Pseudotaxus chienii can occupy diverse habitats with environmental heterogeneity and thus provides an ideal material for investigating the process of population adaptive evolution. Here, we characterize genetic and expression variation patterns and investigate adaptive genetic variation in P. chienii populations.Elucidating the effects of geography and selection on genetic variation is critical for understanding the relative importance of adaptation in driving differentiation and identifying the environmental factors underlying its occurrence. Adaptive genetic variation is common in tree species, especially widely distributed long-lived species. FST outlier, redundancy analysis, and latent factor mixed model methods to detect molecular signatures of local adaptation. We identified 244 associations between 164 outlier SNPs and 17 environmental variables. The mean temperature of the coldest quarter, soil Fe and Cu contents, precipitation of the driest month, and altitude were identified as the most important determinants of adaptive genetic variation. Most candidate unigenes with outlier signatures were related to abiotic and biotic stress responses, and the monoterpenoid biosynthesis and ubiquitin-mediated proteolysis KEGG pathways were significantly enriched in certain populations and deserve further attention in other long-lived trees.We generated population transcriptome data and identified 13,545 single nucleotide polymorphisms (SNPs) in 5037 unigenes across 108 individuals from 10 populations. We observed lower nucleotide diversity (π = 0.000701) among the 10 populations than observed in other gymnosperms. Significant negative correlations between expression diversity and nucleotide diversity in eight populations suggest that when the species adapts to the surrounding environment, gene expression and nucleotide diversity have a reciprocal relationship. Genetic structure analyses indicated that each distribution region contains a distinct genetic group, with high genetic differentiation among them due to geographical isolation and local adaptation. We used P. chienii, genomic data revealed signatures of divergent selection associated with environmental variables. Our research provides SNPs, candidate unigenes, and biological pathways related to environmental variables to facilitate elucidation of the genetic variation in P. chienii in relation to environmental adaptation. Our study provides a promising tool for population genomic analyses and insights into the molecular basis of local adaptation.Despite the strong population structure in The online version contains supplementary material available at 10.1186/s12864-021-07682-3. Dissecting the distribution of genetic variation across landscapes helps us to understand the ecological and evolutionary processes under climate change. The influence of natural selection on genetic variation and expression variation in natural populations has received increasing attention in studies on adaptive evolution and molecular ecology . As specWith the development of sequencing technology, next-generation sequencing (NGS) has made it possible to obtain genome-wide scale sequence information across populations, greatly promoting the investigation of adaptive evolution and molecular ecology in nonmodel species . PreviouMiscanthus lutarioriparius individuals from 10 populations and found genes related to photosynthetic processes and responses to environmental stimuli such as temperature and reactive oxygen species. Sun et al. (2020)   and geographic factors alone [c]  explained 8 and 1% of the variation at all loci, respectively; however, they explained 23% of the genetic variation when considered jointly [b] (adjusted R2 = 0.2331). Using outlier loci, pure environmental factors [a] explained 11% of the genetic variation , and pure geographic factors [c] explained 1% of the genetic variation . Environmental factors and geographic factors together explained 53% of the genetic variation (adjusted R2 = 0.5276)  than other gymnosperms, such as Cupressus chengiana (π = 0.0077), Cupressus duclouxiana (π =0.0031), and Cupressus gigantea (π = 0.0029) P. chienia π =0.00, and Cuptomation . The mea= 0.341) . Furtherbreeding , 36.P. chienii populations/groups were generally high (FST > 0.15), except for a few pairs of populations in geographical proximity. The spatial context of natural selection and the balance between the strength of divergent selection and migration rates between populations/groups are of great significance to genetic differentiation [P. chienii grows in the understory; therefore, the biological characteristics of wind pollination may not help P. chienii spread pollen over a long distance. Additionally, the seed cone of P. chienii possesses a cup-like, fleshy, white aril [Taxus species [P. chienii seeds. From ecological and evolutionary perspectives, the species has been exposed to stressors or diverse environments, which has resulted in genetic differentiation and genetic heterogeneity. A few populations in geographical proximity showed relatively low genetic differentiation, possibly due to high migration rates and a high rate of effective pollen and seed dispersal. In this study, geographic distance and environmental distance were correlated with genetic differentiation , suggesting that IBD and IBE were important to the divergence among P. chienii populations. These results further revealed that the genetic differentiation among the P. chienii populations was mainly the result of geographical isolation and habitat heterogeneity.The genetic differentiation levels among ntiation , 38. Theite aril  and main species , which lGenerally, a higher level of genetic differentiation indicates a stronger population structure. Genetic differentiation among populations with different geographic distributions was found in the ADMIXTURE and PCA analyses in our study. The ADMIXTURE results suggested the presence of three major genetic clusters and a smaller cluster (DXG) across the species’ natural range Fig. , whereasP. chienii genome during the evolutionary process [Gene expression variation among populations may be due to developmental, environmental, genetic or other biological effects, which are essential for adaptive evolution . Underst process . Compare process . Higher P. chienii, analysis of genomic data revealed signatures of divergent selection associated with environmental variables. The FST outlier approach, RDA, the LFMM method, and selective sweep analysis were used to detect signatures of local adaptation among P. chienii populations and identify unigenes under selection. Environmental association studies have been more robust in identifying loci under selection and can also provide context for selection forces [P. chienii populations. We further applied RDA to dissect the individual roles of environmental factors and geographic factors and their confounding effect on genetic variation. Our RDA showed that population divergence in P. chienii was strongly shaped by the joint effect of environmental factors and geographic factors, and environmental factors were more important than geography, a pattern consistent with local adaptation. Environmental differences among populations may constitute key factors maintaining genetic differentiation despite high relative migration rates between local populations. It follows that environmental differences among populations are closely related to the maintenance of genetic variation and that local adaptation may be the main driving force of these patterns. However, a large part of the variation remains unexplained. This may be due to several factors that cannot be fully resolved. First, although we included a large number of environmental variables in our study, many other unmeasured ecological forces may also play a role. Second, other evolutionary forces that maintain local genetic diversity, such as balancing selection, may weaken the signal of locus-environment associations [Local adaptation to environmental variables is generally believed to play a major role in the diversification of species, but its contribution relative to those of other evolutionary forces is rarely quantified. Despite the strong population structure in n forces . Testingciations . Third, Pinus massoniana) [Camellia sinensis) [Kandelia obovata [Taxus chinensis) [T. chinensis [In this study, we found evidence for local adaptation signals to genetic variation associated with environmental variables. We detected 164 SNPs residing in 127 unigenes as candidate targets of adaptive importance. The GO annotation analysis of 127 unigenes showed that the majority of the unigenes were related to abiotic and biotic stress responses, which is of particular interest for future population genomic research. In the biological process category, oxidation-reduction process, signal transduction, and protein phosphorylation were the most represented GO terms, which is consistent with the findings of adaptability studies in conifers and other plants. These three biological processes were shown to be involved in the drought response in Masson pine (soniana) . The oxiinensis)  and adap obovata . Protein obovata . Signal  obovata . Within inensis) . Additioinensis) , 54. Prehinensis . Althougq-values < 0.05) for 87 candidate unigenes. Terpenoids play an important role in abiotic and biotic stresses and are involved in the defense against pathogens and herbivore attack in conifers [Picea abies and Pinus halepensis [P. chienii in the ZJ group. Ubiquitin-mediated proteolysis was significantly enriched  for candidate unigenes for habitat adaptation in the HN group. Ubiquitin-mediated proteolysis impacts many aspects of plant growth and development, including plant hormone signal transduction, reproduction, and abiotic stress responses [P. chienii.The allele frequency of SNPs changes under selection pressure, thereby rapidly maximizing adaptability in different environments. Niche differentiation was detected for GX vs. ZJ and HN vs. ZJ; thus, we further used selective sweep analysis to identify the unigenes underlying divergent adaptation in the ZJ, GX, and HN groups. We found 87 candidate unigenes for habitat adaptation in the ZJ group. The KEGG pathway of monoterpenoid biosynthesis was significantly enriched , particularly in the distribution range of the JX group of P. chienii. In the case of rapid climate change, if P. chienii populations cannot adapt to increasing drought, ecological benefits will be greatly damaged. According to a global assessment, due to droughts, high temperatures, and insect outbreaks under climate change, the mortality rate of forest trees may increase [P. chienii. The identified candidate unigenes are directly or indirectly related to biotic or abiotic stress responses. For instance, Cluster-242,496.114750, encoding an LRR receptor-like serine/threonine-protein kinase, is associated with pathogen and disease resistance [P. chienii. Future climate trends in the distribution range of P. chienii, including increased temperature and decreased precipitation, will pose challenges to P. chienii in terms of its environmental adaptation. Our research provides SNPs and candidate unigenes related to environmental variables to facilitate elucidation of the genetic variation and structure of P. chienii in relation to environmental adaptation.Detecting associations between SNPs and environmental variables helps us recognize the ecological, bioclimatic, and topographic variables that influence genetic variation. We found that the fraction of absorbed photosynthetically active radiation, soil Cu content, soil Zn content, percent tree cover, aspect, and precipitation of the driest month were associated with the most outlier SNPs when using the LFMM method  and kept at the Herbarium of Sun Yat-sen University (No: ds-2018-1001–ds-2018-1010).P. chienii were collected from first-year branches across the distribution range in China in May 2018 . The purity and integrity of the extracted RNA were detected using a NanoDrop spectrophotometer  and an Agilent 2100 Bioanalyzer , respectively. An RNA integrity number > 6.0 was required for cDNA synthesis and library construction. One microgram of RNA from each individual was used for cDNA library preparation.The Illumina library for each individual was constructed using the NEBNext Ultra™ RNA Library Prep Kit  following the manufacturer’s protocol. Poly(A) mRNA was enriched from total RNA using oligo (dT) magnetic beads. Then, the poly(A) mRNA was fragmented into small pieces using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5×). The RNA fragments were reverse transcribed into first-strand cDNA using random hexamer primers and M-MuLV Reverse Transcriptase (RNase H-). Subsequently, second-strand cDNA was synthesized using dNTPs, DNA polymerase I, and RNase H. The purified double-strand cDNA was end-repaired and A-tailed, and then Illumina paired-end adapters were ligated. The library fragments were purified using AMPure XP beads  to select cDNA fragments with lengths of 250–300 bp. Then, PCR was performed with Phusion High-Fidelity DNA polymerase, universal PCR primer and index (X) primer. After size selection and PCR amplification, qualified cDNA libraries were sequenced on the Illumina NovaSeq platform, generating paired-end reads with a length of 150 bp.P. chienii.Raw Illumina RNA-Seq reads were filtered via in-house Perl scripts. Clean reads were obtained by removing the reads with more than 10% ambiguous bases (“N”), adapter reads, and Qphred scores ≤20 bases with more than 50% from the raw reads. Finally, clean reads of 108 individuals were de novo assembled to obtain the final unigenes with Trinity v.2.4.0 . The finP. chienii using Bowtie 2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) with default parameters. Duplicate reads were excluded by Picard tools ver. 1.96 (http://broadinstitute.github.io/picard/); then, the reads were sorted and indexed in BAM format using SAMtools ver. 1.4 with default settings [Clean reads for each individual were mapped to the reference sequences of settings . mpileupsettings  to identhttps://vcftools.github.io/index.html) with nonoverlapping 1000-bp genomic windows. Pairwise genetic differentiation (FST), observed heterozygosity (HO), and expected heterozygosity (HE) were calculated using Arlequin ver. 3.5.1.2 [FST was determined using 1000 permutations. Wright’s inbreeding coefficient (FIS) was estimated for each population using the basic.stats function in the R package ‘hierfstat’ [Based on the SNP data, the nucleotide diversity (π) per site was calculated using VCFtools ver. 0.1.11  was set from 2 to 10. Cross-validation error (CV error) was used to determine the most likely number of clusters. The lowest CV error indicated the optimum K value. GCTA ver. 1.93.2 software [P. chienii individuals. The first two components were plotted for P. chienii to explore its genetic structure.The population genetic structure of the 108 individuals was examined in ADMIXTURE ver. 1.23  using thsoftware  was usedAMOVA was used to assess the extent of genetic structure within and among populations as implemented in Arlequin, and the significance was determined for 1000 permutations.The best model of nucleotide substitution was identified with PhyML 3.0 , 74 usinP. chienii using Bowtie 2 of the RSEM software (http://bowtie-bio.sourceforge.net/Bowtie2/index.shtml). The readcount values of each unigene for 108 individuals were obtained. Considering the influence of the gene length and sequence depth on the fragments, all readcounts were normalized to the FPKM values. The FPKM values were calculated using the formula below: FPKM = (109 × C)/(N × L), where C is the number of fragments mapped to the transcript, N is the total number of mappable reads, and L is the length of the transcript [2 transformation was performed. Quartile analysis was used to filter out values greater than 1.5 times the interquartile range [2-transformed FPKM values larger than 0 were retained.The clean reads of each individual were mapped to the reference sequences of anscript . The exple range , 78. In Ep) was evaluated as the average FPKM value of the individuals from the population. Expression diversity (Ed) was calculated as the gene expression variation in the population. The formulas for Ep and Ed were based on Xu et al.’s (2015) method. To estimate the gene expression level relationship among populations (Ep similarity), we calculated Pearson’s correlation coefficients (r) based on the average correlation coefficients of 108 individuals. The significance of the relationship between genetic distance and Ep similarity in populations was tested with a Mantel test using 1000 random permutations. To detect the relationship between nucleotide diversity and expression diversity within populations, Pearson’s correlation coefficients (r) of π and Ed for 16,225 unigenes in each population were calculated. Additionally, Pearson’s correlation analysis was performed between π and Ep among populations. The ‘HMISC’ package in R was used for Pearson’s correlation analysis, and the significance was determined for 1000 permutations [Gene expression variation in populations was evaluated using Xu et al.’s (2015)  method. utations .P. chienii, a putatively neutral dataset  was performed with the divMigrate function in the R package ‘diveRsity’ [D, GST, and Nm). This approach is a relative bidirectional measure of gene flow using all available genetic differentiation measures to evaluate the consistency of estimates among measures. The confidence interval (95%) of the relative migration rates was calculated with 1000 bootstrap iterations.We pooled the populations into four groups  based on the results from the PCA, phylogenetic tree, and geographical distribution. To assess directional relative migration rates among the 10 populations/four groups of veRsity’  based onhttp://www.worldclim.org). The ecological variables comprised five ecological factors and 20 soil variables. The five ecological factors included the normalized difference vegetation index, percent tree cover, leaf area index, enhanced vegetation index, and fraction of absorbed photosynthetically active radiation, which were obtained from the Land Process Distributed Active Archive Center  based on the moderate resolution imaging spectroradiometer (MODIS) dataset. The annual MODIS layers were generated based on a maximum value composite procedure in the App Store of the ENVI 5.3 package. Then, the layers of different years were averaged to obtain a single layer representing the ecological factor. Twenty soil variables, including pH, electrical conductivity, fresh water content, air-dried water content, organic matter, and total N, P, C, S, Si, K, Ca, Na, Mg, Al, Fe, Mn, Zn, Cu, and Pb, were obtained from published research by our research group [http://www.esri.com/software/arcgis/arcgis-for-desktop). To reduce multicollinearity among variables, the variance inflation factors (VIFs) were calculated for the 19 bioclimatic variables, five ecological factors, 20 soil variables, and three topographic variables using the vif function of ‘usdm’ in R software [The environmental variables include 19 bioclimatic, 25 ecological, and three topographic variables. Bioclimatic data (averaging over the period 1950–2000) comprising 19 bioclimatic variables at a spatial resolution of 2.5 arc-mins were obtained from the WorldClim database , and the significance was determined for 999 permutations.To investigate the roles of environmental and geographic factors in shaping genetic differentiation, we calculated the associations between pairwise P. chienii, including 15 for GX, six for HN, 11 for JX, and 28 for ZJ, were collected from field sampling, published resources, the Chinese Virtual Herbarium , and the Global Biodiversity Information Facility , which covered most of the distribution range of this species.After removing duplicate records, a total of 60 records of http://www.worldclim.org). The VIFs were calculated for the 19 bioclimatic variables to reduce the multicollinearity among the variables. Bioclimatic variables with a VIF > 10 were removed. Five bioclimatic variables were retained, namely, mean temperature of the coldest quarter, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality (CV), and precipitation of warmest the quarter. Maxent ver. 3.3.3 k [P. chienii with the following parameters: replicates, 10; replicated run type, subsample; maximum iterations, 500; and random test points, 25. The AUC values were used to predict the performance of the models, with AUC values closer to 1.0 indicating better model performance. ENMTools ver. 1.4.4 [D and the standardized Hellinger distance (I) were calculated to measure niche overlap in group pairs. Identity tests of six comparisons  were performed.Current climatic data (averaging over the period 1950–2000) comprising 19 bioclimatic variables at a spatial resolution of 2.5 arc-mins were obtained from the WorldClim database , the LIG (approximately 130–114 kya), and the LGM (approximately 21 kya). Climate layers for the LGM and future at a spatial resolution of 2.5 arc-mins and LIG at a spatial resolution of 30 arc-secs were obtained from the WorldClim database.To examine the effects of past and future climatic shifts on the four groups of FST outlier approach in BayeScan ver. 2.1 [FST coefficient (β) shared by all loci and a locus-specific FST coefficient (α) shared by all the populations [q-values in BayeScan, and SNPs with a q-value lower than 0.001 were considered outlier SNPs. We also calculated the locus-specific FST coefficient (α), where a positive value indicates diversifying selection, while a negative value indicates purifying/balancing selection. In the analysis of the correlation between genomic variation and environmental variables, we considered only SNPs with positive α values and ignored SNPs with negative α values.To determine if there were unigenes putatively under selection in the 10 populations, we implemented an ver. 2.1 . It has ulations . All parFST values and π ratios  for group pairs. The π ratios were subjected to log2 transformation. The regions with FST and log2(π ratio) values in the top 5% were considered candidate outliers subjected to strong selective sweeps. Then, all outliers were assigned to corresponding unigenes.Based on niche overlap analysis, the ecological differentiation of GX vs. ZJ and HN vs. ZJ was valid. Therefore, we further used selective sweep analysis to detect adaptation. We calculated the E-value of 1.0 × 10− 5. To identify significantly enriched biological functions and pathways, we performed GO and KEGG enrichment analyses. The GO enrichment analysis of candidate unigenes was performed using the ‘GOseq’ R package based on the Wallenius noncentral hypergeometric distribution [q-values  were used to test the statistical significance of GO and KEGG enrichment, with q-values < 0.05 considered significant.We performed functional annotation of the candidate unigenes containing outlier SNPs identified in BayeScan and selective sweep analysis. Hmmscan of HMMER ver. 3.1  was usedribution . KEGG paribution . q-valueWe utilized RDA, a multivariate method, to detect the relative importance of environmental and geographic variables to genetic variation. RDA, as a canonical ordination method, allows for calculation of the variance in response variables explained by a number of explanatory variables . To avoid spatial autocorrelation, longitude and latitude coordinates of each individual were transformed using distance-based Moran’s eigenvector maps . We used environmental variables  was set to 4 based on the ADMIXTURE result. The analysis was run with a burn-in of 100,000, 50,000 iterations, and 10 repetitions. The p-values were used to test for statistical significance, with p-values < 0.005 indicating significant correlations. Candidate unigenes that were significantly related to environmental variables were subjected to functional annotation.A univariate method for LFMM was also used to test correlations of outlier SNPs with environmental variables using the ‘LEA’ package in R . LFMM isAdditional file 1 Summary of the reads sequenced for 108 Pseudotaxus chienii individuals.Additional file 2 Summary of the unigenes for Pseudotaxus chienii.Additional file 3. The length distribution of unigenes.Additional file 4. The mapping rates between the clean reads of each individual and the reference sequences.Additional file 5 The pairwise FST values between the 10 Pseudotaxus chienii populations.Additional file 6 The pairwise FST values between the four Pseudotaxus chienii groups.Additional file 7 Gene expression analysis based on FPKM data in Pseudotaxus chienii. (a) The distribution of population gene expression (Ep). (b) The distribution of expression diversity (Ed).Additional file 8 The relationship between nucleotide diversity (π) and expression diversity (Ed) of unigenes in each population.Additional file 9 The relationship between nucleotide diversity (π) and population gene expression level (Ep) among 10 populations.Additional file 10 The relationship between population expression similarity (Ep similarity) and genetic distance.Additional file 11. Independent contributions of five bioclimatic variables for four groups.Additional file 12 Predicted potential distributions for the four groups of Pseudotaxus chienii . (a) Present day. (b) Last interglacial . (c) Last glacial maximum . (d) Future .Additional file 13. The annotation of the unigenes containing outlier SNPs.Additional file 14. Gene ontology (GO) annotation of the candidate unigenes containing outlier SNPs identified in BayeScan.Additional file 15. Gene ontology (GO) enrichment analysis of 642 unigenes containing outlier SNPs.Additional file 16. The annotation of the 87 candidate unigenes for Zhejiang (ZJ) group habitat adaptation.Additional file 17. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the 87 candidate unigenes for habitat adaptation in the Zhejiang (ZJ) group.Additional file 18. The annotation of the three candidate unigenes for habitat adaptation in the Hunan (HN) group.Additional file 19. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of three candidate unigenes for habitat adaptation in the Hunan (HN) group.Additional file 20. The annotation of the 17 candidate unigenes for habitat adaptation in the Guangxi (GX) group.Additional file 21. The significant environmental and geographic variables retained by the initial step-forward selection method for all loci and outlier loci.Additional file 22. Outlier SNPs associated with environmental variables identified using LFMM.Additional file 23. The annotation of the unigenes containing outlier SNPs associated with environmental variables.Additional file 24 The environmental variables with variance inflation factors (VIFs) < 10 for 10 Pseudotaxus chienii populations.


Aquilegia buergeriana var. buergeriana distributed in four mountain regions in the Japanese Alps. We also examined the genetic relationship between yellow‐ and red‐flowered individuals, to see if color differences caused genetic differentiation by pollinator isolation. Genetic relationships among 16 populations were analyzed based on genome‐wide single‐nucleotide polymorphisms. Even among populations within the same mountain region, pollinator size varied widely, and the average spur length of A. buergeriana var. buergeriana in each population was strongly related to the average visitor size of that population. Genetic relatedness between populations was not related to the similarity of spur length between populations; rather, it was related to the geographic proximity of populations in each mountain region. Our results indicate that spur length in each population evolved independently of the population genetic structure but in parallel in response to local flower visitor size in different mountain regions. Further, yellow‐ and red‐flowered individuals of A. buergeriana var. buergeriana were not genetically differentiated. Unlike other Aquilegia species in Europe and America visited by hummingbirds and hawkmoths, the Japanese Aquilegia species is consistently visited by bumblebees. As a result, genetic isolation by flower color may not have occurred.Geographic differences in floral traits may reflect geographic differences in effective pollinator assemblages. Independent local adaptation to pollinator assemblages in multiple regions would be expected to cause parallel floral trait evolution, although sufficient evidence for this is still lacking. Knowing the intraspecific evolutionary history of floral traits will reveal events that occur in the early stages of trait diversification. In this study, we investigated the relationship between flower spur length and pollinator size in 16 populations of  Aquilegia buergeriana var. buergeriana evolved in parallel in different mountain regions as a convergent adaptation to locally abundant large bumblebees.In this study, we demonstrate that long spurs in  Mainly, flower color, spur length, flower orientation, and pistil length have evolved to adapt to each pollinator  by the MIG‐seq (multiplexed inter‐simple sequence repeat genotyping by sequencing) method .Hayashi, . The spuHayashi, . FlowersAquilegia populations in four mountain regions  of the central Japanese Alps , and the average length of the measured spurs was used as the spur length of that individual Figure . CorollaWe also examined spatial autocorrelation  by using the "moran.test" function in the "spdep" package in the R Software Environment ver. 4.0.2  to determine visitor size  were the main visitors, although small bees (Ceratina spp.) also visited occasionally. Bumblebees, which extend their proboscis to the nectar source at the tip of the spur to suck nectar, visited both male and female phases and both yellow and red flowers . Smaller bees could not reach the spur tip to forage for nectar. Furthermore, although they sometimes collected pollen from the flowers, it is unlikely that they contribute to pollination because they rarely moved between plant individuals and did not visit female phase flowers. Because the pollen visitation patterns of the bumblebees and small bee species were very different, we calculated average visitor size for all visitors  and for bumblebees only.As the average visitor size for each plant population, the weighted arithmetic average was calculated from the relative abundance of each visitor species and the size of that species:2.4A. buergeriana var. buergeriana were estimated by a linear mixed‐model (LMM) analysis. In this analysis, spur length of each individual was used as the objective variable, and average visitor size  of each population, average visitor size (only bumblebees) of each population, plant height of each individual, number of flowers per individual, and altitude of each population were used as explanatory variables. Here, plant height and the number of flowers per individual were used as indicators of the nutritional status of the plant, and altitude was used as a representative indicator of non‐biotic environmental factors . Population was added to the model as a random effect.The factors affecting spur length in Before conducting the LMM analysis, the variance inflation factor (VIF) statistic was calculated to check for correlation (multicollinearity) between variables, using VIF = 0.5 as the threshold  of the population, average visitor size (only bumblebees) of the population, plant height, number of flowers per individual, and altitude. Then, using the "dredge" function of the R package "MuMIn," we compared the global model to simple models with fewer explanatory variables. Then, we adopted the model with the lowest Akaike information criterion (AIC). We excluded the nine ON‐1000 population, which was not visited by bumblebees, from this analysis, treating it as a missing value.2.5Leaf samples were obtained from 6–21 individuals in each study population , and DNA was extracted by the CTAB method  were removed. Fourth, after performing a Hardy–Weinberg equilibrium test on each population, we excluded loci where allele frequencies deviated from the Hardy–Weinberg equilibrium at p < .01 in three or more populations.Low‐quality reads and extremely short reads containing adaptor sequences were removed by using trimmomatic 0.39  was calculated 10 times for each possible K value from 1 to 10, and the appropriate number of clusters was estimated based on the ΔK value , and the appropriate number of clusters was estimated based on the ΔK.The following population genetic statistics of SNP sites for each population were calculated with the Stacks populations module: expected heterozygosity 2.6A. buergeriana var. buergeriana reflects geographic distance or spur length differences. In general, populations separated by greater distances are more genetically differentiated than populations close together  between populations. The geographic distance between populations was calculated from the latitude and longitude of the populations, and the difference in the average spur length of each population was used as the trait difference. We calculated the relationship between pairwise FST or FST/(1 – FST) and geographic distance between populations, as well as the relationship between pairwise FST or FST/(1 – FST) and trait difference between populations, following methods in Rousset . PCA results for spur length, corolla diameter, and petal width roughly indicated a morphological separation among populations  and 31.84–40.80 mm (only bumblebees) Table . Five tyns Table . Average3.2p < .0001, Figure The LMM model with the lowest AIC value was the model that included only average visitor size (only bumblebees) as an explanatory variable Table . A very 3.3He, 0.0904–0.1450; Ho, 0.0593–0.2154; π, 0.0616–0.2248; FIS, –0.0608 to 0.2041; Table A total of 16,033,406 raw reads  were obtained by MIG‐seq, and after quality control, 15,510,825 reads  were used for further analyses. After de novo SNP detection and filtering, the MIG‐seq dataset of 232 samples from 16 populations contained 190 SNPs, distributed among the mountain regions as follows: Utsukushigahara region , Norikura+Ontake region , and Iizuna region . Norikura and Ontake regions were combined based on the initial STRUCTURE results. The values of the population genetics parameters varied among populations  explained 28.78% of the variance. The geographical structure of the populations is clearly reflected in a plot of PC2 against PC1  or K = 3 (next most likely)  Figure . The STR) Figure . The appK Figure . StructuK Figure . In part3.4FST, FST/(1 − FST)) was detected (Table A significant relationship between geographic distance and genetic isolation (ed Table . On the 44.1A. buergeriana var. buergeriana was correlated with the average flower visitor size (only bumblebees) of the population; spur length varied with the average visitor size even among spatially close populations in the same mountain region   in fellfield and snowbed environments on different mountains in Japan  , associated with flower visitor size, in the Utsukushigahara and Norikura regions of the Japanese Alps  based on genome‐wide SNP data has shown that they are genetically distinct  based on neutral genes did not differentiate red‐ and yellow‐flowered individuals in those populations. These results suggest that red flower color is maintained in each population merely as a flower color polymorphism. Although subtle plasticity of floral color has been reported in A. buergeriana var. buergeriana may not become genetically isolated? In the central Nagano region, where this study was conducted, bumblebees appear to be abundant and many flowers depend on bumblebees for pollination  throughout the study, it is an important finding to consider the relationship between flower color and pollinator in Aquilegia.Why yellow‐ and red‐flowered individuals in 5A. buergeriana var. buergeriana has occurred independently in different mountain regions, and (2) the few red‐flowered phenotypes that occur within the species may not lead to genetic differentiation. First, given that the independent evolution of floral size in different mountain regions has also recently been reported in L. album var. barbatum  the evolution of spur length in There are no conflicts of interest.Tsubasa Toji: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Visualization ; Writing – original draft . Shun K Hirota: Formal analysis (lead); Methodology (lead); Software (lead). Natsumi Ishimoto: Data curation (lead); Investigation (lead). Yoshihisa Suyama: Formal analysis (supporting); Supervision (lead); Writing – review & editing (lead). Takao Itino: Conceptualization (lead); Funding acquisition ; Project administration (lead); Writing – original draft (lead); Writing – review & editing (lead).Supplementary MaterialClick here for additional data file.


Dioon sonorense–D. vovidesii, two cycads distributed along a latitudinal environmental gradient in northwestern Mexico, to reveal their demographic histories and the environmental factors involved in their divergence. Using genome‐wide loci data, we determined the species delimitation, estimated the gene flow, and compared multiple demographic scenarios of divergence. Also, we estimated the variation of climatic variables among populations and used ecological niche models to test niche overlap between species. The effect of geographic and environmental variables on the genetic variation among populations was evaluated using linear models. Our results suggest the existence of a widespread ancestral population that split into the two species ~829 ky ago. The geographic delimitation along the environmental gradient occurs in the absence of major geographic barriers, near the 28th parallel north, where a zonation of environmental seasonality exists. The northern species, D. vovidesii, occurs in more seasonal environments but retains the same niche of the southern species, D. sonorense. The genetic variation throughout populations cannot be solely explained by stochastic processes; the latitudinal‐associated seasonality has been an additive factor that strengthened the species divergence. This study represents an example of how speciation can be achieved by the effect of the latitude‐associated factors on the genetic divergence among populations.Latitude is correlated with environmental components that determine the distribution of biodiversity. In combination with geographic factors, latitude‐associated environmental variables are expected to influence speciation, but empirical evidence on how those factors interplay is scarce. We evaluated the genetic and environmental variation among populations in the pair of sister species  Dioonsonorense–D. vovidesii from northwestern Mexico. We found that the two lineages split approximately 1 million years ago, and their current geographic separation is concordant with the environmental seasonality variation associated with the latitudinal environmental gradient that, in turn, determines the margin of the Neotropical forests. The divergence that led to speciation was promoted by (a) a geographic component that separated the two neighboring lineages, and (b) an environmental component that increased the distinction between them.We reconstructed the history of lineage divergence that gave origin to the sister cycad species  The effSince latitudinal environmental gradients provide zonations that can influence contrasting evolutionary trends in neighboring lineages  Chemnick, T.J. Greg. & Salas‐Mor. and D. vovidesii Gutiérrez‐Ortega & Pérez‐Farr. co‐occur in parapatry between the parallels ca. 27 and 29° N . In recent studies, we demonstrated that D. sonorense actually consists of two very closely related species that can be distinguished using either genetic, morphological, or leaflet anatomical analyses  clarify the species delimitation; (b) estimate population genetic parameters and reveal the demographic history of lineages; (c) estimate the climatic factors involved in the divergence of the species; and (d) examine the influence of the latitudinal environmental gradient on their genetic diversity and differentiation.In this study, we reveal the mechanisms of speciation involved in the history of divergence between 22.1D. vovidesii and two populations of D. sonorense  and raw sequence filtering were performed following the protocol described in Gutiérrez‐Ortega, Salinas‐Rodríguez, et al.  with maximum distance (M) = 2, enabling the deleveraging (d) and removal (r) algorithms. “Cstacks” was used to create a catalog with a maximum of four mismatches between loci (n = 4). “Sstacks” was used to search the ustacks products into the catalog produced in “cstacks.” “Populations” was used to retrieve SNPs from polymorphic loci that were present in at least 90% of individuals (r = .9). When loci were not detected in a sample, alleles were treated as missing data. Minor alleles with frequency <.05 were excluded. One randomly selected SNP was retrieved from each locus using the option “write_random_snp.” This first screening revealed a total of 1989 loci. Data were exported as a STRUCTURE file format, and all file conversions after this step were done with the programs Formatomatic v0.8.1 , observed (oH) and expected heterozygosity (eH), inbreeding coefficient (F), and Shannon's information index (I) were estimated at the population and the species levels.For the three datasets, phi‐values of pairwise genetic distances among individuals were calculated in GenAlEx v6.5  were tested. Optimal K values were determined using the Delta‐K method of Evanno et al.  over 4eN generations in the past were estimated under a stepwise mutation model assuming constant mutation rates across loci. stF values were used as starting parameter to calculate M. Each run assumed uniform priors ranging from 0 to 0.25 and delta = 0.025 to estimate theta (Θ) and uniform prior distribution between 0 and 2000 with delta = 200 to estimate M. Metropolis‐coupled MCMC consisted of one long chain of 25,000 steps, sampling every 20 steps. Burn‐in period was set to 100,000. The numbers of migrants per generation (mN) were estimated with the formula aMaΘ‐>b = 4maN.Historical gene flow was estimated among populations, among three lineages determined in genetic structure analyses (see Results), and between species, using MIGRATE‐N v3.6 . These options gave a dataset of 4,630 SNPs that were used as the input file in DIYABC. However, in the DIYABC input file, we specified to exclude SNPs with minor allele frequency <.05, and the run used the information of a final dataset consisting of 1,138 SNPs.For this analysis, we first used the 809 neutral SNP datasets. However, despite using alternative scenarios and priors, convergence was not reached in the estimation of all parameters. This was likely due to low informative summary statistics of the 809 SNP datasets  were considered as different lineages; the effective population sizes of these three lineages were equal to N1, N2, and N3 (respectively). Two scenarios assumed that N2 was originated by secondary contact between N1 and N3.Four demographic scenarios were tested, all assuming D. vovidesii (Nc) diverged from the ancestral lineage of D. sonorense (Na) t2 generations ago; N2 derived from Nc t1 generations ago; the three lineages had a change in effective population size t1 generations ago.Scenario 1: The ancestral lineage of D. sonorense (Na) diverged from the ancestral lineage of D. vovidesii (Nc) t2 generations ago; N2 derived from Nc t1 generations ago; the three lineages had a change in effective population size t1 generations ago.Scenario 2: The ancestral lineage of D. vovidesii (Nc) diverged from the ancestral lineage of D. sonorense (Na) t2 generations ago; N2 is the result of an admixture event between Na and Nc t1 generations ago. Na and Nc had a change in effective population size t1 generations ago.Scenario 3: The ancestral lineage of D. sonorense (Na) diverged from the ancestral lineage of D. vovidesii (Nc) t2 generations ago; N2 is the result of an admixture event between Na and Nc t1 generations ago. Na and Nc had a change in effective population size t1 generations ago.Scenario 4: The ancestral lineage of Θ values of Dson and Dvov‐N estimated in MIGRATE‐N were used to roughly calculate N1 and N3 via Θ = 4µeN, considering µ = 6.70325E−10 substitutions per site/year as estimated for Cycadales by De La Torre et al.  and 10–50,000 for Dvov‐S, t1, and t2. We assumed that N1 > N2 < N3, t2 => t1, Na <= N1, Nc <= N3. The levels of genetic diversity of the three lineages, st,F and Nei distances among lineages were used as summary statistics. The run consisted of 8 million simulations.As prior information, the e et al. , and asse et al. . Such esThe most likely scenario was chosen by comparing the posterior probabilities through a logistic regression test  information of herbaria specimens deposited in MEXU, USON, and HEM; this includes specimens that we deposited after fieldwork of this study; and (b) the Global Biodiversity Information Facility . Our dataset gathered a total of 23 occurrence points, nine of D. sonorense and 14 of D. vovidesii. We used the software QGIS (http://qgis.osgeo.org) to retrieve environmental data from the 19 bioclimatic layers of resolution of 1 km2 deposited in WorldClim  and the Mexican layer of altitude deposited in INEGI . This dataset was used to test the differentiation of variables between D. sonorense and D. vovidesii through Welch's ANOVA and the Kruskal–Wallis test implemented in PAST v3.14  were constructed with MaxEnt v3.3.1  falls within the 95th percentile of the expected distribution (p < .05). Niche divergence or niche conservatism can be confirmed if the empirical overlap is lower or higher (respectively) than that estimated for the ENM of any species and the background of the opposite species   of the nine populations and estimated their correlation with latitude and the PC1 and PC2 scores in R 3.5.1 /Phi) to infer differences in the local natural selection regimes among populations groups along the distribution range. This method is based on the null hypothesis that the genetic differentiation of outlier loci of one set of populations (with respect of genetic structure) is correlated with the background neutral genetic differentiation on the same set of populations. Thus, if the assumption is true, a correlation between Phi(outlier) and Phi) will be equal to 1, suggesting that the genetic differentiation of outlier loci in the set of populations can be explained by drift (rather than selection) at the local level /Phi ratio at different spatial scales along the latitudinal environmental gradient: within Dson (Pops1–2), within a group combining the Dson and Dvov‐S populations (Pops1–3), within a group combining the Dvov‐S and Dvov‐N populations (Pops3–9), and within Dvov‐N (4–9). For each group, significant differentiation from a median ratio = 1 was estimated with a t test in the software PAST.We used the ratio between the phi‐value of outlier loci and neutral loci (Phi33.1D. sonorense (Pops 1–2) and D. vovidesii (Pops 3–9) was supported by all methods we used. STRUCTURE made a clear separation of the two species when considering the most optimum clustering, K = 2 , D. vovidesii can be separated into two lineages that here we call Dvov‐S (Pop3) and Dvov‐N (Pops4–9). Similar lineage distinctions were also observed in the PCoA plots when considering the “all 871 loci,” the “809 neutral loci,” or the “62 outlier loci” datasets . The Z‐scores of the f3 statistic that evaluated whether any of the three lineages have experienced events of admixture from the two others were all positive: Z‐scores of Dson, Dvov‐S, and Dvov‐N were equal to 10.48, 13.26, and 5.98, respectively. Therefore, the neutral allelic frequencies found among populations can be explained solely by genetic drift, discarding the possibility of admixture after population/lineage divergence.The TreeMix model that better fitted the data was the one containing no migration edges among populations (likelihood = 188.84). The topology of this tree Figure  showed toH) and expected heterozygosity (eH) were very similar between species . In addition, we found that oH was higher than eH in all D. vovidesii populations, suggesting an excess of heterozygosity in this species. In contrast, D. sonorense populations did not show any excess of heterozygosity.Using the neutral dataset, AMOVA showed that most of the neutral variation is found within populations (65%), whereas 17% of the variation occurs among populations and 18% between species Table . Overalles Table ; Ho and 3.2Θ) of D. sonorense (=0.0101) was lower than that of D. vovidesii (=0.0166)  among populations, among lineages, and between the species was low, all with values <0.5  Table . At the 23 Table . When sely Table . In all 5 Tables , and S11D. sonorense–D. vovidesii  had a demographic expansion that gave origin to an ancestral D. sonorense lineage  t2 generations ago . Later, t1 generations ago , Nc had a population shrink that gave origin to the Dvov‐S lineage  and a population expansion that gave origin to the Dvov‐N lineage   Figure . Assumin) Figure , t1 and 3.3Dioon sonorense inhabits less seasonal, warmer areas with higher isothermality . Also, the habitat of D. sonorense receives higher levels of precipitation than D. vovidesii . The PCA constructed from the variation of 12 noncorrelated bioclimatic variables estimated with 12 of the examined populations was able to summarize up to 99.68% of the total variation into only two PCs  of D. sonorense extends toward the distribution range of D. vovidesii, to the north of the 28th parallel north. The differences on the extensions of niche suitability are indicated by the niche breadth estimations using the B1 (inverse concentration) metric, which was lower in D. vovidesii (0.0913) than it was in D. sonorense (0.2009).The ENMs constructed for both species in MaxEnt showed high predictability and did not overfit the data, with AUC values of 0.983 ± 0.006 and 0.991 ± 0.002 for y Figure . The nicDioon in Sonora, Mexico. In addition, bio2  resulted to be informative for the predictability of D. sonorense but not for D. vovidesii, whereas bio6 (min temperature of the coldest month) was informative for D. vovidesii, but not for D. sonorense. The empirical overlap between ENM of the two species was estimated as I = 0.6466. In the background test, this empirical I index value was equivalent to the null distribution constructed by comparing the ENM of D. sonorense with ENMs constructed from random points sampled from the D. vovidesii background (p > .05). However, the I index was significantly higher than the null distribution constructed by comparing the overlap between the ENM of D. vovidesii and 100 ENM replicates constructed of random points sampled from the background of D. sonorense (p = 0) , bio5 (max temperature of the warmest month), and bio19 (precipitation of coldest quarter) were the variables that contributed the most for the ENM construction Table , suggest) Figure . This la3.4eA, oH, and Θ) and latitude, or PC1 and PC2 scores /Phi ratio was uneven among neighboring population groups throughout latitude , suggesting a stronger effect of balancing selection at higher latitudes.The Phie Figure . At lowe44.1D. sonorense and D. vovidesii. This sister species pair occurs at the northern margin of the genus Dioon, across the Nearctic–Neotropical transition zone in Sonora, Mexico. Despite the absence of a clear geographic barrier, genetic and environmental analyses identified that the separation between species coincides with the 28th parallel north  in northwestern Mexico promoted the divergence between h Figure , in conch Figure .Dioon share a common ancestor  suggests that all Sonoran lineages of r Figure  that disr Figure  througho suggestsr Figure , the exp4.2D. sonorense and D. vovidesii was associated with the latitudinal environmental gradient in multiple abiotic aspects. First, as predicted by Janzen /Phi ratio in the Dvov‐N lineage were <1, suggesting that at the local level, non‐neutral loci are less differentiated among populations than the neutral loci, although this value was marginally significantly lower than 1 /Phi among populations groups along the gradient  are shaped.This study represents an empirical example of how speciation can be achieved through the effect of the latitudinal variation among wild populations. As theory predicts, latitude provided a geographic scene favorable for lineage divergence, in which geographic isolation facilitates neutral divergence, while the effect of seasonality has an additive divergent effect because it promotes contrasting selection regimes. The latitudinal gradient provided a sharp variation in climate seasonality that influenced disparate demographic histories at either side of the 28th parallel north. Such influence of latitude‐associated environmental variation on speciation was predicted by Janzen  and secoNone declared.José Said Gutiérrez‐Ortega: Conceptualization (lead); data curation (lead); formal analysis (lead); funding acquisition (lead); investigation (lead); methodology (lead); project administration (lead); resources (lead); software (lead); visualization (lead); writing–original draft (lead); writing–review and editing (lead). Francisco Molina‐Freaner: Resources (supporting); validation ; writing–review and editing . José F. Martínez: Methodology (supporting); resources (supporting). Miguel Angel Pérez‐Farrera: Resources (supporting); validation (supporting); writing–review and editing (supporting). Andrew Vovides: Validation (supporting); writing–review and editing (supporting). Antonio Hernández‐López: Validation (supporting); writing–review and editing (supporting). Ayumi Tezuka: Data curation (supporting); methodology (supporting). Atsushi J. Nagano: Data curation (supporting); methodology (supporting); resources (supporting). Yasuyuki Watano: Supervision (supporting); validation (supporting); writing–review and editing (supporting). Yuma Takahashi: Validation (supporting); writing–review and editing (supporting). Masashi Murakami: Methodology (supporting); project administration (supporting); supervision (supporting); validation (supporting); writing–review and editing (supporting). Tadashi Kajita: Conceptualization (supporting); project administration (supporting); supervision (lead); validation (supporting); visualization (supporting); writing–review and editing (supporting).Supplementary MaterialClick here for additional data file.


Nomadic groups of conquering Hungarians played a predominant role in Hungarian prehistory, but genetic data are available only from the immigrant elite strata. Most of the 10–11th century remains in the Carpathian Basin belong to common people, whose origin and relation to the immigrant elite have been widely debated. Mitogenome sequences were obtained from 202 individuals with next generation sequencing combined with hybridization capture. Median joining networks were used for phylogenetic analysis. The commoner population was compared to 87 ancient Eurasian populations with sequence-based (Fst) and haplogroup-based population genetic methods. The haplogroup composition of the commoner population markedly differs from that of the elite, and, in contrast to the elite, commoners cluster with European populations. Alongside this, detectable sub-haplogroup sharing indicates admixture between the elite and the commoners. The majority of the 10–11th century commoners most likely represent local populations of the Carpathian Basin, which admixed with the eastern immigrant groups (which included conquering Hungarians). Hungarian history was profoundly determined by the conquering Hungarians , who arrived at the Carpathian Basin from the Eastern European steppe at the end of the 9th century AD as an alliance of seven tribes. The leaders of the alliance (Álmos and his son Árpád) founded a steppe state upon the ashes of the Avar Khaganate ,2, and tHitherto, most genetic studies were focused on the elite graves, as these promised an answer for the origin of the immigrant groups. In , 76 indiThis raises the question of whether the commoners were genetically similar to the elite, and, if so, could they be one and the same population, or did the poorer strata have a different origin? This question was addressed in the first HVR-based study , in whicWe set out to implement a comprehensive study in this matter, and to this end, we selected eight cemeteries archaeologically evaluated as belonging to the 10–11th century commoners, from which we obtained 202 whole mitogenome sequences. Phylogenetic analysis was performed to illuminate the origin of each maternal sub-Hg of the studied remains. We compared the mitochondrial haplogroup compositions of the commoner and elite populations to find out their genetic relationship and applied different population genetic methods to elucidate the relationship of the commoners with other ancient Eurasian populations. For this reason, we also built a comprehensive database of ancient Eurasian populations, which included all available published mitogenome data.In contrast to the small 10th century cemeteries with characteristic grave goods  represenWe made an effort to carry out representative sampling. Thus, graves were selected from each section of the cemeteries . The number of collected, processed and analyzed samples from each cemetery is summarized in The largest 10th century commoner cemetery with 262 graves was excavated in Sárrétudvari-Hízóföld  Appendi, with a Another large commoner cemetery with 637 graves is located in the nearby Püspökladány-Eperjesvölgy  Appendi. This ceThe Ibrány-Esbóhalom commoner cemetery with 269 graves also stretches into the Christian era  Appendi. We analWe studied 36 remains from the Homokmégy-Székes cemetery excavated at the Duna-Tisza Interfluve  with 206Among the studied cemeteries, Magyarhomorog-Kónyadomb  Appendi is an exFrom the Transdanubia region, we included the Vörs-Papkert-B cemetery  Appendi, the 716Finally, we complemented our sample set with a few individuals from the Nagytarcsa-Homokbánya , SzegvárAll pre-PCR laboratory procedures leading to next generation sequencing (NGS) were conducted in the common ancient DNA laboratory of the Department of Archaeogenetics of the Institute of Hungarian Research and Department of Genetics, University of Szeged, Hungary. Details concerning the ancient DNA purification, library preparation, hybridization capture, sequencing and sequence analysis method are given in . We usedThe adapters of paired-end reads were trimmed with the Cutadapt software  in pairehttp://www.ebi.ac.uk/ena) under the accession number: PRJEB40566.The raw nucleotide sequence data of the 202 samples were deposited to the European Nucleotide Archive  networks, as described in [For population genetic analysis, we merged all 169 ConqC data to a single population , excludiThree independent methods were applied to measure the genetic distances of ConqC from other ancient populations. In the first analysis, we reduced the Hg assignments of all samples to major Hgs, which decreased population data to 34 dimensions, which is sufficient to display the main correlations. Then, major Hg frequencies were calculated and principal component analysis (PCA) was conducted, employing the function “prcomp” in R 3.6.3. . We alsoIn a second approach, a traditional sequence-based method was implemented, calculating pair-wise population differentiation values (Fst) with Arlequin 3.5.2.2  from whoIn a third approach, shared haplogroup distance (SHD) values were measured between the populations according to our previous study , which cWe collected a total of 229 samples from the listed sites, but could not obtain DNA from 13 samples. Another 10 samples were excluded from the analysis due to low mitogenome sequence coverage and 3 further samples were excluded due to high contamination values. Using the NGS method combined with target enrichment, we acquired 189 ancient mitogenome sequences, and a further 13 were obtained from whole genome sequencing; thus, we report 202 new mitogenomes in this paper . We obtaWe examined a possible kinship relation between and within cemeteries. We detected 10 pairs of identical mitochondrial haplotypes within cemeteries and 4 pairs between cemeteries , which iAs some of the mitochondrial sub-clades may have specific geographical distribution ,36, we ePhylogenetic trees revealed that, out of the 182 commoner maternal lineages, 23 were unequivocally derived from east Eurasia and 107 were derived from west Eurasia, while 52 are widespread throughout Eurasia. Out of the western Eurasian lineages, 11 have a primarily Caucasus–Middle East distribution A.The 34 investigated samples from Homokmégy-Székes belonged to 30 Hgs . As for From the Püspökladány-Eperjesvölgy cemetery, 31 remains were analyzed. The maternal lineages were classified into 28 Hgs , and theThe newly reported mitogenomes of 31 individuals from Sárrétudvari-Hízóföld belonged to 26 Hgs . In a prThe Ibrány-Esbóhalom cemetery was represented by 26 samples falling to 26 different Hgs . 46.2% oWe sequenced 14 mitogenomes out of the 17 remains from the elite part of Magyarhomorog-Kónyadomb, and their Hg composition was very similar to those of previously studied elite cemeteries ; 35.7% oFrom the 11–12th century commoner part of Magyarhomorog, we sequenced 25 samples which belonged to 22 mitochondrial Hgs , supplemThe cemetery of Vörs-Papkert is another special case, as it was used for centuries by successive populations of Avars, Carolingians and Conquerors populations. Evaluating the entire 28 sample set from this cemetery together G showed The six ConqC graveyards with a meaningful sample size showed a rather similar overall picture, with an average of 12.6% east Eurasian Hgs almost confined to C and D, which allowed us to infer a similar overall east Eurasian impact throughout cis-Danubia.We also investigated a few individuals from other commoner cemeteries, namely four samples from Nagytarcsa-Homokbánya, four samples from Szegvár-Oromdűlő and five samples from Szegvár-Szőlőkalja, resulting in two east Eurasian lineages besides the European ones .We acknowledge that the average of 30 samples per site may poorly represent the individual cemeteries, but the total number of 182 commoner remains  can be rFirst, we compared the major Hg distribution of the conqueror period elite and commoner populations . The hetWest Eurasian Hgs of ConqC and ConqE also show notable differences: Hgs HV, I, M, R, U1, U8 and W occur with moderate frequencies in commoners, while these are completely absent from the elite population. Three Hgs , typically widespread both in east and west Eurasia, show much higher ratio in the elite than in commoners: N’s ratios are 11.61% in the elite population and 3.85% in the commoner population; T1’s ratios are 11.61% in the elite population and 2.75% in the commoner population; and X’s ratios are 4.46% in the elite population and 0.55% in the commoner population. The opposite is true for Hgs H and T2; among commoners, H is the most prevalent Hg with a 33.52% frequency, while in the elite group, its proportion is significantly lower (19.64%); T2 has a 6.59% proportion in the commoner population and a 1.79% proportion in the elite population.As the Hg composition of the studied commoner samples markedly differs from that of the elite, we measured ConqC’s genetic distances from ConqE as well as its distances from 87 published ancient Eurasian populations . PCA obtp < 0.00000); in other words, the probability that the two populations are identical is below 1/100,000.In order to further reveal the genetic relationships of ConqC with other ancient groups, we drew an MDS plot  from linThe novel SHD population genetic method gave similar results, but also revealed new information . ConqE hIn this paper, an attempt was made to provide a genetic description of the common people of the Carpathian Basin who lived in the 10–11th centuries during the period of the Hungarian conquest. Of the 202 obtained mitogenomes, 169 belonged to commoners, while 14 samples from the Magyarhomorog cemetery were revealed to represent a small elite graveyard, not related to the adjacent commoner remains. The elite has been shown to comprise around 30% of the east Eurasian Hgs, including characteristic ones like N1a1a1a1a [The overall Hg composition of the commoner population proved to be significantly different from that of the elite with respect to both east and West Eurasian Hgs, indicating that these two groups likely had different origins. Population genetic analysis definitely clustered ConqC primarily with European and Near Eastern populations, separating them from the elite, suggesting that people with local European origin dominated the ConqC population.The presence of a non-negligible proportion of east Eurasian Hgs in the ConqC population is a clear sign of admixture with eastern immigrants, presumably with Avars and/or Conquerors. This effect distinguishes ConqC from contemporary European populations, as well as from modern Hungarians, in whom east Eurasian Hgs are negligible. Thus, despite their significant differences, ConqC might have admixed with ConqE to some extent.This admixture is clearly validated by the SHD method, as ConqC had the smallest SHD distance from ConqE , meaningThe phylogeographic origin of shared Hgs also signals a possible reciprocal gene flow from ConqC to ConqE, as some of their shared Hgs  were absent from east Eurasia but had been present in the Carpathian Basin from the Neolithic–Bronze Age, as shown in The contemporary local population is descended from previous peoples of the Carpathian Basin, and it has indeed been shown that a large number of people survived to the 10th century from the previous Avar period ,41. The For more accurate conclusions, future investigations are necessary, including high-resolution genome analysis of commoner and elite cemeteries. Additionally, genome data from the pre-Avar, Avar and later Árpádian populations would provide a more complete picture about the exact contribution of subsequent nomadic migrations to the demographic history of the Carpathian Basin.


Ellobium chinense  , which inhabits the salt marshes along the coastal areas of northwestern Pacific, is an endangered species on the IUCN Red List. Over recent decades, the population size of E. chinense has consistently decreased due to environmental interference caused by natural disasters and human activities. Here, we provide the first assessment of the genetic diversity and population genetic structures of northwestern Pacific E. chinense. The results analyzed with COI and microsatellites revealed that E. chinense population exhibit metapopulation characteristics, retaining under the influence of the Kuroshio warm currents through expansion of the Late-Middle and Late Pleistocene. We also found four phylogenetic groups, regardless of geographical distributions, which were easily distinguishable by four unidirectional and stepwise adenine-to-guanine transitions in COI . Additionally, the four COI hotspots were robustly connected with a high degree of covariance between them. We discuss the role of these covariate guanines which link to form four consecutive G-quadruplexes, and their possible beneficial effects under positive selection pressure.The land snail  Ellobium chinense 1, of the family Ellobiidae , is a small, air-breathing, land snail that dwells between-stones in freshwater tidelands near the shoreline and on halophytes such as Zoysia sinica, as shown in Figs. 2. Importantly, E. chinense is a key indicator of the environmental health of the intertidal zone of its habitat. However, over recent decades, its natural habitat and the number of populations and individuals have rapidly declined due to environmental stresses, such as typhoons and tsunamis, and human activities including land reclamation and coastal development; thus, E. chinense has been designated as an endangered species on the South Korean Regional Red List3, as a vulnerable species on the Japanese Regional Red List, and as an endangered species on the International Union for Conservation of Nature .4. On the other hand, the loss of intraspecific genetic diversity dramatically reduces the capacity for species to respond to novel selection pressure and thereby increases their extinction risk5. In recent decades, research on the population genetics of endangered metazoan species has been increasingly conducted because of its central importance in planning both in-situ and ex-situ conservation efforts6. In such research, molecular markers, including specific mitochondrial genes [mainly cytochrome c oxidase I (COI)]7, whole mitochondrial genomes8, and microsatellites9 are consistently used. Given the importance of determining the genetic diversity and population genetic structure of an endangered species in conservation planning, it is surprising that such research has rarely been done with northwestern Pacific E. chinense.Sufficient genetic diversity is important for ensuring the persistence of species, increasing their adaptive potential in the face of environmental change, and avoiding inbreeding depressionE. chinense (sampled in South Korea and Japan). Our analyses were based on the popular DNA barcode region of the mitochondrial gene COI and 10 selected microsatellite markers. In total, 140 COI data (consisting of 139 South Korean samples and one Japanese sample), and 54 microsatellite data (from South Korea only) were analyzed. In addition, we employed four distinct covariate adenine/guanine hotspots observed on COI which are distinct key sequences to easily classify phylogenetic groups of E. chinense; these sites were closely associated with G-quadruplex structure conformation. G-quadruplexes, i.e., four-stranded noncanonical DNA structures, can be spontaneously formed by Hoogsteen bonds between stacked sets of four guanines from each of four separated runs of two, three, or four guanines10. A stable G-quadruplex within the coding sequences (CDS) causes ribosome stalling; protein expression can be enhanced through silent mutations that affect its stability11. Herein, by determining the genetic diversity and structures of populations of E. chinense, we were able to discuss not only the roles of the four covariate hotspots on the CDS of COI in relation to G-quadruplex structure conformation but also the associated beneficial and resistant effects against environmental perturbation such as oxidative stress.In the present study, therefore, we provide the first assessment of genetic diversity among populations of northwestern Pacific COI, 595 bp in length, was sequenced from 113 E. chinense individuals collected from the eleven sites in South Korea  A(207)–A(282)–A(354)–A(420), (b) A–A–G–A, (c) G–A–G–A, and (d) G–G–G–G.The partial fragment of ces Data , we obta77 Table . Accordipes Fig. ; Data S2COI haplotype sequence alignment , the G–A–G–A group (N = 1: ECH35), and the G–G–G–G group .Based on the ent Fig. ; Data S2ogy Fig. , it was ree Fig. D was recE. chinense. Within the A–A–A–A and A–A–G–A groups, parsimoniously informative A → G transition events were found at the sites 120  and 183 , with a few exceptional cases of G → A at the sites 216 , 372 , and 429 .As shown in Table E. chinense, ranging from 0.00749 (population BG) to 0.01042 (SC) with an average of 0.00865, whereas the haplotype diversity was very high across these populations, ranging from 0.924 (YG) to 1.000 (SC and JB) with an average of 0.939). All values of Tajima's D and Fu's FS were congruently negative, with averages of − 1.87100 (P < 0.05) and − 2.75886 , respectively, indicating that the signature of demographic expansions recently occurred in E. chinense.As indicated in Table FST values between nine E. chinense populations were highly low and statistically non-significant, ranging from − 0.21177 (JB and GY) to 0.09091 (JB and JK), indicating the lack of genetic differences among populations. Analysis of molecular variance (AMOVA) for the nine populations of E. chinense in South Korea and Japan  → A–A–Ğ–A → Ğ–A–Ğ–A → G–Ğ–G–Ğ (derived). The other local A → G transition events occurred within the A–A–A–A group   Fig. ; these cCoA Fig. a, being COI characterized by unidirectional A → G transitional changes: G-(75 bp)–G-(72 bp)–G-(66 bp)–G. Additionally, the covariance analysis indicated that there was a high degree of covariance among these –G-7 bp–G-(665 bp)–G-7 bp–G- or three (for G3) G-quadruplexes can form a stacked structure. Considering the typical features of G-quadruplex structures, each of the putative motifs searched for G-quadruplex structure formation is likely to exhibit two G-quadruplexes stacked together  varied greatly among loci, from 14 (ECHm41) to 29 (ECHm36). The number of effective alleles per locus (NE) ranged from 5.377 (ECHm24) to 11.858 (ECHm40), with the mean of 9.495. Overall, the effective number of alleles (NE = 9.495) was smaller than the mean number of alleles (NA = 13.4). The expected mean heterozygosity (HE) per locus ranged from 0.793 (ECHm24) to 0.913 (ECHm40), while the average value was 0.883. The observed heterozygosity per locus (HO) was from 0.533 (ECHm24) to 0.969 (ECHm40), with a mean value of 0.806. Thus, the expected heterozygosity (HE = 0.883) was larger than the observed heterozygosity (HO = 0.806). The average of the genetic differentiation coefficient FST was estimated relatively low to be 0.038; it ranged from 0.020 (ECHm40) to 0.051 (ECHm27). Table E. chinense.The ten polymorphic microsatellite loci were amplified from 54 individuals collected from the four populations  of ea Table , and theFST values, showing the degree of genetic differentiation among pairs of populations, were statistically significant (P < 0.05) only between the populations BG and HS (FST = 0.120) and HK and HS (FST = 0.0184). In contrast, pairwise RST was not significant among populations. Generally, pairwise genetic differentiation among populations was low, indicating that the E. chinense populations were not genetically differentiated from each other.According to Table E. chinense populations.The distributions of molecular genetic variation among and within populations, as estimated by AMOVA, are shown in Table E. chinense individuals were further examined to determine population stratification. The optimal number of clusters, K, according to the method of Evanno et al.19, was 2 ; thus, the ancestry proportions were plotted for each individual given two clusters. All individuals were equally admixed  high haplotype diversity but low nucleotide diversity, (2) lack of genetic differences among populations, and (3) low genetic differentiation (frequent genetic flows between populations). Given the general metapopulation characteristics, it is reasonable to conclude that E. chinense populations inhabiting South Korea and Japan are a single broad metapopulation that has arisen due to repeating extinctions and migrations caused by continuous and rapid changes in environmental and habitat conditions during the Late-Middle and Late Pleistocene , which was succeeded by the appearance of warm sea currents in East Asia, the South China Sea, North Pacific Ocean, and Japanese islands23. After the Mid-Pleistocene Transition (1.25–0.77 Maa), which is also known as the Mid-Pleistocene Revolution, divergences of Ğ–A–Ğ–A (0.61 Ma) and G–Ğ–G–Ğ (0.55 Ma) also occurred during interglacial periods , as shown in Fig. COI haplotype numbers and demographic population expansion, as expected from the negative values of Tajima's D and Fu's FS neutrality tests in the present study, may have occurred largely during the Late-Middle Pleistocene  and Late Pleistocene , and might have continued to the Last Glacial Maximum (0.027–0.002 Ma)  in DNA or RNA sequences26. In the nuclear genome, G-quadruplex motifs have been associated with genome instability and gene expression defects; however, they are mainly recognized as regulatory structures that control gene expression or positively affect telomere capping27. Recent studies28 have reported that G-quadruplex structures can form in the mitochondrial genome and even on the CDS in protein-coding genes29. Although G-quadruplexes typically have harmful effects, those formed in mitochondrial genomes can help in the response to certain environmental stresses such as oxidative stress30. Deficiency in COI within mitochondria caused by G-quadruplexes could result in positive selection bias that reduces the production of reactive oxygen leading to less oxidative damage and a selective advantage during competition with other mitochondria within the same cell, which could generate homoplasmy for COI deficiency. Hershman et al.31 suggested that cells with cytochrome c oxidase deficiency are apoptosis resistant and therefore more likely to survive. Active COI oxidizes cytochrome c, which activates pro-caspase 9 leading to apoptosis. Assuming that the four guanine hotspots link to create four G-quadruplex structures, they may function in the regulation (mainly inhibition) of COI expression at the transcription or translation levels, and in modulating (mainly reducing) the concentration of activated COI within the mitochondria. This would reduce oxidative damage and increase resistance to apoptosis with lower reactive oxygen production. Such G-quadruplex conformation-related oxidative stress and apoptosis resistance could provide a selective advantage in competition with other mitochondria in a cell. According to a previous study on the energy metabolism of the garden snail Helix aspersa, natural selection can reduce energy metabolism in a land snail32. Intriguingly, the slow behaviors of land snails that have led to reduced energy consumption might be associated with downregulation of COI expression modulated by G-quadruplex motifs on the CDS of COI  following the manufacturer’s protocol and then maintained at − 20 °C until further use. The efficiency of the genomic DNA extraction and the purity of the extracted DNA were examined with a spectrophotometer .In total, 103 individuals of la Figs. ; Table 1COI gene of E. chinense, PCR was conducted using two universal primers: LCO1490 (5′-GGT CAA CAA ATC ATA AAG ATA TTG G-3′) and HCO2198 (5′-TAA ACT TCA GGG TGA CCA AAA AAT CA-3′). These primers, which were developed for invertebrates by Folmer et al.35, can be used for PCR amplification of a partial mitochondrial COI fragment, i.e. a DNA barcoding region, prior to sequencing. The PCR mixtures were prepared to a total volume of 50 μl containing 2 μl of 10 pM of each primer, 1 μl of 10 mM dNTP mix, 5 μl of 10× Taq DNA polymerase reaction buffer, 0.25 μl of 5 U/μl DiaSter Taq DNA polymerase, and 2 μl of total genomic DNA. Thermal cycling conditions were as follows: denaturation at 94 °C for 2 min, 35 cycles of 94 °C for 40 s, 52 °C for 1 min, and 72 °C for 1 min, and a final extension step of 72 °C for 7 min, which followed a cool down step at 4 °C. The PCR products were electrophoresed on 1% agarose gel including eco-dye and examined under UV light; they were purified using a QIAquick PCR Purification Kit  and then sequenced using the primers applied in PCR and an ABI Prism 3730 DNA sequencer  with a BigDye Termination Sequencing Kit . The determined DNA sequences were manipulated in BioEdit v7.2.536, and finally verified by visual inspection.To amplify a partial fragment of mitochondrial E. chinense37; these were tested using some representative E. chinense samples collected in the present study. The initial screenings discovered ten primer pairs showing clear and reproducible profiles; these were selected for subsequent genetic analyses of E. chinense populations , 10 mM dNTPs, 10 pmol of M13 modified forward primer, 10 pmol of each reverse primer and M13 primer fluorescently labeled with 6-FAM , with 10× incubation mix including MgCl2, 5 units HANLAB Taq DNA polymerase, and filled with distilled water. PCR cycling conditions comprised an initial denaturation phase at 94 °C for 5 min, followed by 34 cycles of 94 °C for 30 s, 54 °C for 30 s, and 72 °C for 30 s, and finally an extension step at 72 °C for 5 min. The tagged PCR product (0.2 μl) was added to 9.8 μl of Hi-Di formamide loading buffer, sized precisely using 0.2 μl of GeneScan 500 LIZ Size Standard  and subsequently run on an ABI 3730 × 1 automated sequencer . Allele size calling and genotypic analyses were performed using GeneMapper Software ver. 4.1 .A total of 30 microsatellite markers that have recently been developed for ns Table . A taileE. chinense, the 140 obtained COI sequences were aligned with Clustal X39  was conducted in ARLEQUIN v.3.541 to partition the genetic variance within and among populations. Pairwise FST values were calculated to assess the pattern of population differentiation. A neutrality test was performed by calculating Tajima's D42 and Fu's Fs43 via ARLEQUIN v.3.541 to detect the historical existence of demographic expansion. To examine whether E. chinense has passed through recent expansion or frequent migration among neighbor demes, a mismatched distribution analysis was conducted based on the distribution of the observed pairwise nucleotide site differences and the expected values obtained assuming the populations of constant size or under the growth15. Based on the sequence alignment set of 58 COI haplotypes  with the GTR + I + G model and 1000 bootstrapping replicates. Additionally, a phylogenetic network was generated via the neighbor-net algorithm44. Furthermore, a statistical parsimony haplotype network was constructed with a 95% connection limit using TCS v.1.2.145; this was used to assess the genealogical relationship at the intraspecific level and to infer biogeography by constructing a COI haplotype network. To evaluate and visualize genetic differences among populations, principal coordinates analyses (PCoA) were conducted via DARwin v.6.0.946, which ordinated genetic distance estimates calculated with the haplotype data used in this study.To elucidate the level of genetic diversity and structure at the interpopulation and/or intrapopulation levels in X39 Data . The numpes Fig. ; Data S237, we reexamined the genetic diversity and structure of E. chinense populations in comparison to the results inferred from COI haplotype data. The PCR primers selected as microsatellite markers in the present study exhibited highly informative values of polymorphism information content (PIC > 0.5). Based on these microsatellite markers, genetic variability was estimated in terms of the number of alleles averaged over 10 loci (NA), observed heterozygosity (HO), and expected heterozygosity (HE) across populations and across loci using GenALEx v.6.547. Furthermore, FIS was calculated for each population and for each locus, based on Wright's F-statistics48. The genetic differentiation coefficient (FST) was estimated using GenALEx v.6.5; additionally, tests for deviation from the Hardy–Weinberg Equilibrium between the microsatellite loci were performed using this software. To characterize the overall population genetic structure, we applied a Bayesian clustering approach implemented in STRUCTURE v.2.3.449 which identifies the number of potential genetic clusters (K) in the dataset without a priori information on geographical or population identifiers. An admixture model was used with correlated allele frequencies applying burn-ins of 2 × 105 and runs of 105 repetitions for each value of K varying from 1 to 8. Three iterations were completed for each tested K value. The true number of K was identified based on the approach of Evanno et al.19 by applying Structure Harvester v.0.6.9450. In addition, we investigated isolation by distance (IBD) was examined by correlating the matrix of pairwise geographic distances between sampling locations with two correspondent matrices of genetic differentiation estimates: FST (estimated based on haplotype frequency) and фST (considering the level of distance among haplotypes)51. Geographic distances were calculated as the shortest pathways along the coastal line.Using the 10 microsatellite loci Table  selectedCOI haplotypes . This calculation process was repeated up to 10 times.To screen for possible G-quadruplex-forming sequence motifs in COI Fig. , we usedE. chinense was conducted based on COI haplotype sequences via the BEAST 2.6.053. The divergence time was estimated using the strict molecular clock algorithm under the calibrated-Yule tree prior. For the calibration point, the previously estimated age of the subfamily Carychiinae (from the family Ellobidae) was used as the basal node . The age of Carychiinae was given by Weigand et al.54, who among others reported that it originated no earlier than the beginning of the early Cenozoic (about 65 Ma)54. The best-fit nucleotide substitution model was selected by jModelTest55 as GTR + I + G. The posterior distributions of parameters were estimated using 1,000,000 MCMC generations with sampling every 1000 generations. Additionally, an effective population size value was determined in Tracer 1.756. TreeAnnotator 2.6.057 was used to produce a maximum clade credibility tree with a median height after removing the initial 25% of trees as burn-in. FigTree 1.4.258 was used to visualize the topology of the resultant consensus tree.Divergence time estimation of the nodes on the phylogeny of Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


The mean exclusion chance in trios and duos were 0.999999319 and 0.999802969 respectively. Multiple biostatistics methods, such as principal component analysis, genetic distances analysis, phylogenetic reconstruction, and structure analysis was used to reveal the genetic relationships among the studied Henan Han group and other 26 reference groups from 1,000 Genomes Project. As expected, the Henan Han population was clustered with East Asian populations, and the most intimate genetic relationships existed in three Han Chinese populations from Henan, Beijing and South China, and showed significant differences compared with other continental groups. These results confirmed the suitability of the 38 X-InDel markers both in individual identification and parentage testing in Han Chinese population, and simultaneously showed the potential application in population genetics.Insertion/deletion (InDel) polymorphisms, as ideal forensic markers, show useful characteristics of both SNPs and STRs, such as low mutation rate, short amplicon size and general applicability of genotyping platform, and have been used in human identification, population genetics and biogeographic research in recent years. X-chromosome genetic markers are significant in population genetic studies and indispensable complements in some complex forensic cases. However, the population genetic studies of X-chromosome InDel polymorphisms (X-InDels) still need to be explored. In this study, the forensic utility of a novel panel including 38 X-InDel markers was evaluated in a sample of Han population from Henan province in China. It is observed that the heterozygosities ranged from 0.0054 to 0.6133, and the combined discrimination power was 1–9.18 × 10 Forensic genetic research is devoted to the pursuit of ideal genetic markers that can be used to the routine forensic casework including individual identification, paternity and kinship testing, race and species identification, biological ancestry analysis et al. Short tandem repeat markers (STRs) have been widely used in routine forensic casework. However, due to the defects of high spontaneous mutation rate, the limitation of detecting degraded samples, stutter peak et al., STRs occasionally produced inconclusive results, especially in some deficient paternity cases or some cases with only degraded tissue as DNA source . To overX-chromosome markers recombine along the whole chromosome during female meiosis in females and are transmitted to both female and male descendants, however, are entirely transmitted to female offspring in males . Due to Han Chinese who makes up 91.50% of Chinese population has shown the regional genetic diversities because of national amalgamation in history . Han ChiOur study was approved by the Ethics Committee of Xi’an Jiaotong University Health Science Center . Each volunteer signed an informed consent, giving his permission to the analysis and publishing of anonymized genetic data obtained from his biological sample. A total of 268 fingertip blood samples were gathered from unrelated individuals  of Chinese Han population in Henan province (HNC). The authentication was performed to ensure those volunteers are indigenous people of Henan province. All the volunteers meet the conditions that the past three generations are Han group and have no trans-regional migration.http://grch37.ensembl.org/Homo_sapiens/Info/Index), as shown in The blood samples were placed in the sample acquisition card and then were dried and stored at room temperature. Meanwhile, we collected 38 InDels raw data and allele frequencies of 26 populations worldwide spanning five continents from the 1,000 Genomes Project . The analyzed panel including 38 X-InDel markers, namely rs10671504, rs11277082, rs1160845, rs143123845, rs149102585, rs16367, rs16368, rs16397, rs16637, rs17394, rs199731653, rs2307707, rs2307741, rs2308033, rs2308280, rs25581, rs3048996, rs3077884, rs3215490, rs34763847, rs35574346, rs35954471, rs363794, rs4030406, rs45449991, rs56820033, rs57608175, rs57843641, rs58595330, rs59605609, rs60283667, rs71671860, rs3216913, rs10699224, rs3859989, rs61260787, rs36094418 and rs79829945. The localizations of the different markers were previously described in our published article . Separatp values were corrected by the Bonferroni procedure  and Hardy-Weinberg Equilibrium (HWE) between all pairs of the 38 X-InDel loci, and rocedure . HWE wasrocedure . LD was rocedure . Allele rocedure . Fisher’https://www.omicstudio.cn/tool). Phylogenetic neighbor-joining (NJ) tree was reconstructed by MEGA software v7.0 using Nei genetic distance matrices. On the basis of raw data of female individuals, population genetic structure was analyzed by the Structure version 2.3.4.21  except for rs4030406 locus (p = 0.007). The male and female allele frequencies of rs4030406 locus are shown respectively, and the male and female allele frequencies of the other 37 X-InDel loci were combined for calculation. Unless otherwise specified, the female allele frequency of rs4030406 locus is used for calculation. The 29 insertion allele frequencies out of 38 loci for Henan Han population range from 0.3000 to 0.7000. As a whole, the allele frequency distributions of the 38 X-InDel loci are relatively balanced in Henan Han population.To evaluate the differences in allele frequencies of the X-InDel loci between males and females, we first carried out fisher’s exact test, and found that there were no statistical significance between males and females  were found among the 38 X-InDel loci, which indicated that we can estimate both the forensic characteristics of the single locus (except for the three blocks) and the combined forensic efficiency of the 38 X-InDel loci in our following analysis. Forensic parameters for the 38 X-InDel loci in Henan Han population are shown in To ensure samples can represent the population, Hardy-Weinberg equilibrium (HWE) testing was performed, and no deviations from HWE , Japanese, Chinese Dai and Vietnamese Kinh were separated each other. The studied Henan Han population was plotted in the middle of the East Asian populations and presented close genetic affinities to Han Chinese in Beijing and Han Chinese in South China.To further reveal population genetic similarities and divergences of Henan Han population and other 26 reference populations, the pairwise Nei’s genetic distances between studied Henan Han population and the other 26 reference populations were calculated and the result was shown in the form of line chart . We obseFurthermore, the matrix of Nei’s genetic distances was applied to draw a heatmap plot. As shown in To more visually exhibit the genetic similarities and divergences among the 27 populations, a Neighbor-Joining (N-J) phylogenetic tree was constructed based on the allele frequencies of 38 X-InDel loci, as shown in To analyze the population ancestry component among the 27 populations, we conducted structure analysis based on 34 X-InDels  raw genotype of 1,377 females. The detection of the number of genetic groups that best fit the data was performed by an online program STRUCTURE HARVESTER  and the In the present study, we first described a novel panel of 38 X-InDels and investigated the forensic efficiency in Henan Han population. The results showed that this panel is powerful as a complementary tool for forensic individual identification and parentage testing of trios. Based on the allele frequencies and raw data of the 38 X-InDels, we conducted principal component analysis, calculated Nei’s genetic distances, and constructed phylogenetic tree and structure analysis to illustrate the genetic affinity and population ancestry component among Henan Han population and 26 populations worldwide. It is observed that the grouping from ancestry component analysis and genetic affinity revealed by the panel of 38 X-InDels are generally consistent with geographical classifications. Meanwhile, we noticed that mixed-race populations could be distinguished clearly from the continental population groups by using the panel of 38 X-InDels. However, sub-population identification within continental population groups was failed due to the limitation of X-InDels. It is expected that compound markers which are adopted to infer biogeographic ancestry would improve accurate classification of populations and sub-populations. In conclusion, this study makes a solid foundation of the population data for the application of the 38 X-InDel markers panel in individual identification, parentage testing and biogeographic ancestry analysis, though much more regional and national population data still need to be collected.