Juglans regia L.) has a long cultivation history, given its highly valuable wood and rich nutritious nuts. The Iranian Plateau has been considered as one of the last glaciation refugia and a centre of origin and domestication for the common walnut. However, a prerequisite to conserve or utilize the genetic resources of J. regia in the plateau is a comprehensive evaluation of the genetic diversity that is conspicuously lacking. In this regard, we used 31 polymorphic simple sequence repeat (SSR) markers to delineate the genetic variation and population structure of 508 J. regia individuals among 27 populations from the Iranian Plateau.Common walnut . Genetic differentiation among the populations was moderate (FST = 0.124), and genetic variation within the populations (79%) significantly surpassed among populations (21%). The gene flow (Nm = 1.840) may have remarkably influenced the population genetic structure of J. regia, which can be attributed to anthropological activities and wind dispersal of pollen. The STRUCTURE analysis divided the 27 populations into two main clusters. Comparing the neighbor-joining and principal coordinate analysis dendrograms and the Bayesian STRUCTURE analysis revealed the general agreement between the population subdivisions and the genetic relationships among the populations. However, a few geographically close populations dispersed into different clusters. Further, the low genetic diversity of the Sulaymaniyah (SMR) population of Iraq necessitates urgent conservation by propagation and seedling management or tissue culture methods; additionally, we recommend the indispensable preservation of the Gonabad (RGR) and Arak (AKR) populations in Iran.The SSR markers expressed a high level of genetic diversity (J. regia populations, whereas ecological and geological variables did not act as strong barriers. Moreover, the data reported herein provide new insights into the population structure of J. regia germplasm, which will help conserve genetic resources for the future, hence improving walnut breeding programs’ efficiency.These results reflected consistent high geographical affinity of the accession across the plateau. Our findings suggest that gene flow is a driving factor influencing the genetic structure of The online version contains supplementary material available at 10.1186/s12870-023-04190-2. Genetic diversity has been constantly found to be essential for adapting the population to environmental changes . GeneticJuglans L. belongs to the Juglandaceae family, which includes ca. 21 deciduous tree species [Juglans regia L., also called common walnut, English walnut, or Persian walnut , is monoecious and heterodichogamous with 2n = 2x = 32 [ species ; It is a species -6. Jugla 2x = 32 . The spe 2x = 32 , 9. High 2x = 32 -12.Common walnut is one of the most essential fruit trees cultivated primarily for edible nuts since ancient times. It is highly supported that walnut originated from the Iranian Plateau -15; prevJ. regia cultivation is 4.48 × 104 ha, producing 3.57 × 105 tons of nuts in shell, and is ranked the third globally [The Iranian Plateau is situated at the upper plate of the Arabia-Eurasia collision zone . The plaglobally .Juglans regia formed fragmented forests in the west of Asia during the Pleistocene [Juglans pollen was present throughout the Upper Paleolithic in Georgia and northwestern Iraq. Glacial refugia often show more values of genetic diversity conservation than postglacially colonized regions [istocene . It has istocene , 24 that regions . The Ira regions .J. regia populations. Nowadays, the walnut in the plateau is distributed in different ecological environments and geographic conditions. As elevation increases, climate, and physicochemical properties are also affected, which may increase population variation. These conditions might support the potential of the Iranian Plateau walnut as a rich gene resource.The Iranian Plateau contains several mountain ranges, such as the Alborz, Zagros, Kopet Dagh, Baluchistan, and Hindu Kush, etc.; hence, they might have facilitated the genetic divergence of J. regia and the lack of an accurate, comparative, and large-scale molecular study, the extent and structure of the J. regia genetic diversity across the Iranian Plateau have never been undertaken. To fill the gap, based on a comprehensive population-level field sampling of common walnut from four countries across the Iranian Plateau, we aim to 1) evaluate the genetic diversity of walnut populations using microsatellites, 2) assess the genetic differentiation and structure of walnut populations, and 3) propose the recommendations for germplasm conservation and resource development and utilization.Simple sequence repeats (SSRs) or microsatellites, due to their codominant inheritance, being genome- and locus-specific, high polymorphism , 27, havNE) varied from 1.024 (JM5446) to 3.964 (BFU-Jr38), with an average of 2.379. Observed heterozygosity (HO) and expected heterozygosity (HE) varied from 0.012 (JM5446) and 0.023 (JM5446) to 0.697 (JS09) and 0.744 (JS12), with a mean of 0.437 and 0.511, respectively.All the primers produced polymorphic fragments where the average number of alleles across marker loci was 6.129 and ranged from 3 (in JM5446) to 11 (in JS12) (Table I) was 0.976 and ranged from 0.072 (JM5446) to 1.598 (BFU-Jr38) across loci. In addition, unbiased expected heterozygosity (uHE) of individual loci ranged from 0.023 at JM5446 to 0.748 at BFU-Jr38 and averaged at 0.512 alleles per locus. The Fixation index (F) ranged from 0.020 at JR05 to 0.545 at BFU-Jr277 and averaged 0.164 (Table PIC) varied from 0.022 (in JM5446) to 0.743 (in CUJRD462), with a mean of 0.503.The mean Shannon’s Information index (J. regia (Table NA) averaged 3.141 and varied from 2.323 (GMR) to 3.419 (TPR and GDR). The mean number of effective alleles (NE) across 27 populations was 2.067, ranging from 1.789 (GMR) to 2.284 (AKR). The observed heterozygosity (HO) and expected heterozygosity (HE) across all populations varied from 0.347 (ZAR) to 0.522 (AKR) and 0.371 (SMR) to 0.495 (RGR), with an average of 0.438 and 0.437, respectively.The genetic diversity parameters at the population level were considerably different across populations of ia Table . The numNE = 2.284, HO = 0.522, HE = 0.492 and I = 0.864), GDR , and RGR  showed high levels of genetic diversity, while those with low values were observed in SMR  and GMR . The fixation index (F) averaged 0.005, ranging from -0.201 (GMR) to 0.170 (ARR). The low level of fixation index identifies the deficiency of heterozygosity in the J. regia populations. The high values of uHE were observed in populations RGR (0.508) and AKR (0.507), whereas the low uHE values belong to populations SMR (0.381) and TKR (0.394). AR values ranged between 2.040 (GMR) and 2.565 (RGR). As with the case of uHE, population RGR also represented a high AR level. The mean polymorphic loci (PPL) percentage across 27 populations was high (91.40%) and ranged from 80.65% for TKR to 100% for EHR (Table Nm) between populations averaged 1.840. The highest value of Nm was observed between ARR and TKR populations (0.857), whereas the lowest value belongs to TAR and TPR populations (5.614) (Table SThe populations AKR (FST) was moderate, ranging from 0.042 between TPR and TAR to 0.225 between ARR and TKR. Moreover, pairwise comparative analysis of Nei’s genetic distance values represents minimum range of 0.046 between ETR and ESR to 0.200 between ARR and TKR  between J. regia populations.The genetic differentiation coefficient  mainly contained 213 individuals from 11 populations, including GSR, GDR, SAR, TER, QAR, RGR, RUR, MZR, TPR, TAR, and TKR. The second comprises 292 accessions collected from 16 populations . The admixtures were grouped between the above groups  populations. Cluster 2 arises from 13 populations, including HMR, JBR, GMR, AKR, EHR, KNR, WER, NGR, ZAR, KHR, ARR, ESR, and ETR (blue). Cluster 3 contained eight populations in QAR, TER, SAR, GDR, GSR, SMR, KDR, and KSR (yellow). These results were consistent with the results from the PCoA  were grouped in cluster 1 (green). While cluster 2 (blue) encompassed the populations of HMR, JBR, GMR, AKR, EHR, KNR, WER, NGR, ZAR, KHR, ARR, ESR, and ETR. Accessions in cluster 3 were mainly from the west of Iran (yellow), with the GSR population being an exception, not following this pattern. Populations from the Center and north of Iran, including QAR, TER, SAR, and GDR, were grouped in cluster 4 (orange)  Fig. .FST) and environmental factors. The results stipulated a weak correlation between genetic distance and geographical distance  varied from 0.042 to 0.225, with an average of 0.124 within 508 J. regia individuals  and AKR/JBR (0.044) showed that these populations share many common alleles and are closely related. The adjacency might justify the genetic affinity between these populations.The gene flow and genetic differentiation coefficient are critical parameters for realizing the population structure . The gens Table S. Accordiogeneity . The preP < 0.001), and 79% was attributed to the differences within populations. Significant variation within populations and a small variance between populations could be a result of the wind pollination system [The results of the AMOVA analysis indicated that 21% of the variation belonged to the differences among populations  was 1.84; if Nm is = 1, which indicates high-intensity gene flow between populations [J. regia populations. Therefore, the differentiation within populations was remarkably greater than between populations. In contrast to previous studies [J. regia.The mean gene flow . The genetic structure of some populations in the plateau was not influenced by geographic distribution; for example, grouping accessions from the different regions in a similar cluster  could result from low genetic variation among the populations suggesting that the genetic structure of J. regia populations does not always correspond to their geographical regions. The reduced genetic structure, even in broad geographic barriers including the Lut desert, Alborz and Zagros Mountain ranges, can be attributed to human-mediated gene flow among the populations. Further investigation to identify the determinant reasons is needed.The relationship between molecular data of r = -0.19, P = 0.029). It could be speculated that the genetic differentiation of J. regia populations in the Iranian Plateau might be affected by altitude. Nevertheless, further research using additional samples from the neighboring countries such as Afghanistan and Pakistan, and other molecular markers  will be helpful to provide a more accurate conclusion on the driving force behind the population structure in the Iranian Plateau. In addition, considering the presence of many commercial walnut cultivars around the world, it is suggested to include commercial cultivars with comrephesive sampling scheme in future research for a better population genetic comparison between native populations and native plantation.The partial Mantel test also exhibited a weak correlation between the genetic distance and altitude , observed heterozygosity (HO) and expected heterozygosity (HE), genetic differentiation coefficient (FST), gene flow (Nm), fixation index (FIT), polymorphic information content (PIC), inbreeding coefficient (FIS), unbiased expected heterozygosity (uHE) across loci, Nei’s genetic distances between populations, and analysis of molecular variance (AMOVA) were performed in GeneAlex v.6.5 [AR) was assigned with the R package “hierfstat”(https://cran.r-project.org/web/packages/hierfstat) package v.3.0.7 [FST) and Nei’s genetic distance (DA) between pairs of populations was performed using Arlequin v.3.5 [To assign the level of the genetic diversity of loci and populations, genetic diversity analysis, such as number of alleles  in any of the genetic clusters were defined as distinct genetic groups, and the individual with low probabilities (Q < 0.80) were treated as “admixture”.The genetic assignment of each individual was implemented in STRUCTURE v.2.3.4 . The run v.0.6.1 , and rep v.0.6.1 . Finally v.0.6.1  was used v.0.6.1 . The vala et al. , individJ. regia populations was acquired by applying the neighbor-joining (NJ) method with 1000 bootstrap replicates in Populations v.1.2.31 [FST genetic distance, altitude, and geographic (km) differences for the dataset of J. regia. Significance was evaluated by conducting 1000 permutations.Moreover, structure in the distribution of genetic differentiation was plotted by principal coordinates analysis (PCoA) using Nei’s genetic distance in R package “ggplot2” . In addiv.1.2.31 . R packav.1.2.31  was usedv.1.2.31  for R vev.1.2.31  for corrAdditional file 1: Table S1. Detailed sampling information of 27 Juglans regia populations across the Iranian Plateau. Table S2. Detailed information for the 31 pairs of primers and their combination for multiplex PCR. Table S3. Population pairwise FST and Nei’s genetic distance (DA). Lower-left part: FST; upper-right part: DA. Table S4. Gene flow (Nm) among populations. Fig. S1. Neighbor-joining tree of 508 individuals, colors correspond to Fig. Fig. S2. Principal Co-ordinates Analysis (PCoA) analysis of 508 individuals based on Nei’s genetic distance (DA). The groups were defined according to STRUCTURE analysis . Fig. S3. Geographical distribution of the genetic structure of 27 populations. Pie charts show the genetic proposition of each cluster in STRUCTURE analysis (K=4). Fig. S4. Principal Co-ordinates Analysis (PCoA) of 508 individuals based on Nei’s genetic distance (DA). The groups were defined according to STRUCTURE analysis . Fig. S5. Neighbor-joining tree of 508 individuals. Colors correspond to Fig. S4. Fig. S6. Genetic isolation by distance of 27 populations in the Iranian Plateau. (a) genetic distance and geographical distance , (b) genetic distance and altitude .


Khanty are indigenous Siberian people living on the territory of Western Siberia, mainly on the territory of the Khanty-Mansiysk and Yamalo-Nenets Autonomous Okrugs. The present study is aimed at a comprehensive analysis of the structure of the Khanty gene pool and their comparison with other populations of the indigenous population of Southern and Western Siberia. To address the issues of genetic proximity of the Khanty with other indigenous peoples, we performed genotyping of a wide genomic set of autosomal markers using high-density biochips, as well as an expanded set of SNP and STR markers of the Y-chromosome in various ethnic groups: Khakas, Tuvans, Southern Altaians, Siberian Tatars, Chulyms (Turkic language family) and Kets (Yeniseian language family). The structure of the gene pool of the Khanty and other West Siberian and South Siberian populations was studied using a genome-wide panel of autosomal single nucleotide polymorphic markers and Y-chromosome markers. The results of the analysis of autosomal SNPs frequencies by various methods, the similarities in the composition of the Y-chromosome haplogroups and YSTR haplotypes indicate that the Khanty gene pool is quite specific. When analyzing autosomal SNPs, the Ugrian genetic component completely dominates in both samples (up to 99–100 %). The samples of the Khanty showed the maximum match in IBD blocks with each other, with a sample of the Kets, Chulyms, Tuvans, Tomsk Tatars, Khakas, Kachins, and Southern Altaians. The degree of coincidence of IBD blocks between the Khanty, Kets, and Tomsk Tatars is consistent with the results of the distribution of allele frequencies and common genetic components in these populations. According to the composition of the Y-chromosome haplogroups, the two samples of the Khanty differ significantly from each other. A detailed phylogenetic analysis of various Y-chromosome haplogroups made it possible to describe and clarify the differences in the phylogeny and structure of individual ethnospecific sublines, to determine their relationship, traces of population expansion in the Khanty gene pool. Variants of different haplogroups of the Y-chromosome in the Khanty, Khakas and Tuvans go back to their common ancestral lines. The results of a comparative analysis of male samples indicate a close genetic relationship between the Khanty and Nenets, Komi, Udmurts and Kets. The specificity of haplotypes, the discovery of various terminal SNPs confirms that the Khanty did not come into contact with other ethnic groups for a long time, except for the Nenets, which included many Khanty clans. The study of the structure of the gene pools of populations ofvarious Siberian regions is one of the priority areas of modernhuman genetics and helps to reveal in detail some of the issuesrelated to their ethnogenesisThe Khanty are an indigenous people living on the territoryof Western Siberia, mainly on the territory of the Khanty-Mansiyskand Yamalo-Nenets Autonomous Okrugs, as well as theTyumen Region. Small groups of Khanty live in the north ofthe Tomsk Region and in the Komi Republic. According tothe All-Russian census of 2010, the number of Khanty was30,943 people, of which 61.6 % lived in the Khanty-MansiAutonomous Okrug and 30.7 %, in the Yamalo-Nenets AutonomousOkrug. The Khanty have three large ethnographicgroups that coincide with the groups of their language dialects– northern, southern and eastern, and the southern (Irtysh)Khanty were Turkified and became part of the Siberian Tatars,having mixed with them, and were also assimilated by Russiansettlers .Khanty populations are of considerable interest for populationgenetic studies, both due to the relatively poor knowledgewith the involvement of modern genomic technologies, anddue to the specificity of the gene pools of their individualgroups that developed under conditions of long-term geneticisolation.The settlement of the Khanty in antiquity was very wide –from the lower reaches of the Ob in the north to the Barabasteppes in the south and from the Yenisey in the East to theTrans-Urals, including the rivers Northern Sosva and Lyapin,as well as part of the rivers Pelym and Konda in the west.Since the 19th century, the Mansi began to move beyond theUrals from the Kama and Ural regions, being pressed by theKomi-Zyryans and Russians. From an earlier time, part of thesouthern Mansi also left to the north in connection with thecreation in the XIV–XV centuries of the Tyumen and Siberiankhanates – the states of the Siberian Tatars, and later (XVI–XVII centuries) with the development of Siberia by the Russians.In the XVII–XVIII centuries, the Mansi already livedon Pelym and Konda. Part of the Khanty also moved fromthe western regions to the east and north (to the Ob from itsleft tributaries), which is recorded by the statistical data of thearchives. Their place was taken by the Mansi. So, by the endof the XIX century, there was no Ostyak population left on therivers Northern Sosva and Lyapin: they either moved to the Obor merged with the newcomers .In the north, the Khanty came into contact with the Nenets,some of them were assimilated by them, which is confirmed byethnographic data, as well as our study of the tribal structureof the Gydan Nenets according to Y-chromosome markers. The migration of the Khanty to thenorth and east continued into the 20th century. By the 20thcentury, the southern Khanty were almost completely assimilatedby the Tatars and RussiansHistorically, the Khanty population was not homogeneouseither in language or culture. Some scientists divide the Khantylanguage into two large groups – western and eastern, whileothers still subdivide the western dialects into southern andnorthern. In anthropological terms, the Khanty are the mostcharacteristic representatives of the Ural type, which alsoincludes the Mansi, Selkups, Nenets, Baraba Tatars, Shors,Northern Altaians and Khakas. The closest relatives of theKhanty in origin, language and culture are the Mansi .The purpose of this study is a comprehensive analysis ofthe structure of the Khanty gene pool and the reconstructionof their origin in comparison with other populations of theindigenous population of Southern and Western Siberia. Toaddress the issues of genetic proximity of the Khanty withother indigenous peoples, genotyping of a wide genomic set ofautosomal markers using high-density biochips, as well as anexpanded set of SNP and STR-markers of the Y-chromosomewas performed in various ethnic groups: Khakas, Tuvans,Southern Altaians, Siberian Tatars, Chulyms (Turkic languagefamily) and Kets (Yeniseian language family).The material of the study was DNA samples of men andwomen from two populations of the Khanty in the villageof Russkinskaya, Surgut district and the village of Kazym,Beloyarsky district of the Khanty-Mansi Autonomous Okrug.The sampling of primary biological material (venous blood)from donors was carried out in compliance with the procedureof written informed consent for the study. For each donor, aquestionnaire was compiled with a brief pedigree, indicatingethnicity and places of birth of ancestors. An individual wasassigned to a given ethnic group based on their own ethnicidentity, their parents and place of birth.For the analysis of Y-chromosome haplogroups and haplotypesof the Khanty, 120 DNA samples of men from the villageof Russkinskaya (N = 64) and the village of Kazym (N = 54)of the Khanty-Mansi Autonomous Okrug were used. For genotypingon high-density microchips, unrelated samples fromthe village of Kazym (N = 30) and the village of Russkinskaya(N = 26) were selected. Other populations of the indigenouspopulation of Siberia are represented by: Chulyms (N = 22),Khakas , Southern Altaians , Kets , TomskTatars , Tuvinians .Genome-wide genotyping data were obtained using InfiniumMulti-Ethnic Global-8 (Illumina) microarrays for SNPgenotyping, including over 1.7 million markers. The materialwas deposited in the bioresource collection “Biobank of thePopulation of Northern Eurasia”.Autosomal SNP (single nucleotide polymorphism) genotypearray clustering and quality control were performed usinga protocol developed by  using GenomeStudio(Ilumina. GenomeStudio) (genotyping module v2.0.3), asoftware package that Illumina developed for various genomicanalyses. For filtering, normalizing and calculating standardgenomic statistics and indicators, the standard set of programs,including vcftools, bcftools, and plink, proved to be optimal.To analyze linkage blocks identical in origin, the RefinedIBD algorithm  wasused, which shows more accurate results compared to thealgorithms built into plink. The genotypes were preliminarilyphased using the Beagle 5.1 software . To compare the populations, the sumsof the average lengths of blocks identical in origin  were obtained between pairsof individuals.The tSNE method was used to analyze genetic relationshipsbetween populations. The NGSadmix method  and the ADMIXTURE program  were used to analyze the componentcomposition and the amount of impurities in individuals andpopulationsTo study the composition and structure of Y-chromosomehaplogroups, two systems of genetic markers were includedin the study: diallelic locuses represented by SNPs and polyallelichighly variable microsatellites (YSTRs). With the help of138 SNP markers, the belonging of the samples to differenthaplogroups was determined. The classification of haplogroupsis given in accordance with the data of the InternationalSociety for Genetic Genealogy (website www.isogg.org).Analysis of STR haplotypes within haplogroups was performedusing 44 STR markers of the non-recombining partof the Y chromosome . STR markerswere genotyped using capillary electrophoresis on an ABIPrism 3730 genetic analyzer. Genotyping of SNP markerswas performed using PCR and subsequent analysis of DNAfragments using RFLP analysis.Experimental studies were carried out on the basis of theCenter for the Collective Use of Research Equipment “MedicalGenomics” . The constructionof median networks of Y-chromosome haplotypes was carriedout using the Network v.10.2.0.0  using the Bandelt mediannetwork method . The generation ageof the observed diversity of haplotypes in haplogroups wasestimated using the ASD method based on the mean square differences in the number of repeatsbetween all markersThe large array of data on autosomal SNPs obtained as a resultof genotyping of high-density microarrays in samples of theKhanty and other indigenous Siberian peoples makes it possibleto characterize the gene pool of the studied samples inthe most detailed way using various methods. Genotyping ofan extended set of specific Y-chromosome SNPs from varioushaplogroups makes it possible to describe the molecularphylogeneticand phylogeographic structure of individualY-chromosome haplogroups much more accurately.After processing the data on the results of a microarraystudy to filter the progenotyped samples and perform furthercalculations, a search was carried out among the mestizoKhanty using the NGSadmix program. The algorithm of thisprogram makes it possible to determine the ratio of ancestralcomponents from NGS data with a relatively shallow coveragedepth. The calculation principle is similar to other programssuch as FRAPPE and ADMIXTURE, but NGSadmix, unlikethem, works effectively when there is statistical uncertaintyin individual genotypes. The NGSadmix method, when runon the data array we formed, showed that almost all Khantysamples do not have crossbreeding, which is fully consistentwith the data from the DNA donor questionnaire. Crossbreedingwith Russians (up to 30 %) was found only for one manfrom the village of Russkinskaya. His belonging to the EuropeanY-chromosomal lineage R1b1a1b-L407 confirms themiscegenation on the paternal side. This sample was excludedfrom further calculations.The obtained data on the frequencies of SNPs in the studiedsamples were used to elucidate the genetic relationshipsbetween the population samples included in the work. Fordimensionality reduction, spatial analysis, and identification of genetic components, we settled on two algorithms: tSNEand ADMIXTURE. The tSNE method makes it possible tomore clearly divide the data array into separate ethnospecificgroups of samples compared to the PCA method.Genetic relationships of the Khantywith other populations of Western and Southern SiberiaWhen analyzing the data array on the frequencies of autosomalSNPs using the tSNE method at the level of individual samples. It is shown that the two samples of the Khanty arevery close, while the samples of the Kazym and RusskinskayaKhanty do not intersect on the graph and are separated fromeach otherThe Khanty are characterized by specific features of thegene pool and do not cluster with other populations. Comparedwith subethnic groups of the Khakas and Southern Altaiansfrom different settlements, more geographically distantsamples of the Khanty demonstrate a much greater geneticcloseness. The samples of the Kets and Tomsk Tatars are closestto the Khanty. The genetic distances between the Khantyand the populations of Southern Siberia are much greater.Samples that are ethnically and geographically close to eachother are located quite close in the Fig. 1, but each sample isincluded in a separate ethnospecific cluster. The exception isonly a few single samples of the Khakas.Component composition of the gene pool of populations.Modern methods used in genomic studies and newbioinformatic approaches make it possible to reliably identifyancestral genetic components of different origins in the genepool of various populations and individuals. To identify individualgenetic components in the gene pool of the studiedpopulations, the ADMIXTURE program was used, whichmakes it possible to identify the mixed composition of a setof individuals based on genotype data and, thereby, to makeassumptions about the origin of the population.Modeling using ADMIXTURE has recently become one ofthe main methods of analysis in the study of the gene poolsof modern and ancient human populations, allowing you toanalyze the same data at different hierarchical levels. Whenthe number of ancestral components is set to more than two, inmost of the studied populations, a genetic component specificto the Khanty is revealed, which is most clearly manifested inthe analyzed array of population samples at K = 8, which canbe interpreted as the “Ugric” genetic layer in the gene poolof modern populations. The Khanty are characterized by thedominance of this component, which is their genetic basis. A significantproportion of this component is also found in the Kets  and Tomsk Tatars (up to 5–9 %).Previously, it was shown that this component also occupiesa significant share in the gene pool of the populations of theVolga-Ural region – the Bashkirs (up to 25 %), Maris (up to20 %), Komi, Udmurts and Chuvashs (up to 15 %). It is presentwith less frequency in almost all South Siberian samples,among the Tuvans, Chulyms, Altaians, and Khakas of Sagays(from 5 to 10 %) .The dominance of the Ugric component in all Khantysamples, starting from K = 3, and the almost complete absenceof other genetic components in their genomes at the individualand population level, indicates that their ancestral populationswere in genetic isolation for a very long time. This suggeststhat the ancient Ugric population of the modern territory ofthe Khanty settlement did not mix with other ethnic groupsand confirms the absence of other groups of migrants from theterritory of Southern Siberia and the steppe zoneThe result obtained shows that the overall picture of thedistribution of the components is in good agreement with thegeographical location of the studied populations, binding toa specific region, anthropological and linguistic differences.This information makes it possible to more accurately judgethe similarities and differences between the compared populations,the composition of ancestral components, as well as theprocess of formation of their gene pool.Identical in origin clutch blocks. As a result of bioinformaticsprocessing of genotyping data from high-densitybiochips of various Siberian populations, an analysis wasmade of the coincidence of DNA fragments common in originbetween populations and individuals. A segment with identicalnucleotide sequences is IBD in two or more individualsif they inherit it from a common ancestor without recombination,that is, in these individuals the segment has a commonorigin. The expected length of an IBD segment depends onthe number of generations since the last common ancestor.One of the applications of the analysis of genome regions ofcommon origin is the quantitative assessment of the degreeof relationship between individuals, which can also supplementinformation on the genetic relationships of populations.The samples of the Khanty showed the maximum match inIBD blocks with each other (6 %), then with a sample of theKets (1.45 %), Chulyms (0.71 %), Tuvans (0.35 %), TomskTatars (0.33 %), Khakas Kachins (0.32 %), and SouthernAltaians (0.28 %). At the same time, among the Khanty, agreater coincidence of IBD blocks is observed in Russkinskaya(23.5 %), compared with Kazym (18.1 %).The degree of overlap of IBD blocks between the Khanty,Kets, and Tomsk Tatars is consistent with the results of tSNEand ADMIXTURE in terms of the distribution of allele frequencies and common genetic components in these populations.At the same time, in Khanty population from Russkinskaya,who have the largest sum of average lengths of IBDsegments between pairs of individuals, the greatest contributionis made by IBD longer than 10 cm (42–46 %), whichindicates a strong recent inbreeding within the population.To confirm this, the FROH inbreeding coefficient was calculatedfor all individuals for the three classes of homozygosityblocks (ROH). For the West Siberian populations, the Chu-lym population (0.0292), the Kazym (0.0280) and RusskinskayaKhanty (0.0266) and Kets (0.0259) populations, whichare close in value, have the maximum values. Among the SouthSiberian populations, including the Altaians, Tomsk Tatars,Tuvans and Khakas, the maximum value was also foundfor the sample of Khakas-Sagays from the foothill Tashtypregion (0.0318), twice as high as the Khakas-Kachins of theplain Shirinsky region. The minimum value is typical for theTomsk Tatars (0.0071).There is a high correlation for FROH > 1.5 with the sumof mean IBD segment lengths (IBD > 1.5 cM) betweenpairs of individuals within Siberian populations . To calculate the Spearman correlation coefficient,cor.test was used in the R program. The ratio of thesum of the average lengths of IBD segments (IBD > 1.5 cM)between pairs of individuals to the coefficient of genomicinbreeding (FROH > 1.5) in the Russkinsskaya Khanty ishigher than in Kazym Khanty. These indicators of genomicinbreeding and distribution of IBD lengths within Khantypopulations are in good agreement with their territorial isolationand confirm the absence of recent gene flows betweenpopulations for hundreds of yearsHaplogroups of the Y-chromosome. As a result of the analysisof the frequency of occurrence of the used SNP markersin the studied samples of the Khanty, eight haplogroups of theY-chromosome were identified. According to the compositionand frequencies of haplogroups, the samples of Russianand Kazym Khanty men are very different from each other.Only two haplogroups are present in both samples (see theTable).Thirty-nine samples belong to the N1a2b1b1 subline inthe Russkinskaya Khanty, and only three in the Kazymones. Terminal for this line, the Khanty have SNPs Y68212,Y70717, Y70315, Y70327. This Khanty subline is close to theN1a2b1b1 variantsin the Chulyms  and Khakas-Kachins .The haplogroup N1a2b1b1 among the Khanty is ethnospecificand does not coincide in terminal SNPs and haplotypeswith the dominant among the Nenets N1a2b1b1a~  .A feature of the ethnic composition of the majority of theSouth Siberian peoples is the presence of clans (seoks), wherekinship is counted along the male line. Such a generic structureis typical for the Shors, Khakas, northern and southernAltaians, and Teleuts. All other samples of men from variousWest and South Siberian populations  belong to others sublinesof haplogroup N1a2b. The median network of haplotypes demonstrates a stellate phylogeny in the Khanty witha recent founder effect and a predominance of the ancestralhaplotype in frequency.The specific cluster of Khanty haplotypes is equidistantfrom all seoks of the Khakas-Kachins. The age of this clusteramong the Khanty was 858 years (SD = 338 years), whichis approximately one and a half to two times higher than theage of the clusters of the Kachin seoks Khaskha – 487 years(SD = 153 years), Yzyr – 501 years (SD = 203 years), Sokhkhy– 585 years (SD = 215 years)  andChulym Turks 667 years (SD = 194 years). Thus, the Khantyin this haplogroup have a direct genetic connection with theKachins, Chulyms and Nenets, whose ancestral lines divergedquite a long time ago and reflect their connection with thepeoples of the Samoyedic language group.The second haplogroup N1a2b2a1  is common for two Khantysamples (previously designated as the European N1b-Elineage). This subline was found among the Bashkirs, KazanTatars, Komi, Mari, Karelians, Vepsians, Finns and Russians(https://www.yfull.com/). Phylogenetically closest to the Khantyalong this line are the Komi samples. Ethnospecific branchesof the Khanty and Komi unite SNPs Y65017 and Y89655, notfound in other populations. The Khanty and Komi have theleast ancient common ancestor for this haplogroup, comparedto other European populations.According to the YFull website, this branch split from theancestral line about 2800 years ago. Theoretically, there aretwo options for the appearance of this haplogroup among themodern Khanty and Komi: 1) inheritance from a commonancient ancestral group of Ugric tribes; 2) the recent mixingof Khanty with ethnic Komi migrants to Siberia. However,the results of the analysis of genomic data using NGSadmix,ADMIXTURE, IBD blocks and differences in terminal SNPsof Y-chromosomes do not confirm the second variant. TheYSTR haplotypes of this line in the Khanty and Komi alsodiffer by several mutations. Previously, V.N. Pimenoff et al.suggested in their work that when the Ob-Ugric Khanty andMansi went to the western slopes of the Ural Mountains andto the north-west of Siberia, a unique association N1b-A andN1b-E was formed . This combinationof N1b sublines in the Khanty and Mansi suggests a recentconfluence of the western and eastern lineages in North WesternSiberia. Our new data do not contradict this versionAll other haplogroups are represented only in individualsamples of the Khanty. The haplogroup N1a2b2b1~ (Z35076)includes three samples of the Kazym Khanty. The lineageN1a2b2b1~  closest toit is also common among the Komi. The Udmurts, Tatars,Chuvashs and Bashkirs have its more modern line (B226).The YSTR haplotypes of this haplogroup in the Komi and Udmurtsare closer to each other than to the Khanty samples. Thepresence among the Khanty and Komi of two haplogroups,N1a2b2a1 and N1a2b2b1~, with ethnospecific terminal SNPsand different haplotypes indicates their inheritance from fairlyancient common ancestors, most likely part of the early Ugricpopulation of these territories.Thirteen samples of the Kazym Khanty belong to the haplogroupN1a1a1a1a2a1c1~ . Seven of themhave the surname Pyak, which is Nenets in origin, referring tothe Forest Nenets. All seven of these samples have very closehaplotypes and are descendants of a relatively recent commonNenets ancestor. In the questionnaires of these men, who considerthemselves Khanty, Nenets ancestors were indicated onthe paternal line with different depths. The remaining six menof this haplogroup differ in haplotypes from the Pyak genusIn our study of the Taz Nenets , itwas found that all men representing the Khanty origin of theSalinder, Lar and Tibichi clans completely belong to thishaplogroup. Representatives of these genera formed in theXVIII–XIX centuries in the lower reaches of the Ob as aresultof the development of the Nenets large-herd reindeerhusbandryand the involvement of part of the northern Khantyin it . All haplotypes of the Kazym Khantyof this haplogroup differ significantly from the haplotypes ofthe Taz Nenets.The other five samples of the Kazym Khanty belong to thehaplogroup N1a2b1b1b1~ . All previouslysurveyed Nenets men from the Vanuito phratry belonging tothe Vanuito, Puiko and Yaungat clans, and the Purungui clanof Khanty origin, belong to it. Four samples of the Khanty differin haplotypes from the Nenets, but one almost completelycoincides with them. Such a division into haplotypes specificto the Khanty and close to the Nenets coincides with the data on the haplogroup N1a1a1a1a2a1c1~. It is obvious that thegene pool of the Kazym Khanty includes precisely the variantsof these haplogroups of Khanty origin, but relatively recentlymarriages were also made with the Forest Nenets. The absenceof these haplogroups in the Russkinskaya Khanty is in goodagreement with the data on the distribution of IBD blocks andthe coefficient of genomic inbreedingThe distribution of various haplogroups of the N cladeof the Y-chromosome in the studied populations is in goodagreement with the frequency of the Ugric genetic component.Phylogenetic analysis of Y-chromosomal sublines andhaplotypes of various haplogroups of the N clade shows thatthe center of origin and distribution of the carriers of theUgric component in Southern, Western Siberia and EasternEurope is the territory of modern Altai and Sayan Mountains.The obtained results are well comparable with the data ofethnology, anthropology and linguistics on the contributionof the Uralic component to the formation of various peoplesof the Altai-Sayan and the historical areas of Ugric and otherlanguages of the Uralic language family.Almost 40 % of men from Kazym belong to the haplogroupQ1b1a3b1a2~ . The lineageQ1b1a3b1a2~  specific to the Kets populationis closest to it. In addition to the Kets, this variant alsoprevails among the Selkups from the Tomsk Region and theKrasnoyarsk Region. A more distant line Q1b1a3b1a~  is common in Tuvan populations, with amaximum frequency in the eastern mountainous regions ofTuva (up to 25 %). Khanty samples show a specific haplotypespectrum with a recent founder effect that is not observed inthe Kets .The distribution of these sublines in the populations ofthe Khanty, Kets, and Tuvans is in good agreement with theshares of matches in IBD blocks between them, the tSNEplot, and the distribution of the Ugric genetic component inthese populations over the autosomal part of their gene pool.The presence of this lineage among the Khanty is not due torecent borrowing from other aboriginal populations (Kets andSelkups), but to the fact that it was already part of the settlingancestral groups.Three men from the village of Russkinskaya have a completelydifferent haplogroup of the Q clade – Q1a2b~ . This is a very rare haplogroup notfound in other Siberian populations. It is presented with themaximum frequency among the ethnic Turkmens of Karakalpakstan,Iran and Afghanistan . In most other ethnic groups, its frequencyis very low. Khanty haplotypes are quite different from otherpopulations. Most likely, the presence of this line among themis not a consequence of recent miscegenation, but is a legacyof the Ugric groups that migrated from southern Siberia andthe Urals to the north.The last haplogroup, which includes 16 Khanty men fromthe village of Russkinskaya, is R1a1a1b2-Y43850. The haplotypesof all samples are quite close, which indicates a recentfounder effect .Khanty-specific terminal SNPs are S7280, FGC687, andFGC38304. The R1a1a1b2-Y43850 variants closest to thislineage are represented with a high frequency in the Khakasand Shors, and less frequently in the Tuvans and Northern Altaians.According to YFull, this haplogroup is approximately3800 years old. All of these patterns belong to four differentlineages that split a long time ago. The age of the haplotypecluster in the Khanty was 933 years (SD = 336 years), whichis approximately one and a half times less than the age of theSouth Siberian lines. The Khakas seok Piltir is 1469 years old(SD = 342 years) (Y39884 хY43109). The lineage of this haplogroup(Y62155.2) specific for the Biryusa Khakas seoks ofTuran, Khyzyl Khaya and Shor seoks of Tartkyn, Shor-Kyzaiand Kara-Shor has approximately the same age – 1315 years(SD = 227 years). The branch with a wider distribution in theSayan-Altai populations (Y43109) is even older – 1566 years(SD = 350 years). The difference in SNP and STR among theKhakas, Shors, Tuvans, and Northern Altaians is greater thanwith the Khanty.A strong heterogeneity of the studied samples of the Khantyin terms of the composition and frequencies of varioushaplogroups is shown. The phylogeny of various lineages oftwo haplogroups, N1a2b1b1 and R1a1a1b2-Y43850, indicatestheir South Siberian origin in the Khanty gene pool. The territoryof the Sayan and Altai was the primary focus of thegeneration of diversity and the expansion of the number ofancestral groups of carriers of these haplogroups in Siberia.It is most likely that the distribution of most Y-chromosomehaplogroups among the Khanty occurred during the initialsettlement of the Ugric tribes to the north and west.It is necessary to take into account the fact that the rangeof modern Khanty is located to the north of the territory oftheir ancestors. The West Siberian and Volga-Ural regionswere the place of secondary generation of diversity, but notthe formation of the N1a2 haplogroup itself. At the moment,there is no final opinion regarding the place of formation ofthe ethnoi of the Uralic language family, but numerous data,including the results of studies of the phylogeny and phylogeography of clade N haplogroups, point to Southern Siberia.Linguistic paleontology points to the Proto-Ural ecologicalarea as a territory limited in the west by the Ural Range, in thenorth by approximately the Arctic Circle, in the east by thearea of the lower reaches of the Angara and PodkamennayaTunguska and the middle reaches of the Yenisey, in the southby approximately the modern southern border of the WestSiberian taiga from the northern foothills of the Sayan andAltai to the lower reaches of the Tobol and the Middle Uralsinclusively .Thus, the gene pool of the two Khanty populations is a heterogeneousset of Y-chromosome haplogroups, but very similarin autosomal markers. The expanded composition of terminalSNPs for the identified haplogroups made it possible to describein detail and clarify the differences in the phylogenyand structure of individual ethnospecific sublines, to determinetheir relationship, and traces of population expansion in theKhanty gene pool. The results of a comparative analysis ofmale samples indicate a close genetic relationship of theKhanty with the Altai-Sayan Khakas and Tuvans, as well aswith the Nenets, Komi, Udmurts and Kets. The specificity ofhaplotypes and the detection of various terminal SNPs indicatethat the Khanty did not come into contact with other ethnicgroups for a long time. The only exception is the Nenets,which included many Khanty clans. For the northern populationof the Kazym Khanty, Y-chromosomal lines show a smallcontribution of the Forest Nenets.The results obtained do not contradict the generally acceptedversions of the Khanty ethnogenesis, but allow us to takea fresh look at this process. The main factor in the formationof the Khanty gene pool was their territorial genetic isolationand later mixing with the newcomer Samoyed population,which, when switching to tundra reindeer husbandry, led to astrong demographic growth of their clans as part of the Nenets.The relatively low genetic diversity in autosomal SNPsand the rather high level of inbreeding in the Khanty confirmthis. New information about the structure of the Khanty genepool is an important addition to the existing anthropological,archaeological, ethnological and linguistic data on their formationand kinship with other peoples.The authors declare no conflict of interest.Alexander D.H., Lange K. Enhancements to the ADMIXTURE algorithmfor individual ancestry estimation. BMC Bioinformatics. 2011;12:246. DOI 10.1186/1471-2105-12-246.Alexander D.H., Novembre J., Lange K. Fast model-based estimationof ancestry in unrelated individuals. Genome Res. 2009;19(9):1655-1664. 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Ormosia henryi is a valuable rare woody species in EBLFs that is particularly sensitive to habitat loss. In this study, ten natural populations of O. henryi in southern China were sampled, and then genotyping by sequencing (GBS) was applied to elucidate the standing genetic variation and population structure of this endangered species.The evergreen broadleaved forest (EBLF) is an iconic vegetation type of East Asia, and it contributes fundamentally to biodiversity-based ecosystem functioning and services. However, the native habitat of EBLFs keeps on decreasing due to anthropogenic activities. O. henryi populations, 64,158 high-quality SNPs were generated by GBS. Based on these markers, a relatively low level of genetic diversity was found with the expected heterozygosity (He) ranging from 0.2371 to 0.2901. Pairwise FST between populations varied from 0.0213 to 0.1652, indicating a moderate level of genetic differentiation. However, contemporary gene flow between populations were rare. Assignment test and principal component analysis (PCA) both supported that O. henryi populations in southern China could be divided into four genetic groups, and prominent genetic admixture was found in those populations located in southern Jiangxi Province. Mantel tests and multiple matrix regression with randomization (MMRR) analyses suggested that isolation by distance (IBD) could be the possible reason for describing the current population genetic structure. In addition, the effective population size (Ne) of O. henryi was extremely small, and showed a continuous declining trend since the Last Glacial Period.In ten O. henryi is seriously underestimated. Artificial conservation measures should be applied as soon as possible to prevent O. henryi from the fate of extinction. Further studies are needed to elucidate the mechanism that leading to the continuous loss of genetic diversity in O. henryi and help to develop a better conservation strategy.Our results indicate that the endangered status of The online version contains supplementary material available at 10.1186/s12870-023-04231-w. The evergreen broadleaved forest (EBLF) is the typical vegetation biome in the subtropical regions of China with a range from 22 to 34°N latitude and 99 to 123°E longitude. Considering that the other areas in the same latitude were covered by desert or semi-desert, this biome type is unique owing to its dependence on East Asian monsoon , the fatSingle nucleotide polymorphisms (SNPs) are the most abundant and universal variations across the genome, thus making them very useful markers for population genetic analysis , 8. WithOrmosia Jacks.  comprises about 150 species of trees and exhibits a striking disjunct geographical distribution between the New World- and Asian and Australasian wet tropics and subtropics [Ormosia, and there are approximately 35 Ormosia species distributed in southern China. Among these species, O. henryi is a rare and endangered evergreen tree species with multiple usages. It is highly prized not only for the durable and attractive wood, but also as an excellent ornamental plant [O. henryi was recently found to be a new bioactive resource with potential antidepressant activity [O. henryi in China have dramatically declined due to over-logging, habitat fragmentation and difficulties in regeneration. O. henryi has been classified as a national second-class protected wild plant in China and was evaluated as Vulnerable (VU) by IUCN. Previous studies of O. henryi focused on its bioactive ingredients, medical usages and tissue culture [O. henryi, genotyping-by-sequencing (GBS) method was adopted in this study. We identified genome-wide SNPs from ten natural populations of O. henryi in southern China to address the following questions: (1) to assess the genetic diversity and population structure of O. henryi, (2) to evaluate the impact of geography and environment on genetic differentiation of O. henryi and (3) to infer the demographic history of O. henryi. All these results will provide a molecular basis for the conservation of O. henryi natural populations and provide a technical reference for their rational utilization.The genus btropics . It is abtropics , 15. Chial plant , 17. O. activity . However culture –20. By fO. henryi was 1,364,689,951 bp in length (including N) and served as the reference genome for GBS data analysis.After removing reads with adapters and reads with low quality, a total of 190,771,231 pairs of reads were obtained, reaching 57.23 Gb of clean data. A total of 4,066,523 contigs were assembled by SOAPdenovo software, and the maximum and N50 contig lengths were 54,197 bp and 656 bp, respectively , expected heterozygosity (He), and nucleotide diversity (π) were 0.1460, 0.1594 and 0.1132, respectively, suggesting a low level of genetic diversity in O. henryi populations. At the population level, genetic diversity based on the three statistics showed different trends of ranking. The Ho values ranged from 0.2282 (XF) to 0.2874 (SX) with an average of 0.2675, while the He values ranged from 0.2371 (XF) to 0.2901 (AY) with an average of 0.2669. The nucleotide diversity (π) varied between 0.1216 (YK) and 0.1429 (JH) with an average of 0.1312. The results showed that PIC values varied from 0.2023 to 0.2335 with an average of 0.2213. The inbreeding coefficient (FIS) values among populations were close to zero, ranging from -0.0232 to 0.0224  and geographic distance for the 10 O. henryi populations  and environmental distance  or between geographic distance and environmental distance . The results of partial Mantel tests were similar. Pairwise FST/(1-FST) displayed a significant correlation with geographic distance after controlling for environmental distance , but no significant correlation was observed between pairwise FST/(1-FST) and environmental distance after controlling for geographical distance . The multiple matrix regression with randomization (MMRR) analysis revealed the relative contributions of IBD and IBE, and further confirmed strong and significant effects of IBD  but weak and non-significant effects of IBE  on population genetic differentiation.Mantel tests showed a significant correlation between pairwise Ne) of 3.4 (95% CI: 2.6—13.6) for all the O. henryi individuals. The Ne estimates of JH, XF and AY populations were larger than 40, while the Ne estimates of other populations were below 20  and expected heterozygosity (He) of O. henryi were 0.1460 and 0.1594 at the species level respectively, which were lower than those of some other tree species revealed by SNP markers, for example, Cinnamomum camphora  is much lower than that of O. hosiei  by IUCN, which is supposed to be rarer than O. henryi . The endangered status of O. hosiei with high genetic diversity is likely due to the recent population declines caused by human activities, and the high level of genetic diversity observed in O. hosiei could be a transitory state [Ne) of O. henry began to decline during the Last Glacial Period (LGP) and the contemporary Ne estimate was very low at the species level. Thus, the relatively low genetic diversity of O. henryi appears to be a result of both evolutionary history and human-induced population declines.Generally, species or populations with higher genetic diversity are more capable of adapting to environmental changes , 22. In = 0.322, ), Quercu 0.2272, ) and Euc= 0.232, ). Notabl3–0.835, ), which ry state , 26, 27.Ho and He were similar but quite different from that based on nucleotide diversity (π). Considering the three statistics  together, the AY population had relatively high genetic diversity despite only eight individuals being found in our field investigation. Contemporary Ne is usually smaller than the real population size [Ne was estimated in AY population, suggesting that there may be more undiscovered individuals of O. henryi in AY population.Our results showed that the rankings of genetic diversity of different populations based on ion size . A relatO. henryi was mainly maintained within populations, with a moderate and significant genetic differentiation detected among populations . It is widely accepted that IBD and IBE are two main models of population genetic differentiation, but IBD and IBE are not mutually exclusive and can jointly contribute to population divergence [O. henryi. Seeds of O. henryi are dispersed over short distances mainly by gravity [O. henryi does not regenerate well in the understory and seedlings are rarely seen [O. henryi may also attract arboreal frugivorous birds and rodents for long-distance seed dispersal like the other tree species of Ormosia [O. henryi populations has not developed, resulting in isolation. Populations with low genetic diversity are very sensitive to genetic drift. The factors above may enhance the effect of IBD. In addition, physical barriers such as mountains, rivers or man-made landscapes can facilitate genetic divergence by obstructing dispersal among populations [Castanopsis fargesii [Schima superba [Sassafras tzumu [. On the other hand, no significant effects of IBE were detected by either Mantel tests or MMRR analysis. A strong pattern of IBE is typically interpreted as evidence that divergent selection associated with environmental heterogeneity is maintaining population differentiation [O. henryi populations was mainly driven by neutral processes like genetic drift, but not local adaptation to different climate conditions.Many forest tree species show a high level of genetic variation within populations, with relatively low genetic differentiation among populations . As expevergence , 31. In  gravity . Howeverely seen . Seeds o Ormosia , 34, butulations , 36. In fargesii , Schima  superba  and Sassas tzumu . On the ntiation . In consO. henryi was identified by the ADMIXTURE analysis, PCA and phylogenetic analysis, supporting the division of four groups. Echoing the IBD pattern, the population structure of O. henryi also exhibited variation with geographic distance. The JH population located in the range of Nanling Mountains is geographically isolated from other populations. The genetic composition of JH population was distinct from other populations regardless of the K value. The NH, SC, MTS, SX and JO populations from the third group located in the adjacent areas of Wuyi Mountains were closely related, and some individuals even clustered with individuals from other populations on the phylogenetic tree . According to the results of PCA and ADMIXTURE analysis, the LQ population from the fourth group was genetically more similar to the populations in the third group than the YK population. The AY and XF populations in southern Jiangxi province were observed with clear genetic admixtures, suggesting that southern Jiangxi province may be a transition zone between eastern and western populations.Understanding the population structure of a threatened species can help identify conservation units and develop effective conservation strategies . SignifiO. henryi, it probably relies primarily on short-distance, gradual dispersal. Without habitat destruction, there can be high levels of gene flow among O. henryi populations. Once the population connectivity is broken by the processes such as habitat fragmentation and human activities, gene flow will be restricted and a significant population structure will generate.Gene flow plays an important role in shaping the spatial genetic structure . Small, O. henryi endangerment situation. The contemporary Ne estimates of O. henryi were much lower than the numbers generally suggested in the 50/500 rule. A Ne of 50 is recommended to avoid short-term inbreeding depression, and a Ne of 500 to maintain the long-term evolutionary potential of a species [Ne since the LGP. Accordingly, only a small number of individuals were found in most natural populations during our field investigation. Many isolated trees were found distributed in fragmented habitats such as roadsides, riversides and beside villages. The low Ne estimates, along with the low level of genetic diversity and the observed small population size are warning signs that O. henryi is at increased risk of genetic erosion and should therefore be subject to conservation intervention urgently. Moreover, a moderate level of genetic differentiation and strong IBD pattern were detected in the ten O. henryi populations from southern China. If the conservation and management of O. henryi is not strengthened in time, genetic differentiation among O. henryi populations will further increase, eventually leading to a more endangered status.The exploration of genetic diversity and population structure can provide vital information for the conservation and management of threatened species . Our pop species , 46. TheO. henryi has attracted much attention for its valuable timber, high ornamental value, and potential medicinal value. In recent years, O. henryi has been gradually used in the transformation of low-quality and inefficient forests such as pure fir forests and pure pine forests in southern China. However, there are still some problems in the conservation and utilization of O. henryi such as inadequate ex-situ conservation, arbitrary selection of provenances in the reforestation process and lack of good varieties. Considering the underestimated endangered status of O. henryi, urgent actions should be taken for both in-situ and ex-situ conservation. In addition, genetic assessment of natural populations outside the highly suitable areas should be conducted to determine a more comprehensive conservation strategy. Based on the findings of this study, several conservation measures are suggested here: (i) In situ conservation should be further strengthened. Priority should be given to the protection of large populations with high genetic diversity in natural forest stands. Maintaining their existing natural habitats is one of the most important ways to protect them. Our results have divided the ten populations in highly suitable areas into four genetic groups, which can be protected as separate evolutionary units. (ii) Ex-situ conservation should be carried out for the long-term survival of O. henryi. The establishment of germplasm resource nurseries and low-temperature seed banks is recommended. Individuals from different genetic groups especially superior trees should be collected to ensure genetic representativeness. (iii) Reintroduction and restoration programs are necessary for increasing population size and preventing further isolation. Seed germination of O. henryi is limited by its hard, dense seed coat and inhibitory substances [bstances , 47, whibstances . TherefoO. henryi in southern China. Based on the GBS method, we found a low level of genetic diversity, a moderate level of genetic differentiation and significant genetic structures in natural O. henryi populations. Isolation by distance (IBD) is considered as the main reason for the current population genetic structure. Continuous declining trend of the effective population size was suggested, which occurred during the Last Glacial Period. The results from the current genomic data combined with our field investigation suggest that the endangered status of O. henryi is seriously underestimated. Both ex situ and in situ conservation measures should be applied as soon as possible to prevent O. henryi from the fate of extinction. To gain more complete scenario, we call for more studies to decipher the mechanism that leads to the continuous loss of genetic diversity in O. henryi. Our population genomic study provides valuable information for the conservation and management of O. henryi and contributes to the further understanding of preserving the biodiversity in EBLFs. As our sampling area did not cover the entire range of O. henryi, the genetic diversity and differentiation of O. henryi may have been underestimated in this study. In addition, we lacked an in-depth understanding of the important biological characteristics of O. henryi, such as mating system, seed dispersal and natural population regeneration. Therefore, further studies are needed to clarify the endangerment mechanism of O. henryi.In this study, we performed a genome-wide population genetic investigation on natural populations of O. henryi suggest that, under current climatic conditions, the core distribution of O. henryi is mainly concentrated in Jiangxi, Hunan, Fujian, Zhejiang provinces in southern China [O. henryi in its core distribution range, 129 O. henryi individuals were collected from 10 populations in the four provinces of southern China . A paired-end DNA library was prepared and sequenced with Illumina HiSeq X-ten platform. After removing reads with adapters and reads with low quality, the survey genome assembly was performed using SOAPdenovo [One APdenovo  softwareMseI and NlaIII) were used to digest previously extracted DNA. Barcoded adapters were ligated to the digested fragments, and multiplex amplification by PCR was performed. All the samples were mixed, and the desired fragments were selected for GBS library construction in Novogene Co., Ltd . Paired-end sequencing was performed on an Illumina HiSeq sequencing platform.The GBS libraries were prepared following the method developed by Elshire et al. . Two reshttp://hannonlab.cshl.edu/fastx_toolkit/). The obtained clean reads were aligned to the previously assembled survey genome of O. henryi using bwa v0.7.8 [https://broadinstitute.github.io/picard/) was used to sort bam files by coordinates and mark PCR duplicates. SNP calling was performed using bcftools v1.7 [The barcoded adapter sequences of raw reads were removed by the process_radtags program in the STACKS v2.1 software . Raw reaa v0.7.8  with thea v0.7.8 . Picard ols v1.7  with defols v1.7 .O. henryi, observed heterozygosity (Ho) and expected heterozygosity (He) for each population was estimated by Arlequin v3.5 [FIS) and polymorphism information content (PIC) was calculated according to the formula [FST) between populations were calculated by Arlequin v3.5. The contemporary gene flow among populations was estimated by BA3-SNPs program [6, and a sampling frequency of 1000. Mixing parameters for allele frequencies, inbreeding coefficients and migration rates were adjusted to achieve acceptance rates between 0.2 and 0.6 as recommended by the software's manual. Five independent runs with different random seeds were performed. Migration rates were considered significant if the 95% credible sets did not include zero.To fulfil neutrality assumptions in population genetic analyses, we detected potential SNPs under selection using the Bayesian method as implemented in BayeScan v2.1 . To asseuin v3.5 , and nuc formula , 61. Pai program  with 10 The population genetic structure was investigated using phylogenetic tree construction, principal component analysis (PCA) and the model-based evolutionary clustering approach as implemented in ADMIXTURE v1.2 . An unroAMOVA analysis was carried out by Arlequin v3.5 to quantify the genetic variation among and within the genetic groups inferred by the population structure analyses. The significance levels for the variance components were tested using 1,000 permutation steps.https://worldclim.org/, [FST /(1- FST) between populations was calculated by Arlequin v3.5. Mantel tests were performed to assess the associations between genetic distance, geographic distance and environmental distance with 9999 permutations in R package vegan. The relative role of geography (IBD) and environment (IBE) on population divergence was also evaluated using multiple matrix regression with randomization (MMRR) implemented in the "MMRR" function of R [βD) and IBE (βE) were determined after 9999 permutations.To investigate the roles of geographic and environmental factors in shaping the spatial genetic differentiation, we calculated: (a) the correlation between environmental and geographic distance, (b) isolation by distance (IBD) and (c) isolation by environment (IBE). The geographic distance matrix was calculated based on the GPS coordinates of sampling sites using GenAlex v6.51 . A totalim.org/, ). To avoim.org/, . Pairwision of R . MMRR alNe) for each population was estimated using the linkage disequilibrium method in NeEstimator v.2.1 [O. henryi was inferred using Stairway Plot 2 [https://github.com/isaacovercast/easySFS). The mutation rate for O. henryi was taken from literature-based estimates for genome-wide mutation rates and was eventually set at 4e-9 per site per year [Contemporary effective population size . Six SNPs colored in red were probably under selection. Fig S2. Cross-validation (CV) errors of 10 repeat runs under different K values. Fig. S3. Demographic history of three genetic groups of Ormosia henryi inferred by Stairway Plot 2. The x-axis indicates the time before the present, and the y-axis represents the historical effective population size. The first genetic group were not used in the demographic history analysis due to the small number of individuals.


Larimichthyscrocea) is one of the most economically valuable marine fish in China and is a notable species in ecological studies owing to a serious collapse of wild germplasm in the past few decades. The stock division and species distribution, which have important implications for ecological protection, germplasm recovery, and fishery resource management, have been debated since the 1960s. However, it is still uncertain even how many stocks exist in this species. To address this, we evaluated the fine-scale genetic structure of large yellow croaker populations distributed along the eastern and southern Chinese coastline based on 7.64 million SNP markers. Compared with the widely accepted stock boundaries proposed in the 1960s, our results revealed that a climate-driven habitat change probably occurred between the Naozhou (Nanhai) Stock and the Ming-Yuedong (Mindong) Stock. The boundary between these two stocks might have shifted northwards from the Pearl River Estuary to the northern area of the Taiwan Strait, accompanied by highly asymmetric introgression. In addition, we found divergent landscapes of natural selection between the stocks inhabiting northern and southern areas. The northern population exhibited highly agminated signatures of strong natural selection in genes related to developmental processes, whereas moderate and interspersed selective signatures were detected in many immune-related genes in the southern populations. These findings establish the stock status and genome-wide evolutionary landscapes of large yellow croaker, providing a basis for conservation, fisheries management and further evolutionary biology studies.The large yellow croaker (The online version contains supplementary material available at 10.1007/s42995-023-00165-2. Larimichthyscrocea) is a marine fish that lives in the northwestern Pacific, generally in temperate nearshore seas and estuaries. The great abundance of fishery resources made it an economically important species in Northeast Asia, including China, Korea, and Japan. However, due to serious overfishing, wild populations of this species have suffered a serious collapse since the 1970s , Min-Yuedong stock , and Daiqu stock  , as a new generation of genetic markers, are plentiful in the genome and easy to access. Population structure has been assessed based on genome-wide SNPs in many fishes, such as sharks  to compare the two groups. For observed heterozygosity (HO) and the inbreeding coefficient (F), populations were partitioned into Zhoushan (ZS), Fuding (FD), and Zhanjiang (ZJ) vs. the other five populations. The ZS-FD-ZJ group had significantly lower HO values and higher F values than the other group , indicating higher levels of inbreeding. Based on expected heterozygosity (HE) and nucleotide diversity (π), populations were partitioned into FD and ZJ vs. the other six populations. The FD and ZJ populations had significantly lower HE and π values than that of other populations . These results suggest that large yellow croakers inhabiting these areas  have relatively low genetic diversity.We estimated basic population genetic parameters to determine the status of each population (Table S4 in Supplementary file 1). We first employed the K-value was set to 2 initially and updated by adding 1 for each repetition. When K = 2, the DQ stock and MD stock shared the same ancestry, while the NH stock was assigned to a separate group. In the model with K = 3, each of the three stocks identified by PCA had a unique hereditary constitution. And no additional effective information can be provided when set K greater than 3. Mixed ancestry was less common in the NH stock than in the other stocks. Gene introgression was generally in the direction of southern to northern populations. A phylogenetic tree constructed based on polymorphic SNPs using the maximum likelihood (ML) algorithm also revealed a very clear population structure coinciding with the results of the PCA and ancestry proportion analysis .We then investigated the genetic structure of these populations via multiple methods. A principal component analysis (PCA) revealed that the sampled large yellow croakers can be divided into three distinctive stocks, i.e., Nanhai (NH), Mindong (MD) and Daiqu (DQ) Fig. C. The NHP = 2.691E−5) between the total length of shared haplotypes and position along the coastline. Populations closer to the two ends of the habitats (ZS and XW), in general, showed longer shared haplotypes (apart from ZJ and FD).Shared haplotypes, also known as identity-by-descent segments, are valid indicators of introgression events among populations and stocks  in the NH and MD stocks. The NH stock had an average nSHF that was 2.7 times higher than that of the MD stocks in the PT-FF shared haplotypes. Among these, 11,585 windows had log2-transformed relative SHFs (rSHFs) higher than 2.5, implying a higher chance that they originated from the NH stock. However, only 1763 windows had rSHFs lower than − 2.5 , 55,694 windows overlapped with shared haplotypes between the PT and FF populations . The time since the habitat changed is insufficient for the spread of introgressed segments to other sites in the new stock. Therefore, shared haplotypes in these segments should be more frequent within the original stock. Based on this concept, we determined the direction of introgression events by comparing the normalized shared haplotype frequencies (R2 = 0.151) but not with Fst or Pi , which indicates the degree of genetic differentiation between a pair of populations. Pairwise Fst values were 0.0356 ± 0.0212 .Dcsl), the “as the crow flies” distance (Dcrf), the distance along latitudes (Dlat), and the difference by maximum, minimum, and mean SST  using ordinary least squares (OLS) to identify significant patterns among large yellow croaker populations (Table S7 in Supplementary file 1). The best IBD model was obtained using Dlat   and Dcsl , did not show significant effects on the genetic differentiation of these populations (Table S8 in Supplementary file 1). Among climatic variables, both SSTmean  and SSTmin  were important determinants of genetic differentiation among large yellow croaker populations  and isolation-by-environment (IBE) are the two most common patterns explaining genetic distances in wild animal populations  might be unreliable due to the high multicollinearity . Hence, we employed three additional methods robust to multicollinearity to decompose the variance explained by the multiple linear regression (MLR) model. We employed LASSO (least absolute shrinkage and selection operator) regression and elastic net regression to rebuild the MLR model and disentangle the unique contributions of predictors in the MLR model by a weighting procedure . These two predictors had positive coefficients  in the model. All weighted MLR indices also supported the importance of SSTmin and SSTmean. The validity, structure, and commonality coefficient ranked SSTmin as the most effective predictor. SSTmean had the greatest importance among predictors by unique coefficients, general dominance, and relative weights . Notably, 89.47% of DQ PSRs were near fixation, whereas only 0.16% of PSRs were under recent positive selection. In contrast, 51.97% of NH PSRs were near fixation and 27.96% of NH PSRs showed evidence of recent positive selection  in the DQ and NH stocks, respectively . Moreover, NF PSRs overlapped with 153 functional genes with a total overlap length of 2.29 Mb and a generic sequence proportion of 44.34%, whereas only 13 genes overlapped with RS PSRs, resulting in a generic sequence proportion of 27.80%. The largest PSR was a DQ-NF type PSR located between 17.97 and 19.24 Mb on Chromosome 7 with a length of 1.27 Mb  and divided them into six categories based on the type of PSR with which they overlapped . These included 107 and 55 unique DQ and NH PSGs. We then performed an overrepresentation analysis (ORA) followed by enrichment analyses to identify biological processes and clusters associated with climatic adaptation (Table S13 in Supplementary file 1). DQ PSGs were enriched in 16 functional categories, including 12 GO Biological Processes, 2 Reactome Gene Sets, and 2 KEGG pathways, involving 38 unique functional genes. It is worth noting that 10 of 16 terms were closely linked to the developmental processes, such as "regulation of angiogenesis" , "ear development" , and "pronephros development" . The development-related terms involved 25 DQ PSGs. Nearly half of these (12) were located on the longest PSR on Chromosome 7. The NH PSGs were enriched in nine GO Biological Processes and one KEGG pathway, involving 29 unique PSGs. Among these, five terms were related to immune system processes or responses to stimuli, such as “leukocyte differentiation” (GO: 0002521), “lymphocyte differentiation” (GO: 0030098), and "double-strand break repair" (GO: 0006302).Although the classification of wild large yellow croaker populations in coastal waters of China has been debated for a long time, this issue remains unresolved. By a detailed literature search, we identified two general views. The traditional division is the DQ-MYD-NZ three-stock system initially proposed by Tian and Xu in the 1960s  and the Adriatic Sea stock of beluga sturgeon (Husohuso) have become extinct , Kellet's whelk (Kelletiakelletii), California spiny lobster (Panulirusinterruptus), and Pacific ridley (Lepidochelysolivacea) . Winter mortality is size-selective and can exert strong selective pressure on animals, especially ectotherms, such as fish, in temperate regions  three-stock system. However, the genetic structure and asymmetric introgression indicated that the boundary between the NH and MD stocks may have moved from the estuary of the Pearl River to the Taiwan Strait. Based on our IBE analysis, we deduced that this habitat change was closely associated with climate change in the past few decades. The substantially different landscapes of selective signatures from the northernmost and southernmost stocks presumably arose from differences in responses to long-term cold and heat stress.We collected 104 large yellow croakers from eight sites in eastern and southern Chinese coastal waters Fig. . Reads wClean reads were aligned using BWA-MEM (version 0.7.17-r1188) (Li and Durbin HO), expected heterozygosity (HE), and F values, for each population using the R package hierfstat (version 0.5–7) (Goudet π) was calculated using VCFtools (version 0.1.15)   was set from two to ten for each calculation. A ML phylogenetic tree was constructed based on all samples. Firstly, RAxML (version 8.2.12)   Goudet . NucleotAll SNP loci were phased using beagle (version 5.2) with default parameters, except for a window length of 4.0 cM and a window overlap of 0.5 cM. Then, pairwise shared haplotypes were extracted from each chromosome using RefinedIBD (version 17Jan20.120) with a window size of 10 cM, minimum reported LOD score of 3.0, minimum reported haplotype length of 0.025 cM, and trimmed length when calculating LOD score of 0.0025 cM. All shared haplotypes between individuals sampled from the same site were filtered.The direction of introgression between the NH and MD stocks is determined based on a basic concept; given the short time since the habitat change, introgressed segments cannot yet spread to other sites in the new stock. Therefore, shared haplotypes on these segments should be more frequent within the original stock. The frequency of shared haplotypes (SHF) was estimated as described previously .We divided the genome into 25 kbp windows with a step size of 5 kbp. If a window overlapped with a shared haplotype longer than 10 kbp, it was regarded an introgression event. The number of introgressions between all pairs of populations (sites) was calculated per window. As the total number of pairwise comparisons differed between the groups, these numbers were normalized, ranging from zero (no shared haplotype tract detected) to one . Then, the direction of gene introgression across the boundary between the NH and MD stocks was determined by the relative shared haplotype frequency (rSHF) of introgression, which was calculated using the following equation:NH and nSHFMD were evaluated by a parametric test (ANOVA) and two non-parametric tests . All tests were carried out using the “stats” module of the "scipy" Python package (version 1.7.1)  and transformed into "Mercator" projections using the R package "sf" (version 0.9.7). Dcsl was modeled as the total length of diagonal lines of all 0.1 km grids passed through the coastline between two sites. SST values were obtained from the Kaplan Extended SST V2 dataset available at https://psl.noaa.gov/ . An in-house Python script was used to parse the NetCDF4 file under the "netCDF4" Python library (version 1.5.5.1). The annual SSTmax, SSTmin, and SSTmean were calculated after removing the first five outliers.The genetic distance Fst/(1 − Fst) was calculated using VCFtools . Regularized least squares (RLS) models were built using the "linear_model" module imported from the Python library "sklearn" (version 0.24.0) to deal with multicollinearity among variables when decomposing their contribution to genetic differentiation. RLS is a family of relatively new linear regression techniques with high tolerance to multicollinearity among variables. It has been widely used for variable selection by setting coefficients of less important variables to zero in machine learning, data mining, and bioinformatic approaches (Tibshirani π), extent of haplotype homozygosity (Rsb), Tajima's D, and composite selection score (CSS). The fixation index (Fst) and raw nucleotide diversity (π) were calculated using VCFtools (version 0.1.15)   tests at a significance level of P < 0.05.Signatures of selection in the large yellow croaker genome were detected by comparing the DQ and NH stocks. The genome was divided into 25 kbp windows with a sliding length of 5 kbp. Various indices of positive selection were calculated within each window, including the fixation index (Fst), nucleotide diversity  Supplementary file 2. A Word document includes 10 supplemental figures (DOCX 2366 KB)Below is the link to the electronic supplementary material.