs, 46 AIM-DIPs and 24 AIM-SNPs to estimate the degree of genetic admixture.Fifteen autosomal STRs were typed in 70 Pilagá, 54 Wichí, 121 Toba and 37 Mocoví belonging to the Mataco-Guaycurú-speaking group  and admixed populations from the provinces inhabited by the Native American sample donors: Formosa, Chaco and Santa Fe. A sample from Buenos Aires Province was also included since this region contains the most cosmopolitan population and experienced the highest influx of internal immigration.Figure Slatkin’s linearized genetic distances, depicted in the multi-dimensional scaling (MDS) plot shown in Fig. AMOVA taking into account the urban population vs Toba (pooled into one group) vs the rest of the Mataco-Guaycurú groups revealed that 6.6% of the variation was among groups (p = 0.00684 +/− 0.00231) and no significant variation among populations within groups (0.11339 +/− 0.00819)  was observed in three haplotypes in Pilagá and five haplotypes in Wichí. One of these haplotypes was the most frequent haplotype in Wichí 29.3%) and in the global Mataco-Guaycurú sample. Two of six haplotypes were previously described in the Toba group from northern Chaco Province by Toscanini 9.3% and Figure An MDS plot of the Q1a3a Y-haplotypes is presented in Fig. Only 23 of 210 (10.9%) Y-haplotypes were non-Amerindian. The number of non-Amerindian Y-hgs increased from north  to south .A total of 245 haplotypes of the entire mtDNA D-loop region are summarized in Supplementary Material Figure Genetic distances based on mitochondrial control region sequencing data were statistically significant in most cases, except for those of Toba-Ch with Toba-F and Toba-SF. The highest genetic distances were observed between Mocoví and all other groups . Several unique mutations in the D-loop region differentiated the Mataco-Guaycurú sequences.Figure et al.16 studied this hg, compiling a list of D1j haplotypes belonging to Argentinian populations, mainly from central areas of the country. In this study, García proposed a local origin of D1j in the Sierras Pampeanas region of Argentina. In addition, this hg was observed in the Mapuche group . Notably, the haplotypes observed in the present work differed from all previously described haplotypes due to their unique mutations .Sub-hg D1j was found in four samples from Mocoví and Toba but not observed in the EMPOP database. García et al.18 (in northeastern Argentina) and in the Chiriguano Native American group19, all with different sequences.Sub-hg D1e was observed in 27 samples from the Toba and Wichí groups. Disregarding insertions at 309, one group of 17 samples and another of 7 samples were identical (including private mutations). In addition, three single haplotypes, two with different private mutations and one identical to the nodal haplotype, comprised the D1e set in the Mataco-Guaycurú sample. This sub-hg was observed twice by Bobillo et al.18 or García et al.16.Sub-hg D1f was observed in only one sample of Toba from Formosa, with a deletion at position 16,166. This haplotype does not match the other D1f samples previously described by Bobillo 18 but has not been observed in any other aboriginal group previously investigated by our team: Mapuche and Tehuelche17, Chiriguano19 or Mbyá - Guaraní20.Sub-hg D4j2 was observed in 8 Toba samples and was the most frequent hg in Pilagá (15/56). This hg was observed in one sample from southern Argentinaet al.21; B2h in 2 samples from Buenos Aires by Bobillo et al.18).Regarding sub-hgs belonging to the B2 branch Fig. , we founet al.19).Other sub-hgs, including A2ag (in one Mocoví) and A2ah (in one Toba), were not found or were infrequent in other Native American groups from Argentina  and to recent migration events that led to their present-day dwellings.In agreement with the conclusion of Demarchi In clear contrast with the homogeneity of Toba, the other groups showed peculiar attributes concerning genetic structure, gene flow and uniparentally transmitted markers.As expected, and based on historical records, the gene pool of Wichí contained the largest aboriginal component among the groups; this group was the most genetically distant from the other groups. Isolated conditions were reflected in all the polymorphic genetic markers tested and underscored by the centroid method, revealing the lowest gene flow among the aboriginal groups included herein. In addition, the smallest allele size range and number of alleles per locus for all the studied loci were observed in Wichí. The hypothesis that a recent bottleneck can still be detected in these groups is being evaluated by expanding the number of DNA markers (manuscript in preparation).The Native American component decreased from north to south, i.e., from Wichí to Mocoví. This pattern can be explained by the higher internal migration experienced by Toba and Mocoví because of the incorporation of the horse as a tool of power and conquest during colonial times. In contrast to these groups, Wichí remained pedestrian in the northern range of their distribution, in Formosa and Salta Provinces, with reduced contact with other groups.Currently, the natural environment is severely affected by intense farming and highly technical agricultural practices, forcing Native American descendants to migrate to urban areas to search for work opportunities. Table 12 reported that 52.6% (10/19) of Pilagá sample donors carried a non-Native American-specific Y-hg. In contrast, we found that only 5.5% (4/72) exhibited the non-Amerindian signature on the Y chromosome. This difference may be due to a difference in sample sizes and/or sampling sites  haplotypes differed, with 31% shared by different Mataco-Guaycurú-speaking groups. The additional genetic information provided herein (entire D-loop sequencing data) allowed us to increase the information about this marker, such as the haplotype diversity and haplotype discrimination power within each hg , although the D-loop haplotype sequences were different from those of Mataco-Guaycurú reported in this study.Recently published work on the Alto Paraná region of ParaguayBased on autosomal and patrilineal compositions (55.5% of Y-haplotypes were non-Amerindian), Mocoví were the most admixed group. Furthermore, genetic distance representation via MDS plots Fig.  placed tStatistical information obtained from population genetics analysis is presented in the supplementary information since it might be of interest in molecular anthropology as well as for forensic purposes. Some Y-STRs and mtDNA haplotypes found in the analysed populations have been uploaded to specific databases, such as the Y-chromosome Haplotype Reference Database (YHRD) and EMPOP. Further sequencing of the full mitochondrial genome could expand knowledge of the sub-hg diversity of the aboriginal populations investigated.This work complements previous research carried out by other groups and by our team, who have worked on the genetic characterization of different aboriginal groups inhabiting Argentina and whose main goal is to increase knowledge about the native populations that persist despite the multi-ethnic admixture process taking place in present-day Argentina.Sample donors read and signed a written informed consent statement. Research projects and consent statements were approved by the Bioethics Committee of the School of Pharmacy and Biochemistry, Buenos Aires University, Argentina . The research was performed in accordance with relevant guidelines, and signed informed consent was obtained from all donors. A set of 890 individuals was analysed, including 282 Native American tribesmen who spoke Mataco-Guaycurú and inhabited three Argentinean provinces , 608 individuals from urban populations of four provinces (the abovementioned provinces and Buenos Aires Province), and three parental populations from the CEPH panel. Table 24.DNA was extracted from either blood samples or liquid saliva by using conventional protocolsWe typed Amelogenin and fifteen STRs: D3S1358, TH01, D21S11, D18S51, Penta E, D5S818, D13S317, D7S820, D16S539, CSF1PO, Penta D, VWA, D8S1179, TPOX, and FGA. Experimental procedures and analyses were performed according to the manufacturer’s protocol .We analysed seventeen Y-STRs included in the AmpFlSTR YFiler Kit  in all the samples, except in TobaSF and Mocoví samples, which were analysed with twelve Y-STRs included in the PowerPlexY kit (Promega Corp.).The haplotypes included in this study have been deposited in the YHRD .25.The Y-SNP M3-C/T (Q1a3a1 hg) was analysed by real-time PCR followed by high-resolution melting analysis as previously described27. The analysed samples as well as results from the structure analysis are summarized in Supplementary Material A total of 307 reference samples from the CEPH panel, 161 samples from Mataco-Guaycurú speakers  and 200 samples from the urban populations were typed with a panel of 46 AIM-DIPs. In addition, 247 reference samples, 103 Mataco-Guaycurú samples and 162 urban population samples were analysed with 24 autosomal SNPs (AIM-SNPs). A total of 46 AIM-DIPs and 24 AIM-SNPs were amplified as previously described19. All sequencing analyses were performed with both forward and reverse primers (with at least four to six primers for each sample). Electropherograms were edited with Sequencher v 5.3 software . Hgs were determined using the EMPOP v4/R11 Query Search tool (http//www.empop.org). All sequences obtained and reported herein were deposited in EMPOP under accession number EMP0066728.We sequenced the entire mtDNA D-loop . Amplification, purification and electrophoresis conditions were as previously describedTable 29.Allele frequencies, HWE, gene and haplotype diversities, genetic distances and heterozygosity were determined with Arlequin v3.5.2.230, employing 15 autosomal STRs31, 24 AIM-SNPs and 46 AIM-DIPs. Genotypes of the parental populations, including the Sub-Saharan African, European and Native American populations, were obtained from published data derived from CEPH Panel samples. For STRUCTURE analysis, five iteration rounds were used. The number of parental populations (k) was set from two to five. Monte Carlo-Markov chain simulation, including a burn-in step of 10,000 iterations followed by 20,000 iterations for data gathering, was performed for each round. The number of populations assumed was initially set to the number of parental populations. An admixture model and independent allele frequencies were used. The most likely value for the number of parental populations (k = 3) was determined using the online program Structure Harvester, which enables implementation of the Evanno method32. Further data analysis was performed using CLUMPP33. Graphical representation was performed with Ancestry Painter software .Admixture analysis was performed with STRUCTURE v2.3.4 software14. Calculations were performed with Geno Cline V.1.1 software (http://genocline.sourceforge.net). Three sets of samples were considered as the complete set: CEPH Panel parental samples, urban Argentinean samples and Mataco-Guaycurú speaker samples. Then, each group was sequentially removed. Regression plots reflecting the proportion of heterozygotes (H′) in each population expected under HWE against the distance from the centroid (ri) were created in Microsoft Excel 2016.Gene flow extent was analysed by using autosomal STR allele frequency data by means of the centroid method proposed by Harpending and Wardn(1-Σpi2)/(n-1) (where n is the sample size and pi is the frequency of the ith haplotype35). For statistical purposes, the length of DYS389II was subtracted from that of DYS389I36. The YHRD database (http://www.yhrd.org) was used to search global haplotype frequency distributions. Y-Hgs were inferred using Whit Athey’s Haplogroup Predictor (http://www.hprg.com/hapest5/index.html), and Native American specific Hg was confirmed with the SNP M3-Q3. A matrix of normalized Slatkin’s genetic distances was represented as an MDS plot using XLSTAT (Addinsoft Corp) software.Y-chromosome haplotype diversity was calculated as http://www.fluxus-engineering.com/ 37). Y-STR mutations were weighted according to Qamar et al.38MJ networks were obtained with Network 5.0.1.1 software (Dataset 1-aDataset 1-bDataset 2Dataset 3


The genetic origins of Uralic speakers from across a vast territory in the temperate zone of North Eurasia have remained elusive. Previous studies have shown contrasting proportions of Eastern and Western Eurasian ancestry in their mitochondrial and Y chromosomal gene pools. While the maternal lineages reflect by and large the geographic background of a given Uralic-speaking population, the frequency of Y chromosomes of Eastern Eurasian origin is distinctively high among European Uralic speakers. The autosomal variation of Uralic speakers, however, has not yet been studied comprehensively.Here, we present a genome-wide analysis of 15 Uralic-speaking populations which cover all main groups of the linguistic family. We show that contemporary Uralic speakers are genetically very similar to their local geographical neighbours. However, when studying relationships among geographically distant populations, we find that most of the Uralic speakers and some of their neighbours share a genetic component of possibly Siberian origin. Additionally, we show that most Uralic speakers share significantly more genomic segments identity-by-descent with each other than with geographically equidistant speakers of other languages. We find that correlated genome-wide genetic and lexical distances among Uralic speakers suggest co-dispersion of genes and languages. Yet, we do not find long-range genetic ties between Estonians and Hungarians with their linguistic sisters that would distinguish them from their non-Uralic-speaking neighbours.We show that most Uralic speakers share a distinct ancestry component of likely Siberian origin, which suggests that the spread of Uralic languages involved at least some demic component.The online version of this article (10.1186/s13059-018-1522-1) contains supplementary material, which is available to authorized users. The linguistic landscape of North Eurasia is dominated by three language families—Turkic, Indo-European (IE) and Uralic. It has recently been shown that the spread of Turkic languages was mediated by gene flow from South Siberia . SimilarThe Uralic family contains 40–50 different languages –11 and cThe question as which material cultures may have co-spread together with proto-Uralic and Uralic languages depends on the time estimates of the splits in the Uralic language tree. Deeper age estimates 6,000 BP) of the Uralic language tree suggest a connection between the spread of FU languages from the Volga River basin towards the Baltic Sea either with the expansion of the Neolithic culture of Combed Ware, e.g. ,000 BP o, 17, 26 Previous genetic studies have shown that demographic histories of Uralic-speaking populations inferred from maternally inherited mitochondrial (mtDNA) and paternally inherited Y chromosomes (chrY) are different. MtDNA studies of Uralic speakers suggest that the distribution of Western and Eastern Eurasian components is mostly determined by geography –32. ThusOur goal in this study was to test whether the Uralic-speaking peoples share recent common genetic ancestry in their genomes. Specifically, we tested whether the clear signal of migration between East Eurasia and Europe that is present in the distribution of paternal lineages could be also detected in the patterns of autosomal variation. It has been shown earlier that the genetic landscape of northern and northeastern European populations displays affinities with Siberia –45 and tFST-distances between populations . On the UPGMA tree based on these FST-distances . Overall, and specifically at lower values of K, the genetic makeup of Uralic speakers resembles that of their geographic neighbours. The Saami and (a subset of) the Mansi serve as exceptions to that pattern being more similar to geographically more distant populations  data for pairs of populations  with high versus low probability to share their patrilineal ancestry in chrY hg N   segmentsTo study the fine-scale genetic structure of Uralic-speaking populations and to reconstruct past admixture events in their history, we used the haplotype-based approach implemented in fineSTRUCTURE  and GLOBThe natural clusters defined by fineSTRUCTURE were further used to study admixture history with Globetrotter analysis, which was applied in two different setups. First, we performed the ‘full’ analysis, where every recipient population could copy from any other donor group Fig. a involveIn the VUR Fig. a, all poThe excess of admixture events between closely related geographical neighbours may mask traces of subtle genetic contacts with distantly related populations . TherefoIn the European set, only the Uralic-speaking ‘Finnic’ and ‘Saami’ clusters have a detectable (more than 3%) amount of admixture with West Siberian sources Fig. b, even iThe Uralic- and non-Uralic clusters from the VUR have different admixture histories. Turkic speakers  contain three European donors . The ‘Samoyed’ cluster shows evidence for a complex one-date multiway admixture shaped by multiple regionally diverse surrogates dominated by West/Central Siberian and Khanty-Mansi groups. The ‘Samoyed’ cluster is, together with South and West Siberians, also a major contributor to an admixture event in a separate Samoyed-speaking group—Nganasans . The only Uralic-speaking population that did not fit into the tested model with five ancestral components were Hungarians. The qpGraph estimates of the contributions from the Siberian component show that it is the main ancestry component in the West Siberian Uralic speakers and constitutes up to one third of the genomes of modern VUR and the Saami  = − 0.00263 (± 0.0008); Z-score = − 3.0691)] and Lithuanians .We then used the software  to test ami Fig. . It dropFST distances and the fineSTRUCTURE coancestry based matrix using the data of shared chunk counts and two types of FSTs with both mtDNA and chrY  as did all the genetic distances . This is consistent with the expectation that haplotype-based distances capture more recent signals of shared ancestry that are more relevant to recent history of language expansions. For mtDNA and chrY distances, correlation was not significant after correcting for geography. Thus, our findings indicate a clear relationship between autosomal genetic distances and lexical distances among Uralic-speaking populations, even when the effect of geographical distance is taken into account. The non-significant finding with respect to mtDNA and chrY data may reflect greater noise in these haploid loci. It is also worth noting that geographical distances significantly predict autosomal and chrY distances (but do not predict mtDNA distances) when keeping the lexical distance constant  that is primarily present in most Uralic speakers Fig. . AssuminOur Mantel test is consistent with the presence of common genetic substrate in most of the Uralic speakers  the Uralic speakers are genetically most similar to their geographical neighbours; (2) nevertheless, most Uralic speakers along with some of their geographic neighbours share a distinct ancestry component of likely Siberian origin. Furthermore, (3) most geographically distant Uralic speaking populations share more genomic IBD segments with each other than with equidistant populations speaking other languages and (4) there is a positive correlation between linguistic and genetic data of the Uralic speakers. This suggests that the spread of the Uralic languages was at least to some degree associated with movement of people. Moreover, the discovery of the Siberian component shows that the three known major components of genetic diversity in Europe  are not enough to explain the extant genetic diversity in (northeast) Europe.UNESCO Atlas of the World’s Languages in Danger [. In this study, DNA samples of a total of 15 Uralic-speaking populations from Europe and Western Siberia were collected from the present-day spread area of corresponding Uralic languages  based on whole genome single nucleotide polymorphisms (SNPs)  in a window of 200 SNPs (sliding the window by 25 SNPs at a time), due to the possible effect of background LD on PCA and structure-like analysis. To exclude possible close relative pairs (first and second degree) among the individuals, the software KING v1.4 [A total of 135 samples from this study were genotyped using the Illumina 610K, 650K, 660K or 1M SNPs arrays  and analysed for the whole-genome variation together with published genotype data  from K = 3 to K = 20 to observe the deviation of the results between individual runs  shares more derived alleles with the Uralic-speaking population (C) compared to the non-Uralic-speaking population (D); if D > 0, a test population (B) shares more derived alleles with the non-Uralic-speaking population (D) compared to the Uralic-speaking population (C). We used Yorubas as an outgroup (A) and one out of four westernmost Uralic-speaking populations: Saami from Sweden, Finns, Estonians and Hungarians; and one out of three European non-Uralic speakers: French (representing West Europeans), Poles (representing East Europeans) and Swedes (representing North Europeans) as a fixed pair . We ran the D-statistics test . Only the D-values with |Z-score| > 3 were considered significant.We calculated atistics   implemented in the ALDER v1.03 software  to test f3 analysis of our modern and published ancient human genotyping data with the AdmixTools v3.0 software package [f3-statistic  is a function of shared branch length between X and Y in the absence of admixture with the outgroup [f3-statistics in the form of  to investigate the shared history of a set of 47 European, Siberian and East Asian populations, including the Uralic speakers and 216 ancient genomes  were computed with Uralic-speaking populations and their geographical neighbours as test populations, and all European and Siberian populations from the EBC Illumina dataset as comparison populations. The analyses  when we modelled ancient and modern European populations through several admixture events shown in Fig. We ran the  package  on a mer package , Jones e package  and Saag package  resultinWe present new genotype data of 1578 mtDNAs and 994 chrY of Uralic-speaking individuals, which include also all those individuals genotyped for autosomal markers. MtDNA hgs were determined by genotyping the variation of the first hypervariable segment (HVS-I) and coding region markers of mtDNA  that are universal, maximally resistant to borrowings and temporally stable. The Uralic basic vocabulary data and cognate assessments were achieved from the available literature. The data was collected by one single person (Jyri Lehtinen) which ensured equal quality of the data throughout the languages. In total, we had 226 meanings based on the meanings listed in Swadesh 100 and 200 lists , 94 and ST [ST for both mtDNA and chrY [ST for autosomal SNPs and (4) total variation distances (TVD) [To assess correlation between genetic, linguistic  and geographic distances for the Uralic-speaking populations, we employed the Mantel test  using PeST  values cand chrY , (3) Weiand chrY  pairwisees (TVD)  between cor.test. Results are presented in Additional file Pearson correlation coefficients between two variables—the frequency of the k9 ancestral component Fig.b and theAdditional file 1:Table S1. Autosomal data used in the study. (XLSX 28 kb)Additional file 2:Table S2. Ethnolinguistic characteristics of studied Uralic populations. (PDF 15 kb)Additional file 3:Figure S1. Principal component analysis of the studied Uralic-speaking populations in Eurasian context. Figure S2. FST distances of the studied Uralic-speaking populations in the global context. Figure S3. ADMIXTURE analysis. Figure S4.D-statistics. Figure S5. fineSTRUCTURE tree. Figure S6. fineSTRUCTURE heatmap. Figure S7. Admixture dates of ALDER. Figure S8. Outgroup f3 test between modern and ancient samples. Figure S9. Outgroup f3 results plotted pairwise against each other. Figure S10. Admixture graphs. Figure S11. Phylogenetic network of Y chromosome hg Q. Figure S12. Trees constructed from linguistic and genetic data of Uralic peoples. Figure S13. Comparison of autosomal and X chromosomal outgroup f3 statistics for Estonians, Udmurts and Khanty. (PDF 11550 kb)Additional file 4:Table S3. Mitochondrial DNA data of Uralic speakers. (XLSX 541 kb)Additional file 5:Table S4. Mitochondrial DNA haplogroup frequencies in Eurasia. (XLSX 71 kb)Additional file 6:Table S5. Y chromosome haplogroup frequencies in Eurasia. (XLSX 22 kb)Additional file 7:Table S6. Correlation analysis of k9 ancestral component and chromosome Y haplogroup N sub-haplogroups. (XLSX 16 kb)Additional file 8:Table S7. Average identity-by-descent-segment share in Northeast European populations. (XLSX 50 kb)Additional file 9:Table S8. Average identity-by-descent-segment share in Central and East European populations. (XLSX 44 kb)Additional file 10:Table S9. Average identity-by-descent-segment share in Siberian populations. (XLSX 41 kb)Additional file 11:Table S10. Globetrotter results. (XLSX 48 kb)Additional file 12:Table S11. Details of Globetrotter regional analysis. (PDF 50 kb)Additional file 13:Table S12. ALDER vs Globetrotter dates. (XLSX 210 kb)Additional file 14:Table S13. Outgroup f3 statistic. (XLSX 305 kb)Additional file 15:Table S14. Data matrices for correlation analysis and geographical coordinates of studied populations. (XLSX 38 kb)Additional file 16:Table S15. Mantel test results. (XLSX 11 kb)Additional file 17:Table S16. Y chromosomal hg Q STR data. (XLSX 16 kb)


We capitalize on the widespread colonization of Chinook salmon Oncorhynchus tshawytscha in South America, mediated by both dispersal pathways, to address these issues using data from a panel of polymorphic SNPs. First, genetic diversity and the number of effective breeders (Nb) were higher among artificial than natural populations. Contemporary gene flow was common between adjacent artificial and natural and adjacent natural populations, but uncommon between geographically distant populations. Second, genetic structure revealed four distinct clusters throughout the Chinook salmon distributional range with varying levels of genetic connectivity. Isolation by distance resulted from weak differentiation between adjacent artificial and natural and between natural populations, with strong differentiation between distant Pacific Ocean and Atlantic Ocean populations, which experienced strong genetic drift. Third, genetic mixture analyses revealed the presence of at least six donor geographic regions from North America, some of which likely hybridized as a result of multiple introductions. Relative propagule pressure or the proportion of Chinook salmon propagules introduced from various geographic regions according to government records significantly influenced genetic mixtures for two of three artificial populations. Our findings support a model of colonization in which high‐diversity artificial populations established first; some of these populations exhibited significant admixture resulting from propagule pressure. Low‐diversity natural populations were likely subsequently founded from a reduced number of individuals.Genetics data have provided unprecedented insights into evolutionary aspects of colonization by non‐native populations. Yet, our understanding of how  The former relates to human‐mediated releases or propagule pressure, intentional and unintentional, of non‐native individuals into the receiving environment; the latter relates to spread of non‐native individuals from established populations via gene flow. Populations founded via artificial dispersal may involve multiple introductions with expected increases in genetic diversity , (ii) spatial patterns of genetic structure, and (iii) the degree of genetic admixture among non‐native populations.Invasion biology has historically benefited from a partnership with population genetics to clarify evolutionary aspects of the establishment and spread of colonizing species  native to the Northern Hemisphere are important subjects in ecology and evolutionary biology because they exhibit diverse life histories , are lacking. Anadromous salmonids that spawn in freshwater but feed in the ocean as adults are especially appropriate to study as they can rapidly colonize unoccupied habitats by dispersal. They can become quickly established, extending their distribution at their native  is the largest‐bodied Pacific salmon species, recognized for its importance for recreational, commercial, and subsistence fisheries in North America and Asia. Chinook salmon were also repeatedly introduced to South America via several government‐sponsored and private initiatives during most of the twentieth century, both in Chile and in Argentina, to develop recreational and commercial fisheries as well as net‐pen aquaculture  through their South American distribution, including Pacific Ocean and Atlantic Ocean basins  populations contributed to establishment of non‐native Chinook salmon using analyses of genetic mixtures. We predicted whether genetic mixtures were consistent with relative propagule pressure or the proportion of Chinook salmon propagules introduced from various geographic regions according to historical government records.Here we analyzed nine Chinook salmon collections taken from s Figure , using as Figure  and have22.1We covered the entire distributional range of Chinook salmon among Pacific Ocean and Atlantic Ocean basins in South America by supplementing archived samples of seven populations  following the protocols from the manufacturer. Some isolates from Chinook salmon carcasses contained low concentrations of DNA (<10 ng/μl), and these generally yielded low‐quality genotypes that were excluded from analyses. DNA was screened for a suite of 191 SNPs  proportions or showed evidence of linkage disequilibrium (LD) using GENEPOP v4.2  and expected heterozygosity (HE) for each collection using GENALEX  were calculated in FSTAT  was calculated in NeEstimator using the linkage disequilibrium (LD) approach  threshold values for minor allele frequencies dependent on sample sizes n ≥ 25 or n < 25, (ii) random mating system, and (iii) jackknifing to calculate 95% CIs  Jombart  on multiθ estimator. Exact tests of population differentiation for the null hypothesis θ = 0  between population sampling sites were estimated using the tracking line option in QGIS : Petrohue River (PET), Cobarde River (COB), and Prat River (PRA). Two primary sources of data were reviewed. First, we accessed databases belonging to Chile's Under Secretariat of Fisheries (SUBPESCA), Department of Aquaculture, to identify the geographic origin of Chinook salmon eggs importations for captive breeding taken place between 1977 and 2000, including geographic coordinates of stocking and farming operations. Introduction records from earlier periods were ignored because they likely failed to establish  and 0.303 . Artificial populations showed higher diversity than natural populations  and 13 . Artificial Nb estimates averaged 156, whereas natural Nb estimates averaged 32. Neither HE  nor LD Nb was correlated to sample size , implying sampling bias failed to explain differences in HE and LD Nb among populations.Values for ns Table ; HE flup < .01 threshold  as well as in the opposite direction (VAR to PRA). We also observed dispersal among adjacent natural populations . We observed additional long‐distance, bidirectional dispersal with four immigrants between COB in the Pacific Ocean and CAT in the Atlantic Ocean, two populations separated by nearly 4000 km of coastal distance. We found no evidence for connectivity between northern (ALP and PET) and southern populations , other than one possible immigrant from PIC (a hatchery population) to PET.We identified 14 first‐generation immigrants at the ld Table . Gene fl3.3artificial events in Pacific Ocean sites located north , even though admixture was evident between artificial Pacific Ocean sites located south (between COB and PRA). Natural populations from the Pacific Ocean (VAR and SER) clustered geographically close to artificial populations (COB and PRA). Atlantic Ocean sites SAC and CAT were the exception as they showed substantial divergence from all Pacific Ocean sites.DAPC identified four clusters of Chinook salmon from South America: (1) ALP; (2) PET‐PIC; 3) COB‐VAR‐SER‐PRA; and (4) SAC‐CAT  and θ = 0.231 (PIC vs. CAT) and were all significantly higher than 0, suggesting weak to strong spatial genetic structure  with only 1 of 200 backcrossed individuals misassigned to a third, and closely related, parental geographic region  Pacific Northwest—Washington Coastal, (iii) Lower Columbia River—Willamette; (iv) Columbia River—Deschutes, (v) Oregon‐California Coast, and (vi) California Central Valley  and Lower Columbia River—Willamette (56.9%). Individual Bayesian assignment was consistent with results above and further confirmed evidence of hybridization among donor populations as some individuals showed ancestry to two or more geographic regions : (i) ey Table . Artifics Figure .3.4.3Puget Sound‐South British Columbia  and PRA  for which genetic mixtures comprised higher than 95% genotypes from Lower Columbia River‐Willamette, yet 100% propagules were introduced via Vancouver in British Columbia, Canada, assigned to Puget Sound‐South British Columbia  in South America using a population genetics approach. Although Chinook salmon show strong philopatry, they also colonize new habitats via dispersal. Our genetic survey of nine non‐native populations covered nearly 5500 km and provided resolution across the distributional range to evaluate how colonization of this species has unfolded during almost four decades. Below we discuss how our findings informed and supported predictions regarding genetic diversity, the evolution of genetic structure, and population admixture.We used multilocus genotypes (from both non‐native and native and donor populations), historical records of introduction, and various statistical approaches to evaluate the roles of 4.1Artificial populations had on average higher genetic diversity and larger estimates of LD Nb than natural populations. This is consistent with two, nonmutually exclusive hypotheses. First, increased HE among artificial populations may reflect admixture during captive breeding or in situ following introduction of multiple lineages  than originally stocked populations. Yet, our estimates of LD Nb need to be interpreted with caution due to some drawbacks. First, samples were taken in different years and comprised different life stages and thus applied to slightly different periods. Waples  sampled at year i apply to previous years, likely year i–j where j is the age at which adults reproduce. Estimates of LD Nb from juveniles  sampled at year i apply to the same year  rather than Nb as samples span one entire generation of Chinook salmon , suggesting that artificial populations were likely established first and natural populations were subsequently founded (VAR to PRA in the Pacific Ocean). We also observed gene flow between natural populations . Further, we found evidence for long‐distance dispersal between Pacific Ocean and Atlantic Ocean basins (COB to CAT and vice versa). Both strategies may be common among successful invasions , even though some amount of gene flow has occurred between artificial  and natural  populations, especially south of 45°S. Also, PRA was stocked with Chinook salmon that likely founded PET  carrying genes from Lower Columbia River‐Willamette region.We additionally found that measures of relative propagule pressure and mixture analyses for Chinook salmon populating Cobarde River (COB) were inconsistent, suggesting that the demographic effects of propagule pressure cannot be generalized, and that history and chance or deterministic factors may affect invasion success .SNP multilocus genotypes for this study are available in DRYAD under accession number doi: (None declared.DG‐U, CR‐R, JEC, MAP, JES, and LWS contribute to conception and design of the work; DG‐U, DC‐R, BE, EA, SSM, FV‐A, MAP, and TPQ contributed to acquisition, analysis, and interpretation of data for the work; DG‐U and DC‐R drafted the manuscript; DG‐U, CR‐R, JEC, TPQ, JES, and LWS revised the manuscript critically for intellectual content; and all authors approved the final version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. 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.


Cupressus gigantea) that occurs at higher latitude and elevation on the Qinghai-Tibet Plateau into a related species (C. duclouxiana), which has likely aided the latter species to extend its range by colonizing cooler and drier mountain habitats during postglacial periods. We show that 16 introgressed candidate adaptive loci could have played pivotal roles in response to diverse stresses experienced in a high-elevation environment. Our findings provide new insights into the evolutionary history of Qinghai-Tibet Plateau plants and the importance of introgression in the adaptation of species to climate change.Introgression may act as an important source of new genetic variation to facilitate the adaptation of organisms to new environments, yet how introgression might enable tree species to adapt to higher latitudes and elevations remains unclear. Applying whole-transcriptome sequencing and population genetic analyses, we present an example of ancient introgression from a cypress species ( Yazhen Ma et al. find evidence of an ancient introgression from cypress into a related species from the Qinghai-Tibet Plateau, which aided in the colonization of cooler and drier mountain habitats. They identify 16 introgressed loci that may provide protection against high-elevation stressors. It is argued that to survive such change, terrestrial species might migrate across land by tracking favorable conditions or persist in situ either through adaptive phenotypic plasticity3 and/or genetic modification caused by natural selection favoring advantageous standing genetic variation and mutations4. A further, but until recently, overlooked potential source of genetic variation that can aid adaptation to climate change is introgression from closely related species6.Rapid climate change is poised this century to be one of the greatest global challenges to the stability and cohesion of ecosystems12. Such adaptive introgression resulting from hybridization and backcrossing with another species15 has the potential to allow adaptation in recipient populations at rates considerably higher than if mutation was the only source of variation18. Though adaptive introgression has long been considered an important evolutionary mechanism13, with notable examples reported in both plant and animal genera22, most studies of adaptive introgression in plants using a genomic approach have focused on short generation species (but see refs. 23). A major challenge to detecting introgression between species with long generation times and large effective population sizes, such as trees, is to distinguish between the causes of shared polymorphisms due to incomplete lineage sorting or introgression25. Notwithstanding this difficulty, a growing number of studies have shown that introgression occurs in at least some tree genera (reviewed in refs. 27), particularly from local tree species into widespread or invasive ones28, involving genomic fragments exhibiting low rather than high intraspecific gene flow29. How commonly such introgression is adaptive, however, remains a topic of active research.Recent studies on humans, dogs, cattle, sunflowers, poplars, and oaks suggest that introgression from other species can be a very important source of new genetic variation in the adaptation of species to new environments31 with clines reflecting local adaptation across environmental gradients31 and ecotypic variation is often recorded4. Moreover, candidate loci associated with adaptive changes across environmental gradients are increasingly implicated in studies of adaptive divergence in trees34. Although evidence of adaptive introgression in trees is accumulating , only recently has detailed evidence of it emerged from genomic analyses in model tree genera such as poplars37 and oaks38. In contrast, little is known of it occurring in conifers, partly due to their huge genome size39 which makes reference genome assembly difficult. Moreover, very few studies have focused on introgression across elevation gradients (but see ref. 27), especially in topographically complex regions, such as the Qinghai-Tibet Plateau and adjacent regions in Asia (but see ref. 40). Here, we examine the possible role of introgressive hybridization in the adaptation of a cypress (Cupressus) species to higher elevations and latitudes in the Qinghai-Tibet Plateau and neighboring areas.Adaptive genetic variation is common within tree speciesCupressus gigantea, which occurs along the Yarlung Tsangpo river valley in Tibet (Xizang), grows at higher altitudes (2950–3430 m.a.s.l.) relative to other species in the genus41, whereas C. duclouxiana, occupies a wider altitude range (1400–3300 m.a.s.l.), and is mainly distributed in central and northwest Yunnan, and southwest Sichuan, China42. Previous work has indicated a close phylogenetic relationship between these two species based on cpDNA variation43, and an absence of gene flow between them based on nuclear microsatellite markers44. Importantly, populations of C. duclouxiana in the northern and southern parts of its range are genetically divergent, forming two separate groups based on microsatellite variation44. The northern populations tend to occur at elevations and latitudes between those of southern C. duclouxiana and C. gigantea populations, although their habitat is similar to that of C. gigantea. Thus, both C. gigantea and northern populations of C. duclouxiana occur in mountain valleys that are cooler and drier than the habitat of southern populations of C. duclouxiana. The distribution of C. duclouxiana is also notably more strongly influenced by human activities than C. gigantea, with relict populations found around temples and in remote areas. Here, we tested whether adaptive introgression from C. gigantea to northern C. duclouxiana enabled the latter to colonize mountain habitats.Cupressus species to address the following questions: What is the genetic affinity of northern to southern populations of C. duclouxiana and to populations of C. gigantea? What is the level and direction of gene flow between the different genetic groups detected? What role might adaptive introgression have played in shaping the distribution pattern of genetic variation in this complex, enabling C. duclouxiana to grow over a wide range of elevations? Our results suggest that both C. duclouxiana and C. gigantea are monophyletic; however, genetic introgression from C. gigantea to northern C. duclouxiana is supported by multiple lines of evidence. We detected 1285 loci likely to be introgressed from the former to the latter, with 16 of these identified as candidate adaptive loci that might play pivotal roles in response to diverse stresses in high-elevation environments. These loci were positively selected in C. gigantea and northern C. duclouxiana, but not in southern C. duclouxiana. This introgression most likely occurred during the last glacial maximum (ca. 0.022 million years ago) or during earlier glacial periods, and contributed to the adaptation of northern C. duclouxiana to cooler and drier conditions in mountain habitats.We analysed RNA-sequences from leaf samples representing both Cupressus duclouxiana trees (from 11 populations) and 35 C. gigantea trees (from 12 populations)  and mean annual temperature between 12.6 and 20.8 °C  nr (non-redundant) database. After aligning transcriptome reads from all individuals to the reference transcriptome and undertaking stringent quality filtering, we identified a total of 878,614 single nucleotide polymorphisms (SNPs) across all 65 individuals of C. duclouxiana and C. gigantea examined. For 513,615 of these SNPs, covering 16,884 unigenes, there were no missing data across all samples. The transcriptome-wide average values for both indicators of nucleotide diversity, θπ and θw, were higher for C. duclouxiana  than for C. gigantea  and further separated northern from southern populations of C. duclouxiana along PC2   Fig. . Analysitea Fig. . Some ofals Fig. . When K C. gigantea and the northern and southern types of C. duclouxiana were further explored by a refined identical-by-descent (IBD) approach46. Pairwise comparisons between individuals showed that haplotype sharing was much more common between C. gigantea and northern C. duclouxiana (50 of 350 pairwise comparisons indicated sharing) than between C. gigantea and southern C. duclouxiana (only 6 of 700 pairwise comparisons indicated haplotype sharing)  Fig. . As expe.14 Fig.  and hybr.14 Fig. , indicat48. Of the 16 candidate migration models compared , while the northern and southern types of C. duclouxiana diverged approximately 3.2 Mya  declined in both species until approximately 0.6–1.0 Mya 53 for all unigenes detected across the whole transcriptome. First, we identified candidate signals of introgression only if the D-statistic was greater than or equal to 0.7. Next, we extracted those with averages (over 200 bp windows) of both D and fdM significantly different from zero (P < 0.01). Because introgressed regions generally show lower absolute genetic divergence, we further calculated sequence divergence (dXY) for each candidate introgressed locus and compared this with the whole transcriptome mean dXY. In combination, these tests detected a subset of 1285 candidate loci introgressed from C. gigantea into northern C. duclouxiana. We performed Gene Ontology (GO) enrichment analysis for these introgressed loci and identified 65 enriched GO terms (corrected P < 0.05), including response to salt stress, response to extracellular stimulus, detoxification, and response to metal ion , respectively, and a maximum of 7 overlaps among three samplings were observed after 100,000 replications. These 16 loci were, therefore, considered as introgressed genes likely to be important to the adaptation of northern C. duclouxiana to its local environment at high latitudes and elevations on the Qinghai-Tibet Plateau , the ecological niche models for the middle Holocene and last glacial maximum predicted a wide potential distribution for C. duclouxiana, but a narrow one for C. gigantea, during both periods .Jackknife tests of ecological niche models showed that temperature seasonality contributed most to model predictions for the distribution of ods Fig. . These dods Fig. . For botmum Fig. . Based oCupressus gigantea and C. duclouxiana, are highly divergent genetically. This was evident from phylogenetic, principal component, and genetic clustering analyses conducted on a large set of transcriptomic SNPs. Our analyses also showed that within C. duclouxiana a northern group of populations is genetically divergent from a southern group, and further that individuals of the northern group are of mixed ancestry between C. gigantea and southern C. duclouxiana with most of their ancestry equating to that of southern C. duclouxiana. A similar pattern of differentiation is also evident for morphological characters. Thus, while C. gigantea has a wider terminal shoot diameter than both northern and southern types of C. duclouxiana, the terminal shoot diameter of northern C. duclouxiana is greater than that of southern C. duclouxiana  genetic group when K = 358. Confirmation that individuals of northern C. duclouxiana were hybrid emerged from a HIest analysis  blocks analysis  of northern relative to southern C. duclouxiana (Supplementary Table f statistic and estimates of sequence divergence (dXY) on each unigene, to distinguish between these two possibilities. These analyses clearly identified 1285 loci likely to be introgressed from C. gigantea into C. duclouxiana.Further evidence that northern na Figs. , 2b origsis Fig. , the sliC. gigantea into northern than southern C. duclouxiana came from coalescent-based simulations  was approximately 7 times greater than into southern C. duclouxiana (2.10 × 10−7) and that gene flow in the opposite direction was much lower in each case . Highest levels of gene flow were estimated to have occurred, as expected, between northern and southern C. duclouxiana (6.90 × 10−6 in the forward direction vs. 2.05 × 10−6 in the reverse direction).Additional evidence that introgression has occurred at a much higher rate from ons Fig. . These iC. gigantea into northern C. duclouxiana, 16 were positively selected in both C. gigantea and northern C. duclouxiana but not in southern C. duclouxiana. These may be considered as candidate loci that have contributed to the adaptation of C. gigantea and northern C. duclouxiana to local environments, with some directly involved in the ability of plants to grow at higher latitudes and elevations relative to southern C. duclouxiana  produces A. thaliana plants that have a small leaf area, a dwarf phenotype and strong tolerance to salt and drought stress63. Thus, it is feasible that introgression of particular alleles of these and other genes  C. gigantea was distributed to the south of its current distribution and overlapped the distribution of C. duclouxiana  and the RNeasy Kit  approach. RNAs with poly (A) tails were purified from total RNA using oligo (dT) magnetic beads before fragmenting into short sequences and synthesizing and purifying cDNAs with a PCR extraction kit (QiaQuick). RNA sequencing was performed using paired-end libraries with 300 bp insert size on an Illumina HiSeq 2000 instrument with 125 bp read length. Raw reads were filtered for downstream analyses, during which reads were removed that contained adapters, poly(N) or were of low-quality.Leaf samples were collected from a total of 65 individuals comprising 30 ees Fig. . In addiC. duclouxiana individual were assembled into contigs using Trinity v2.0.666 with default parameters. Transcripts of less than 200 bp were removed and longest transcripts  were selected for the final assembly. CD-HIT-EST v4.6 (https://github.com/weizhongli/cdhit) was used in the final assembly to eliminate redundancies. To obtain high-quality contigs for further annotation and analysis, we removed sequences showing high similarity with known non-coding RNA sequences in the Rfam database (http://rfam.xfam.org/), and also contigs assigned to microbial , fungal, virus and bacterial sources (sequences downloaded from the NCBI database). In addition, sequences for which 50% of bases aligned with sequences in UTRdb (http://utrdb.ba.itb.cnr.it/) or contained <200 non-UTR bases were excluded. In this way, a final reference set of 85,401 contiguous expressed sequences (unigenes) was obtained. We compared all high-quality unigenes against the NCBI nr database using BLASTX67. Functional classification of GO categories for these sequences was performed using the Blast2GO program68.Filtered reads from one C. duclouxiana and C. gigantea sample, high quality reads were aligned to the reference transcriptome using Bowtie2 v2.2.5 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) with default parameters. To identify the ancestral state of these two species and for the identification of positively selected genes (see below), we also mapped the reads of one C. funebris sample, one J. microsperma sample and nine C. chengiana samples to the reference assembly. Picard v1.128 (https://github.com/broadinstitute/picard) was used to remove PCR duplicates and to assign read group information containing library, lane and sample identity. RealignerTargetCreator and IndelRealigner in GATK v3.3 (https://software.broadinstitute.org/gatk/) were used to realign indels. With parameters set as -q 20 -Q 20 -t DP -m 2 -F 0.002, we executed the mpileup command in SAMtools v1.2 (https://github.com/samtools/samtools) to identify SNPs. Sites with coverage depth <2 and >250 and mapping quality <20 were filtered out. SNPs were retained only if they were present in 60 percent of data across all sampled individuals. For all filtered sites in both species, we defined alleles that were the same as those found in C. funebris and J. microsperma as the ancestral allelic state.For each C. duclouxiana and C. gigantea samples, based on SNP variation, was constructed using RAxML v8.1.2469 and the GTRGAMMA model for heuristic tree search, with C. funebris and J. microsperma as outgroups. Estimates of nucleotide diversity, θπ  and θw , were calculated using VCFtools v0.1.14 (http://vcftools.sourceforge.net/) and the equations of Watterson71, respectively. The pairwise genetic differentiation (FST) between species or groups was estimated according to Weir and Cockerham72. To further investigate the population structure of C. duclouxiana and C. gigantea, a principal component analysis (PCA) was conducted on SNP variation using EIGENSOFT v6.0 (https://github.com/DReichLab/EIG) after converting the SNP variant calling format to binary ped format using VCFtools and PLINK v1.07 (http://zzz.bwh.harvard.edu/plink/). Significance levels of principal components were determined using the Tracy-Widom test and the first two significant components were plotted. In addition, the software Frappe v1.145 was used to examine population structure with number of clusters (K) ranging from 2 to 10 and each run comprising 10,000 iterations. Also, a refined identity-by-descent (IBD) blocks analysis46 was performed using the algorithm from BEAGLE v4.1 (window = 600 overlap = 50 ibd = true ibdtrim = 20 ibdlod = 3.0) to detect shared haplotypes between individuals of both species. Finally, the heterozygosity and hybrid index of each of the 65 individuals were calculated using the R package HIest47 on 144,027 SNPs that had no missing data for each parental population  and for which parental populations had allele frequency differences exceeding 0.4.A maximum likelihood (ML) phylogenetic tree for fastsimcoal248 to infer divergence times and gene flow based on the multidimensional site frequency spectrum for C. gigantea and the northern and southern populations of C. duclouxiana. To minimize the effects of selection on demographic inference, only SNPs at fourfold degenerate sites  were analyzed. SNPs located within 5 bp of each other were also excluded, as were those that significantly deviated (P < 0.05) from Hardy-Weinberg equilibrium when tested using VCFtools. The mutation rate per site per generation was estimated as: µ = D × g/2T, where D is the observed frequency of pairwise differences between the two species, T is the estimated divergence time and g is the estimated generation time. Average generation time (g) was set to 50 years and estimated divergence time between Juniperus and Cupressus was set to 62.8 Mya according to previous field surveys and studies of other Cupressaceae species73, respectively. These values yielded an estimated mutation rate of 7.0 × 10−9 mutations per site per generation. Parameter estimates were obtained using the composite ML approach for 16 models that differed in presence or absence of migration  to 5 (3.85%) in all 65 individuals, i.e., 130 alleles, as rare and calculated the proportion of them shared only between two of the three populations in each frequency category. Because rare alleles are expected on average to have originated only recently, they will be limited to a single population or species if recent gene flow is absent65. Hence, this approach helps to determine whether introgression has occurred recently between the species and populations examined.To test the relative levels of gene flow between D statistic51 and a modified f statistic (fdM)53 to identify potential loci introgressed from C. gigantea to the northern population of C. duclouxiana, using C. funebris and J. microsperma as outgroups. The D statistic was used to examine the phylogenetic distribution of derived alleles at loci that display either an ABBA or BABA allelic configuration using Eq.  are the four taxa compared and pij is the derived allele frequency of a site i in population j.Based on the tree topology presented in Supplementary Fig. f statistic, fdM, was calculated based on Eq. /S and PD is the population (P2 or P3) that has the higher frequency of the derived allele; if pi2 < pi1, then fdM = S/-S and PD is the population (P1 or P3) that has the higher frequency of the derived allele53.The modified d on Eq. :2\documeZ-tests to determine if each D or fdM value was significantly different from zero, indicating potential gene flow.For each of all the assembled unigenes, the standard error was calculated using a moving block bootstrap approach with optimal block size being 200 bp. All tests were followed by two tailed dXY) of the whole transcriptome and dXY for each unigene were calculated between C. gigantea and northern C. duclouxiana as a complementary analysis to the D statistic and modified f statistic74, using python scripts . For each candidate introgressed locus, dXY was calculated using a smaller block size of 200 bp for moving block bootstrapping. If the mean dXY of a putatively introgressed locus was lower than the mean value of the whole transcriptome, the two values were compared statistically using a Mann–Whitney U-test. The Benjamini-Hochberg false discovery rate method was performed to decrease false positive rate for all tests .To rule out false positive introgressed loci, resulting from incomplete lineage sorting, mean pairwise sequence divergence (http://www.bioconductor.org). Fisher’s exact tests with weight01 algorithms were employed to examine the significance of enrichment, with corrected P-values < 0.05 considered significant.Having identified candidate introgressed loci in the above way, GO enrichment analysis was conducted using the TopGO package in Bioconductor  from southern C. duclouxiana may have been recipients of gene flow from northern C. duclouxiana  value of the target population was calculated using Eq. (T = −log(1–FST) is the population divergence time T in units scaled by the population size.Two methods, the population branch statisticC. gigantea or northern C. duclouxiana) was denoted as A and the number of fixed differences between the target population and both control populations (southern C. duclouxiana) and outgroup (C. funebris) was denoted as B. We compared the ratio of A/B for each unigene to the transcriptome-wide average and tested the null hypothesis A(unigene)/B(unigene) = A(transcriptome-wide)/B(transcriptome-wide) using Pearson's chi-square test for the 2 × 2 contingency table.For the Hudson-Kreitman-Aguadé tests, the number of polymorphic sites in the target population  for the Hudson-Kreitman-Aguadé test were recognized as positively selected genes in C. gigantea/northern C. duclouxiana. We carried out the GO enrichment analysis for positively selected genes using the same method applied to candidate introgressed loci.Unigenes with the highest 10% of the population branch statistic and a significant C. gigantea, northern and southern C. duclouxiana, we compared the diameter of the last ramification shoots with leaves. Nine individuals across three populations for each group were examined, and for each individual five of the last ramification shoots were randomly collected, preserved as herbarium specimens and measured. Mann–Whitney U-test was used to test the differences of diameters among groups.To quantify morphological differentiation between http://data.cma.cn). Principal component analysis and graphical illustrations were performed with R v3.4.0.To better illustrate the habitat differentiation among the three groups, a scatter plot for latitude against elevation of all populations was constructed. Subsequently, we conducted a principal component analysis on climate data (26 climate variables) obtained from ten climate stations  and at the last glacial maximum (0.022 Mya) using the following parameters: random test points = 25; replicates = 20; type = subsample; maximum iterations = 5000. Information from 56 presence sites for C. duclouxiana and 17 for C. gigantea were collected from field investigations and herbarium records (http://www.gbif.org). Data for 19 bioclimatic variables for each period were retrieved from the WorldClim database (http://www.worldclim.com) with a resolution of 2.5 arc-min. For the mid-Holocene and last glacial maximum, paleoclimate data, based on three different GCMs , were employed. To avoid multicollinearity, we examined pairwise Pearson correlations (r) among the bioclimatic variables and eliminated one of the variables in each pair with r > 0.7. Seven variables were finally retained . The AUC (area under the receiver operating characteristic curve) values were used to compare predictive performance of one model with another and values above 0.9 indicated better model performance. Jackknife tests were also performed to identify which variables contributed the most individually. In addition, we downloaded altitude information from the SRTM elevation database (https://www2.jpl.nasa.gov/srtm/). The elevation for each predicted species distribution model (SDM) was extracted using QGIS v2.18.10 (https://qgis.org/en/site/). Welch’s t-test was employed to examine divergence between the average altitude values of the SDMs for C. duclouxiana.To explore the distributional shifts of the two Changes in distributional shifts of the two species in response to recent Quaternary climate change as revealed by our ecological modeling analysis provide an environmental context to the effects of adaptive introgression on the recent demography of these two species.t-test were performed using R v3.4.0. Z-test was performed using perl module Statistics::Zed. Mann-Whitney U-test was performed using perl module Statistics::Test::WilcoxonRankSum. Benjamini-Hochberg FDR correction was performed using perl module Statistics::Multtest. All the results within this study are reproducible.Pearson's chi-square test and Welch’s Further information on research design is available in the Supplementary InformationReporting Summary


Rhodeus amarus) is a small fish that is a brood parasite of freshwater mussels and is widespread across continental Europe. Its range recently expanded, following sharp declines in the 1970s and 1980s. We investigated its genetic variability and spatial structure at the centre of its distribution at the boundary of three watersheds, testing the role of natural and anthropogenic factors in its genetic structure.Anthropogenic factors can have a major impact on the contemporary distribution of intraspecific genetic diversity. Many freshwater fishes have finely structured and locally adapted populations, but their natural genetic structure can be affected by river engineering schemes across river basins, fish transfers in aquaculture industry and conservation management. The European bitterling (CYTB) revealed that bitterling colonised central Europe from two Ponto-Caspian refugia, which partly defines its contemporary genetic structure. Twelve polymorphic microsatellite loci revealed pronounced interpopulation differentiation, with significant small-scale differentiation within the same river basins. At a large scale, populations from the Baltic Sea watershed (middle Oder and Vistula basins) were distinct from those from the Black Sea watershed (Danube basin), while populations from rivers of the North Sea watershed  originated from the admixture of both original sources. Notable exceptions demonstrated the potential role of human translocations across watersheds, with the upper River Oder  inhabited by fish from the Danube basin (Black Sea watershed) and a population in the southern part of the River Elbe (North Sea watershed) basin possessing a signal of admixture from the Danube basin.Sequences of mitochondrial cytochrome B  contains supplementary material, which is available to authorized users. Quaternary climatic changes have significantly affected the genetic diversity and structure of populations. The current interspecific and intraspecific diversity of the European biota has been substantially shaped by cycles of expansion and contraction of species ranges (e.g. ). ThermoThe geographic patterns and genetic diversity hotspots of freshwater taxa often differ from those of terrestrial taxa. The historical dynamics of range contractions, expansions and shifts in freshwater species are less well understood than those of terrestrial taxa . FreshwaHuman-mediated translocations are an important recent influence on species distributions and population connectivity –17, partRhodeus amarus Bloch (Cyprinidae) in the central part of its current distribution. The bitterling is a thermophilic species currently distributed across much of continental Europe, excluding Fennoscandia, the Iberian peninsula, and northern parts of European Russia [Here, we investigate fine-scale population genetic structure of a small freshwater fish, the European bitterling, n Russia –29. Thisn Russia  and no cn Russia , limitinn Russia  and usesn Russia . Human-an Russia , Italy [n Russia  and Denmn Russia . The Eurn Russia . Bitterln Russia , making The current wide distribution of the bitterling across Europe contrasts with a marked decline in its abundance in the 1970s and 1980s that led to its designation as an endangered species in several European countries , 37. WhiCyprinus carpio L.) aquaculture in western Europe 5–10 centuries ago also are possible [Previous phylogeographic studies demonstrated that the European bitterling persisted during colder Quaternary conditions in southern refugia and colonised the continent from two main regions –29. Firspossible , 29. Apppossible , 40, 41.b; CYTB) and a set of 12 nuclear microsatellite markers. We concentrated sampling in the Czech Republic, where the main divides among three major European watersheds exist  to test whether the genetic structure of the bitterling in central Europe is contingent upon the boundary of three major watersheds, and (2) to compare the roles of natural and anthropogenic processes in formation of the contemporary genetic structure in the study area. In broader terms, our aim was to contribute to understanding of the impacts of artificial translocations, historical events and demographic processes in shaping present genetic diversity of freshwater organisms using a non-commercial fish in a hydrographically diverse area.b (CYTB) gene  from 28 populations, composed of 409 newly sampled and genotyped individuals and 282 individuals from a previous study . We focuHWE) test was performed for each locus in all populations using Markov chain methods (“Exact probability test”) in Genepop 4.0.10 [He) and observed (Ho) heterozygosities in GENETIX 4.05 [AR) corrected for minimum sample size (N = 9) using the rarefaction method in FSTAT 2.9.3.2 [NA) at each locus and population was estimated in FreeNA [A Hardy-Weinberg equilibrium (p 4.0.10 . GeneticTIX 4.05 . Because 2.9.3.2 . The pron FreeNA . The pron FreeNA .FST [FST significantly higher than zero) was tested by 1000 permutations in GENETIX 4.05 [K = 1–10 was completed using an admixture model and correlated allele frequencies model (λ = 1). The program was run with 10 independent simulations for each value of K, each of 106 iterations, following a burn-in period of 105 iterations. The likelihood of K (Ln Pr(X|K)), was used to infer the best number of real populations in the datasets  against ln-distance between sample sites [Isolation by distance was analysed for the group of Danube populations (DAN1-DAN10) by regressing pairwise estimates of le sites  measuredFCT) was calculated, and the significance of partitioning of molecular variance among groups was assessed by 10,000 bootstraps [Hierarchical Analysis of Molecular Variance , based otstraps .FST) and stepwise mutations (RST) on genetic differentiation in SPAGeDi v1.5 [FST is a measure of population differentiation [RST is an FST analogue based on allele size, and pRST is RST computed after allele-size permutation. The hypothesis RST = FST predicted that genetic drift had an equal effect on genetic divergence as stepwise mutations. The alternative hypothesis (RST > FST) predicted that stepwise mutations were more heavily involved in population differentiation [RST values (pRST) and their 95% confidence intervals (CIs) for testing whether RST > pRST [To examine temporal aspects of the observed divergence, we compared the relative impact of genetic drift  are taken into account. We acknowledge, however, that an alternative view  might be that the ABC analysis rests on the assumption that differences among populations are solely due to mutations.The analyses of genetic structure revealed that some populations (sample ELB3: Sázava River (SAZAVA); and samples ODR1-ODR3: the Czech part of the River Oder (CZODER), see above) did not cluster according to watersheds. We inferred their detailed history via Approximate Bayesian Computation  implemeThree groups of populations were created according to the Bayesian assignment of their genetic structure for testing the origin of hydrographically misclassified populations. First, SAZAVA (North Sea) was formed by the ELB3 population , the Elbe group (CZELBE – North Sea) composed of ELB1, ELB2, ELB4 , and a group of geographically proximate populations associated with western tributaries of the River Morava (WMORAV – Black Sea) consisting of populations DAN8-DAN10 . Second, the Czech Oder populations  consisted of ODR1–3 , the River Oder and Vistula populations from Poland  comprising ODR4, VIS1, VIS2 , and populations from geographically close rivers in the lower and middle reaches of the River Morava and its tributaries (NMORAV – Black Sea) formed by DAN1, DAN2, and DAN4-DAN7 .Rser11 and 2 bp in all other markers used), and the generalized stepwise model was used as the mutation model . All miel  of each scenario was determined with the 1% of the simulated data sets closest to the observed data (Euclidian distances). The posterior parameter distributions were estimated from the 1% of the simulated data sets closest to the most likely scenario . Model cRser04 (7.8%) and Rser09 (7.3%), possibly representing homozygotes for null alleles. The same loci had relatively high frequencies of null alleles estimated in FreeNA  . To avoid potential bias caused by these loci, population-level genetic variability was additionally analysed using a reduced dataset of 9 loci . All analyses of genetic structure among populations are based on the complete dataset across all 12 loci because high allelic polymorphism and presence of null alleles had a negligible effect on the analyses of population structure. The analysis of mitochondrial variability is based on 83 CYTB sequences from 26 populations.Genotyping success for microsatellites was high 96.1%), and multilocus genotypes were obtained for 688 individuals from 27 populations. Most missing genotypes were at loci %, and muHWE was demonstrated in 41% of populations (11 of 27) when calculated over all 12 loci, although only a single population deviated from the HWE in the reduced dataset with 9 loci . This population (ITA) arose from a recent introduction [HWE (deficit of heterozygotes) may be due to inbreeding or to HWE not yet having been achieved. The range of allelic richness (AR) for 9 loci was 2.33–5.05  were strongly correlated , one Czech population in the River Elbe (ELB4), and two populations in the Czech part of the River Oder . The highest genetic diversities were detected in the middle part of the Danube basin , an apparently admixed population from the River Rhine basin  and, surprisingly, an introduced Italian population (ITA) CYTB haplFST values were significantly different from zero in 98.9% of population pairs  and lower Morava (DAN2), and among three populations in the middle reaches of the River Morava (DAN4) and its geographically proximate tributaries, the Rivers Bečva (DAN7) and Olšava (DAN5). The genetic distance between Danube basin populations was significantly correlated with their hydrographic distance , suggesting a strong role of isolation by distance.We detected a high level of genetic structuring and inferred a significant role of genetic drift. The pairwise rs Table . The FSTFactorial correspondence analysis generally separated the three watersheds along the first axis Fig. , but sevK criterion  is considered a frequent bias of this method [K = 2  in central Europe  provided a concordant outcome  located in the Czech Republic clustered with geographically proximate populations from the Danube watershed. The ELB3 population (River Sázava) was more similar to the Danube watershed populations, but was apparently admixed with population from the Elbe (North Sea watershed) and Danube watersheds  model for ope Fig.  and demoope Fig.  and 4 anope Fig.  and 4. Sope Fig.  and 4, wK > 7  in central Europe demonstrated additional fine-scale substructuring. They were split into three clusters in models for  7 Figs.  and 4. TFCT = 0.057, p < 0.001). After removal of the mismatched populations ELB3 and ODR1-ODR3 (see above), the explained component of variation increased to 14% . This finding indicates that the origin of these mismatched populations was not primarily driven by vicariance among watersheds.Watersheds were important factors in the genetic structuring of bitterling populations in central Europe. Grouping populations according to watersheds explained a significant proportion (5.7%) of genetic variability  showed significant differences, suggesting that genetic differences within this lineage were caused primarily by genetic drift. On the contrary, one of three pairwise comparisons between the ELB3 population and three geographically proximate Danube populations (DAN8-DAN10) was significant, suggesting some role for stepwise mutations besides genetic drift in population differentiation. The pairwise comparisons among the Czech Oder populations (ODR1-ODR3) did not show any significant differences between FST and RST, and the same was found for the comparison between Czech Oder populations  with geographically proximate populations from the River Morava and its tributaries . On the contrary, 3 out of 9 pairwise RST comparisons (33%) between Czech Oder and Polish  populations were significantly higher than FST  is estimated to be 130 years ago , following a bottleneck of an estimated 50 (6.84–97.4) individuals.The populations from the Czech part of the River Oder  most likely originated exclusively from the River Danube populations   , especially in the Slovakian parts of the Rivers Danube and Tisza. These populations likely retained relatively high genetic variability during recent range contractions and may have served as refugia for subsequent smaller-scale recolonisations. Two other populations were genetically diverse. The bitterling population of the River Po basin in Italy is known to be of recent origin . Its higR. amarus was typically found in populations at the periphery of the current range  was genetically more similar to bitterling populations from the western part of the Black Sea drainage. Second, bitterling populations from the River Oder  in the Czech Republic were closely similar to populations from the geographically proximate, but hydrographically disconnected, River Morava basin (Black Sea watershed).Cottus gobio [Barbus barbus [Cobitis elongatoides [Carassius gibelio [Neogobius melanostomus [The River Elbe has been recently colonized from the Danube basin by other freshwater fishes, such as us gobio , Barbus s barbus  and Cobigatoides , probabl gibelio , or colonostomus . In a prnostomus , we shownostomus . The conThe second case of discordance between hydrography and genetic structure includes all three Czech bitterling populations sampled from the River Oder . All three populations possessed nuclear and mitochondrial genotypes suggesting their non-admixed origin from the geographically proximate River Morava basin (Black Sea drainage), followed by genetic differentiation by random drift in bottlenecked populations. In contrast, the Polish population from the River Oder clustered with an adjacent population from the River Vistula basin belonging to the same watershed. The large genetic distance between Czech and Polish Oder basin populations pertains at least in part to the inference that mutations, in addition to simple genetic drift, were implicated in the microsatellite marker differences detected between Czech and Polish Oder populations of the bitterling. It is possible that the Czech part of the River Oder basin was not naturally colonised by the bitterling prior to its introduction from the Danube basin, although more than three populations must be examined to test this hypothesis.The microsatellite analysis revealed profound genetic structuring of bitterling populations on a relatively small geographical scale. Such a high level of genetic differentiation contrasts with the diversity of mitochondrial DNA, with only two lineages evident across continental Europe  demonstrated fine-scale genetic differentiation. The populations were divided into three groups Fig. , with a The genetic structure of the European bitterling in central Europe is affected primarily by the interplay of Quaternary climatic fluctuations and barriers between watersheds. In addition, we detected anthropogenic effects on the genetic variability of some bitterling populations, with one population admixed from two different watersheds and a group of geographically proximate populations misclassified from expectation on the basis of hydrography. Given the lack of natural or artificial connections between adjacent drainages, dispersal across watersheds likely involved intentional or unintentional stocking in the course of fish cultivation or as an effect consequent of angling. Local translocations within a species’ range can cause spread of non-native lineages outside their former range. It becomes apparent that such cryptic invasions are more widespread than commonly recognized , with apAdditional file 1:Protocols for genotyping microsatellites and mtDNA. (DOCX 1965 kb)Additional file 2:Figure S1. Evaluation of 10 runs in STRUCTURE 2.3.3 [K = 2 to K = 10. (a) Likelihood (ln Pr(X|K)) of models in STRUCTURE for increasing number of hypothetical populations (K); (b) Estimation of the best K division using the ΔK criterion of Evanno et al. [Figure S2. Detailed genetic structure of the bitterling populations in central Europe. For K = 3 and K = 8. (http://qgis.org). Table S1. Genetic variability of populations. Genetic variability was assessed by 12 and 9 loci. (DOCX 294 kb)Additions to genetic analyses. o et al. . Figure Additional file 3:Table S1. Summary of scenarios used in the approximate Bayesian computation analysis to infer the origin of the Sázava river population and three Czech Oder basin populations. Table S2. The prior parameter distributions used in five scenarios for the origin of SAZAVA origin in DIYABC 2.0.4. Table S3. The posterior parameter distributions of SAZAVA population. Table S4. The posterior parameter distributions of CZODER population. Figure S1. Graphical schemes of scenarios used for analysis in DIYABC 2.0.4 to infer of origin of the SAZAVA population. Figure S2. Graphical schemes of scenarios used for analysis in DIYABC 2.0.4 to infer of origin of the CZODER population. (DOCX 29 kb)Approximate Bayesian Computation analyses.