Mauritia flexuosa L.f. palm is known as the “tree of life” given its importance as fundamental food and construction resources for humans. The species is broadly distributed in wet habitats of Amazonia and dry habitats of the Amazon and Orinoco river basins and in the Cerrado savanna. We collected 179 individuals from eight different localities throughout these habitats and used microsatellites to characterize their population structure and patterns of gene flow. Overall, we found high genetic variation, except in one savanna locality. Gene flow between populations is largely congruent with river basins and the direction of water flow within and among them, suggesting their importance for seed dispersal. Further, rivers have had a higher frequency of human settlements than forested sites, contributing to population diversity and structure through increased human use and consumption of M. flexuosa along rivers. Gene flow patterns revealed that migrants are sourced primarily from within the same river basin, such as those from Madeira and Tapajós basins. Our work suggests that rivers and their inhabitants are a critical element of the landscape in Amazonia and have impacted the dispersal and subsequent distribution of tropical palm species, as shown by the patterns of genetic variation in M. flexuosa.The  During my residence in the Amazon district I took every opportunity of determining the limits of species, and I soon found that the Amazon, the Rio Negro and the Madeira formed the limits beyond which certain species never passed. The native hunters are perfectly acquainted with this fact, and always cross over the river when they want to procure particular animals, which are found even on the river's bank on one side, but never by any chance on the other. On approaching the sources of the rivers they cease to be a boundary, and most of the species are found on both sides of them.. We selected 10 microsatellites previously designed for 2.3Vegan v 2.4‐6 package , expected heterozygosity (He), and the inbreeding coefficient (F) using Arlequin v 3.5 . The eight collection sites  were divided into four groups based on major geographic areas: Madeira basin , Tapajos basin  and BVI and XAP. Significance was tested using 1,000 permutations with a 95% confidence interval. Population genetic structure was also measured using the Bayesian assignment method STRUCTURE v 2.3.4, which uses genotypes to assign individuals to a genetic group without a priori assumptions of populations  present in the data set from 1 to 10 using 20 iterations per K. We used a maximum of ten populations to allow for the possibility that a sampled location is substructured into more than one population. We used the ΔK method of Evanno, Regnaut, and Goudet  was conducted using the sum of squares size difference  to 0.63  among all populations, but none within basins (Table FST (mean 0.08) and RST (mean 0.09) suggested low population structure, and even lower within river basins  and the Tapajós basin  individuals from Amazonia are assigned into two clusters. The XAP Cerrado population was inferred as an independent genetic cluster, together confirming the results obtained by FST and RST. BVI individuals had either admixed genotypes or shared ancestry within the Tapajós basin cluster. The population graph analysis showed that populations from the same river basin are highly connected, as in the case of JUR and TPI rivers that flow into the TAP  species of the Peruvian Amazon . The area surrounding BVI (Roraima State) is thought to be the “center of origin” for many plant species . The BVI population is the least inbred of these two according to the fixation index  and Madeira  have less gene flow. Pairwise immigration estimates show that the MAD population receives immigrants consistent with river flow that moves from the GUA to MAM then MAD localities along the Madeira basin. Overall there is a strong contribution of migrants mostly within basins, supporting the role of exchange within basins as part of the seed dispersal agents in the population diversity and structure of M. flexuosa.Our population genetic structure and migration analyses suggest that the distribution of rivers is an important factor in the population structure of Amazonian Myricaria laxiflora , yet our population genetic structure results are congruent with the fact that rivers are a key element of the landscape in Amazonia. Rivers influence animal distributions such as birds . Unlike other populations in our sampling, this high value could be explained by this area being within an indigenous community, who have used and managed this palm intensively for human consumption and for making handcrafts for hundreds of years  population, despite being located on the riverbank and having several other populations of Bertholletia excelsa (Brazil nut), for example, is strongly linked to areas populated by indigenous groups in Amazonia , and we open the possibility that it has adapted to re‐colonizing habitats disrupted by fire.In the Gran Sabana of Venezuela and likely in the contiguous Roraima savannas of Brazil , the gallery forest was more dominant than the savannas 3,100–1,800 Cal yr BP (Leal et al., M. flexuosa, could aid conservation and management strategies. Also, the importance of rivers in maintaining population connectivity that are geographically distant is here shown for M. flexuosa, which can act as an umbrella for associated species and the environmental that thrive with it. Our results from M. flexuosa may be used as a first step toward building a model for other studies of plants whose dispersal is heavily influenced by rivers.The study sites from this work are currently undergoing increasing deforestation and other modifications of forest landscapes. Hydrological connectivity in Amazonia is increasingly disrupted by dynamic and multifaceted drivers (Ritter et al., None declared.M.T.P., A.B.R., C.J.D.S., and A.C.J. conceived the research. N.L.S., J.C.A., and M.A.A.B. conducted the fieldwork. N.L.S., A.P.V., and M.S.B.B. conducted the lab work. N.L.S. and F.P.Z. did the analyses. C.D.B. and A.C.J. wrote the manuscript with all authors contributing and approving the final manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Erinaceus europaeus) in Denmark.Low genetic diversity can lead to reduced average fitness in a population or even extinction. Preserving genetic connectivity across fragmented landscapes is therefore vital to counteract the negative consequences of genetic drift and inbreeding. This study aimed to assess the genetic composition and consequently the conservation status of a nationwide sample of European hedgehogs (O), observed (HO), and unbiased expected (uHE) heterozygosity, inbreeding coefficient (FIS), percentage of polymorphic loci (P%) and tested for deviations from Hardy-Weinberg equilibrium (HWE). We used linear models to test for potential anthropogenic effects on the genetic variability of hedgehogs with iHO, uHE, P% and FIS as response variables, and assessed the demographic history of the population.We applied an adaptation of the genotyping by sequencing (GBS) technique to 178 individuals from six geographically distinct populations. We used a Bayesian clustering method to subdivide individuals into genetically distinct populations. We estimated individual observed (iHE of 0.126–0.318 and a mean HO of 0.124–0.293 in the six populations. The FIS was found to be significantly positive for three of the six populations. We detected a large heterogeneity of iHO values within populations, which can be due to inbreeding and/or fragmentation. FIS values decreased with increasing farmland density, but there was no significant association with human population or road density.The Danish hedgehog population is composed of three genetic clusters. We found a mean P% of 54.44–94.71, a mean uHWe found a low level of genetic variability and evidence for genetic substructure and low effective population size, which are all consequences of habitat fragmentation. We failed to detect signs of a recent population bottleneck or population increase or decline. However, because the test only identifies recent changes in population size, we cannot reject the possibility of a longer-term decline in the Danish hedgehog population. Erinaceus europaeus, hereafter referred to as “hedgehog”), is a widely-distributed nocturnal generalist predator species found in a range of habitats in the British Isles, New Zealand and continental Europe, from Iberia and Italy northwards into Scandinavia [The western European hedgehog  can be an important mortality source [In addition to habitat fragmentation, poisons, pollution and competition can affect hedgehog populations. The widespread application of molluscicides, insecticides and rodenticides can negatively influence hedgehog populations via secondary poisoning and elimination of prey items –32 and py source , 33–36. y source , 33–37.Maintaining gene flow across fragmented landscapes is necessary to prevent the negative consequences of genetic drift, reduced genetic diversity, and inbreeding depression, which can result in less viable and less fertile offspring , 39. TheOf particular concern in this context are the degrees of heterozygosity and inbreeding. Hedgehogs are promiscuous and have hetero-paternal superfecundation , which mE. europaeus and E. roumanicus in the central European contact zone between the two species. They found that a homogenous population of E. europaeus, had been divided by two large rivers in the Czech Republic (Vlatava and Elbe), into a subpopulation in the western part, and two subpopulations with a mosaic location pattern in the eastern part of the country. They found a significantly lower observed heterozygosity (HO) than expected heterozygosity (HE) in five of the nine microsatellite loci studied (n = 131), with a mean HO of 0.695 and a mean HE of 0.687, using the mitochondrial control region and nuclear microsatellites. In the UK, a study of 42 individuals in an isolated population of hedgehogs in the Regent’s Park, London (166 ha), showed a low genetic diversity, with a mean HE of 0.197, and a mean HO of 0.198 (nloci = 6) [2 fragmented landscape in Oxfordshire, UK and found a statistically significant genetic differentiation among the studied populations  and a mean HO of 0.7. According to the authors, these results indicated that the hedgehogs of Oxfordshire had a restricted dispersal which may have been caused by human-mediated barriers such as roads and train tracks in the landscape [2 (nloci = 10). The population of hedgehogs in Zurich were divided into three genetic clusters, separated by two rivers and the major transportation axes. Genetic diversity measures were similar between the three clusters, and the F coefficients (FIS) were low. Mean HE ranged between 0.569–0.627 for the three clusters, and the mean HO ranged between 0.523–0.631 [E ranging between 0.197 and 0.687.Previous genetic research on hedgehogs has been conducted using microsatellites , 41–53. oci = 6) . Becher oci = 6)  detectedandscape . Braakerandscape  studied Hedgehogs have a long history in Denmark. Archaeological evidence from Mesolithic sites (Maglemosian cultures) confirms their presence in 7500 BC and other evidence suggests that they immigrated in the early Preboreal around 9550 BC . Researc2 [Denmark consists of the large peninsula Jutland and several islands of varying sizes. The larger islands are connected by long bridges (0.75–17 km), which hedgehogs are unlikely to cross, isolating the local hedgehog populations in the different areas of Denmark. The total area of Denmark is 43,000 km2 , and 62%2 . Altoget2 –59. The 2 , and an 2 .For this study we adapted and optimised the use of a second-generation genotyping technique, genotyping by sequencing (GBS) for the genetic analysis of European hedgehogs.e) through genetic signatures.The main aims of this study were: i) To develop and test a set of SNPs which can be used for investigating the genetic structure and variability of the European hedgehog on a broader scale ii) to evaluate the patterns of the genetic diversity distribution in the Danish hedgehog populations, iii) to investigate potential anthropogenic effects on the genetic variability of the hedgehogs and iiii) to estimate the historical changes in their effective population size  and ligated to adapters [DNA was extracted from muscle and skin tissues (1–2 mg) using the DNeasy Blood and Tissue kit  and subsequently digested with adapters . The ligadapters .https://github.com/brwnj/fastq-multx) allowing one mismatch in order to remove the barcode sequences from the sequenced reads.The i7 barcodes of the dual-barcoded sequenced reads were demultiplexed using bcl2fastq2 version 1.0.0  allowing zero mismatch. The i5 barcodes were demultiplexed using Fastq-multx version 1.02.772 . Burrows-Wheeler Aligner (BWA) was used to align the reads against the hedgehog (Erinaceus europaeus) using the EriEur2.26 reference genome. Only reads with a mapping quality of at least 30 were used for the further analysis. Variants were called using The Genome Analysis Tool Kit’s HaplotypeCaller, and joint genotyping was performed using GenotypeGVCFs. Initial filtering was performed using SelectVariants and filtered for SNPs, bi-allelic sites, and mapping quality > 30 [Adaptor quality trimming were accomplished by Trim Galore using default parameters (ity > 30 . Minor aIS coefficient (FIS), unbiased expected (uHE) and observed (HO) heterozygosity. These were all estimated using GENALEX v. 6.5 [O) was calculated for every individual, plotted in a box plot, and tested for significant differences among locations with a one-way ANOVA. The pairwise comparisons among locations were conducted with a Tukey’s HSD test. The mean iHO´s within every population were ranked from the lowest to the highest value and plotted together in order to show the populations with the lowest and the highest levels of iHO.We assessed genetic diversity using four quantities: The percentage of polymorphic loci (P%), the FX v. 6.5 . In addiST), and, for each population, a test for departure from Hardy-Weinberg equilibrium (HWE) was performed using GENALEX v. 6.5.Furthermore, the degree of genetic differentiation among the six populations was quantified with fixation index (F6 Markov chain Monte Carlo (MCMC) iterations and 105 burn-in period on the basis of independent allele frequencies and admixture ancestry model. Additionally, we ran STRUCTURE of K = 1–7 separately for every population to test if further substructuring could be detected within the particular populations. The accurate number of populations (K) was determined according to the ΔK formula [In addition to measuring genetic differentiation and variability, population genetic structure of the sampled individuals (n = 178) was assessed using a Bayesian clustering method, implemented with STRUCTURE v. 2.3, which clusters individuals into genetically distinct populations/groups (K) based on their allelic frequency at multiple loci and minimise deviations from HWE within groups . To illu formula  using th formula . In addi formula  and plot formula .Tests for recent population declines or expansions in the population size were performed for every population with the program BOTTLENECK v. 1.2, after 15,000 iterations assuming an infinite allele model (IAM) according to a deficiency of the rare alleles and an excess of the heterozygosity. The model assumes that a population (large size) is in mutation-drift equilibrium and that as a consequence of a severe restriction in size the loss of alleles  occurs at a higher pace that the loss of the genic heterozygosity. Consequently, a deficiency in the total number of alleles is detected when compared to the number of expected alleles in a large population with the same observed heterozygosity. The deficiency in allele number will only be apparent until the population reaches the mutation-drift equilibrium again , 71.O, uHE, FIS and % polymorphic loci (%P) and the explanatory variables were human population density per km2 [2 [2 [To investigate whether the differences in genetic variability found between the hedgehog populations were associated with human population density in their area, road density and farmland density of the areas from which the studied hedgehogs derived, we fitted linear models in R . The res per km2 , 74, farr km2 [2 , and kilm2 [2 [2  (The datm2 [2 [2 ).O, uHE, FIS and %P) we first fitted models with a single explanatory variable at a time (i.e. three models for each of the three response variables), followed by linear models with two explanatory variables at a time. Then we fitted models that included all three explanatory variables as main effects. Lastly, we prepared models that included two explanatory variables at a time (e.g. road density and farmland density) and the interactions between them. We tested the significance of the interaction term by comparing models that included the interaction term to those that did not include the interaction term, using an ANOVA test.For each of the four response variables , mean individual heterozygosity iHO and the F coefficient (FIS), varied with location  and the highest in Jutland south of the Limfjord (JSL). The measure of uHE was very similar in Funen (FN) and Jutland north of the Limfjord (JNL). Mean iHO values from Lolland and Falster (LFA), Jutland north of the Limfjord (JNL) and Zealand (Z) were almost identical. Of the three statistically significant and positive FIS values  the levels were similar between Jutland south of the Limfjord (JSL) and Zealand (Z), but markedly lower on Funen . The Tukey’s test found several significant differences of the mean iHO between populations , followed by Z, JNL, LFA and finally FN which showed the highest starting levels of iHO  values between populations were highly significant (p < 0.001) with a FST range from 0.034 between JNL and JSL and FST = 0.321 between JNL and BH .O, uHE and % polymorphic loci (%P) for each of the six populations and farmland density, road density or population density, fitted as single main effects . Models fitted with farmland density and road density, as well as all three explanatory variables at a time, as main effects, also showed significant associations between farmland density and FIS.Testing for effects of farmland density, population density and road density in the particular areas from where the hedgehogs derived on the genetic variability observed in the study, we found no statistically significant associations between iH density . FIS decPrevious studies have compared the applicability of microsatellite and SNP as linkage mapping markers , 77, menE ranging between 0.197 and 0.6872. A calculated mean HE of 0.197 and a mean HO of 0.198 (n = 42) was reported in an isolated population of hedgehogs in Regent’s Park, London [O of 0.7 (n = 160) was determined in a study from Oxfordshire, UK [E of 0.6872, a mean HO of 0.695 and a mean FIS of 0.0686 was determined in hedgehogs from the Czech and Slovak Republics (n = 131) [E ranged between 0.569–0.627 (n = 147) and the mean FIS ranged between -0.006–0.070 for three genetic clusters in Zurich, Switzerland [E varying between 0.126–0.318 and a mean HO varying between 0.124–0.293 for the Danish hedgehogs studied . However, individuals from Jutland south of the Limfjord did in fact have the highest measures of heterozygosity (HO and uHE) of all the areas tested in the present study  . Mean HEtzerland . In our  studied . As Denmnt study , Table 2O values for each of the six populations ranked from the lowest to the highest values within each population  even if partially overlapping with it, and is clearly separated from the first cluster . The island of Bornholm, which was previously connected to the continent around northern Germany, became an island in late Preboreal approximately 8000 BC . The resorth Sea . The perST values were all highly significant, however, because several of the populations investigated are not in HWE the FST values should be interpreted with caution, as one of the assumptions for a correct estimate test is that the populations which are compared, are panmictic.The FIS) of 16.9%, but the HWE test was still negative. This could be due to the small sample size or the large standard errors of uHE and HO.JNL, LFA and BH had a low genetic variability . HoweverJSL, FN and Z showed a low genetic variability , and as An additional possible factor, which could have contributed to the deviations from HWE, is the effect of humans relocating hedgehogs into foreign habitats. During the past 20–30 years, the rehabilitation of sick, orphaned and injured wild hedgehogs has become an established practice in many western European countries. Denmark has a number of working hedgehog rehabilitation centres, where volunteers care for the hedgehogs with the purpose of releasing the surviving individuals back into the wild. In 2013 the Danish Nature Agency prepared guidelines for the care of wildlife, instructing rehabilitators to refrain from moving mammals over water, and ensuring that rehabilitated wildlife would be released back into their original habitat . InjuredOne drawback to our citizen science sample collection methods was that we did not have precise and reliable records of the geographical location of the samples. This had consequences for the analyses we could carry out because analyses such as Isolation by Distance (IBD) and the Mantel test are very sensitive to even small deviations from the precise location point. We therefore refrain from interpreting the conducted Mantel and IBD tests. Future collection of genetic samples should endeavor to collect precise locations for the samples.e or a combination between these factors which could be caused by habitat fragmentation and/or the large amount of farmland in Denmark. As intensified agricultural practises increase, arable land is gradually becoming a less suitable habitat for hedgehogs. The decline in the hedgehog population of the UK has even been found to be more severe in rural areas than urban [The low level of genetic variability can be explained by inbreeding, genetic substructure and extremely low Nan urban . Two-thian urban . In compan urban , where pan urban , 43, 48.IS) in our study, but the effect was surprisingly that FIS decreased with increasing farmland density. This may be due to the tendency for a lower degree of road-associated fragmentation in farmland areas and less traffic in general. Or perhaps because the hedgehogs, which are able to survive in this habitat type, can move more freely in their search for mates, because the limitations of movement primarily present in urban areas such as buildings, fences and traffic-laden roads, are less pronounced in farmland areas. Further research is needed to understand this effect.We found an effect of farmland density on the F coefficients , which has likely influenced the statistical power of the tests.We also tested whether the human population density per area for each population of hedgehogs studied, had an effect on iHe to 0.4 Ne generations). Therefore, we cannot reject the possibility that the population is declining and or have declined drastically before the scope of 0.2 to 0.4 Ne generations.The software BOTTLENECK 1.2. failed to detect signs of population bottlenecks or increase in population size. However, the software only detects decreases or increases in population size, which has occurred recently (within 0.2 NO = 0.124–0.293). We detected differences between the mean iHO in the populations, which indicate some degree of inbreeding and fragmentation. The 178 Danish hedgehogs tested could be divided into six geographically distinct populations based on the Danish island structure and hence isolation by water. This division was furthermore confirmed by the pairwise fixation index (FST). The STRUCTURE analysis determined that the six populations were distributed inside three genetic clusters. Investigating the potential anthropogenic effects on the genetic variability of the hedgehogs, we discovered that the inbreeding coefficient (FIS) decreased with increasing farmland density, but we found no evidence for an effect of human population or road density. We found evidence for genetic substructure and low effective population size for all populations, which are all consequences of habitat fragmentation. Furthermore, no signs of a recent population bottleneck or population increase or decline were detected.We adapted the GBS technique with the application of 2902 SNPs per individual to investigate the genetic structure and variability of the European hedgehog on a broader scale. By applying the technique to samples from 178 Danish hedgehogs, we found a low genetic variability Click here for additional data file.S2 TableA presentation of the dataset from GENEPOP.(TIFF)Click here for additional data file.S3 Table2 describes the area of the regions measured in km2 [2 in the regions [2 in the regions [2 in the regions [The data applied in the analyses with linear models to investigate anthropogenic effects on hedgehog heterozygosity. Area kmd in km2 . Populat regions , 74. Far regions , and Roa regions .(TIFF)Click here for additional data file.S4 TableO between the six populations: Jutland north of the Limfjord (JNL), Jutland south of the Limfjord (JSL), Funen (FN), Zealand (Z), Lolland and Falster (LFA) and Bornholm (BH).Tukey’s test matrix for testing pairwise significant differences of the mean iH(TIFF)Click here for additional data file.S1 FigO) estimated for the six populations: Jutland north of the Limfjord (JNL), Jutland south of the Limfjord (JSL), Funen (FN), Zealand (Z), Lolland and Falster (LFA) and Bornholm (BH).Box plot of the individual heterozygosity (iH(TIFF)Click here for additional data file.S2 FigO values for each of the six populations ranked from the lowest to the highest values within each population.Plot of the iH(TIFF)Click here for additional data file.S3 FigLikelihood plot of STRUCTURE results. Ln P(D) is the mean likelihood of K, the number of simulated clusters. The most likely K is that where ln P(D) is maximized.(TIFF)Click here for additional data file.S4 FigThe figures are representing the Likelihood plots of results for each of the populations run separately in STRUCTURE . Ln P(D) is the mean likelihood of K, the number of simulated clusters. The most likely K is that where ln P(D) is maximized. Jutland north of the Limfjord (JNL), Jutland south of the Limfjord (JSL), Funen (FN), Zealand (Z), Lolland and Falster (LFA) and Bornholm (BH).(TIFF)Click here for additional data file.S5 FigPrincipal Component Analysis of the PC1  and PC2  and the convex hulls. The following colours are indicating the six discovered populations: JNL: Jutland north of the Limfjord (Black); JSL: Jutland south of the Limfjord (Purple); FN: Funen (Red); Z: Zealand (Orange); LFA: Lolland and Falster (Blue); BH: Bornholm (Green).(TIFF)Click here for additional data file.


Fokienia hodginsii is a Tertiary relict conifer of the monotypic genus Fokienia (Cupressaceae s.l.). Currently, the species is distributed in southern China, northern Vietnam, and northern Laos and listed as a “near threatened” species by the IUCN. In this study, a total of 427 individuals of F. hodginsii were sampled from China and Vietnam to characterize its genetic diversity and population differentiation. Based on the profiles of 12 simple sequence repeat (SSR) markers, we observed a high level of genetic diversity in F. hodginsii at the species level (He =0.635), albeit slightly lower than that of its sister species Chamaecyparis obtusa. Signals of bottleneck events were detected in the populations GXDMS, GXHJ, V‐PXB, and V‐HB, probably due to Pleistocene glaciations or overexploitation in recent years. Pronounced genetic differentiation (Fst = 0.157) was found in this species. The inbreeding index (Fis = 0.176 ± 0.024) indicated that F. hodginsii has a mixed mating system. Significant correlation was found between the pairwise genetic differentiation and geographic distance , suggesting that genetic differentiation among the populations follows the model of isolation by distance (IBD). STRUCTURE analysis and principal coordinate analysis revealed that these populations were divided into four groups: the western China group located mainly in the Yunnan–Guizhou Plateau, the central China group located mostly in the Luoxiao Mountains and Nanling Mountains, the eastern China group located in the Wuyi Mountains and the Vietnam group containing two populations in Vietnam. The different terrains and elevations of populations may be the most likely factors leading to the differentiation between the western China group and the central China group, while the geographic isolation caused by the lack of appropriate habitats may greatly contribute to the differentiation between the central China group and the eastern China group. Based on the results, some conservation suggestions for this species are provided, such as establishing seed orchards and multiple nature reserves. It is also the only family of gymnosperms that is present on all continents except Antarctica  contains only one extant species, Fokienia hodginsii (Dunn) Henry et Thomas , the vulnerable species by the Information System of Chinese Rare and Endangered Plants (https://rep.iplant.cn/protlist), National Secondary Protected Plants in China and a K‐class protected plant species in Vietnam  markers, codominant markers with good reproducibility and high variability, are one of the best tools to understand species genetic diversity and population structure , the effective number of alleles (Ne), the expected and observed heterozygosities , the Shannon information index (I) and the fixation (inbreeding) index (Fis). Additionally, FSTAT version 2.9.3.2 , the unbiased estimate of Wright's F‐statistic , the overall intrapopulation inbreeding coefficient (Fis) and the interpopulation genetic differentiation coefficient (Fst), Weir & Cockerham, Fst between paired populations. Based on pairwise Fst, gene flow between populations (Nm) was further estimated with the following formula: Nm = (1 − Fst)/4Fst  and used to calculate the differentiation matrix. Mantel tests  and the differentiation matrix of geographic distances or abiotic‐climate variables were performed with GenAlEx with 1,000 random permutations  between pairs of loci and deviation from Hardy–Weinberg equilibrium (HWE) for each locus/population combination were tested using ARLEQUIN version 3.1  software . Then, STRUCTURE HARVESTER  was conducted based on the Jaccard distance between populations using MVSP software  for each population ranged from 2.967 to 3.365 (average: 3.193 ± 0.067). The value of Na ranged from 2.917 to 3.750 (average: 3.465 ± 0.044), Ne ranged from 2.461 to 3.106 (average: 2.861 ± 0.034), and He and Ho ranged from 0.551 to 0.669 (average: 0.635 ± 0.005) and 0.475 to 0.583 (average: 0.523 ± 0.007), respectively. After Bonferroni corrections, no loci showed deviations from Hardy–Weinberg equilibrium (Supporting Information Table Fis (inbreeding coefficient) averaged across all loci ranged from 0.048 to 0.326 . Among the 22 populations in China, GXDMS and GXHJ harbored the lowest genetic diversity . In contrast, the populations FJDYS, FJHBL, HNMS and SCHGX showed the highest genetic diversity (He = 0.662–0.669 and Ho = 0.521 – 0.563).Populations V‐PXB and V‐HB, located in Vietnam, had the lowest genetic diversity  was 0.176 ± 0.024, the total‐population inbreeding coefficient (Fit) was 0.308 ± 0.019, the interpopulation genetic differentiation coefficient (Fst) was 0.157 ± 0.019, and the gene flow (Nm) was estimated to be 2.013 ± 0.698 , ranging from 0.009 (between FJDYS and FJFHS) to 0.234 , latitudinal changes , precipitation differentiation , elevational changes , and average temperature changes , the central China gene pool (mainly in red), and the western China gene pool  and the Vietnam gene pool  than in Chamaecyparis obtusa (He = 0.780), the sister species of F. hodginsii  of F. hodginsii are similar to those of regional species (He = 0.65) and long‐lived woody perennial species (He = 0.68). Allelic diversity (Na) and expected heterozygosity (He) are also commonly used to estimate the genetic diversity in natural populations  are slightly lower than those of C. obtusa , albeit higher than those of other conifer species, such as Cryptomeria japonica . This phenomenon agreed with previous reports that most populations in Vietnam harbor low genetic diversity , and signals of bottleneck events were also detected in these two populations , while the genetic differentiation among populations of F. hodginsii (Fst = 0.157 ± 0.019) is weak; however, the value of Fis was 0.176 ± 0.024, indicating a mixed mating system in which inbreeding occurred frequently. The genetic differentiation among populations of F. hodginsii (Fst = 0.157 ± 0.019) is also in accordance with that of other mixed‐breeding species of seed plants , and much lower than that of entomophilous species (Fst = 0.21) . For the species C. obtusa, much higher genetic diversity was maintained within populations (91.7%), and genetic differentiation among populations was lower (Fst = 0.039). The Fis value estimated for C. obtusa was only 0.034, indicating a random mating system. Therefore, the different levels of genetic differentiation among the three species may be caused primarily by the differentiation of mating systems.Most conifers have high levels of genetic diversity within populations and low levels of differentiation among populations  Nybom, . This paFst/(1 − Fst)) and geographic distance , suggesting that the genetic differentiation among populations follows the model of isolation by distance (IBD), that is, the differentiation among populations is strongly associated with geographic distance. Such a phenomenon was also observed in C. obtusa  and precipitation , their correlations were rather weak compared to those with geographic distance . It was observed that the flowering period of F. hodginsii is delayed with a decrease in temperature and precipitation (Hou et al., Although significant correlations were also found between genetic differentiation and climatic variables in the sampled locations, such as average temperature (4.3K value was at K = 3. The assignment results for K = 3 showed that the two populations in Vietnam were clustered with the eastern China group. In contrast, the results for K = 4 showed that the Vietnam populations were separated from the eastern China group and clustered as a fourth group. However, the populations located in Vietnam are located far away from those in eastern China, and the climatic conditions are much different between the two regions. It is surprising that the two populations in Vietnam were clustered with the eastern China group and not the western China group, which is much closer to Vietnam in terms of geographic distance. More molecular data need to be analyzed to understand this pattern.The STRUCTURE model based on 12 loci identified three as the most likely number of genetic clusters, as the highest ΔK = 4 were the same as the results from SAMOVA and PCoA. Therefore, it is reasonable to divide all populations into four groups: the eastern China group, the central China group, the western China group, and the Vietnam group. The terrain of China from west to east forms a flight of three steps, commonly called the “Three Steps”. The first step located in southwestern China mainly includes the Qinghai‐Tibetan Plateau, which has an elevation above 4,000 m. The second step lies in central and western China with an elevation of 1,000–3,000 m and includes the Xuefeng Mountains, Qinling Mountains, and Yunnan–Guizhou Plateau. The third step spans all remaining regions, covering eastern and southern China with an elevation of 500 m (Huang et al., F. hodginsii is delayed with an increase in elevation. Therefore, the change in topography may be the main reason for the population differentiation between the western China group and the central China group. Based on the specimen records and our field collections, the distribution of F. hodginsii is continuous between the western China group and the central China group; thus, populations located near the border, such as GXJX and GXHP, may receive gene flow from both groups and ultimately harbor mixed gene pools.In this study, the assignment results for F. hodginsii were found. Therefore, the plain area between the central and eastern China groups may have limited the gene flow between them and led to genetic differentiation, as we have found that isolation by distance was the main reason for genetic differentiation of F. hodginsii. However, it was surprisingly that the population JXSQS, located in the eastern China group, was closer to the central China group genetically (Figure F. hodginsii was often planted around the tombs and temples in China.Population differentiation was also found between the central China group and the eastern China group even though both of them are located on the third step. It was found that the central China group belongs to the Guangdong and Guangxi Hills while the eastern China group belongs to the Zhejiang and Fujian Hills, and between them, most areas are plains with a low elevation where no specimen records of y Figure . It is p4.4F. hodginsii and the initial significant genetic differentiation among its populations found in this study are encouraging. However, we found recent bottleneck events in the populations GXDMS, GXHJ, V‐PXB, and V‐HB, suggesting that individual populations may suffer from a dramatic decline in population size. As a Tertiary relict species, the range of this conifer contracted sharply during the Pleistocene glaciations, and our field investigations also showed that the F. hodginsii populations have been overexploited since the 1980s, especially in the last ten years. For the conservation of this species, measures should be taken to increase the number of individuals and avoid the destruction caused by human activities. Ex situ conservation and breeding can also be considered to maintain the greatest within‐species genetic variation, especially for the populations GXHJ and GXDMS, with higher inbreeding coefficients. Establishing seed orchards is also a good method, which could preserve favorable genes and prepare for breeding in the future. According to the results from STRUCTURE, the optimum number of groups is 4; thus, we also should establish seed orchards for these four groups to preserve their genotypes. In addition, establishing multiple F. hodginsii nature reserves, such as the Daiyunshan National Nature Reserve and Nanling National Nature Reserve, is needed, and the communities containing F. hodginsii should be classified as absolute protection areas to avoid human destruction.Genetic diversity plays an important role in determining the survival and adaptability of a species (Liao et al., None declared.Liao, W.B. and Fan, Q. designed the research. Guo, W. and Huang, Y.SH. collected the samples. Yin, Q.Y., Huang, Y.L. and Zhou, R.CH. generated the data. Yin, Q.Y., Chen, S.F. and Zhou, R.CH. analyzed and interpreted the data. Yin, Q.Y. wrote the manuscript, and Chen, S.F. and Zhou, R.CH. edited the manuscript.The primers used in this study are shown in Table  Click here for additional data file. Click here for additional data file.


Palaemonetes sinensis, is an economically important freshwater shrimp in China, and the study of genetic diversity and structure can positively contribute to the exploration of germplasm resources and assist in the understanding of P. sinensis aquaculture. Microsatellite markers are widely used in research of genetic backgrounds since it is considered an important molecular marker for the analyses of genetic diversity and structure. Hence, the aim of this study was to evaluate the genetic diversity and structure of wild P. sinensis populations in China using the polymorphic microsatellite makers from the transcriptome.The Chinese grass shrimp, P. sinensis from transcriptome, and analyzed for differences in genetic diversity and structure in multiple wild P. sinensis populations in China. Totally of 319 individual shrimps from seven different populations were genotyped to find that allelic polymorphisms varied in two to thirteen alleles seen in the entire loci. Compared to other populations analyzed, the two populations including LD and SJ showed lower genetic diversity. Both the genetic distance (D) and Wrights fixation index (FST) comparing any two populations also indicated that LD and SJ populations differed from the other five populations. An UPGMA tree analysis showed three main clusters containing SJ, LD and other populations which were also confirmed using STRUCTURE analysis.Sixteen polymorphic microsatellite markers were developed for P. sinensis populations. All the polymorphic microsatellite makers are believed useful for evaluating the extent of the genetic diversity and population structure of P. sinensis. Compared to the other five populations, the LD and SJ populations exhibited lower genetic diversity, and the genetic structure was differed from the other five populations. Therefore, they needed to be protected against further declines in genetic diversity. The other five populations, LP, LA, LSL, LSY and LSH, are all belonging to Liaohe River Drainage with a relatively high genetic diversity, and hence can be considered as hot spots for in-situ conservation of P. sinensis as well as sources of desirable alleles for breeding values.This is the first study where polymorphic microsatellite markers from the transcriptome were used to analyze genetic diversity and structures of different wild  Palaemonetes sinensis , also known as Chinese grass shrimp, a small freshwater shrimp belonging to the Palaemonidae family distributed in China, Myanmar, Vietnam, Japan, southeastern Siberia and Sakhalin and has both ecological and ornamental value [P. sinensis is commonly distributed in the Liaoning, Jilin, Heilongjiang, Hebei, Jiangsu, Fujian and Yunnan provinces [P. sinensis is an attractive shrimp due to its striking appearance, appealing flavor [P. sinensis is very popular in both domestic and foreign markets [P. sinensis population has gradually diminished. As such, there has been interest in P. sinensis aquaculture to potentially alleviate fishing pressure on the wild population by meeting consumer demand with farmed shrimp and through stock enhancement. Nevertheless, there are only a handful of studies investigating P. sinensis morphology and more work is needed to understand the biology of this species [P. sinensis using molecular biology are rarely reported, only some studies have used microsatellite markers in other species of the Palaemonidae family, including Macrobrachium rosenbergii [Macrobrachium nipponense [Scylla paramamosain). Research in related species of snapping shrimps with highly duplicated genomes proved that microsatellites acquired from the transcriptome were more likely to work effectively than those developed through traditional methods from the genome [P. sinensis [P. sinensis, which has the potential to aid in the conservation and improvement of this shrimp species. In view of the broad distribution of its wild populations, the questions over whether they are genetically similar or do exhibit differences attributable to their locations or drainage basins are still unsolved. The objectives of the current study were to validate variations of transcriptome-derived microsatellite markers and to analyze their underlying genetic background of different wild P. sinensis populations. It is hoped that this work could make a positive contribution to the molecular genetic analyses of P. sinensis populations and eventually serve as a basis for the improvement and sustainable conservation of P. sinensis aquaculture in China.Sollaud, 911, alsoal value . In Ching flavor  and use g flavor . Due to  markets –6. Howev species , 6. At penbergii  and Macrpponense . In the pponense  identifisinensis . MoreoveP. sinensis, totaling 319 individual shrimps , NE (3.628), He (0.724) and Ar (7.478), and c2591_g1_i1 was the least polymorphic locus, with the lowest NA (2), NE (1.006), He (0.006) and Ar (1.213) values.Seven wild populations of P < 0.01), whereas the other seven loci showed no significant differences (Table P < 0.01)  (es Table . All pop1) Table . HoweverSY Table . In geneP. sinensis populations were shown in Table NA, He and Ar values, while the LSH population exhibited highest NE and Ho values. In the LD, LP, LA, LSL and SJ populations, FIS coefficients varied from 0.093 to 0.322, suggesting significant deficiencies of heterozygotes with 95% confidence interval. In the other two populations, FIS values did not show significantly different from zero.Data for all parameters of genetic diversity for the seven Ne) and the mean ratio of the number of alleles to the range in allele size (M ratios) of each population were also listed in Table Ne values for LP and LSH showed to be the highest (infinite), whereas the lowest Ne value was 106.1 as shown in LA. M ratios among all populations ranged from 0.826 to 0.908, which revealed that no population experienced reduction in effective size. Reduction in effective size only occurs when M < 0.68 [P > 0.05).Estimated of effective population size (M < 0.68 . PotentiFST) and Cavalli-Sforza and Edwards’ genetic distance (D) [P < 0.01) (Table The pairwise Wrights fixation index (ance (D)  values w1) Table .Table 4FST values among the seven populations reflected their geographic relationships. Firstly, there was a very great genetic differentiation between SJ (the only population from Huaihe Drainage Basin in Shandong Province) and the other six populations  (FST > 0.25). Secondly, LD (Related River Drainage Basin) and all populations from Liaohe River Drainage Basin  exhibited a great genetic differentiation between (0.15 < FST < 0.25). Finally, in Liaohe River Drainage Basin, there was a moderate genetic differentiation among LP, LA, LSL, LSY and LSH (0.05 < FST < 0.15), meanwhile, LSL and LP, LSL and LSY populations showed little genetic differentiation (FST < 0.05) [FST values revealed significant differences among the populations (P < 0.01).Basically, the D values) was significantly correlated . The D value between the populations displayed similar pattern as FST values. For example, most D values between SJ and the other six populations including SJ vs LA, SJ vs LSL, SJ vs LYS, and SJ vs LSH, were larger than 0.20, suggesting that they were genetically disparate populations [D values between LD and populations of Liaohe Drainage Basins were also higher than those between latter populations.As analyzed by the Mantel test, the correlation between geographical distances and a pairwise comparison with genetic distances  to 3.545 (SJ into LP). Most long-term gene flow between populations were symmetric , except those between SJ and LP, LSY and LSL, and SJ and LSY  related with different numbers of genetic clusters K, calculated from structure analysis of 319 individuals of NA value ranging from 2 to 13 (mean = 5.8), which is similar with studies investigating transcriptome-derived microsatellite markers in other decapod [M. rosenbergii [M. nipponense [P. sinensis so far, these polymorphic microsatellite makers and further mining of transcriptomic data may helpful for future research of P. sinensis and its related species.One of the main objectives of this study was to detect polymorphisms in transcriptome-derived microsatellite makers and to further understand the genetic diversity and structure of the wild populations from different locations. Compared with genomic derived microsatellites, transcriptome-derived microsatellites have several advantages including high efficiency, strong transferability, and correlation with potential genes . Althoug decapod . Additioenbergii  and M. nmean = 5., which iNE, Ho and He values in seven P. sinensis populations ranged from 1.441–1.867, 0.158–0.369 and 0.226–0.379, respectively, showing that the genetic diversity of the populations was lower than that of other species of the Palaemonidae family [FIS values. The LD and SJ populations showed lower genetic diversity as indicated by NE, Ho and He values, which may be a result of a small Ne value, inbreeding and a limited number of shrimp samples. Results in the current study indicated that the Ne values of the four populations LD, LA, LSY and SJ, were lower than the values suggested by Franklin et al. [P. sinensis. In addition, due to time and space constraints in sampling, a certain number of individuals may have been from a few number of spawning shrimps, which would affect genetic diversity through an increase in genetic identity [The mean n et al. . This maidentity .P. sinensis populations did not experience a bottleneck effect or a recent decline in quantity. Therefore, in future breeding projects, the level of inbreeding, brood shrimp population size and genetic diversity must be considered since domestication probably leads to a reduction in genetic variation due to genetic drift, selection and inbreeding [Most of the populations analyzed in this study showed a departure from HWE. This result was similar to several other studies which microsatellites derived from transcribed sequence data significantly depart from HWE –23. Thisbreeding .In this present study, subjects are collected from three geographically isolated drainage basins. LD population belongs to Related River Drainage in East Liaoning Peninsula; the SJ population belongs to Huaihe River Drainage in north China and the other five populations  come from Liaohe River Basin in central Liaoning Province. Specifically, LSL and LSY come from a closed lake and a semi-closed reservoir respectively; LA and LSH belong to inland tributaries; LP locates close to the mouth of Liaohe River.P. sinensis, which accounted for 17.24% of the total genetic variation, was much lower than that within populations. Pairwise D and FST values were consistent with these results. According to Thorp [D values between most of the populations of P. sinensis indicated that they were closely related populations. Likewise, excluding SJ and LD populations, the pairwise FST values among the other five populations were low to moderate [FST values among five populations indicated they might share the same ancestors. Even so, the significant correlation between geographical and genetic distances as well as the gene flow values indicated that five populations from Liaohe River Basin were separate due to the habitat fragmentation. The divergence between SJ and the other six populations as well as the divergence between LD and the other five populations were due to long-term geographic separation. These outcomes were consistent with the Bayesian analysis in genetic structure simulations, which also revealed that the SJ and LD populations were much different from the other populations.AMOVA analysis revealed that the genetic differentiation among populations of to Thorp , the paimoderate . The sliP. sinensis populations in China. All the polymorphic microsatellite makers are believed useful for evaluating the extent of genetic diversity and population structure of P. sinensis. Compared to the other five populations, the LD and SJ populations exhibited lower genetic diversity due to the lower NE, Ho and He. All populations showed normal L-shaped distributions and may not have experienced a bottleneck or recent reductions in effective size. The AMOVA analysis revealed that genetic variation among populations was 17.24% and much lower than that within populations. D and FST values between any two populations indicated that the LD and SJ populations differed from the other five populations. The UPGMA tree and the STRUCTURE analysis also supported the result. Therefore, they needed to be protected against further declines in genetic diversity. Among the seven populations, LP, LA, LSL, LSY and LSH populations were all from Liaohe River Drainage with a relatively high genetic diversity, and hence can be considered as hot spots for in-situ conservation of P. sinensis as well as sources of desirable alleles for breeding values. In future, further development of transcriptome-derived microsatellite markers is necessary for more detailed investigation on the genetic variation, genetic structure, and molecular markers-assisted selection (MAS) of P. sinensis. Overall, this study provided a theoretical basis for the protection, rational use and genetic breeding of germplasm resources.In this study, 16 polymorphic transcriptome-derived microsatellites were screened and used to assess the genetic diversity and structure among wild P. sinensis were collected in 2016 from Sha River in Liaoning Dalian , Shuangtaizi River in Liaoning Panjin , Yangliu River in Liaoning Anshan , Longwei Lake in Liaoning Shenyang , Yangshi reservoir in Liaoning Shenyang , Liao River in Liaoning Shenyang Huangjia  and Dushan Lake in Shandong Jining   [http://bioinfo.ut.ee/primer3-0.4.0/). Primers were examined using varying PCR conditions and PCR amplified products were evaluated by agarose gels. PCR reactions were performed by ABI 2720 thermocycler . DNA samples were subsequently amplified for the polymorphic loci in single PCR reactions. PCR reactions were 15 μL, containing 50-100 ng template, 10 × buffer 1.5 μL, Mg2+ (25 mmol·L− 1) 1.5 μL, dNTPs (each of 10 mmol·L− 1) 0.25 μL, each of forward primer and reverse primer (10 μmol·L− 1) 0.15 μL, Taq polymerase 1 U, and ddH2O. Denaturation for 3 min at 94 °C was followed by 35 cycles made up of 25 s at 94 °C, 25 s at the 54 °C and 60 s at 72 °C. The final step was a prolonged extension of 10 min at 72 °C, followed by 4 °C hold. All individual genotypes were scored after the PCR products were resolved on Applied Biosystems 3730XL Genetic Analyzer  and the product size was analyzed by GeneMarker version 2.2.0. .More than 50 microsatellite loci were selected from the data of P. sinensis populations (Table NA), the number of effective alleles (NE), observed heterozygosity (Ho) and expected heterozygosity (He) of each locus of each population was calculated using POPGENE 1.32 [r) in each locus was estimated using software FreeNA [Ar) and Wright’s FIS values with 95% confidence intervals were calculated according to Weir and Cockerham using FSTAT version 2.9.3.2 software [Sixteen polymorphic loci were used to detect genetic variation among ns Table . The numENE 1.32 . Null ale FreeNA  in whichsoftware . Tests fsoftware  and a MaNe) for each population was calculated using the gametic disequilibrium method implemented in LDNe 1.31 [Ne value using Arlequin 3.5.2.2 [The estimates of effective population size  and analysis of molecular variances (AMOVAs) were calculated using Arlequin 3.5.2.2 [Pairwise Wrights fixation index .Pairwise gene flow among all seven populations was calculated by using software Migrate-n 4.4.3 , 33 throGenetic structures among populations analysis was performed via STRUCTURE v2.3.3  using BaAdditional file 1: Table S1.P-value for deviation from Hardy-Weinberg equilibrium for each locus in each population.Additional file 2: Table S2. Comparative migration estimation among all pairs of P. sinensis populations for coalescent (MIGRATE) estimator along with 95% confidence intervals.Additional file 3: Table S3. Cumulative gene flow for each population of P. sinensis.


After the colonization of the Americas by Europeans and the consequent Trans-Atlantic Slave Trade, most Native American populations in eastern Brazil disappeared or went through an admixture process that configured a population composed of three main genetic components: the European, the sub-Saharan African, and the Native American. The study of the Native American genetic history is challenged by the lack of availability of genome-wide samples from Native American populations, the technical difficulties to develop ancient DNA studies, and the low proportions of the Native American component in the admixed Brazilian populations (on average 7%). We analyzed genome-wide data of 5,825 individuals from three locations of eastern Brazil: Salvador (North-East), Bambui (South-East), and Pelotas (South) and we reconstructed populations that emulate the Native American groups that were living in the 16th century around the sampling locations. This genetic reconstruction was performed after local ancestry analysis of the admixed Brazilian populations, through the rearrangement of the Native American haplotypes into reconstructed individuals with full Native American ancestry . We compared the reconstructed populations with nonadmixed Native American populations from other regions of Brazil through haplotype-based methods. Our results reveal a population structure shaped by the dichotomy of Tupi-/Jê-speaking ancestry related groups. We also show evidence of a decrease of the diversity of nonadmixed Native American groups after the European contact, in contrast with the reconstructed populations, suggesting a reservoir of the Native American genetic diversity within the admixed Brazilian population. Tapuia (mostly Macro-Jê-speaking populations) inhabited the hinterlands. However, this coastal Tupi continuum was broken by Tapuia in the mouth of the Paraíba River, in the Southern Bahia, and in the Maranhão areas  , making In conclusion, we present a new approach that allowed a high-resolution study of the population substructure and the genetic history of the Native American ancestral populations of three current Brazilian urban admixed populations. We analyzed, traced, and compared the Native American component of the Brazilian Atlantic Coast (North-East and South) and the Brazilian Plateau, an area where most of Native American populations vanished or admixed to become urban admixed populations. A similar approach could be applied to other admixed populations, even those with genetic components in extreme low frequency, therefore expanding the boundaries of the study of extinct populations beyond the limitations given by the availability of ancient DNA. Moreover, having started from current individuals, we focus on the study of populations that, by definition, were the ancestral of current Brazilians.n = 1,246), Bambui (n = 926), and Pelotas  cities genotyped by the Illumina HumanOmni2.5–8v1 array (n = 4) and Arara (n = 4) from Carib linguistic family and Xavante (n = 11) from Jê linguistic family. From the Tupi linguistic stock, the data set includes six populations: Guarani Kaiowá (n = 10) and Guarani Ñandevá (n = 7) from Guarani branch within Tupi-Guarani linguistic family; Urubu-Kaapor (n = 3) from Northern branch within Tupi-Guarani linguistic family; Karitiana (n = 5) from Mondé linguistic family; and Surui (n = 4) and Zoro (n = 1), from the Arikem family  missing in more than 10% of the individuals and individuals with more than 10% of missing SNPs were excluded. Those SNPs that failed Hardy–Weinberg test at 0.05 significance threshold were also excluded. The kinship coefficients for each possible pair of individuals within a population were computed using the method implemented in the Relatedness Estimation in Admixed Populations software as described in Data were phased with SHAPEIT  with a p. The number of generations since the admixture event (parameter -G) was fixed at 20 (∼500 years) and the number of trees to generate per random forest (parameter -t) in 500. Window lengths (parameter -w) were set to 0.2 cM.Local ancestry was performed with a data set composed of 1,845,872 shared SNPs between the Illumina HumanOmni2.5–8v1 array and phase III 1000 Genomes Project individuals. The data set comprised the analyzed populations from For each of the three admixed Brazilian samples , local ancestry Native American fragments were rearranged without breaking nor overlapping fragments, using each fragment only once, to build chromosomes with full Native American ancestry that configured the reconstructed Native American individuals see  online.rearranged chromosome of the future reconstructed individual was reconstructed by picking up fragments from the fragments list sorted by the first position, from the beginning to the end of the chromosome. The only condition was that the subsequent fragment could not overlap the previous, which means that the start position of the next fragment had to be higher than the end position of the previous one  was run in each chromosome with the following flags: -in, -iM, and -i 15. Thus, the output for each receptor individual was iterated 15 times to find the best n and M values  and the mean value for all the individuals per each autosome was computed. Once the parameters were established, ChromoPainter was run again to compute the squared coancestry matrix. Finally, all autosomal chromosomes were summed to obtain the genome-wide squared coancestry matrix, in which all samples are individually considered as recipients and donors . Before running FineStructure, fs combine was run in order to compute the parameter c, needed for FineStructure. FineStructure (fs fs) was run in two steps: mcmc and tree computation with three random seeds set.oPainter  were usen and M for Data set A were the same as the ones computed for the Data set B because the computational limitations in running ChromoPainter with a large data set. We set clusters from the fineSTRUCTURE results of Data set B for subsequent analyses. We set the consensus clusters at the dendrogram height of 3, as the lowest height that allowed us to analyze the Guarani individuals as a single cluster and not as two mixed clusters of Guarani Ñandeva and Guarani Kaiowá populations.This process was run with two sets of populations: Data set A and Data set B in order to compare reconstructed Native American individuals with their original admixed individuals from each population . The parameters n and M parameters were computed again for each chromosome from the average values of the individuals used in this run. The distributions of the total chunklength received by the recipient populations for a given donor were compared between them. The significance mean difference was tested by Wilcoxon test. Differences accounting for a Bonferroni multiple test corrected P value lower than 0.005 were considered significant.ChromoPainter v2 was used to obtain a nonsquared coancestry matrix, where a restricted set of populations can play as haplotype donor populations. We run ChromoPainter in this way with the Data set B, where the reconstructed individuals were the recipient individuals and the nonadmixed Native Americans are the possible donors. The Effective population size (Ne) was computed with IBDNe through intrapopulation IBD values of 4-cM windows, which were computed by IBDseq . The valThe genetic structure of the admixed Brazilian populations is driven by the admixture proportions of Native American, European, and sub-Saharan African components. A PCA shows most individuals from Bambui, Pelotas, and Salvador spread between the sub-Saharan African and European individuals, and to a lesser extent, toward the Native American individuals A, in agrIn order to analyze and compare the substructure of the Native American component between the individuals from Bambui, Pelotas, and Salvador, we reconstructed, for each population, Native American individuals with rearranged chromosomes made of the Native American ancestry haplotypes of the admixed Brazilian individuals , we were able to reconstruct 45 diploid individuals for Bambui, 197 for Pelotas, and 51 for Salvador with full Native American ancestry. These reconstructed individuals do not show evidence of putative European or sub-Saharan African ancestries neither in and ADMIXTURE analysis  online nAfter the rearrangement of the chromosomes, the reconstructed Native American individuals cluster with other nonadmixed Native Americans and present a genetic structure correlated with continental geography. A PCA B shows tTherefore, in order to recover the population structure hidden in the reconstructed Native American individuals and overcome the limitations of the allele frequency-based methods, we use more-sensitive haplotype-based methods. The FineStructure dendrogram built with Native American samples  online rHaplotype-based methods, like ChromoPainter, can mitigate and partially overcome the effect of genetic drift  and alloIn the ChromoPainter analyses, each recipient individual is an independent run, which enables the comparisons between recipient populations as described above. However, the genome of a recipient individual has a given total length, and then the increase in the total length of the haplotypes shared with a donor population  can produce the decrease of the total length of the haplotypes shared with another donor population . To discard artifacts, we thus repeated the previous analyses with all Native American clusters as donor populations excluding the Guarani and, in a parallel, we did the same analyses excluding the Xavante. This allowed us to see if the signal detected from one of the two populations was artifactual and caused by the true signal of the other. In the first case, when excluding Guarani of the analysis, the signal from Xavante persists and the significant differences observed in the previous analyses are still significant. To compensate the absence of Guarani as a donor, a new significant difference appears from another Tupi population: Tupi-Madeira gives more haplotypes to Pelotas than to Salvador  online. n parameter from ChromoPainter  and Jê (Xavante) components shapes the structure of the three reconstructed populations. Our results show higher Jê ancestry in Bambui and lower in Pelotas, compensated with, respectively, lower and higher Tupi ancestry, particularly Guaraní. Unexpectedly, the reconstructed Native Americans from Salvador have more Jê ancestry than the reconstructed Native Americans from Pelotas, but they do not show significant differences in the amount of Tupi ancestry. We hypothesize that the observed higher Jê ancestry in Salvador could be a wider signal related to Macro-Jê populations, which were populations closer to Salvador. This reinforces the idea of Salvador as a crossroad of both components and challenges the thought of a long continuum of a large coastal population of Tupi ancestry. Alternatively, a Macro-Jê Native American ancestral component could have been introduced from neighboring hinterland areas during the configuration of the admixed population of Salvador. However, the original approximate Bayesian computation (ABC) approach performed with the present samples showed that the Native American component was mostly introduced in the admixed populations of Bambui, Pelotas, and Salvador soon after the arrival of the Europeans to the Brazilian coast  suggestiThe effective population sizes of the reconstructed Native American populations are similar to other Native American groups and lower than non-Native American groups. Very recently, ancestry specific estimations for effective population sizes through IBDNe have been successfully analyzed in admixed American populations comparing ancestry specific effective population size evolution along time within the same population through IBD, which allows a fine evaluation of the effective population size in recent generations . In thisHere, we have contrasted the effective population size of the reconstructed Native American populations with other Native American populations. We observe that they show a similar behavior to other Native American populations in pre-Columbian times (until 1500 CE). At this point Native American populations, except the reconstructed groups, show a decline in their effective population size. This abrupt fall in the population size might be associated with the demographic impact suffered by Native American groups after the arrival of the Europeans, either as direct causatives or as a source for a shift of the pathogenic environment to which Native American populations had adapted. Amazonian populations experience this fall some generations after than southern populations, coincident with their later contact time with European populations  online.Interestingly, Guarani populations are an exception within Native Americans and stop their population size decline 12 generations ago. We also observe mixed genetic clusters from Guarani Kaiowá and Ñandevá in the FineStructure analysis, indicating a possible more recent admixture process of these groups. Both observations could be a signal of the demographic effect caused by the expulsion of the Jesuits and the end of the Guarani Jesuit reductions at the 17th century, where other Guarani peoples had been forced to the integrate to the European culture and to adopt the catholic faith. This major historical event caused the relocation of the Guarani people, to their actual neighbor locations in Paraguay, Northern Argentina, and Northern, Southeastern, and Midwestern states of Brazil .In contrast, the reconstructed Native American populations do not show signals of effective population size decrease and they even experienced an increase after this period. These results could suggest that admixed Brazilians might have acted as a reservoir of the diversity present before 1500 CE in each of the geographical locations analyzed. However, there are several factors that may have affected the effective population size estimates of the reconstructed populations after the date of the admixture. Regarding the methodology, the main possible source of bias relies on the fact that the amount of individuals in identity by descent in a certain genomic window could eventually increase as an artifact of splitting the window region from a single real individual to various fragments that end up in different reconstructed individuals , in a region where most Native American populations no longer exist as nonadmixed populations. The genetic structure of the reconstructed Native American populations reflects a dichotomic ancestry related to Tupi- and Jê-speaking populations and suggests the contact between these Native American groups in the east coast of Brazil in precolonial times. In addition, we track the decay of the effective population size of Native American populations due to the advance of the colonization process. Similarly, we find weak but significant signals that point to the existence of a genetic reservoir of Native American diversity in the admixed populations of Brazil, although our contribution to corroborate this hypothesis is limited. This approach opens the door for the study of other ancestral populations that have experienced a similar population reduction without requiring the availability of ancient DNA. Further studies would clarify the demographic fluctuations of the populations described in this study, expand the population structure characterization detailed here, and contribute to the knowledge of the demographic history of South America before 1500 CE.Genome Biology and Evolution online.evz161_Supplementary_DataClick here for additional data file.


Understanding the underlying genetic structure of human populations is of fundamental interest to both biological and social sciences. Advances in high-throughput genotyping technology have markedly improved our understanding of global patterns of human genetic variation. The most widely used methods for collecting variant information at the DNA-level include whole genome sequencing, which remains costly, and the more economical solution of array-based techniques, as these are capable of simultaneously genotyping a pre-selected set of variable DNA sites in the human genome. The largest publicly accessible set of human genomic sequence data available today originates from exome sequencing that comprises around 1.2% of the whole genome (approximately 30 million base pairs).To unbiasedly compare the effect of SNP selection strategies in population genetic analysis we subsampled the variants of the same highly curated 1 K Genome dataset to mimic genome, exome sequencing and array data in order to eliminate the effect of different chemistry and error profiles of these different approaches. Next we compared the application of the exome dataset to the array-based dataset and to the gold standard whole genome dataset using the same population genetic analysis methods.Our results draw attention to some of the inherent problems that arise from using pre-selected SNP sets for population genetic analysis. Additionally, we demonstrate that exome sequencing provides a better alternative to the array-based methods for population genetic analysis. In this study, we propose a strategy for unbiased variant collection from exome data and offer a bioinformatics protocol for proper data processing.The online version of this article (10.1186/s12864-018-5168-x) contains supplementary material, which is available to authorized users. The investigation of the ethnogenesis of human populations is made possible by population genetic studies, through comparing genetic makeup and frequencies of the selected variants or alleles, and also by computing their genetic distance from the rest of the studied population or their level of admixture , 2. CompUltimately, application of the Whole Exome Sequencing (WES) method had spread and gained popularity, as WES is cost effective for routine genetic diagnosis of rare inherited diseases, and extensive databases have been generated containing thousands of publicly accessible exomes , Principal Components Analysis (PCA), f3− and f4−statistics, admixture) by utilizing the commonly used tools: EIGENSOFT [Thus, we assessed the usability of exome data in comparison with the other approaches in population genetic analyses. In order to compare the practicality of each strategy –  – we generated three subsets of the same publicly available experimental data , accordingly . The results we obtained from these datasets were compared using the most widely used population genetic calculations  relative to the total genetic variance (T). Its values can range from 0 to 1, where FST = 0 implies panmixia (absence of any differentiation among subpopulations) and FST = 1 implies complete divergence between populations.Migration, admixture, adaptation, and genetic drift lead to genetic diversity between human populations. By studying genetic diversity within and between populations, we could reconstruct how these populations are related to one another. Fixation index a measure of genetic structure developed by Sewall Wright , 12 can Principal Components Analysis is the most widely used approach for identifying ancestry differences among a group of individuals , 14. Whef3-statistics is used for testing the relationship of three populations [3 is negative, then C does not appear to form a simple tree with A and B, but instead appears to be a mixture of A and B. If F3 is zero, it indicates the absence of admixture, while a positive F3 value implies simple tree-like relations among A, B and C . Because of the complex genetic ancestry of human populations, there usually exists more than one possible model for any studied case. The f4-statistics has been developed in order to test alternative hypothetical trees [f4-statistics is used to estimate the admixture proportion of a test population (PC) under the assumption that we have a correct historical model. In this test, PA and PB are the potential contributors and PI is a reference population with no direct contribution to PC.F-statistics measure shared genetic drift among sets of populations and can be used to test simple and complex hypotheses about admixture events between populations . The f3-ulations  by allowal trees . The f4-Admixture analysis is based on the maximum likelihood estimation of individual ancestries from multi-locus SNP genotype datasets , 18. It Because there is no publicly available WGS data from modern Hungarians and since our results indicate that the use of exome data is suitable for population genetic analysis, we carried out population genetic analysis of modern Hungarians based on their exome data (HUN EXOME dataset). Based on our results, we also evaluated a strategy to filter exome data and offer the most suitable parameters for using them in population genetic analysis.) between tightly linked markers causes certain haplotypes to be more frequent than expected and large blocks of markers in complete LD can seriously distort the eigenvector/eigenvalue structure [–indep-pairwise algorithm of Plink to 10,000 kb, while maintaining the recommended 10 SNPs increment and r2 threshold of 0.1. The variant counts and the effect of LD pruning on the different datasets are summarized in Additional file Some population genetic approaches like PCA or admixture analysis are based on the assumption of linkage equilibrium. Thus, it is important that linked markers are pruned from the datasets as linkage disequilibrium  compared to the GENOME (~ 52%) and EXOME (~ 55%) datasets. As expected, the larger pruning window affected mostly the EXOME dataset , while the GENOME (~ 11%) and BEADCHIP (~ 9%) datasets were affected to a lesser degree.ST value between each studied population and compared the results of the different datasets  and European populations and the African and East Asian populations. We observed that FST values calculated on the basis of the BEADCHIP dataset were systematically overestimated between populations originating from different super-populations.For each dataset, we calculated the pairwise FFor each dataset, we performed the PCA analysis of all samples without outlier removal. The different datasets show a remarkably similar overall picture by the first two eigenvectors Fig. -c. The rIn order to investigate whether the BEADCHIP and EXOME datasets represent similar population relations within each super-population in comparison to the GENOME dataset, next we performed the PCA analysis restricted to each super-population using default outlier removal . The detailed PCA of the populations for each super-population showed that the indicated eigenvectors and values were very similar in all three datasets Fig. -e. The gf3-statistics for all possible combinations of population triads and plotted the resulting F3 values Fig. In order to compare the usability of the three datasets we calculated the 3 values is almost identical to GENOME results (Pearson correlation r = 0.9998), while the BEADCHIP data  with the F3 values calculated from the GENOME dataset. The differences are confined to the larger positive F3 values in our analyzed populations  values where X denotes all possible test populations were calculated for each dataset  error for different models K = 3 to 10) for each dataset. Since the absolute CV error values were significantly higher in the BEADCHIP dataset compared to the GENOME and EXOME datasets, we displayed the relative CV values compared to the lowest fitting model (K = 3) that resulted the highest CV error for each dataset  with almost identical CV errors. Since the analysis of the different dataset suggested different best fitting admixture models, we visualized both models (K = 6 and K = 9) for each dataset.The analysis of the best fitting admixture model (K = 6) suggested by both the GENOME and EXOME datasets produced very similar admixture results  suggested by the BEADCHIP dataset produced very similar admixture results for each datasets  of the analyzed populations for each SNP and visualized it as a density plot Fig. .Fig. 4DeFigure The PCA of the HUN-EXOME dataset  value  is the same as observed in other European populations  and exonic variants (~ 50%), although only a portion of the exonic variants are functional. In order to make the various approaches comparable, we used the same curated HGP 1 kG genomic variant data to select the subsets of the GENOME, EXOME and BEADCHIP datasets (see in detail in the Methods section). The number of variants was comparable in all unpruned datasets  and the European populations were very slightly (0.001–0.002) but systematically smaller using the EXOME dataset. On the other hand, the FST values between the African and East Asian populations were very slightly (0.001–0.002) but systematically larger compared to the GENOME FST distances. Since this slight difference was systematic between the random GENOME and the EXOME dataset , we hypothesize that a portion of functional variants are accountable for this phenomenon. However, the deviation (~ 1–3%) is still only a portion of what was observed in the BEADCHIP dataset. The comparison of FST values of the BEADCHIP dataset to the GENOME dataset revealed that the pairwise FST distances between populations of different super-populations were systematically larger (~ 1–12%), and that the extent of the difference appears to be correlated to the phylogenetic distance. On the contrary, we detected almost no differences between the FST distances of populations within the same super-population, except in the highly admixed AMR super-population. We assume that this is due to a general overrepresentation of differentiating SNPs (AIMS) and imbalances in the selection and proportion of the marker composition in the pre-selected BEADCHIP dataset. This hypothesis is also supported by the observed FST values of the admixed ASW population. The ASW population is a sub-population of African Americans in the Southwestern United States who originated from West-Africa, and later mixed with Caucasian and American Indians. Accordingly, the BEADCHIP data places this population closer to its admix sources - the European (EUR) and Admixed American (AMR) super-populations - than those of the GENOME FST values, indicating that the overrepresentation of AIMs distorts the true population distances. As FST can be used to estimate coalescence times [ST values - such as observed using the BEADCHIP dataset - may lead to bias in these estimations.The fixation index is one of the most commonly used statistics in population genetics, which is a measure of population differentiation due to genetic structure. The Fce times , 21, sigST results, the admixed ASW and ACB populations displayed slightly different relations to other populations. We assume that again, the overrepresentation of AIMs in the BEADCHIP dataset is responsible for the increased eigenvalues and the different relations of the admixed ASW and ACB populations. Nonetheless, the genetic relationship among the studied populations was still comparable.For each dataset the PCA analysis showed both similar topology and comparable relations between the analyzed populations. We observed that for the first two eigenvectors of the whole dataset Fig.  the eige3 values .On the other hand our analysis supports that f4-statistics is robust to SNP ascertainment  and deviation from zero is only observed if the test population is admixed [Comparison of the three datasets showed that the GENOME and the EXOME data gave almost identical FThe admixture analysis of different datasets suggest different best-fit admix modelsSince the BEADCHIP dataset resulted in higher CV errors and a number of very similar alternative models, it appears that this dataset is less conclusive for pinpointing the true admixture model. Nevertheless, the admixture analysis of the different datasets resulted very similar admixture components for all of the tested models  quality parameters and pipeline specific fix tresholds which is not always optimal for analyzing all genomic regions with different read depths (due to inter-sample and/or target region variances).. Thus, in order to minimize the bias, we applied a number of criteria for exome data processing before using them for population genetic analyses. Hence, we excluded the low-coverage data, based on the real coverage profile of our aligned NGS reads, as these data may have led to ambiguous variant calls. We excluded all read length variations (INS/DELS/DUPS) from the analysis since the same variant may be represented ambiguously by different variant callers. We also excluded potentially pseudogenic, conservative and repetitive regions where reads could be ambigiously mapped to multiple genomic regions and the proportion of the alternatively aligned sequencing reads may lead to differences depending on the threshold values or the pipeline applied for the analysis.3 analysis of HUN-EXOME dataset also suggests a small but significant (high Z scores) South-Asian-HUN admixture  deviates most from the GENOME dataset. The reason behind this phenomenon may be twofold. First, the pre-selection of SNPs in the array is not necessarily a uniform representation of all of the analyzed populations, and second, the increased proportion of AIMs is shifting the balance towards highlighting the differences between populations. The increased proportion of AIMs makes the BEADCHIP dataset suitable for sensitive exploration of admixture components. However, it could also lead to deviations of the Fixation index, specific cases of f3-statistics, PCA eigenvalues, suggested admixture model, and admixture rates. Thus, all derived parameters  estimated from these statistics are prone to bias in case of array-based data. WES data, on the other hand, is not affected by this bias and is suitable for population genetic analyses. Based on the usability of the EXOME dataset we would encourage everyone to use and to publicly share WES sequences with the correct indication of ethnic and geographical origin, which could contribute towards a better understanding of the genetic relationships among human populations.samtools bedcov (version 1.3.1) algorithm [MAPQ0 reads . We excluded sex chromosomes from the analyzed regions. As a result, we generated a BED coordinate list  best practice [-stand_emit_conf 10 -stand_call_conf 30 in the HaplotypeCaller module, and --ts_filter_level 99.0 in the variant recalibration  module. We filtered the variants to include only high quality SNPs (PASS filter) in the final high coverage/unique bed regions. We also filtered out multi-allelic variants and variants that had < − 0.5 InbreedingCoeff .Our previously published BAM files of Hungarian whole exomes  were alilgorithm  with the version .3.1 algopractice  using thWe used the publicly available variants from the higly currated VCF files of the Human Genome Project 1 kG phase 3 dataset . We exclbedtools intersect algorithm using the genomic coordinates of the high-coverage high-quality exome BED coordinate list that we established using the Hungarian exome data. Second, the GENOME dataset was created on the basis of a homogeneously dispersed genomic coordinate list that spanned all somatic chromosomes  and array-based approaches, we created three SNP datasets  established upon the following rules: first, the EXOME dataset . This resulted in comparable variant numbers for each dataset . The 1 kG annotated joint VCF was re-coded to plink.ped and plink.map formats. Plink (version 1.9) was used to generate the binary bed format files used in the downstream analysis [In order to prepare the BEADCHIP dataset . The resulting joint VCF were re-coded to plink.ped and plink.map formats and we used plink to generate the binary bed format file . Because of the structure of the human genome, it was expected that exome variants would yield non-homogeneously dispersed markers. Therefore, we performed the LD pruning using different sliding window sizes  to prune linked markers in order to identify the best parameters. For both window sizes, we kept the recommended 10 SNPs increment and r2 threshold of 0.1 to allow pruning of markers by the same linkage criteria. In order to allow unbiased comparison of the three methods, we used the same LD pruning parameters  for each dataset.LD pruning was carried out using the ST matrix and the PCA analysis of populations were performed by the EIGENSOFT software (version 6.1.3) [The pairwise Fn 6.1.3)  on the Ln 6.1.3) .3 tests were carried out by the qp3Pop program of ADMIXTOOLS [4 tests were calculated using the fourpop algorithm of TreeMix software (version 1.13). For each dataset, the corresponding F4 values were calculated (where X refers to the given test population). F3 and F4 values of the tests were visualized by ggplot2.The FMIXTOOLS  for each--cv option for K = 3 to 11 values using 20 iterations and randomized seeds [Admixture analysis was performed by the ADMIXTURE software (version 1.3.0) using the LD pruned datasets with the ed seeds . The besAdditional file 1:Table S1. Number of high-quality biallelic variants in GENOME, EXOME, BEADCHIP, and HUN-EXOM datasets before and after LD pruning. Table S2. f3-statistics of Hungarian and selected South Asian populations indicate admixture which could be attributed to the Gypsies living as an ethnic minority in Hungary. Figure S1. The f4  statistics estimates the East Asian and African components for each dataset. Negative values indicates East Asian gene flow, while positive values indicate dominant African genetic components. Figure S2. Calculated CV error for different admixture models (K=3 to 10). The best fitting model of admixture indicated was K=6 in the GENOME, EXOME and HUN-EXOME datasets, however, it was K=9 in case of the BEADCHIP dataset. Figure S3. Admixture analysis of 25 populations with the K=6 admixture model. The major topology and admixture components were very similar in each dataset for all analyzed populations but the minor admix components were systematically overestimated using the BEADCHIP dataset. Figure S4. Admixture analysis of 25 populations with the K=9 admixture model. Besides the minor admix components overestimation using BEADCHIP this admixture model suggested that the European super-population was split into two and the African super-population was split into three admixing components. Figure S5. PCA of the HUN-EXOME dataset confirms that genetically, modern Hungarians belong to the European super-population and within the Europeans, two genetic components exist with a North-South European gradient. Figure S6. F4 values of the HUN-EXOME dataset indicates that Hungarians have higher East Asian admixture component than British but lower than Finnish population. Figure S7. Admixture analysis of 25 populations using the HUN-EXOME dataset for the A) K=6 and B) K=9 admixture models. Besides the major Europian admixture component in few Hungarian individuals minor East Asian admixture components also detected. Data Supplementary Data was deposited to: https://figshare.com/s/e91794c7141a7eb16255. (PDF 2255 kb)Additional file 2:Table S3. The calculated FST and FST difference data used for analyses. FST matrix of populations based on the GENOME, EXOM, BEADCHIP datasets. The difference between the FST matrix of the GENOME and EXOME, GENOME and BEADCHIP datasets. FST matrix of populations based on the HUN-EXOME dataset. (XLSX 34 kb)