Willkommia of Centaurea  to test for reticulate evolution and recurrent hybridizations between nearby populations. For this, we combine analyses of genetic diversity and structuring, gene flow and spatial correlation, and ecological niche modeling. Our results support the contention that the current genetic structure of the three species is the result of historical gene flow at sites of secondary contact during the glacial periods, followed by isolation after the retraction of populations to the middle-upper areas of the mountains during the interglacial periods. The extent and direction of the gene flow was determined largely by the location of the populations on mountainsides oriented toward the same valley or toward different valleys, suggesting the intermountain valleys as the areas where secondary contacts occurred.Late Neogene and Quaternary climatic oscillations have greatly shaped the genetic structure of the Mediterranean Basin flora, with mountain plant species tracking warm interglacials/cold glacials by means of altitudinal shifts instead of broad latitudinal ones. Such dynamics may have enhanced population divergence but also secondary contacts. In this paper, we use a case example of subsection  Climatic changes since the end of the Neogene have greatly modeled the flora of the Mediterranean Basin in terms of species distributions and divergence . PleistosensusensuCentaurea  Garcia-Jacas, Hilpold, Susanna, and Vilatersana of the genus Centaurea L. , triggered by the Pleistocene climatic oscillations in the complex local topography  Blanca, and C. sagredoi Blanca. Phylogenetic analyses have shown that C. pulvinata and C. sagredoi are sister species, while C. gadorensis is genetically differentiated from them . Only two populations are known, one on the northern mountainside and the other on the southern mountainside. Centaurea gadorensis can be found in the southernmost mountain range, the Sierra de Gádor, where up to seven populations have been reported on basic soils (limestone). However, two populations of this species are located on the southern side of Sierra Nevada, a large massif situated geographically between Sierra de los Filabres and Sierra de Gádor on schists  we study the population genetic differentiation at intra- and interspecific level using different methodological approaches and thus gather evidence of the reticulate evolution pattern they could have followed; (2) we examine suitable distributions for the species at the present and the Last Glacial Maximum ; 3) we determine how geographical, climatic, and topographic variation over space and time explain the pattern of population genetic structure.The geographically restricted three-species system allows us to investigate the reticulate evolution proposed in the subsection trnT–trnL of the chloroplast DNA (cpDNA). Second, for the distribution study, we use ecological niche modeling (ENM) to build the specific paleodistribution models.For these tasks, we use two complementary approaches. First, for the genetic study, we analyze the genetic variation using a combination of two different molecular markers with different evolutionary rates and modes of inheritance: nuclear loci for microsatellites, and nucleotide sequences of the intergenic spacer C. sagredoi and C. pulvinata , and five populations of C. gadorensis (three from Sierra de Gádor and two from Sierra Nevada). The populations were taxonomically assigned to each of the three species according to the review of the genus in the Iberian Peninsula (C. gadorensis (PEC and REV) were sampled in early summer of 2012. To increase the sampling of C. gadorensis in the Sierra de Gádor, we sampled the PEC and REV populations in the summer of 2014. For each population, leaf samples from 30 adult individuals  were collected. The individuals were randomly collected, trying to cover the maximum possible area of each population while maintaining a minimum distance of one meter between individuals.A total of 10 populations were sampled throughout the distribution range of the three species . These ieninsula . All the13D10, 21D9) and in CD37, 42CM27, 25CM6, CM17, CM15), and following the PCR conditions explained in those studies. We selected these loci from all the ones described in ® 3100-Avant Genetic Analyzer . Alleles were scored using GENEMARKER v1.85 .For microsatellite analyses, the genomic DNA of the 300 sampled individuals  was extracted from silica-dried leaves using the kit NucleoSpin Plant II (Macherey-Nagel), following the manufacturer’s instructions. All individuals were genotyped for seven microsatellite loci as described in trnT–trnL was amplified and sequenced for a subsample of 10 individuals per population , using the primers a and b . The resulting sequences were aligned using the Clustal algorithm in the alignment editor BIOEDIT v7.0.5.3 .The genotype matrix used in this article is available on Figshare at f) were estimated with the software INEST v2.2  and a null model  using the Deviance Information Criterion (DIC)  corrected for null alleles. GENODIVE was used to estimate the mean number of alleles per locus (A) and the number of private alleles. Allelic richness (Ar) was calculated using FSTAT. All these statistics were computed for each population and for each species (by pooling populations) across all loci. When multiple tests were involved, the sequential Bonferroni-type correction was applied to test for significance (H) diversity were calculated using ARLEQUIN v3.5.2.2 (Genotypic linkage disequilibrium and the departure from Hardy-Weinberg expectations were tested with FSTAT v2.9.4  and GENOEST v2.2 . INEST tEST v2.2 . To teston (DIC) . The modificance . For cpDv3.5.2.2 .FST values were calculated for each pair of species and populations using GENODIVE, for microsatellite data, and for each pair of species with ARLEQUIN for cpDNA. Second, the Bayesian algorithm implemented in STRUCTURE v2.3.3 (K) in our microsatellite data. The number of clusters tested ranged from one to 11, with 10 replicates per K, using the admixture model and correlated allele frequencies. The burn-in period and Markov Chain Monte Carlo (MCMC) iterations were set to 50,000 and 106, respectively. The optimal number of clusters was estimated with the online tool STRUCTURESELECTOR    and the   . Third,   . The funed error . Finallyed error  was useded error . The sigrror , while the rest has a mean frequency < 0.05 (see the section “Results”). To test the possible effect of the null alleles at the 21D9 locus on FST values and genetic structuring analyses, we repeated some of these analyses, considering null alleles , so this locus was not expected to cause significant problems in the analyses. It has been suggested that biases are negligible when null alleles are present at frequencies below 0.200  between all individual populations using a Bayesian assignment test with the software BAYESASS v1.3 . In addition, the extent and direction of historical gene flow among populations were estimated by calculating the historical mutation-scale migration rates (M) with MIGRATE-N v3.6.4 (6), with uniform prior distribution both for Θ and M . The effective number of migrants per generation (Nm) among populations was estimated by using the formula 4Nm = ΘM (m) values, because those determined from MIGRATE-N were mutation scaled (M), and mutation rates (μ) for microsatellites for the genus Centaurea were not available. Analyses were carried out at the CIPRES bioinformatic facility  gene flow between the populations, we estimated migration rates (ASS v1.3 . As progN v3.6.4 . We ran 4Nm = ΘM . We coulfacility .FST distances (FST/(1 − FST)] and geographical, climatic, and topographic factors by multiple matrix regression with randomization . These analyses were used to test the genetic similarity between populations considering the topography and the orientation of the mountainsides on which the populations occur  because the number of wild occurrences was not sufficient to establish reliable models (C. gadorensis were gathered from the Threatened Flora Information System (FAME) of the Andalusian regional government (r ≥∣0.9∣), we selected the variable that contributed most to the model  and had a clear response curve . The selected variables were isothermality (bio3), maximum temperature of the warmest month (bio5), minimum temperature of the coldest month (bio6), and precipitation seasonality (bio15). The distribution model under current conditions was then projected to the LGM using paleoclimatic layers simulated by the Community Climate System Model v4 . As these layers were available only at 2.5 arc-min resolution, they were interpolated to 30 arc-sec using the “Spline with Barriers” tool implemented in ARCGIS v10.2 .The maximum entropy algorithm implemented in MAXENT was employed to evaluate the potential distribution of e models . Presencvernment . From ea4 CCSM4;  and the C. gadorensis (11), we used a methodology based on a jackknife (or ‘leave-one-out’) procedure to test the model  surveyed showed up to four alleles for many individuals of the three species. Since the diploid nature of some of these individuals was corroborated by visualizing their chromosomes in root meristematic cells from germinating seeds (following the protocol in 13D10 locus) was attributed to duplications of this locus within the genomes of the three species studied. Consequently, the locus 13D10 was excluded from the statistical analyses. There was no evidence of linkage disequilibrium for microsatellite loci (only loci 42CM27 and CM15 showed linkage disequilibrium in the CAL population of C. pulvinata). With regard to the Hardy-Weinberg equilibrium (HWE), 14 of 60 population-by-locus tests deviated significantly, from which 10 corresponded to the 21D9 locus . Null allele frequency was low for all combinations of loci and populations (mean < 0.05); only the bi-allelic locus 21D9 had a mean null allele frequency higher than 0.1 (mean = 0.157). Therefore, only null alleles in 21D9 appeared to explain the deviation from HWE. The alleles found and their observed frequencies for the different microsatellite loci, and null allele frequencies, are shown in the trnT–trnL sequence alignment was 559 base pairs in length, and it included five variable positions and seven indels.One of the microsatellite loci . At the population level, genetic-diversity values (HE) ranged from 0.54 of CAL (C. pulvinata) to 0.69 of REV (C. gadorensis), and those of allelic richness from 3.80  to 5.75  (f > 0) and the null model (f = 0) resulted in inbreeding being significant in CAS, REV (C. gadorensis), RAG, CAL (C. pulvinata), and GAR (C. sagredoi); and also in C. pulvinata and C. sagredoi when the overall population estimation was made , whereas C. pulvinata showed five and C. gadorensis three.In general, all three species showed very similar values of genetic-diversity indices , with C.agredoi) . The commade see . CentaurFCT = 0.046, P = 0.001) than the genetic structuring between populations within species , and that the genetic differentiation between species was lower , whereas individuals of the RAG and CAL populations were clearly assigned to the C. sagredoi cluster . The results interpreted using the method of K = 9) as the most likely group structure  and it coincided with a second highest ΔK peak (C. gadorensis) presented a mixture of individuals belonging to differentiated genetic clusters . The RAG population (C. pulvinata) showed a clearly mixed ancestry involving the genetic cluster for GAR (C. sagredoi). PEC population (C. gadorensis) was the only population with Q < 0.5 in any inferred cluster and, therefore, could not be assigned to a specific cluster. All clusterings, from K = 2 to K = 11, are represented in the The Bayesian clustering method, as implemented in STRUCTURE, recognized two genetic clusters as the uppermost hierarchical level of genetic partitioning according to the highest ΔK peak . These ctructure , conside ΔK peak . The meaC. gadorensis from the other two species, and the second discriminant component separated the SUF population of C. sagredoi from C. pulvinata populations and GAR of C. sagredoi, which was intermingled with C. pulvinata populations  or geographically (according to the mountain range), but rather mainly by population (C. gadorensis) and SER (C. pulvinata), and the most frequent in GAR  and MON . Only one other haplotype (H2) was found in more than one population, whereas the remaining haplotypes were exclusive to single populations  and the very high interpopulation differentiation . The species are not clearly differentiated with the chloroplastidial marker, where the coefficient of fixation between species was much lower than that between populations and was at the limit of significance .The results of BAYESASS clearly indicated no current exchange of genes , but did not identify any contribution of the climatic or topographic variables . On the other hand, when we tested the effect that the mountainside orientation of populations exerted on genetic differentiation, the MMRR analysis identified a significant contribution , and between populations on nearby mountain ranges oriented toward the same valley . On the contrary, there was no significant genetic similarity among other distance classes, including intramountain range populations on mountainsides with different orientations  and statistical significance (P < 0.001) in the jackknife test. For the present time frame, several middle-elevation  mountain areas within the study area appear to be suitable for C. gadorensis. Such areas included peripheral mountains of both Sierra Nevada and Sierra de los Filabres, most of Sierra de Gádor (with the exception of its mountaintops), and other small mountain ranges. By contrast, the intermountain valleys, usually below 1,000 m, appeared to be unsuitable , suggesting that the lineage distribution is the result of historical phylogeography. All this evidence, together with the genetic admixture found within the morphologically well-characterized populations  and the almost absence in the geographically isolated population SUF , by the effect of their orientation. This finding supports that they are the result of migration at high altitudes from contact areas and not of remaining e valley , regardle valley , which ie valley , supportC. sagredoi and C. pulvinata  on the northern mountainside of Sierra de los Filabres (K = 3), and C. sagredoi (GAR) or with C. pulvinata would have been rather unlikely.The geographical distribution of the populations of ulvinata , togetheulvinata , 6, fit ulvinata , 2. A sia Nevada . In thisFilabres  is the lC. gadorensis (As in the case between the southern mountainside of Sierra de los Filabres and the northern mountainside of Sierra Nevada, there is a clear genetic connectivity between populations located at the southern mountainside of Sierra Nevada and the northern mountainside of Sierra de Gádor, all belonging to dorensis . Althougdorensis . On the dorensis .C. gadorensis, it is somewhat surprising to find genetic differences between LAR and MON populations despite the general pattern of genetic similarity between neighboring populations on the same mountainside (see K = 9, K = 7 (FST value within the species, and LAR harbors the H4 haplotype while MON shows mainly H1 (Within 9, K = 7 , and the9, K = 7 . Both poainly H1 . Such geainly H1 . Geneticainly H1 .K = 9, On the other hand, our results also show the genetic signature left by population-divergence processes in allopatry. Population isolation, as a consequence of fragmentation processes and/or bottlenecks/founder events, generally results in populations with especially low levels of genetic diversity in the cpDNA (as result of the smaller effective size of the cpDNA compared to the nuclear genome; e.g., HE for the 10 populations studied = 0.616; HE = 0.420; HE = 0.620; Centaurea subsect. Phalolepis using almost the same set of microsatellite loci (mean HE = 0.587 and 0.504 for the Greek and Anatolian taxa, respectively; Centaurea subsect. Phalolepis and now in the subsect. Willkommia. The reason for such polymorphism in Greece and Turkey was the occurrence of populations within mountainous, environmentally stable refugia, and the same reason may also account for our present results, given that all three species are distributed within the Sierra Nevada/Gata refuge (Centaurea species by increasing it during the secondary contacts (Genetic diversity detected with microsatellites within the species studied is substantial (mean a refuge . It is wa refuge . Endemisa refuge . In addicontacts .SB-MS and JL-P performed the laboratory work. VS-S, JL-P, SB-MS, and RV analyzed the data. NG-J and VS-S conceived and designed the studies. GB and RV participated in the design. VS-S drafted the manuscript and all the authors participated in the editing of the manuscript. All the authors read and approved the final manuscript.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.


Garrulax courtoisi, which has only two known wild populations in Wuyuan, Jiangxi and Simao, Yunnan, China. We carried out the first conservation genetic study, in order to provide useful implications that allow for successful ex situ conservation and management of the Blue-crowned Laughingthrush.Captive populations permit research and conservation of endangered species in which these efforts are hardly implemented in wild populations. Thus, analysing genetic diversity and structure of captive populations offers unique opportunities. One example is the critically endangered Blue-crowned Laughingthrush, Using the novel microsatellite markers developed by whole-genome sequencing, we genotyped two captive populations, from the Ocean Park Hong Kong, which are of unknown origin, and the Nanchang Zoo, which were introduced from the Wuyuan wild population since the year 2010–2011, respectively. The genetic diversity of captive Blue-crowned Laughingthrush populations was estimated based on genetic polymorphisms revealed by a new microsatellite data set and mitochondrial sequences. Then, we characterised the population structure using STRUCTURE, principal coordinates analysis, population assignment test using the microsatellite data, and haplotype analysis of mitochondrial data. Additionally, we quantified genetic relatedness based on the microsatellite data with ML-Relate.Our results showed equally low levels of genetic diversity of the two captive Blue-crowned Laughingthrush populations. The population structure analysis, population assignment test using the microsatellite data, and haplotype analysis of the mitochondrial data showed weak population structuring between these two populations. The average pairwise relatedness coefficient was not significant, and their genetic relatedness was quantified.This study offers a genetic tool and consequently reveals a low level of genetic diversity within populations of a critically endangered bird species. Furthermore, our results indicate that we cannot exclude the probability that the origin of the Hong Kong captive population was the wild population from Wuyuan. These results provide valuable knowledge that can help improve conservation management and planning for both captive and wild Blue-crowned Laughingthrush populations. Many endangered species require captive breeding to save them from extinction, as they are incapable of surviving in inhospitable natural environments because of direct or indirect human impacts in the form of habitat loss, overexploitation, pollution, or introduced predators, competitors, or diseases . For conBecause of their relatively high level of polymorphism, repeatability in genotyping and high PCR amplification success, microsatellites are advantageous genetic tool to address conservation and behavioural genetic patterns in threatened species towards their conservation and management issues . A key pGarrulax courtoisi, is listed as ‘Critically Endangered’ by the IUCN Red Data Book  and the Nanchang Zoo (NCZ). Specifically, we attempted to learn two aspects. First, whether the two captive populations have a low level of genetic diversity and a sign of inbreeding, as expected for an endangered species. Second, whether the introduced origin of the population in the OPHK was from Wuyuan wild population, using the population of the NCZ as a reference.The OPHK population was introduced in 1989, and the existing population size is 16 individuals. Both the source and individual relationships of Blue-crowned Laughingthrush are unknown because of foot ring loss and lack of records. The NCZ population in Jiangxi was introduced in 2010 and 2011 from the wild Blue-crowned Laughingthrush population in Wuyuan County, Jiangxi Province, and is the only captive population that has a confirmed source in the world. In this population, six individuals are from the wild population and the other seven individuals are their descendants.Since this study involved sampling of endangered species, all the animal operations were approved by the Institutional Ethical Committee of Animal Experimentation of Sun Yat-sen University and strictly complied with the ethical conditions by the Chinese Animal Welfare Act (20090606). And all sampling procedures were performed with assistance of veterinarians or zoo keepers.We collected Blue-crowned Laughingthrush samples from 23 individuals of two captive populations: 14 individuals from the long-established OPHK population and nine individuals from the recently established NCZ population . Fresh bWe sequenced the whole genome from one sample of Blue-crowned Laughingthrush (No. SYSb6040) from OPHK, and then used it as the draft genome. P5 and P7 adapters were ligated to the fragments after the genomic DNA was digested. The P5 adapter contains a forwards amplification primer site, an Illumina sequencing primer site, and a barcode. The selected fragments were end-repaired and 3′ adenylated, and these fragments were PCR amplified with P5- and P7-specific primers. Our library was validated using the Agilent Technologies 2100 Bio-analyzer and ABI StepOnePlus Real-Time PCR System. After adapter ligation and DNA cluster preparation, the samples were sequenced using a Hiseq X-10 sequencer .Raw data from this single individual were processed by removing adapter sequences and subsequently removing the reads. Sequences with a low-quality rate ) greater than or equal to 50% and with more than 10% unknown (‘N’) bases were removed. The final read length was trimmed to 82 nucleotides (minimum length). Then, the high-quality sequences were selected to assemble the reference scaffolds. The genome was assembled using a short-read assembly method in SOAPdenovo2 . A de BrWe identified the microsatellites by screening the sequence data for di-, tri-, tetra-, and penta-nucleotide motifs with a minimum of six, five, five, and five repeats, respectively, by using the polymorphism information from the Blue-crowned Laughingthrush draft genome. Then we designed the primers in MSATCOMMANDER v.1.0.8  and PrimThe 30 selected loci were arranged into eight PCR multiplex sets (two to four loci per set); each forwards primer was labelled with fluorescent dye on the 5′ end of the forwards primers, and the sequence GTTTCTT was placed on the 5′ end of the reverse primer . PCR amp2 (2.5 mM), 0.5 μL of each primer (10 mM), and 0.5 μL (one unit/μL) KOD DNA polymerase . The PCR cycling conditions were as follows: an initial denaturation step of 4 min at 94 °C followed by 35 cycles of 40 s denaturation at 94 °C, 40 s annealing at 56 °C, and 90 s extension at 72 °C, followed by a final 10 min extension at 72 °C. The purified products were sequenced with both forwards and reverse primers using a BigDye Terminator v.3.1 Cycle Sequencing Kit  according to the manufacturer’s guidelines. The products were sequenced on an ABI Prism 3730 Automated DNA sequencer .To infer maternal relatedness of sampled individuals, partial mitochondrial cytochrome b (cytb) sequences were amplified and sequenced using the primers L14995 and H16065 . PCR ampAN), average allelic richness (RA), observed heterozygosity (OH), and expected heterozygosity (EH) using the remaining 12 loci in GenAlEx v.6.5.1 (ISF) for each population and assessed the significance of this index based on 10,000 permutations in Arlequin v.3.5.For each microsatellite locus, we calculated the frequency of null alleles using Cervus v.3.0.7 ; then, w v.6.5.1 . We alsoeN) of 1,000 for the base population, we performed 1,000 runs and generated eight predefined levels of population differentiation , with sample sizes, numbers of markers, and allele frequencies corresponding to the empirical data. The proportion of significant outcomes (P < 0.05) then corresponded to an estimate of power. The OH at each locus was tested for equal allele frequencies by both Pearson’s traditional contingency chi-square and Fisher’s exact tests. The information from all loci was then combined by summing the data from chi-square and Fisher’s methods  for the entire dataset. We then used Structure Harvester v.0.6.8 (K), the posterior probability, Ln P(D) increased per K, and ΔK means the second order rate of change of the Ln P(D) with respect to the number of clusters were estimated and compared. The final results for individual memberships were visualised by bar plot in DISTRUCT v.1.1  using GenAlEx v.6.5.1 : R = 0.25; full-siblings: R = 0.5; parent–offspring: R = 0.5) was used to determine the proportion of a specific relatedness category. To assess the likelihood of a given relatedness category relative to the other three categories, a likelihood ratio test using a 95% confidence level and 1,000 simulations was carried out in ML-Relate  was calculated in ML-Relate , and theL-Relate .The whole genome of the endangered Blue-crowned Laughingthrush was first assembled using the high-coverage (approximately 40×) sequence reads. After processing of the approximately 41.18 Gb of raw data and removal of ambiguous barcodes, about 39.1 Gb of clean data were retained. The assembly generated 1,089,819 scaffolds larger than 100 bp, with an N50 contig size of 1,297 bp and an N50 scaffold size of 4,548 bp. The microsatellite detection generated 70,310 markers with 31,216 di-nucleotide repeats, 21,195 tri-nucleotide repeats, 10,892 tetra-nucleotide repeats, and 7,007 penta-nucleotide repeats.AN for each population was approximately 3.25, and AN of the NCZ population was slightly higher than that of the OPHK population. We found low to moderate genetic diversity with a mean OH of 0.36–0.45 and a mean EH of 0.34–0.44. Moreover, we found no sign of inbreeding at the loci, with ISF ranging from −0.283 (p = 0.923) to −0.214 (p = 0.784)  .STF ≥ 0.075  to detect genetic substructure if the true  ≥ 0.075 .STF value . This is because 10 out of 12 loci had non-significant and low STF values , with only two exceptions   were assigned to the OPHK population. For the NCZ population, 77.8% of the individuals (n = 7) were assigned to the NCZ population, whereas 22.2% of the individuals  were assigned to the OPHK population  . For thestimator  suggester (K = 1 . Since ten K = 2 . It cleaen K = 2 . Assignmpulation .MH423582–MH423604). The average level of genetic diversity was similar between OPHK and NCZ populations  are negative in both OPHK population and NCZ population , and this might provide genetic rescue that prevented loss of genetic diversity. The results of the NCZ population may be related to the short amount of time (<10 years) that this population has been captive. And our result of relatedness estimator may also support these results, most value of the pairwise relatedness  were measured based on a very small sample size for each captive population  and genotyped at 12 loci. Because both HWE tests and F-statistics lie the assumption of an infinite population size  are provided for each pair of individuals at upper diagonal. Genetic kinship between each pair of individuals that has the highest likelihood at lower diagonal , and the pairwise relatedness based on the Queller and Goodnight estimator .Click here for additional data file.10.7717/peerj.6643/supp-3Supplemental Information 3ST values of 0.001, 0.0025, 0.005, 0.01, 0.02, 0.05, 0.075) were generated using POWSIM. Statistical power was defined as the proportion of times the null hypothesis of equal allele frequencies across populations was rejected using a chi-square test or a Fisher’s exact test.Datasets with eight predefined levels of population differentiation (FClick here for additional data file.10.7717/peerj.6643/supp-4Supplemental Information 4Click here for additional data file.


Gadus morhua L.): the Northeast Arctic cod (NEAC) population and the coastal cod (CC) population. The objectives of the current study were to identify genomic islands of divergence and to propose an approach to quantify the strength of selection pressures using whole-genome single nucleotide polymorphism (SNP) data. After applying filtering criteria, information on 93 animals  and 3,123,434 autosomal SNPs were used.Two distinct populations have been extensively studied in Atlantic cod (Four genomic islands of divergence were identified on chromosomes 1, 2, 7 and 12, which were mapped accurately based on SNP data and which extended in size from 11 to 18 Mb. These regions differed considerably between the two populations although the differences in the rest of the genome were small due to considerable gene flow between the populations. The estimates of selection pressures showed that natural selection was substantially more important than genetic drift in shaping these genomic islands. Our data confirmed results from earlier publications that suggested that genomic islands are due to chromosomal rearrangements that are under strong selection and reduce recombination between rearranged and non-rearranged segments.Our findings further support the hypothesis that selection and reduced recombination in genomic islands may promote speciation between these two populations although their habitats overlap considerably and migrations occur between them. Gadus morhua L.) exploits different ranges of salinity and temperature across an extensive geographical distribution. However, due to a large effective population size and to gene flow between habitats [Marine fish species are often distributed across a variety of habitats, which makes these organisms interesting models to study the interaction between gene flow and natural selection. Atlantic cod  population and the stationary population known as coastal cod (CC) , 3. GeneHb-I) alleles [pantophysin (Pan I) (now named synaptophysin like 1 or SYPL1) [Pan I locus, the frequency of the Pan IB allele is above 90% in the NEAC population, whereas the Pan IA allele predominates in the CC population [b locus [In addition to dissimilarities in migratory and feeding features, there are also clear differences in growth rate and age at maturity between these two populations . Moreove alleles , 10 micr alleles , and allr SYPL1) . At the pulation , microsapulation  and at t[b locus . It has [b locus . This ha[b locus . Involve[b locus . In cod,[b locus , 15–26 uRecent developments have enabled next-generation sequencing technology to compare individual complete genomes with high precision. The objectives of our study were to identify genomic islands of divergence and to propose an approach to measure the strength of the natural selection within each genomic island in the NEAC and CC populations and their crosses using whole-genome single nucleotide polymorphism (SNP) data.Pan I locus [Genomic information from 111 animals belonging to the National cod breeding program of Nofima in Tromsø (Norway) was used. More specifically, year-classes (YC) from 2003, 2004 and 2005 were formed as progeny of wild Atlantic cod. Since the generation interval of Atlantic cod is 3 years, the data from YC 2006, 2007 and 2008 represent the first generation (F1) of the progeny of the selected fish from YC 2003, 2004 and 2005, respectively. Accordingly, the data from YC 2009 represent the second generation (F2) of the progeny of the selected fish from YC 2006. Each sire was mated to two dams, while each dam was mated to one sire. Furthermore, some sires and dams were also mated across YC to create genetic links between YC. The base population of the breeding program consisted of fish that were sampled from different geographical areas along the coast of Norway. The dataset consisted of fish with pure (CC and NEAC) and crossed origins . The mean coancestry for the CC × NEAC individuals was smaller (0.702 ± 0.003), and the corresponding value of Mean coancestry across the genome did not differ significantly between CC (0.712 ± 0.004) and NEAC individuals (0.718 ± 0.006)  value across the genome between the CC and NEAC populations was 0.062 ± 0.020. Using the SNPs that were genotyped on each chromosome, more precise genetic differentiation coefficients were obtained across the chromosomes  homozygous for the rearranged segment , 2) heterozygous for the rearranged segment , and (3) homozygous for the non-rearranged segment   were used to confirm the population identification, which would bias the allocation of non-rearranged regions to the CC and NEAC population. Thus, the selection pressures for the entire rearranged regions were higher than that for the individual SNPs within these regions.The resulting In this study, genomic islands of divergence were identified and estimates of selection pressures in these genomic islands were obtained in two populations of Atlantic cod. The results indicate that natural selection is more important than genetic drift on these detected genomic islands of divergence.FST showed that the degree of divergence is higher for the chromosomes that carry these genomic islands  estimated here agrees well with FST values from previous studies, which range from 0.024 to 0.065 [The genomic islands of divergence identified on chromosomes 1, 2, 7 and 12 showed differences in ome Fig. . Resultsnds Fig. . Howeverto 0.065 , 24. Theto 0.065 , 22.The method used to estimate the migration rate between the two populations is well known and was previously described in the literature (e.g. ). A majoThe difference in frequency at individual SNPs is caused by the difference in the frequency of the rearrangement but the former is usually smaller than the latter. This is because the rearrangement will often carry the allele that is most common on the non-rearranged chromosome. Consequently, the frequencies of individual SNPs lead to underestimate the strength of selection. If some recombination occurs, the selection pressure on the causal loci will be stronger than that on the neutral loci, and the average selection pressure across the loci will further underestimate the selection pressure at the most important alleles that drive the divergence in these regions.Assuming that a particular rearrangement only occurs once and does not recombine with the non-rearranged region, all the chromosomes that carry this rearrangement will descend from one original chromosome. Consequently, they will all carry the same alleles at each SNP within the rearrangement, and the original rearranged haplotype is thus monomorphic. Accordingly, the alternative region to the rearranged region will only occur in the wild-type haplotypes, and will be private to the wild-type haplotypes. This means that an increased private allele frequency points to wild-type haplotypes, which corresponds to the CC population, and the NEAC population corresponds to the rearranged haplotypes. It may be noted that the genome of NEAC individuals also has regions of increased private allele frequencies, which are due to mutations that occurred after the rearrangement. These later mutations may have drifted to considerable allele frequencies due to the originally low-frequency of the rearrangement, i.e. due to a hitchhiking effect.The principal component analysis within the genomic islands revealed three genotypic groups Fig. . For theCurrently, uggested , 26 thatWhole-genome SNP data for Atlantic cod were used to investigate genomic islands of divergence in the CC and NEAC populations. Our results show that gene flow between the populations was sufficient to limit divergence between the two populations except at four genomic islands. The high resolution of the SNP data used in this study enabled us to precisely locate four genomic islands of divergence on chromosomes 1, 2, 7 and 12 in the NEAC and CC cod populations and to estimate the natural selection pressures that lead to their divergence. The estimates of the selection pressures showed that natural selection was substantially more important than genetic drift in shaping the diverged regions on chromosomes 1, 2, 7, and 12.Additional file 1: Figure S1. Patterns of coancestry  between populations CC and NEAC in all chromosomes . The showed values are genetic differentiation coefficients smoothed by using a sliding window approach. Figure S3. Private allele frequency in populations CC (left column) and NEAC (right column) in all chromosomes . A sliding window approach was used to smooth the private allele frequency. Figure S4. Principal components analysis in populations CC and NEAC in all chromosomes . First two principal components on the SNP data available.


Corynespora cassiicola is a destructive plant-pathogenic fungus causing widespread target spot epidemics, including outbreaks on cotton, soybean, and tomato in the southeastern United States. Previous studies revealed that populations from the three hosts are genetically distinct and host specialized. Although variation in aggressiveness to cotton and tomato were observed, no genetic diversity was detected within populations sampled from each of these hosts. We aimed to gain a better understanding of the emerging target spot epidemics by developing microsatellite markers for C. cassiicola to assess genetic variation, population structure, and to infer modes of reproduction and mechanisms of dispersal. Two hundred sixty-five isolates from cotton, soybean, tomato, and other host plants were genotyped with 13 microsatellite markers. Genotypic diversity revealed genetic variation within each of the populations collected from different hosts, with the population from cotton dominated by clonal genotypes and showing the least genetic diversity. In addition, C. cassiicola populations on different host species were genetically distinct and structured based on host species. No association between genetic and geographic distances was identified in the tomato populations, and the association in cotton populations was low. However, significant regional geographic structure was detected in the soybean populations of C. cassiicola. These results further support previous findings of introduced host specialized isolates or the evolution of more aggressive strains on each host. The lack of geographic structure suggests that the clones on cotton and tomato spread rapidly, or similar founder populations were established by human-mediated dispersal, and that dispersal is not limited. However, regional geographic structure of populations on soybean suggests limited dispersal among more established populations of C. cassiicola, or genetic differences in founder populations that colonized different geographic areas. Phytophthora infestans, which causes late blight of tomato and potato [Phytophthora ramorum, which causes the widespread outbreaks of sudden oak death in the Pacific Northwest forests, where studies on the genetic diversity and reproductive mode revealed that clonal lineage NA1 dominates in the region and no evidence of sexual reproduction has been detected [Cryphonectria parasitica, which causes chestnut blight, where studies have provided evidence for clonal expansion in Europe [Asexual reproduction is common among many fungal and oomycete plant pathogens, resulting in clonal populations with parents and offspring that are genetically identical . Althougd potato , where ed potato ; Phytophdetected ; and Cryn Europe .Corynespora casssiicola (Berk & Curtis) Wei is causing target spot epidemics on cotton (Gossypium hirsutum) [Glycine max) [Solanum lycopersicum) [C. cassiicola collected from a variety of crop, ornamental, and weed species worldwide has been described [act1, caa5, and ga4). The PL were associated with the host of origin and pathogenicity but were widely distributed geographically. Phylogenetic analyses of C. cassiicola particularly from cotton, tomato, and soybean in the southeastern U.S. showed three genetically distinct populations that clustered based on the host species of origin, with isolates from cotton and soybean belonging to PL1 and isolates from tomato belonging to PL4 [irsutum) –15, soybine max) –18, and ersicum)  in the sersicum)  where thescribed , 22. Sixg to PL4 . FurtherC. cassiicola, conidia have been observed in the field serving as survival structures on plant debris and as the source of inoculum for primary as well as secondary infections during the growing season [C. cassiicola are not yet known.Determining the spatial genetic structure of emerging pathogen populations assists in understanding how pathogens spread and helps in identifying sources of inoculum –24. Infeg season . HoweverC. cassiicola, which belongs to the phylum Ascomycota, class Dothideomycetes, order Pleosporales [C. cassiicola resulting from asexual reproduction [The life cycle of sporales  is consioduction , 28–30. oduction  showed eoduction , 31.Population genetic analyses can improve our ability to understand emerging epidemics , 32. PatC. cassiicola and genotyping a much larger sample of isolates from a wider geographic scale, including isolates from cotton and soybean target spot epidemics in Brazil. We were interested in determining if epidemics in the U.S. could be caused by similar populations to those causing epidemics in Brazil. Specifically, we wanted to assess the genetic variation within host specialized populations of C. cassiicola to determine if they are clonal reproducing, and if they are geographically structured or if dispersal is widespread.Our goal was to gain a better understanding of the emergence of the target spot epidemics on cotton, soybean, and tomato. This included developing polymorphic microsatellite markers for C. cassiicola were collected from infected leaves of cotton, soybean, tomato, cucumber, hydrangea, Mandevilla, pepper, and sesame and fruits of tomato from the southeastern U.S. . After 3 days of incubation in the dark at 25°C, an agar plug containing mycelia from the edge of one of the colonies was transferred to a new dish of qPDA. All isolates were processed for DNA extraction using a rapid, high yield mini-prep method for fungi [4OAc was added, vortexed on high for approximately 10 seconds, placed on ice for 15 min, and then centrifuged at 12,500 rpm for 3 min. The supernatant was transferred to another sterile tube, 700 μL isopropanol was added, it was thoroughly mixed, and then centrifuged at 12,500 rpm for 5 min. Once completed, the supernatant was carefully poured off. The remaining pelleted DNA at the bottom of the tube was rinsed twice with 1 mL of 70% EtOH, dried, and re-suspended with 50 μL of sterile H2O. DNA was stored at -20°C. Genomic DNA was obtained from cotton and soybean isolates in Brazil.Two hundred sixty-five isolates of ern U.S. . The reqor fungi . Prior tC. cassiicola were developed through a next generation sequencing approach. High quality genomic DNA was extracted from two C. cassiicola isolates from cotton, CM13 and CVa5  and then centrifuged at 14, 000 g for 10 min. The supernatant was then added to 100 μl RNase A (10 mg/ml) and incubated at 37°C for 120 min. Isopropanol at equal volume and sodium acetate at 1/10 volume were then added and incubated at 25°C for 5 min. These were centrifuged at 14, 000 g for 30 min and the supernatant was discarded. The resulting pellet was rinsed twice with 70% ethanol and air-dried overnight. The DNA pellet was subsequently dissolved in 100 μl deionized H2O. Genomic DNA was submitted to the Georgia Genomics and Bioinformatics Core  for library preparation of each isolate and Illumina sequencing using a NextSeq platform based on a paired-end 150-bp (PE150) protocol.Microsatellite markers for and CVa5 , using tC. cassiicola from diverse hosts, yet polymorphic within cotton isolates, the forward and reverse reads of CM13 and CVa5 were trimmed and mapped to a C. cassiicola reference genome  [Hevea brasiliensis) isolate using default settings in Geneious v.6 (Biomatters). Using Phobos 3.3.11 [To identify microsatellite loci shared among isolates of  234811)  from a rs 3.3.11  we ident5'-CAGTCGGGCGTCATCA) for use with a labelled fluorescent primer in a three-primer reaction [5'-GTTTCTT) [C. cassiicola isolates, including CVa5 and CM13 from cotton, as well as Ssta1 from soybean, and TCl3 from tomato. PCR amplification was performed in a 10-μl reaction volume containing 1 μl of 10–200 ng/μl genomic DNA template, 1 μl of 10× PCR buffer , 1 μl of 2.5 mM dNTPs, 0.5 μl of 10 μM forward primer, 0.5 μl of 10 μM reverse primer, and 0.1 μl ExTaq . Thermal cycling conditions had an initial denaturation for 5 min at 95°C followed by; 28 cycles of 30 s at 95°C, 90 s at 57°C, and 30 s at 72°C; and a final elongation of 30 min at 60°C. Amplification of a product was confirmed by electrophoresis on a 1% (w/v) agarose gel with 1× TBE buffer.Primers flanking repeat regions that were polymorphic for the two cotton isolates were designed in Primer3 . Each foreaction  in downsGTTTCTT)  was addeC. cassiicola isolates representative of different hosts and geographic regions  and 9.9 μl of Hi-Di formamide (Applied Biosystems). Samples were incubated at 95°C for 5 min and placed immediately on ice. Fragment analysis was conducted at the GGBC on an Applied Biosystems 3730x1 96-capillary DNA Analyzer. Microsatellite Plugin in Geneious v.6 (Biomatters) was used to score alleles and loci were distinguished based on expected size range.PCR primers that produced single products of the expected size for the four isolates in the initial screen were further tested for polymorphism on a panel of 15  regions . The thrC. cassiicola. Primers that produced single peaks in the expected size range and amplified markers that were polymorphic among the panel of fifteen diverse isolates  and 9.9 μl of Hi-Di formamide (Applied Biosystems). Samples were incubated at 95°C for 5 min and placed immediately on ice. Fragment analysis was conducted as described above. Microsatellite Plugin in Geneious v.7 was used to score alleles and loci were distinguished based on fluorescent dye and expected size range. The capability of the microsatellite loci to resolve genotypes was determined by plotting the proportion of multilocus genotypes against the number of loci analyzed with 1,000 randomizations of the data in poppr package in R.Multiplex PCR was performed using the Type-it Microsatellite PCR Kit (Qiagen) following the manufacturer’s instructions, except reactions were scaled to 10 μl. Multiplex reactions consisted of 5 μl of 2× Type-it Master Mix, 1 μl of 10× primer mix , 1 μl of DNA template, and RNAse-free water. Thermal cycling conditions were the same as described earlier. PCR products were diluted 1:15 in double-distilled Hg) was estimated in MLGsim 2.0 [Ĝ), which is the probability that two individuals taken at random will have unique multilocus genotypes, was estimated for the overall population that included all 265 isolates and for populations from each host in MultiLocus v.1.3b [H) was estimated in poppr package in R [Measures of allelic and genotypic diversity, clonal composition, and population genetic structure, as well as relationships among haplotypes from different host populations were based on the multilocus genotype data generated by the developed microsatellite markers. The number of observed alleles and gene diversity for each locus were estimated using the poppr package performed in R . Gene diGsim 2.0 . Genotyps v.1.3b . The totage in R .K-means clustering of principal components, where K is the optimal number of clusters and is inferred as the number of clusters where the Bayesian information criterion (BIC) increases or decreases by a negligible amount. PCoA was generated based on pairwise genetic distances of genotypes of all isolates. The resulting principal coordinate values generated for PC1 and PC2 for each isolate in each population  were compared using one-way analysis of variation (ANOVA). To examine relationships among all isolates and isolates from tomato, cotton, and soybean populations, minimum spanning networks were constructed using the Bruvo’s genetic distance model [To visualize patterns of genetic variation and determine if populations are structured, discriminant analysis of principal components (DAPC) in R  and prince model  in the Pce model .Mantel Test [PT/(1−ΦPT)) to account for sample size errors and non-parametric distribution and the logarithm of geographic distance.To identify regional geographic genetic structure and provide insight on the modes of pathogen dispersal during the epidemics, a tel Test  was perftel Test  by compaC. cassiicola isolates from cotton, tomato, and soybean when tested for PCR amplification and electrophoresed on a gel.A total of 227 and 224 scaffolds were obtained for the CM13 and CVa5 draft genomes, respectively, when mapped to the 244 scaffolds of the reference genome, Corca1 . Of thesC. cassiicola isolates from different hosts and geographic origins in the southeastern U.S., 14 of the 19 markers that amplified were reproducible and polymorphic. Two multiplex reactions were developed for high throughput genotyping of isolates. Each reaction contained 7 primer pairs that varied in allele sizes or fluorescent dye label, and were optimized by slightly modifying the concentrations of primers of loci with inconsistent amplification or low peaks. One marker was removed because when mixed with other primer pairs it did not amplify consistently or produce scoreable alleles, even after attempts at optimization. Thus, 13 microsatellite markers  was relatively high, especially for a haploid system.To determine the sufficient number of microsatellite loci that can distinguish genotypes, we plotted the proportion of genotypes and the number of loci sampled . Five loC. cassiicola isolates genotyped with 13 microsatellite markers, we detected 71 unique multilocus genotypes with genotypic diversity of 0.79 for all isolates  and principal coordinates analysis (PCoA) of the genotype data. For DAPC, K = 10 optimal clusters were identified  in the southeastern U.S. for both PC1  and PC2 .The PCoA analysis  showed cC. cassiicola based on Bruvo’s genetic distance model  and soybean  populations (R2 = 0.0048 of p = 0.223) was detected for the tomato population  that is distantly related to the lineage that most of the other isolates belong (PL1) [To improve our understanding of the genetic diversity, population structure, modes of reproduction, and mechanisms of dispersal of m Brazil . The micmbalance . The isong (PL1) , 22, whiC. cassiicola from both cotton and tomato  of reproduction will affect how rapidly populations overcome host resistance or evolve to evade control by fungicides. We now know that target spot epidemics in the southeastern U.S. are caused by clonally reproducing, host-specialized populations that have some genotypic diversity.Determining the genetic diversity and population structure of S1 TableIsolate name, host, MLG, collection location, and allele size for 13 loci.(XLSX)Click here for additional data file.


Myotis dasycneme) is a rare species classified as “Near Threatened” with a wide but patchy Palearctic distribution. A total of 277 samples representing populations in Denmark, Germany, Latvia, Hungary, and Russia were used in the genetic analyses; 224 samples representing Denmark, Germany, and Russia were analyzed at 10 microsatellite loci; 241 samples representing all areas were analyzed using mitochondrial D‐loop and cytochrome B sequences. A Bayesian clustering approach revealed two poorly resolved clusters, one representing the Danish and German groups and the other the Russian group. However, significantly different pairwise FST and DEST estimates were observed between the Danish and German groups and between the Danish and Russian groups suggesting a recent population structure. These conflicting results might be attributed to the effect of migration or low resolution due to the number of microsatellite markers used. After concatenating the two mitochondrial sequences, analysis detected significant genetic differentiation between all populations, probably due to genetic drift combined with a founder event. The phylogenetic tree suggested a closer relationship between the Russian and Northern European populations compared to the Hungarian population, implying that the latter belongs to an older ancestral population. This was supported by the observed haplotype network and higher nucleotide diversity in this population. The genetic structuring observed in the Danish/German pond bat stresses the need for a cross‐border management between the two countries. Further, the pronounced mtDNA structuring, together with the indicated migration between nearby populations suggest philopatric female behavior but male migration, emphasizes the importance of protecting suitable habitat mosaics to maintain a continuum of patches with dense pond bat populations across the species' distribution range.Conservation genetics is important in the management of endangered species, helping to understand their connectivity and long‐term viability, thus identifying populations of importance for conservation. The pond bat ( In Germany, the pond bat has a fragmented distribution, occurring mainly in the northern and western states (www.bfn.de). Schleswig‐Holstein harbors the biggest known population and a large hibernaculum with more than 1,000 individuals .  based on variation in ten microsatellite markers, the control region (CR), and cytochrome B (CytB) of the mitochondrial DNA (mtDNA). The microsatellite markers were used to (a) assess the genetic diversity, (b) estimate population structure, and (c) evaluate immigration/emigration rate and direction (gene flow) within the populations in Denmark and Germany and between them and samples from Russia using assignment test and exploring migration network based on various population structure estimates. Further, as pond bat populations have declined during the last decades . DNA from buccal samples was extracted using the Isohelix DNA Isolation Kit following the manufacturer's protocol . From fecal samples, DNA was extracted using the Biolab Products Crystal Stool DNA Kit (Biolab Products) adjusting the manufacturer's protocol for a smaller amount of sample. The extraction was conducted in the ancient‐DNA laboratory at the Centre for Baltic and Scandinavian Archaeology (ZBSA), Schleswig‐Holstein State Museums Foundation, Germany.DNA from wing samples was extracted using Qiagen DNeasyMyotis myotis .Ten microsatellite markers developed for A 521 bp part of the mitochondrial cytochrome b and 247 bp of the D‐loop region of mitochondrial control region were amplified using the following primers: MyoF ATGACCAACATTCGAAAATCTC, MyoR ATGTTAAAGTTAGGAGATCTGC; and MyCR‐F TTAATTACTAATCAGCCCATGCC, MyCR‐R1 GTTGTTGTGTTGTATGTCCTG. Amplification was conducted in a reaction volume of 12.5 µl using Amplicon DNA polymerase master mix (AMPLICON) and 10 µM of each primer, in a standard PCR using one cycle for 5 min at 95°C, 35 cycles at annealing temperatures of 48 and 41°C, respectively, and extension time for 7 min at 72°C. DNA extractions were stored at −20°C. The amplified DNA strands obtained from German, Latvian, Hungarian, and Russian samples were Sanger‐sequenced one way at the Institute of Clinical Molecular Biology, Kiel University, Germany, while the amplified DNA strands from the Danish samples were sequenced forward and reverse at MACROGEN Inc. . The sequences were later analyzed using Sequencher 5.3 (Gene Code).2.32.3.1Genetic variation, estimated as observed and expected heterozygosity, and tests for goodness of fit to the Hardy–Weinberg equilibrium were performed in FSTAT Goudet,  and GenA2.3.2K, expected (K = 3 in this instance) to account for the unbalanced sample size. The results of the tests were based on 1,000,000 iterations, a 100,000 burn‐in period. All samples were combined, and STRUCTURE was run with K = 1 to K = 7 and 10 replicates without prior information regarding the sample's origin. The clusters of individuals forming the number of populations with the highest likelihood were assigned to sampling locations. As it might be difficult to infer the number of clusters represented due to an effect of isolation by distance (IBD) and extensive admixture . FST is dependent on the variability of the used markers, while DEST is independent of this within‐population diversity .MrBayes was run twice using the default settings involving two independent MCMC runs in each and four chains. The default sample frequency of 500 and diagnostic frequency of 5,000 and run length of 1,000,000 were used, discarding 25% of the samples of burn‐ins and run until the standard deviation of split frequencies was below 0.01, which is indicative of convergence according to the authors .The datasets used to estimate genetic diversity, population structure, and migration based on the 10 microsatellite markers (Supporting Information Appendix D9 and D15 in the Danish sample (Supporting Information Appendix HE = 0.676 ± 0.092), German (HE = 0.673 ± 0.094), and Russian samples (HE = 0.659 ± 0.095). Analysis for genotypic linkage disequilibrium revealed that one pair of loci (G9 and H29) showed linkage  (data not shown). At the mtDNA level, the highest level of genetic diversity in the CR was observed in the Russian sample , while the lowest level for both estimates was observed in the Latvian sample where only one haplotype was found. Unique mt haplotypes were observed in all populations except Latvia; Russia had seven while Denmark and Germany had one and two unique haplotypes, respectively, and Hungary three , then the German population , except for the nucleotide diversity where the Hungarian sample had the second highest . The Danish sample had the lowest diversity   as the most probable structure  (data not shown).The results of IBD were nonsignificant despite the method applied, Φ3.33.3.1The result of the assignment test Table , showed Seven out of the 81 sampled pond bats in the German population were identified to be putative FGM from Denmark, which was more than expected by chance . Among the Danish samples, four out of 120 sampled pond bats were possible FGM from Germany, but this might be due to chance alone .Assessment of the migration direction including the Russian sample using DivMigrate Network Figure  suggeste3.43.4.1No bottleneck effects were observed in the Danish and German pond bat populations Table  despite 3.4.2Fs estimate . The opposite was observed in the Danish and German samples where the SSD and Raggedness Index or Raggedness Index was significant suggesting a stable or declining population.The demographic population history of the investigated areas provided signs of population expansion Table . A signimate Fu,  in the R3.5The total number of observed concatenated haplotypes was 23  separated the Hungarian haplotypes, while the third (3) separated the Russian haplotypes from the other areas. Furthermore, a rather close relationship between the Danish and German pond bats was observed and the two populations shared some of the concatenated haplotypes.The genetic relationship among the concatenated haplotypes analyzed using the Bayesian approach implemented in MrBayes 3.2.6 , the present study offers a first indication of the genetic constitution of the species. A very close recent genetic relationship was revealed between the Danish and German populations by the microsatellite analysis. The multilocus pairwise 4.1Myotis myotis) in the contact zone between European and Anatolian populations  and the noctule bat: the former showing strictly female philopatric behavior , but no significant correlation was discovered, illustrating closer genetic relationship and shorter geographical distance between the populations. This may suggest that the genetic pattern indicates the existence of geographical barriers and/or historical colonization events.4.3The assignment test and detection of FGM between Danish and German populations illustrated a close genetic relationship between the two populations. The high percentage of inconclusive assignments to the populations can probably be attributed to the low number of markers used. However, some individuals sampled in the German roosts might be FGM from Denmark suggesting migration between the two areas, but a source–sink relationship was not detected. These results concur with the observations of the ringed German pond bat female that visited a hibernaculum in Denmark  are needed to provide more information about the genetic structuring and colonization processes following the latest glacier period. Higher resolution genetic analysis, for example, involving RAD sequencing (Baird et al., The Danish wing samples were collected in accordance with the Danish Animal Welfare Act. The German wing samples were collected by Dr Florian Gloza‐Rausch, scientific director of Noctalis, Bad Segeberg, Germany, under the permission and ethical approval provided by the State Agency for Agriculture, Environment and Rural Areas, Schleswig‐Holstein. The Hungarian wing samples were collected by Péter Estók under permission no. 14/2138‐7/2011.The authors declare no conflict of interests.LWA, RD, HJB, FGR, FK, and ME conceived the study. RD, HJB, GP, PE, OLO, MO, MG, FK, and ME did the fieldwork. LWA, RD, EN, and MO did laboratory analysis. LWA and RD conducted the data analysis with contribution from EN. FGR secured funding. All authors helped drafting the manuscript and gave approval before publication. Click here for additional data file.


The valley of the Magdalena River is one of the main population pathways in Colombia. The gene pool and spatial configuration of human groups in this territory have been outlined throughout three historical stages: the Native pre-Hispanic world, Spanish colonization, and XIX century migrations. This research was designed with the goal of characterizing the diversity and distribution pattern of Y-chromosome lineages that are currently present in the Tolima and Huila departments . Historic cartography was used to identify the main geographic sites where the paternal lineages belonging to this area have gathered. Twelve municipalities were chosen, and a survey that included genealogical information was administered. Samples collected from 83 male volunteers were analyzed for 48 Y-SNPs and 17 Y-STRs. The results showed a highly diverse region characterized by the presence of 16 sublineages within the major clades R, Q, J, G, T and E and revealed that 93% (n = 77) of haplotypes were different. Among these haplogroups, European-specific R1b-M269 lineages were the most representative (57.83%), with six different subhaplogroups and 43 unique haplotypes. Native American paternal ancestry was also detected based on the presence of the Q1a2-M3* and Q1a2-M346*(xM3) lineages. Interestingly, all Q1a2-M346*(xM3) samples  carried allele six at the DYS391 locus. This allele has a worldwide frequency of 0.169% and was recently associated with a new Native subhaplogroup. An in-depth phylogenetic analysis of these samples suggests the Tolima and Huila region to be the principal area in all Central and South America where this particular Native lineage is found. This lineage has been present in the region for at least 1,809  years. Similar to most South American populations, the genetic and cultural background of Colombians has been shaped by complex population dynamics: first, the peopling of Colombia by Native American inhabitants; second, the Spanish conquest and slave trade; and third, the different waves of migration and movements that characterized the XX century and the first decade of the XXI century ,2. Over Several genetic studies have been performed in Colombia using different types of molecular markers  to analyze numerous populations in different regions of the country ,14,15,16Such investigations have demonstrated that populations from urban areas usually present higher levels of non-Native admixture and uniparental-lineage diversity. In addition, these studies have shown how the proportions of Native American, African and European lineages vary throughout the country. The highest frequencies of African genetic contribution have been found on the Caribbean coasts and islands and in the Pacific Coast region. In contrast, Native lineages are found in higher proportions in Native populations, mainly from the eastern part of the country, including the Amazon and rural areas of southwest and northern Colombia. The European contribution is present throughout the country. This diversity reflects a nonuniform genetic pool in Colombian communities and regions.In the history of Colombian peopling, three routes have been suggested: one following the Pacific coast, another traveling into the Colombian mountains  and the last, along the Atlantic coast .2) and is densely populated. Nearly 79% of the entire population of Colombia lives in the Magdalena watershed, amounting to a demographic density of 120 inhabitants km2 ), to evaluate migration, admixture, and ancestry patterns and to reveal additional microevolutionary processes associated with population structure.This study was conducted on samples collected by the Population Genetics and Identification Group of the Universidad Nacional de Colombia in the Tolima and Huila region in 2012.Prior to the field phase, a historic cartography was performed on both departments to identify the main geographic sites where the paternal lineages that shaped the region have gathered over time, by the analysis of demographic, economic and political variables of the territory from the colony time to the present  protocol.This research was approved by the ethics committee of the Faculty of Science of the Universidad Nacional de Colombia.Seventeen Y-chromosomal STR loci were amplified using the AmpFlSTR Yfiler PCR Amplification kit, following the manufacturer’s instructions . Separation and detection of the amplified products were performed in an Applied Biosystems PRISM 310 genetic analyzer. Allele assignations were performed by comparison with a reference ladder included in the kit, and using the nomenclature recommended by the International Society for Forensic Genetics (ISFG) . These awww.yhrd.org): accession number YA004510.The Y- chromosomal data for all 83 donors have been submitted to the open-access Y-STR Haplotype Reference Database . This software is based on a Bayesian approximation using allelic frequencies calculated from haplotype collections taken from published papers and databases [To select the best set of SNP markers to be typed, Y-STR haplotype information was used to predict the most likely haplogroup of each sample, using an online tool , in six previously described multiplex reactions including the markers: (i) SRY1532, M213, M9, M70, M22, Tat, 92R7, M173, and P25 ; (ii) M2For all multiplex reactions, PCRs were performed in a 10 μl volume containing 5 μl of the Qiagen multiplex PCR kit, 1 μl of primer mix (each primer at 2 μM), 3.5 μl of water, and 0.5 μl of DNA (5–20 ng/μl). The PCR thermocycling conditions consisted of an initial denaturation at 95°C for 15 minutes, followed by 35 cycles at 94°C for 30 seconds, 60°C for 90 seconds and 72°C for 60 seconds, and a final extension at 72°C for 10 minutes. After confirming the amplification in a polyacrylamide gel , 1 μl of the PCR product was purified using 0.5μl of Exo-SAP-it (USB), and incubated at 37°C for 15 minutes, followed by inactivation of the enzyme at 85°C for 15 minutes.University of Pará), Brazil.The SNaPshot multiplex reactions were performed in 5 μl containing 2 μl of SNaPshot multiplex mix (Applied Biosystems), 1.5 μl of the purified PCR product and 1.5 μl of a mix of the single base extension (SBE) primers. The reaction protocol consisted on 25 cycles at 95°C for ten seconds, 50°C for five seconds and 60°C for 30 seconds. The final products were purified with 1μlof SAP (USB) at 37°C for one hour, followed by a denaturation step at 85°C for 15 minutes. The SBE products were run in an Applied Biosystems 3130 Genetic Analyzer and analyzed using the GeneMapper software v4.0, at the Human Genetics and Medicine Laboratory, Biological Sciences Institute at the UFPA  was performed using Arlequin software 3.5.1.2  to evaluwww.fluxustechnology.com), which assumes a stepwise mutation model for STRs. In this analysis, DYS385 was excluded because it corresponds to two loci that cannot be differentiated via the applied typing method [Phylogenetic relationships between Y-chromosome haplotypes among American and Asian populations were inferred for the Native American Q lineage  and visug method , while fg method . In the g method  and using method .−4/locus/25 years [Estimates of the time to the most recent common ancestor (TMRCA) were determined using rho statistics, implemented in the NETWORK program, employing a mean effective mutation rate for Y-STRs of 6.9 × 1025 years . The anc25 years .To compare the Y-chromosome haplogroup frequencies identified in this study with those from 30 other Colombian samples, a principal component analysis (PCA) was performed using Multiple Statistical Package (MVSP) software, version 3.1 .FST = -0.00133; p = 0.50255+/-0.0016); these two groups were therefore were analyzed as one population.The Y-chromosome haplotypes and haplogroups observed in Tolima and Huila are listed in The haplogroup diversity of the Tolima and Huila region was 0.8692. Haplotype diversity was evaluated using the information obtained from 17 STR loci, revealing a high diversity rate of 0.9680, characterized by 93% (n = 77) unique haplotypes. Shared haplotypes were observed for haplogroups E1b1b-M78, E1b1b-M81, Q1a2-M346*(xM3), R1b-U106, and R1b-S116* . The hapMost studies conducted in admixed Colombian populations have identified a male European contribution ranging from 58% to 94% and representation of Native lineages ranging from 1% to 38% ,11,27,50The present study revealed a 16.86% frequency of Y-chromosomes belonging to haplogroup Q . IndividHaplogroup Q1a2-M346*(xM3) was identified in the same proportion as haplogroup Q1a2-M3. This haplogroup has been found in samples from Argentina, Chile, Bolivia, some studies from North America, the northern part of South America, and Siberia ,53.Using a phylogenetic approach, the possibility of past minor founder effects was evaluated in the Native American lineages. In the Q1a2-M3* network , 310 hapAs illustrated in To identify patterns and matches between the unconventional Q1a2-M346*(xM3) DYS391*6 samples from the Tolima-Huila territory, a phylogenetic analysis was performed, using 193 previously reported Q-M242*(xM3) haplotypes from Asian and American populations  Tables. The above analysis revealed high diversity in the Tolima and Huila Native American lineages. The Y-chromosomes from this study belonging to Q1a2-M3* and Q1a2-M346*(xM3) did not match samples from East Asia or from the American continent, including Northern, Central, and Southern populations.The results obtained from Q1a2-M346*(xM3)-DYS391*6 Y-chromosomes fit a model of isolation after colonization, which most likely erased the footprints of this lineage dispersion route from modern Native populations. This finding could indicate already extinct haplotypes or very low-frequency haplotypes in the current Native communities that have not been reported previously.Nevertheless, DYS391*6 is not a private allele of a subhaplogroup within Q-M242*-(xM3); it has also been reported in individuals carrying the C-M130, O2-M95, Q-M242, Q-M3, and Q-M120 haplogroups, who mainly belong to the Han ethnic group, from Southeast Asian populations . Howeverhttp://yhrd.org/) [For this reason, a broad search was conducted using the YHRD database (rd.org/) , and 334Furthermore, related studies have obtained data from Central and South American samples. Battaglia et al.  reportedNevertheless, many of the reported DYS391*6 samples were not assigned to a haplogroup; however, their haplotype information is valuable for assessing similarities and differences among these chromosomes. To evaluate the similarity among the DYS391*6 haplotypes found in the Asian and Latin American data, a phylogenetic network was created . Based oIn 2013, Battaglia and collaborators  suggesteThe Coyaima Cariban linguistic family is one of the tribes belonging to the Pijao Native American group, which is only found in the Tolima department. When 15 Y-STRs were used to compare the haplotypes from our Q1a2-M346*(xM3)-DYS391*6 samples with the Coyaima data reported by Jota et al.  classifiThe phylogenetic relationships reported to date between all people in the Central and South American populations with allele six at DYS391 who were typified as Q-M242*(xM3) were further analyzed using YFiler profiles . In thisFrom this analysis, there were three main findings: (i) the Tolima and Huila region is characterized by the highest frequency of Q1a2-M346*(xM3)-DYS391*6 chromosomes (21 out of 25 haplotypes); (ii) 50% of these 25 haplotypes are different (N = 13); and (iii) the haplotype variability of these characteristic chromosomes is high in the Tolima and Huila samples, with nine of 13 Y-haplotypes differing.These results indicate the middle Magdalena River region as the principal zone within all of Central and South America where this particular Native lineage is found. It is possible that all of the collected samples belong to the new sublineages Q1a2-SA02 and Q1a2-SA03 reported by Jota et al. . HoweverAdditionally, it is fundamental to bear in mind the frequency of this allele in Panama. As mentioned above, this population exhibits 12 reported haplotypes, six of which are found in the Ngöbe Native group (these data were not included in the comparison because less than 10 STRs were genotyped). Panama is the country with the second-highest frequency of the Q*-DYS391*6 lineage. Grugni et al.  proposedThe Isthmo-Colombian area has been described by O'Connor and Muysken  as a terIn addition, a recent study of ancient human bones belonging to a Native Colombian group belonging to the Chibcha “Muisca” family from the IX to XVI centuries A.D. in the eastern Colombian Andes identified one male carrying allele 6 at locus DYS391 . Based oThe narrow Isthmus of Panama is a likely bottleneck that should have allowed only a comparatively small number of nomadic hunters, fishermen, and gatherers to enter the northern Andean region via the Atrato, Cauca, and Magdalena rivers and tributaries through the northern reaches of the Colombian mountain ranges belonging to the greater Andean mountain range ,61.After subsequent demographic events such as isolation, genetic drift, and gene flow between Native American populations, speakers of different languages, such as Chibcha and Cariban languages, could have initiated the incorporation of this new Native lineage among local settlers of the middle Magdalena River region. Their descendants then survived through colonial times and waves of violence during the XIX and XX centuries. Today, they are still present at a high frequency in the Tolima and Huila departments and are part of the Colombian genetic pool.The influx of male Europeans represented the greatest legacy in both the Tolima and Huila departments 83.14%). These haplotypes were distributed among 14 different subclades, within haplogroups R, E, G, J, and T , which was characterized by high haplogroup diversity, including six sublineages: R1b-S116*, R1b-U152, R1b-M529, R1b-M153, R1b-U106, and R1b-M167 . AdditioThese results regarding haplogroups and diversity reflect the migration dynamics during colonization and at the end of the XIX century in Tolima and Huila. These dynamics were characterized by the arrival of settlers from various parts of Europe, mainly from the Iberian Peninsula . In that region, the presence, frequency, and diversity levels of these R1b-M269 sublineages are similar to those reported from this analysis ,64,65,66Other frequent haplogroups common in western Europe, such as J members n = 5, , were alEurasian clades T-M70 and G-M201 were also present, at frequencies of 6% (n = 5) and 3.61% (n = 3) respectively, neither of which showed shared haplotypes . HaplogrHaplogroup E, defined by the M96 mutation, is the most common human Y chromosome clade found in Africa . Within The most common sub-Saharan African lineage, E1b1a-M2 , was notFrom the point of view of Colombian history, this is an unexpected result, because approximately 200 thousand slaves arrived in the Colombian territory through the port of Cartagena during the colonial era  and thenHowever, in the last population census of Tolima and Huila, only approximately 38 thousand people  were reported as "Afro-Colombians" .Hence, the absence of this lineage in our sample might be an effect of sample size due to its low population frequency. This low representation may have resulted from the high proportion of European men present in the region, which might have reduced the number of descendants carrying haplogroup "E1b1a" in this area due to their frequency.Based on the data provided in In the presented diagram, the non-Native populations cluster in the first quadrant of the plane, along with the Tolima and Huila populations (corresponding to the populations examined in this study); this group is mainly characterized by high frequencies of European male lineages, including P*-92R7(xM167) and J-12f2a(xM9). In the case of Native populations, these clades are divided into two groups. The first is located in the second quadrant of the plot, where the frequency of Q1a2a*-M3 is close to one . The second group is located in the third quadrant, where Q lineages other than Q-M242*-(xM3) predominate (Waunana and Ingano). These results show clustering among the Tolima and Huila samples and other admixed Colombian populations, mainly based on the high prevalence of this European lineage, illustrating that the European influence is stronger in urban, rather than rural areas.The location of the Tolima and Huila departments in the center of the country, characterized by the rainforest biome  and its Although this territory does currently not include any major Colombian city and is instead composed of many rural municipalities, the Y-chromosome gene pool found in this study is indicative of a population with high diversity and similar frequencies of paternal lineages that are very common in European regions such as Spain or the Iberian Peninsula. This diversity is a consequence of the economic and political historical importance of this region during colonial times and in the XIX century.It is important to note the lack of Y-chromosomes belonging to haplogroup E1b1a-M2 in our sample. This finding does not preclude the possibility that there are descendants of this lineage in the Tolima and Huila departments. Men with this genetic inheritance do exist in the territory, albeit not in high frequencies. The probability of sampling these individuals is therefore lower than for other, more frequent lineages.The analysis also revealed uncommon Native American haplogroups at high frequencies. All archeologic and genetic evidence suggests that the Q1a2-M346*(xM3)-DYS391*6 lineage arose in prehistoric times in the Isthmo-Colombian area. However, after many generational and demographic events, this lineage currently only appears at a high density in and could even be considered near-exclusive to the Tolima and Huila departments. This hypothesis can be confirmed after analyzing more territories along the Colombian Andes in depth.For this reason, it is important to assess new informative SNPs within the Q1a2 clade in Native and non-Native communities to obtain new insights and clues about the origin and dispersion routes that Native American groups have followed throughout time.S1 Fig(PNG)Click here for additional data file.S2 FigThe comparison was made using 15 STR markers. Circles represent haplotypes, with areas proportional to their frequencies; colors indicate the population of origin. The median vectors (absent or extinct haplotypes) are shown in white.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


Surrounded by speakers of Indo-European, Dravidian and Tibeto-Burman languages, around 11 million Munda (a branch of Austroasiatic language family) speakers live in the densely populated and genetically diverse South Asia. Their genetic makeup holds components characteristic of South Asians as well as Southeast Asians. The admixture time between these components has been previously estimated on the basis of archaeology, linguistics and uniparental markers. Using genome-wide genotype data of 102 Munda speakers and contextual data from South and Southeast Asia, we retrieved admixture dates between 2000–3800 years ago for different populations of Munda. The best modern proxies for the source populations for the admixture with proportions 0.29/0.71 are Lao people from Laos and Dravidian speakers from Kerala in India. The South Asian population(s), with whom the incoming Southeast Asians intermixed, had a smaller proportion of West Eurasian genetic component than contemporary proxies. Somewhat surprisingly Malaysian Peninsular tribes rather than the geographically closer Austroasiatic languages speakers like Vietnamese and Cambodians show highest sharing of IBD segments with the Munda. In addition, we affirmed that the grouping of the Munda speakers into North and South Munda based on linguistics is in concordance with genome-wide data. These groups speak languages of four major language families: Indo-European, Dravidian, Austroasiatic and Trans-Himalayan. Studies based on genome-wide genotype data have shown that the majority of present day populations of the Indian subcontinent derive their genetic ancestry to a large extent from two ancestral populations – ancestral northern and southern Indians – of which the former is genetically close to West Eurasian populations6. In addition to these two components, the Munda speakers of the Austroasiatic family share a minor proportion of their genetic ancestry with Southeast Asian populations7. Austroasiatic languages are spoken by more than 100 million people in Mainland Southeast Asia (MSEA) and >10 million Austroasiatic speakers8 of Munda languages live in East and Central parts of India where they are surrounded by Indo-European, Dravidian and Trans-Himalayan languages speakers.Genetically diverse10. Alternatively, considering the deep splits of extant Munda and extinct Para-Munda languages and evidence for independent domestication of rice in India and in Southeast Asia, it has been proposed that Austroasiatic languages could have, instead, spread from India to Southeast Asia11. Given that about 25% of the genetic ancestry of Munda speakers has been shown to be shared with Southeast Asians, unlike in other Indian populations, and, reversely, because Burmese share some ancestry with Indian populations, it has been proposed that the expansion of rice farming may have involved bilateral movement of people7.Considering the widespread sharing of words related to rice agriculture in all main branches of Austroasiatic, it has been proposed that this language family co-expanded with farming in MSEA and that the speakers of Munda languages spread to India as part of this farming expansion13. Such a contrasting distribution of maternal and paternal lineages among the Munda speakers is a classic example of ‘father tongue hypothesis’14. However, the temporality of this expansion is contentious16. Based on Y-STR data the coalescent time of Indian O2a-M95 haplogroup was estimated to be >10 KYA13. Recently, the reconstructed phylogeny of 8.8 Mb region of Y chromosome data showed that Indian O2a-M95 lineages coalesce within a clade nested within East/Southeast Asian within the last ~5–7 KYA17. This date estimate sets the upper boundary for the main episode of gene flow of Y chromosomes from Southeast Asia to India.Studies analysing mtDNA and Y chromosome markers have revealed a sex-specific admixture pattern of admixture of Southeast and South Asian ancestry components for Munda speakers. While close to 100% of mtDNA lineages present in Mundas match those in other Indian populations, around 65% of their paternal genetic heritage is more closely related to Southeast Asian than South Asian variation7, therefore in the present study, we generated and assembled large body of contextual genome-wide genotype data from Southeast Asia as well as from South Asia  in context of 978 other samples (including 46 newly reported samples) from 72 populations mainly from India, Southeast Asia and East Asia. The Munda speakers are divided into North Mundas (NM) and South Mundas (SM) based on linguistic affinities. List of all the populations, sample sizes, and some additional information on the dataset can be found in Supplementary Table 18 and a linguistic isolate Nihali.We first analysed Munda genomes with ADMIXTURE and PCA in context of other South and Southeast Asian populations and found that Munda share about three quarters of their genetic ancestry  roughly reflects geographical locations of studied populations  and Ho (NM) speakers, as they yielded significantly negative f3 values (indicative of admixture) when tested together with populations from India and Southeast Asia  with Dravidian speaking Chenchus  and Indo-European speaking Chamar  when disregarding Nihali and Gond tribes as Nihali, a language isolate, are possibly related to Munda and the Gond are reported to have received gene flow from the Munda18. From Southeast Asia the sharing is highest with Mah Meri  and Temuan  tribes from Peninsular Malaysia, followed by Jakun and Che Wong from the same area . The formation of the two groups seen on Fig. i.e., the Southeast Asian share of the Munda speakers’ genomes has diverged from present day Southeast Asians more recently than the South Asian part from present day South Asians. This result has to be taken with caution as we found correlation between the shared IBD segment lengths and the average heterozygosity in these populations  and long (>1 cM), we noticed that the two sources, South Asian and Southeast Asian populations, clearly form two distinct groups based on shared segment length patterns Fig. . Both, m21 to determine the relative proportions of West, Southeast and South Asian ancestries in Munda speakers, using a number of modern and ancient West Asian populations, Lao, Burmese, Cambodia or Mah Meri , and Onge or Paniya as proxies for the three Asian components  approach implemented in qpGraph23. The admixture midpoint was 3846 (3235–4457) years ago for South Mundas, which may point to the time of arrival of the Southeast Asian component in the area, and 2867 (1751–4525) years ago for North Mundas  years old17, while the variation within Munda speakers has been estimated to derive from a single male ancestor who lived 4 300 (+−200) years ago15. The latter date estimate is very similar to ours and implies a significant male-specific founder event as part of the admixture process.While the ALDER dates that we obtained are, to our knowledge, the first estimates of the time of admixture of the Munda speakers based on genome-wide data, estimates from previous studies, based on other types of data, have yielded much earlier dates for the spread of Austroasiatic populations in India. Diamond and Bellwoodet al.7 that the Mundas lack one ancestral component  of East Asian chromosome Y haplogroup O2a at the background of almost no detectable East Asian mitochondrial DNA signal pointing to a sex-biased nature of admixture between Austroasiatic speakers and their local Indian neighbouring populations. We used outgroup f3 analysis to contrast allele frequency patterns on the X chromosome versus those on the autosomal chromosomes to clarify the maternal side of this sex-biased admixture event. Our analysis revealed that on X chromosome, a Dravidian speaking group, North Kannadi, is relatively more similar to Munda speakers than on autosomes, while on autosomes Lao, Vietnamese and Burmese from Southeast Asia and Sino-Tibetan speaking Kuki from India have relatively higher f3 values than on X chromosome , where South Munda speakers have also higher (0.73) average frequency of haplogroup O2a than North Munda speakers (0.62)7. This finding is consistent with our proposed scenario where South Munda kept isolated after the admixture event, while North Munda received additional admixture from local Indian groups, which diluted Southeast Asian component and blurred the signs of the sex-specific nature of the admixture event as the latter admixture pulse in North Munda was not sex-specific anymore.In Chaubey P < 0.0001). These components were maximised in East and Southeast Asian samples. Smaller amount of Lao ancestry in North Mundas was also shown by qpAdm analysis  which clustered with Asur and Ho samples from North Munda  is a suitable grouping criteria for genetic analyses. Here we take a glance at the genetic relationship between different North and South Munda populations. PCA of only Munda populations displayed North and South Mundas as separate groups, except one Juang and one Kharia individual fell together with North Mundas on first two principal components , dataset was further pruned by excluding SNPs with pairwise genotypic correlation r2 > 0.4 in a window of 200 SNPs sliding the window by 25 SNPs at a time39. This left us 155743 SNPs.All the samples were filtered with plink v1.940 on pruned data of the whole filtered dataset . To get some idea of the Munda speakers’ genetic structure in context of other Asian populations, we ran ADMIXTURE 1.23 program41 with random seed number generator on the LD pruned data set one hundred times at K = 2 to K = 18  were treated as one population due to small sample size. We ran outgroup f3 statistic as f3 = (SouthMunda/Ho(NM), X, Yoruba) to find the closest modern populations from our data set for South and North Munda.To test the admixture, we ran three-population formal test of admixture21. Only individuals for which X chromosome information existed in the dataset, could be used; for individuals present in this analysis, see Supplementary Table To compare the sharing of SNPs on X chromosome to autosomes in Munda speakers and other relevant populations from South and Southeast Asia, outgroup f3 statistics of the form f3 were computed separately for X chromosomes and autosomes of the same individuals using AdmixTools-1.121 testing the following South and North Munda populations  as a three ways mixture of all possible combinations of West , East  and South  Asian groups and using as outgroups the following groups: Natufian, WHG, Han, Kankanaey, Karitiana, MbutiLaz2016, Papuan, Ust_Ishim, Yorubas44. To retrieve the percentage for West Asian component, we modelled the Mundas as a mixture of IranianLaz2016, Onge, and Southeast Asian population. For Southeast Asian component, we modelled the Mundas as a mixture of IranianLaz2016, Paniya and Mah Meri population which turned out to be a better fit in qpAdm analysis than Lao, Burmese, and Cambodian. We could not use Iranian and Onge separately with Mah Meri to retrieve Southeast Asian component because Onge itself shares some Southeast Asian ancestry46.To retrieve the admixture proportions, we ran the qpAdm software21 but first, to decide if we should make a graph for every 11 Munda population or group them in some way (e.g. North and South Munda), we ran f4 analysis as described in Lazaridis et al. (2016) supplementary information section 343 using AdmixTools-4.1 software21. There was no clear clustering pattern in the Munda populations and therefore we generated 11 separate qpGraphs.To estimate goodness of fit between data and specified model that involves multiple admixture events and drift, we used qpGraph software23 to infer admixture dates for South Munda, Ho (NM), Santhal (NM), Birhor (NM) and Korwa (NM). We used all the populations spanning from India to Europe from our data set as source 1 and all the populations from East and Southeast Asia as source 2. The population pairs to represent admixture times were chosen based on decay status and LD decay curve amplitude. Standard errors were estimated by jackknifing on chromosomes. We used generation length of 30 years47.We used ALDER19. For this analysis, the data was previously phased with Beagle 3.3.248. A co-ancestry matrix was constructed using ChromoPainter v119 with the default settings. From the co-ancestry matrix, the mean chunk lengths shared between Eurasian populations and Andamanese were extracted.To investigate the relationship between the Munda speakers and Andamanese, we used fineSTRUCTURE20 analysis, where we studied the sharing of DNA segments of identity-by-descent (IBD) between the Munda speakers and other populations in our data set. From the results, we extracted the count of segments shared between every two individuals and found population medians. We did the same with short (<1 cM) and long (>1 cM) segments, to find patterns. We also compared total length of IBD segments shared between individuals from two different populations on average.Beagle was also used in Refined IBDAll the methods were performed in accordance with relevant guidelines and regulations.Supplementary Figures and TablesSupplementary Table S1Supplementary Table S2Supplementary Table S5Supplementary Table S7