Cycas taiwaniana Carruthers is endemic to Hainan Island and is classified as endangered by the International Union for Conservation of Nature (IUCN). To comprehensively understand the genetic dynamics of C. taiwaniana, we sampled 12 wild populations in Hainan Island and one cultivated population in Fujian province, and analyzed the genetic diversity, genetic structure, and demographic history based on the molecular data. Results revealed that C. taiwaniana had relatively low genetic diversity and high genetic differentiation. Haplotypes of C. taiwaniana diversified during the Pleistocene based on the chloroplast DNA (cpDNA) and the concatenated nuclear DNA (nDNA) data. Genetic cluster analyses based on the microsatellite (SSR) data showed that the 12 wild populations were separated into three clusters which could be three evolutionary significant units (ESUs), indicating three basic units of protection were identified. Moreover, we also confirmed the cultivated population FJ derived from the DLS1‐GSL clade. Demographic inference from different data was discordant, but overall, it uncovered that C. taiwaniana had experienced population contraction events twice during the Pleistocene and Holocene, and then expanded recently. Our study elucidated the population genetic characteristics of C. taiwaniana, and guided us to develop targeted conservation and management strategies for this endangered species.Hainan Island had experienced several cold–warm and dry–humid fluctuations since the Late Pleistocene period, resulting in separating and connecting from the mainland several times with the cyclic rise and fall of sea level. The fluctuations can change the biota and ecological environment in the island.  Cycas taiwaniana had relatively low genetic diversity and high genetic differentiation, three evolutionarily significant units were identified and this species had experienced twice population contraction events and expanded recently. By analyzing the genetic diversity, genetic structure and demographic history of C. taiwaniana based on DNA data and microsatellites, specific protection recommendations were formulated for C. taiwaniana. This study provides guidelines for the development of conservation strategies for other species which distributed on Hainan islands. At a time of rapid loss of biodiversity, it is more necessary to study population genetics and historical dynamics of endangered species , most of them are from cultivated plants whereas are extremely rare in the wild  evaluate the genetic diversity, genetic differentiation and genetic clusters of C. taiwaniana, (ii) determine the phylogenetic divergence time estimation, (iii) explore the demographic history of C. taiwaniana by combining with the geological history of Hainan Island, and (iv) provide the basic and practical guidelines for its conservation strategies.In this study, we used four chloroplast DNA (cpDNA) intergenic spacers, four nuclear genes and 10 microsatellites to perform comprehensive population genetics analysis of 22.1C. taiwaniana were collected by field investigation. Among them, the population FJ was a cultivated population. After collection, fresh and healthy leaves were immediately dried in silica gel. Ten individuals from each population were randomly selected for DNA sequencing, 20 individuals were selected for microsatellite analysis, and the populations with fewer than 10 or 20 individuals were all sampled. In total, we used 128 and 188 individuals for DNA sequencing and microsatellite genotyping, respectively. Sampling detail information and distribution were summarized in Table http://desktop.arcgis.com).A total of 13 populations of 2.2atpB‐rbcL, psbA‐trnH, psbM‐trnD, and trnS‐trnG), four nuclear genes  and 10 microsatellites for population genetics analysis, and the details of primers were provided in Table Genomic DNA was extracted using the modified CTAB method Doyle, . We chos2.32.3.1DNA sequences were edited and assembled using the SeqMan  and haplotype diversity (Hd) for 13 populations of C. taiwaniana were calculated from aligned DNA sequences in DnaSP v5.0  were calculated in Permut v1.0 (http://www.pierroton.inra.fr/genetics/labo/Software/Permut). Genetic variation assigned within and among populations was performed with an analysis of molecular variance (AMOVA) using the software Arlequin v3.11  to estimate the divergence time of haplotypes for cpDNA and the concatenated nDNA with C. taiwaniana, we analyzed the Bayesian Skyline Plot (BSP) of cpDNA data and the four nuclear genes respectively and the Extended Bayesian Skyline Plot (EBSP) of all genes with unlinked model in the BEAST program , private alleles (AP), effective number of alleles (AE), expected heterozygosity (HE), observed heterozygosity (HO), Shannon's diversity index (I), fixation index (F), and percentage of polymorphic loci (PPB)) were calculated in GenAlEx 6.51b2 , total genetic diversity for species (HT), and coefficient of gene differentiation (GST) were estimated in FSTAT v1.2  between populations (1000 permutations) were calculated in Arlequin v3.11. Gene flows between pairs of populations were tested based on Wright's principles Nm = (1 − FST)/4FST.The genetic diversity indices  of each population were tested under random mating system in LDNe , and 11 haplotypes (taiH1‐taiH11) were identified (Table Pi) and the total haplotype diversity (Hd) of C. taiwaniana were 0.00035 and 0.730, respectively (Table Hd = 0.200) and nucleotide diversity (Pi = 0.0004). The remaining 12 populations did not have the nucleotide diversity and haplotype diversity .Total length of the combined cpDNA was 3525 bp  in 128 individuals of 13 populations . The Pi and the Hd of C. taiwaniana were 0.00217 and 0.735, respectively  were identified. The Pi and the Hd of C. taiwaniana based on the nuclear gene PPRC were 0.00188 and 0.617, respectively . The Pi and the Hd of C. taiwaniana based on the nuclear gene AAT were 0.00864 and 0.896, respectively . Four nuclear genes showed variable genetic diversity within 13 populations. For gene AC5, population FJ had no genetic diversity  and population WX had the highest haplotype diversity . For gene PHYP, population BLS had the lowest genetic diversity  and population NWH had the highest haplotype diversity . For gene PPRC, population BLS had the lowest genetic diversity  and population SJC had the highest haplotype diversity . For gene AAT, population DLT had the lowest genetic diversity  and population WX had the highest haplotype diversity .The genetic diversity of different populations revealed by different DNA sequence data was inconsistent. Except the population GSL, the other 12 populations had no genetic diversity based on cpDNA data (NP = 5). Allelic richness was lowest in population FJ (AR = 2.200) and highest in population DL (AR = 5.171). The number of alleles (NA) ranged from 2.200 to 6.600, and the effective number of alleles (NE) ranged from 1.889 to 3.756. Shannon's diversity index (I) ranged from 0.587 to 1.356, and population FJ had the lowest I value but population DL had the highest I value. Observed heterozygosity (HO) ranged from 0.375 to 0.648 and expected heterozygosity (HE) ranged from 0.376 to 0.648, respectively. The population DL had the highest HE and population FJ had the lowest HE. The percentages of polymorphic loci (PPB) were high, ranging from 70% to 100%. To sum up, the population FJ had the lowest genetic diversity while the population DL had the highest genetic diversity.A total of 120 alleles were identified at the 10 microsatellite loci Table . DiversiAC5, PHYP, PPRC, and AAT), respectively. Moreover, more variations were partitioned within populations (81.74%) than among populations (18.26%) based on the SSR data. The FST value for the three types of markers ranged from 0.183 to 0.985 with significance, indicating highly significant genetic differentiation among the 13 populations of C. taiwaniana (Table Nm = (1 − FST)/4FST), gene flow was inversely proportional to the FST value. The genetic differentiation between populations based on the cpDNA data was significant. Therefore, there was almost no gene flow between populations. Whereas the genetic differentiation between populations was smaller and the gene flow between populations was larger based on nuclear data. Gene flows between each pair of the 13 populations based on the SSR data were shown in Table Nm = 8.452), and the population NBS had the least with the population FJ (Nm = 0.330). Fixation indices (F) were positive for all 13 populations, with a mean value F = 0.140, which suggests a high level of inbreeding within each population (Table The AMOVA results showed that there were more variations partitioned among populations (98.5%) than within populations (1.5%) based on the cpDNA data, whereas there were fewer variations partitioned among populations  than within populations  based on the four nuclear genes (NST was significantly greater than GST (p < .05) based on the nuclear gene ATT and PHYP, indicating that C. taiwaniana had significant phylogeographical structure  and 0.3187 Mya (nDNA), which indicates that haplotypes diverged in the Pleistocene , with a 50.8% mean support value, separating the 12 populations into two clusters Figure , the fir3.4Fs was more sensitive to population expansion than the other three neutrality test parameters. Based on nuclear genes AC5 and AAT, Fu's Fs were significant negative values, SSD and raggedness index were not significant, and the mismatch distribution analyses showed a unimodal curve, indicating that C. taiwaniana experienced an expansion event  population decline, it continued for a short stable period (20–10 Kya), and then experienced small‐scale population decline. Until 7 Kya, the species began to expand . Under the Mode shift models, the 12 populations had normal L‐shaped distributions, suggesting that C. taiwaniana had not experienced a recent severe bottleneck. Compared with the bottleneck analysis, Garza–Williamson indices can reflect earlier population decline. GWIs of the 12 populations were lower than the critical Mc value of 0.68, which indicating that C. taiwaniana had experienced bottleneck effect in history ; Dioon sonorense ; Zamia loddigesii ; D. edule ) , C. chenii  of C. taiwaniana was similar to other Cycas species , and higher than C. panzhihuaensis (72.23%), C. megacarpa (61.4%), Z. incognita, and Z. melanorrhachis (69.3%–78.9%)  were inconsistent with the BSP results. When the general population experienced expansion or continuous growth in the past, the mismatch distribution curve showed a unimodal Poisson distribution, and the neutrality test significantly deviated from neutral mutation. However, when the population size remained stable, the mismatch distribution analysis showed a multimodal curve distribution, and the neutrality test was not significant. Actually, demographic histories inferred are more complex than the parametric models involved in these approaches. The BSP method is based on coalescent theory and it is more reflective of the population historical dynamics.Climate fluctuation during the Quaternary period had effects on the population historical dynamics of C. taiwaniana were consistent with the seven Cycas species  which had experienced population contractions based on cpDNA data  and then expansion recently (Ca. 7 Kya). During the quaternary period, the glacial and interglacial periods appeared alternately, and the global climate was generally in a state of cooling, but Hainan Island located in the present tropical region was less affected. According to the EBSP result, C. taiwaniana was stable at 200 Kya, indicating that the global climate cooling during the glacial period had relatively little influence on C. taiwaniana in Hainan Island. During interglacial period (200–15 Kya), the temperature rose, glaciers melted, and sea levels rose, causing C. taiwaniana to undergo a population contraction event. The last Great Ice Age occurred about 15 Kya, due to the advent of the ice age, the global climate became colder and the global sea level continued to fall  might face a high risk of losing partial genetic variation. In addition, 12 wild populations of C. taiwaniana were divided into three separated clades, indicating three evolutionary significant units (ESUs) should be managed and protected separately. The three ESUs are ESU1: population NWH; ESU2: population WX, DL, TLF, and NBS; ESU3: population DLS1, DLS2, DLH, DLT, SJC, BLS, and GSL, respectively. Individuals in populations of ESU2 were clumped distribution, which increased inbreeding among them. Inbreeding often occurs when populations have a certain genetic structure. Inbreeding can increase homozygous, leading to the reduction of genetic diversity in small populations, which is extremely dangerous for small groups ; data curation ; formal analysis ; writing – original draft ; writing – review and editing . Hai‐Yan Xu: Data curation . Shu‐Guang Jian: Investigation ; resources . Xun Gong: Conceptualization ; project administration (lead); supervision ; writing – review and editing . Xiu‐Yan Feng: Conceptualization ; data curation ; formal analysis ; funding acquisition (lead); project administration (lead); supervision ; writing – review and editing .The authors declare no conflicts of interest.Figure S1Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.Table S6Click here for additional data file.Table S7Click here for additional data file.Table S8Click here for additional data file.


GAHP is a freely available software package for genetic analysis with bi-parental immortalized heterozygous and pure-line populations. The package is project-based and integrated with multiple functions. All operations and running results are properly saved in a project, which can be recovered when the project is re-open by the package. Four functionalities have been implemented in the current version of GAHP, i.e., 1) MHP: visualization of genetic linkage maps; 2) VHP: analysis of variance (ANOVA) and estimation of heritability on phenotypic data; 3) QHP: quantitative trait locus (QTL) mapping on both genotypic and phenotypic data; 4) SHP: simulation of bi-parental immortalized heterozygous and pure-line populations, and power analysis of QTL mapping. VHP and QHP can be conducted in individual populations, as well as in multiple populations by the combined analysis. Input files are arranged either in the plain text format with an extension name same as the functionality or in the MS Excel formats. Output files have the same prefix name as the input file, but with different extensions to indicate their contents. Three characters before the extension names stand for the types of populations used in analysis. In the interface of the software package, input files are grouped by functionality, and output files are grouped by individual or combined mapping populations. In addition to the text-format outputs, the constructed linkage map can be visualized per chromosome or for a number of selected chromosomes; line plots and bi-plots can be drawn from QTL mapping results and phenotypic data. Functionalities and analysis methods available in GAHP help the investigation of genetic architectures of complex traits and the mechanism of heterosis in plants. A number of mapping methods have been proposed, such as interval mapping  mapping has been extensively applied in genetic studies to dissect the individual genes of complex traits in both animals and plants. Bi-parental segregating populations, such as backcross (BC), doubled haploids (DH), recombinant inbred lines (RIL), and Fping IM; , composiping IM; , multiplping IM; , inclusiping IM; , and mulping IM; . Some frping IM; , QTL Carping IM; , QTLNetwping IM; , MAPQTL ping IM; , and QTLping IM; .via a two-step mapping strategy. In the first step of ICIM, stepwise regression is applied to identify the most-significant regression variables representing the marker genotypes. In the second step, interval mapping is performed on phenotypic values adjusted by marker variables identified in the first step . It should be noted that the pure-inbred lines (or pure lines in short) can be either DHs or RILs derived from two inbred homozygous parents. Relationship between the four populations is shown in 1 hybrid with the first inbred parent. IB2 is generated by the hybridization between the PIL population and the second inbred parent, similar to the backcrossing of F1 hybrid with the second inbred parent. IF2 is generated by the hybridization between two lines in the PIL population, similar to selfing of the F1 hybrid. As each line in population PIL can be maintained by selfing, IB1, IB2 and IF2 can be repeatedly produced like the typical F1 hybrids whenever needed, which is the reason to be called ‘immortalized’. Due to their repeatability, each of the four kinds of populations can be evaluated in multi-environmental trials with replications.Four kinds of populations, which are essentially derived from the same two homozygous parents, can be handled in GAHP for both phenotypic and genetic analysis. These populations are called by bi-parental pure-inbred lines (PIL), immortalized backcross population with the first parent (IB1), immortalized backcross population with the second parent (IB2), and immortalized F1 (or Parent A) and P2 (or Parent B), two bands can be observed in the two parents at one polymorphic marker locus. Markers having no polymorphism or heterozygous in either parent cannot be used. Assuming AA is the genotype of P1, BB is the genotype of P2, and AB is the genotype of their F1 hybrid. Marker types could be coded by numbers, letters, or the mixed numbers and letters. As individual lines in PIL are assumed to be homozygous, only homozygous genotypes in PIL are useful in genetic analysis. Heterozygous genotypes in PIL are treated as missing values. When numbers are used in coding, the two parental bands are coded as 2 and 0, respectively. When letters are used, Parent A is coded as A or AA; Parent B is coded as B or BB. Codes 1, H and AB are acceptable for heterozygotes, and missing values of marker types are coded as -1, X, XX, *, or **. Mixed coding with numbers and capital letters is allowed in the software, but it is not recommended. Missing phenotypic values are represented by “NA”, “na”, “*“, “.“, or “-100”, which will be replaced by population mean in QTL mapping.Both independent population and combined analysis can be conducted in GAHP. For genetic analysis, the genotypic data is only needed for population PIL. Genotypes of heterozygous lines in populations IB1, IB2, or IF2 can be deduced from the genotypes of homozygous lines in PIL. Assuming there are two homozygous parents PIn GAHP, core modules for phenotypic data analysis, QTL mapping, genetic population simulation, and power analysis were written in Intel Fortran 90/95. The interface and core modules for setting parameters, viewing results and drawing figures were written in JAVA. The software runs on Microsoft Windows XP/Vista/7/10/11. GAHP is an integrated and project-based software package. When the software is initiated, the first thing to do is to create a new project or open an existing project. The use of project will assure that all operations and running results are properly saved when the software is closed. When the project is open the next time by the software, previous operations and results can be recovered. Introduced below are the four functionalities implemented in the current version of GAHP.Functionality MHP displays the completed linkage maps in a format (or style) which can be easily modified by users. Linkage maps should have been built by other software packages. Chromosome information and marker positions have to be provided. The input file for MHP consists of three parts: 1) general information on linkage maps, 2) marker number information, and 3) linkage map information. The example given in etc. In the middle is the display window, which shows the detailed information of input or output files. At the right side are the parameter setting and running message windows. No parameter is needed to run functionality MHP. While the input file is properly loaded, the users may click “Run” on the tool bar to run the functionality.Heritability may be the most important concept in quantitative genetics, which quantifies the proportion of genetic variation included in phenotypic values. Analysis of variance (ANOVA) can be used to estimate the variance components, based on which the broad-sense heritability can be estimated in genetic populations. Here the mapping populations can be some or all of the four populations as shown in As many as four populations, i.e. PIL, IB1, IB2, and IF2, can be used in QTL mapping either independently or together in functionality QHP, depending on the populations available. Firstly, the included populations are analyzed independently. Independent analysis is named by the respective population. Secondly, combined analysis is conducted using the included populations as many as possible. Names of the combined analysis are given in In functionality SHP, populations PIL, IB1, IB2 and IF2 are generated for a set of predefined QTLs, and then power analysis is conducted on the simulated populations. Similar to functionality QHP, mapping methods IM and ICIM are provided in SHP. QTL mapping can be conducted in individual populations, as well as in multiple populations by combined analysis. Only one trait can be defined and simulated in one input file. The input file for SHP is composed of five parts: 1) general information of mapping populations, 2) marker number information, 3) linkage map information, 4) gene or QTL information, and 5) genotypic values of the predefined QTLs.2 is used as the reference population to convert between heritability and error variance. Name of each chromosome and number of markers on the chromosome are specified first  and combined QTL mapping (i.e. IBC, IFL, IBL, IBF, or BFL; see the lower left window in Outputs not related to QTL mapping are listed under the ‘Common’ group (see the lower left window in Graphs of LOD score and genetic effects on each chromosome or on all chromosomes are available in the QHP functionality. Similar to QHP, outputting results from functionality SHP are also grouped by names of individual population and combined QTL mapping (see the lower left window in Outputs not related to QTL mapping are listed under the ‘Common’ group (see the lower left window in SHP also provides the graphic option of LOD scores and genetic effects on one chromosome or on all chromosomes, averaged from all simulation runs, which are similar to functionality QHP.2 are such populations, but have the disadvantage in conducting the multi-environmental and replicated phenotyping trials. As one replacement, immortalized F2 populations can overcome the disadvantage and provide the estimates of additive, dominant and epistatic effects. In addition, genotyping is only needed on pure lines in population PIL, which are the direct parents of F1 hybrids consisting of the immortalized population (Heterozygous populations are needed in order to investigate the dominance-related genetic effects, which are critical to understanding the genetic mechanism of heterosis in plants. Conventional bi-parental Fpulation . Immortapulation .https://isbreeding.caas.cn. Users’ manual and sample datasets are automatically included when the package is properly installed in local personal computers. A video tutorial is provided on the software webpage. GAHP can conduct the phenotypic data analysis, and QTL mapping on pure-line populations and their derived immortalized BC and F2 populations, either separately or in combination. Both additive and dominant variances can be estimated by the combined ANOVA in the SHP functionality, by which the broad-sense and narrow-sense heritabilities can be calculated. Both additive and dominant effects of QTLs can be estimated by the combined QTL mapping on immortalized BC and F2 populations in the QHP functionality. Combined mapping utilizes more populations, and improves the estimation accuracy of genetic variances, heritabilities, and positions and effects of QTLs. In addition, GAHP can simulate the four kinds of mapping populations (etc. When new mapping methods are developed, the simulated populations generated by SHP can be used to evaluate their efficiency.GAHP is freely available from ulations , based oulations . Evaluatulations . The SHPIn most QTL mapping packages, only the independent population analysis is provided, such as QTL IciMapping , GACD Z and GAPL2 populations. Therefore, map construction is not considered in GAHP. Instead, functionality MHP is developed in GAHP to display the completed linkage maps. MHP can handle larger number of markers and make higher quality of linkage maps, in comparison with QTL IciMapping. In input files of functionality QHP, genotypes are only needed for population PIL; genotypes of hybrids in immortalized BC and F2 populations can be derived from pure lines and two original inbred parents. When using functionalities VHP and QHP, it is expected that the phenotypic values of different populations are collected in the same environment so as to avoid the effect of environments and genotype by environment interactions.Linkage map used in functionality QHP is based on genotypes of pure lines in population PIL, which should be constructed by other software packages, such as QTL IciMapping . There iTime spent in QTL mapping should be taken into consideration when a large number of markers are included. When populations PIL, IB1, IB2 and IF2 are fixed at a size of 1000, the time spent for SHP to complete one simulation run was around 1, 12 and 55 min for marker numbers 200, 2000 and 20000, respectively. The time spent in one run was to complete four independent population analysis, and one combined analysis. The time spent for independent population analysis was close to that in QTL IciMapping for the same dataset. The time spent for combined analysis is slightly longer than that for independent population. The current version of GAHP can handle a number of markers as much as 50000. In most bi-parental populations, number of polymorphic markers may be much smaller than 50000. When more markers are included, binning analysis can be conducted to reduce the marker number and running time.1 hybrids obtained by suitable crossing designs. The hybrid population derived from a diversity of inbred lines has different structure from population IF2 as discussed in this study, which may require further studies on genetic analysis method. Once developed and validated, the analysis method can be added as a separate functionality to extend the applications of GAHP in genetic studies.At present, only one-dimensional QTL mapping is available in GAHP. In addition to additive and dominant effects, epistasis is also an important source of variation of complex traits, which maintains the additive variance and assures the long-term genetic gain in breeding . Epistas


Upper Mesopotamia played a key role in the Neolithic Transition in Southwest Asia through marked innovations in symbolism, technology, and diet. We present 13 ancient genomes  from Pre-Pottery Neolithic Çayönü in the Tigris basin together with bioarchaeological and material culture data. Our findings reveal that Çayönü was a genetically diverse population, carrying mixed ancestry from western and eastern Fertile Crescent, and that the community received immigrants. Our results further suggest that the community was organized along biological family lines. We document bodily interventions such as head shaping and cauterization among the individuals examined, reflecting Çayönü’s cultural ingenuity. Last, we identify Upper Mesopotamia as the likely source of eastern gene flow into Neolithic Anatolia, in line with material culture evidence. We hypothesize that Upper Mesopotamia’s cultural dynamism during the Neolithic Transition was the product not only of its fertile lands but also of its interregional demographic connections. Neolithic Upper Mesopotamia was a center of cultural innovation; ancient genomes now reveal that it was also a migration hub. Located between the Euphrates and Tigris rivers, the hilly flanks of Upper Mesopotamia were home to the earliest sedentary hunter-gatherers who built the first monumental structures at Göbekli Tepe  (7–13). Here, we address this gap by studying genomic data from Çayönü Tepesi (hereon Çayönü) of the Upper Tigris area , a settlOur study presents genomic data from Çayönü, which we then use to describe (i) the demographic makeup of the Fertile Crescent populations and its relation to material culture affinities observed in the archaeological record, (ii) the Neolithic demographic transition as reflected in genomic diversity at Çayönü and other Neolithic sites, (iii) genetic kinship among coburials in domestic structures at Çayönü, and (iv) the potential contribution of Upper Mesopotamia to Neolithic and post-Neolithic population movements in Anatolia. We also detail the curious case of an infant burial at Çayönü, whom we infer to be a migrant offspring and who presents not only artificial cranial deformation but also one of the earliest known cases of cauterization in her skull.P < 0.05), but comparable to another Central Anatolian site of a similar date, Boncuklu .We studied the skeletal remains of 33 individuals from Çayönü , from which we generated shotgun genomes with depths ranging from 0.016× to 0.49×. (fig. S1 and table S3). High rates of postmortem damage accumulation at read ends, short average fragment sizes , and mitochondrial haplotype-based estimates suggested the authenticity of all 14 libraries  (table S3). With these data, we first estimated genetic kinship among all individual pairs . Two samples, both identified as female infants (cay018 and cay020), were genetically inferred either to belong to the same individual or to be identical twins. Skeletal analyses also suggested that both petrous bones could belong to the same individual. We therefore merged their genomic data and treated these merged data as representing a single individual, reducing our sample size to 13 . We further identified four related pairs of first to third degree (see below) and removed all but one individual among sets of closely related individuals in population genetic analyses .f3 results, D-statistics, and qpAdm analyses   ( Zagros) . Last, wGiven these observations, we first investigated the origins of the genetic structure in Neolithic Southwest Asia. The higher genetic affinity among Upper Mesopotamia (represented by Çayönü), Central Anatolia, and South Levant populations relative to Central Zagros . Howeveret al.  and with observed material culture affinities between the two regions during the Neolithic [e.g., .We next investigated the demographic origins of Çayönü inhabitants. The ancestry , which iP > 0.05; P > 0.05; P > 0.05) and lacked a significant South Levant component found in the rest of Çayönü genomes (P > 0.05). Here, we caution that regions other than the Zagros, but with genetically related populations, including the South Caucasus (table S5) or yet unsampled regions, could also be the source of eastern ancestry in Çayönü. In addition, “Zagros-related ancestry” itself might actually represent human mobility from Northwest Zagros  rather than from Central Zagros, which would also be compatible with Çayönü’s material cultural affinities with Northwest Zagros , South Levant , and Central Zagros (Ganj Dareh), we found that genetic samples from all three Fertile Crescent PPN settlements had higher diversity levels than those of the Central Anatolia PPN and are on a par with those of later-coming PN communities of Central and Western Anatolia  analyses of one Çayönü genome (cay007) with sufficient coverage, using the hapROH algorithm  (37). InOverall, these results suggest that the demographic transition observed in Central Anatolia between the PPN and the PN did not take place in the Fertile Crescent, at least not at the same magnitude. This observation is in line with radiocarbon-based estimates of low population density on the Central Anatolian plateau relative to other regions of Southwest Asia during the early Holocene  (fig. S8). Nevertheless, we cannot fully distinguish between spatial and temporal effects given our small sample size (fig. S9); the question thus deserves being readdressed using larger samples with more intensive dating on skeletons.We hypothesized that the nine individuals who were coburied with others but were not closely related could still belong to the same extended biological families. We investigated this by testing whether each of these coburied pairs was genetically closer to each other than to other Çayönü individuals, using outgroup Our results are largely similar to observations from the contemporaneous Aşıklı and Boncuklu of Central Anatolian PPN , where X was a Neolithic–to–Bronze Age population from Anatolia/Aegean, CHG represents early Holocene Caucasus , and Anatolia EP/Anatolia PPN represents Epipalaeolithic Pınarbaşı and PPN Boncuklu of Central Anatolia, respectively.Last, we investigated the possible role of Upper Mesopotamia as a source of post-7000 BCE eastern gene flow into Anatolia. Eastern gene flow events have been inferred from increasing levels of early Holocene South Caucasus– and Zagros-related ancestry in Anatolian populations, starting by the PN and continuing into the Bronze Age (D-statistics ≈ 0). This result is consistent with Upper Mesopotamia, but most likely not the Caucasus, being the source of eastern gene flow into Central Anatolia and possibly also into Western Anatolia around 7000 BCE. The finding also resonates with archaeological evidence from Çatalhöyük, where the mid-seventh millennium BCE witnesses the first introduction of obsidian from the Bingöl area of Eastern Turkey, the appearances of lithic types akin to “Çayönü tools,” and the growing use of the pressure technique in lithic industries . The observations reported in the present manuscript regarding the mixed west-east ancestry of Upper Mesopotamian Neolithic communities are consistent with those reported in Science.Petrous bones from 33 human individuals from Çayönü, housed at the Hacettepe University in Ankara, Turkey, were used in aDNA experiments . Table SThe experiments were carried out in dedicated aDNA facilities at the Middle East Technical University and Hacettepe University in Ankara, Turkey. To prevent contamination, all equipment and utensils were decontaminated with DNA AWAY or a bleach solution at each use and also in between handling the samples. Ultraviolet (UV)–insensitive solutions were UV-irradiated for 10 min at a distance of 5 cm before use. Negative controls were included at each step of the experiments to be able to track potential contamination originating from the reagents or handling the samples.et al.  were assigned using the “best path” method of pathPhynder  and the option “-randomHaploid.” In this study, we used three different SNP panels. Because the Human Origins Dataset has a relatively limited number of SNPs (~600 K) but covers wider modern diversity than other datasets, we chose it for analyses of the type PCA  and ADMIXTURE. On the other hand, we used the 1240K Dataset in hapROH analysis, which has been optimized using this dataset. Last, since the 1KG Yoruba Dataset includes more SNPs in total, we chose this for analyses such as D tests and kinship estimation, where higher SNP numbers could partly alleviate the low depth of coverage of our sample.After remapping previously published ancient genomes with the above-described procedure (see the “Sequencing data preprocessing” section above), SNP calling was performed in two steps: (i) Pileup files were generated with the SAMtools (v.1.9) mpileup software  (v50.0) 1240K Dataset and extracted Human Genome Diversity Project high-coverage genomes. After calling these positions in Southwest Asian Early Holocene genomes and Çayönü samples, we merged the datasets. The dataset consisted of 1,151,145 autosomal positions to compute ROHs.We downloaded the Allen aDNA Resource (n = 279) from the Simons Genome Diversity Project (SGDP) representing present-day global diversity  greater than 10% in the African Yoruba genome sample  from the 1KG phase 3 dataset . We merged this with genotype files generated from high-coverage genomes (P0) of pseudo-haploid genome pairs in 1–million base pair (Mbp) windows and normalizes these values with the median P0 of the sample, assuming that the median represents a nonrelated pair (P0) values as an estimate of the kinship coefficient (θ). NGSRelate v2 (J1 + 0.5 × (J3 + J5 + J7) + 0.25 × J8. Since our Çayönü genome sample was composed of low-coverage genomes, to increase the resolution of NGSRelate, lcmlkin, and TKGWV2, we provided background allele frequencies calculated from a total of 211 Southwest Asia Holocene individuals including Çayönü and published Anatolia, Levant, and Zagros genomes. All four methods yielded consistent results in many pairs, although READ appears to overestimate θ values relative to other methods in low-coverage pairs.To determine close kin up to third-degree relatedness, we estimated genetic kinship coefficients (θ) between each pair of individuals. To achieve this, we ran four alternative software [READ  were coherently identified as “identical/twin” by all methods. These two libraries were obtained from human remains from the same building, and one library was constructed from a left-side and the other one from a right-side petrous bone. The reconstructed skull showed that both petrouses could belong to the same infant. In addition, both libraries showed similar DNA preservation patterns (see table S3 for human proportions and damage patterns), which would be consistent with the possibility that the bones derived from a single individual. We thus merged these two libraries, leaving us with a total of 13 individuals.Z| > 2 for the normalized P0 in READ analysis (estimated using variance among the genome-wide 1-Mbp windows by the READ software). All methods yielded well-correlated kinship coefficients . One pair (cay004-cay033) was estimated as first-degree kin, two pairs (cay004-cay015 and cay011-cay027) as second-degree kin, and one pair (cay008-cay013) as third-degree kin according to autosomal kinship analysis. All these related pairs were coburied in the same buildings. We excluded one of each pair of close kin  from downstream population genetic analyses to ensure sample independence. Notably, cay008 and cay013 individuals were closer kin than third degree according to X-chromosomal kinship analysis. Both individuals were females; while cay013 was an adult, cay008 individual was a 1.5- to 2-year-old child (table S1).After merging cay018 and cay020, we repeated the genetic kinship analysis with the 13 individuals but using autosomal and X-chromosomal data separately. To increase confidence in the estimates, we stipulated a minimum number of overlapping SNP counts between pairs of individuals, >2000 SNPs when analyzing autosomal data and >200 SNPs when analyzing X-chromosomal data. We further required a significance of |To resolve the pedigree of the relationship between cay008 and cay013, we constructed and analyzed possible maternal and paternal pedigrees of cay008 using the pedsuite package in R . One of these, cay015-cay033, were coburied in the same building, showing a signal of being third-degree related, which was supported by their shared close kinship to cay004. The other pair, cay015-cay027, were buried in different buildings .f3-statistics and D-statistics using the qp3pop and qpDstat programs, respectively, from the ADMIXTOOLS package (v7.0.2) (n = 3) from the SGDP dataset  correction  to detect ROHs, which are long, homozygous stretches of the genome that result from the common ancestry of the maternal and paternal chromosomes (Ne (effective population size)  values. From this, we filtered out the pairs that had <2000 overlapping SNPs. We then applied the “cmdscale” function in R.We summarized the outgroup To perform PCA, we used the “smartpca” (version 16000) software of EIGENSOFT (v7.2.1) (www.cog-genomics.org/plink/1.9/) with parameters “--indep-pairwise 200 25 0.4” and “—maf 0.05,” which retained 179,175 SNPs. After filtering, we selected Western Eurasian modern-day populations (n = 629) and merged them with ancient individuals (n = 307), similar to  and 10 replicate runs with different random seeds. The cross-validation procedure of ADMIXTURE was used to choose the optimal value for K. The LargeKGreedy algorithm of CLUMPP  package. We selected a reference differentially related to left populations covering modern and ancient diversity .We then performed all possible two- and three-way models, adding published genomes representing late Pleistocene and early Holocene populations of Central Anatolia, Zagros, and Levant as surrogates (“left populations”) and Çayönü genomes as targets. We ran all qpAdm analyses with “allsnps: YES” option, which is robust to low-coverage data . To increase the resolution, we added an Anatolian Epipaleolithic individual into the reference set. Here, we only used shotgun sequenced published genomes from Barcın to avoid technical confounding.We compared summed probability distributions of C14-dated individuals using the “stackspd” function from the “rcarbon” package in R (All plots were generated in R (


Heterotis niloticus, is an important component of inland fisheries in West Africa. Nigeria has the largest fishery for African bonytongues, representing ~ 86% of the global total. Recent declines in yields at some Nigerian locations, however, suggest current levels of exploitation may be unsustainable. Habitat degradation also may be impacting some stocks. Despite its commercial and nutritional importance, the African bonytongue has been the subject of scant genetic research to support management. We examined patterns of genetic diversity in natural populations of H. niloticus at four locations in Nigeria, including Kainji Lake, a reservoir on the Niger River in north-central Nigeria, and three southern localities , as well fish from the Ouémé River delta near Porto Novo, Benin. Eighty-five specimens were genotyped for nine microsatellite-loci. Genetic diversity estimates were highest at Kainji Lake, and substantially lower at southern localities. High levels of genetic differentiation were detected between samples from Kainji Lake and those from southern localities. Low, yet significant FST values were observed among samples from southern Nigerian localities that were more differentiated from the sample from nearby coastal Benin. We thus recommend that African bonytongues from the five locations be considered distinct genetic stocks and managed accordingly.Inland fisheries are an important source of protein and income for people in Africa. Their sustainable management can greatly benefit from identification of regional genetic stocks and characterization of their genetic diversity, but such information is lacking for most African freshwater fisheries. The African bonytongue,  Capture inland fisheries in Africa produced an estimated 3,004,437 metric tons in 2018, an amount only surpassed by that from Asia at 7,953,840 metric tons5. Inland fisheries in Africa, however, are facing growing threats that include overexploitation, habitat degradation, and introduction of exotic species that can impact native fish stocks7. Given that human populations and economies are growing rapidly in Africa, impacts from these threats are expected to increase8. The human population in sub-Saharan Africa is projected to double by 20509, and it is estimated that one in every four people will be undernourished in this region10. Inland fisheries will continue to play a critical role in supporting food security and livelihoods in growing populations and economies of the continent. Therefore, it is important to conduct research that informs the sustainable use of these resources. Documentation of genetic diversity patterns of inland fisheries species is needed for identification of management and/or conservation units and for monitoring fishing impacts11. At present, genetic studies are very limited for most African freshwater fisheries, even though such studies have been long recognized as a research priority for the sustainable exploitation of these valuable resources12.Inland fisheries provide an important source of protein, employment, and income for people in Africa9. Freshwater fish are important in the diet of Nigerians2, and demand for fish has increased considerably with population growth. An estimated 420,078 metric tons of freshwater fish were caught in Nigeria in 2017 and 392,188 metric tons in 2018, ranking first and second in harvest, respectively, among African countries13. These quantities are nearly four-fold greater than in 1980, when Nigeria’s population was ~ 73.4 M, and 107,530 metric tons of freshwater fishes were captured. Nigeria is one of the seven countries that are driving most of the growth in global inland fisheries2, and by 2050 could be one of the world's largest consumers of fish14. Abundance of many freshwater fishes in Nigeria, however, appears to be declining due to overfishing15. Of the 266 freshwater fish species documented in 2005, 47 (17%) were reported to be critically endangered, 15 (5%) endangered, 8 (3%) vulnerable, 23 (8%) near threatened, and the status of 129 (48%) was unknown6.Nigeria is the most populous country in Africa, ranking 7th among nations globally (with an estimated population of ~ 206 million). In addition, Nigeria is one of nine countries that collectively will contribute more than half the projected growth of the global population between now and 2050Heterotis niloticus , is a freshwater fish widely distributed in tropical rivers and freshwater lakes of Western and Central Africa, and the Nile Basin, where it supports important commercial and subsistence fisheries16. The African bonytongue has been introduced for aquaculture at several locations across Africa17, and is considered one of the top ten species for future use in global aquaculture18. The greatest harvest of African bonytongues occurs in Nigeria13. Between 1990 and 2019, ~ 362,534 metric tons of this fish were caught in Nigeria, corresponding to 6.6% of the national freshwater fish yield, and ~ 83.4% of the total yield for this species from the entire continent. Recent bonytongue yields represent a sharp increase in comparison to previous decades. For example, in 2010, 15,392 metric tons were caught in Nigeria, representing 85.2% of the total yield for this species from the continent, and 5.2% of the national freshwater fish yield; whereas in 2000, only 7915 metric tons were caught in Nigeria. Current levels of exploitation of this species may be unsustainable in regions of Nigeria where catches appear to be declining19.The African bonytongue, 20. That study detected high levels of genetic differentiation among fish sampled from three river basins: (1) the Niger River at Malanville, a city in northern Benin located upstream from where the river enters Nigeria; (2) a location on the lower Mono River in coastal south-west Benin; and (3) nine localities in the Ouémé-Sô floodplain system in south-central Benin. Within the Ouémé–So river-floodplain system, however, low levels of genetic differentiation were detected among the localities sampled, suggesting that seasonal flooding facilitates gene flow. Low genetic variability was detected for African bonytongues at Malanville, in the Niger River, compared to fish from the Mono and Ouémé-Sô basins20. Malanville is a fishing community where some stocks may be overexploited21. Therefore, it is important to examine other localities in the Niger Basin to determine whether low genetic diversity is a characteristic of H. niloticus in that system.The only prior research on the population genetics of the African bonytongue was conducted in Benin, the country on the western border of NigeriaH. niloticus from four locations in Nigeria where fishing activity is intense. These include Kainji Lake, in north central Nigeria, a reservoir on the Niger River formed by the Kainji Dam, and three localities in southwestern Nigeria close to the Atlantic coast: Ethiope River, Igbokoda River, and Epe Lagoon , Igbokoda River, Epe Lagoon and Ethiope River  carried out in 10 µl reaction volumes containing 50–1000 ng genomic DNA, 5 µl QIAGEN Multiplex PCR Master Mix , 1 µl of the forward (fluorescent labelled) and reverse primer mix and 3 µl of nuclease-free water . Amplifications were carried out using a BIO-RAD Thermal Cycler (T100TM) programmed according to standard multiplex PCR protocols as follows: initial activation step: 15 min at 95 °C, 35 cycles of denaturation at 94 °C for 30 s, annealing at 57 °C for 90 s, extension at 72 °C for 60 s, and a final extension step 60 °C for 30 min. PCR products were run on 2% agarose gel electrophoresis to check for amplification of all loci prior to genotyping, and positive PCR products were then run on an ABI 3130xl automated sequencer using Genescan 400 HD ROX as size standard (Applied Biosystems). Allele calling and sizing for each sample was obtained using Genemarker software (version 1.75).Samples were genotyped for nine microsatellite loci Table  reported23. Tests of linkage disequilibrium (LD) and test of conformity to the expectations of Hardy–Weinberg Equilibrium proportions (HWP) were performed using GENEPOP on the Web 4.725. To control for the occurrence of false positives due to multiple comparisons, we used the Benjamini–Hochberg procedure26 at a false discovery rate (FDR) of 0.05 for assessing the significance of LD and HWP p values. All GENEPOP analyses were performed with a dememorization number of 10,000 and 1000 batches with 10,000 iterations per batch. GenAIEx v. 6.528 was used to calculate the following genetic diversity statistics: number of private alleles (NP), number of private alleles average number of alleles (Na), average number of effective alleles (Ne), Shannon’s information index (I), mean observed heterozygosity (HO), mean expected heterozygosity (He), unbiased expected heterozygosity (uHe), and inbreeding coefficient (FIS). We used MICRO-CHECKER v. 2.2.329 to check for the potential presence of null alleles and scoring errors.To convert input files between software packages, we used PGDSpider v. 2.1.1.3FST values, with and without correction for null alleles, and their corresponding 95% confidence intervals (CI) were estimated using the program FreeNA30. FST values in which the 95% CIs excluded zero were deemed statistically significant. Pairwise population FST values were also estimated using the program Arlequin v.3.5.2.231, which does not correct for null alleles, and their corresponding p values were estimated with 10,000 permutations. The Benjamini–Hochberg procedure at a false discovery rate (FDR) of 0.05 was used to determine whether these FST values were statistically significant. A discriminant analysis of principal component analysis (DAPC) with the R package adegenet v.2.1.133 was conducted to visualize levels of genetic differentiation among the samples from the different locations, defining sampling locations as a priori groups. DAPC is a non-model-based method that maximizes the differences between groups while minimizing variation within groups34; and distances between genetic clusters using a priori groupings reflect underlying FST35. STRUCTURE 2.2.336, which performs model-based clustering with a Bayesian approach, was also used to examine population subdivision. Two models were used: admixture with correlated allele frequencies, and no admixture with correlated allele frequencies. K values from 1 to 5 were tested in ten iterations, with 500,000 steps and a burn-in of 125,000 steps, and all other settings were set to default. GenAlEx was used to construct a genetic distance matrix, from which a principal coordinates analysis (PCoA) was performed to identify population clusters.Population genetic structure was analyzed using several different approaches. Pairwise population 37.Finally, we analyzed the relationship between genetic differentiation and geographic distance by testing for isolation by distance (IBD) using ISOLDE in GENEPOP on the Web 4.7. Two kinds of analyses were performed: (1) between localities; and, (2) between individuals. We used Google Earth Pro to measure distances between sample locations. For the distances between locations within the southern rivers, we measured the shortest straight-line distance. For distances between the Kainji Lake and all other localities, we followed the contour of the Niger River to the closest point to the Ethiope River, and from there we used straight lines. Statistical significance based on the Spearman’s rank correlation coefficient was evaluated using Mantel testsHn45 to 19 in Hn30 .Genotyping scores for all individuals are shown in Dataset 30 Table . DepartuNa) = 8.33; average number of effective alleles (Ne) = 4.56; Shannon’s information index (I) = 1.6; observed heterozygosity (Ho) = 0.73; expected heterozygosity (He) = 0.70; and unbiased expected heterozygosity (uHe) = 0.72. The highest number of private alleles was also observed at Kainji Lake: 21 out of 75 alleles in this locality. This sample had a low inbreeding coefficient (FIS = –0.03). Genetic diversity in the samples from the three southern Nigerian populations were substantially lower: Na ranged between 4.89 (Ethiope River) and 5.67 (Igbokoda); Ne between 2.55 (Igbokoda) and 2.65 (Epe Lagoon); I between 0.97 (Epe Lagoon) and 1.11 (Igbokoda); Ho between 0.44 (Epe Lagoon) and 0.50 (Ethiope River); He between 0.47 (Epe Lagoon) and 0.54 (Igbokoda); uHe between 0.48 (Epe Lagoon) and 0.56 (Igbokoda). In these localities, FIS ranged between –0.01 (Ethiope River) and 0.18 (Igbokoda). Excluding locus Hn11 in Igbokoda, for which null alleles were suggested in this locality, FIS was 0.12, still the highest value. Thus, it is possible that the excess of homozygotes in Hn11 observed in Igbokoda results from inbreeding rather than null alleles. Between two and four private alleles were observed in the southern Nigerian populations . The sample from the Ouémé River in Porto Novo, Benin, had the lowest allelic diversity ; although Ho (0.57) was higher than the southern Nigerian localities, and He (0.49) and uHe (0.53) were within the range observed in these localities. Three private alleles were observed at this locality (out of 32). The lowest inbreeding coefficient was estimated for the sample of this locality (FIS = -0.16). Estimates of genetic diversity per locus per population is presented in Table The sample from Kainji Lake revealed the highest genetic diversity among the five populations examined Table : averageFST values obtained with the program FreeNA were practically identical with and without correction for null alleles, indicating that null alleles, if present, did not bias results. The highest FST values were obtained for the pairwise comparisons that included Kainji Lake , using the p values obtained for the pairwise comparisons without correcting for null alleles.ke Table . AverageFST results . Although high, this level of differentiation is smaller than that observed (average FST = 0.27) between fish from Malanville, also in the Niger River, and southern Benin localities in Mono River and the Ouemé- Sô river floodplain system20. Differentiation between fish from Kainji Lake and those collected in the southern localities was supported by results from all genetic differentiation analyses, as well as the high number of private alleles (21) observed in Kainji Lake. The large geographic distance between Kainji Lake and southern populations is probably an important factor restricting gene flow of the African bonytongue between the two regions. A pattern of IBD was detected only when analyses included the sample of Kainji Lake. In addition, movements of the African bonytongue must be affected by the presence of the Kainji Dam, completed in 196839. The dam constitutes an effective barrier for upstream movement of fishes from the river below, although fish can move downstream from the lake through the spillway. Nonetheless, the dam appears to have impacted downstream populations after its construction. Bonytongues were rarely caught at Faku, just below the dam, and Awuru, ~ 25 km downstream from the dam, in 1973 and 197439. Our sample from Kainji Lake included a few specimens from Faku, and our genetic analyses grouped them in the same cluster as fish obtained from upstream of the lake, suggesting gene flow occurs from the lake to reaches just below the dam. The African bonytongue also became less frequent in landings in the second half of 1968 and in 1969 at Jebba and Pategi, ~ 100 km and ~ 220 km below the dam, respectively40. Between 1970 and 1972, however, bonytongues comprised a large percentage of the fish biomass landed at these locations, as well as at Lokoja, ~ 400 km downstream the dam. This proportion dropped substantially in these three below-dam localities between 1974 and 197539, which may be due to local exploitation. It is important to investigate genetic differentiation of African bonytongues at these below-dam localities, where this species has historically been a valuable fishery component.The greatest genetic differentiation was observed between fish from Kainji Lake and those collected in the southern localities (average 20 and Kainji Lake (this study) suggest restricted gene flow between these two localities on the Niger River separated by a river distance of ~ 230 km. Microsatellite datasets for these samples were not analyzed together because of potential bias when combining datasets from different studies41. Marked differences in mean expected heterozygosity (He) for these samples, however, suggest highly restricted gene flow between these two areas of the Niger River. He was 0.57 in Malanville (n = 12), whereas He was 0.73 in the sample from Kainji Lake (n = 23). Mean He is expected to be fairly insensitive to potential bias due to differences in sample size43, and can be useful for comparisons between different studies44.Comparisons between microsatellite results for bonytongues from Malanville in BeninFST = 0.11). This level of differentiation is higher than the one reported in southern Benin between fish from Mono River and localities in the Ouemé- Sô river floodplain system (average FST = 0.09). Average divergence among southern Nigeria localities (average FST = 0.05) is higher than the reported between localities within the Ouemé- Sô river floodplain system (average FST = 0.03). A pattern of IBD was not detected among southern localities in this study. An ocean connection and the presence of brackish waters in Lagos Lagoon, which is connected to the western portion of Epe Lagoon, may represent effective barriers to gene flow for H. niloticus between Porto Novo and Epe Lagoon. According to our results, gene flow between upstream areas of the Ouémé River Basin and rivers connecting to Epe Lagoon also appears restricted. Significant pairwise FST values among the southern Nigerian localities suggest restricted gene flow among them, despite potential surface water connections provided by a complex network of waterways and annual flooding. Igbokoda River and Epe Lagoon are connected through a water channel that joins the Lekki Lagoon, which is connected to the eastern portion of Epe Lagoon. Thus, it appears that although movement of fish between the two localities may occur, it is not enough to prevent subtle genetic differentiation. Igbokoda and Ethiope rivers are separated by > 150 km. Flooding and water channels may facilitate movement of fish between both localities, although the lower portion of the Igbokoda River has brackish water that may act as a barrier to dispersal. STRUCTURE analyses did not detect subdivision among fish from southern locations, even though simulations have shown that this method can detect substructure in cases where FST values are as low as 0.03 when ten highly variable microsatellite loci are used45. Thus, it is possible that the microsatellite loci we used in this study are not variable enough for STRUCTURE to detect substructure with FST values of 0.11. In the previous study from Benin, most STRUCTURE analyses did not detect genetic differentiation between fish from the Mono River and those from localities in the Ouemé- Sô River floodplain system, even though all FST pairwise values were significant (average FST was 0.09), and other methods also clearly showed this differentiation20.Among southern locations, gene flow appears to be more restricted between the lower Ouémé River in Benin (Porto Novo) and the southern Nigeria rivers sampled , but not at smaller scales . Most of the pairwise FST values in the Nile tilapia study were significant, except for pairwise comparisons among localities separated by some of the shortest distances. Significant genetic differentiation in the Nile tilapia was usually observed between populations separated by more than ~ 90 km. Interestingly, Nile tilapia samples from the only two localities sampled in the Niger River, Malanville and Mopti, which are separated by ~ 1400 km, show high genetic similarity, suggesting high levels of gene flow. This contrasts with what we observed for the African bonytongue in the Niger River, for which high genetic differentiation appears to occur at comparatively shorter distances within this river, i.e., between Malanville and Kainji Lake (~ 230 km), and between Kainji Lake and the lower Niger portion (~ 700 km).Studies of large-scale patterns of genetic structure of other important capture fisheries are limited in West Africa. A recent study of the Nile tilapia Arapaima gigas47. This species, which is distributed throughout the Amazon River Basin, is the only other living member of the Arapaiminae48, and both species construct nests where they lay and protect eggs. The predatory arapaima protects free-swimming larvae much longer  than the omnivorous African bonytongue (a few days), and grows much larger (over 2 m and 100 kg compared to Heterotis at 1 m and 10 kg). At a fine scale , the two species tend to exhibit genetic homogeneity47. At a meso-scale , both species exhibit low but significant values of genetic differentiation. Finally, at the largest scale (e.g. > 1300 km in Arapaima and > 510 km in Heterotis), the highest levels of genetic differentiation were observed in both species. Thus, despite occupying two separate continents these two sister taxa exhibit similar patterns of genetic differentiation.An effect of floodplain connectivity and geographic scale has also been reported in the African bonytongue’s closest living relative, Na values for this fish are similar or much lower than the average (Na = 9.1) reported for freshwater fishes49. Heterozygosity includes values considerably higher or lower than the average heterozygosity (H = 0.54) reported for freshwater fishes49. The sample from Kainji Lake had the highest heterozygosity  and second highest allelic diversity (Na = 8.33) among all African bonytongue populations examined to date. The sample from the Oueme–Sô river-floodplain system in Benin has the highest Na (9.25) for African bonytongues, but with a much larger sample examined (n = 184), and the second highest heterozygosity 20. Genetic diversity values for fish from the southern Nigeria localities are much lower , and one of these localities, the Igbokoda River sample, shows high levels of inbreeding (FIS = 0.18). The high genetic diversity in Kainji Lake contrasts with the low genetic diversity observed at Malanville, Benin , the only other Niger River locality sampled to date, and where high inbreeding was also observed (FIS = 0.20)20. Low genetic diversity at Malanville could be associated with intense fishing pressure20. Although we observed low allelic diversity (Na = 3.56) for the sample from the Oueme–Sô River at Porto Novo, Benin, Ho (0.57) was more similar to the value previously reported for the Oueme–Sô river-floodplain, and He (0.49) was similar to that observed for samples from southern Nigeria. The smaller Na and He values obtained for Porto Novo compared to the Oueme–Sô river-floodplain system are likely due to the small (n = 6) sample size43. Values of Na more similar to the one obtained in Porto Novo were previously obtained for some localities with small sample sizes within the Oueme–Sô river floodplain system20. For a sample of n = 6 in the Ouemé River channel, values were Na = 3.38, Ho = 0.48 and He = 0.66; whereas for a sample of n = 10 in the Sô River channel, values were Na = 3.62, Ho = 0.55 and He = 0.61.A broad range of genetic diversity values are observed in African bonytongue populations of Nigeria and Benin. 20 report low levels of genetic differentiation among fish collected from this system, with sampling localities separated by up to ~ 75 km (average FST = 0.03). In that study, the fish from the Ouemé- Sô river floodplain system were highly differentiated from those collected in the Mono River and the Niger River locality of Malanville. The southern Nigerian populations sampled in this study show subtle, albeit statistically significant differentiation; we thus recommend that they be managed as local stocks. These genetically distinct populations could constitute valuable genetic resources for future use in aquaculture.The observed patterns of genetic differentiation indicate that African bonytongue from each of the localities examined in this study correspond to differentiated populations , and should thus be treated separately for conservation and management. The Kainji Lake population is highly differentiated from the southern populations. In the south, the sample from Porto Novo is highly differentiated from southern Nigerian populations. Porto Novo is part of the Ouemé- Sô river floodplain system in Benin, for which Hurtado et al.51, and illegal fishing activities, including the use of prohibited gear , fish poisoning, and explosives, have exacerbated overfishing in this lake52. Environmental pollution also impacts fish and people who consume them; and high levels of heavy metals have been detected in fish from the area53. The Ethiope River flows through a densely populated area of Nigeria’s Delta state, where pollution has impaired water quality in some stretches, with documented effects on macroinvertebrates54. Effects of degraded water quality in this river have not yet been shown to impair fish survival directly55. The Igbokoda River is an important fishing location in the Ilaje local government at Ondo State, a major oil-producing state. Unsafe levels of heavy metals have been found in water samples and fish in this river56. Consumption of fish is also considered unsafe in other rivers located in the Ilaje local government due to oil and industrial pollution57. Given the low quality of its riverine environment, and high levels of inbreeding detected in this study, urgent attention needs to be paid to the African bonytongue in Igbokoda River. A decade ago, Epe Lagoon was reported to have diverse and abundant fish stocks threatened by a rapidly growing population in the Lagos metropolitan area58. Anthropogenic activities appear to largely contribute to pollution in Epe Lagoon. Recent studies of Epe lagoon reported the presence of benzene, toluene, ethylbenzene and xylene (BTEX) and unsafe levels of Polycyclic Aromatic Hydrocarbons (PAH) in H. niloticus; as well as high levels of heavy metals in sediment of the lake, and a high prevalence of an intestinal parasite in H. niloticus, which may be attributable to the water pollution60.Multiple activities threaten the sustainability of the African bonytongue stocks we have identified in Nigeria. Overfishing compromises the sustainability of fishery resources in Kainji LakeFST values were obtained for pairwise comparisons among fish from the southern localities. Based on these results, we suggest that populations of the African bonytongue in each of the Nigerian localities examined in this study be treated separately for conservation and management. Given the observed levels of genetic differentiation, understanding patterns of genetic diversity in the African bonytongue throughout the rest of its range is needed for adequate delineation of management stocks. We recommend that populations located in different rivers that are not part of the same floodplain system and are separated by > 70 km, or populations within a major river separated by > 200 km, be treated as separate management units pending genetic evidence.Consistent with a previous study conducted in Benin, we found significant genetic structure among African bonytongue samples from locations in Nigeria. The highest values of genetic differentiation were observed between Kainji Lake and the southern localities examined. We found evidence of gene flow from Kainji Lake to the area just below the dam, but the large distance between Kainji Lake and southern populations probably restricts gene flow between the two regions. Relatively low but significant Supplementary Information 1.Supplementary Table S1.Supplementary Table S2.


The Oxus Civilisation  was the main archaeological culture of the Bronze Age in southern Central Asia. Paleogenetic analyses were previously conducted mainly on samples from the eastern part of BMAC. The population associated with BMAC descends from local Chalcolithic populations, with some outliers of steppe or South-Asian descent. Here, we present new genome-wide data for one individual from Ulug-depe (Turkmenistan), one of the main BMAC sites, located at the southwestern edge of the BMAC. We demonstrate that this individual genetically belongs to the BMAC cluster. Using this genome, we confirm that modern Indo-Iranian–speaking populations from Central Asia derive their ancestry from BMAC populations, with additional gene flow from the western and the Altai steppes in higher proportions among the Tajiks than the Yagnobi ethnic group. Homo sapiens. It has played a key role in human history, having served for millennia as a connection between Europe and Asia on the one hand, and Siberia and southern Eurasia on the other. This area exhibits a high genetic, linguistic, and ethnological diversity. Today, several ethnic groups are present in Central Asia belonging to two linguistic families: Indo-Iranian—composed of the Tajik and Yagnobi populations, traditionally sedentary agriculturalists—and Turco Mongol that encompasses Uzbeks, Turkmens, Karakalpaks, Kazakhs, and Kyrgyz, traditionally nomadic herders and organized into patrilineal descent groups.Central Asia was one of the first regions outside of Africa populated by Genetic analyses led by our team on about 40 Central Asian populations brought to light a major impact of social organization and cultural practices on Central Asia’s genetic diversity. The patrilocal rule of residence favors women’s migrations while increasing the spatial, linguistic, and ethnic structuring of men  and coulGenetic analyses have also helped to understand the origin and history of these populations. First, they evidenced that the two cultural groups are genetically distinct , 2017. IThe development of paleogenomics over the last 15 years has allowed a better understanding of the demographic events that shaped the history of these populations. In this region with a complex migration history, ancient DNA is precious for disentangling the different waves of migration. Furthermore, relying on precise archaeological context made possible the joint study of cultural and demographic changes through time.Notably, the Neolithic way of life developed in the steppe territory in the North  around 4000 BC with the Botaï culture , agropastoral communities are present since 6000 BC D. R.  and as eDespite the publication of genome-wide data from several BMAC archaeological sites, none has been published up to now from Ulug-depe (Turkmenistan). Ulug-depe is one of the biggest proto-urban sites of the BMAC (13 ha), and is located in the formative area of the BMAC, halfway between Namazga-depe and Altyn-depe , at 175 No necropolis has been (yet) found in Ulug-depe, but up to 100 burials from the Bronze Age have been discovered inside the houses . Inside To better understand the genetic diversity of Bronze Age Ulug-depe, we performed a paleogenetic analysis of one of these individuals.Thirteen samples from the BMAC period and three from Iron Age have been selected, based on the overall preservation . They sp2, 1 × PCR buffer II, and 1 U of AmpliTaq Gold DNA polymerase (Applied Biosystems). 1 µl of PCR product was loaded on a 2% agarose gel to check for positive results.All the preamplification steps were carried out in the clean room, dedicated to ancient DNA, of the Paleogenomic and Molecular genetics platform, set in the Musée de l’Homme . Ancient DNA extraction was performed for the 15 samples using a protocol adapted from ©); libraries were amplified using 10–12 PCR cycles and purified on a MinElute column (Qiagen ©). Analyses on a LabChip ® GX provided an estimated size distribution of fragments with a peak length of 150–250 bp.The ancient DNA extract was converted into a TruSeq Nano Illumina library following the manufacturer’s protocol with slight modifications that account for the ancient DNA damage. First, DNA was not fragmented; only 25 µl of ancient DNA extract was used; after end-repair, the libraries were purified using a MinElute column (Qiagen ULG75 from grave 75) with more than 1% of endogenous DNA content. For this sample, a genomic capture was performed using the myBaits Expert Whole Genome Enrichment (WGE) kit (Arbor Biosciences) and the manufacturer’s instructions were followed. Baits were formed from the genomic DNA of three individuals of different  ancestries. After enrichment, libraries were re-amplified using 12 PCR cycles.Genome-wide data was successfully produced from the only sample (Captured libraries were sequenced on a NextSeq 500 (2 × 75 bp) instrument on the IGenSeq platform.aln command and the parameter “−l 1,024.” Mapped reads were filtered out for mapping quality <25 with SAMtools 1.940. Duplicates were removed using Picard MarkDuplicates (http://picard. sourceforge.net), and the reads were realigned using GATK 3.5 IndelRealigner . For positions with more than one base call, one allele was randomly chosen with a probability equal to the frequency of the base at that position.We similarly processed the new sample as follows. We mapped sequencing reads to the human reference genome (GRCh37) using BWA-0.1.17  with theealigner . Finallyealigner  was usedools 1.9  to extraWe calculated the contamination rate on the genome using AuthentiCT .Biological sex was determined using Rx  and Ry  statistimergeit from eigensoft. This first merge is called the “1240 k-dataset” and includes 1,233,013 SNPs. We provide all metadata about the ancient dataset in Among ancient human genomes from Eurasia from Paleolithic to Middle Age , DNA seqFor some analyses, it was then merged with 22 modern Indo-Iranian genomes  indicates that the latter lost Iranian Neolithic ancestry since Bronze Age, probably linked to a gene flow from a population with low Iranian Neolithic ancestry, like most of the Steppe populations. Nevertheless, the low coverage of ULG75 may not give us enough sensitivity to identify all the gene flows.We do not obtain significantly negative D-stats for Yagnobis, but the closer genetic proximity between ULG75, show that it belongs to the genetic BMAC cluster, represented by individuals from Gonur-depe, Dzharkutan, Bustan, and Sappali-tepe. This reinforces the integration of Ulug-depe into the Oxus Civilization, at a population level. Strontium and oxygen isotope analyses of Early and Middle Bronze Age individuals from Ulug-depe (Ancient DNA analyses of a Middle Bronze Age individual from Ulug-depe, lug-depe  have shoULG75 suggests if these migrants were present in Ulug-depe, they were not part of the ancestors of this individual. From an isotope point of view, the individuals contemporaneous of ULG75 revealed little mobility and extensive use of the direct surrounding; no foreigners could have been identified (On the other hand, previous genetic analyses on Dzharkutan, Gonur-Depe, Bustan, and Sappali-tepe Bronze Age populations have shown that in addition to the BMAC genetic cluster, first- or second-generation migrants from various parts of Eurasia were present within the Oxus. Interestingly, these outliers cluster with two different populations: one is related to steppe populations, from Northern Central Asia, while the second to the population from the Indus periphery. Due to the poor preservation of DNA in Ulug-depe, a comparison of the genetic diversity inside the site was not feasible. However, the absence of genetic ancestry coming from either of these populations in entified .As in From a genetic point of view, the non-outlier BMAC individuals are strongly related to southerner populations such as the Iranian Neolithic, an observation that is also congruent with archaeological observations. The Turan archaeological sequence appears to mirror those found in South-western Asia. As early as the late seventh millennium BC, the Neolithic communities of South Central Asia have the same pattern of subsistence as those in the Near East and Iran; by the end of the fourth millennium, they also share the technological base for the production of pottery, metal, irrigation, etc., with numerous shreds of evidence showing material contact with the Iranian Plateau . These cThis genetic continuity still goes on. A comparison of modern Indo-Iranian populations with the ancient genome  has showULG75 genome can be modeled with as much as 24% of its ancestry derived from Indus Periphery. Thus, the small amount of South-Asian ancestry found in the modern Turan population may be explained by an ancient, low, continuous, gene-flow.A more limited gene flow with the South-Asian populations has also been evidenced in Tajiks but not in Yagnobis, suggesting that it occurred mainly after the split between these two Indo-Iranian–speaking groups. On the other hand, outliers with a high amount of the Indus Periphery group are found in several BMAC groups, such as Bustan, and the This analysis shows the strength of adding ancient DNA data to better understand the evolution of genetic diversity in Central Asia. Several questions remain about the timing of the different demographic events and call for more paleogenetic data, despite the difficulties due to the low preservation of ancient DNA in this warm, arid region.


Juglans mandshurica is a native tree species in Northeast China. Due to habitat destruction and human disturbance, its population size has sharply decreased. Currently, information on molecular markers of J. mandshurica is limited and cannot meet the needs of germplasm resource evaluation and molecular marker-assisted breeding of J. mandshurica. Based on transcriptomic data from three tissues , we developed expressed sequence tag-simple sequence repeats (EST-SSRs) for J. mandshurica, and 15 polymorphic EST-SSR primers were initially selected. The average number of alleles (Na), expected heterozygosity (He), and the polymorphic information content (PIC) at different loci were 18.27, 0.670, and 0.797, respectively. Population genetic diversity analysis revealed that the average Na, He, and Shannon information indices (I) for 15 J. mandshurica populations were 6.993, 0.670, and 1.455, respectively. Among them, population Hunchun exhibited the highest genetic diversity , while population Heihe exhibited the lowest genetic diversity . STRUCTURE analysis, neighbor-joining method cluster analysis, and principal coordinate analysis showed that the 343 individuals of J. mandshurica from 15 populations were clustered into three categories. Category 1 (green) had 147 individuals from eight populations in Qingyuan, Caohekou, Jian, Ningan, Yongji, Baishishan, Helong, and Maoershan; category 2 (blue) had 81 individuals from three populations in Hulin, Boli, and Sanchazi; and category 3 (red) had 115 individuals from four populations in Heihe, Hunchun, Fangzheng, and Liangshui. Analysis of molecular variance (AMOVA) showed that genetic variations among and within individuals accounted for 16.22% and 21.10% of the total genetic variation, respectively, indicating that genetic variations within populations were greater than genetic variations among populations. The average genetic differentiation coefficient (Fst) and gene flow (Nm) between different populations were 0.109 and 4.063, respectively, implying moderate levels of genetic differentiation and gene flow. Based on the genetic diversity characteristics of different populations, we proposed various genetic conservation strategies for J. mandshurica. Juglans mandshurica Maxim., a deciduous tree of the Juglandaceae family, is native to northern and northeastern China and is mainly distributed in the Changbai Mountains and Lesser Khingan Mountains at elevations of 500–1,000 m. It is also distributed in Russia, Japan, and Korea and can withstand low temperatures of −50°C. As an ancient fruit tree species, it is economically valuable  use Illumina HiSeq 2000 to sequence the RNA of J. mandshurica and to develop polymorphic EST-SSR primers using the obtained transcriptome data; (2) analyze the genetic diversity of different J. mandshurica populations in northeastern China using the developed EST-SSR primers; and (3) propose genetic conservation strategies for J. mandshurica in northeastern China.In recent years, with advances in molecular biology and high-throughput sequencing technologies, molecular markers, transcriptome sequencing, and genome sequencing have been gradually applied to study the genetic structure, genetic diversity, and relatedness among or within species, which have allowed the exploration of the evolutionary origin of species at the individual and population levels. Molecular marker technologies, such as SRAP Wang, , ISSR W, and SSRJ. mandshurica populations were collected from the main distribution areas of Heilongjiang, Jilin, and Liaoning Provinces in Northeast China  Co., Ltd., Beijing, China and Biomarker Technologies Co., Ltd., Beijing, China. Based on transcriptome data, SSRs were detected and localized by the MIcroSAtellite identification tool (MISA) (http://pgrc.ipk-gatersleben.de/misa/)   were collected from a healthy 2O. The PCR conditions were as follows: predenaturation at 94°C for 4 min; 30 cycles of denaturation at 94°C for 30 s, annealing at 60°C for 30 s, and extension at 72°C for 30 s; and a final extension step at 72°C for 10 min. All PCR products were sent to Sangon Biotech (Shanghai) Co., Ltd., Shanghai, China for capillary electrophoresis sequencing. Ultimately, 15 pairs of polymorphic EST-SSRs were selected and characterized  , and a ucterized , and 15 Na), effective number of alleles (Ne), Shannon information index (I), observed heterozygosity (Ho), expected heterozygosity (He), overall fixation index (Fit), inbreeding coefficient (Fis), genetic differentiation coefficient (Fst), gene flow (Nm), number of private alleles (NPA), and Hardy–Weinberg equilibrium (HWE) were calculated by the GenAlEx 6.502 program   repetitions. The LnP (K) and ΔK plots were generated by the online tool Structure Harvester (http://taylor0.biology.ucla.edu/structureHarvester/)  genetic distances between populations were calculated by PowerMarker 3.25 program   to 33 (NEJM-6), with a mean of 18.27, whereas the Ne ranged from 1.728 (NEJM-4) to 6.170 (NEJM-6) with a mean of 3.937. The I ranged from 0.635 (NEJM-4) to 2.005 (NEJM-6) with a mean of 1.455. The Ho ranged from 0.132 (NEJM-4) to 0.775 (NEJM-9) with a mean of 0.564; He ranged from 0.322 (NEJM-4) to 0.831 (NEJM-6) with a mean of 0.670. The PIC ranged from 0.662 (NEJM-5) to 0.912 (NEJM-6) with a mean of 0.797. The Fit ranged from 0.141 (NEJM-9) to 0.812 (NEJM-4) with a mean of 0.317; Fis ranged from 0.025 (NEJM-8) to 0.590 (NEJM-4) with a mean of 0.177; Fst ranged from 0.039 (NEJM-7) to 0.541 (NEJM-4) with a mean of 0.184; Nm ranged from 0.212 (NEJM-4) to 6.191 (NEJM-7) with a mean of 1.967; and NPA ranged from 1 (NEJM-9) to 9 (NEJM-15) with a mean of 3.867, accounting for 21.17% of Na.A total of 240 EST-SSRs were randomly selected and synthesized for polymorphism investigation . A totalJ. mandshurica using the primers shown in Na among the different populations was from 4.200 (Heihe) to 8.133 (Fangzheng and Maoershan), and the mean value was 6.933. For Ne, the range was from 2.894 (Heihe) to 4.651 (Helong), with an average of 3.937. For I, variations ranged from 1.158 (Heihe) to 1.617 (Hunchun), with a mean value of 1.455. Variations in Ho ranged from 0.477 (Jian) to 0.665 (Fangzheng), with a mean of 0.564, whereas variations in He ranged from 0.605 (Heihe) to 0.723 (Hunchun), with a mean value of 0.670. NPA varied from 1  to 10 (Hulin) with a mean value of 3.867. The fixation index (F) varied from 0.026 (Heihe) to 0.326 (Maoershan) with a mean value of 0.177. In this study, F > 0 indicates the absence of heterozygotes and excess of pure heterozygotes in the J. mandshurica population and the presence of inbreeding. Furthermore, the Hunchun population showed high genetic diversity , whereas the Heihe population exhibited a relatively low level of genetic diversity .Genetic diversity analysis was performed on 343 individuals from 15 populations of J. mandshurica population in Northeast China was analyzed using the STRUCTURE program. The optimal number of clusters (K) was determined by comparing the LnP (D) and ΔK based on the rate of change in LnP (D). The LnP (D) value increased continuously with the number of clusters (K) from 2 to 10 based on estimated posterior probability of genotype data, and the inflection point appears when K = 3 and K = 5  (The genetic structure of the nd K = 5 . Two proobserved . Because yellow) . CategorJ. mandshurica, principal coordinate analysis (PCoA) was performed using Nei's genetic distances of 343 individuals from 15 populations . Moreover, 21.10 and 62.68% of the genetic variations were attributed to variations among individuals and within individuals, respectively, whereas variations among populations accounted for the total genetic variance of 16.22%, indicating that genetic variations within populations were much higher compared to genetic differentiation among populations. The Fst value was 0.162 (0.15 < Fst < 0.25), indicating a moderate level of genetic differentiation among populations. The Fis value was 0.252 (Fis > 0), indicating the loss of heterozygotes and the presence of inbreeding between populations. The Fst  and annual sunshine (r = 0.2592), but with weak correlation coefficients. These results indicate that the molecular traits of J. mandshurica populations were less influenced by environmental factors.Mantel tests were performed to explore the correlations between molecular traits and environmental factors. The results showed that except for  factors . Na had J. mandshurica resources basically existing in a wild or semi-wild state, genetic improvement of J. mandshurica is lagging  were the most abundant motif class, followed by dinucleotide repeats (AG/CT), while the most common trinucleotide repeats were GAA/TTC and AAG/CTT. Comparable findings have been reported for Vigna angularis , while the Heihe population had the lowest . Bai et al.  for both populations was small but >0, indicating an excess of pure heterozygotes in both populations and a slight inbreeding phenomenon  refers to the movement of genes within and among populations, which in the plant kingdom is mainly achieved through the migration or movement of genetic material carriers, such as pollen, seeds, spores, and vegetative bodies. It affects the degree of genetic variation in the population . We show that the J. mandshurica population in Northeast China has high genetic diversity, indicating that the endangerment of J. mandshurica is not the cause of genetic variation but is mainly affected by human disturbance and habitat destruction and degradation  and the Scientific Research Start-up Funds of Jilin Agricultural University (No. 2021002).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Heterodera avenae is one of the major nematode pests on cereals that can cause 10%–90% crop yield losses worldwide. Through extensive sampling on wheat and grasses, the Chinese population of H. avenae is widely identified in virtually all wheat growing regions in China, with H1 being the predominant haplotype. The monoculture of wheat in north China might have been the key driver for the prevalence of H1 population, which should date no earlier than the Han Dynasty (202 BCE–220 CE). Molecular phylogenetic and biogeographic analyses of Chinese H. avenae suggest a Pleistocene northwest China origin and an ancestral host of grasses. We assume that the prosperity of Heterodera in this region is a result of their preference for cooler climate and various grass hosts, which only appeared after the uplift of Qinghai‐Tibetan Plateau and aridification of Inner Asia. Nematode samples from the current and historical floodplains show a significant role of the Yellow River in the distribution of Chinese H. avenae. Whereas mechanical harvesters that operate on an inter‐provincial basis suggest the importance in the transmission of this species in eastern China in recent times. This study highlights the role of environmental change, river dynamics, and anthropogenic factors in the origin and long‐distance dissemination of pathogens.Reconstructing the dispersal routes of pathogens can help identify the key drivers of their evolution and provides a basis for disease control. The cereal cyst nematode  Through phylogeographic and population genetic analyses, we aim to elucidate the following: (i) the clear genetic structure of CHA across China, (ii) the geographical and ecological origin of CHA, and (iii) its primary dispersal pathway.22.1A total of 633 sites were sampled from wheat field and grass during several surveys between 2010 and 2021, which covered nearly all major wheat growing areas of China as well as alpine meadow in northwest China Tables . Since t2.22O on a sterilized glass slide, punctured by a needle under a dissecting microscope. The released eggs and J2s were crushed by sterilized needle and transferred together with water suspension into an Eppendorf tube containing 20 μl of worm lysis buffer solution . The tube was frozen for at least 10 min at −20°C, and 1 μl proteinase K (1.2 mg/ml) was added before incubation for 1 h at 65°C and 10 min at 95°C. After incubation, the tube was centrifuged and kept at −20°C until use. A fragment of the mtCOI gene was amplified using two primer pairs, the details are listed in Appendix DNA was extracted from the eggs and second‐stage juveniles (J2s) released from a single cyst. Cysts were soaked in double distilled water for 10–20 min in advance, and one cyst was put into 20 μl ddH2.3For mtCOI‐based study, the newly obtained sequences of CHA were analyzed together with other related sequences in downloaded from GenBank. Multiple alignments of mtCOI gene were generated using MAFFT implemented in TranslatorX .Both long and short mtCOI fragments were used for phylogeny analyses, and duplicated haplotypes sequences were pruned from the dataset prior to analysis. The Bayesian inference analyses were performed in MrBayes 3.2.3 , the individual inbreeding coefficient (Fis), as well as Analysis of Molecular Variance (AMOVA), were calculated among and within the populations. PCoA and Bayesian clustering were used to examine the population grouping. The possible bottleneck effect was tested based on three different models . Population structure was inferred by using Bayesian clustering program STRUCTURE v2.3.4   Figure . Among t) Figure , and oneRoegneria kamoji Ohwi next to wheat fields in the floodplain of Xinyi River of Jiangsu Province , Qinghai (QH), and Tianzhu (TZ) population on grass Figures  and 2a. e Figure . Likewise Figure , respectThe sampling of the old courses of the Yellow River suggested that CHA is widely distributed, with six independent populations being recovered Figure . These pK = 2 or K = 3) without grass samples , sistering to H. australis . The European and Middle East populations of H. avenae were placed in a well‐supported clade (PP = 1), but appeared to be paraphyletic, with H. arenaria nested inside. The species delimitation analyses split this clade as five (GMYC), two , and one (ABGD) species. Although cryptic species may exist within H. avenae, none of them supporting H. arenaria as a valid species.The Bayesian phylogeny analysis suggested that newly recovered CHA populations were clustered together with s Figure . Speciess Figure . The numnd Moens , was nes3.3H. pratensis + CHA + H. australis (node 1) was possibly a northwest China lineage that split from other cyst nematodes around 0.74–1.74 million years ago (mya) in the Pleistocene epoch. The clade consisting of major wheat‐parasitic CHA (node 2) diverged around 0.49–1.59 mya, probably from central China or northwest China. The lineage represented by typical grass‐parasitic populations (node 4) probably arose between 0.65 and 1.57 mya, while an intermediate clade contains both grass‐ and wheat‐parasitic populations (node 3) arose between 0.76 and 1.91 mya. Both nodes 3 and 4 may have a northwest China origin.The divergence time and historical distribution estimates for each lineage are depicted in Figure 3.4H. avenae without significant inter‐population variation , two, and five species depending on the methods used, but none of them included CHA , suggesH. avenae group, although the average cyst size trends to be interspecifically conserved, it is clear they overlap in range and differ among populations . Therefore, the formation of Qinghai‐Tibetan Plateau and their local environment were likely the key for Heterodera spp. diversification.Our biogeographic reconstruction indicates that northwest China is a center of Heterodera spp. diversification. Indeed, our population genetic analysis revealed that the wheat Heterodera spp. in Qinghai‐Tibetan Plateau has much higher diversity in comparison to those from central China.The Qinghai‐Tibetan Plateau uplift throughout much of the Cenozoic (the last 66 million years) is one of the most important tectonic events in the Earth's history , global cooling resulted in an intensification of such aridity  Stelter, 1984. This species has relatively recognized history, which was probably introduced to China in the late 1950s, when the wheat cultivars from Albania were brought to China . Through this population bottleneck, few superior genotypes were able to expand their populations rapidly and soon become predominant, which is similar to the process of Founder effect.The Yellow River efficiently delivers various cysts haplotypes across its basin. However, the prevalence of haplotypes H1 population is related to the cultivation of wheat. The extensive monoculture of wheat sharply reduced the genetic variation of Recent findings  and the Special Fund for Agro‐scientific Research in the Public Interest (Grant numbers 200903040 and 201503114). TL acknowledges the Zhejiang Provincial Qianjiang Talent Scheme (Grant number QJC2002001). We thank Drs. Yulong Li and Gaofeng Wang for their assistance in sampling or providing samples. Prof. Fengbao Zhang is also acknowledged for providing photos of the Loess Plateau.The authors declare no competing interest.Appendix S1–S4Click here for additional data file.