Population genetic structure analysis distinguished the four conservation flocks from the village chicken populations. Of the four flocks, the Ovambo clustered closer to the village chickens particularly those sampled from South Africa. Clustering of the village chickens followed a geographic gradient whereby South African chickens were closer to those from Zimbabwe than to chickens from Malawi. Different conservation flocks seemed to have maintained different components of the ancestral genomes with a higher proportion of village chicken diversity found in the Ovambo population. Overall population LD averaged over chromosomes ranged from 0.03 ± 0.07 to 0.58 ± 0.41 and averaged 0.15 ± 0.16. Higher LD, ranging from 0.29 to 0.36, was observed between SNP markers that were less than 10 kb apart in the conservation flocks. LD in the conservation flocks steadily decreased to 0.15 (PK) and 0.24 (VD) at SNP marker interval of 500 kb. Genomewide LD decay in the village chickens from Malawi, Zimbabwe and South Africa followed a similar trend as the conservation flocks although the mean LD values for the investigated SNP intervals were lower. The results suggest low effective population sizes particularly in the conservation flocks. The utility and limitations of the iselect chicken SNP60K in village chicken populations is discussed.Extensively raised village chickens are considered a valuable source of biodiversity, with genetic variability developed over thousands of years that ought to be characterized and utilized. Surveys that can reveal a population's genetic structure and provide an insight into its demographic history will give valuable information that can be used to manage and conserve important indigenous animal genetic resources. This study reports population diversity and structure, linkage disequilibrium and effective population sizes of Southern African village chickens and conservation flocks from South Africa. DNA samples from 312 chickens from South African village and conservation flocks ( Extensively raised village chickens are considered a valuable source of biodiversity, with genetic variability developed over thousands of years, that could be useful in future for improvement in response to climate change and consumer demands Delany, . This diLinkage disequilibrium (LD), defined as non-random association of alleles at two or more loci Hedrick,  is a useThe advent of whole genome sequencing and high density SNP genotyping technologies has resulted in increased marker density and facilitated estimation of LD in a number of domesticated animals including chickens. The completion of the first draft of the chicken genome , Malawi , and Zimbabwe (Zim). These chicken populations are raised by smallholder communal farmers under village chicken farming systems characterized by low input management, uncontrolled mating systems and intermixing of flocks within and between villages , Eastern Cape (n = 26) and Northern Cape (n = 35) provinces, and four conserved flocks of Venda , Naked Neck , Potchefstroom Koekoek  and Ovambo  that are kept at the Agriculture Research Council Poultry Breeding Resource at Irene in Pretoria. Detailed sampling of these populations was described by Mtileni et al. (n = 92), AEZ3 (n = 34), and AEZ5 (n = 10). The detailed sampling of Zimbabwe chicken populations is described by Muchadeyi et al.  described in the previous studies . SNP calling was done using Illumina Genome Studio v2.0. The genotype input file was converted into a PLINK (v1.07)  as well as the inbreeding co-efficient of each population using PLINK (v1.07) software  ≤0.02 and this resulted in a total sample of 311 chickens across the seven populations. There were 54,115 SNPs available to estimate observed and expected heterozygosity indices  was then performed to illustrate the relationship among the extensively raised chicken populations using the Golden Helix SNP Variation Suit (SVS) version 8.1  was determined as that which had the lowest cross validation error (CV-error).In addition, ADMIXTURE 1.23 software , (iv) with missing genotypes (>5%) as well as for individual chickens with missing genotypes (>5%) and high kinship (IBD ≥0.45) using PLINK (v1.07) , Zimbabwean (n = 121) and South African field populations (n = 62) and conservation flocks of NN (n = 15), OV (n = 9), VD (n = 12), and PK (n = 18), respectively. The level of identity by descent in the resultant data sets were 0.022, 0.038, 0.060, and 0.072 for the PK, OV, NN, and VD conservation flocks and 0.002, 0.006, and 0.004 for the South African, Malawian, and Zimbabwean village chickens, respectively. These individual population data sets were used for the estimation of linkage disequilibrium and associated estimates.SNP data for the individual populations were quality-controlled in order to remove SNPs (i) on sex chromosomes or those there were not mapped, (ii) with MAF ≤5%, (iii) those that deviated from Hardy-Weinberg equilibrium (HWE) (r2 estimation was used to measure LD between pairs of SNPs within a chromosome and population using PLINK (v1.07) program   was conducted using the Generalized Linear Model procedure (Proc GLM) in the SAS (2011) to determine the effects of chromosome, population, the interaction of chromosome-by-population, and SNP marker interval (bp) on LD using the following model:r2ij was the pairwise LD; μ was the overall population mean and Popi was the effect of the ith chicken population from Malawi, Zimbabwe or South Africa; Ggaj was the effect of the jth chromosome 1–28; and SNPint represented the effects of SNP interval which were defined as the distance between markers (number of base pairs) and fitted as a covariate with regression coefficient b. The F-test from the ANOVA analysis was used to determine the significance of factors included in the model at P ≤ 0.05. Linkage disequilibrium decay was estimated genomewide for all subpopulations. Sliding window bins for LD decay were set at 10, 20, 40, 60, 100, 200, 500, 1000, 2000, and 5000 kb for chromosomes 1–28. An additional analysis of the macro-chromosomes 1–5 was done with bins up to 10,000 kb.where: Ne, recombination frequency and expected LD (r2) was determined using the following equation from Corbin et al.  to the corresponding genetic length ic using the formula: ci = ōixi, where ōi is the average ratio of Morgans per kilo base pair on chromosome i, which was taken from the physical lengths of the chicken genome v74  and the populations were therefore significantly inbred (P ≤ 0.05) inbred. Heterozygosity estimates and inbreeding coefficients were high in the conservation flocks compared to the village flocks. Of the conservation flocks, Ovambo chickens had the lowest levels of inbreeding.Observed heterozygosity values averaged 0.62 ± 0.003 across all seven populations. Overall, HResults of the first principal component showed the conserved Venda, Ovambo, Naked Neck and Potchefstroom Koekoek chickens from South Africa grouped into four distinct clusters separated from the village chicken populations. Of the four flocks, the Ovambo clustered closer to the village chickens particularly those sampled from South Africa. Clustering of the village chickens followed a geographic gradient whereby South African chickens were closer to those from Zimbabwe than to chickens from Malawi. The chickens from Malawi clustered together with some chickens from Zimbabwe Figure .K-value for admixture was K = 6  among chromosomes, populations and their interaction as well as with SNP marker interval  followed by Naked Neck (>0.33) and least in Ovambo and Potchefstroom Koekoek (0.29). LD in the conservation flocks steadily decreased to 0.15 (PK) and 0.24 (VD) at SNP marker interval of 500 kbp. A sudden increase in LD was observed at 500 kbp SNP interval. Genomewide LD decay in the village chickens from Malawi, Zimbabwe and South Africa followed a similar trend as the conservation flocks but the mean LD values at different SNP intervals were lower  at t generations ago for the village and conservation flocks, respectively. The adjusted LD based estimates of Ne indicated low effective population size of 49–57 in the village chickens and of 31–50 in the conservation flocks 97 generations ago. The graphs also illustrate a steady decrease in effective population size from over 8500 to below 60 within 9000 generations for the village chickens  or were in linkage disequilibrium. Conversely, over 80% of the SNPs of the panel were used for LD analysis. The number of monomorphic markers observed in this study was about 5 fold lower than those reported in Qanbari et al.  in commePopulation structure analysis grouped the conservation flocks into four distinct clusters that were different from the village chickens sampled from the communal farming areas of South Africa, Zimbabwe, and Malawi Figures , 3. ThisConservation chickens were sampled from closed populations, kept at the Agricultural Research Council in South Africa, ranging from 100 to 150 chickens/flock (van Marle-Köster et al., Whilst it is evident that the conservation flocks diverged from the village chicken populations they were founded from, results from ADMIXTURE indicated that the Potchefstroom Koekoek and Naked Neck have retained single and unique ancestral genomic components from the founder flocks Figure  and coulThe village chicken samples were obtained from multiple agro-ecological zones within a country except for the chickens of Malawi that were obtained from a single agro-ecological zone. The clustering of the village chickens followed a geographical gradient in which the South African chickens were least related to the Malawian chickens. Higher levels of divergence between village chickens from Malawi and South Africa could be explained by geographical distance from each other, lack of gene flow between the two countries and isolated evolution occurring in these populations. Village chickens of South Africa and Zimbabwe had more within population diversity, as indicated by their wide spread clusters, than the conservation flocks. This could be due to a combination of founder effects in the conservation flocks as well as gene flow between the two countries.Gallus gallus v2.1 (Groenen et al., Linkage disequilibrium was calculated using 28 of the 38 chicken autosomal chromosomes that were represented on the Illumina iSelect SNP60K bead chip. The 10 autosomes not used for this analysis are micro chromosomes that were not included in the design of the 60K bead chip as they were not yet covered by the genome build The overall LD values between populations showed significant differences between populations with higher LD observed in the conservation flocks and low LD in the village chicken populations kept by smallholder farmers. Variation in LD between the conservation flocks and village chicken populations could be an indication of different population histories and the influences of different evolutionary mechanisms in terms of bottleneck effect, genetic drift, selection and mutations in different population categories. The least diverse Table  and highThe current study also indicated a significant LD decay with increased marker intervals, which generally is a function of increased recombination events with increased genetic distance (Megens et al., Over and above the expected trends in LD decay with increased marker distances, LD was moderately high and remained well above 0.2 at marker distances of up to 500 kb when using genomewide SNP data and upto 1000 kb for macro-chromosomes 1–5 in the conservation flocks Figures . On the Analysis of trends in effective population size from LD values suggested low effective population sizes particularly in the conservation flocks. Results showed a decrease in genetic variation over time in both conservation and village chicken flocks which could be due to poor management, inbreeding as a result of population sub-structuring within villages or population bottlenecks that could have been experienced during the development of these populations Figures . The oveOverall, the study demonstrated the utility of the Illumina chicken iselect SNP 60K panel in extensively raised and conservation flocks with limitations due to high proportion of monomorphic and less polymorphic SNPs. Only a subset of independent SNPs could be used for population structure analysis. The study observed population divergence resulting in clear population boundaries between the conservation and the village flocks. High levels of population diversity were observed in the village chickens as well as the Ovambo conservation flock. A relatively high LD that persisted over longer SNP intervals was observed in the South African conservation flocks and not the village chicken populations. This LD pattern seems to be consistent with low effective population sizes and loss of diversity in conservation populations which could be an effect of small size of the founder populations and them being raised as closed populations prone to the effects of inbreeding and genetic drift.Khulekani S. Khanyile carried out the laboratory analyses, statistical analyses, and interpretation of the data and drafted the manuscript. Farai C. Muchadeyi and Edgar F. Dzomba assisted with the acquisition of funding, designing and execution of the experiment and revised the manuscript critically for important intellectual content.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plasmodium falciparum clinical isolates were collected in Ghana from Kintampo in the central forest-savannah area, and Navrongo in a drier savannah area ~350 km to the north with more seasonally-restricted transmission. Parasite DNA was sequenced and paired-end reads mapped to the P. falciparum reference genome.Genome wide sequence analyses of malaria parasites from widely separated areas of the world have identified contrasting population structures and signatures of selection. To compare relatively closely situated but ecologically contrasting regions within an endemic African country, population samples of High coverage genome wide sequence data for 85 different clinical isolates enabled analysis of 121,712 single nucleotide polymorphisms (SNPs). The local populations had similar proportions of mixed genotype infections, similar SNP allele frequency distributions, and eleven chromosomal regions had elevated integrated haplotype scores (|iHS|) in both. A between-population Rsb metric comparing extended haplotype homozygosity indicated a stronger signal within Kintampo for one of these regions (on chromosome 14) and in Navrongo for two of these regions (on chromosomes 10 and 13). At least one gene in each of these identified regions is a potential target of locally varying selection. The candidates include genes involved in parasite development in mosquitoes, members of variant-expressed multigene families, and a leading vaccine-candidate target of immunity.P. falciparum populations of Ghana, three narrow genomic regions showed evidence indicating local differences in historical timing or intensity of selection. Sampling of closely situated populations across heterogeneous environments has potential to refine the mapping of important loci under temporally or spatially varying selection.Against a background of very similar population structure and selection signatures in the The online version of this article (doi:10.1186/s12864-015-1746-3) contains supplementary material, which is available to authorized users. Plasmodium falciparum [P. falciparum) in a brief diploid stage that occurs after parasite mating in the mosquito each transmission cycle [Malaria is a globally important disease which exhibits major differences in local epidemiology and ecology, with great variation within Africa where most cases are caused by lciparum . Local dlciparum , and mallciparum . Converslciparum . Malarialciparum , 6, withon cycle , and thuon cycle .Plasmodium falciparum revealed significant global population structure [P. falciparum population structure in Southeast Asia may be important for understanding the current emergence of Artemisinin antimalarial drug resistance [mdr1 and crt) and anti-folate (dhfr and dhps) resistance that reflect differences in historical drug use among the countries [gdv1 that is essential for parasite transmission [Recent genome-wide analyses of tructure , 10, contructure , 12. Locsistance –16. In csistance , 18, altsistance , 20, Thesistance , and Guisistance . Differeountries , 19, 22.smission . HoweverP. falciparum population samples were analysed from two different areas of Ghana (‘S molecular form’), An. coluzzii (previously termed as the ‘M molecular form’ of An. gambiae), An. arabiensis, and An. funestus [An. arabiensis and An. coluzzii may be more common in the north than in central areas of the country [P. falciparum populations analysed here were not significantly different, and most of the strong selective signatures were evident in both populations, several of these signatures mapping to loci encoding known targets of antimalarial drugs and immunity. However, haplotype-based tests indicated a small number of chromosomal regions at which signatures of recent directional selection were stronger in one or other population, and these putatively contain one or more genes under locally varying selection with potential relevance to malaria control.To test for selection that might occur due to varying patterns of transmission seasonality, varying antimalarial drug use, or other causes, ana Fig. . Clinicaach year , 24, whiach year , 26. Majfunestus , 27, witP. falciparum reference genome sequence, enabling high quality genome-wide SNP calling and analysis for 85 isolates (45 from Kintampo and 40 from Navrongo) passing the quality filtering described in the Methods  in Kintampo, and from 0.17 - 0.99 (mean 0.74) in Navrongo . The proportions of isolates with FWS scores above 0.95, indicating infections dominated by single genotypes at the time of sampling, were 24 (53 %) of 45 isolates in Kintampo and 20 (50 %) of 40 isolates in Navrongo. Within each isolate, the majority allele at each SNP (with the highest number of mapped reads) was counted towards analyses of population-based allele frequencies for the following tests.The within-infection diversity in each isolate was assessed using the on index , 28. TheR2 = 0.71) in the distributions of Tajima’s D values across all genes  [The SNP allele frequency spectra in each of the two local populations were examined by calculation of Tajima’s D value for the 4,048 genes with 3 or more SNPs in at least one of the two populations   using thFST was calculated for each of the 34,646 non-singleton SNPs genome-wide , and only 51 SNPs had FST values above 0.1, with the highest FST value being 0.16 .Principal component analysis using the 107,221 SNPs with no missing data in the complete dataset of 85 isolates did not show any separation between isolates sampled from the Kintampo and Navrongo populations  for all SNPs above allele frequencies of 5 % in the combined population sample from Ghana  [trap gene (ID Pf3D7_1335900) encoding the thrombospondin related adhesive protein, also known as sporozoite surface protein 2, which is vital for hepatocyte cell invasion and is a leading vaccine candidate antigen to which some naturally acquired immune responses are allele specific [Rh2a, Rh2b, and msp7 genes encoding polymorphic merozoite proteins involved in erythrocyte invasion. The region on chromosome 14 contains the crmp4 gene (Pf3D7_1475400) encoding the cysteine repeat modular protein 4 which is essential for mosquito transmission [ccp1 gene (Pf3D7_1475500) encoding an LCCL (Limulus coagulation factor C) domain-containing protein expressed in parasite gametocytes [phist and hyp multigene families.Three of the four Rsb signatures  coincide with regions that had high |iHS| signatures Figs.  and 7. Todb.org) . The funspecific . This chsmission , adjacenetocytes . The regFWS, which is inversely related to the level of genomic complexity per infection, had a similar range of values in each population. The overall levels of within-infection parasite genomic complexity in each of the populations sampled here are similar to those estimated for other highly endemic West African populations in Burkina Faso, Mali, and Guinea, and higher than for a population of lower endemicity in The Gambia [FWS value close to 1.0 is dominated by a single population of identical or closely related parasites [The overall population genetic structure of parasites at both of the endemic sites sampled in Ghana was very similar despite differences in transmission seasonality and local vector species abundances. The within-isolate fixation index e Gambia , 17, 28.arasites , diversearasites .FST values between the populations were very low, with the small proportion of SNPs having FST values above 0.1 not departing from random expectations accounting for sampling variance. A previous comparison of two West African parasite populations with different levels of endemicity (sites ~1000 km apart in Guinea and The Gambia) also showed very low FST values throughout most of the genome [gdv1, PF3D7_0935400) [FST = 0.002 for the gdv1 coding SNP that was previously shown to be differentiated between The Gambia and Guinea).Similar overall allele frequency distributions observed in both of the sampled populations was not surprising, given their proximity (~350 km apart) and a high level of gene flow generally among West African malaria parasite populations , 17, 18.ama1 gene on chromosome 11, encoding an important merozoite target of acquired immunity with extensive sequence polymorphism. This is likely to be subject to occasional positive selection of new alleles added to the repertoire of existing alleles maintained by balancing selection [crt, dhfr, and dhps) highlight the role of selection by antimalarial treatment across Ghana. Strongest selection on these genes will have occurred when chloroquine and pyrimethamine-sulphadoxine were widely used in antimalarial therapy in Ghana, until Artemisinin Combination Therapy (ACT) replaced them as official first line treatment in 2005, and there may have been local differences in the decay of these signatures following the introduction of ACT. It is also possible that there is some limited ongoing selection, due to use of sulphadoxine – pyrimethamine for intermittent preventive treatment of malaria in pregnancy [In contrast, haplotype-based approaches indicated that selection has probably been operating on multiple other loci. Of the 13 genomic windows containing elevated |iHS| scores, marking loci likely to have been under recent directional selection in Ghana, 11 were observed in both of the local population samples. The most significant window identified a large region located towards one end of chromosome 6, as observed previously in other population samples from Senegal, The Gambia, and Guinea , 22, 36,election . Strong regnancy , continuregnancy  or selectrap gene which encodes a vaccine-candidate target of immunity, genes involved in parasite development in mosquitoes, and members of variant-expressed multigene families. Haplotype based tests of directional selection on malaria parasites in West Africa have previously identified selection occurring over very recent time frames, with strong signatures linked to the use of chloroquine and the antifolate drugs sulphadoxine-pyrimethamine as first line malaria therapies [P. falciparum. Our results here, using a combination of the |iHS| and the Rsb tests, provide evidence of subtle differences in local selection signatures in the two Ghanaian populations that are likely to represent ongoing or very recent selection events. Future studies to investigate selection operating over longer time frames, and heterogeneity among more distantly related populations with potentially differing demography, may employ additional tests including coalescent-based approaches [Three regions, on chromosomes 10, 13 and 14, with high |iHS| values in both of the local populations here were also marked as having stronger evidence of extended haplotypes in one or other of the populations by the Rsb cross-population analysis. This has yielded windows that each contain candidate genes that may be subject to population-specific differences in selection, including the herapies , 19, 22.proaches –42.Studies to conduct genome-wide analyses on malaria parasites from a larger number of populations sampled across heterogeneous environments are likely to be able to refine the mapping of loci influenced by local selective processes, and would also indicate the geographical scales of such signatures of selection. Coupling these approaches with epidemiological analyses, including further details of local transmission by different vector species, should facilitate future studies to not only detect signatures of selection but identify the causal processes underlying them.This study compares population structure and genomic signatures of selection on malaria parasites in two closely situated but ecologically contrasting endemic areas. Parasite genome sequencing yielded high coverage of more than 120,000 SNPs for population genomic analyses of 45 clinical infection isolates from an area with malaria transmission for most of each year, and 40 isolates from an area with seasonally restricted transmission. The local populations had similar profiles in most respects, particularly with regard to overall SNP allele frequency distributions, and proportions of mixed genotype infections. Eleven different chromosomal regions showed elevated integrated haplotype scores in both populations, but another metric comparing extended haplotype homozygosity (the between-population Rsb index) indicated differences in the strength of the signal between the populations for three of these chromosomal regions. A stronger signal was detected within one population for a narrow region of chromosome 14, whereas a stronger signal was seen in the other population for small regions on chromosomes 10 and 13. These include genes that are potential targets of locally varying recent selection, such as those involved in parasite development in mosquitoes, members of variant-expressed multigene families, and a leading vaccine-candidate target of immunity. Even in a situation of closely situated populations with very similar population structure and shared selection signatures, analysis across heterogeneous environments has potential to refine the mapping of important loci under temporally or spatially varying selection, including those of potential relevance to epidemiology and control of infection.Blood samples were collected from 146 clinical malaria cases attending Ghana government health facilities in 2011 and 2012, at Kintampo  in Brong-Ahafo Region of central Ghana, and Navrongo  in Kassena-Nankana East Municipality, in the Upper East Region of northern Ghana  or blood smear and had not taken antimalarial drugs during the last 72 h preceding the sample collection. A total of 146  whole blood samples (up to 5 ml) were collected into heparinised vacutainer tubes  and centrifuged. After removal of the plasma and buffy coat, the red cells were depleted of leukocytes by lymphoprep density gradient centrifugation and subsequently passing them through Plasmodipur® filters , and then frozen at −20 °C. DNA was extracted from each frozen blood sample using the QIAamp blood midi kit  prior to whole genome sequencing.Approval to collect and analyse the clinical samples was granted by the Ethics committees of the Ghana Health Service, the Noguchi Memorial Institute for Medical Research, University of Ghana, the Kintampo Health Research Centre, the Navrongo Health Research Centre and the London School of Hygiene and Tropical Medicine. Written informed consent was obtained from parents or other legal guardians of all participating children, and additional assent was received from the children themselves if they were 10 years or older. Antimalarial treatment and other supportive care was provided to the children according to the Ghana Health Service guidelines. Patients were eligible for recruitment into the study if they had uncomplicated clinical malaria, were aged 2–14 years, tested positive for P. falciparum positive clinical samples was processed for library preparation and whole genome sequencing at the Wellcome Trust Sanger Institute by paired-end 101 base pair genome sequencing on an Illumina HiSeq platform. Sequence read files for each of these isolates have been deposited in the European Nucleotide Archive  using BWA-MEM version 0.7.5a [var, rifin and stevor families or if they were positioned within repetitive sequences as identified by Tandem Repeat Finder [n = 45, Navrongo n = 40) and 121712 biallelic SNPs remained for analysis after filtering.DNA extracted from the 146 n 0.7.5a  with defn 0.7.5a  as previn 0.7.5a . For eact Finder . The datFWS fixation index [FWS scores approaching 1.0 are considered to have a single predominant genotype.Within-infection genomic diversity in relation to the total local population diversity was determined using the within-isolate on index , 28. Brion index . IsolateFST [FST analysis missing data for some isolates were excluded on a per SNP basis. For Tajima’s D analysis missing data was excluded by removal of individual isolates on a gene by gene basis due to the observation that the majority of missing data clustered within a small number of isolates. Expected genome wide FST distributions were simulated by random assignment of individuals to each population using 1000 replicates and a sampling with replacement strategy. Signatures of directional selection were identified within each population using the standardised |iHS| [P. falciparum populations (in Guinea and The Gambia) [Analysis of allele frequency distributions, including between-population FST  and withFST  was perfed |iHS| , which wed |iHS| . Also used |iHS|  in the Red |iHS| , which a Gambia) , we had  Gambia) . OverlapThe datasets supporting the results of this article are deposited in the European Nucleotide Archive, with multiple accession numbers provided in the article (listed in Additional file


Naja atra) ranges from southeastern China south of the Yangtze River to northern Vietnam and Laos. Large mountain ranges and water bodies may influence the pattern of genetic diversity of this species. We sequenced the mitochondrial DNA control region (1029 bp) using 285 individuals collected from 23 localities across the species' range and obtained 18 sequences unique to Taiwan from GenBank for phylogenetic and population analysis. Two distinct clades were identified, one including haplotypes from the two westernmost localities (Hekou and Miyi) and the other including haplotypes from all sampling sites except Miyi. A strong population structure was found  with high haplotype diversity (h = 1.00) and low nucleotide diversity (π = 0.0049). The Luoxiao and Nanling Mountains act as historical geographical barriers limiting gene exchange. In the haplotype network there were two “star” clusters. Haplotypes from populations east of the Luoxiao Mountains were represented within one cluster and haplotypes from populations west of the mountain range within the other, with haplotypes from populations south of the Nanling Mountains in between. Lineage sorting between mainland and island populations is incomplete. It remains unknown as to how much adaptive differentiation there is between population groups or within each group. We caution against long-distance transfers within any group, especially when environmental differences are apparent.The vulnerable Chinese cobra ( Naja are a clade of elapid snakes that occur in Africa and Asia. They have attracted worldwide attention for their unique hood-spreading behavior and deadly toxicity. The genus currently contains 28 oviparous species in four subgenera: Afronaja, Boulengerina, Naja and UraeusNaja are primarily African. The 11 Asian cobras in the subgenus Naja are thought to comprise a lineage that spreads from west to east, from Africa through India Naja cobras can be found in China, the Chinese cobra N. atra and the monocled cobra N. kaouthiaNaja atra occurs in the southeastern provinces (including Taiwan) of China south of 30°17′N north latitude, northern Vietnam and Laos; N. kaouthia ranges from southwestern China to south-southeastern Asia Cobras of the genus N. atra is much more widely distributed and commonly found than N. kaouthia, and is among the snake species that have long been used by local people as traditional food and medicine. The long-term unsustainable exploitation of N. atra has made it one of the most vulnerable snake species N. atra has not only exacerbated the decline of wild populations, but also disrupted their lineage relationships due to escapes and uncontrolled releases of wild-caught individuals. As a “top-of-the-food-chain” predator, N. atra is important to its ecosystem because it can keep the populations of its various prey species  under control In China, N. atra have shown that population genetic structure is correlated with geography. Using mitochondrial DNA markers (cyt b and ND2) to examine relationships among five mainland populations, Lin et al. (2008) found that three eastern populations  cluster together, while two southern populations in Baise and Quanzhou of Guangxi Province form another clade N. atra in Taiwan into four independent evolutionary significant units. Combining mitochondrial cyt b gene and microsatellite markers, Lin et al. (2012) found that: (1) mitochondrial DNA data reveal two main  and one minor  clades; (2) microsatellite data divide the eastern + southeastern China clade further into two genetic clusters; and (3) the Luoxiao and Nanling Mountains are inferred to be the major barriers causing lineage sorting N. atra in mainland China into three independent management units N. atra conducted independently in mainland China and Taiwan have been limited by incomplete sampling, not allowing the development of a generally applicable conservation plan for the species. Thus, further work through more intensive and large-scale sampling within the species' entire range by using the same genetic markers is needed to reveal its overall genetic diversity and population genetic structure. Our aim is to find different lineages across this vulnerable snake's range and take them into account for conservation plans.Understanding population structure and genetic diversity is valuable for wildlife conservation because of its importance in determining the integrity and viability of natural populations. Previous studies on th site), defined 52 haplotypes. Sixteen haplotypes were unique to a single population. The number of haplotypes within each locality ranged from 1 to 12 (h) ranged from 0  to 0.920 (the southern Taiwan population), and nucleotide diversity (π) from 0  to 0.0057 (the Hekou population)  were obtained from 390 individuals (of which 105 were from Taiwan  1 to 12 . Haplotyulation) . For theThe phylogenetic tree resulting from ML revealed two clades . This fiClade B had a complex pattern with two core haplotypes, H1 and H42, shared among geographically separated populations, and had broader mutation connections and higher frequencies . H1 was We defined three groups separated by the Luoxiao and Nanling Mountains: [eastern China], [southern China and Vietnam] and [western China] . There wP<0.0001). A significant proportion (34.1%) of the total variation in the mitochondrial DNA data could be attributable to differences among groups. A significant proportion of variation also occurred among populations within groups (41.9%) and within populations (24.0%).Using analysis of molecular variance, significant population structure was found for 27 populations and three groups  from the Miyi and Hekou populations was even more distant from all other haplotypes, separated by at least 44 mutation steps In the present study, cobras from the western (green), southern (red) and eastern (blue) population groups are at the base, in the middle and at the top of the ML phylogenetic tree, respectively . This reN. atra, shaping the current genetic structure of populations.An association between geographic region and haplotype within Clade B was apparent, with two “star” clusters represented by haplotypes mainly from either the eastern or the western populations . SignifiThe haplotypes found in the four Taiwan populations were close to those found in the eastern mainland populations. All 19 haplotypes (H1–H19) found in Taiwan were clustered on the right side of the haplotype network. The 18 unique haplotypes were no more than four steps from the core haplotype H1 , and neaN. atra to migrate between mainland China and Taiwan.Similar to what has been observed between mainland and island populations in the northeastern (Zhoushan) and southern (Hainan) parts of the cobra's distributional range b gene. The study shows that the two mountain ranges are important physical barriers limiting gene flow in N. atraN. atra adjoin in Anhui, Hubei and Jiangxi provinces in the central-east part of China, indicating a “west–east division” in the species. Well differentiated evolutionary lineages and a similar “west–east division” have also been reported for other squamate reptiles such as the sharp-snouted pit viper Deinagkistrodon acutusGloydius brevicaudusTakydromus septentrionalisT. septentrionalis, for example, all the haplotypes from the Yudu population east to the Luoxiao Mountains are clustered within the western lineage Populations on the two different sides of the Luoxiao and Nanling Mountains often do not share haplotypes, although they are geographically very close. This result is consistent with an earlier study using 12 microsatellite loci and the cyt N. atra that are less likely to disperse are more prone to suffering substantial losses in genetic diversity resulting from habitat loss and fragmentation The phylogenetic tree showed a two-way dichotomy between Clades A and B, which provides no evidence that one clade is older than the other, as would be expected in a dispersal scenario. The two-way dichotomy might be the consequence of vicariant events subdividing widespread populations. More than a half of the total genetic variation occurred among the regions and among populations within a region, indicating high levels of geographical structuring and restricted gene flow. This population genetic structure could be largely due to the fact that this widespread snake has a limited ability to disperse. Herpetofauna including The neutrality test and mismatch distribution analysis suggest that the eastern populations showed population expansion. This is also supported by the haplotype network . Here, aD. acutus, a snake that has almost the same distributional range as N. atra, with populations of the former species in eastern China also experiencing expansion 0.226–0.252 million years ago The expansion of eastern populations occurred some 0.273 million years ago. This is consistent with the result reported for N. atraN. atra occurred in the early last interglacial, when was a global warming period, which provided the opportunity for cobras of this species to disperse, colonize, and undergo in situ genetic differentiation.Prolonged exposure to temperatures lower than 9°C is lethal to N. atra will have the potential to disrupt lineage relationships. Therefore, we caution against the release of cobras into the wild unless their origins can be determined with confidence. The ex-situ conservation benefits gene flow and more easily reaches the minimum surviving quantity to maintain populations, but the genetic similarity between populations should be considered before executing such conservation plans. Besides the westernmost population group (Hekou and Miyi), three population groups  separated by the Luoxiao and Nanling Mountains can be identified, but how much adaptive differentiation is present between these groups or within each group is currently unknown. Therefore, we also caution against long-distance transfers within any group, especially when environmental differences are apparent. Comparatively speaking, in-situ conservation by protecting and recovering habitats of N. atra is more sound and viable than other measures.Studies on snake species with large and continuous ranges have generally shown the existence of independent management units for different lineages that should be taken into account for conservation plans We collected 285 adult cobras larger than 900 mm snout-vent length from 23 localities covering the species' whole range in mainland China (including Zhoushan and Hainan Islands) and Vietnam . Effort - CCA CAA AGC ATT GTT CTT GTA AAC C T-3′) and PH1 (5′-GAA GCT TGC ATG TAT AAG TAG GG-3′) Preserved tissue samples were used to extract total genomic DNA using EasyPure Genomic DNA Extraction Kit (TransGen Biotech). DNA was resuspended in TE buffer  and stored at −80°C until ready for use. We used primers PL1 (5′N. atraThe mtDNA of cobras has two control regions (CR1 and CR2). CR1 is located between tRNA-Pro and tRNA-Phe, containing the replication origin of heavy chain and the transcription start site of duplexes; CR2 is located between tRNA-Ile and tRNA-Leu h) and nucleotide diversities (π) Ophiophagus hannah and the banded krait Bungarus fasciatus (GenBank Accession number EU921899 and EU579523 respectively) as outgroups. ML analysis was carried out by a heuristic search of 10 random addition analyses with tree-bisection-reconnection (TBR) branch swapping using PAUP* 4.0b10 We compiled and aligned sequences using MEGA 5.1 SSD) and the Harpending's raggedness index D test Fs test τ = 2utt), where τ is the date of the growth or decline measured in units of mutational time and u is the mutation rate per sequence and per generation. The approximate time of expansion in years was calculated by multiplying t by the generation time of N. atra, estimated as four years based on the approximate time at which animals become mature D. acutusWe performed hierarchical analysis of molecular variance (AMOVA) in ARLEQUIN 3.5


Arabidopsis thaliana. These populations inhabit west Himalaya, an undersampled region. Using 19 genomic SSR and 11 chloroplast markers they determined that these populations are highly structured and genetically distinct from the rest of the world populations. They also observed that the populations were structured at the altitudinal level. Additionally their molecular clock analysis showed that these populations are not recent introductions and have inhabited the Himalayan region for about 130,000 years.In this study Tyagi et al. determined the genetic diversity and population structure of the model plant,  Arabidopsis thaliana, as a model plant, has been studied in great detail including its probable origin, local as well as global genetic diversity pattern, population structure, adaptation, etc. However, no such studies have so far been reported from the Indian Himalayan region. Here, we describe a comprehensive study on the genetic diversity and population structure of A. thaliana from an altitudinal range of 700–3400 m above mean sea level the highest altitudinal range reported so far. We also compare these populations with previously reported worldwide populations. A total of 48 accessions representing six populations were analysed using 19 microsatellites and 11 chloroplast markers. Genetic diversity analysis indicated populations to be highly diverse and comparable with worldwide populations. STRUCTURE, principal coordinate and isolation by distance (IBD) analyses showed that genetic variation in different populations is structured at geographical and altitudinal level. Further analyses indicate that these populations are genetically distinct from the rest of the world populations. Different parameters of the demographic expansion model support a rapid expansion. Based on mismatch distribution, the initial time of expansion of west Himalayan populations was found to be about 130 000 years. Bayesian analysis of divergence time indicated that these populations have a long evolutionary history in this region. Based on the results of genetic diversity parameters, demographic expansion and divergence time estimation, it appears that west Himalayan populations may be the source of the west–east expansion model.The natural genetic variation within a plant species is primarily a consequence of its phylogeography and evolutionary history. This variation largely determines its present-day population structure.  An understanding of the amount and distribution of genetic variability and structure in natural populations is fundamental in ecological genetic studies. The amount of genetic variability found within and among populations will both influence the response to, and be influenced by, selective pressures and evolutionary processes such as gene flow and genetic drift , whereasArabidopsis thaliana, commonly known as rock cress, is an annual weed having a worldwide geographical distribution. It has been extensively employed as a model plant not only for plant molecular genetics but also for ecological and evolutionary studies , which have been reported from this region, viz. Kas-1 and Kas-2, collected from Kashmir region of India, but concerns have been raised about the origin of Kas-1 accession, as it stands aloof from the rest of Asian accessions in phylogenetic studies  to establish the relationship of A. thaliana of west Himalaya with respect to other accessions of the world and (iii) to determine the probable origin of west Himalayan populations and their expansion. We attempt to provide here the link between populations of China and west Himalaya and further extend the demographic model proposed by A. thaliana will greatly enhance our understanding of global population structure and evolution of this model species.In the present study, our goals were (i) to estimate the genetic diversity of A. thaliana accessions were collected from six different populations located at Dehradun  designated as low altitude; Dhapa  and Munsiyari , designated as medium altitude and Sangla , Koksar  and Chitkul , designated as high-altitude localities of the west Himalayan region . The minimum and maximum distances between populations were 4 km (Mun and Dha) and 384 km (Kok and Mun), respectively. The accessions were named by the first three letters of the name of the nearest location or village from where samples were collected, followed by a numeric indicating the individual accession number. The habitats of the populations varied from human-disturbed lawn to undisturbed mountainous river valley and slopes [seeSupporting Information—Table S1].A total of 48 Tm of primer pairs) and extension at 72 °C for 1 min followed by a final extension step at 72 °C for 5 min. The amplified products were cleaned up using the Qiaquick PCR purification kit . Sequencing PCR was performed using ABI Prism BigDye Terminator v3.1 cycle sequencing Kits (Applied Biosystems) in 10 µL reaction volume containing 2 µL cleaned PCR product as template, 1 µL of either forward or reverse primer, 0.5 µL of RRmix and 1.75 µL of 5× dilution buffer. The products were ethanol precipitated and denatured with Hi-Di formamide and sequenced on ABI 3730XL Automated DNA Sequencer (Applied Biosystems).Genomic DNA was isolated from field-collected leaf samples using the Qiagen™ DNeasy Plant mini kit (USA) following the manufacturer's protocol. The quality of the DNA was checked on 0.8 % agarose gel and quantified using Nanodrop spectrophotometer . For amplification of CP loci, the same set of primer pairs was used as described in 2, 0.2 mm deoxyribonucleotides, 4 pmol fluorescently labelled M13 primer, 4 pmol of forward primer with M13 tail and 8 pmol of MS reverse primer, 2 µL Promega buffer and 0.5 U of Promega Taq polymerase. The PCR reactions were performed in the following conditions: 94 °C (1 min), then 30 cycles at 94 °C (1 min)/specific annealing temperature (56–60 °C) for each MS marker, (1 min)/72 °C (1 min), followed by 8 cycles at 94 °C (1 min)/53 °C (1 min)/72 °C (1 min) and a final extension at 72 °C for 10 min. The amplified products were run on a capillary-based 3730xl DNA Analyser (Applied Biosystems) and the products were precisely sized for major, comparable and conspicuous peaks using GeneMapper v4.0 (Applied Biosystems), using default quality indicators of GeneMapper. Details of the MS data are shown in Supporting Information—Table S2.All the samples were genotyped for 19 previously described MS loci  through We sequenced 11 CP loci from 8 individual plants collected from each of the 6 locations . Each sequence was visually inspected and matched with its corresponding electropherogram. Only the high-quality sequences (Phred score >20) were considered for analysis. Besides these 528 sequences, we included the sequences of worldwide samples provided in for comparative analyses. The average sequence length of our samples was slightly shorter than the sequence length obtained by viz. genetic diversity measures, such as the percentage of polymorphic loci (%PL), mean number of alleles (Na), mean number of effective alleles (Ne), mean allelic richness (Rs), mean private allelic richness (Rp), mean gene diversity (Hs), number of multilocus haplotypes (Nh), expected heterozygosity (He) and observed heterozygosity (Ho) were estimated for the three data sets using software programs GENALEX 6.5 (n = 2) using HP-rarefaction tools to avoid sampling disparity.Three sets of population data were considered for analysis: (i) 48 accessions from six different populations from west Himalaya using 19 MS markers, (ii) 48 west Himalayan accessions using 11 CP loci and (iii) 48 west Himalayan accessions and 77 worldwide accessions using 11 CP loci from ALEX 6.5 , FSTATv ALEX 6.5  and HP-RALEX 6.5 . AllelicFST like statistics. Statistical significance of AMOVA was tested from 20 000 permutations. To determine whether the genetic distances between populations were determined by geographic distance or difference in their altitude, IBD analysis between populations was performed. To determine the IBD, partial Mantel test (PMT) were performed. This test allows two pairwise distance matrices to be compared, while controlling for the effect of a third. Partial Mantel test was performed between pairwise FST and the corresponding paired population matrix of geographic distances while controlling for pairwise altitudinal distances. Alternatively PMTs were performed between pairwise FST and pairwise altitudinal distances while controlling for geographic distances. Pairwise FST between six populations were calculated using the Arlequin program. Geographic distances between location pairs were calculated using Geographic Distance Matrix Generator v1.2.3 software  using Arlequin v3.5 , which csoftware . Partialsoftware . To deteK, STRUCTURE was run in 20 replicates with assumed K from 2 to 10. The K value where the posterior probability (ln P(D)) began to plateau was selected as the true K (P(D) began to plateau at K = 6 [seeSupporting Information—Fig. S1], which was also incidentally the number of our populations. To determine how the individuals of populations respond to increasing K, we plotted STRUCTURE results from K = 2 to K = 6. Principal coordinate analysis was performed using Nei's distance matrix as implemented in GENALEX 6.5, and the resulting eigenvalues were plotted using k-means clustering implemented in cluster package R v3.2 (http://www.R-project.org). Phylogenetic relationships among accessions were determined by the neighbour-joining (NJ) method using PAUP*4.0 . The haplotype with the highest out group weight as estimated by TCS was considered as the ancestral haplotype.Haplotype diversity in the combined sequence data was determined using DnaSP v5  and ‘sum of squared deviation’ (SSD) values were determined to infer the demographic expansion event. To estimate the time since expansion, substitution rate of 2.9 × 10−9 changes per site per year for non-coding sequence of the A. thaliana CP was considered as reported by t is the expansion time in number of generations, τ is the mode of the mismatch distribution and μ is the mutation rate per generation for the entire DNA sequence.To test the neutral mutation hypothesis, different statistical tests such as Tajima's D  and Fu ad F test  were conOlimarabidopsis cabulica and A. thaliana  and XJalt, XJqhx (CHN) from their parent data set and included in the rest of the world data set, as the phylogenetics and haplotype network analyses grouped these accessions with the rest of the world (see Results). Four independent BEAST runs of 3 × 107 generations saving logs every 200 generations were performed. Results of four BEAST runs were combined and then subsampled at a lower frequency using Log Combiner v1.8.0; this produced 3 × 104 age estimates under the coalescent constant size tree prior and the uncorrelated lognormal relaxed clock. The count of beast runs was decided to achieve an effective sample size of at least 100 for all TMRCA estimates. The MCMC data output for each individual run was visualized using Tracer v1.5. Chronogram trees were generated using TreeAnnotator v1.8.0 program.The divergence time between the accessions was estimated using Bayesian methods implemented in BEAST v1.8.0 . All the 11 CP loci were found to be polymorphic. At population level, Deh and Chi populations were found to be the most and the least diverse with respect to %PL and Na using CP markers. The He (assuming panmictic populations) was higher than the Ho. The haplotype diversity of each of the six populations was 1.0 indicating high degree of CP sequence diversity across all the populations . On the basis of MS data, all the six populations differed genetically from each other since all the FST values between pairs of populations were significant  [seeSupporting Information—Table S3]. Partial Mantel tests of pairwise geographic distances [seeSupporting Information—Table S4] and corresponding FST values  showed a significant correlation  indicating IBD, whereas no significant correlation was found when pairwise FST were correlated with pairwise altitudinal differences .In the phylogenetic analysis using NJ method with MS markers, clustering of accessions at two hierarchical levels was observed. The three major branches represented accessions on the basis of their defined altitudinal class, whereas branches arising from external nodes formed clades representing accessions clustered according to population affiliation, except Mun and Dha that clustered under a single clade. All the other accessions clustered according to their population affiliation with minor intermixing Fig. . NeighboSupporting Information—Fig. S3). STRUCTURE analysis indicated that at K = 2, the populations were grouped into two: one representing populations of low and medium altitude, and the other one representing populations of high altitude. At K = 3, all the populations grouped according to their altitudinal assignment (described in Methods). Further, at K = 4 and 5, San formed a separate group from Chi and Kok, and at K = 6, all the populations except Mun and Dha formed separate groups. San showed large within-population variations at K = 5 and 6 . The results of comparative genetic diversity parameters between the three groups are depicted in Table Supporting Information—Table S6). Two haplogroups (I and II) were detected, where Haplogroup I consisted of all the accessions from west Himalaya and Yangtze River, and Haplogroup II consisted of the rest of the world accessions including accessions from Xinjiang province and Kas-1. The Haplogroup I was connected to Haplogroup II by two nucleotide character states . The ancestral haplotype analysis showed one haplotype (CHN-AHyxx) consisting of 10 accessions from the Yangtze River and one accession from west Himalaya (Kas-2) belonging to Haplogroup I, and another haplotype (CZE Ta-0) belonging to Haplogroup I, consisting of five accessions, had the highest and outgroup weights  among all the detected haplotypes. This indicates that these two haplotypes are the ancestral haplotypes for the respective haplogroups [seeSupporting Information—Table S5].In the concatenated data set of 11 CP loci, 1.5 % were parsimony informative characters (PIC). The genetic relationship among west Himalayan and the rest of the world accessions was first analysed by constructing NJ trees with complete deletion of gaps. This method resulted in a poorly supported tree (data not shown). However, inclusion of indels greatly increased the statistical branch support without changing the clustering pattern as observed with complete deletion methods  at P < 0.05. Tajima's D value for the concatenated data set was also negative and highly significant . Similarly, Fu and Li's D*, D, F* and F test values were also negative for all but trnG. The combined values were also negative and significant at P < 0.01 [seeSupporting Information—Table S6], indicating a rapidly expanding population when compared with the Yangtze River population (Tajima's D = −1.17)  were smaller than that of Yangtze River . These results provided evidence of rapid population expansion in the west Himalayan population. Based on the τ value, the initial time of expansion of west Himalayan populations and Yangtze River populations was found to be ∼130 000 and ∼84 000 years, respectively.Tajima's [seeSupporting Information—Table S8]. The TMRCA of combined populations of western Himalaya and Yangtze River was estimated to be 0.45 mya , which was exactly same as the TMRCA of western Himalaya lineage. The TMRCA of Yangtze River populations stood at 0.29 . Further, the origin of the rest of the world was found to be 0.62 mya . Although the tree prior for A. arenosa and A. thaliana split was 5.0 mya, the relaxed molecular clock found it to be 6.61 mya . The rest of TMRCA reference points, O. cabulica  and A. suecica , were within the range of the given priors.Using Bayesian approaches, we estimated the divergence time with 95 % highest probability density (HPD) The chronogram resulting from Bayesian phylogenetic time tree analysis had two distinct clades. One included accessions from west Himalaya, Yangtze River, and the other included all the accessions from the rest of the world accessions. The chronogram clearly showed a split between European and Himalaya–Yangtze River accessions at 0.49 mya Fig. .Figure A. thaliana, a well-studied model plant species, has found no detailed studies from this region. We collected accessions of A. thaliana from the west Himalaya ranging altitudes of 700–3400 m a.m.s.l. The populations were collected from diverse habitats of highly disturbed lawn (Deh) to mountainous slopes (Mun) to undisturbed mountainous river valley (Chi). To the best of our knowledge, this is the first report on A. thaliana populations from altitudes ranging from such a large altitudinal range.The Himalayan region is one of the mega-biodiversity hot spots of the world, bestowed with rich flora and fauna  formed one group distinct from the rest of the world accessions. This polytomous clustering was primarily attributable to lack of PIC's. The two strongly supported branches in the NJ tree were obtained as a result of the use of indels as phylogenetic information. Several other studies involving regional , New Delhi, Project: SIMPLE (BSC-109).The study was supported by S.R. designed and conceptualized the study. A.T., S.S., P.M., A.S., A.M.T. and S.N.J. conducted the experiments. S.R. and A.T. wrote the manuscript.None declared.The following additional information is available in the online version of this article –Table S1. Detailed geographical locations of sample collection sites and their habitats.Table S2. Details of 19 MS loci data.Table S3. Pairwise genetic distances (FST) between west Himalayan populations. All the values are highly significant (P < 0.0001).Table S4. Pairwise geographical distances between west Himalayan populations (in km).Table S5. Results of haplotype analysis of west Himalayan (WH) populations and the rest of the world (RW) accessions using CP markers. Showing outgroup weights (in decreasing order) of the haplotypes and the corresponding accessions in the haplotype.Table S6. Nucleotide diversity (π) and the results of neutral mutation hypothesis tests for the 11 CP marker data sets of WH populations. ns, not significant (P > 0.05); s, significant (P < 0.05); vs, very significant (P < 0.01).Table S7. Results of mismatch distribution analysis using CP markers of west Himalaya (WH) and Yangtze River (YR) populations.Table S8. Divergence time estimation. Mean divergence time in million years ago of outgroup species , rest of world (RW), west Himalaya (WH), west Himalaya + Yangtze River (HY) and Yangtze river (YR). Also included is 95% upper and lower higher probability density (HPD) and effective sample size (ESS).Figure S1. Mean probability (D) of ln(K).Figure S2. Consensus NJ tree of 1000 bootstrap replicates for WH accessions  constructed using concatenated sequences of 11 CP markers. The tree was constructed employing indel coding method MCIC. Figures on the nodes represent bootstrap branch support.Figure S3. Percentage contributions to the genetic variability of the co-ordinates calculated using principal co-ordinate analysis of west Himalayan samples on the basis of MS markers. x-axis = co-ordinate, y-axis = percentage contribution of the corresponding co-ordinate to the overall variability.


Arbutus unedo L. is an evergreen shrub with a circum-Mediterranean distribution that also reaches the Eurosiberian region in northern Iberia, Atlantic France, and a disjunct population in southern Ireland. Due to the variety of climatic conditions across its distribution range, the populations of A. unedo were expected to display local adaptation. Conversely, common garden experiments revealed that diverse genotypes from a range of provenances produce similar phenotypes through adaptive plasticity, suggesting the action of stabilizing selection across its climatically heterogeneous range. Nonetheless, since a uniform response might also result from extensive gene flow, we have inferred the population structure of A. unedo and assessed whether its extended and largely one-dimensional range influences gene flow with the help of AFLP genotypes for 491 individuals from 19 populations covering the whole range of the species. As we had anticipated, gene flow is restricted in A. unedo, providing further support to the hypothesis that stabilizing selection is the most likely explanation for the homogeneous phenotypes along the range. The Euro-Siberian populations were not particularly isolated from the Mediterranean. Instead, there was a distinct genetic divide between the populations around the Mediterranean Sea and those sampled along Atlantic coasts from northern Africa up to Ireland. This genetic structure suggests the action of historic rather than biogeographic factors as it seems consistent with a scenario of independent glacial refugia in the Atlantic and Mediterranean portions of the range of A. unedo. Genetic exchange was likewise restricted within each set of populations. Nevertheless, isolation-by-distance (IBD) was stronger, and FST increased faster with distance, along the Atlantic, suggesting that gene flow might be larger among Mediterranean populations. Genetic diversity was significantly lower in NW Iberia and Ireland than in other populations whereas Ireland was more closely related to populations in NW Iberia than to a population in Atlantic France, suggesting a postglacial stepping-stone colonization of the Atlantic coast. Altogether, our results show that stabilizing selection is able to homogenize the phenotypic response even when population structure is strong, gene flow is constrained, and the phylogeographic past is complex. The future of a species, in terms of adaptive ability and evolutionary potential, depends on its genetic diversity and quantitative traits. These can be affected by the spatial distribution of the species as it influences the amount of gene flow among populations  is a predominantly Mediterranean evergreen shrub or small tree  were interpreted as evidence that the populations of the strawberry tree may be under stabilizing selection despite its extended and climatically variable range , it could be speculated that the populations that encircle the Mediterranean might fit an isolation-by-distance (IBD) pattern if gene flow is restricted to movements along the shoreline. Finally, it would be reasonable to anticipate that the disjunct Irish enclaves may be genetically eroded given their peripheral, isolated position  whether the largely one-dimensional distribution of the strawberry tree has restricted gene flow; (ii) whether the climatic contrast between Eurosiberian and Mediterranean populations has reduced the genetic exchange between them; and, in an effort to contribute to the Lusitanian debate, (iii) whether the Irish population is more closely related to French or Iberian ones. To fulfill these aims, we conducted a range-wide survey of Arbutus unedo is a diploid species  , A. pavarii Pampanini (Libyan coasts), A. canariensis Veill. (Canary Islands) and A. unedo L. . Leaf samples were collected from 19 sites covering nine countries  following manufacturer's instructions with minor modifications: incubation times at cell lysis and protein precipitation steps were extended up to 2 h and up to 20 min, respectively. Samples were processed in batches of 23 samples (from at least five populations) plus 1–2 negative controls with no tissue. Ten percent of the samples were extracted twice on different batches. The quality of the extracted DNA and negative controls was checked on 1.5% agarose gels.Given that AFLP performance can be sensitive to reaction conditions  , using the squared Euclidean distance between AFLP phenotypes. The significance of the ΦPTvalues was estimated after 9999 random permutations of individuals among populations performed with GenAlex 6.5 .Allele frequencies were also employed to estimate genetic differentiation between individuals as Milligan . SignifiFST values) and geographic distance (km) among populations was tested with the Mantel test implemented in the IBD Web Service 3.23 , and six iterations per K were performed for K ranging from 1 to 15 . The value of K that captured most of the structure in our data was determined using the approach originally proposed by Pritchard et al.  that is closely related to Moran's I  was investigated with GenAlex 6.5 . Moreover, IK was significantly less diverse than EP (0.28 ± 0.016). No population showed private bands.Nine primer combinations produced a total of 226 reproducible AFLP loci, 125 (55.3%) of which were polymorphic for the whole data set (5% criterion) and were kept for further analyses. The number of polymorphic loci per population ranged from 94 (IK in the northern Atlantic edge of the distribution range) to 114  than the Atlantic group . Besides, two private bands were detected in some, but not all, Mediterranean populations.Since the analyses of population structure revealed two clearly separated sets of populations (see below), separate estimates of diversity were obtained for each group. The Mediterranean group  had slightly more polymorphic loci (113 vs. 111) and significantly higher gene diversity  and ΦPT statistics (0.105) revealed the occurrence of highly significant genetic differences among populations (P < 0.001), although most of the variation occurred within populations , and the pattern of pairwise FST values depicted by the nMDS revealed two sets of populations: the set of circum-Mediterranean sites and the set of demes sampled along the Atlantic coast  clearly surpassed the values registered for other Mediterranean sites . In comparison, three Atlantic populations visibly separated from the Atlantic set. Interestingly, the three divergent populations included the two sites sampled at the northern (IK) and southern (MD) edges of the distribution range of A. unedo along the Atlantic and the site sampled on northwest Iberia (EP). Average pairwise FST values for these three populations ranged from 0.075 to 0.103, clearly exceeding the estimates recorded among other Atlantic sites (0.045–0.062).Both the global t Figure . The strFCT estimates increased with larger K values but reached a plateau for K = 4 . The optimal partition separated the circum-Mediterranean set from the samples collected along the Atlantic. Most of the Atlantic populations fell together in a single group but IK (northern edge) and MD (southern edge) were held separately. EP separated from other populations for K = 5, but the FCT estimate for this structure  indicates that keeping EP as a separate entity does not improve the proportion of genetic variance attributed to differences between groups of populations. Unlike nMDS, SAMOVA provided not support to the hypothesis that FM should be regarded as particularly differentiated from other Mediterranean populations.SAMOVA results corroborated the partitioning of populations in two sets as well as the presence of a few genetically differentiated populations Table . As expeA. unedo (19 populations), but the correlation geographic and genetic distance (as FST) improved slightly when geographic distance was measured as a straight-line  rather than avoiding conspicuous barriers to dispersal . Separate analyses for each set of populations (straight-line geographic distances only) revealed that both the slope and the strength of the relationship changed from one area to another  and steeper (slope = 6.65 × 10−5 ± 1.00 × 10−5) in the Atlantic than in the Mediterranean . Since the highest FST estimates recorded within the Mediterranean were consistently produced by comparison with a single population (FM), Mantel calculations were repeated with FM excluded from the Mediterranean group  and yielded an even flatter relationship (slope = 2.30 × 10−5 ± 3.17 × 10−6). Similar results were obtained with Nei's genetic distance between populations instead of FST.The Mantel test revealed the existence of IBD throughout the entire range of r Figure . The cor) for the 19 populations was 7 ; a majority of individuals (91.4%) were assigned to a single genetic cluster. Admixture clustering graphs reinforced the split between Atlantic and Mediterranean because most of the genetic clusters were largely region-specific . The combined autocorrelogram was non-significant  and showed a very flat profile around the null hypothesis of r = 0.Finally, spatial autocorrelation analyses failed to detect any evidence of fine-scale SGS. The test of heterogeneity among populations indicated that the 19 sites displayed a similar lack of spatial pattern  obtained in our analyses, together with the fact that most of the genetic variation was found within populations, could be regarded as a sign that gene flow might still play a role in the genetic structure of the strawberry tree. Nonetheless, it should be recaled that ΦPT measures are strongly influenced by the amount of within population genetic variation, which in our case were high. Hence, considerable genetic variation observed within the populations of the strawberry tree, rather than gene flow, might possibly explain the moderate ΦPT value detected in our study. Furthermore, a comparable restriction in gene flow was also observed with cpDNA markers while studying the phytogeography of A. unedo  was previously shown to be indistinguishable from the behavior found in plants collected throughout the species range  than avoiding barriers (i.e. around the Mediterranean). Consequently, the circum-Mediterranean distribution does not seem to be a determinant of the restricted gene flow suggested by the IBD pattern. Moreover, genetic differentiation was generally lower among circum-Mediterranean populations than among Atlantic populations separated by similar geographic distances, suggesting that gene flow around the Mediterranean, although restricted, has been higher than between Atlantic demes . Instead, there is a separation between the populations located around the Mediterranean basin and those found along Atlantic coasts from North Africa up to Ireland. The genetic partition involves two private AFLP loci in the circum-Mediterranean group, suggesting that the split may have taken place long time ago . In comparison, populations within each genetic group  can be separated by 2000 km. Several other widespread Iberian trees also show an ancient and often remarkably clear-cut divide between populations from the Mediterranean and from the Atlantic regions of the Iberian Peninsula. The genealogical concordance between multiple co-distributed species can be interpreted as evidence that the responsible evolutionary forces must have had widespread effects at the level of biotic communities and ecosystems Avise, . In the A. unedo, reinforcing the conclusion that the uniform phenotype found in range-wide common gardens must be due to a combination of adaptive plasticity and stabilizing selection, where the latter selects for a norm of reaction that develops a similar phenotype in individuals from different populations  and FPU fellowship AP-2009-0962 (Ministerio de Educación). This research was also supported by the European Regional Development's Fund (ERDF).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Finally, we used the ALDER method and inferred admixture dates (~9th–17th centuries) that overlap with the Turkic migrations of the 5th–16th centuries. Thus, our results indicate historical admixture among Turkic peoples, and the recent shared ancestry with modern populations in SSM supports one of the hypothesized homelands for their nomadic Turkic and related Mongolic ancestors.The Turkic peoples represent a diverse collection of ethnic groups defined by the Turkic languages. These groups have dispersed across a vast area, including Siberia, Northwest China, Central Asia, East Europe, the Caucasus, Anatolia, the Middle East, and Afghanistan. The origin and early dispersal history of the Turkic peoples is disputed, with candidates for their ancient homeland ranging from the Transcaspian steppe to Manchuria in Northeast Asia. Previous genetic studies have not identified a clear-cut unifying genetic signal for the Turkic peoples, which lends support for language replacement rather than demic diffusion as the model for the Turkic language’s expansion. We addressed the genetic origin of 373 individuals from 22 Turkic-speaking populations, representing their current geographic range, by analyzing genome-wide high-density genotype data. In agreement with the elite dominance model of language expansion most of the Turkic peoples studied genetically resemble their geographic neighbors. However, western Turkic peoples sampled across West Eurasia shared an excess of long chromosomal tracts that are identical by descent (IBD) with populations from present-day South Siberia and Mongolia (SSM), an area where historians center a series of early Turkic and non-Turkic steppe polities. While SSM matching IBD tracts (> 1cM) are also observed in non-Turkic populations, Turkic peoples demonstrate a higher percentage of such tracts ( Centuries of nomadic migrations have ultimately resulted in the distribution of Turkic languages over a large area ranging from Siberia, across Central Asia to Eastern Europe and the Middle East. Despite the profound cultural impact left by these nomadic peoples, little is known about their prehistoric origins. Moreover, because contemporary Turkic speakers tend to genetically resemble their geographic neighbors, it is not clear whether their nomadic ancestors left an identifiable genetic trace. In this study, we show that Turkic-speaking peoples sampled across the Middle East, Caucasus, East Europe, and Central Asia share varying proportions of Asian ancestry that originate in a single area, southern Siberia and Mongolia. Mongolic- and Turkic-speaking populations from this area bear an unusually high number of long chromosomal tracts that are identical by descent with Turkic peoples from across west Eurasia. Admixture induced linkage disequilibrium decay across chromosomes in these populations indicates that admixture occurred during the 9th–17th centuries, in agreement with the historically recorded Turkic nomadic migrations and later Mongol expansion. Thus, our findings reveal genetic traces of recent large-scale nomadic migrations and map their source to a previously hypothesized area of Mongolia and southern Siberia. Linguistic relatedness is frequently used to inform genetic studies  and hereThe extant variety of Turkic languages spoken over this vast geographic span reflects only the recent (2100–2300 years) history of divergence, which includes a major split into Oghur (or Bolgar) and Common Turkic , 6. ThisThe earliest Turkic ruled polities (between the 6th and 9th centuries) were centered in what is now Mongolia, northern China, and southern Siberia. Accordingly, this region has been put forward as the point of origin for the dispersal of Turkic-speaking pastoral nomads , 4. We dPrevious studies based on Y chromosome, mitochondrial DNA (mtDNA), and autosomal markers show that while the Turkic peoples from West Asia (Anatolian Turks and Azeris) and Eastern Europe  are generally genetically similar to their geographic neighbors, they do display a minor share of both mtDNA and Y haplogroups otherwise characteristic of East Asia –15. ExpeSeveral studies using genome-wide SNP panel data describe the genetic structure of populations in Eurasia and although some include different Turkic populations , 20–24, Here we ask whether it is possible to identify explicit genetic signal(s) shared by all Turkic peoples that have likely descended from putative prehistoric nomadic Turks. Specifically, we test whether different Turkic peoples share genetic heritage that can be traced back to the hypothesized IAH. More specifically, we ask whether this shared ancestry occurred within an historical time frame, testified by an excess of long chromosomal tracts identical by descent between Turkic-speaking peoples across West Eurasia and those inhabiting the IAH. To address these questions we used a genome-wide high-density genotyping array to generate data on Turkic-speaking peoples representing all major branches of the language family .STRUCTURE-like [K = 3 through K = 14 groups) . Overall, alleles from the Turkic populations sampled across West Eurasia showed membership in the same set of West Eurasian genetic clusters, k1–k4, as did their geographic neighbors. In addition, the Volga-Uralic Turkic peoples  also displayed membership in the k5 cluster, which contained the Siberian Uralic-speaking populations (Nganasans and Nenets) and extended to some of the European Uralic speakers . However, in most cases the Turkic peoples showed a higher combined presence of the “eastern components” k6 and k8 than did their geographic neighbors.To characterize the population structure of Turkic-speaking populations in the context of their geographic neighbors across Eurasia, we genotyped 322 new samples from 38 Eurasian populations and combined it with previously published data (see URE-like  approachURE-like  to explo groups) . As showulations . Geograp3-statistics [3-statistics) [3 with the most negative f3-statistics  , 31. We atistics  and consp < 2.2×10–16) between the rate of IBD sharing decay and geographic distance in our set of Eurasian populations. This distance-dependent pattern is likely shaped by both isolation-by-distance and gene flow: many of the populations are admixed (the negative f3-statistics in p < 2.2×10−16). To identify populations for which IBD sharing with Turkic populations departs from a distance-dependent decay pattern, we first computed IBD sharing (the average length of genome IBD measured in centiMorgans) for each of the 12 western Turkic populations with all other populations in the dataset . Some of the non-Turkic neighbors, such as Adyghe, Maris, Udmurts, and North Ossetians, also shared a relatively high number of IBD tracts . For longer IBD tracts of 3–4 cM, statistical evidence for higher IBD sharing becomes weaker in some Middle Eastern and Caucasus  samples. By weaker evidence, we mean that a statistically significant excess of IBD sharing was restricted to a subset of the four candidate ancestors tested. In the Volga-Ural region, for the same class of segments (3–4 cM), only Bashkirs continued to show strong evidence for gene flow, while Tatars and Chuvashes do not. For these two Turkic populations, not all tests were statistically significant because the background group, from which permuted samples are drawn, contained the Finnic speaking Mari population, which shows comparable levels of Asian admixture  and IBD According to historical records, the Turkic migrations took place largely during ~5th–16th centuries (little is known about earlier periods) and partly overlap with the Mongol expansion. Assuming 30 years per generation, the common Siberian ancestors of various Turkic peoples lived prior to and during this migration period between 20 and 53 generations ago. The expected length of a single-path IBD tract inherited from a common ancestor that lived ~20–53 generations ago ranges between 2.5 cM and 0.94 centiMorgans see  fell witThe admixture-induced linkage disequilibrium signal in an admixed population is “restored” using two surrogates (reference populations) and when several such pairs are tested, ALDER allows the comparison of their genetic proximity to true mixing populations based on the amplitude of the weighted LD curve . It shouAlthough we report a single admixture date for each population, we note that it is likely that the contemporary Turkic peoples were established through several migration waves –4, 37. Ielite dominance scenario, that is, intrusive Turkic nomads imposed their language on indigenous peoples due to advantages in military and/or social organization.Our ADMIXTURE analysis  revealedp < 0.05). This might be explained by the fact that we collected Maris samples in the Republic of Bashkortostan, where they seemingly intermarried with Bashkirs to some extent. Finally, some of the Siberian populations are in fact migrants in their current locations. For example, Yakuts, Evenkis, and Dolgans largely stem from the Lake Baikal region, which is essentially the SSM area [When the Turkic peoples settled across West Eurasia are compared with their non-Turkic neighbors, they demonstrate higher IBD sharing with populations from SSM and Northeast Siberia . There aSSM area . It turnvice versa [Another important outcome of our IBD sharing analysis is the finding that two of the three SSM populations that we consider “source populations” or modern proxies for source populations are both Mongolic-speaking. This observation can be explained in several ways. For example, one may surmise that the Mongol conquests, starting in the 13th century, were accompanied by their demographic expansion over the territories already occupied, in part, by Turkic speakers, and this led to admixture between Turkic and Mongolic speakers. Alternatively, it is also probable that the ancestors of Turkic and Mongolic tribes stem from the same or nearly the same area and underwent numerous episodes of admixture before their respective expansions. The latter explanation is indirectly testified by a complex, long-lasting stratigraphy of Mongolian loan words in Turkic languages and ce versa . The firce versa . TherefoFinally, our IBD sharing analysis suggested that the SSM area is the source of recent gene flow. This area is one of the hypothesized homelands for Turkic peoples and linguistically related Mongols. While the presence of the Mongol empire over this territory is well recorded, historical sources alone are insufficient to unambiguously associate this area with the Turkic homeland for several reasons: some of the Turkic groups speaking the Oghuric branch of Turkic were attested westerly in the Pontic-Caspian steppes in the mid-late 5th century CE. This is geographically distant from the SSM area, and temporarily much earlier than the Göktürk Empire was established in the SSM area. Thus, our study provides the first genetic evidence supporting one of the previously hypothesized IAHs to be near Mongolia and South Siberia.The gene flow from the SSM area that we inferred based on our IBD sharing analysis should also be detected using an alternative approach such as ALDER, which is based on the analysis of linkage disequilibrium (LD) patterns due to admixture. Using the ALDER method, we tested all possible combinations of reference populations in our dataset. LD decay patterns observed among western Turkic populations were consistent with admixture between West Eurasian and East Asian/Siberian populations  than their Kipchak-speaking neighbors in the Volga region. According to historical sources, when the Onogur-Bolgar Empire (northern Black Sea steppes) fell apart in the 7th century, some of its remnants migrated northward along the right bank of the Volga river and established what later came to be known as Volga Bolgars, of which the first written knowledge appears in Muslim sources only around the end of the 9th century . Thus, tDifferences in admixture dates for the three Oghuz speaking populations  were notable and their geographical locations suggest a possible explanation. Anatolian Turks and Azeris, whose Central Asian ancestors crossed the Iranian plateau and became largely inaccessible to subsequent gene flow with other Turkic speakers, both have evidence of earlier admixture events  than Turkmens. Turkmens, remaining in Central Asia, showed considerably more recent admixture dating to the 14th century, consistent with other Central Asian Turkic populations and most likely due to admixture with more recent, perhaps recurrent, waves of migrants in the region from SSM.elite dominance model for Turkic language dispersal. We also showed that almost all the western Turkic peoples retained in their genome shared ancestry that we trace back to the SSM region. In this way, we provide genetic evidence for the Inner Asian Homeland (IAH) of the pioneer carriers of Turkic language, hypothesized earlier by others on the basis of historical data. Furthermore, because Turkic peoples have preserved SSM ancestry tracts in their genomes, we were able to perform admixture dating and the estimated dates are in good agreement with the historical period of Turkic migrations and overlapping Mongols expansion. Finally, much remains to be learned about the demographic consequences of this complex historical event and further studies will allow the disentangling of multiple signals of admixture in the human genome and fine scale mapping of the geographic origins of individual chromosomal tracts.In summary, our collection of samples, which covered the full extent of the current distribution of Turkic peoples, shows that most Turkic peoples share considerable proportion of their genome with their geographic neighbors, supporting the All subjects signed personal informed consents and ethical committees of the institutions involved approved the study.2 > 0.4) in a window of 1,000 SNPs, sliding the window by 150 SNPs at a time. This resulted in a dataset of 174,187 SNPs. Another dataset with a dense marker set for IBD sharing and wavelet transform admixture dating analyses was prepared. For this, the 1M-Duo genotyped samples  according to manufacturers’ specifications. Our data was combined with published data from Li et al. , Rasmuss samples  were excK = 3 to K = 14) genetic clusters or “ancestral populations” , whereas runs using higher K values have more variable LLs. Relying on the low level of variation in LLs (LLs < 1) within a fraction (10%) of runs with the highest LLs, we assume that the global log-likelihood maximum was reached at K = 3 to K = 11 and K = 13 to K = 14 , which estimates the predictive accuracy of the model at a given K. In our setting clustering solutions at K = 5, 6, 7, and 8 showed better predictive power than other K values (K) to discuss further, we draw from the CV results and restrict ourselves to the Ks that likely converged at the global LL maximum for the particular model. In addition, we acknowledge that clustering solutions at different Ks may reflect the hierarchical nature of human population structure. In this study, we are interested in distinguishing regional groupings, like Northeast Asia and Europe, and possible admixture between such groups. In sum, we found that the clustering solution that met our selection criteria best was K = 8.We inferred population structure in our dataset using a model-based clustering method implemented in the ADMIXTURE software. Because the ADMIXTURE algorithm expects SNPs to be unlinked, we used an LD pruned marker set of 174,187 SNPs. We ran ADMIXTURE assuming 3 to 14  and then excluded combinations that contained matching source and target populations. Altogether, 97548 combinations were prepared and subjected to the three-population test implemented in the qp3Pop software [3-statistic together with a corresponding negative Z-score when the target population tested is admixed with a source related to the given source populations. We considered the target population to be significantly admixed when its Z-score was less than -1.64  (−7 and then a quantile function for the corrected α was computed using the qnorm function in the R package. The Z-scores that were significant after Bonferroni correction (Z-score < -4.89) were denoted with two asterisks in We constructed all possible sets of target and source populations in our dataset fsoftware . This teed test) . AssuminWe used the fastIBD algorithm  implemenPatterns of IBD sharing between modern populations can bear information about historical events . AccordiChromosomal tracts that were IBD between two populations were first sorted into bins (classes) based on their length: 1–2, 2–3, 3–4, 4–5 cM, and then the total length was divided within each bin (class) by sample size to obtain the average IBD sharing for each population pair tested. From here onward, we refer to this statistic as IBD sharing. It was shown previously that IBD sharing between populations decays exponentially with distance between samples . To testEven if there is a statistically significant correlation between IBD sharing and geographic distance in the data, spatial patterns of recent ancestry between real populations are unlikely to follow a distance-dependent decay pattern ideally. One way to detect the departure from the expected distance-dependent decay pattern is to compute parameters that describe the relationship between IBD sharing and distance in a population set where you do not expect any deviation. These parameters can then be used to compute the expected range of IBD sharing for a given pair of populations at a given distance and report deviations, if any. In our dataset, it was difficult to define a population subset that was completely devoid of samples with departures from the expected distance-dependent decay pattern. Therefore, a comparative approach was used, in which the IBD sharing pattern in our dataset with Turkic populations and without them was compared. Because departures from the distance-dependent decay pattern may already exist in the dataset without Turkic peoples, this test determines whether Turkic peoples have more extreme departure due to, for example, a long range migration from Asia, as suggested by our ADMIXTURE analysis. Therefore, the test dataset only included western Turkic populations from the Middle East, Eastern Europe, the Caucasus, and Central Asia. The overall goal is to find systematic (correlated) differences between these western Turkic populations and their geographic neighbors. The null hypothesis was that differences between western Turkic populations and their geographic neighbors are random. To perform this analysis, sets of geographic neighbors were defined for each of the 12 western Turkic populations See  and IBD p-value.A permutation test was designed to verify whether the excess of IBD sharing that western Turkic populations demonstrate with SSM populations is statistically significant. IBD sharing (as described previously) between a western Turkic-speaking population and each of the three SSM populations  that show high accumulated IBD sharing was calculated. A permutation procedure was then used to test whether observed excess of IBD sharing that a given Turkic population demonstrates can be expected by chance among its non-Turkic neighbors. For each Turkic population, their geographic neighbors were pooled and 10,000 random samples of the same size were generated (as for the Turkic population tested). For each random sample, IBD sharing with the three SSM populations and Evenkis was calculated. Obtained IBD sharing statistics from permuted samples were compared to that of Turkic populations from the same region and the number of tests showing equal or higher values was divided by the total number of permutations to obtain a We used ALDER  to test 0 receives migrants from another population and grows for 300 generations until it reaches an effective population size of 10,000 individuals. We modeled different amounts of migrants, replacing 5%, 10%, 15%, 20%, …, 75% of the recipient population. Each admixture scenario was repeated 100 times and a random sample of 20 individuals was drawn to compute WT coefficients. The WT coefficients from these 100 independent runs were used to construct a 95% confidence interval for a given admixture scenario. Simulated WT coefficients with 95% confidence interval were plotted against the known number of generations since admixture , and the raw Illumina genotype data through the data repository of the National Center for Biotechnology Information—Gene Expression Omnibus (NCBI-GEO) under accession number GSE66157.The genome-wide SNP data generated for this study can be accessed through the data repository of the Estonian Biocentre  shown here likely each converged to a global likelihood maximum as > 10% of runs  with the highest log-likelihood (LL) converged to essentially to the same solution with a log-likelihood difference of > 1 LL units. We plotted the runs with the highest LL at each K.Each individual is represented by a vertical (100%) stacked column indicating the proportions of ancestry in (PDF)Click here for additional data file.S2 FigPanels A, B, C, and D show sequence of steps in the analysis.(PDF)Click here for additional data file.S3 FigCircle position corresponds to population location. Circle color indicates the amount of excess IBD sharing (shown in the Legend) that this population shares with all 12 randomly selected geographic neighbors. Populations with IBD sharing exceeding the 0.90 quantile are shown with the “plus symbol”. Panels A–J show IBD sharing signals for different randomly selected combinations of geographic neighbors. All the results are based on IBD tracts of 1–2 cM.(PDF)Click here for additional data file.S4 FigPanels A, B, C, and D show results for different segment size bins . For each population ordered along the x–axis, IBD sharing is computed with four populations , and Evenkis) from the SSM area. Each Turkic-speaking population (shown in red) is grouped with its respective geographic neighbors using parentheses. The grouped geographic neighbors were pooled and used to perform a permutation test as described in the M&M section. The red number under the Turkic population name shows how many SSM populations demonstrate a statistically significant excess of IBD sharing with a given Turkic population. Overlapping parentheses show Turkic-speaking populations with shared geographic neighbors.(PDF)Click here for additional data file.S5 FigThe blue curve shows the relationship between the WT coefficient and time since admixture in a growing population. Each curve summarizes the outcome from 100 forward simulations. Thus, the bold curve shows the average WT coefficient over 100 simulations and the blue shaded area shows the 95% confidence interval. Horizontal lines in red, green, and blue show point estimates of the WT coefficient for different Turkic-speaking populations. The intersection point between the horizontal line and the blue curve gives the admixture time estimate, shown with dashed vertical lines.(PDF)Click here for additional data file.S6 FigFor clarity, the y-axis is shown in two sections.(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 FigPanel A) ALDER method with and without LD pruning. Panel B) SPCO method with and without LD pruning.(PDF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 TableFor each target population, we computed f3-statistics using all possible pairs of source populations, and report the pair that produces the most negative f3-statistic. One asterisk indicates that the f3-statistic is significant at p = 0.05 (one-tailed test), i.e. Z-score = 1.64. Two asterisks indicate that the negative f3-statistic is significant after Bonferroni correction for multiple hypothesis testing; that is, the Z-score is more than 4.89 standard errors below zero.(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 TableAdmixture dates in years were estimated as the number of generations multiplied by 30 years. Absolute dates or Calendar dates were estimated as 2000 − (30 × generations).(XLS)Click here for additional data file.S5 Table(XLS)Click here for additional data file.