Cyperus fuscus as a model. Using microsatellite markers, we found no difference in genetic diversity levels between soil seed bank and above‐ground population and only moderate differentiation between the two fractions. One possible interpretation is the difference in short‐term selection during germination under specific conditions (glasshouse versus field) resulting in an ecological filtering of genotypes out of the reservoir in the soil. River populations harbored significantly more genetic diversity than populations from the anthropogenic pond types. We suggest that altered levels and patterns of dispersal together with stronger selection pressures and historical bottlenecks in anthropogenic habitats are responsible for the observed reduction in genetic diversity. Dispersal is also supposed to largely prohibit genetic structure across Europe, although there is a gradient in private allelic richness from southern Europe  to northern, especially north‐western, Europe , which probably relates to postglacial expansion out of southern and/or eastern refugia.Many ephemeral mudflat species, which rely on a soil seed bank to build up the next generation, are endangered in their natural habitat due to the widespread regulation of rivers. The aim of the present study was to elucidate the role of the soil seed bank and dispersal for the maintenance of genetic diversity in populations of near‐natural river habitats and anthropogenic habitats created by traditional fish farming practices using  Cyperus fuscus, possibly as a result of differences in short‐term selection during germination under specific conditions (glasshouse versus field) resulting in an ecological filtering of genotypes out of the reservoir in the soil. River populations, however, harboured significantly more genetic diversity than populations from fishponds and fish storage ponds, supposedly because of differences in the strength of selection pressures, historical bottlenecks, and altered levels and patterns of dispersal.Using microsatellite markers, we found no difference in genetic diversity levels between soil seed bank and above‐ground population and only moderate differentiation between the two fractions in the mudflat species  Muddy sRegulation of the majority of the world's rivers has led to a dramatic loss of floodplain habitats  Does the persistent soil seed bank function as a “genetic memory”? If the seed bank really worked as a genetic memory, we would expect higher genetic diversity in the soil. Does the genetic composition of the above‐ground population differ from that of the soil seed bank? If selection acted as a filter on the alleles present in the soil during germination and growth of the above‐ground individuals, we would expect genetic differentiation between the seed bank and the above‐ground population. (b) Do populations from river and anthropogenic habitats differ in their levels of within‐populational variation? We would expect river and anthropogenic populations to differ, because they are exposed to different habitat conditions, which in turn influence determining factors of within‐populational variation such as dispersal, drift, and selection. Are the populations from river and anthropogenic habitats genetically differentiated? If they were exposed to divergent selection pressures, we would expect them to be genetically differentiated. (c) The first two questions were studied in Central Europe, but Cyperus fuscus, as many other mudflat species, occurs sporadically all over Europe—for short periods of time and at different points in time—meaning that populations are isolated spatially and temporally from each other. Our third question therefore was whether populations across a larger geographic area are genetically differentiated. If the action of drift and/or selection was stronger than gene flow, we would expect genetic differentiation. Moreover, we wanted to know whether European populations differ in levels of within‐populational variation. If current and/or previous environmental conditions better supported the survival of Cyperus fuscus in some regions, we would expect higher levels of variation in these regions.Little is known about the genetic structure of mudflat species. We studied neutral genetic variation in 22.1Cyperus fuscus is an annual, self‐compatible, and highly plastic graminoid native to the Mediterranean and temperate Eurasia .We studied 31 populations belonging to one of the three habitat types  in Central Europe and 49 additional populations across Europe. The localities were roughly assigned to one of four coarse biogeographical regions  following the manufacturer's instructions. We amplified 21 polymorphic microsatellite loci previously isolated from ernhardt . In tota2.42.4.1HI), expected heterozygosity under the Hardy–Weinberg equilibrium (HS), and the inbreeding coefficient (FIS) were calculated using GenAlEx version 6.5  and private allelic richness (pAr) based on 10 randomly sampled alleles. Because we wanted to put pAr of the above‐ground fraction in relation to that of the soil fraction, we used only the above‐ground and the soil fractions of a specific population as input for the program, omitting all other populations. This was done separately for each population. Differences in genetic diversity estimates were evaluated by means of linear mixed models using the procedure MIXED of SAS version 9.4 . Fraction, habitat as well as their interaction were used as fixed factors. Differences between least square means were tested for significance by Tukey–Kramer post hoc tests.For the analysis of genetic diversity within the soil and above‐ground fractions of populations, only fractions of populations with at least five amplified individuals were considered. Because it was not possible to get five or more unambiguously genotyped individuals from both soil fractions from every site Appendix , individ2.4.2To detect first‐ and second‐generation migrants from the genotype data, we performed Bayesian inference of recent migration using BayesAss version 3.0.4  as well as among the three sampling plots within populations, we conducted analyses of molecular variance (AMOVA) in Arlequin version 3.5  was used to produce a Neighbor‐Net network with SplitsTree4 version 4.14.5  were included (regardless of their level of human impact and proximity to running water). The populations were grouped according to Appendices 2.5.2FST; as above) and geographic (linear) distance (generated from the population coordinates in GenAlEx) between all populations analyzed  in R version 3.5.1  with the same settings as before, but pAr was estimated for all populations across Europe in a single step. Because pAr is dependent on the density of the sampled populations, which differs considerably between biogeographical regions, we also estimated it after arbitrarily thinning out populations (two arbitrary selections of populations), so that only four populations remained in each region. Differences in genetic diversity estimates were evaluated by means of linear mixed models with the same settings as before using biogeographical region as fixed factor. To better understand genetic diversity in the biogeographical regions, Ar and pAr were also estimated for the regions as a whole  using rarefaction analysis with 22 randomly sampled alleles per region. In order to balance the geographic area occupied by populations in a biogeographical region between the regions, Ar and pAr were also evaluated by rarefaction analysis for just two regions as a whole .3All 21 microsatellite markers were polymorphic in our sample. None of the scored plants had more than two distinct alleles per marker, so that all plants were interpreted to be diploid. The number of alleles per marker in the entire sample ranged from 5 to 40 with a mean of 12.4. There were 260 different alleles altogether. Observed heterozygosity was usually considerably lower than expected heterozygosity. This explains the high inbreeding coefficients Figures  and S3.3.1No significant differences were detected in genetic diversity estimates between the above‐ground and the soil fraction in all three habitat types Figure .Inspection of the trace of the log likelihood of the analysis of recent migration showed that it somehow stabilized after ~ 4‒5 million iterations. Of course, one must keep in mind that only a small fraction of potential source populations, namely the localities analyzed here, have been sampled. For this reason, one cannot interpret the results as actual dispersal events from a particular source population, but rather as indications of the general region of origin of immigrants. The proportion of immigrants into the above‐ground and soil fractions of populations did not differ significantly in all three habitat types Table.The values of differentiation among fractions within populations were in a similar range as among sampling plots within populations Table. The higFST distance matrix  than populations from the two anthropogenic habitats, and the latter did not differ significantly  than for fishpond populations  or fish storage pond populations . The mean expected percentage share of immigrants from up‐ or downstream with respect to total migration along the river system  was 51.3 (SD = 8.4) for immigrants from upstream and 48.7 (SD = 8.4) for immigrants from downstream.The summary results of the analysis of recent migration Figure showed tAMOVA revealed a moderate variation of 8.1% among the three habitat types Table. Most vaIn the Neighbor‐Net network, populations from river and anthropogenic habitats are not substantially separated Figure. Most ri3.3AMOVA revealed that in the case of populations of the six large river systems , remarkable 14.7% variation was found among river systems compared with 9.1% variation among biogeographical regions Table. On the The Neighbor‐Net network Figure is “bushThere is a significant pattern of isolation by distance across all populations analyzed in Europe Figure. The ManAr = 2.30, HS = 0.41) than populations from the other biogeographical regions in Europe, whereas private allelic richness was highest in the Mediterranean populations ] as reported for Medicago lupulina, another annual , self‐compatible plant with a soil seed bank  is slightly lower than the one reported for Medicago lupulina [mean = 0.92 (range = 0.74 to 1.00)], but—given the rather large population sizes . In contrast, the fishpond regions in western and southern Bohemia (populations F2‒9 and S4‒10) and south‐western Moravia (population F10) are mainly formed by mineral‐poor acidic soils. Consequently, Cyperus fuscus was rare in most parts of southern Bohemia until the 1950s, where it occurred mainly in eutrophic water bodies in settlements . Due to frequent water‐level fluctuations in dry and warm regions, suitable habitats with exposed fine muddy sediments may appear with higher frequency in the landscape. In the present Mediterranean climate, on the other hand, quickly desiccating exposed substrata‐like sand or gravel may not support similarly large populations of 4.5Cyperus fuscus is classified as vulnerable in Austria, the Czech Republic, Great Britain, Liechtenstein, and Switzerland (Bornand et al., Freshwater wetland habitats and particularly riverine floodplains face a dramatic decline (Hein et al., The authors declare no conflict of interest.K.G.B., H.G., K.Š., and K.T. designed the experiment; J.B. and G.K. acquired the data; J.B., K.G.B., H.G., K.Š., and K.T. analyzed and interpreted the data; J.B., K.Š., and K.T. drafted the article, and all other coauthors critically revised and approved it.Appendix S1‐S5Click here for additional data file.


ST-values between this and other Southeast and East Asian populations revealed significant but relatively low differences, indicating a close relation. Heteroplasmic positions were observed in 12.2% of hair samples confirming the frequent appearance of heteroplasmic positions in hairs. This dataset will complement existing data as an mtDNA reference for forensic investigations.Mitochondrial DNA (mtDNA) control region sequences from hair samples of 213 individuals from Thailand were analyzed using Sanger sequencing. A total of 170 different haplotypes were identified, of which 146 occurred only once (unique haplotypes). The dataset showed a random match probability of 0.87% and a haplotype diversity of 0.9960. The samples were assigned to 85 different haplogroups with B5a, F1a1a, and M being the most frequent ones. Pairwise FThe online version of this article (10.1007/s00414-020-02303-2) contains supplementary material, which is available to authorized users. Mitochondrial DNA (mtDNA) analysis has become a routine approach in forensic casework where STR markers cannot be used. The associated estimation of the frequency of obtained mtDNA profiles in the respective population sample is based on the availability of suitable population data sets. MtDNA data from Thailand has already been published –5. HowevHair samples were obtained from 213 unrelated individuals of both sexes living in southern, central, northern, and northeastern Thailand Fig. . The samTotal DNA of 10–12 hairs per individual was extracted with the MagCore® Genomic DNA Tissue Kit  at the University of Khon Kaen . PCR and sequencing of the entire control region were performed at the University Medical Center Freiburg – University of Freiburg  as described in  using prwww.haplogrep.uibk.ac.at) [www.empop.online) [A total of 213 full double strand control region sequences were successfully analyzed and aligned to the revised Cambridge Reference Sequence (rCRS) , 9 usingk.ac.at)  and EMPO.online)  based onMtDNA data quality was controlled using the EMPOP tool NETWORK . FurtherIntra- and inter-population statistical analysis was done using Arlequin v3.5.2.2 . NumbersST-values.We compared our data with six other Southeast and East Asian populations from recent studies , 16–20 oWe obtained 213 high-quality mtDNA control region sequences from Thailand to establish reference data  in the Thai population is 12.86, which is in the dimension of that observed in other Asian populations Table . The lowST-values between population samples were relatively low and similar, indicating a close relation between populations. Higher variance was found between Southeast Asian populations  and South Korea (FST 0.037–0.051), whereas genetic variance between Hong Kong and South Korea was low (FST 0.016). All FST-values between Thailand and other population samples were significant  ESM 2(XLSX 22 kb)


Aedes aegypti (yellow fever mosquito) and Ae. albopictus (Asian tiger mosquito) are both common throughout the Indo-Pacific region, where 70% of global dengue transmission occurs. For Ae. aegypti all Indo-Pacific populations are invasive, having spread from an initial native range of Africa, while for Ae. albopictus the Indo-Pacific includes invasive populations and those from the native range: putatively, India to Japan to Southeast Asia. This study analyses the population genomics of 480 of these mosquitoes sampled from 27 locations in the Indo-Pacific. We investigated patterns of genome-wide genetic differentiation to compare pathways of invasion and ongoing gene flow in both species, and to compare invasive and native-range populations of Ae. albopictus. We also tested landscape genomic hypotheses that genetic differentiation would increase with geographical distance and be lower between locations with high connectivity to human transportation routes, the primary means of dispersal at these scales. We found that genetic distances were generally higher in Ae. aegypti, with Pacific populations the most highly differentiated. The most differentiated Ae. albopictus populations were in Vanuatu, Indonesia and Sri Lanka, the latter two representing potential native-range populations and potential cryptic subspeciation respectively. Genetic distances in Ae. aegypti increased with geographical distance, while in Ae. albopictus they decreased with higher connectivity to human transportation routes. Contrary to the situation in Ae. aegypti, we found evidence of long-distance Ae. albopictus colonisation events, including colonisation of Mauritius from East Asia and of Fiji from Southeast Asia. These direct genomic comparisons indicate likely differences in dispersal ecology in these species, despite their broadly sympatric distributions and similar use of human transport to disperse. Our findings will assist biosecurity operations to trace the source of invasive material and for biocontrol operations that benefit from matching genetic backgrounds of released and local populations.The arbovirus vectors  Ae. aegypti and Ae. albopictus are highly invasive and transmit dengue and other arboviruses. This study investigates the genetics of these mosquitoes in the Indo-Pacific region, where 70% of global dengue transmission occurs and where both species have established widespread invasions by hitch-hiking on human transport vessels. We compared patterns of genetic differentiation to determine the pathways these species have taken while spreading through the Indo-Pacific, and to better understand how they disperse. We sequenced DNA from 480 mosquitoes sampled from 27 locations in the Indo-Pacific, and found many genetic differences between the two species. Populations of Ae. aegypti, which is not native to the region, tended to be genetically different from each other, and populations in the Pacific Ocean were particularly divergent. Aedes albopictus populations were generally more similar to each other, though genetically different populations in Sri Lanka and Indonesia point to these having a different history to other populations. Genetic differences between Ae. aegypti populations were larger when populations were geographically distant, while differences between Ae. albopictus populations were larger when populations likely had limited access to human transportation. These results will help improve strategies for controlling these species and stopping their spread around the world.The mosquitoes  Aedes aegypti (yellow fever mosquito) and Ae. albopictus (Asian tiger mosquito) [Aedes aegypti, the primary regional vector, has a putative native range of West Africa [th c. [Aedes albopictus has a hypothesised native range that includes Japan, China, northern India, and parts of Southeast Asia [Ae. albopictus is thought to have occurred more recently than Ae. aegypti, with colonisation likely involving successive waves [The Indo-Pacific region, here defined as encompassing the Indian Ocean, the western and central Pacific Ocean, and the coastal territories therein, is the site of 70% of global dengue transmission . Infectiosquito) ,3, both t Africa  and is tt Africa , while ia [th c. . Aedes aast Asia . Regionave waves ,10.Ae. aegypti and Ae. albopictus (hereafter Aedes spp.) exhibit stratified dispersal. This consists of two distinct processes: short-range, active dispersal and long-range, passive dispersal [Aedes aegypti is generally highly localised, as measured by both traditional mark-release-recapture methods  or curr99 m/gen ). Similaecapture . At the directly  or inferdirectly ,17. Dispdirectly –21.Aedes spp. has many adverse consequences. Both species are presently continuing to colonise new tropical, subtropical and, for Ae. albopictus, temperate regions [Aedes spp. populations can spread advantageous alleles that reduce the efficacy of local control programs [The unrestricted spread of  regions , and fut regions . In addiprograms . Incursiprograms ,20,25, aAedes spp. can help efforts to prevent, control, and prepare for future threats from these species. As well as being a critical focus for dengue control efforts, the Indo-Pacific presents a useful location to investigate these processes due to the broad sympatry of Aedes spp. within the region. Investigating the genetics of sympatric taxa can help link patterns of genetic differentiation to the forces that shape population structure [Understanding the forces that influence dispersal, gene flow, and population structure in tructure ,27. For tructure –30, whiltructure .Aedes spp. from the Indo-Pacific region at spatial scales from ~20 km (inter-city) to ~16,000 km (trans-oceanic). Dispersal at these scales largely proceeds along the network of shipping routes that link each inhabited coastal location with every other location , a common occurrence in Aedes spp. [Mosquitoes were genotyped using a pipeline that has been described elsewhere  and is d aegypti  to obtai aegypti . We usedtie v2.0  to align aegypti  and Ae. bopictus  mitochonbopictus  and Aalobopictus  to obtaides spp. . As someAe. aegypti and 256 Ae. albopictus genotypes. We used the Stacks program ref_map to build catalogs containing all mosquito genotypes from each species, from which we called single nucleotide polymorphism (SNP) genotypes at RAD stacks using default Stacks settings. Only SNPs scored in at least 75% of genotypes from each population were used in analyses.We retained 224 Ae. aegypti and 256 Ae. albopictus genotypes to retain biallelic SNPs with < 20% missing data, with minimum depth of coverage of 3, and maximum depth of coverage of 45. Genotypes were phased and missing data imputed in Beagle v4.1 [We investigated genetic structure among individual mosquito genotypes using three methods: discriminant analysis of principal components (DAPC ); sparsegle v4.1  using deadegenet [K genetic clusters. We ran the ‘find.clusters’ algorithm to determine the optimal K for 1 ≤ K ≤ N, with N equal to the number of populations . We ran 1x109 iterations and selected the value of K with the lowest Bayesian information criterion (BIC). We then repeated the above analysis, selecting the value of K with the lowest Akaike information criterion (AIC), a less conservative measure than BIC.DAPC was run in the R package adegenet . In DAPCK ancestral lineages, where K is unknown a priori. To determine which value of K was optimal for summarising the variation in each species, we set 1 ≤ K ≤ N and ran 100 iterations of the sNMF algorithm for each K, selecting the K with the lowest cross-entropy across all runs. For the chosen K we selected the iteration with the lowest cross-entropy for visual presentation.We ran sNMF in the R package LEA . This anTo generate PCA-UMAPs, we adapted code provided in Diaz-Papkovich et al. . UMAP prn) would not bias analyses, while still including as many genotypes as possible. To balance these aims, we subsampled each dataset ten times, sampling (with replacement) from each population a number of genotypes (five) equal to the minimum n among all populations. For each subsample we used the Stacks program populations to calculate FST’ [O), nucleotide diversity (π), number of private alleles and proportion of missing data for each population. We used the results from the ten subsamples to calculate the mean and 95% confidence intervals of each estimated parameter.For investigations of population-level genetic structure, we were cautious to process and filter the data so that uneven population sample size  and Colombo-2. We investigated this divergence further by calculating the folded allele frequency spectrum for each Ae. albopictus population in VCFtools [Analysis of the data indicated a deep genetic division within the Sri Lankan VCFtools , using tAedes spp. using distance-based redundancy analysis (dbRDA), performed in the R package vegan [Ae. albopictus subpopulation Colombo-1 from analyses , which lists all airline routes active at June 2014. For each sampled population, we used the total number of active flight routes at the largest international airport of that city or town. This gave values of between 2 (Christmas Island) to 408 (Singapore). Shipping and aerial connectivity scores for each population are listed in Tables Ae. aegypti  or Ae. albopictus  populations.Our measure for connectivity to shipping routes was derived from previously published raster data of 799,853 commercial shipping tracks from October 2004 to October 2005 . We used p 10.5.1  to calcupcnm. We likewise calculated geographical distance matrices for each species, and transformed these into principle coordinates.The connectivity of each population pair to the shipping and aerial networks was calculated as the average of their connectivity scores for that network type. These average scores were used to construct pairwise connectivity matrices of each transportation type for each species. As independent variables in a dbRDA must be rectangular, these matrices were transformed into principle coordinates using the function ST’ as the dependent variable. To test for effects of connectivity to transportation networks, we first determined scaled geographical distance vectors with which to condition the model, by building dbRDAs where each vector was treated as a separate independent variable, and selecting vectors with P < 0.01. We assessed shipping and aerial connectivity separately. We built dbRDAs using the scaled vectors of connectivity as the independent variable and FST’ as the dependent variable, with the model conditioned using the significant vectors for geographical distance. All dbRDAs were built using the function capscale. Marginal significance of variables was assessed using anova.cca with 9,999 permutations.To test for isolation by distance in each species, we built dbRDAs using the scaled vectors of geographical distance as the independent variable and FST’ estimates, and used the regression coefficients as indicators of directionality. These analyses were not used to determine significance of variables due to non-independence among pairwise terms.Once transformed into principal coordinates, geographical and connectivity variables ceased to indicate directionality, and thus dbRDAs could only ascertain significance of associations and not the direction of these associations. To determine directionality, we ran linear regressions on the normalised pairwise scores of these variables against normalised FAe. aegypti , Australia (cream), and all central and Western populations (purple)  and Colombo-2. As BIC is a conservative measure, these results represent broad genetic groupings, with more detailed variation described in sNMFs (DAPC determined that west BIC  was used(purple) . DAPC ofbopictus  showed ain sNMFs , PCA-UMAin sNMFs  and 5, ain sNMFs .Ae. aegypti  largely reflected the high level of structuring between populations, with five of the ten ancestral lineages almost wholly confined to single locations: Jeddah, Colombo, Taiwan, New Caledonia and Vanuatu. Proximate population pairs such as Cairns and Townsville, Kuala Lumpur and Singapore, and Ho Chi Minh City and Nha Trang each had common ancestral lineages, as did Fiji and Kiribati, despite their distance of separation. The most putatively admixed population was Bangkok, which showed genetic similarities with the other Southeast Asian populations, but not with either Taiwan or Colombo.The sNMF of  aegypti  found K Ae. albopictus  and 35,984–57,639 . Pairwise FST’ estimates for Ae. aegypti and Ae. albopictus are listed in ST’ estimates were generally consistent across the ten subsamples. FST’ confidence intervals in Ae. aegypti were on average only 5.3% the size of the mean; for Ae. albopictus, they were only 6.4% the size of the mean. Mean HO and π estimates were also mostly consistent among subsamples within populations, and are listed in Numbers of nuclear SNPs retained for population-level analyses had ranges of 46,151–54,647 , indicating high genetic similarity. Australian Ae. aegypti populations were genetically very similar, and there was a network of similarity among Southeast Asian populations  but not in Ae. albopictus . Linear regression indicated that this relationship was positive (regression coefficient = 0.52). When each scaled vector for geographical distance was treated as an independent variable, one was significant at P < 0.01 in Ae. aegypti and none were significant in Ae. albopictus.The dbRDAs assessing isolation by distance indicated that geographical distance was significantly associated with FST’ in Ae. albopictus was significantly associated with both shipping  and aerial  transportation routes , and one in Indonesia. Certain populations outside the native range showed clear signs of recent invasion from these regions; specifically, Mauritius from East Asia, and Fiji and Christmas Island from Southeast Asia. Aedes albopictus populations in Indonesia were highly differentiated from all others, despite their proximity to other Southeast Asian populations. In Colombo, we observed two distinct Ae. albopictus subpopulations, one of which may represent an ancestral native-range population or cryptic subspecies. Overall these results indicate that Aedes spp. have established invasions in the Indo-Pacific along different pathways, and that recent gene flow patterns are different between the two species. Our findings also point to various regions of interest in the Ae. albopictus native range that require further investigation.Here we present the first direct comparison of genome-wide genetic structure in the globally-invasive, dengue-vectoring mosquitoes smission , and thismission , increassmission . Furthersmission . Our comAe. aegypti fit with the hypothesis that Ae. aegypti began its invasion of the Indian Ocean region from regions to the west [Ae. aegypti populations were in the western Pacific populations of Australia, the Pacific Islands and Taiwan, suggesting that these regions do not share an invasion history with Indian Ocean regions. Separate invasions of Australia and the Pacific Islands also seem likely, based on the strong differentiation between Australian Ae. aegypti and those of nearby Pacific Islands such as Vanuatu and New Caledonia.The patterns of divergence observed in the west ,6. Putatthe west ,70, and Ae. aegypti populations may reflect an earlier invasion of these regions, possibly from the Americas. However, these divergences could instead be due to a lack of gene flow into these populations following invasion, with strong genetic differentiation produced by the effects of drift following founder events [Ae. albopictus genetic structure Click here for additional data file.S1 TableST' for Ae. aegypti in the Indo-Pacific region; (S1B Table) pairwise FST' for Ae. albopictus in the Indo-Pacific region; (S1C Table) heterozygosity and nucleotide diversity for Ae. aegypti in the Indo-Pacific region; (S1D Table) heterozygosity and nucleotide diversity for Ae. albopictus in the Indo-Pacific region; and (S1E Table) FST when calculated with five genotypes compared with when calculated with 16 genotypes.These tables show means and upper and lower confidence ranges of: (S1A Table) pairwise F(XLSX)Click here for additional data file.S1 FigAe. aegypti, K = 15 was used, while K = 11 was used for Ae. albopictus. The map uses a Mollweide projection with a central meridian of 120°E. The map was produced in arcmap 10.5.1, using shapefiles made available by www.naturalearthdata.com.Colours indicate cluster membership. Circles are sized relative to population sample size. For (TIF)Click here for additional data file.


We did not observe significant differences in genetic diversity among historical samples and modern representatives of Kholmogor and Yaroslavl breeds. The unbiased expected heterozygosity values were 0.726–0.774 and 0.708–0.739; the allelic richness values were 2.716–2.893 and 2.661–2.758 for the historical and modern samples, respectively. Analyses of STF and Jost’s D genetic distances, and the results of STRUCTURE clustering, showed the maintenance of a part of historical components in the modern populations of Kholmogor and Yaroslavl cattle. Our study contributes to the conservation of biodiversity in the local Russian genetic resources of cattle.Analysis of ancient and historical DNA has great potential to trace the genetic diversity of local cattle populations during their centuries-long development. Forty-nine specimens representing five cattle breeds , dated from the end of the 19th century to the first half of the 20th century, were genotyped for nine polymorphic microsatellite loci. Using a multiple-tube approach, we determined the consensus genotypes of all samples/loci analysed. Amplification errors, including allelic drop-out (ADO) and false alleles (FA), occurred with an average frequency of 2.35% and 0.79%, respectively. A significant effect of allelic length on ADO rate ( Since ancient times, cattle have been an essential element in the subsistence of humankind because they have provided humans with food, clothing, and draught force. Modern animal breeding uses a small number of highly productive commercial breeds originating from a limited number of elite sires . Most ofThe most prominent local breeds of black and white cattle, bred in the Central and Northern parts of European Russia, are Kholmogor and Yaroslavl . They orSeveral types of DNA markers have been used for genetic studies of modern cattle populations; the most widely used are mtDNA polymorphisms , microsaAnalysis of ancient and historical DNA ,23,24,25Studies of DNA derived from ancient samples tend to focus on the mitochondrial genome , becauseThe main problem associated with microsatellite genotyping of DNA derived from ancient and museum specimens are the amplification errors, such as false homozygotes, also known as allelic drop-out (ADO), and false alleles (FA) ,38, whicIn the present study, using a multiple-tube approach, we generated microsatellite genotypes for 49 historical samples dated from the end of the 19th century to the first half of the 20th century, representing four native Russian cattle populations of the Central and Northern parts of European Russia, and compared them with modern representatives of the same breeds that have been maintained until now to trace their biodiversity and characterise the relationship between both populations.n = 22), Yaroslavl , Novgorod , and Great Russian cattle . Historical samples of Holland cattle  were also included in the study because of their active importation to old Russia during the 19th century and the first half of the 20th century, and their possible contribution to the formation of Russian cattle breeds. A sample of modern representatives of Kholmogor  and Yaroslavl  breeds was used for comparison. A sample of modern Holstein breed  was used as an out-group.Cattle skulls dated from the end of the 19th century to the first half of the 20th century, which are stored in the craniological collection of the Museum of Livestock, named after E.F. Liskun , were used as the source of historical samples. A total of 46 historical samples of Russian local cattle populations were included in the study, including Kholmogor , according to the manufacturer’s instructions. Animal tissue samples were collected by trained personnel under strict veterinary rules in accordance with the rules for conducting laboratory research (tests) in the implementation of the veterinary control (supervision) approved by the Council Decision Eurasian Economic Commission No. 80 (10 November 2017). The sperm samples were provided by the artificial insemination stations.The quantitative and qualitative characteristics of the obtained DNA were evaluated by measuring the concentration of double-stranded DNA (dsDNA) using a Qubit™ fluorimeter  and determining the ratio of the absorption at 260 and 280 nm (OD260/280) on a NanoDrop 8000 instrument .The risk of genotyping errors decreases if more template DNA of good quality are available for PCR; thus, we set the threshold for concentration of dsDNA at 1 ng/µL and the limits for OD260/OD280 ratio at 1.6–2.0. The samples with DNA concentration lower than the threshold and out of the OD ratio limits were discarded, and new DNA extractions were carried out using an increased amount of bone powder or by replacing the DNA extraction kit.2, a 0.5 mM primer mix, 1 unit of Taq polymerase , and 1 µL of genomic DNA. After the initial denaturation , 35 cycles were performed at the following temperature and time regimens: 95 °C, 20 s; 63 °C, 30 s; and 72 °C, 1 min. The final extension was performed at 72 °C for 10 min. To check the PCR reagents for DNA contaminations, we included negative control (PCR reaction without DNA template) in each PCR experiment. Fragment analysis was performed on an ABI3130xl genetic analyser  using GeneScan™-350 ET ROX as a fragment standard. The Gene Mapper software v. 4  was used to determine the fragment lengths. Allele sizes were standardised according to ISAG International Bovine (Bos Taurus) STR typing comparison test 2018–2019.Nine cattle microsatellite loci were selected from the list of microsatellite markers recommended by the International Society of Animal Genetics (ISAG) ; the selTo determine the consensus genotypes, we used a modified multiple-tube approach, proposed by Mondol et al.  and usedBased on genotyping results of the five PCR replicates, we calculated the “quality indices” (QIs) for each sample/locus as described by Miquel et al. . The mosp) for each locus were calculated according He et al. [Amplification failure for each locus was calculated as the number of positive PCRs divided by the number of replicates. For ADO and FA rate calculations, we used the protocol proposed by Broquet and Petit . The ADOe et al. .We checked the presence of null alleles using FreeNA  and MICROH) heterozygosity, unbiased expected heterozygosity (UHE), unbiased inbreeding coefficient (UFIS), and rarefied number of alleles (RA), which measures the contribution of alleles weighted by the sample size [Observed  from 2 to 8 (corresponds to the number of studied populations), performing 10 independent runs for each K. STRUCTURE HARVESTER [K that explained the sample structure. The program CLUMPAK [http://clumpak.tau.ac.il, was used to analyse multiple independent runs at a single K value. Average pairwise similarity scores as a measure of the constancy over runs, implemented in CLUMPAK [The genetic structure was investigated using an admixture model with the option of correlated allele frequencies in the STRUCTURE 2.3.4 program . We set ARVESTER  with theARVESTER  was used CLUMPAK  availabl CLUMPAK , were usp) at each locus were less than threshold (p < 0.001)  . Amplifi< 0.001) .In total, ninety-five microsatellite alleles were identified in eight cattle populations, including eighty-one alleles from historical populations and eighty alleles from modern populations. All loci were polymorphic except for BM1824 in historical Novgorod cattle, which could be explained by the small sample size. While loci TGLA122 and TGLA227 were characterised by the highest variability , loci BM1824, SPS115, and TGLA126 were less polymorphic  .Details of variability measurement across 439 individuals of historical and modern cattle populations are summarised in UHE) values were 0.726–0.774 and 0.708–0.739; the allelic richness (RA) values were 2.716–2.893 and 2.661–2.758 for the historical and modern samples, respectively. The highest values of UHE and RA were detected in Great Russian cattle , whereas the lowest were detected in Holland cattle . We observed a significant deviation from the Hardy–Weinberg equilibrium in heterozygote number, only for historical samples of the Kholmogor breed (heterozygote excess), which has been indicated by a negative value of UFIS (−0.80).We did not observe any significant differences in genetic diversity between the historical and modern populations of Kholmogor and Yaroslavl cattle. The unbiased expected heterozygosity , 0.995 (K = 3), 0.984 (K = 4), 0.987 (K = 5), 0.987 (K = 6), 0.984 (K = 7), and 0.970 (K = 8). Results of the STRUCTURE analysis for the number of clusters from 2 to 4 are presented in K = 2, Holsteins are clearly differentiated from historical and modern Russian local cattle populations, thereby indicating their development from different ancestral populations. We observed the visible presence of Holstein-specific genetic components in part of the historical samples of the Kholmogor and Yaroslavl breeds and in Holland cattle. At K = 3, the modern Yaroslavl breed was clearly separated from the Kholmogor breed; several modern Kholmogor cattle revealed admixture signals. At this K value, the cluster structure of modern Kholmogor and Yaroslavl cattle was similar to that of historical populations of the respective breeds. Analysis at K = 4 showed that both modern Kholmogor and Yaroslavl breeds maintained the historical components in their cluster structures.The highest or K = 2 . The aveOwing to centuries of breeding history, local breeds of cattle have formed a unique gene pool that makes them a valuable source of biodiversity ,3,4,5. S2r = 0.729, p = 0.01). Among the genotyping errors, ADO occurred more frequently compared to FA , and agreed with the results of other studies of museum and ancient samples [2r = 0.620, p = 0.05). For seven out of nine loci, the longer allele failed to amplify more often than the shorter allele; however, the differences were significant (p < 0.05) only in loci TGLA122 and TGLA126  and false alleles (FA) ,38. As t samples ,80. We o TGLA126 .STF-based network (STF-based and Jost’s D networks (Genetic diversity level, detected in historical populations, was comparable with those in modern populations of Kholmogor and Yaroslavl breeds , and agr network A, Jost’s network B, and ST network  showed anetworks A,B, and networks , confirmThe obtained result of the current study are clearly encouraging and are useful for managing modern populations of the studied breeds following the principle of sustainable animal breeding , while sBy using a multiple-tube approach, we managed to extract consensus genotypes at nine microsatellite loci for historical specimens of the Russian local cattle breeds dated from the end of the 19th century to the first half of the 20th century. Conducted comparative studies detected the maintenance of the historical genetic components in modern Kholmogor and Yaroslavl cattle populations. Results of our research confirmed the great potential of studying historical DNA of livestock species for elucidation of breed history and selection of breeds and individuals for germplasm conservation.


Astyanax mexicanus from the river basins of the Gulf of Mexico slope are small freshwater fish that usually live in large groups in different freshwater environments. The group is considered successful due to its high capacity for dispersal and adaptation to different habitats, and the species present high morphological variability throughout their distribution in Mexico. This has produced the most extreme morphotype of the group; the hypogeous or troglobite, which has no eyes or coloration, and is probably the cause of taxonomic uncertainty in the recognition of species across the entire range. Most studies of A. mexicanus have mainly focused on cave individuals, as well as their adjacent surface locations, providing an incomplete evolutionary history, particularly in terms of factors related to dispersal and the potential corridors used, barriers to gene flow, and distribution of genetic variability. The aim of the present study is to determine the population structure and the degree and direction of genetic flow in this complex taxonomic group, incorporating geographic locations not previously included in analyses using microsatellite loci. Our aim is to contribute to the knowledge of the intricate evolutionary history of A. mexicanus throughout most of its range.A. mexicanus, which have been widely sampled along the Gulf of Mexico slope, in a genetic population analysis using 10 microsatellite loci.The present study included a set of several cave and surface locations of Ten genetic populations or lineages were identified. In these populations, gene flow was recorded at two time periods. Historical gene flow, both inter and intra-basin, was observed among surface populations, from surface to cave populations, and among cave populations, whereas recording of contemporary gene flow was limited to intra-basin exchanges and observed among surface populations, surface to cave populations, and cave populations. Astyanax is a Neotropical genus found in North American basins, reaching as far north as the Bravo River hydrographic system  to similar environments , and currently, are even considered to be different species   . TherefoAstyanax mexicanus constitute an excellent study model group, not only because of their widely studied traits associated with parallel evolution, but also due to their high capacity for dispersal across a wide geographic area that has allowed colonization of different habitats and the occurrence of distinct recurrent introgression events over time .Total DNA was extracted from fin tissue using a DNeasy blood and tissue kit from Qiagen. Ten polymorphic microsatellite loci developed for the genus Astyanax  were stuAstyanax  through Astyanax . AllelicIn order to detect the presence of null alleles at every locus and in each location, the Brookfield 1 Index  and the O), and the fixation index (FIS), using GENETIX 4.05 (R) was calculated in the function allele.rich in PopGeReport. v3.0.4 (Genetic variability within populations (as estimated by STRUCTURE) was evaluated by the number of alleles (N), number of alleles per locus (Na), effective number of alleles (Nae), observed heterozygosity  were performed by multiple assessment  was performed using the ADEGENET package  in R CorA. mexicanus [(CC)+], subclades of A. aeneus , and the clade of A. hubbsi ; (C) hydrographic basin criteria considering the seven independent river basins included in the present study ; (D) Localities were grouped according to their degree of genetic differentiation as measured by the FST, which resulted in the grouping of those locations that presented non-significant differentiation levels . Ten thousand permutations were used in each of these analyses.Analysis of molecular variance (AMOVA) was conducted for four different partitions of the 16 locations using ARLEQUIN 3.5 . The folK = 1 to K = 16, including all locations, and for K = 1 to K = 12, without cave locations, that is, including only surface locations. The mean and variance of the log likelihoods for each K were calculated from 10 independent runs of 100,000 iterations in order to determine the highest posterior probability K. To estimate the true number of clusters represented along the sampled range of Astyanax spp. . Four distinct Mantel tests were performed as follows: (1) using all 16 locations; (2) using only the surface populations; (3) using only the locations located north of the TMVB and (4) using only the locations south of the TMVB. A total of 10,000 permutations were required to estimate the 95% upper tail probability of the matrix correlation coefficients.The pattern of isolation-by-distance (IBD) among locations was assessed by performing a Mantel test, based on pairwise e generations  were estimated between locations. Different migration model parameter distributions were used between every location detected by MIGRATE. Microsatellite mutation was modeled as a continuous Brownian process and the mutation rate was set to constant for all loci. A static heating scheme was used with four chains and temperatures , for which the initial Φ and m values were generated from the FST option. Exponential priors were placed for both Φ (bounded between 0 and 50.0) and M (bounded between 0 and 100.0). The analyses were conducted using 10 replicates of a single long Markov chain, with 100,000 steps recorded for every 100 generations and the first 500,000 trees per run discarded as burn-in. Three patterns of migration were estimated in A. mexicanus from the Gulf of Mexico slope: among surface populations, among cave populations, and from surface to cave. Gene flow from cave to surface has been regarded as a nonexistent event, as reflected in its negligible estimate  was estimated using BAYESASS 3.04 . This prAm145a for GV/TR, locus Ast02 for AL/LCA/SLP and SAB/TIN, and loci Ast10 and Am106b for CAT. Nevertheless, the HW multilocus did not show deviation from equilibrium , and FreeNA detected the presence of null alleles at five loci in different genetic populations: locus Ast09 for CC, locus Am241b for SF, locus Am145afor GV/TR, loci Am122b and Am106b for AL/LCA/SLP, and locus Am106bfor CAT, but, none of them at frequencies greater than 0.15  and minimal intra-group variance    . STRUCTU = 0.00) . The Pán = 0.00) , which w values) ; Table 2 values) .FST, DAPC, AMOVA, and STRUCTURE agree that the number of homogeneous genetic groups along the Atlantic slope is ten, consisting of three cave populations, and seven surface populations.In summary, the results of R2 = 0.07, P = 0.908; R2 = 0.067, P = 0.125, respectively). A fourth Mantel test, using all surface locations except for CAT (11 locations), also recorded a significant correlation between genetic and geographic distances  and 12 (surface only) locations, the results showed a non-significant correlation  were presented by caves locations , except for the highest genetic flow values recorded among distinct basins. Of particular note is the TR location from the Soto la Marina basin that presented a symmetric flow with the CC population, and the same TR that presented an asymmetric flow toward the cave population PCH. All three of these populations are assigned to A. mexicanus  among locations and populations show low values  ranged from 1 to 23 (average = 8.30), number of effective alleles (Nae) ranged from 1 to 11.8 (average = 4.0), allelic richness (AR) ranged from 1 to 13.9 (average = 5.8), observed heterozygosity (Ho) presented values from 0 to 0.923 (average = 0.485), expected heterozygosity (uHe) ranged from 0 to 0.921 (average = 0.556), and the fixation index (FIS) ranged from −0.292 to 0.665 (average = 0.145). As expected, the cave populations  presented the lowest values of genetic variability and the highest values of the fixation index, while the surface populations showed higher variability and lower values of the fixation index , the three cave populations differentiated considerably  also revealed a marked separation between cave and surface populations  was observed among the following seven genetic clusters of surface populations, revealing a hydrographic divergence pattern: CC (Bravo basin), SF (San Fernando basin), GV+TR (Soto la Marina basin), V (Tuxpan basin), AL+SLP+LCA (Pánuco basin), CAT , and TE+TA+RTZ   in which all 12 surface locations were analyzed. Thus, the pattern of differentiation among surface populations is structured by the hydrographic basins along the Atlantic slope of Mexico. Based on SNPs nuclear loci and practically the same location sampling as used in the present study, Bramocharax). Recent findings confirmed that this population belongs to the genus Astyanax (sensuThe third level of genetic differentiation (a basin) ; Fig. 1.Astyanax . AlthougAstyanax  within tAstyanax . The hetax sensu.FST = 0.05–0.10) involved the AL, SLP, and LCA locations within the Pánuco basin, and locations from the Grijalva-Usumacinta population, in addition to the CAT population in relation to the Grijalva-Usumacinta population (A. mexicanus and A. aeneus lineages (FST = 0.01–0.02), occurred only among surface populations within the same river basin, particularly in the northern Soto la Marina and southern Grijalva-Usumacinta basins  whose surface relatives were extinct, while other cave populations were considered more recent, and therefore closer to current surface fish populations. This scenario involved two types of cave populations derived from an ancient invasion and a recent invasion  considers that a cave population within “Sistema de los Sabinos” experienced a partial fragmentation, implying the occurrence of an isolation stage with low connectivity as indicated by the historical gene flow (e2) scenario consists of independent isolation events in each of these caves derived from a common surface ancestor, which would imply two independent cave invasions in “Sistema de los Sabinos”, with subsequent secondary contact. These scenarios could both reflect the very dynamic karst system in northeastern Mexico, associated with the formation of new caves, and/or incidents of separation or fusion of these systems , Herichthys cyanoguttatus (Baird & Girard 1854), and Micropterus salmoides (Lacepède 1802). There are two likely connections: (1) a hypothetical ancestral corridor through the intermediate headwaters of the San Fernando River Basin, and (2) an ancestral coastal system like the current Laguna Madre, an extensive coastal lagoon into which the Bravo, San Fernando, and Soto la Marina rivers flow , there were four cases of moderate to high gene flow . The higers flow . The tol lagoons . This inA. aeneus s. stricto from Guerrero, Mexico in the Pacific slope. Further, in the same study genetic exchange was found among the A. aeneus population and the Choy surface population  and ML cave population. Rather than regarding these two gene flow events as evidence of direct connection between populations, the authors interpreted them as signals of ancient hybridization in the genome. Based on the above, an alternative explanation to the scenarios of the connections discussed above, is that the historical gene flow signal observed in the present study, indicates a past genetic exchange in a common ancestral area where the lineages that preceded the present populations met. Finally, the low estimates of historical gene flow recorded between populations located north and south of “Punta del Morro” confirm that this volcanic mountain is a biogeographic barrier to freshwater fish  and Soto la Marina (TR population) basins towards southern populations  in the Pánuco basin . The othter fish .The significant and unidirectional contemporary gene flow between surface populations observed only between locations in the same basin , suggests a continuous corridor between source and target locations.FST value (0.27). Estimates of both the differentiation index and gene flow gave results similar to those recorded among surface populations from different river basins , number of alleles (Na), effective alleles (Nae), allelic richness (R), observed heterozygosity (Ho), expected heterozygosity (He) and fixation index (F). (*) Hardy-Weinberg disequilibrium after the Bonferroni adjustment with Click here for additional data file.10.7717/peerj.10784/supp-5Supplemental Information 5P ≤ 0.05). For the meaning of acronyms, see Estimation conducted according to the Brookfield 1 estimator (B) in the MICRO-CHECKER  only locations to the north of the Trans- Mexican Volcanic Belt . (B) all surface locations, except for Catemaco. Click here for additional data file.10.7717/peerj.10784/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.10784/supp-9Supplemental Information 9Allele sizes of micosatellites loci.Click here for additional data file.


Kumhars, a group of people who inhabit large parts of northern India. We compared 27 previously published Kumhar SNP genotype data sampled from Uttar Pradesh in north India to various modern day and ancient populations.The population structure of the Indian subcontinent is a tapestry of extraordinary diversity characterized by the amalgamation of autochthonous and immigrant ancestries and rigid enforcement of sociocultural stratification. Here we investigated the genetic origin and population history of the Kumhars have high ASI ancestry, minimal Steppe component and high genomic proximity to the Kurchas, a small and relatively little-known population found ~ 2500 km away in Kerala, south India. Given the same, biogeographical mapping using Geographic Population Structure (GPS) assigned most Kumhar samples in areas neighboring to those where Kurchas are found in south India.Various approaches such as Principal Component Analysis (PCA), Admixture, TreeMix concurred that We hypothesize that the significant genomic similarity between two apparently distinct modern-day Indian populations that inhabit well separated geographical areas with no known overlapping history or links, likely alludes to their common origin during or post the decline of the Indus Valley Civilization (estimated by ALDER). Thereafter, while they dispersed towards opposite ends of the Indian subcontinent, their genomic integrity and likeness remained preserved due to endogamous social practices. Our findings illuminate the genomic history of two Indian populations, allowing a glimpse into one or few of numerous of human migrations that likely occurred across the Indian subcontinent and contributed to shape its varied and vibrant evolutionary past. The Indian subcontinent and adjoining regions in South Asia have been a cradle for several waves of human migration during Paleolithic, Neolithic Periods, Bronze and Iron Age –6. The gKumhars, a north Indian population that has likely been practicing endogamy over long periods of time, as evidenced by their Identical by descent (IBD) scores that are significantly higher than that of the Ashkenazi Jews and the Finns [Kumhars are found throughout large parts of northern, western, and eastern India, as well as in Pakistan. The name ‘Kumhar’ is derived from the Sanskrit term ‘Kumbhakar’, which literally means earthen-pot makers alluding to their ancestral way of earning a living [Kulal or Kalal, a term phonetically similar to Kulala, a group of people from Kasaragod district of the southern Indian state of Kerala, whose traditional occupation is also pottery. The phonetic similarity between the two terms is potentially due to their common origin from the Yajurveda, an ancient Vedic Sanskrit text where potters were termed as ‘Kulals’ [Kumhars and examined their genomic similarity with other populations from the Indian subcontinent. To this end we assessed 27 previously published Kumhar samples, which were sampled from the north Indian state of Uttar Pradesh and compared to 2013 modern day South Asian populations [Here we have investigated the population history of the he Finns . Kumharsa living . Interes‘Kulals’ . In thisulations , 23.Ashkenazi Jews, Kalash and other Pakistani populations, and Shia Iranians from Hyderabad clustering at one extreme of the cline, and Juang and other AAA populations congregating at the other extreme , the remaining clustered with ASI and AAA samples, largely overlapping with tribal populations from Kerala such as the Kurchas. According to PCA, the only North Indian population that revealed genomic proximity to Kumhars, were the Syons from Uttarakhand. Further we note that the three above-mentioned outlier Kumhar samples overlapped with a population cluster that largely comprised of various non-Brahmin backward castes from Uttar Pradesh.Principal component analysis (PCA) of South Asian samples exhibited previously described , 26 ANI eme Fig.  Fig. S2.FST between Kumhars and 63 selected populations across India using Weir and Cockerham approach [Kumhars to be genetically almost identical to Kurchas from Kerala in southern India (weighted FST = 0.0008)  followed by Vishwabrahmins from Andhra Pradesh, south India (weighted FST = 0.0192) and Chakkiliyans from Tamil Nadu, south India (weighted FST = 0.0196), and farthest from largely homogeneous populations from Kerala and Tamil Nadu such as Adiyan (weighted FST = 0.044), Paniyas (weighted FST = 0.054), Narikuruvar (weighted FST = 0.055), Pulliyar (weighted FST = 0.056), Malaikuravar (weighted FST = 0.07) and Ulladan (weighted FST = 0.077). Weighted pairwise FST between Kurchas and the same 63 populations across India revealed similar results, indicating that Kurchas are genetically more similar to Kumhars than any other Indian populations including its neighboring ones , Juang , Ashkenazi Jews and Shia Iranians , Vysya , Kumhar and Kurchas , Palliyar , Pulliyar , Malaikuravar, Narikuruvar and Hakki Pakki , Ulladan , ATB , and Siddi  populations were assigned to distinct clusters. Congruent with previous studies [k3, likely derived from Bronze Age Steppe populations), red  and tan . Component k5, which was assigned to Kumhars and Kurchas, was found to be present in discernibly higher proportion among most non-Brahmin south Indian populations, indicating genomic similarity between Kumhars and ASI populations potentially linked to their common origin and admixture history. Further, the Bronze Age Steppe ancestry proportion was found to be the lowest in Kumhars compared to other populations from Uttar Pradesh and Uttarakhand , and were also found to be outliers in principal component analysis. Both PCA and ADMIXTURE analysis suggested that origin of these three Kumhar samples was divergent from the rest.The genomic ancestry of all 2040 individuals present in the URE v1.3 . The low studies , 16, 26,FST and ADMIXTURE analyses, the ML tree generated by TreeMix revealed high degree of genetic relatedness between Kumhars and Kurchas, with Kurumba  and Sugali (Andhra Pradesh) populations as their sister groups  or an AASI related (Juang) population or a backward caste population  from north and central India  likely coincides with or after the decline of the IVC [We applied ALDER v.1.02  to inves the IVC , 31.Kumhar samples was performed using the GPS algorithm. Barring stockplate_14_C2, stockplate_14_C4, stockplate_14_C6, and CCMB_PL_9_298, GPS assigned all 23 Kumhar samples to south-western Karnataka across Western Ghats in southern India with 16 being localized at the Karnataka-Kerala border , Steppe-related (Steppe-MLBA) and Iran-Turan-related (Indus_Periphery) as Left  and O8 was used as the ‘Right’ outgroup (see Methods). Congruent with ADMIXTURE analysis, qpAdm analysis revealed that Kumhars (10.5%) are among the three populations from Uttar Pradesh, north India with low Bronze Age Steppe ancestry , the Steppe ancestry reduced to 9.5% and Kumhars emerged as the population with the second lowest Steppe ancestry after Sahariyas. It is noteworthy that Onge, Steppe_MLBA and Indus_Periphery ancestral component in Kumhars, after removal of the three outliers , was found to be very similar to that in Kurchas  and Kurumans from Kerala , and Chakkiliyans from Tamil Nadu , in south Indian reflecting the significant genomic proximity between Kumhars and ASI populations.We used qpAdm  implemenqpAdm  to estim Kumhars 0.5% are Kumhars (W) and Kurchas (Y)  employed in this study, indicating gene flow between Kumhars, a group of people who traditionally worked as potters and are found over large parts of north, west and east India.Populations from the Indian subcontinent are envisaged as a Pleistocene gene pool , 34–36 aPottery is the art of creating objects from non-metallic minerals, such as earthenware, porcelain by molding them when wet and subsequently firing them at high temperatures. It was practiced by potters referred to as Kumhars in northern, western and eastern regions of the Indian subcontinent. The origin of pottery in the Indian subcontinent can be traced back to cord-impressed style of ceramic ware from the Mesolithic period, found at the site of Lahuradewa, dating back to 7000–6000 BCE . EvidencKumhar SNP genotype data [In India, Kumhars and their Southern counterparts such as Kulals , Kummara (Andhra Pradesh and Telangana), Kumbara and Kummari (Andhra Pradesh) are synonymous with pottery. We interrogated 27 previously published ype data  and compstockplate_14_C2, stockplate_14_C4, stockplate_14_C6), all Kumhars congregated with those of ASI and AAA ancestries, largely overlapping with tribal populations from Kerala, in southern India, such as the Kurchas , followed by Kurumbas from Kerala (weighted FST = 0.019), Vishwabrahmins from Andhra Pradesh, south India (weighted FST = 0.0192) and Chakkiliyans from Tamil Nadu, south India (weighted FST = 0.0196) e of IVC , 18.Kumhars and other ethnic groups from the same geographic regions in Uttar Pradesh and its adjoining state of Uttarakhand, in north India. The Kumhars appeared to have an overwhelming ASI ancestral component; 24 out of 27 Kumhars appeared to be identical (> 98%) to a small population known as the Kurchas from the Wayanad district in the south Indian state of Kerala, which is approximately 2500 km south of the region from where the Kumhar samples were obtained. Similar to the Kumhars little is known regarding the Kurcha population and to the best of our knowledge there is no existing literature that describes any anthropological or historical connection between Kurchas with either the Kumhars or the Kulalas.In brief, we found very little similarity between Kumhars and Kurchas, which despite significant geographic isolation have remained almost identical, genetically, shining light on how largescale population movements that spanned across the Indian subcontinent over extensive periods of time together with imposition of sociocultural hierarchies have contributed to its diverse evolutionary heritage.Here, we propose that the significant genomic similarity between two apparently distinct modern day Indian populations that correspond to well separated geographical areas separated by ~ 2500 km with no known overlapping history or links likely alludes to their common origin during or after the decline of IVC; subsequently the two populations likely migrated towards opposite ends of the Indian subcontinent but their genomic integrity was preserved owing to stringent enforcement of endogamy. Our findings illuminate the population history of two Indian groups, allowing a glimpse into one or few of numerous of human migrations that likely occurred across the Indian subcontinent and have shaped its varied and vibrant evolutionary past. Overall our findings help to reconstruct the genomic history of two Indian populations, the Kumhars are significantly higher than that of the Ashkenazi Jews and the Finns, consistent with social practices of consanguinity among them [Kumhar population [Previous reports have indicated that the IBD scores for ong them . This ispulation .As mentioned earlier, Kumhars are distributed throughout North India and Pakistan. However, since we did not genotype/sequence any Kumhar sample and our study was completely based on previously published SNP genotype data, we were limited in terms of sample size and distribution. The same is applicable for the Kurchas. We could only employ four Kurcha samples in this study due to unavailability of Kurchas in the published datasets. It can be speculated that sampling across various parts of India can significantly improve the robustness of the study.Kumhars with various south Indian groups, including the so far little known Kurchas, offering some insight into the latter’s genomic history and likely predisposition to genetic disorders. It further underscores the importance of uncovering founder events among Indian populations and prioritizing them for studies dissecting genetic diseases and their underlying etiology.Overall, our results reflect the high genomic similarity of Modern South Asian only dataset we merged two previously published datasets [Kumhar samples, and corresponding to total 265 Indian ethnic groups, and assessing 91,781 single nucleotide polymorphisms (SNPs). The Modern South Asian only dataset was then merged with two ancient DNA datasets comprising of 294 and 362 ancient individuals, respectively (N = 2696) [Ancient-Modern dataset comprising of 4575 individuals, and assessing 91,768 SNPs. File conversions and manipulations were performed using EIG v7.2 [https://www.cog-genomics.org/plink2/).To generate the datasets , 23 usindatasets . This da = 2696) , 40. Fin = 2696)  across tEIG v7.2  and PLINEIG v7.2  (https:/Modern South Asian only dataset (N = 2040) was used for all genome-wide SNP analyses to describe fine-scale population structure recapitulating the population history of Kumhars.FST between Kumhars and 63 selected populations across India using the Weir and Cockerham approach [We calculated mean and weighted pairwise approach  implemenapproach . PLINK eThe fine population structure of the modern South Asians was delineated using Principal Component Analysis (PCA) implemented in PLINK v1.9  using –pK) was determined by minimizing the cross-validation error (CVE) using a –cv flag to the admixture command line. The lowest CVE was estimated for K = 11  tree for 84 selected populations comprising of all 35 populations from Uttar Pradesh and Uttarakhand, all 10 populations from Kerala, and 39 populations across the rest of India by using TreeMix v.1.12  in orderKumhars and other South Asians considering a generation time of 28 years. Kumhars were included as the ‘admixpop’ (admixed population) and the remaining South Asian populations present in the Modern South Asian only dataset were used as the ‘refpops’ (reference populations).We applied ALDER v.1.02  to compuKumhars. We curated the reference dataset using the rest of south Asians present in the Modern South Asian only dataset except Siddis and Ashkenazi Jews.Biogeographical analysis was performed using the Geographical Population Structure (GPS) algorithm, which has been successfully used to reconstruct history of several populations worldwide –53. GPS qpAdm [Ancient-Modern dataset comprising of 4575 ancient and modern-day samples from across the world was employed for this analysis. In accordance with existing literature [qpAdm analysis. All South Asians were modelled as a combination of three source populations namely Andaman Islanders (Onge), Steppe-related (Steppe-MLBA) and Iran-Turan-related (Indus_Periphery) as Left  as already described [Mabuti.DG, SHG, EHG, Ganj_Dareh_N, Anatolia_N, West_Siberia_N, Han and Karitiana our ‘Right’ outgroup populations (O8).We used qpAdm  implemenqpAdm  to estimterature , three aescribed . We usedqpDstat function implemented in AdmixTools v5.1 [To investigate whether Kumhar is genetically closer to Kurcha than to any other South Asian group used in this study, we employed ols v5.1  in orderPop1 (Kumhar) Pop2 (Modern South Asian populations): Pop3 (Kurcha) Pop4 (Onge).Onge was used as an outgroup since it has been disconnected from the mainland populations long while ago. Here, while positive Z-scores will indicate gene flow between Kumhar and Kurcha, negative scores will indicate gene flow between other South Asian population(s) and Kurcha.Additional file 1. Supplementary Material


Gasterosteus aculeatus) is a remarkable system to study the genetic mechanisms underlying parallel evolution during the transition from marine to freshwater habitats. Although the majority of previous studies on the parallel evolution of sticklebacks have mainly focused on postglacial freshwater populations in the Pacific Northwest of North America and northern Europe, we recently use Japanese stickleback populations for investigating shared and unique features of adaptation and speciation between geographically distant populations. However, we currently lack a comprehensive phylogeny of the Japanese three-spined sticklebacks, despite the fact that a good phylogeny is essential for any evolutionary and ecological studies. Here, we conducted a phylogenomic analysis of the three-spined stickleback in the Japanese Archipelago.The three-spined stickleback (Gasterosteus nipponicus).We found that freshwater colonization occurred in multiple waves, each of which may reflect different interglacial isolations. Some of the oldest freshwater populations from the central regions of the mainland of Japan (hariyo populations) were estimated to colonize freshwater approximately 170,000 years ago. The next wave of colonization likely occurred approximately 100,000 years ago. The inferred origins of several human-introduced populations showed that introduction occurred mainly from nearby habitats. We also found a new habitat of the three-spined stickleback sympatric with the Japan Sea stickleback (These Japanese stickleback systems differ from those in the Pacific Northwest of North America and northern Europe in terms of divergence time and history. Stickleback populations in the Japanese Archipelago offer valuable opportunities to study diverse evolutionary processes in historical and contemporary timescales. The presence of phylogenetically independent lineages adapting to similar environments offers great opportunities to investigate the roles of natural selection in phenotypic evolution . FurtherBecause transition from marine to freshwater habitats occurred in multiple lineages , 8, we cGasterosteus aculeatus) are a remarkable system to study the genetic mechanisms underlying this transition ) diverged at 24 ka (95HDI = 14–34 ka) BP.The topology of the species tree obtained by the SNAPP analyses was identical among all the runs and were generally congruent with the ML trees Fig. , Fig. S3As for the North American and European populations, the divergence of an East Pacific freshwater population (Little Campbell Stream) from the rest occurred at 128–129 ka (95HDI = 76–182 ka) BP. European and Pacific populations excluding this old Pacific freshwater lineage diverged 72–73 ka (95HDI = 45–99 ka) BP. The East Pacific marine population from the estuary of Little Campbell River diverged from the Japanese Pacific Ocean marine population 38–39 ka (95HDI = 22–53 ka) BP. North European freshwater (Grosser Ploener See) and marine (Lemvig) populations diverged 13 ka (95HDI = 7–19 ka) BP.G. aculeatus clade ), Aizu , and Ono (FW Ono [F14]) were estimated to have diverged at approximately 100 ka BP. This is still before the latest Pacific–Atlantic split, which has been suggested to have occurred when the Bering Strait closed somewhere between 34 and 75 ka BP during the last glacial period . Other fG. aculeatus. The waters inhabited by the Aizu and Ono populations are drained by the rivers that flow into the Sea of Japan, where G. aculeatus is absent at present  were derived from either Aizu or Nasu populations. Although non-native populations can provide opportunities to study the process of adaptation to novel habitats on a contemporary timescale  and FW Towada [FN5]) were genetically similar to imescale , 32, theimescale . Native imescale .G. aculeatus and G. nipponicus at the eastern end of Hokkaido (Okinebe [Oki]). Based on the Q values of the ADMIXTURE analysis, among 32 fish analyzed, two individuals were F1 hybrids and one was a backcross to G. nipponicus. Okinebe Pond is relatively small  and is connected to the Pacific Ocean by a short stream approximately 200 m in length. The frequency of hybrids in this pond is relatively high compared to previously investigated sympatric habitats. Previous genomic studies have shown that sympatric habitats can differ in the magnitude of reproductive isolation and hybridization [In addition to previously reported sympatric habitats , 61, 62,dization , 38. ThiG. aculeatus and G. nipponicus enable us to test whether the same genomic loci are resistant to introgression or likely to introgress between closely related species [Stickleback populations in the Japanese Archipelago offer valuable opportunities to study a wide spectrum of evolutionary processes in historical and contemporary timescales. First, Japanese freshwater populations provide phylogenetically independent and geographically distant replicates of stickleback freshwater populations. Using these systems, we can test the extent to which causative alleles and genes are shared among independent lineages adapting to similar environments and what factors determine the probabilities of sharing the same alleles and genes –5. Secon species . By char species , 65. In G. aculeatus, G. nipponicus, and possibly their hybrids are supposed to be included.All sticklebacks were collected with seine nets and minnow traps as described previously , 66, 67 Genomic DNA was isolated using a DNeasy Blood & Tissue Kit . Double digest RAD sequencing (ddRAD-seq) was performed as described previously . BrieflyG. aculeatus collected from PO Akkeshi (P4), G. nipponicus from JS Akkeshi (J4), and Gasterosteus wheatlandi, we used the previously reported whole genome sequences [G. aculeatus from FW Aisaka (FN4) and FW Towada (FN5) were derived from a previous study [G. aculeatus from northern Europe, the sequences of two randomly selected samples from the marine population reported previously [Additionally, we used publicly available whole genome sequence (WGS) data  or those in masked regions or on ambiguous nucleotides in the reference sequence were discarded. Samples with excessively missing genotypes (> 80%) were excluded with BCFtools. This process resulted in a dataset of 97,145 SNPs genotyped in a total of 310 samples.The pre-processed variant calls from ddRAD-seq and WGS were merged using BCFtools. Block substitutions were decomposed into their constituent SNPs using vt. Indels, invariant sites, and sites on the sex chromosomes of K from one to nine. The results were summarized and visualized using CLUMPAK [https://clumpak.tau.ac.il/index.html).In order to investigate genetic differentiation and potential introgression among the stickleback populations in the Japanese Archipelago, we first used a model-based likelihood clustering algorithm implemented in ADMIXTURE v1.3.0 . We sele CLUMPAK  on the wWe also conducted principal component analyses (PCA), using the adegenet v2.1.1 package , 87 of RG. aculeatus and G. nipponicus, which would violate basic assumptions of phylogenetic reconstruction methods and bias tree topology and branch lengths. Hybrid individuals were identified using the ADMIXTURE analysis described in the section of Q values assuming K = 2. When both a Q value for the G. aculeatus cluster and that for the G nipponicus cluster at K = 2 were < 0.875, that individual was classified as a hybrid. The identified hybrids were concordant with those detected by PCA  phylogenetic trees were constructed with RAxML-NG v0.9.0  based onK from one through four. Identification of hybrid individuals was conducted based on Q values from the ADMIXTURE analysis assuming K = 3. If the Q values of an individual for any cluster did not exceed 0.875, it was classified as a hybrid. As a result, three putative hybrids from Duwamish and Big Soos were removed  BP following our previous study [Root divergence was used as the calibration point. We adopted two previously published estimates as the time of divergence between us study , estimatus study  based onus study , we inclus study , and witG. aculeatus  and that of two European populations were set as constraints. We used a script by Matschiner [6 generations starting from different initial trees. Trees were sampled every 5000 steps and checked for convergence to the stationary distribution and a sufficient effective sample size (ESS > 200) using Tracer v1.7.1 [Prior for the divergence time was specified to follow a log-normal distribution with means in real space to the respective divergence times , and with a standard deviation of 0.18 so that 95% intervals of the two priors do not overlap. We fixed a population parameter theta, which is proportional to the product of effective population size and mutation rate per site, to be equal across lineages with a uniform prior, following Stange et al. [tschiner  to prepar v1.7.1 . The firr v1.7.1 . Maximumr v1.7.1  and visur v1.7.1 .Additional file 1: Fig. S1. Cross-validation errors for each K from the ADMIXTURE analyses for Japanese populations.Additional file 2: Fig. S2. Scatter plots of principal components of genetic differentiation in the Japanese populations based on 813 SNPs. The contributions of each principal component are shown in the parentheses.Additional file 3: Fig. S3. Time-calibrated species trees of representative populations of Gasterosteus aculeatus and G. nipponicus inferred with SNAPP based on 2022 SNPs. Results of three independent runs are shown. The trees recorded in a run are overlaid by the maximum clade credibility tree. Posterior probabilities of each node are shown. Each bar indicates the 95% highest posterior density interval of the node height.  Calibrated with root divergence at 680 ka BP.  Calibrated with root divergence at 1.38 Ma BP. Individuals from Japanese freshwater populations of G. aculeatus are highlighted in blue.Additional file 4: Table S1. Information of collection sites.Additional file 5: Table S2. Sample informationAdditional file 6: Fig. S4. Summary of the flow of bioinformatic analyses.Additional file 7: Fig. S5. (A) Bar plots showing the results of the population structure analyses of East Pacific samples based on 3790 SNPs with ADMIXTURE (K = 2–4). Individuals are represented as vertical bars with different colours being proportional to the genotypes belonging to each genetic cluster. (B) Cross-validation errors for each K from the ADMIXTURE analyses of the East Pacific populations.