Correlation between genetic, geographic and linguistic distances of Balto-Slavic populations
A Mantel test was applied to compare the roles which geography and language have played in shaping the genetic variation of the Balto-Slavic populations (
Fig 5, Tables I,J in
S1 File). The test was performed independently for the three genetic systems, with all three exhibiting a very high correlation with geography (0.80–0.95) and slightly lower (0.74–0.78) correlation with linguistics (Table J in
S1 File). Because the linguistic pattern itself is highly correlated with geography (
Fig 5), partial correlations were considered to distinguish between the direct and indirect influences of geography on the two other systems. The correlations with linguistics became much lower whilst all three genetic systems maintained high correlations with geography (Table J in
S1 File).
Fig 5
Correlations between matrices of genetic, geographic and linguistic distances among Balto-Slavic populations.
Discussion
Two major genetic substrata are embedded in the gene pools of Slavs
The results of our study have shown the close genetic proximity of the majority of West and East Slavic populations inhabiting the geographic area from Poland in the west, to the Volga River in the East (
Fig 2A and 2B, Tables A,B in
S1 File). Some mtDNA haplotypes of hgs H5, H6, U4a were more frequent in the genomes of West and East Slavic speakers, providing thereby further evidence for the matrilineal unity of West and East Slavs [
28,
36] as well as continuity of mtDNA diversity in the territory of modern Poland for at least two millennia [
38].
In contrast to this apparent genetic homogeneity of the majority of West and East Slavs, the gene pool of South Slavs, who are confined to the geographically smaller Balkan Peninsula, differs substantially and shows internal differentiation, as testified by their NRY and autosomal variation (
Fig 2A and 2B;
Fig 3, Tables A,B in
S1 File). Consequently, we suggest that there is a “central-east European” genetic substratum in West and East Slavs, exemplified by NRY hgs R1a and the k3 ancestry component, and a “south-east European” one, featuring NRY hgs I2a and E plus the k2 ancestry component for South Slavs (
Fig 2A and 2B,
Fig 3, Table K in
S1 File; Tables A,B in
S1 File). Notably, the “south-east European” component does not extend to the whole Balkan Peninsula, as South Slavs are differentiated from Greek sub-populations except Macedonian Greeks (
Fig 2A,
Fig 4B) [
55].
The importance of these substrata in shaping the genetic diversity of the present-day Slavs is evident from the observed lower IBD relatedness between the combined group of East-West Slavs and South Slavs than with north-east Europeans, including Baltic speakers (
Fig 4A). The latter reside within the East European Plain and presumably represent the “central-east European” pre-Slavic substratum (
Fig 4A, Table G in
S1 File). AMOVA results also support the substrata prevalence, because genetic variation among Slavic branches (which assimilated different substratum populations) strongly exceeds intra-branch variation (Table H in
S1 File). The influence of geography in shaping the Slavic genetic heritage (
Fig 5, Table J in
S1 File) led to the same conclusion, because if substratum importance is the major factor shaping the genetic relationships among present-day Slavic-speaking populations, these will not reflect the relationship among expanding Slavic languages, but should instead reflect the relationships between pre-Slavic populations, which can be approximated by geographical distances between them.
Demographic mechanisms shaping the gene pool of Slavic speakers
Most West and East Slavs of Central-East Europe form genetically a compact group of populations that, as a general rule, differ from their western (Germanic-speaking) and eastern (Finno-Ugric-speaking) neighbors (
Fig 2A and 2B;
Fig 4A and 4B). However, so-called ‘contact’ zones of this group with non-Slavic peoples are characterized by various patterns of genetic clines or sharp genetic borders [
27,
32,
56–
58]. For example, there is a pronounced genetic proximity between Czechs and their immediate Germanic neighbors in the west (
Fig 2A and 2B,
Fig 3) [
27,
58] that could be attributed to the pre-Slavic gene pool formation of Central-East Europeans. In contrast, a clear genetic border exists nowadays between Poles and their immediate western neighbors Germans, and even between a West-Slavic-speaking minority–Sorbs–and their German host population (
Fig 2B, Tables A,B in
S1 File) [
43,
59]. It has been suggested, that this genetic boundary predates massive resettlements of people after World War II, and could have been shaped during medieval migrations of Germanic and Slavic peoples in the Vistula and Oder River basins [
60]. In the north-east, a largely autochthonous (pre-Slavic) component is detected in the gene pool of Russians from northern regions of the European part of Russia (
Fig 2A, 2B and 2C,
Fig 3), which agrees with previous anthropological [
61,
62] and genetic [
32,
45,
56,
63] studies and supports substantial admixture of expanding Slavs with indigenous populations and, perhaps, language shift in the latter.
Taken together, several mechanisms including cultural assimilation of the autochthonous populations by expanding Slavs while maintaining the pre-Slavic genetic boundaries, and
in situ gene pool shaping, are needed to explain the genetic patterns observed on the eastern, north-eastern and western margins of the current ‘Slavic area’ within Central-East Europe.
The presence of two distinct genetic substrata in the genomes of East-West and South Slavs would imply cultural assimilation of indigenous populations by bearers of Slavic languages as a major mechanism of the spread of Slavic languages to the Balkan Peninsula. Yet, it is worthwhile to add here evidence from the analysis of IBD segments: the majority of Slavs from Central-East Europe (West and East) share as many IBD segments with the South Slavs in the Balkan Peninsula as they share with non-Slavic populations residing nowadays between Slavs (
Fig 4A and 4B; Table G in
S1 File). This even mode of IBD sharing might suggest shared ancestry/gene flow across the wide area and physical boundaries such as the Carpathian Mountains, including the present-day Finno-Ugric-speaking Hungarians, Romance-speaking Romanians and Turkic-speaking Gagauz. A slight peak at 2–3 cM in the distribution of shared IBD segments between East-West and South Slavs (
Fig 4A and 4B) might hint at shared “Slavonic-time” ancestry, but this question requires further investigation.
Expansion of Slavic languages took place in an area already occupied by speakers of the Baltic languages [
49,
50]. Despite significant linguistic divergence between extant East Baltic and Slavic languages (
Fig 1) [
7], Baltic populations are genetically the closest to East Slavs (
Fig 2A and 2B, Table K in
S1 File) [
45,
64–
66] and here we found that they bear the highest number of shared IBD segments with the combined group of East-West Slavs (
Fig 4, Table G in
S1 File). The presence of a substantial “Baltic substratum” in the genomes of extant Slavs within East Europe might in part explain their genetic closeness to each other and difference from some neighboring non-Slavic groups.
A synthesis
Comparing genetic and linguistic reconstructions with geography has a long tradition in human population genetics [
67]. Here, we have studied the autosomal, NRY and mtDNA diversity of all Balto-Slavic populations in the context of their linguistic variation and geography. A remarkable agreement between these five systems was found: correlation coefficients range from 0.68 to near the maximum (0.95). This agreement between datasets from different systems supports the reliability of the results and in most cases, when drawing a conclusion, we could find one supported by the majority of the systems analyzed. In particular, we found that autosomal and NRY compositions and geographic affiliations of the Balto-Slavic populations form a triad, all variables of which are very similar to each other.
Combining all lines of evidence, we suggest that the major part of the within-Balto-Slavic genetic variation can be primarily attributed to the assimilation of the pre-existing regional genetic components, which differed for West, East and South Slavic-speaking peoples as we know them today.
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Materials and Methods
Ethics Statement
The DNA samples analysed in the study were collected after having obtained written informed consent. The procedure has been approved by Ethics Committees of the appropriate Institutions, including the Research Ethics Committee of the University of Tartu (UT REC) (no 225/T-9) and the Ethics Committee of the Research Centre for Medical Genetics, Russian Academy of Sciences.
Datasets
Three datasets NRY, mtDNA and autosomal SNP representing populations speaking Balto-Slavic languages were assembled.
The NRY data comprises 6,079 samples, including 1,254 reported here for the first time and 1,138 samples updated from previous work (Table L in
S1 File).
The mtDNA data include 6,876 samples, 917 are reported here for the first time (Table C in
S1 File).
The autosomal SNP data include 1,297 worldwide individuals including 70 reported here for the first time (Table M in
S1 File); this dataset encompasses in total 296 samples representing Balto-Slavic populations.
S1 Text: Datasets provides extended information on dataset assemblage. All samples reported here for the first time were collected after informed consent was obtained from each participant.
Genotyping
40 binary NRY markers were genotyped using the TaqMan (Applied Biosystems) technology as described [
68]. MtDNA analyses included HVS1 sequencing and genotyping of coding region SNPs defining mtDNA hgs [
69] (mtDNA tree Build 15 (30 Sep 2012). The autosomal SNP genotypes were generated with the Illumina 660K array and combined with published data (Table M in
S1 File).
S1 Text: Methods provides details about the autosomal SNP pre-processing performed before all analyses.
MDS, PCA and ADMIXTURE
MDS analysis based on genetic distances [
70] was performed for the NRY and mtDNA datasets (Tables C, K, N in
S1 File). PCA was performed for the autosomal dataset using the
smartpca program of the EIGENSOFT package [
71]; sets of Illumina-Affymetrix cross-platform SNPs (around 57k of LD-pruned SNPs), encompassing available Balto-Slavic populations, were used. Genomic ancestry components in Balto-Slavic speakers in the context of worldwide populations were inferred with ADMIXTURE [
51]; sets of only Illumina cross-platform SNPs (around 200k shared LD-pruned SNPs between the 610K, 650K and 660K arrays) were used (Table M in
S1 File). See
S1 Text: Methods for choosing the value of K which best models the ancestry components in our dataset.
Analysis of pairwise segments IBD
We aimed to compare the level of IBD relatedness between the combined group of East-West Slavs (group1)
vs South Slavs (group2) (i.e. IBD relatedness within Slavs) to the IBD relatedness between each group of Slavs
vs their respective neighboring groups of mostly non-Slavic populaitons (Table F in
S1 File lists populations in each group,
S3 Fig shows schematically the geographic location of each population groups). To this end we: a) calculated an average number of IBD segments per pair of individuals (ibd-statistic) between the group of East-West Slavs (group1) and South Slavs (group2), i.e. within-Slavic IBD sharing, and between each Slavic group and their respective geographic neighbors; b) compared the within-Slavs ibd-statistic with the ibd-statistics for each Slavic group and groups 3–9. The fast IBD (
fIBD) algorithm [
53] implemented in BEAGLE (
http://faculty.washington.edu/browning/beagle/beagle.html) was used to detect pairwise IBD segments. Sets of Illumina-only cross-platform SNPs (around 500k shared SNPs between the 610K, 650K and 660K arrays) were used in the analysis. See
S1 Text: Methods for detailed information about the experimental design and statistical approach applied.
AMOVA and Mantel tests
AMOVA (implemented in the Arlequin 3.11) was applied to estimate genetic differentiation when Balto-Slavic populations were grouped according to the three hierarchical levels of the tree of Balto-Slavic languages (
S1 Text: Methods, Table H in
S1 File,
S5 Fig). Mantel tests were performed in Arlequin 3.11 [
72] to calculate the coefficients of the pairwise and partial correlations between matrices of genetic (mtDNA, NRY and whole genome SNP), linguistic and geographic distances (Table I in
S1 File).
S1 Text: Methods provides additional details for Mantel tests analysis.
Lexicostatistical reconstruction of Balto-Slavic languages
20 wordlists of extant Balto-Slavic languages were used to reconstruct their phylogeny. The consensus tree (
Fig 1, Fig G in
S2 File) was drawn manually based on the set of trees produced by different phylogenetic methods. The method implying individual relative index of stability for each Swadesh item [
73,
74] was used for the node dating.
S2 File, Figs A-C in
S2 File, and Tables A,B in
S3 File contain detailed information about lexicostatistical reconstruction of the Balto-Slavic languages.
Supporting Information
S1 Dataset
(zip-archive).
- bslav.dbf, bslav.var, bslav.inf, lexical dataset in STARLING format (multistate matrix with synonyms allowed). This dataset exported in MS EXCEL format is available as Table A in S3 File.
- bslav.nex, the same dataset as a binary matrix in NEXUS format.
- *.tre, some of the discussed trees in NEWICK format;
- NEXUS files for NeighborNet networks.
(ZIP)
Click here for additional data file.(270K, zip)
S1 Fig
PC1vsPC2 plot based on whole genome SNP data (PC1 = 0.53; PC2 = 0.34).
(PDF)
Click here for additional data file.(56K, pdf)
S2 Fig
ADMIXTURE plot (k2-k20) (A). Box and whiskers plot of the cross validation (CV) indexes of all runs of the ADMIXTURE analysis (B). Log-likelihood (LL) scores of all runs (C). Variation in LL scores in the fractions (5%, 10%, 20% shown in dark green, middle green and light green, respectively) of runs that reached the highest LLs) (D).
(PDF)
Click here for additional data file.(2.3M, pdf)
S3 Fig
Schematic representation of groups of populations used in the IBD analysis.
Populations within each group are listed in Table F in
S1 File. Source of the Europe contour map:
http://www.conceptdraw.com/How-To-Guide/geo-map-europe.
(PDF)
Click here for additional data file.(116K, pdf)
S4 Fig
Distribution of the average number of IBD segments between group of East-West Slavs and their geographic neighbors.
Russians from Northern region of European part of Russia are considered separately from the group of north-east Europeans. The x-axis indicates ten classes of IBD segment length (in cM); the y-axis indicates the average number of shared IBD segments per pair of individuals within each length class.
(PDF)
Click here for additional data file.(34K, pdf)
S5 Fig
Hierarchical levels of genetic variation used in AMOVA.
(PDF)
Click here for additional data file.(250K, pdf)
S1 File
Table A in S1 File. Matrix of pairwise Nei distances (DNei) between Balto-Slavic populations based on Y-chromosome data. Table B in S1 File. Matrix of mean population pairwise FST for Balto-Slavic populations calculated from autosomal SNP data. Table C in S1 File. Frequencies of the mtDNA haplogroups in Balto-Slavic and some other European populations. Table D in S1 File. Matrix of pairwise Nei distances (DNei) between Balto-Slavic populations based on mtDNA data. Table E in S1 File. Predicting the country affiliation for 53 Balto-Slavic populations from their Y-chromosomal composition. Table F in S1 File. Groups of populations used in IBD analysis. Table G in S1 File. Summary statistics of IBD analysis. Table H in S1 File. Analysis of molecular variance (AMOVA) in Balto-Slavic populations. Table I in S1 File. Matrices of geographic (a), lexicostatistical (b) and genetic (c,d,e) distances between Balto-Slavic populations used in Mantel Tests. Table J in S1 File. Results for Mantel tests on genetic, lexicostatistical and geographic distances. Table K in S1 File. Frequencies of the NRY haplogroups in Balto-Slavic populations. Table L in S1 File. Frequencies of NRY haplogroups in 29 Balto-Slavic populations presented here for the first time. Table M in S1 File. Populations used in whole-genome SNP analyses. Table N in S1 File. Frequencies of the NRY haplogroups in non-Balto-Slavic populations of Europe.
(XLSX)
Click here for additional data file.(224K, xlsx)
S2 File
(Linguistics: Datasets; Methods; Results).
Fig A in S2 File. Geographical distribution of extant Slavic and East Baltic languages and dialects used in the study. Map was prepared by Yuri Koryakov. Fig B in S2 File. Dated phylogenetic tree of the Balto-Slavic lects produced by the StarlingNJ method from the multistate matrix (binary nodes only). Bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%). Fig C in S2 File. Phylogenetic tree of the Balto-Slavic lects produced by the NJ method from the binary matrix in the SplitsTree4 software. Bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%). Branch length reflects the relative rate of cognate replacement as suggested by SplitsTree4. The BioNJ method yields the same topology. Fig D in S2 File. Phylogenetic tree of the Balto-Slavic lects produced by the UPGMA method from the binary matrix in the SplitsTree4 software. Bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%). Branch length reflects the relative rate of cognate replacement as suggested by SplitsTree4. Fig E in S2 File. Consensus phylogenetic tree of the Balto-Slavic lects produced by the Bayesian MCMC method from the binary matrix in the MrBayes software. Bayesian posterior probabilities are shown near the nodes (not shown for stable nodes with P ≥ 0.95). Branch length reflects the relative rate of cognate replacement as suggested by MrBayes. Fig F in S2 File. Optimal phylogenetic tree of the Balto-Slavic lects produced by the UMP method from the binary matrix in the TNT software. Bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%). Branch length reflects the relative rate of cognate replacement as suggested by TNT. Fig G in
S2 File. Manually constructed consensus phylogenetic tree of the Balto-Slavic lects based on the StarlingNJ, NJ, BioNJ, UPGMA, Bayesian MCMC, UMP methods. Ternary nodes result from neighboring binary nodes, joined together, if the temporal distance between them ≤ 300 years. The gray ellipses additionally mark two joined nodes, which cover binary branchings that differ depending on the method. Probability values are shown in the following sequence: NJ/Bayesian MCMC/UMP (“x” means that P ≥ 0.95 in an individual method; not shown for nodes with P ≥ 0.95 in all methods). StarlingNJ dates are proposed. Fig H in S2 File. NeighborNet network of the Balto-Slavic lects (without Slovenian) + German. Produced in the SplitsTree4 software; bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%). Fig I in S2 File. NeighborNet network of the Balto-Slavic lects (without Slovenian) + Demotic Greek. Produced in the SplitsTree4 software; bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%). Fig J in S2 File. NeighborNet network of the Balto-Slavic lects (without Slovenian) + German + Demotic Greek. Produced in the SplitsTree4 software; bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%). Fig K in S2 File. NeighborNet network of the Balto-Slavic lects (with Slovenian) + German. Produced in the SplitsTree4 software; bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%). Fig L in S2 File. NeighborNet network of the Balto-Slavic lects (with Slovenian) + Demotic Greek. Produced in the SplitsTree4 software; bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%). Fig M in S2 File. NeighborNet network of the Balto-Slavic lects (with Slovenian) + German + Demotic Greek. Produced in the SplitsTree4 software; bootstrap values are shown near the nodes (not shown for stable nodes with bootstrap value ≥ 95%).
(PDF)
Click here for additional data file.(1.5M, pdf)
S3 File
Table A in S3 File. Lexical dataset (multistate matrix with synonyms allowed). Table B in S3 File. Reverse distance matrix generated from the multistate matrix (Table A in S3 File) in the Starling software. Table C in S3 File. Distance matrix, generated from the binary matrix (bslav.nex (deposited in S1 Dataset)) in the SplitsTree4 software.
(XLSX)
Click here for additional data file.(58K, xlsx)
S1 Text
(Genetics: Datasets, Methods).
(DOCX)
Click here for additional data file.(59K, docx)
Acknowledgments
We are grateful to all the volunteers who have made this study possible by donating their blood samples. We thank V. Ferak, M. Nelis, J. Klovins, and A. Kouvatsi for assistance in sampling. We thank B. Browning for valuable discussion of results of the IBD analysis. We thank Yu. Koryakov for the geographical map of Baltic and Slavic languages distribution, designed especially for this study. Computational analyses of whole genome data were performed on High Performance Computing Center, University of Tartu, Estonia.
The members of the Genographic Consortium are: Li Jin, Hui Li, & Shilin Li (Fudan University, Shanghai, China); Pandikumar Swamikrishnan (IBM, Somers, New York, United States); Asif Javed, Laxmi Parida & Ajay K. Royyuru (IBM, Yorktown Heights, New York, United States); R. John Mitchell (La Trobe University, Melbourne, Victoria, Australia); Pierre A. Zalloua (Lebanese American University, Chouran, Beirut, Lebanon); Syama Adhikarla, Arun Kumar, GaneshPrasad, Ramasamy Pitchappan, Arun Varatharajan Santhakumari (Madurai Kamaraj University, Madurai, Tamil Nadu, India); R. Spencer Wells and Miguel G. Vilar (National Geographic Society, Washington, District of Columbia, United States); Himla Soodyall (National Health Laboratory Service, Johannesburg, South Africa); Elena Balanovska & Oleg Balanovsky (Research Centre for Medical Genetics, Russian Academy of Medical Sciences, Moscow, Russia); Chris Tyler-Smith (The Wellcome Trust Sanger Institute, Hinxton, United Kingdom); Daniela R. Lacerda & Fabrício R. Santos (Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil); Jaume Bertranpetit, Marc Haber & Marta Melé (Universitat Pompeu Fabra, Barcelona, Spain); Christina J. Adler, Alan Cooper, Clio S. I. Der Sarkissian & Wolfgang Haak (University of Adelaide, South Australia, Australia); Matthew E. Kaplan & Nirav C. Merchant (University of Arizona, Tucson, Arizona, United States); Colin Renfrew (University of Cambridge, Cambridge, United Kingdom); Andrew C. Clarke & Elizabeth A. Matisoo-Smith (University of Otago, Dunedin, New Zealand); Jill B. Gaieski, Amanda C. Owings & Theodore G. Schurr (University of Pennsylvania, Philadelphia, Pennsylvania, United States). A lead author of the Genographic Consortium is R. Spencer Wells (
gro.sgn@sllewps).
Funding Statement
This work was supported by Russian Science Foundation (grant 14-14-00827 to OB, M. Chuhryaeva, AA and VZ), Programme of the Presidium of Russian Academy of Sciences "Molecular and cell biology", Russian Foundation For Basic Research (grants 13-04-01711, 13-06-00670, 13-04-90420); Ukrainian State Fund for Fundamental Researches (grant F53.4/071); the European Union European Regional Development Fund through the Centre of Excellence in Genomics to the Estonian Biocentre; by the European Commission grant 205419 ECOGENE to the Estonian Biocentre; the Estonian Basic Research Grant SF 0270177s08 and by Institutional Research Funding to the Estonian Biocentre from the Estonian Research Council IUT24-1; the European Commission grant 205419 ECOGENE to the Estonian Biocentre; the Wellcome Trust 098051 to CTS; the Lithuanian part was supported by the LITGEN project (VP1-3.1-ŠMM-07-K-01-013), funded by the European Social Fund under the Global Grant Measure. Center for Genomics and Transcriptomics (CeGaT GmbH) provided support in the form of salaries for author LM, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of LM are articulated in the ‘author contributions’ section.
Data Availability
The whole genome SNP data generated in this study are available in the National Center for Biotechnology Information – Gene Expression Omnibus (NCBI GEO accession number GSE71049) as well as in PLINK format in our website at
www.ebc.ee/free_data. The NRY dataset is presented in Table N in
S1 File; mtDNA HVS1 sequences are available in the National Center for Biotechnology Information (GenBank accession numbers KT261802–KT262718).
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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4558026/
@Ројалиста @Kyrios @Krishna @FDDD @Kor @Lanselot @Miki 75 @suave poznat ovaj rad?
