The island of Sardinia has been of particular interest to geneticists for decades. The current model for Sardinia’s genetic history describes the island as harboring a founder population that was established largely from the Neolithic peoples of southern Europe and remained isolated from later Bronze Age expansions on the mainland. To evaluate this model, we generate genome-wide ancient DNA data for 70 individuals from 21 Sardinian archaeological sites spanning the Middle Neolithic through the Medieval period. The earliest individuals show a strong affinity to western Mediterranean Neolithic populations, followed by an extended period of genetic continuity on the island through the Nuragic period (second millennium BCE). Beginning with individuals from Phoenician/Punic sites (first millennium BCE), we observe spatially-varying signals of admixture with sources principally from the eastern and northern Mediterranean. Overall, our analysis sheds light on the genetic history of Sardinia, revealing how relationships to mainland populations shifted over time. Ancient DNA analysis of early European farmers has found a high level of genetic affinity with present-day Sardinians. Here, the authors generate genome-wide capture data for 70 individuals from Sardinia spanning the Middle Neolithic to Medieval period to reveal relationships with mainland European populations shifting over time. Subsequent work on genome-wide variation in ancient Europeans found that most “early European farmer” individuals, even when from geographically distant locales  have their highest genetic affinity with present-day Sardinian individuals6. Accumulating ancient DNA (aDNA) results have provided a framework for understanding how early European farmers show such genetic affinity to modern Sardinians.The whole-genome sequencing in 2012 of “Ötzi”, an individual who was preserved in ice for over 5000 years near the Italo-Austrian border, revealed a surprisingly high level of shared ancestry with present-day Sardinian individuals7, spreading through Anatolia and the Balkans9 while progressively admixing with local hunter-gatherers10. Major movements from the Eurasian Steppe, beginning about 3000 BCE, resulted in further admixture throughout Europe14. These events are typically modeled in terms of three ancestry components: western hunter gatherers (“WHG”), early European farmers (“EEF”), and Steppe pastoralists (“Steppe”). Within this broad framework, the island of Sardinia is thought to have received a high level of EEF ancestry early on and then remained mostly isolated from the subsequent admixture occurring on mainland Europe2. However, this specific model for Sardinian population history has not been tested with genome-wide aDNA data from the island.In this framework, Europe was first inhabited by Paleolithic and later Mesolithic hunter-gatherer groups. Then, starting about 7000 BCE, farming peoples arrived from the Middle East as part of a Neolithic transition15. Archeological evidence suggests Sardinia was not densely populated in the Mesolithic, and experienced a population expansion coinciding with the Neolithic transition in the sixth millennium BCE16. Around this time, early Neolithic pottery assemblages were spreading throughout the western Mediterranean, including Sardinia, in particular vessels decorated with Cardium shell impressions 17, with radiocarbon dates indicating a rapid westward maritime expansion around 5500 BCE18. In the later Neolithic, obsidian originating from Sardinia is found throughout many western Mediterranean archeological sites19, indicating that the island was integrated into a maritime trade network. In the middle Bronze Age, about 1600 BCE, the “Nuragic” culture emerged, named for the thousands of distinctive stone towers, known as nuraghi20. During the late Nuragic period, the archeological and historical record shows the direct influence of several major Mediterranean groups, in particular the presence of Mycenaean, Levantine and Cypriot traders. The Nuragic settlements declined throughout much of the island as, in the late 9th and early 8th century BCE, Phoenicians originating from present-day Lebanon and northern Palestine established settlements concentrated along the southern shores of Sardinia21. In the second half of the 6th century BCE, the island was occupied by Carthaginians , expanding from the city of Carthage on the North-African coast of present-day Tunisia, which was founded in the late 9th century by Phoenicians23. Sardinia was occupied by Roman forces in 237 BCE, and turned into a Roman province a decade later24. Throughout the Roman Imperial period, the island remained closely aligned with both Italy and central North Africa. After the fall of the Roman empire, Sardinia became increasingly autonomous24, but interaction with the Byzantine Empire, the maritime republics of Genova and Pisa, the Catalan and Aragonese Kingdom, and the Duchy of Savoy and Piemonte continued to influence the island26.The oldest known human remains on Sardinia date to  ~20,000 years ago28. Pioneering studies found evidence that Sardinia is a genetic isolate with appreciable population substructure31. Recently, Chiang et al.32 analyzed whole-genome sequences33 together with continental European aDNA. Consistent with previous studies, they found the mountainous Ogliastra region of central/eastern Sardinia carries a signature of relative isolation and subtly elevated levels of WHG and EEF ancestry.The population genetics of Sardinia has long been studied, in part because of its importance for medical genetics34 found evidence for more genetic turnover in Gallura  than Ogliastra. Modi et al.35 sequenced mitogenomes of two Mesolithic individuals and found support for a model of population replacement in the Neolithic. Olivieri et al.36 analyzed 21 ancient mitogenomes from Sardinia and estimated the coalescent times of Sardinian-specific mtDNA haplogroups, finding support for most of them originating in the Neolithic or later, but with a few coalescing earlier. Finally, Matisoo-Smith et al.37 analyzed mitogenomes in a Phoenician settlement on Sardinia and inferred continuity and exchange between the Phoenician population and broader Sardinia. One additional study recovered β-thalessemia variants in three aDNA samples and found one carrier of the cod39 mutation in a necropolis used in the Punic and Roman periods38. Despite the initial insights these studies reveal, none of them analyze genome-wide autosomal data, which has proven to be useful for inferring population history39.Four previous studies have analyzed aDNA from Sardinia using mitochondrial DNA. Ghirotto et al.Here, we generate genome-wide data from the skeletal remains of 70 Sardinian individuals radiocarbon dated to between 4100 BCE and 1500 CE. We investigate three aspects of Sardinian population history: First, the ancestry of individuals from the Sardinian Neolithic (ca. 5700–3400 BCE)—who were the early peoples expanding onto the island at this time? Second, the genetic structure through the Sardinian Chalcolithic  to the Sardinian Bronze Age (ca. 2300–1000 BCE)—were there genetic turnover events through the different cultural transitions observed in the archeological record? And third, the post-Bronze Age contacts with major Mediterranean civilizations and more recent Italian populations—have they resulted in detectable gene flow?Our results reveal insights about each of these three periods of Sardinian history. Specifically, our earliest samples show affinity to the early European farmer populations of the mainland, then we observe a period of relative isolation with no significant evidence of admixture through the Nuragic period, after which we observe evidence for admixture with sources from the northern and eastern Mediterranean.40, (2) the Late Neolithic to Bronze Age Seulo cave sites of central Sardinia41, (3) the Neolithic Sites Noedalle and S’isterridolzu42, (4) the Phoenician-Punic sites of Monte Sirai23 and Villamar43, (5) the Imperial Roman period site at Monte Carru (Alghero)44, (6) medieval remains from the site of Corona Moltana45, (7) medieval remains from the necropolis of the Duomo of San Nicola46. We sequenced DNA libraries enriched for the complete mitochondrial genome as well as a targeted set of 1.2 million single nucleotide polymorphisms (SNPs)47. After quality control, we arrived at a final set of 70 individuals with an average coverage of 1.02× at targeted SNPs  and a median number of 466,049 targeted SNPs covered at least once per individual. We obtained age estimates by either direct radiocarbon dating (n = 53), previously reported radiocarbon dates (n = 13), or archeological context and radiocarbon dates from the same burial site (n = 4). The estimated ages range from 4100 years BCE to 1500 years CE , Early Copper Age , Early Middle Bronze Age , and Nuragic . For the post-Nuragic sites, there is substantial genetic heterogeneity within and among sites, and so we perform analysis per site when grouping is necessary . Figure We organized a collection of skeletal remains . For some analyses, we grouped the modern Sardinian individuals into eight geographic regions  and the remainder . As with other human genetic variation studies, population annotations are important to consider in the interpretation of results.To assess the relationship of the ancient Sardinian individuals to other ancient and present-day west Eurasian and North-African populations we analyzed our individuals alongside published autosomal DNA data  being with Western European Neolithic and Copper Age populations  and southern France (France-N). When projecting ancient individuals onto the top two principal components (PCs) defined by modern variation, the Neolithic ancient Sardinian individuals sit between early Neolithic Iberian and later Copper Age Iberian populations, roughly on an axis that differentiates WHG and EEF populations, and embedded in a cluster that additionally includes Neolithic British individuals Fig. . This reons Fig. . Pairwisons Fig. .Fig. 3Gep = 0.376 for Sar-MN, Table Ancient Sardinian individuals are shifted towards WHG individuals in the top two PCs relative to early Neolithic Anatolians Fig. . Analysip = 0.38 to reject the model of one population being the direct source of the other); followed by other western populations high in EEF ancestry, though with poor fit  the southern French Neolithic individuals (France-N) are consistent with being a single source for Middle/Late Neolithic Sardinia , we were able to infer mtDNA haplotypes for each individual and Y haplotypes for 30 out of 34 males. The mtDNA haplotypes belong to macro-haplogroups HV (n = 20), JT (n = 19), U (n = 12), and X (n = 1), a composition broadly similar to other European Neolithic populations. For Y haplotypes, we found at least one carrier for each of three major Sardinia-specific Y founder clades  that were identified previously based on modern Sardinian data51. More than half of the 31 identified Y haplogroups were R1b-V88 or I2-M223  that contain admixture between Nuragic Sardinia, one northern Mediterranean source  and one eastern Mediterranean source   Table C, D. Maln) Table B, but apBecause of limited sample sizes and ancestral source mis-specification, caution is warranted when interpreting inferred admixture fractions; however, the results indicate that complex post-Nuragic gene flow has likely played a role in the population genetic history of Sardinia.To more directly evaluate the models of post-Nuragic admixture, we obtained aDNA from 17 individuals sampled from post-Nuragic sites. The post-Nuragic individuals spread across a wide range of the PCA, and many shift towards the “eastern” and “northern” Mediterranean sources posited above Fig. . We conf36. We also found several Y haplogroups absent in our Neolithic trough the Nuragic period sample  and two Medieval individuals (SNN002 and SNN004). We also observed J1-L862 in one individual from a Punic site (VIL007) and E1b-L618 in one medieval individual (SNN001). Notably, J1-L862 first appears in Levantine Bronze Age individuals within the ancient reference dataset and is at about 5% frequency in Sardinia today.Consistent with an influx of novel ancestry, we observed that haplogroup diversity increases after the Nuragic period. In particular, we identified one carrier of the mtDNA haplogroup L2a at both the Punic Villamar site and the Roman Monte Carru site. At present, this mtDNA haplogroup is common across Africa, but so far undetected in samples from SardiniaWe used individual-level qpAdm models to further investigate the presence of these new ancestries  is shifted towards mainland populations of southern Europe, and the median for Campidano (southwest Sardinia) shows a slight displacement towards the eastern Mediterranean Fig. a. A thre9 and later in EEF Iberians14, in which they make up the majority of Y haplogroups, but have not been detected in Neolithic Anatolians or more western WHG individuals. These results are plausible outcomes of substantial gene flow from Neolithic populations that spread westward along the Mediterranean coast of southern Europe around 5500 BCE . We note that we lack autosomal aDNA from earlier than the Middle Neolithic in Sardinia and from key mainland locations such as Italy, which leaves some uncertainty about timing and the relative influence of gene flow from the Italian mainland versus from the north or west. The inferred WHG admixture fraction of Middle Neolithic Sardinians was higher than that of early mainland EEF populations, which could suggest a time lag of the influx into Sardinia (as HG ancestry increased through time on the mainland) but also could result from a pulse of initial local admixture or continued gene flow with the mainland. Genome-wide data from Mesolithic and early Neolithic individuals from Sardinia and potential source populations will help settle these questions.Our analysis of genome-wide data from 70 ancient Sardinian individuals has generated insights regarding the population history of Sardinia and the Mediterranean. First, our analysis provides more refined DNA-based support for the Middle Neolithic of Sardinia being related to the early Neolithic peoples of the Mediterranean coast of Europe. Middle/Late Neolithic Sardinian individuals fit well as a two-way admixture between mainland EEF and WHG sources, similar to other EEF populations of the western Mediterranean. Further, we detected Y haplogroups R1b-V88 and I2-M223 in the majority of the early Sardinian males. Both haplogroups appear earliest in the Balkans among Mesolithic hunter-gatherers and then Neolithic groups12 and also with many earlier Neolithic and Copper age populations across mainland Europe, where local admixture increased WHG ancestry substantially over time10. We observed remarkable constancy of WHG ancestry (close to 17%) from the Middle Neolithic to the Nuragic period. While we cannot exclude influx from genetically similar populations , the absence of Steppe ancestry suggests genetic isolation from many Bronze Age mainland populations—including later Iberian Bell Beakers13. As further support, the Y haplogroup R1b-M269, the most frequent present-day western European haplogroup and associated with expansions that brought Steppe ancestry into Britain13 and Iberia14 about 2500–2000 BCE, remains absent in our Sardinian sample through the Nuragic period (1200–1000 BCE). Larger sample sizes from Sardinia and alternate source populations may discover more subtle forms of admixture, but the evidence appears strong that Sardinia was isolated from major mainland Bronze Age gene flow events through to the local Nuragic period. As the archeological record shows that Sardinia was part of a broad Mediterranean trade network during this period19, such trade was either not coupled with gene flow or was only among proximal populations of similar genetic ancestry. In particular, we find that the Nuragic period is not marked by shifts in ancestry, arguing against hypotheses that the design of the Nuragic stone towers was brought with an influx of people from eastern sources such as Mycenaeans.From the Middle Neolithic onward until the beginning of the first millennium BC, we do not find evidence for gene flow from distinct ancestries into Sardinia. That stability contrasts with many other parts of Europe which had experienced substantial gene flow from central Eurasian Steppe ancestry starting about 3000 BCE54.Following the Nuragic period, we found evidence of gene flow with both northern and eastern Mediterranean sources. We observed eastern Mediterranean ancestry appearing first in two Phoenician-Punic sites . The northern Mediterranean ancestry became prevalent later, exemplified most clearly by individuals from a north-western Medieval site (San Nicola Necropoli). Many of the post-Nuragic individuals could be modeled as direct immigrants or offspring from new arrivals to Sardinia, while others had higher fractions of local Nuragic ancestry . Substantial uncertainty exists here as the low differentiation among plausible source populations makes it challenging to exclude alternate models, especially when using individual-level analysis. Overall though, we find support for increased variation in ancestry after the Nuragic period, and this echoes other recent aDNA studies in the Mediterranean that have observed fine-scale local heterogeneity in the Iron Age and later14 and central Italy54, where variation in individual ancestry increased markedly in the Iron Age, and later decreased until present-day. In terms of the fine-scale structure within Sardinia, we note the median position of modern individuals from the central regions of Ogliastra and Nuoro on the main PCA , our results support that the shared EEF ancestry component could explain their genetic affinity despite their geographic separation.The Basque people, another population high in EEF ancestry, were previously suggested to share a genetic connection with modern Sardinian individualsups Fig. , ref. 1458, and are not unexpected given that the Punic center of Carthage on the North-African coast itself has roots in the eastern Mediterranean. Interestingly, the Monte Sirai individuals, predating the Villamar individuals by several centuries, show less North-African ancestry. This could be because they harbor earlier Phoenician ancestry and North-African admixture may have been unique to the later Punic context, or because they were individuals from a different ancestral background altogether. Estimated North-African admixture fractions were much lower in later ancient individuals and present-day Sardinian individuals, in line with previous studies that have observed small but significant African admixture in several present-day South European populations, including Sardinia60.Beyond our focal interest in Sardinia, the results from individuals from the Phoenician-Punic sites Monte Sirai and Villamar shed some light on the ancestry of a historically impactful Mediterranean population. Notably, they show strong genetic relationships to ancient North-African and eastern Mediterranean sources. These results mirror other emerging ancient DNA studies33 versus the sub-populations that are accessible at archeological sites). As such, caution should be exercised when generalizing from the sparse sampling typical for many aDNA studies, including this one.As ancient DNA studies grow, a key challenge will be fine-scale sampling to aid the interpretation of shifts in ancestry. Our sample from Sardinia’s post-Nuragic period highlights the complexity, as we simultaneously observe examples of individuals that appear as novel immigrant ancestries  and of individuals that look more contiguous to the past and to the present . This variation is likely driven by differential patterns of contact—as might arise between coastal versus interior villages, central trading centers versus remote agricultural sites, or even between neighborhoods and social strata in the same village. We also note that modern populations are collected with different biases than ancient individuals  has emerged due to recent genetic drift. Overall, the history of isolation, migration, and genetic drift on the island has given rise to an unique constellation of allele frequencies, and illuminating this history will help future efforts to understand genetic-disease variants prevalent in Sardinia and throughout the Mediterranean, such as those underlying beta-thalassemia and G6PD deficiency.With these caveats in mind, we find that genome-wide ancient DNA provides unique insights into the population history of Sardinia. Our results are consistent with gene flow being minimal or only with genetically similar populations from the Middle Neolithic until the late Bronze Age. In particular, the onset of the Nuragic period was not characterized by influx of a distinct ancestry. The data also link Sardinia from the Iron Age onwards to the broader Mediterranean in what seems to have been a period of new dynamic contact throughout much of the Mediterranean. A parallel study focusing on islands of the western Mediterranean provides generally consistent results and both studies make clear the need to add complexity to simple models of sustained isolation that have dominated the genetic literature on Sardinia40. The second was from the Seulo Caves project41, an on-going project on a series of caves that span the Middle Neolithic to late Bronze Age near the town of Seulo. The project focuses on the diverse forms and uses of caves in the prehistoric culture of Sardinia. The Neolithic individuals from Sassari province as well as the post-Nuragic individuals were collected from several co-authors as indicated in Supplementary Information Section 42. The fourth are a collection of post-Nuragic sites spanning from the Phoenician to the Medieval time. All samples were handled in collaboration with local scientists and with the approval of the local Sardinian authorities for the handling of archeological samples . For more detailed description of the sites, see Supplementary Information Section The archeological samples used in this project derive from several collection avenues. The first was a sampling effort led by co-author Luca Lai, leveraging a broad base of samples from different existing collections in Sardinia, a subset of which were previously used in isotopic analyses to understand dietary composition and change in prehistoric Sardinia61 and then converted into double-stranded libraries retaining62 or partially reducing63 the typical aDNA substitution pattern resulting from deaminated cytosines that accumulate towards the molecule’s termini. After indexing PCR62 and differential amplification cycles, the DNA was shotgun sequenced on Illumina platforms. Samples showing sufficient aDNA preservation where captured for mtDNA and  ≈1.24 million SNPs across the human genome chosen to intersect with the Affymetrix Human Origins array and Illumina 610-Quad array47. The resulting enriched libraries were also sequenced on Illumina machines in single-end or paired-end mode. Sequenced data were pre-processed using the EAGER pipeline64. Specifically, DNA adapters were trimmed using AdapterRemoval v265 and paired-end sequenced libraries were merged. Sequence alignment to the mtDNA (RSRS) and nuclear (hg19) reference genomes was performed with BWA66 , duplicates were removed with DeDup64 and a mapping quality filter was applied (MQ≥30). For genetic sexing, we compared relative X and Y-chromosome coverage to the autosomal coverage with a custom script. For males, nuclear contamination levels were estimated based on heterozygosity on the X-chromosome with the software ANGSD67.The ancient DNA (aDNA) workflow was implemented in dedicated facilities at the Palaeogenetic Laboratory of the University of Tübingen and at the Department of Archaeogenetics of the Max Planck Institute for the Science of Human History in Jena. The only exception was for four samples from the Seulo Cave Project which had DNA isolated at the Australian Centre for Ancient DNA and capture and sequencing carried out in the Reich lab at Harvard University. Different skeletal elements were sampled using a dentist drill to generate bone and tooth powder, respectively. DNA was extracted following an established aDNA protocol68. We used custom python scripts (https://github.com/mathii/gdc3) to generate pseudo-haploid genotypes by sampling a random read for each SNP on the capture array and setting the genotype to be homozygous for the sampled allele. We then screened for first degree relatives using a pairwise relatedness statistic, and identified one pair of siblings and one parent-offspring pair within our sample , and nuclear contamination <6% (average 1.1%), and after inspection of contamination patterns . We assigned haplogroups following the nomenclature proposed by the PhyloTree database build 17 (http://www.phylotree.org)72 and for Sardinian-specific haplogroups36.Data originating from mtDNA capture were processed with schmutzihttps://isogg.org/tree . We merged this data with our set of calls and identified markers available in both to create groups of equivalent markers for sub-haplogroups. Our targeted sequencing approach yielded read count data for up to 32,681 such Y-linked markers per individual. As the conventions for naming of haplogroups are subject to change, we annotated them in terms of carrying the derived state at a defining SNP. We analyzed the number of derived and ancestral calls for each informative marker for all ancient Sardinian individuals and reanalyzed male ancient West Eurasians in our reference dataset. Refined haplotype calls were based on manual inspection of ancestral and derived read counts per haplogroup, factoring in coverage and error estimates.To determine the haplotype branch of the Y chromosome of male ancient individuals, we analyzed informative SNPs on the Y-haplotype tree. For reference, we used markers from Ancient DNA datasets from Western Eurasia and North Africa: We downloaded and processed BAM files from several ancient datasets from continental Europe and the Middle East50. To minimize technology-specific batch effects in genotype calls and thus downstream population genetic inference, we focused on previously published ancient samples that had undergone the capture protocol on the same set of SNPs targeted in our study. We processed these samples through the same pipeline and filters described above, resulting in a reference dataset of 972 ancient samples. Throughout our analysis, we used a subset of n = 1,088,482 variants that was created by removing SNPs missing in more than 90% of all ancients individuals (Sardinian and reference dataset) with at least 60% of all captured SNPs covered.58. We merged individuals into groups  and called genotypes on the Human Origin autosomal SNPs to create a dataset similar to the other modern reference populations. For analyses on province level, we used a subset where at least three grandparents originate from the same geographical location and grouped individuals accordingly .73. We additionally filtered out rare variants with minor allele frequency  on two large-scale datasets of modern genotypes from Western Eurasia and North Africa  and Sardinia . For both datasets, we normalized the genotype matrix by mean-centering and scaling the genotypes at each SNP using the inverse of the square-root of heterozygosity74 . The outgroup-f3 statistic can be interpreted as a measure of the internal branch length of a three-taxa population phylogeny and thus does not depend on genetic drift or systematic error in one of the populations that are being compared77.We measured similarity between groups of individuals through computing an outgroup-78. We fixed the ancestral allele counts to n = 106 to avoid finite sample size correction when calculating outgroup f3.We used the ancestral allelic states as an outgroup, inferred from a multi-species alignment from Ensembl Compara release 59, as annotated in the 1000 Genomes Phase3 sites vcff3 and f4-statistics that test for admixture were computed with scikit-allel using the functions average_patterson_f3 and average_patterson_d that implement standard estimators of these statistics77. We estimated standard errors with a block-jackknife over 1000 markers (blen=1000). For all f-statistics calculations, we analyzed only one allele of ancient individuals that were represented as pseudo-haploid genotypes to avoid an artificial appearance of genetic drift—that could for instance mask a negative f3 signal of admixture.The f3 statistic does), we estimated average pairwise FST and its standard error via block-jackknife over 1000 markers, using average_patterson_fst from the package scikit-allel. When analyzing ancient individuals that were represented as pseudo-haploid genotypes, we analyzed only one allele. For this analysis, we removed first degree relatives within each population. Another estimator, average_hudson_fst gave highly correlated results (r2 = 0.89), differing mostly for populations with very low sample size (n ≤ 5) and did not change any qualitative conclusions.To measure pairwise genetic differentiation between two populations  to assess whether a specific reduced-rank model, representing a particular admixture scenario, can be rejected in favor of a maximal rank (“saturated”) model for the matrix of f4-values12. We report p-values under the approximation that the LRT statistic is χ2 distributed with degrees of freedom determined by the number of “left” and “right” populations used in the f4 calculation and by the rank implied by the number of admixture components. The p-values we report are not corrected for multiple testing. Formal correction is difficult as the tests are highly correlated due to shared population data used across them; informally, motivated by a Bonferroni correction of a nominal 0.01 p-value with 10 independent tests, we suggest only taking low p-values (<10−3) to represent significant evidence to reject a proposed model. The full qpAdm results are discussed in Supp. Note We estimated admixture fractions of a selected target population as well as model consistency for models with up to five source populations as implemented in qpAdm (version 810), which relates a set of “left” populations  to a set of “right” populations (diverse out-groups)Further information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Peer Review FileReporting SummaryDescription of Additional Supplementary Files


Y-chromosomal short tandem repeats (Y-STRs) are widely used in human research for the evaluation of population substructure or population differentiation. Previous studies show that several haplotype sets can be used for the evaluation of population differentiation. However, little is known about whether each Y-STR in these sets performs well during this procedure. In this study, a total of 20,927 haplotypes of a Yfiler Plus set were collected from 41 global populations. Different configurations were observed in multidimensional scaling (MDS) plots based on pairwise genetic distances evaluated using a Yfiler set and a Yfiler Plus set, respectively. Subsequently, 23 single-copy Y-STRs were characterized in the evaluation of population differentiation using the mean of allele frequency difference (mAFD) between populations. Our results indicated that DYS392 had the largest mAFD value (0.3802) and YGATAH4 had the smallest value (0.1845). On the whole, larger pairwise genetic distances could be obtained using the set with the top fifteen markers from these 23 single-copy Y-STRs, and clear clustering or separation of populations could be observed in the MDS plot in comparison with those using the set with the minimum fifteen markers. In conclusion, the mAFD value is reliable to characterize Y-STRs for efficiency in the evaluation of population differentiation. Over the past decade, Y-chromosomal short tandem repeats (Y-STRs) have been widely used in human research and forensic practice to supplement information retrieved from autosomal DNA profiling. Y-STRs can be applied for acquiring male genotypes from stains of unbalanced male/female DNA mixture ,2,3, forBecause of the male-specific inheritance pattern and haploid nature of the Y chromosome, it is more sensitive to genetic drift and founder effect for Y-linked markers than for autosomal ones. Y-STRs are widely used for the evaluation of population substructure or population differentiation by testing pairwise genetic distances. The clustering or separation of populations can further be visualized using multidimensional scaling (MDS) analysis based on the similarity of pairwise genetic distances between populations. Previous studies show that several haplotype sets, such as the minimal haplotype set, Yfiler set, PowerPlex Y23 set and Yfiler Plus set, can be used in the evaluation of population differentiation ,9,10,11.In this study, we characterized 23 single-copy Y-STRs adopted in a Yfiler Plus set in the evaluation of population differentiation using the mean of allele frequency difference (mAFD) between populations. Haplotype profiles of the Yfiler Plus set were compiled from 41 global populations, and an evaluation of population differentiation using the Yfiler set and Yfiler Plus set was performed. Further, the mAFD values of 23 Y-STR loci between 41 populations were calculated, respectively, and the reliability of the mAFD to characterize Y-STRs in the evaluation of population differentiation was estimated based on pairwise genetic distances and MDS analysis from a series of fifteen-marker sets.A total of 41 populations with haplotypes of 27 Y-STR loci in a Yfiler Plus set were collected . To facin is the total number of alleles at a locus, and if-terms denote the frequency of the ith allele in the two populations. As for every Y-STR, the mean of the AFD across all population pairs was obtained based on the AFD for every population pair. Here, the mAFD values of every single-copy Y-STRs were computed using an in-house Python script.The allele frequency difference (AFD) of each Y-STR locus between two populations was defined as follows according to previous reports :AFD=12∑ip values were calculated at a significant level of 0.05 using 10,000 permutations. In addition, MDS analysis estimating similarity quantitatively among populations was performed with the software IBM SPSS® Statistics version 22 . The output of MDS is a plot that reveals the relational structures of objects, where similar objects cluster together, and dissimilar ones are far from each other.The evaluation of population differentiation using a Yfiler set and a Yifler Plus set was carried out. Pairwise genetic distances of these two marker sets were gauged using Arlequin v3.5 , and a lFor validating the efficacy of the mAFD in the evaluation of population differentiation, nine marker sets containing 15 Y-STRs were constructed along with the stepwise reduction of mAFD value (step size: one marker) using the 23 Y-STRs evaluated. Pairwise genetic distances between populations for these Y-STR sets were quantified and compared. To visualize the differences in pairwise genetic distances between populations, MDS analysis of all marker sets was conducted.To evaluate the population differentiation, the pairwise genetic distances between 41 populations were calculated based on a Yfiler set and Yfiler Plus set, respectively, and population differentiation was visualized in a MDS plot. On the whole, the distribution of populations was in accordance with the biogeographic pattern based on pairwise genetic distances tested using a Yfiler set or a Yfiler Plus set A,B. HoweIn this study, the same population sets were employed to evaluate population differentiation using a Yfiler set and a Yfiler Plus set, which can reduce the bias from sampling using different population sets. The Yfiler Plus set seems to more clearly cluster or separate populations than the Yfiler set in MDS analysis A,B, whicTo characterize Y-STRs in the evaluation of population differentiation, the mAFD values of 23 Y-STRs were calculated as described in Material and Methods section. As shown in In this study, the mAFD is described to evaluate the variance of genetic markers in populations, which is somewhat similar to Rogers’ distance ,18. RogeThe difference of allelic frequency between populations might be affected by many factors, such as founder effect, genetic drift and mutation rate. The mutation events of each Y-STR occur independently in the population, although the evaluation of genetic distances is based on the Y-STR haplotypes. A low mutation rate for Y-STRs might make the difference of allelic frequency more sensitive to genetic drift and founder effect. In contrast, a high mutation rate for Y-STRs might help to rapidly establish a permanent allelic configuration in the population. In this study, the mutation rate did not show a close correlation with the mAFD . SimilarTo validate the efficacy of the mAFD in the evaluation of population differentiation, 23 Y-STR markers were ranked in a descending order based on the mAFD, and nine sets were established using a sliding-set method with a set of 15 markers and a step size of one marker. The pairwise genetic distances calculated using these sets could be varied , and theTo visualize population differentiation, MDS analysis was performed based on pairwise genetic distances obtained using the sets with the top fifteen markers and the minimum fifteen markers, respectively. As shown in Genetic distance is defined as the extent of genetic differentiation between populations or species. Previous studies demonstrate the capacity of several haplotype sets in the evaluation of population differentiation through genetic distance, and little attention has been paid to the selection of genetic markers. Unlike autosomal markers, which can reach Hardy–Weinberg equilibrium, Y-STRs are paternally inherited. Currently, it remains unknown whether a Y-STR locus in a haplotype is suitable for the evaluation of population differentiation. Although the method based on the mAFD value for the evaluation of single Y-STR is relatively simple in this study, our results show that the mAFD is suitable for characterizing Y-STRs in the evaluation of population differentiation. In the future, the introduction of more global populations can improve the evaluation of the mAFD value for each Y-STR.


Margaritifera margaritifera in Southern Sweden, and in relation to the population size and life history of its hosts, Salmo trutta and S. salar. Mussel populations were genetically differentiated into two clusters, further subdivided into four clusters and distinct conservation units. Regardless of host species, the genetic differentiation was lower among mussel populations sustained by sea-migrating than by resident hosts, while the genetic diversity was higher in mussel populations sustained by sea-migrating than by resident hosts. Genetic diversity of mussel populations was positively related to host abundance. Mussel population size was positively related to high genetic diversity of mussels sustained by resident hosts, while low mussel population size sustained by sea-migrating hosts had a high genetic diversity. The results of our study suggest a combined influence of mussels and host fish on genetic structure of unionoid mussels. We suggest to conserve not only mussel population sizes and host fish species, but also consider host migratory/resident behaviour and abundance when designing conservation programs.Many landscape and biotic processes shape the genetic structure of populations. The genetic structure of species with parasitic stages may also depend on the life history and ecology of their host. We investigated population genetic structure of the mussel  Recent anthropogenic processes such as habitat destruction and fragmentation often lead to reduced population sizes and connectivity, reducing the genetic diversity and increasing the genetic differentiation4. In addition, life history traits and species interactions affect the genetic structure of populations at different spatial and temporal scales5.Several historic processes shape the structure of landscapes, with consequences for the genetic structure of populations at local and regional scales9. Such processes may result in differences in the genetic structure of post-parasitic sedentary population, depending on the host species, but also among strains of the same host species11. Population size is another factor affecting the genetic structure, where a high population size and effective population size, respectively, often positively affects genetic diversity12. In addition, the host population size is a factor that potentially affects the genetic structure of species with parasitic life stages. Many host individuals may increase the chance for each individual parasite to infest a host and pass on their genes to the next generation, sustaining the genetic diversity of the post-parasitic population. Mobile and dense host populations might thus increase the potential for high numbers of spawning parasitic individuals infecting their hosts, and connect sub-populations of post-parasitic populations, thereby sustaining the genetic diversity and decreasing the genetic differentiation.Species with parasitic life stages are dependent on the life history and ecology of the host, where differences in mobility patterns of the host may influence the genetic structure of post-parasitic life stages13. Combined with the connectivity within and among river systems, host fish mobility may thus affect the genetic structure of mussel populations. Mussel populations sustained by host fish species with a high mobility may thus show less differentiation and higher genetic diversity compared to mussel populations sustained by stationary host fish species14. Such genetic structure may also be more accentuated between areas with high versus areas with low connectivity between mussel populations. In addition, population sizes of host fish typically differ manifold among mussel species and populations15, with potential consequences on the genetic diversity of the mussel populations. Thus, the higher the host fish population size and abundance, the higher potential for high genetic diversity of parasitic mussel larvae on the fish, and consequently for the mussels leaving the host fish entering the benthic mussel population.The threatened unionoid mussels are obligate parasites on one or more fish species, and the mobility pattern of the host fish is one factor affecting the distribution and passive movement of the musselsMargaritifera margaritifera) is parasitic on Atlantic salmon  and/or brown trout  for 10–12 months, whereupon they fall off the fish and become sedentary mussels16. Atlantic salmon, with their high abundances of young-of-the-year (YOY) individuals, is generally considered as the primary host for the mussel in river reaches where their distribution overlap. Sea-migrating brown trout, also with high abundances of YOY individuals, appear to be the host in river reaches where they spawn and Atlantic salmon has no historical distribution. Finally, in tributaries higher up in river catchments, tributary resident brown trout, with comparatively low abundances of YOY individuals, are functional hosts20.The threatened freshwater pearl mussel  populations are genetically differentiated among drainages (2) the genetic differentiation is higher between mussel populations sustained by tributary resident brown trout compared to mussel populations sustained by sea-migrating brown trout and Atlantic salmon (3) the genetic diversity is higher in mussel populations sustained by sea-migrating brown trout and Atlantic salmon compared to mussel populations sustained by tributary resident brown trout. We also hypothesize that the genetic diversity increase both as a function of mussel population size and host fish abundance. Based on this knowledge, one last aim is to recommend management strategies and conservation programs.18  were performed in a total volume of 10 µl, with the following components: 2 µl DNA, 0.35 µl 2.5 mM dNTP, 1 µl 10XB and 0.075 µl Taq DNA polymerase (with MgCl2 in buffer) and different amounts of primers . Two different PCR reactions were used for the 6 microsatellites. TD 55–45 for MarMa 5167, 4277, 4143 and 4315 and TD 60–50 for MarMa 3621 and 3116. PCR was done with an initial denaturation in 96 °C for 1 min, followed by 30 cycles of 96 °C for 20 s, 50 °C for 20 s and 60 °C for 4 min, and extension in 72 °C for 1 min. PCR fragments were separated using polyacrylamide gels and were analyzed in CEQ 8000 Genetic Analysis System (Beckman Coulter).Thirteen published microsatellite loci27. Number of observed (NA) and private alleles (NPA), observed (HO) and expected heterozygosity (HE), inbreeding coefficients (FIS), and deviations from the Hardy–Weinberg equilibrium (HWE) were determined in GENEPOP v.4.129. Significance exact tests were estimated by a Markov chain method after 10,000 randomizations. Presence of null alleles was tested for each locus using MICROCHECKER30.All individuals were genotyped for the 6 loci, followingFST fixation index31 in FSTAT 2.9.3.232 with significance being assessed with 1.000 permutations with adjustment for multiple comparisons using the sequential Bonferroni method. The impact of null alleles on FST estimation was assessed by comparing FST estimates before and after correction for null alleles. Null alleles were found to have a minimal or none impact on FST estimates and later analysis, hence all subsequent analyses were conducted on data uncorrected for null alleles. For a tree-based analysis, FST were calculated and used with PopTree233 to produce a Neighbour-Joining (NJ) tree based on population allele frequencies.Global genetic differentiation among populations was assessed with the 34. Correlated allele frequencies, an admixture model without prior population information for individuals, was assumed. Fifteen independent runs were made for K = 1–20 with each run consisting of a burn-in of 105 Markov-chain Monte Carlo steps, followed by 5 × 105 steps. Selection of the most likely number of genetic clusters (K) was based on the second order rate of change in probability between successive K values as described in35 and implemented in STRUCTURE HARVESTER36. In systems with hierarchical population structure, STRUCTURE typically best resolves the highest level of population subdivision35. To resolve lower levels of subdivision, STRUCTURE analyses were also conducted separately for each of the main clusters identified , among populations within clusters (FSC) and among populations (FCT) was assessed, and significance of F-statistics was tested using 10,000 permutations.Tests for genetic differentiation between the clusters and groups identified in STRUCTURE and DAPC analyses were conducted using AMOVA in ARLEQUIN v.3.5.1.37 as implemented in the BOTTLENECK software was applied, assuming a two-phase mutation model (TPM)41. TPM was parameterized with 70% single-step mutations, assuming a conservative variance among multiple steps of 30, with the significance being assessed using Wilcoxon sign-rank test with 10,000 iterations. Rates of recent migration (m) among populations/clusters identified in STRUCTURE, as well as its direction, were estimated using the Bayesian multilocus genotyping procedure implemented in BAYESASS v.3.042. The program was run after 106 MCMC iterations, with a burn-in of 105 iterations and a sampling frequency of 1,000 with default values for all parameters.To infer for recent demographic changes, the moment-based method ofHO), and expected (HE) heterozygosity, and inbreeding coefficient (FIS) were compared between mussel populations having sea-migrating brown trout or Atlantic salmon as host fish and mussel populations having a tributary resident brown trout population as host fish, using Mann-Whiney U-tests.To test our hypothesis that migratory and resident life history of the host fish can influence the genetic diversity of the mussel populations, the observed (HO) and expected (HE) heterozygosity, and inbreeding coefficient (FIS), stepwise regressions were run for these genetic parameters as dependent variables, with the abundance of YOY brown trout and mussel population size, and the abundance of 1 + brown trout and mussel population size, respectively, as independent variables. Mean abundance of YOY brown trout and 1 + brown trout living sympatrically with mussel populations were included in these regressions. Mussel populations with a former distribution of anadromous sea-trout and Atlantic salmon populations, but where barriers had cut-off their migration route between the Baltic Sea and the mussel populations, were not included in these analyses. Atlantic salmon does not exist in these areas today, while brown trout abundances are dramatically reduced when only tributary resident brown trout populations can spawn upstream of the migration obstacles. Thus, if the genetic composition of mussels is influenced by their host fish abundances as we hypothesized, the present brown trout abundances are not reflecting their historical abundances, when they were potentially influencing the genetic diversity of these long-lived mussels, given that the majority of mussels that we find today were recruited before the migration obstacles were constructed.As we hypothesized that host fish abundance positively affects the genetic diversity of the mussel populations, mean brown trout abundances over thirty-one years (1986–2005) were extracted from the Swedish electrofishing database. To see if the abundance of YOY brown trout, the abundance of brown trout older than one year (1 +) and mussel population size were related to observed (HO) and expected (HE) heterozygosity, and inbreeding coefficient (FIS) using linear regression. Lastly, mussel population sizes were also related to the genetic variables within each drainage, using linear regressions.Mussel population sizes were estimated based on the Swedish standard survey protocol for the freshwater pearl mussel. In addition to the stepwise regressions, mussel population size from all rivers were also related to observed (NA) were between 13  and 42 (Not). Nine out of 17 populations did not exhibit any private alleles (NPA), while the remaining ranged from one to four except in the population Not which had 11 private alleles. A large number of sampling localities showed significant deviations from Hardy–Weinberg expectation, manifested as deficit of heterozygotes. Inbreeding coefficient values (FIS) were positive for all populations, ranging from 0.02 (Bra) in the southernmost populations to 0.62 (Alg) in the northernmost populations. All inbreeding coefficient values were statistically significant (p < 0.001). The expected heterozygosity (HE) ranged between 0.18 and 0.64, while the observed heterozygosity (HO) ranged between 0.08 and 0.56. The expected and observed heterozygosity’s both had their highest values in the southernmost drainages  Neighbour-Joining tree (NJ-FST)  Fig.  reveals ST) Fig.  and 2.Fi35 based on the first STRUCTURE analysis (K = 1–20) indicated that two is the most likely number of clusters present in the full dataset  identified two additional groups, i.e. one corresponding to Sil, Nat, Bra and Mie populations and the other to Pau and Not populations  evidenced a well-structured population pattern, revealing a lower differentiation within geographically close southern mussel populations sustained by Atlantic salmon and Sea-migrating brown trout. The exception was the Mie population, which showed a high differentiation from the geographically close drainages Sil, Nat and Bra, and the highest differentiation to all other mussel populations. The genetic differentiation was generally high to extremely high within and among the main drainages where the mussel populations were sustained by tributary resident brown trout populations  and expected (HE) heterozygosity were both higher in mussel populations with sea-migrating brown trout / Atlantic salmon as host fish  than in mussel populations with tributary resident brown trout population as host fish  . The inbreeding coefficient (FIS) were significantly lower in mussel populations with sea-migrating brown trout / Atlantic salmon as host fish (FIS: 0.13 ± 0.063) than in mussel populations with tributary resident brown trout population as host fish (FIS: 0.43 ± 0.16) .The results of the sea-migratory vs. tributary resident life history of the host fish on genetic diversity and inbreeding coefficient between mussel populations showed that the observed (HO) and expected (HE) heterozygosity, and inbreeding coefficient (FIS), the abundance of YOY brown trout and mussel population size, and the abundance of brown trout older than one year and mussel population size, respectively, were tested in stepwise regressions. Mussel population size did not have any significant effect on the observed (HO) and expected (HE) heterozygosity, or the inbreeding coefficient (FIS), and was excluded in all multiple regressions, both when regressed with the abundance YOY brown trout and the abundance of brown trout older than one year (p > 0.05). There was however a significant positive relationships between the abundance of YOY brown trout and observed heterozygosity (HO) , and between the abundance of YOY brown trout and expected heterozygosity (HE) , but no relationship between the abundance of YOY brown trout and inbreeding coefficient (FIS) (p = 0.059). For older brown trout, there were no significant relationships between the abundance of brown trout older than one year or mussel population size and observed heterozygosity (HO) , between the abundance of brown trout older than one year and expected heterozygosity (HE) , or between the abundance of brown trout older than one year and inbreeding coefficient (FIS)   or observed heterozygosity (HO) or the mean inbreeding coefficient (FIS) (p > 0.05). Analyses were also performed for populations within each drainage. There was a significant relationship between population size and the expected heterozygosity (HE), for the Göta Älv drainage , but not between any other genetic variables for any drainage.The mussel population sizes from all rivers were also related to the genetic variables, but there were no significant relationships between mussel population size and the expected heterozygosity  which had lower genetic diversity and higher differentiation compared to the three geographically close mussel populations sustained by sea-migrating brown trout. Genetic drift may explain some of these differences in this extremely small populations, but also the fact that the population may partly by sustained by lake-migrating brown trout. Also, even if these four mussel populations had high genetic diversity, the mussel population in Nat sustained by sea-migrating brown trout seemed to have gone through a bottleneck. This may possibly be a result of the low population size, which shows that also these populations are sensitive and need prioritization in management.52. These results also highlight the complex interactions and factors structuring the genetic composition of threatened mussels with a parasitic life stage.Interestingly, brown trout abundance, including tributary-resident and sea-migrating populations, appears to positively affect the genetic diversity of mussel populations. Many brown trout individuals over a large distribution may in fact increase the potential for a high number of spawning mussel individuals to infect their host fish. This should result in high infection rates of the parasitic mussel larvae with a diverse genetic composition, sustaining the genetic diversity of the benthic mussel population. Host fish abundance can thus be one key to understand population genetics also of other unionoid mussel species. Such relations between host fish abundance and genetic diversity may add to factors making the unionoid mussels highly threatened43. This represents two conservation units mixed over the Göta älv and Norrström main drainages, one conservation unit in the Emån main drainage, and one conservation unit in the four geographically close southernmost drainages sustained by sea-migrating brown trout. We also suggest considering resident/migratory life history of the host when designing management strategies and conservation programs.The present study shows that population genetic studies can be of high importance when protecting and managing threatened unionoid mussels. We suggest that the freshwater pearl mussel populations in the study area should be divided into four conservation units21 and their host fish. This implies that there seem to be a special need to protect mussel populations sustained by tributary-resident brown trout populations from further decline10. In such areas, the high frequency of fragmentation by migration obstacles (i.e. dams and hydropower plants) reduce the connectivity for the host fish10, and is a probably a large threat to gene flow between mussel populations21. In contrast, even small mussel populations can be genetically diverse when sustained by sea-migrating brown trout, even if their density is also important for genetic. In addition, host fish abundance appear to positively influence genetic diversity, why managers should make sure that fish abundance stays intact over time.The results of our study also call for attention on conservation of population sizes and abundances of the mussels53. According to the results from our study and others54, we suggest that population genetic structure assessments are vital before such activities are performed. Information about conservation units is a first step to evaluate which mussel strains to use in translocation and propagation programs. Careful investigations of the genetic structure of sub-populations should however be prioritized, given the high genetic differentiation among mussels from different tributaries, especially for mussels sustained by tributary-resident brown trout21. In addition, co-evolutionary considerations should also be considered, since disruption of locally adapted mussels and host fish strains living in sympatry can result in changes in recruitment success57. Investigations of the genetic structure should thus also be applied to geographically close mussel populations sustained by sea-migrating brown trout and Atlantic salmon, even with their generally low genetic differentiation and high genetic diversity. In summary, our results highlight the complex management for threatened species with life cycles including a parasitic stage on a host.Given the decreasing population sizes and extinctions of the freshwater pearl mussel, translocation and propagation are often consideredSupplementary Figures.Supplementary Table.


Vietnam features extensive ethnolinguistic diversity and occupies a key position in Mainland Southeast Asia. Yet, the genetic diversity of Vietnam remains relatively unexplored, especially with genome-wide data, because previous studies have focused mainly on the majority Kinh group. Here, we analyze newly generated genome-wide single-nucleotide polymorphism data for the Kinh and 21 additional ethnic groups in Vietnam, encompassing all five major language families in Mainland Southeast Asia. In addition to analyzing the allele and haplotype sharing within the Vietnamese groups, we incorporate published data from both nearby modern populations and ancient samples for comparison. In contrast to previous studies that suggested a largely indigenous origin for Vietnamese genetic diversity, we find that Vietnamese ethnolinguistic groups harbor multiple sources of genetic diversity that likely reflect different sources for the ancestry associated with each language family. However, linguistic diversity does not completely match genetic diversity: There have been extensive interactions between the Hmong-Mien and Tai-Kadai groups; different Austro-Asiatic groups show different affinities with other ethnolinguistic groups; and we identified a likely case of cultural diffusion in which some Austro-Asiatic groups shifted to Austronesian languages during the past 2,500 years. Overall, our results highlight the importance of genome-wide data from dense sampling of ethnolinguistic groups in providing new insights into the genetic diversity and history of an ethnolinguistically diverse region, such as Vietnam. Mainland Southeast Asia (MSEA) is of great interest in terms of ethnolinguistic diversity and deep population history. The early settlement of anatomically modern humans in MSEA dates back to at least 65 thousand years ago (ka)  and is awww.gso.gov.vn; accessed the General Statistics Office of Vietnam in September 2019), comprising 54 official ethnic groups; 110 languages are spoken in the country . Vietnam has a population size of more than 96 million people  data from 22 Vietnamese ethnolinguistic groups, speaking languages that encompass all five families in MSEA. We incorporate published data and analyze the allele and haplotype sharing within the Vietnamese groups and between them and both nearby modern populations and nearby SEA ancient samples. Our results provide new insights into the genetic diversity of these ethnolinguistically diverse groups, including their recent interactions and demography.We genotyped individuals from 22 Vietnamese ethnolinguistic groups  and mergWithin modern Vietnamese groups, individuals from the same language family are mostly placed together B. There K = 6 . With respect to the ancient samples, the pink source is enriched in the Tianyuan and Hoabinhian samples, whereas the blue source is enriched in the Neolithic samples. The blue source decreases in ancient samples younger than the Neolithic, with a concomitant increase of the green, purple, or black sources. Specifically, the green and black sources increase in the Bronze Age and historical samples in Vietnam. The black source also increases in the historical samples from Malaysia, whereas the purple source is enriched not only in the Iron Age sample from Long Long Rak (Thailand) but also in the Neolithic sample from Oakaie (Myanmar).We next performed an ADMIXTURE analysis and found that the lowest cross-validation error occurs at K = 6 . Under t6 , than to other Vietnamese groups. Also, the HM-speaking Hmong and the TK-speaking Colao and Lachi stand out in lacking the black and blue sources, whereas the ST-speaking Sila and the AA-speaking Mang lack the black and green sources. The remaining Vietnamese groups present fairly similar profiles  that are also similar to the Dai from southern China.K are associated with higher cross-validation errors, they can nevertheless provide additional insights  approaches to investigate demography and recent contact based on haplotype sharing.The above analyses (PC and ADMIXTURE) are descriptive analyses that provide an overview of the relationships of the populations analyzed. To further explore and quantify these relationships, we used outgroup f3 statistic indicate more shared drift, and hence a closer relationship, between two test populations since their divergence from the outgroup population.Higher values of the outgroup f3 results within Vietnamese groups  and Kinabatagan , except for the AN groups. The f3 values normalized to range from 0 to 1 tend to be especially high (>0.95) with the historical sample from Hon Hai Co Tien. The AN-speaking Ede/Giarai as well as the AA-speaking Mang/Khomu show higher f3 values with the Neolithic samples from Tam Pa Ling (Laos), Tam Hang (Laos), Gua Cha , and Man Bac (Vietnam). The f3 values with the Neolithic sample from Oakaie (Myanmar), Bronze Age sample from Nui Nap (Vietnam), and Iron Age sample from Long Long Rak (Thailand) are generally high with all groups except the AN groups. The smallest f3 values are those with the Paleolithic sample from Tianyuan and the Hoabinhian samples, and the f3 values with these samples show little variation among Vietnamese groups.When compared with ancient samples , all theWe next investigated interactions within/between populations within the past ∼3 ka by analyzing IBD . The numAlthough IBD sharing within populations provides insights into population size changes, IBD sharing between populations provides insights into recent contact and/or shared ancestry; the longer the shared IBD blocks, the more recent the interaction. We analyzed IBD blocks in three categories: 1–5, 5–10, and >10 cM ; these cf3 and IBD-sharing results, we selected the Htin Mal (AA), Atayal (AN), Miao (HM), Dai (TK), and Chinese Lahu (ST) as the representative source populations for the five language families in Vietnam. We then calculated f4 statistics of the form f4 to test if each Vietnamese group shares any excess ancestry with any of the representative source populations, compared with the southern Han Chinese. Significantly positive Z-scores indicate excess shared ancestry between the Vietnamese group and the source population, whereas significantly negative Z-scores indicate excess shared ancestry between the Vietnamese group and southern Han Chinese. The resulting Vietnamese f4 profiles are heterogeneous within each language family . We found that the AA and AN groups indeed shared excess ancestry with the Hoabinhian sample from Pha Faen, most of the Neolithic samples except for the samples from Oakaie  and Nam Tun, and the historical samples from Supu Hujung and Kinabatagan  shows that the first split separates the Onge from a branch leading to the ST-speaking Chinese Lahu and the HM-speaking Miao (Z = −2.235). This graph maintains the same branching order for the Chinese Lahu, Miao, and Dai  for the Vietnamese AA groups (Z = −3.642), but even so this graph retains the Kinh/Muong versus Khomu/Mang division  shows different histories for the Giarai and Ede versus the Cham .The best-fitting graph for the AN groups (worst-fitting the Cham . The GiaZ = −1.462) indicates that the Hmong and Pathen share an ancestor with the Atayal, whereas the Dao are admixed from an ancestor of the Atayal and a node derived from the ancestor of the Hmong (Z = 2.627) in which the Dai share an ancestor with the Atayal; the Miao are not modeled as admixed but share ancestry with the Hmong, Atayal, and Dai; the Pathen are admixed between an ancestor of the Hmong and an ancestor of the Atayal/Dai; and the Dao are admixed between the same or a closely related ancestor of the Hmong (our data are insufficient to distinguish between these two possibilities) and an ancestor of the Dai , the TK groups Thai, Lachi, Nung, and Tay form a clade with the Dai , with the Lolo most closely related to the Atayal  form a clade together with the Atayal in the best-fitting graph , and the beginning of their separate migrations into MSEA dates to ∼2.5 ka . The TK supplementary fig. S18f3 and IBD analyses than other neighboring ST groups  but also indicates more recent interactions between the ST and HM groups, within the past few hundred years.The ST family originated in northern China ∼7 ka  and thenT groups . CompareT groups , and 8F.T groups  and 7. IWe have analyzed newly generated genome-wide SNP data for the majority group Kinh plus 21 smaller ethnic groups from Vietnam. These ethnolinguistic groups speak languages that encompass the five major language families in MSEA. Our study shows extensive genetic diversity of the Vietnamese ethnolinguistic groups that is associated with heterogeneous ancestry sharing profiles in each language family. In contrast to previous studies suggesting a largely indigenous origin of the Vietnamese, we find evidence for extensive contact, over different time periods, between Vietnamese and other groups. However, the linguistic diversity is not completely in agreement with genetic diversity. In particular, the HM and TK groups in Vietnam demonstrate extensive interactions with populations speaking languages belonging to different families. Moreover, different AA groups show different affinities with other ethnolinguistic groups , whereas the AN groups likely reflect language shift involving AA groups. This study highlights the importance of dense sampling of ethnolinguistic groups, combined with genome-wide data from both extant and ancient sources, to gain insights into the history of an ethnolinguistically diverse region such as Vietnam.the General Statistics Office of Vietnam  and the Ethnologue  and the ancient samples, using smartpca from EIGENSOFT with “lsqproject” and “autoshrink” options. We performed heatmap visualization of downstream PCs using the pheatmap package in R version 3.6.0. For running the ADMIXTURE program, we also first estimated the allele frequency of the inferred ancestral populations  using the less isolated modern populations and then projected the more isolated modern populations and the ancient samples with the -P option. From K = 2 to K = 15, we performed 100 replicates for each K with random seeds. Finally, we used pong version 1.4.7 , shown in We used PCA and ADMIXTURE version 1.3.0  to visuaon 1.4.7  to visuaf3- and f4-statistics from ADMIXTOOLS version 5.1  were used to measure the shared drift between populations X and Y since their divergence from the outgroup. We performed heatmap visualization of f3 profiles using the pheatmap package in R. f4-statistics of the form f4 were used to formally test whether W or X shares more ancestry with population Y. We used Mbuti as the outgroup for all analyses; to ensure there is no excess shared ancestry between any test population and the outgroup, we also repeated the outgroup f3-statistics with French as the outgroup. To avoid attraction to deep outgroups and minimize potential noise from DNA damage patterns in ancient samples, we performed an additional set of f4-statistics using French as the outgroup and only transversions.We used admixr version 0.7.1  to compusion 5.1 , with siWe used SHAPEIT version 2.r904 , 2014 wiWe used refinedIBD  to identf4 analyses. Only the ancient samples with <65% missing data were used here in order to have at least 20,000 SNPs for the model estimation. As the ancient samples are not closely related to the Vietnamese groups in the global admixture analysis, and their inclusion decreases the number of SNPs while increasing the complexity of the modeling, we decided to use only the present-day source populations for dissecting the Vietnamese admixture graph. We first modeled an admixture graph with only the related modern source populations, which we call the backbone populations. For each language family and the backbone populations, we pruned the SNPs as we did in the population structure analyses and calculated allele frequencies with PLINK. Using the covariance of the allele frequency profiles as input, we first ran TreeMix version 1.12  The data will only be used for studies of population history, 2) the data will not be used for medical or disease-related studies, or for studies of natural selection, 3) the data will not be distributed to anyone else, 4) the data will not be used for any commercial purposes, and 5) no attempt will be made to identify any of the sample donors.Molecular Biology and Evolution online.msaa099_Supplementary_DataClick here for additional data file.


Apocynum venetum L. is an important medicinal plant that is mainly distributed in the coastal areas and northwest of China. In addition to its high medical and economic value, its adaptation to saline-alkali and coastal saline lands makes A. venetum an ideal candidate for use in vegetation restoration. To date, the study of A. venetum has been limited in the northwest region of China, little attention has been paid to the genetic diversity and population structure of A. venetum populations in the coastal region. Here, we performed transcriptome sequencing of total RNA from A. venetum leaves and developed efficient expressed sequence tag-simple sequence repeat (EST-SSR) markers for analyzing the genetic diversity and population structure of A. venetum in the coastal region.A. venetum exhibited low level of genetic diversity  and moderate level of genetic differentiation among the populations  in the coastal region. Although the contemporary (mean mc = 0.056) and historical (mean mh = 0.106) migration rates among the six A. venetum populations were moderate, a decreasing trend over the last few generations was detected. Bayesian structure analysis clustered six populations into two major groups, and genetic bottlenecks were found to have occurred in two populations .A total of 86,890 unigenes were generated after de novo assembly, and 68,751 of which were successfully annotated by searching against seven protein databases. Furthermore, 14,072 EST-SSR loci were detected and 10,243 primer pairs were successfully designed from these loci. One hundred primer pairs were randomly selected and synthesized, twelve primer pairs were identified as highly polymorphic and further used for population genetic analysis. Population genetic analyses showed that A. venetum in the coastal region and determined conservation priorities based on these findings. The large dataset of unigenes and SSRs identified in our study, combining samples from a broader range, will support further research on the conservation and evolution of this important coastal plant and its related species.Using novel EST-SSR markers, we evaluated the genetic variation of  Coastal habitats, which are located in the transition zone between terrestrial ecosystems and marine ecosystems, are characterized by unique, complex ecosystems with high ecological value . Their u2, with an annual growth rate of 10,000 hm2   , A. veneA. venetum populations across the Yancheng coastal region. Genetic structure analysis grouped all of the sampled populations into two clusters, and in the majority of populations, a number of admixed plants existed  were found for A. venetum, indicating an absence of inbreeding in the coastal populations of this species  . Under the persistent pressures of anthropogenic and climate disturbances, whether natural A. venetum populations, especially those with low genetic diversity and a small population size, have a sufficient capacity to survive following disturbance or adapt to future environmental change still requires long-term monitoring.Gene flow is crucial for population resilience and persistence following habitat disturbances and environmental changes. This process may provide a source of recruitment and maintain genetic diversity by introducing adaptive alleles from other populations –55. In tted Fig. . Negatives Table . In acco venetum . Due to spersion , 59. TheA. venetum leaves by using the Illumina sequencing platform and successfully developed twelve polymorphic EST-SSR markers in A. venetum. Using these informative makers, we detected a low level of genetic diversity and bottleneck events in A. venetum populations across their natural distribution in the Yancheng coastal region of Jiangsu Province. Although population connectivity between A. venetum populations has been maintained by the high seed dispersal ability of the species, considering the ongoing anthropogenic activities, long-term monitoring and conservation strategies should be implemented to better protect these small populations. The conservation of available genetic diversity is essential to enable the continued utilization of this economically important plant. Based on the results of this study, we suggested that populations that have undergone bottlenecks  and those with a high level of genetic diversity (population XY), should be given conservation priority. In addition, there are not enough comparative data available on the extent of genetic diversity in A. venetum across its global biogeographical ranges, and further studies combining samples from arid regions and more markers should be conducted to gain a more precise understanding of the genetic diversity, population structure, and evolutionary history of this important plant species.In this study, we acquired a high-quality transcriptome from A. venetum were collected from 10 to 24 individuals in six wild populations, representing most of the distribution area of this species in Jiangsu Province  to extract the total genomic DNA according to the manufacturer’s protocol. Besides, fresh leaves of three individuals from the LD population were immediately frozen in liquid nitrogen and stored at − 70 °C for total RNA extraction. Total RNA from these individuals was extracted using the QIAGEN RNeasy Plant Mini Kit following the manufacturer’s procedures . Then the quantified total RNAs were sent to Novogene Bioinformatics Institute  for further processing.A total of 103 leaf samples of First, the RNA concentration and integrity of the samples were measured using the Qubit RNA Assay Kit in a Qubit 2.0 Fluorometer  and the RNA Nano 6000 Assay Kit with the Agilent Bioanalyzer 2100 system . Then, for each sample, cDNA library were construction and sequenced by using equal amounts of qualified RNA according to the standard procedures of Novogene Bioinformatics Institute . The NEBNext® Ultra™ RNA Library Prep Kit for Illumina®  were used to generate sequencing libraries. After purification and quality assessment, the three library preparations were sequenced on the Illumina HiSeq platform .http://www.expasy.ch/sprot); Pfam (Protein family database); the KOG (Clusters of Orthologous Groups of proteins) database and KO (KEGG Orthology) database. Gene ontology (GO) annotation [https://www.genome.jp/kegg/) were determined with an E-value cut-off of 1e-5. The WEGO [Raw RNA-seq reads were processed using in-house Perl scripts to remove reads containing adapters and ploy-N sequences (greater than 5%) and reads with more than 20% low-quality bases . Then, we used Trinity software  with thenotation  of the unotation . The KyoThe WEGO  were useThe MicroSAtellite (MISA) program  was usedPCR amplification was carried out in a 15 μL total reaction volume containing 20–40 ng genomic DNA, 7.5 μl of 2 × Taq PCR MasterMix  and 0.3 μM of each primer. The PCR procedure included an initial denaturation for 3 min at 94 °C, followed by 35 cycles of 30 s at 94 °C, 30 s at optimal annealing temperature for each locus, and 15 s at 72 °C, followed by a final extension of 5 min at 72 °C. The PCR products were checked using silver-stained nondenaturing polyacrylamide gels. Then the optimized SSR primers were further labelled with 6-FAM or HEX fluorescein dye . After PCR amplification, allele identification and genotyping were performed with GeneMarker version 2.2.0 .NA), polymorphism information content (PIC), expected heterozygosity (HE) and observed heterozygosity (HO) of each EST-SSR locus were estimated with Cervus 2.0 [enepop version 4.2 with a Bonferroni correction [icro-Checker version 2.2.3 [A. venetum, the number of alleles (NA), allelic richness (AR), expected heterozygosity (HE), observed heterozygosity (HO), inbreeding coefficient (FIS) and differentiation among populations (FST) were calculated using Fstat version 2.9.3.2 [The number of alleles  was set from 1 to 6, assuming an admixture model and independent allele frequencies. Ten independent runs were conducted for each K with a burn-in of 10,000 and 100,000 Markov Chain Monte Carlo replicates. The most possible K value was chosen by calculating ΔK [tructure Harvester [Population structure was investigated by using the S program  implemenating ΔK  in Strucarvester .A. venetum populations was estimated using BayesAss version 1.3 [7 iterations with a burn-in of 106 generations. Ten replicate runs were conducted with a different initial seed. We assessed the model convergence by comparing the posterior probability densities of parameter estimates across these ten runs. The results presented were from the best-fit run. We also estimated historical gene flow using a coalescent-based Bayesian method implemented in the program Migrate-n version 3.6 [7 iterations with a burn-in of 105. The static heating scheme was used with four chains at different temperatures  [Neμ, where Ne is effective population size, μ is the average mutation rate of microsatellites as 10− 3 per generation) and scaled immigration rates M  between pairs of populations over around 4Ne generations were estimated simultaneously. The number of immigrants per generation Nm was estimated by the equation Nm = ΘM/4. A Wilcoxon signed-rank test was conducted to compare historical and contemporary gene flow estimates among the A. venetum populations.Contemporary inter-population migration between sion 1.3 . The delsion 1.3 . We perfsion 3.6 . The pro0,000.0) . Two parFST/1-FST) values was evaluated. Statistical significance was tested with 1000 permutations of the Mantel test via the R package Vegan [ottleneck version 1.2.02 [A. venetum populations in the Yancheng coastal region have undergone significant reductions in the effective population size (Ne). The first methodology compares the heterozygosity expected (HE) at Hardy-Weinberg equilibrium with the heterozygosity expected at mutation-drift equilibrium (Heq) [Ne generations. The second methodology is based on the allele frequency distribution, which is more appropriate for detecting population declines that have occurred more recently (approximately the last few dozen generations) [A. venetum population. P-values were assessed for statistical significance at the 0.05 level.To test for isolation-by-distance (IBD), the correlation of pairwise geographical distance  and genetic distance (ge Vegan . Wilcoxon 1.2.02  were useum (Heq) , which irations) , 80. ForAdditional file 1: Fig. S1. Length distribution of assembled unigenes (A) and the number of reads mapped to each unigene (B) that generated from A. venetum transcriptome.Additional file 2: Fig. S2. KOG classification of assembled unigenes of A. venetum.Additional file 3: Table S1. Migrate-n results of historical gene flow and BayesAss results of contemporary gene flow with 95% confidence among the Apocynum venetum populations.Additional file 4: Fig. S3. Correlation between genetic differentiation and log geographical distance for the A. venetum populations in Jiangsu Province.Additional file 5 Table S2 The SSR type, expression level and KEGG annotation of 88 flavonoid biosynthesis related genes in A. venetum.


Hippotragus niger, have existed in zoos and privately owned ranches in North America since the 1910s. Unknown founder representation and relatedness has made the genetic management of this species challenging within zoos, while populations on privately owned ranches are managed independently and retain minimal‐to‐no pedigree history. Consequences of such challenges include an increased risk of inbreeding and a loss of genetic diversity. Here, we developed and applied a customized targeted sequence capture panel based on 5,000 genomewide single‐nucleotide polymorphisms to investigate the genomic diversity present in these uniquely managed populations. We genotyped 111 sable antelope: 23 from zoos, 43 from a single conservation center, and 45 from ranches. We found significantly higher genetic diversity and significantly lower inbreeding in herds housed in zoos and conservation centers, when compared to those in privately owned ranches, likely due to genetic‐based breeding recommendations implemented in the former populations. Genetic clustering was strong among all three populations, possibly as a result of genetic drift. We propose that the North American ex situ population of sable antelope would benefit from a metapopulation management system, to halt genetic drift, reduce the occurrence of inbreeding, and enable sustainable population sizes to be managed ex situ.As we enter the sixth mass extinction, many species that are no longer self‐sustaining in their natural habitat will require ex situ management. Zoos have finite resources for ex situ management, and there is a need for holistic conservation programs between the public and private sector. Ex situ populations of sable antelope,  Mean kiwww.sourcepopulation.org, Wildt et al., www.conservationcenters.org), aims to tackle this challenge by bringing together urban zoos, conservation centers, and privately owned ranches to manage select species collectively as metapopulations. The SPA’s initial focus is on four ungulate species  that are not only of high conservation value, but also have large population sizes in the North American private sector relative to the number of animals in the AZA’s Species Survival Plan (SSP®) programs for these species  SSP® ) and masked from downstream analyses. Variants were called using bcftools mpileup , our VCF file was transformed into a.ped PLINK format file and filtered for variants with a minor allele frequency below 0.05, variants with missing call rates exceeding 0.05 and pruned for variant pairs in linkage disequilibrium (LD) with an R2 correlation coefficient of 0.5 or greater . Finally, we used PLINK to filter any SNPs deviating from Hardy–Weinberg equilibrium, with significance set to p < .001. After filtering, all 111 sable antelope remained and 3,954 of the initial 5,000 SNPs were retained, which were used for all downstream population genetic analyses.Sequencing reads from 111 sable antelope were aligned to the sable antelope reference genome . We performed Wilcoxon–Mann–Whitney tests comparing population heterozygosity between our three North American populations . Statistical significance was set at p < .05.Individual observed heterozygosity was calculated using PLINK  and one non‐HBD class using a predefined zoomodel . kR values are approximately double the number of generations from the time of inbreeding, and we can therefore track inbreeding events that have occurred since the establishment of the captive sable population in North America approximately 8.4 generations ago  using the R package SNPRelate . Figure Out of the 5,000 SNPs selected for the design of the myBaits3.2We analyzed the aligned reads using the samtools flagstat tool from the SAMtools package  and Fossil Rim  comparing North American sable antelope populations showed that each population formed an obvious cluster Figure . However4This study is the first to compare genetic diversity, genetic clustering, and genetic drift among a subset of sable antelope managed in zoos, conservation centers, and ranches, and to use this information to propose and support metapopulation management between traditionally isolated populations. Using a customized SNP panel developed within this study, our findings indicate that captive sable antelope populations managed by the Association of Zoos and Aquariums (AZA) retain significantly greater genetic diversity and have significantly lower inbreeding coefficients than populations managed in the privately owned ranches. Southern sable antelope were brought in to captivity in North America during the 1910s to 1950s . The genetic clustering observed between our North American captive populations closely emulates the artificial gene flow (or lack thereof) occurring through transfer events. For example, Fossil Rim Wildlife Center, an AZA and SPA managed population, has transferred bulls to the privately owned ranch populations studied herein, and this is reflected in our principal component analysis . The SPA is an initiative to manage multiple species as metapopulations spanning public and private sectors , to our North America sable antelope, as to understand how genomic diversity has differentiated since the founding of the North American ex situ populations.The authors thank: Jill M. Piltz , Dr. Douglas L Armstrong (Omaha’s Henry Doorly Zoo and Aquarium), Dan Beetem (The Wilds), Kevin and Cole Reid (Stewards for Wildlife), Jimmy and Justin Gregory (Austin Savanna), Hayden Kelly (Griffin Point Ranch), and anonymously participating Conservation Centers for Species Survival’s Source Population Alliance members for providing samples. We thank Alison Devault from Arbor Biosciences for the construction and quality checking of the sable antelope myBaits array and for comments on the manuscript. We also thank Dr. Janine Brown, Smithsonian Conservation Biology Institute for substantial discussions during early stages of this project and funding support and Ms. Jill Piltz, Co‐Ordinator ‐ AZA Sable Antelope Species Survival Plan for regular feedback and guidance throughout this project. K.P.K. and the research reported herein was supported by the Smithsonian Institution Competitive Grants Program, the Sichel Endowment, and the Phil Reed Fund. G.T. was supported in part by funding provided through the Smithsonian Institution’s Short‐Term Visitor Fellowship program.None declared.J.E.M, B.S.P. D.E.W. and K.P.K designed the study; H.H. coordinated sample collection from Fossil Rim; B.S.P. coordinated the collection and curation of all samples; S.C.R. and K.R.M. performed DNA extractions, library preparation and capture enrichment; G.T., P.D. and K.P.K. designed the sable antelope targeted sequence capture array; R.M.G., G.T., P.D., B.S.P., C.W.E. and K.P.K. planned the analysis approach; R.M.G. performed variant calling and population genetic analyses; G.T. visualized the distribution of SNPs across the sable antelope genome; G.F. provided management and transfer information for captive sable antelope and guidance for research implementation. R.M.G. drafted the manuscript and all authors revised the manuscript and approved and contributed to the final version.Fig S1Click here for additional data file.Fig S2Click here for additional data file.


Halyomorpha halys (Hemiptera: Pentatomidae) has caused extensive damage to global agriculture. As a high-risk pest for many countries, including New Zealand, it is important to explore its genetic diversity to enhance our knowledge and devise management strategies for BMSB populations. In this study, two mitochondrial genes, Cytochrome c oxidase I (COI) and Cytochrome c oxidase II (COII) were used to explore the genetic diversity among 463 BMSB individuals collected from 12 countries.In the past decade, the brown marmorated stink bug (BMSB), Hd) and nucleotide diversity (π) were high while the neutrality (Fu’s Fs) values were negative for the BMSB populations in the native countries, China, and Japan. For the BMSB populations from the invaded countries, the Fu’s Fs values were negative for populations from Chile, Georgia, Hungary, Italy, Romania, Turkey, and USA, indicating that those populations are under demographic expansion. In comparison, the Fu’s Fs values were positive for the populations from Austria, Serbia, and Slovenia, revealing a potential population bottleneck. Analysis of molecular variance (AMOVA) suggested that significant genetic difference exists among the BMSB populations from China, Japan, and the invasive countries.In total, 51 COI and 29 COII haplotypes of BMSB were found, which formed 59 combined haplotypes (5 reported and 54 novel). Of these, H1h1 was the predominant haplotype. The haplotype diversity  but low in those countries which have been invaded by the species. The analysis indicated that multiple invasions of BMSB occurred in Europe and the USA. The study also revealed three ancestral lines and most of the novel haplotypes were evolved from them. Moreover, we observed two genetic clusters in the invasive populations that are formed during different invasion events. Our study provided a comprehensive overview on the global haplotypes distribution thus expanding the existing knowledge on BMSB genetic diversity that potentially could play an important role in formulating feasible pest management strategies.The online version contains supplementary material available at 10.1186/s12863-021-00961-8. Halyomorpha halys  (Hemiptera: Pentatomidae) is a highly polyphagous insect, which feeds on more than 300 host plants [Brown marmorated stink bug (BMSB), ys Stål, 855 , Japan and the Korean peninsula –11 and hMitochondrial DNA (mtDNA) sequence analysis is one of the most widely used methods to examine genetic diversity and determine the locality of origin of an invasive species , and hasA total of 441 COI sequences (657 bp each) were obtained from 463 BMSB individuals collected from the 12 countries  for the COI sequences of BMSB specimens submitted in Barcode of Life Data System (BOLD) : BIN AAMA total of 450 COII sequences 518 bp) were obtained from the 463 BMSB individuals , with Hd values of 0.942 and 0.858, and nucleotide diversity (π) values of 0.00238 and 0.00327, respectively , Serbia  and Slovenia . In contrast, little to no haplotype diversity was observed in the BMSB samples collected from Chile, Georgia, Hungary, Italy, Romania, Turkey and the USA. Therefore, two genetic groups were defined based on the Hd values obtained from the haplotype analysis: group A  and group B . It is noteworthy that in Hungary five sampling sites were studied, of which at two sites no haplotype diversity was observed, while the other three sites showed variable diversity with an Hd value from 0.038 to 0.5 and a π value from 0.00085 to 0.0017036, with an overall Hd value of 0.107 and a π value of 0.00028. This indicates that the invasion of BMSB in Hungary may have come from genetically distinct populations.Japan and China had the highest haplotype diversity (p < 0.02) and − 29.707 (p < 0.02) (Table p < 0.02) for group A  suggesting that group A was under population expansion. In comparison, the haplotype diversity was slightly higher with an average of 0.63 for group B , but with Fu’s Fs values of 1.453 (p > 10), indicating that group B was under bottleneck.In the neutrality test, the Fu’s Fs statistic values were very low in the two native countries of BMSB, China and Japan, with − 7.852   using the FSA) Fig. . The recSA) Fig. . The BMSST value was 0.71 (p < 0.05), indicating that the genetic variation among populations was high.The AMOVA  showed that the genetic variation among the 12 populations contributed 71.26% while variation within population contributed 28.74%. The overall FThe haplotype network of the BMSB individuals further revealed the widespread occurrence of H1h1 and H3h3, except the populations from Japan, whereas all the other haplotypes were mainly detected in the native countries Fig. . The anaThis study revealed 51 COI haplotypes (36 novel) and 29 COII haplotypes (20 novel) from 463 BMSB individuals of 12 countries. However, most of these haplotypes (80%) were detected only once , Shaanxi (China), Japan and the USA were h1 67.8%), h3 (75%), h11 (81.1%) and h1 (100%). Xu et al.  made a s7.8%, h3 The combined data for COI and COII led to the observation of several ancestral lineages, including h1 (H1h1), h3  and h11 lines . The haplotype networks support that most of the less abundant haplotypes were possibly evolved from these lines.Hd) of BMSB populations from China and Japan was 0.858 and 0.942, respectively, which clearly indicates that the genetic diversity of these two populations was much higher than that of the most of the BMSB populations in the invaded countries (Hd < 0.182). This conclusion agreed with the observations from the study by Xu et al. [The haplotype diversity of BMSB from the native regions is much higher than that of the invaded populations. The haplotype diversity  was negative as that of the ancestral Chinese  and Japanese populations , indicating that the populations belong to genetic group A could have been under population expansion stage [ST values between the populations within the genetic group A indicate that these populations could have originated from the same ancestral line. The close genetic relatedness among the BMSB populations from Chile, Georgia, Hungary, Italy, Romania and Turkey with the USA populations suggests that the late detection in those European countries might have originated from the USA. This also aligns well with the past invasion history of BMSB, where BMSB was first detected in the USA in mid-90s, and then spread throughout the country. Ten years after establishing in the USA, BMSB was detected in Switzerland in 2007 [p > 10), which is consistent with the more recent detection of BMSB in these locations as the positive Fs indicated that these populations were under a population bottleneck [Hd) and nucleotide diversity (π) of these three populations in group B were higher than 0.5 and 0.001, respectively, implying that these countries have been invaded multiple times by BMSB from different origins.The PCoA also revealed that the genetic group A  could have become a relatively independent genetic group. The neutrality test supported this, where the Fu’s Fs value of the genetic group A . All BMSB specimens were stored in 95% ethanol at − 20 °C and the species identity was confirmed by morphological characteristics by MPI entomologists. Total genomic DNA was extracted from each individual specimen using QIAGEN DNeasy® Blood & Tissue Kit with QIAGEN RNase A . The DNA quality and purity were determined using NanoDrop™  and quantified using QuantiFluor™   cytochrome c oxidase subunit I (COI) and cytochrome c oxidase subunit II (COII). The two markers were chosen due to their fairly high variability and large number of sequences previously reported. Partial sequence of the COI (657 bp) and COII 518 bp) genes were amplified using the genomic DNA as a template. The LCO1490 (5′-GGTCAACAAATCATAAAGATATTGG-3′) and HCO2198 (5′-TAAACTTCAGGGTGACCAAAAAATCA-3′)  primer p8 bp geneA comparative barcoding analysis of COI and COII genes from the BMSB cohort of the current study and the publicly available COI and COII sequences from BMSB were conducted. The reference COI and COII sequences were created, respectively, by the following steps. Firstly, previously reported COI and COII barcode sequences of BMSB were downloaded from the GenBank. The COI  and COII  sequences were aligned separately using Geneious software v10.2.5 [All the COI and COII sequences obtained in this study were checked and edited in Geneious software  to removHn), haplotype diversity (Hd) and nucleotide diversity (π) was estimated using DnaSP v6 [Hd values, the BMSB populations in the invaded countries were divided into two groups: genetic group A  and genetic group B . Therefore, the neutrality test was also performed among the BMSB populations from China, Japan, group A, and group B.The genetic diversity as the percentage of each haplotype present from different regions and/or countries was estimated by calculating the number of haplotypes detected at the country, divided by the total number of individuals sampled using Microsoft Excel. Population genetic diversity, as indexed by the number of haplotype (DnaSP v6  to quantST), and the overall genetic variance was calculated by AMOVA . Both calculations were fulfilled using Arlequin 3.5 [ST data (Additional file Genetic differentiation among the BMSB populations was estimated by the fixation index (Fquin 3.5 . To showAdditional file 1. Metadata information of the collected BMSB cohort.Additional file 2. Haplotype information of the BMSB populations.Additional file 3. FST values of all the BMSB populations.