Polygenic risk scores are gaining more and more attention for estimating genetic risks for liabilities, especially for noncommunicable diseases. They are now calculated using thousands of DNA markers. In this paper, we compare the score distributions of two previously published very large risk score models within different populations. We show that the risk score model together with its risk stratification thresholds, built upon the data of one population, cannot be applied to another population without taking into account the target population’s structure. We also show that if an individual is classified to the wrong population, his/her disease risk can be systematically incorrectly estimated. Noncommunicable diseases, also known as chronic diseases, are currently responsible for more deaths than all other causes together . CardiovIt is recognised that CVDs and the type 2 diabetes (T2D) are potentially preventable , 2. For The cause of these diseases is considered to be complex, combining both genetic and environmental factors –4. WhileFor this reason, the polygenic risk score (PRS), the risk metric calculated on several single nucleotide polymorphisms (SNP), weighted by their effect-size estimates , can be seen as an approximation of the total genetic risk and is in the focus of the current research. Starting with a few dozens of markers , 6, PRSsSeveral authors have provided PRS models for indicating low and high risks for different diseases or traits–e.g. CHD , T2D 7]7], schizGenetic risk estimation is mostly based on percentiles of the PRS distribution in the study cohort  and manyIt is shown that PRSs, particularly those that consist of up to hundreds of SNPs, are dependent on the discovery cohort . UsuallyIn this study, we have used two previously published PRSs, both based on thousands of SNPs and compared their distributions within different populations. The populations of interest include Estonia, Europe, America, South-Asia, East-Asia and Africa.i denotes the effect allele count and wi the weight of the i-th SNP for a certain outcome, accordingly. The number of SNPs included in PRS (denoted with n) varies, depending on the trait/disease.PRS is calculated as a sum of weighted effect alleles. The general mathematical formula of the PRS is written as follows:CHD), based on 49310 SNPs [T2D), based on 7502 SNPs [CHD, weights wi are taken to be equal to the estimated effect sizes ßi. In T2D model, an additional parameter πi is used for each SNP to determine the weight, so that wi = βi ⋅ πi. That kind of double-weighting of SNPs helps to minimise the bias arising from the “winner’s curse”. For a better comparison, both PRS are scaled over all samples.We used PRS calculation pipelines from two recently published articles. The first is PRS for predicting the risk of CHD . The odCHD and PRST2D in different populations, we used 1000 Genomes Project data from Phase 3 (October 2014) release [In order to calculate the PRS release . It cont release –a popula release . SNPs we release . No impuPRSs were calculated by using PLINK (v1.9) . Output For the genetic risk estimation, individuals are divided into quintiles, based on the PRS values in each study cohort. In both models the risk is considered to be highest for individuals in the top PRS quintile and lowest for the bottom PRS quintile.Finally, the distributions of the scores in all populations were plotted, quintiles calculated and compared by using R version 3.2.3.As there is no phenotype data in 1000 Genome Project, we were unable to analyse the association between disease prevalence and the risk score within different populations.In order to explore the genetic variability of the input data between populations and to better interpret the outcomes, we also performed a Principal Component Analysis (PCA) of SNPs.CHD and PRST2D are shown in The observed distributions of PRSCHD and PRST2D distributions follow the same pattern: Europeans, including Estonians, are getting lower scores than Americans and South-Asians on both plot. East-Asians and Africans are getting the highest scores. However, when looking at the means of the distributions, the vast shifts between the populations can easily be observed. For instance, the highest quintile of Europeans (people having the highest genetic risk of CHD) have values ranging from -0.28 to 0.82. At the same time, this is approximately the range where Africans have the lowest quintile (-0.45 to 1.39) and therefore should have a lower risk. A similar difference appears in T2D.The order of PRSPCA plot of all samples, based on 7395 SNPs from T2D model, is shown in For each population, the correlation between the first component of PCA, conducted only on the SNP data of that population, and PRS is given in In order to illustrate the differences in effect allele frequencies of SNPs that have the strongest effect in the model, we have taken 20 top SNPs from the T2D model and compared their effect allele frequencies in African and European population in CHD and PRST2D, both containing large number of SNPs, for the samples from different populations and compared their distributions. We found that the distribution plots for both PRS models follow a similar pattern–the distribution parameters are considerably different. Estonians, together with other Europeans, tend to get the lowest and Africans the highest scores among considered demographic groups. Large shifts mean that the absolute ranges of the quintiles can be very different in different populations. The absolute score which in one population indicates the highest risk may mean the lowest risk in the other. If we apply the genetic risk cut-offs from European ancestry in individuals of African ancestry, then everyone would have an extremely high estimated risk level. Although the prevalence of the disease in different populations is indeed slightly different [We calculated two polygenic risk scores PRSifferent , it doesOne might argue that the importance of absolute cut-off values is questionable because usually relative PRS thresholds are used in research instead. This holds only for research domain, where one is mainly interested in the strength of PRS-phenotype association and the absolute values are not important. However, if a PRS is used for personalised risk prediction in clinical practice, absolute thresholds are needed. Therefore, these PRS models together with their absolute thresholds, which were designed to the data of European populations, cannot be applied directly to other populations for risk estimation.These findings are coherent with Martin et al.  who repeCarlson et al. provide an explanation for observing higher scores for non-European populations. They argue that because of the linkage disequilibrium in GWAS studies which are conducted mainly on European ancestries, discovered rare disease-associated variants are often not the true causal variants. As the linkage disequilibrium between causal and associated SNPs varies in different populations, the effect size of the disease-associated variant tends to be over-estimated in non-European ancestries for approximately a quarter of SNPs . We alsoAs a result of different effect allele frequencies, SNP data that is used for PRS calculation already includes the population information. The PCA plot in In order to overcome the PRS distribution shift problem the final score or SNP weights individually have to be adjusted according to the particular population where the score is applied.So far, the large-scale GWAS studies have found relatively little between-cohort heterogeneity in the effects of individual SNPs. We cannot distinguish, whether it is so because there is not enough power to detect that or whether the effect sizes are actually homogenous–research so far supports the latter. Thus, there is no evidence to support differential weighting of individual SNPs. As the differences in PRS distribution depend mainly on different allele frequencies across populations, it seems justified to apply a population-specific correction to the entire PRS , to make the correct decision on general genetic risk level of any given individual.One option is to simply recalculate the PRS distribution cut-offs for given target population by using sample data from the same population as a reference. This would solve the problem relatively easily for homogeneous populations. However, the problem still arises in admixed populations, where an individual might have a mixed set of SNPs from several ancestries and his/her individual cut-off thresholds do not match with the others. In such cases, first, we have to detect all these ancestries and then apply corresponding score adjustments to these populations.However, even in a relatively homogeneous population or discovery-cohort, there is a potential risk of misclassifying an individual into a wrong population which would lead systematically to a wrong risk estimation. It can be observed from How to detect the true mixture of ancestries for an individual effectively and taking it into account when adjusting PRS, remain an open question. We are getting incomparable scores because of the differences of the effect allele frequencies between populations, and at the same time, in order to suppress these differences, we have to know the descent of the individual. That brings us back to the PCA plot where we saw that the sample already holds the information about the ancestry. Can the same information be used for adjusting the score in-place? For instance, by weighting SNPs accordingly, by using the characteristics of the sample. We believe this issue deserves further investigation.Adjusting the weights according to the descent require a trans-ethnic understanding of the disease-associated SNPs. Gathering such information is a tremendous challenge and this might be also the reason, why we did not find any such PRS models during the writing of this paper that has at least thousand SNPs and is built on global GWAS data.S1 File(XLSX)Click here for additional data file.S2 File(ZIP)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.


Tai-Kadai (TK) is one of the major language families in Mainland Southeast Asia (MSEA), with a concentration in the area of Thailand and Laos. Our previous study of 1234 mtDNA genome sequences supported a demic diffusion scenario in the spread of TK languages from southern China to Laos as well as northern and northeastern Thailand. Here we add an additional 560 mtDNA genomes from 22 groups, with a focus on the TK-speaking central Thai people and the Sino-Tibetan speaking Karen. We find extensive diversity, including 62 haplogroups not reported previously from this region. Demic diffusion is still a preferable scenario for central Thais, emphasizing the expansion of TK people through MSEA, although there is also some support for gene flow between central Thai and native Austroasiatic speaking Mon and Khmer. We also tested competing models concerning the genetic relationships of groups from the major MSEA languages, and found support for an ancestral relationship of TK and Austronesian-speaking groups. The geography of Thailand encompasses both upland and lowland areas, and Thailand is one of the most ethnolinguistically-diverse countries in Mainland Southeast Asia (MSEA). With a census size of ~68 million in 2015, there are 70 different recognized languages belonging to five different major language families: Tai-Kadai (TK) (90.5%), Austroasiatic (AA) (4.0%), Sino-Tibetan (ST) (3.2%), Austronesian (AN) (2.0%), and Hmong-Mien (HM) (0.3%) [The recorded history of the CT people or Siamese started with the Sukhothai Kingdom, around the 13th century A.D . HoweverHere we extend our previous study by adding 560 new complete mtDNA genome sequences from 22 groups (mostly from CT) speaking TK, AA, and ST languages; when combined with the previous data , there aSamples were analyzed from 560 individuals belonging to 22 populations classified into four groups: 1) the central Thais (CT) (seven populations: CT1-CT7); (2) the Mon (two populations: MO6-MO7); (3) the TK speaking groups from northern Thailand, including Yuan (four populations: YU3-YU6), Lue (four populations: LU1-LU4) and Khuen (TKH); and (4) the ST speaking Karen  (Table  the centwww.mbio.ncsu.edu/BioEdit/bioedit.html). A multiple sequence alignment of the sequences and the Reconstructed Sapiens Reference Sequence (RSRS) [We generated complete mtDNA sequences from genomic libraries with double indices and mtDNA enrichment based on protocols described previously , 15. Thee (RSRS)  was obtae (RSRS) . The 560Φst, pairwise difference), permutation test, analyses of molecular variance (AMOVA), and a Mantel test of the correlation between genetic and geographic distances were also carried out with Arlequin [Φst matrix by Arlequin [Haplogroup assignment was performed with the online tools Haplogrep  and MitoArlequin . Three tArlequin . To get Arlequin .Φst distance matrix was visualized by a multi-dimensional scaling plot (MDS) using STATISTICA 10.0 . A Discriminant Analysis of Principal Components (DAPC) was employed using the dapc function within the adegenet R package [www.fluxus-engineering.com) and visualized in Network publisher 1.3.0.0.The  package . Median- package  of haplo−8 and 9.883 × 10−8 for data partitioned between coding and noncoding regions [Bayesian skyline plots (BSP) per population and maximum clade credibility (MCC) trees per haplogroup, based on Bayesian Markov Chain Monte Carlo (MCMC) analyses, were constructed using BEAST 1.8.0 . BEAST i regions  and TracAn approximate Bayesian computation (ABC) approach was utilized to test different demographic scenarios concerning the origin of CT populations and the relationships between SEA language families. For the maternal origin of CT populations, we considered the same three demographic scenarios tested in our previous study for the origins of North/Northeastern Thai and Laos populations:  demic dih = 0.83 ± 0.08; haplogroup diversity = 0.73 ± 0.09; S = 99), although this was also the group with the lowest sample size  and Mon from central Thailand (MO7) (MPD = 39.32 ± 17.70 and π = 0.0024 ± 0.00119)  than a geographic classification , indicating that language family seems to be a better indicator than geography of the genetic structure of Thai/Lao populations, however there is much more variation among populations within the same group for both classifications , resistance distance  and least-cost path distance , further supporting the limited impact of geography on the genetic structure of Thai/Lao populations. Furthermore, a DAPC analysis showed that clustering groups by language family resulted in more discrimination among groups than clustering by geographic criteria  and Seak (SK), as found previously  and in tFourteen of the 174 haplogroups occur in at least ten individuals and together account for 33.92% of the 560 sequences; these are F1a1a, B6a1a, F1f, B5a1a, F1a1a1, C7a1, C7a, M*, M12a1a, M21a, M7b1a1a3, R9b1a1a, R9b1a3, and B5a1b1 (Supplementary Table The remaining haplogroups (66.08%), which occur in lower frequency, tend to be more widely distributed, e.g., G2a1 and basal M sublineages in MO7 and subhaplogroups F (x F1a1a and F1a1a1), M7b1a1 and B4 in Lue (LU) and Khuen (TKH) at varying frequencies  are prevalent in almost all groups , with the exception of some AA groups , Karen, and CT groups; these groups have other widespread haplogroups, e.g., D, M12-G, M , A, C, and N (xN9a) Fig. . Apart fFor the combined dataset, we estimated coalescence ages of SEA haplogroups and their sublineages. We analyzed haplogroups that have additional sequences from the present study and have more than five sequences in total Table . The ageThere are many lineages with ages older than 30 kya found in our Thai/Lao samples, e.g. B4, B5, D, F1, F3, M7, M*, M12, M13, M17, M21, M71, M73, M74, M91, R9, R22, N10, and U . In doing so, it is important to keep in mind that we are not testing the relationships of these language families, as that would require linguistic data. However, determining the best-fitting model based on genetic relationships may help discriminate among hypotheses concerning the language family relationships that make predictions about the genetic relationships of populations speaking those languages. We tested five models of the language family relationships Fig. . The obsThe present study adds to our previous study of Thai/Lao mtDNA genome sequences by including 22 additional groups from Thailand, including the AA-speaking Mon (MO), ST-speaking Karen, and several TK speaking groups, especially the CT. Similar to our previous mtDNA study , geneticThe Mon, who were a previously dominant group in MSEA located in present-day southern Myanmar and central Thailand since the 6 to 7th century A.D. , have beThe Karen in Thailand are refugees who migrated from Myanmar starting from the 18th century A.D. due to the influence of Burmese . HoweverThe present results emphasize the common maternal ancestry of CT and other TK speaking groups in MSEA, e.g., Laos and Southern China. Demic diffusion is still the most probable scenario for TK-speaking populations Fig. , possiblFinally, we used simulations to test hypotheses concerning the genetic relationships of groups belonging to different language families. We found that Starosta’s model  providedSpecifically, the best-fitting model suggests that after separation of the prehistoric TK from AN stocks around 5–6 kya in Southeast China, the TK spread southward throughout MSEA around 1–2 kya by a demic diffusion process, accompanied by population growth but with at most minor admixture with the autochthonous AA groups. Meanwhile, the prehistorical AN ancestors entered Taiwan and dispersed southward throughout ISEA, with these two expansions later meeting in western ISEA. The lack of mtDNA haplogroups associated with the expansion out of Taiwan in our Thai/Lao samples has two possible explanations: either the Out of Taiwan expansion did not reach MSEA ; or, if the prehistoric AN migrated through this area, their mtDNA lineages do not survive in modern Thai/Lao populations. Ancient DNA studies in MSEA would further clarify this issue. Moreover, although mtDNA analyses are informative in elucidating genetic perspectives in geographically and linguistically related populations, they have an obvious limitation in that they only provide insights into the maternal history of populations. Future studies of Y chromosomal and genome-wide data will provide further insights into the genetic history of Thai/Lao populations and the role of factors such as post-marital residence patterns and migration in shaping the genetic structure of the region.Supplementary InformationSupplementary Table S2


The Druze are an aggregate of communities in the Levant and Near East living almost exclusively in the mountains of Syria, Lebanon and Israel whose ~1000 year old religion formally opposes mixed marriages and conversions. Despite increasing interest in genetics of the population structure of the Druze, their population history remains unknown. We investigated the genetic relationships between Israeli Druze and both modern and ancient populations. We evaluated our findings in light of three hypotheses purporting to explain Druze history that posit Arabian, Persian or mixed Near Eastern-Levantine roots. The biogeographical analysis localised proto-Druze to the mountainous regions of southeastern Turkey, northern Iraq and southeast Syria and their descendants clustered along a trajectory between these two regions. The mixed Near Eastern–Middle Eastern localisation of the Druze, shown using both modern and ancient DNA data, is distinct from that of neighbouring Syrians, Palestinians and most of the Lebanese, who exhibit a high affinity to the Levant. Druze biogeographic affinity, migration patterns, time of emergence and genetic similarity to Near Eastern populations are highly suggestive of Armenian-Turkish ancestries for the proto-Druze. Contemporary Druze comprise an aggregate of Levantine and Near Eastern communities residing almost exclusively in the mountain regions of Syria , Lebanon , Israel  and Jordan , although with an increasingly large diaspora in the USA1th century A.D., Druzism garnered rapid support in the LevantThe population history of the Druze people, who accepted Druzism around the 11 Lebanon 15,000, I Israel 16,000 andThis question is particularly challenging given the Druze’s original nomadic lifestyle and the development, over time, of their esoteric religion that incorporates Isma’ilism Islam, Judaism, Zoroastrian, Hindu, Christian, Neo-Platonic and Persian influencesDruzism was first reported in Cairo under the sixth caliph of the Fatimid Dynasty, Al-Hakim (996–1021 A.D.) who sent missionaries throughout Arabia and the Middle East (with epistles being recorded as far as India), calling for new adherents to join the religionth century A.D. However, none of these hypotheses are supported by incontestable evidence leaving the population history of the Druze unresolved. This study aims to employ genetic tools to generate a more comprehensive history of Druzism.Several hypotheses have been proposed to explain the history of contemporary Druze based on historic, archaeological and linguistic evidence . These hThe work of Cavalli-Sforza and other investigators has already established the strong relationship between geography and genetics1012131215161719presumed social structure and geographical preference, genetic efforts targeting the Druze have identified several rare, mainly monogenic, recessive disorders like Behçet’s diseaseDue to their unknown population history, remote living and the theoretical prohibition of marriage with non-Druze, the Druze have been characterised by some authors as a ‘population isolate,’ ‘genetic isolate’ and a ‘genetic refugium’ e.g.12To improve our understanding of the population history of Israeli Druze, we performed genome-wide and haplogroup analyses, the results of which were compared with Levantine and non-Levantine populations. Our findings are evaluated in light of the three major hypotheses depicting Saudi Arabian, Near Eastern and Iranian-Iraqi roots for Druze , though biogeographical affinity. For individuals of mixed ancestries, GPS coordinates represent the mean geographical locations of their immediate parental populationsThe rationale of biogeographical analyses is that the population structure of the population of interest, and that of its parental populations, would exhibit high similarity allowing us to infer the biogeographical affinity of the population in question from the known geographical location of the parental populationsd) defined as the minimal Euclidean distances between the admixture proportions of each individual and all members of a population of interest. Small genetic distances indicate high genetic similarity. The median genetic distances for all populations  were relatively small, ranging from 1.6% to 10.2% with a mean of 2.7%.Prior to applying GPS to elucidate the biogeographical affinity of Druze, we sought to trial its accuracy on Afro-Eurasian populations. For that, we analysed over 1,000 individuals belonging to 50 Afro-Eurasian populations and estimated their admixture proportion in respect to nine admixture components corresponding to putative ancestral populations . All theeave-one-out procedure at the population level. Assignment accuracy was determined for each individual based on whether the predicted geographical coordinates were within 500 kilometres (km) or 250 km of the political boundaries of their country or regional location. GPS correctly assigned 77% and 68% of the individuals to less than 500 km and 250 km from their countries, respectively . Using Alderth century and the early 12th century (38.62 ± 5.8 generations ago). This estimate is within the range proposed by two recent studies using the less accurate ROLLOFFLike most Eurasians, the Druze genomes exhibit a mixture of three major components: Mediterranean  obtained using a PCA-based application for biogeography, suggesting a diffused Southern Turkish and Northern Syrian affinity for the DruzeWe next applied GPS to infer the biogeographical affinity of the Druze using all reference populations. GPS positioned nearly all 42 Druze along a trajectory going from the Armenian-Turkish border to Syria . Druze also have a significantly smaller ancient Levantine ancestry (14.9%) compared to other Levantine populations . The ancient Anatolian component distributes nearly evenly (18–22%) among mixed Levantine individuals and exhibits a gradient in Druze (0–17%). These results are in agreement with our findings using modern-day populations that indicated a high similarity between Druze and Near Eastern populations over Levantine populations and the complex population structure of the Druze . However, the K and H haplogroups are not significantly higher in Druze  compared to Lebanese  . There is no significant difference in the maternal haplogroup U between Druze (5.63%) and other Levantine populations (3.06–3.66%) . There is also no significant difference in maternal haplogroup J between Druze (5.63%) and Levantine populations (7.55%) . However, compared to the Syrian population (12.05%) the J haplogroup count is significantly lower in Druze . The combined frequency of the K and L (14.58%) Y haplogroups in Druze is much higher than in other Levant populations (3.6–4.6%) , whereas the Levantine frequencies of J1 and J2 (45–67.6%) are lower in Druze (33.53%)  and higher in Bedouins (67.6%).The Druze belong to nearly all the basal Y chromosomal and mtDNA haplogroups . The most common mtDNA haplogroups  are present in 68.76% of the individuals compared with 94.17% of the individuals that exhibit the most common Y haplogroups . Maternal haplogroups H, K and X are significantly more common in Druze  than in all other Levantine populations   as has been previously observedThe most common Y haplogroups in Druze dominate the area between the Black and Caspian Seas and represent the major lineages among populations inhabiting Western Asian regions, including Turkey, Iran, Afghanistan and the Caucasus2930The present study aims to shed light on the population history of the Israeli Druze and assess the findings in light of three hypotheses advocating Arabian, Iranian-Iraqi or Near Eastern roots  while exAlthough predicted in part to Syria . Altogetth century6The biogeographical affinities of the Druze are unique compared with neighbouring Levantine populations. Only a minute fraction of Lebanese and Syrians share a Turkish affinity , and botth century B.C. and the 2nd century A.D. This multi-origin of the Lebanese  in the range of 10–100 individuals (<80 for the New World founding population)46Evaluating whether Druze are a ‘genetic isolate’ necessitates an understanding of this concept. An ideal genetically isolated population is an endogamous group dating back to ancient times that derived from a small number of individuals who became isolated after a founding event. Such communities would be characterised by minimal mixing and reduced gene flow with neighbouring populations or their potential progenitors, facilitated by strict societal practices, effective geographical barriers or bothIn reality, we estimate that less than 20% of the worldwide populations , and none of the populations exhibiting shorter distances have been denoted a “genetic isolate”. Third, the Druze exhibit high genetic diversity (as evident from their GPS results)  .th century scientists used a range of problematic methods to argue that the Sami people were isolated from the Norwegian minority, often noting their purportedly distinct genetic ancestry. This assumption had many negative consequences for the Sami people, impacting on their territorial and political rights while contributing to the justification of wide-spread and systematic discrimination at the hands of governments. It is fundamental that any attempt at classifying a population, such as the Druze, as ‘genetic isolates’ is approached with caution and is founded on irrefutable genetic evidence as well as historical, sociological and administrative data.Stereotyping populations as ‘genetic isolates’ has been criticised by geneticists and non-geneticists alike. LipphardtOur study has several limitations. First, the modest sample size of the Israeli Druze may have obscured a more complex population structure that exists within and between Israeli and non-Israeli Druze communities, as noted byde novo mutations that segregate in particular families or villages . Between the years 2005 and 2012, participants were tested for either their mtDNA or Y chromosomal haplogroups. Participants tested after 2012 were genotyped on the GenoChip microarray that includes nearly 150,000 non-functionalhttp://geno-web.nationalgeographic.com/geno2/dist/. Our search in this database (October 2015) retrieved 27, 254 and 502 individuals who reported having at least one Lebanese, Palestinian, or Syrian parent respectively, from whom the mtDNA or Y were inherited. Haplogroup assignment was done by the Genographic Project. These data were combined with existing datasets in the literature (Tables S4 and S5). The Druze most common mtDNA and Y haplogroups were defined as haplogroups with frequency ≥5%.The National Geographic Society’s Genographic Project contains genetic and demographic data from over 320,000 anonymous participants who have provided written informed consent for the use of their DNA in genetic studies  was plotted using Matlab Graph function, which uses a square symmetric matrix as an adjacency matrix and constructs a weighted graph with edges corresponding to the nonzero entries of the matrix.Biogeographical analyses were carried out using the Geographic Population Structure (GPS) tool as previously described1010K admixture proportions, GPS requires a reference population set of N populations with both K admixture proportions and two geographical coordinates (longitude and latitude). All supervised admixture proportions were calculated as in Elhaik et al.To infer the geographical coordinates (latitude and longitude) of an individual given GPS finds the biogeographical affinity of a sample by matching its admixture signature with modern-day reference samples of known biogeographical affinity then converting the genetic distances into geographic distances. For unmixed individuals, the inferred location should be interpreted as the region where populations with the most similar admixture proportions to those of the individual are found  as input. At each iteration, we calculated the ratio of the mean square and sum of squares between the groups. If this ratio was <0.9 and there were more than three samples in each cluster, then we accepted the k-component model, whereas smaller clusters were removed.Detailed annotation for subpopulations was unavailable for most populations , though they exhibited fragmented subpopulation structure . To deteleave-one-out’ procedure at the population level. This approach is more rigorous than the ‘leave-one-out’ individual procedure and ensures that the reference panel will not be biased by outliers that do not fit with the genetic profile of the region. Individuals predicted to reside within the political borders of their countries, or less than 200 km outside of them, were retained and used to recompile the reference population set using the technique described above. We resort to the use of country labels, since some of the population data did not have detailed regional information. Elhaik et al.leave-one-out’ procedure at the population level  results for the Druze.The time of admixture events were estimated with Alder v.1.022 > 0.2) in windows of 50 SNPs (sliding the window by 5 SNPs at a time) using indep-pairwise in PLINKTo test the similarity between modern day individuals and ancient Anatolian, Armenian and Levantine populations, we analysed an ancient dataset that comprised of 281 ancient humans−10. This low threshold corresponds to long shared haplotypes (≥1 cM) that are likely to be IBD. Short gaps (<50 indexes) separating long domains were assumed to be false-negatives and concatenatedTo detect IBD segments between Druze and other populations 1763, we 17The Nabataean caravan and trade routes map was plotted according to the map drawn by Augé and DentzerHow to cite this article: Marshall, S. et al. Reconstructing Druze population history. Sci. Rep.6, 35837; doi: 10.1038/srep35837 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


A large-scale systematical investigation of the influence of Pleistocene climate oscillation on mangrove population dynamics could enrich our knowledge about the evolutionary history during times of historical climate change, which in turn may provide important information for their conservation.Sonneratia alba was studied by sequencing three chloroplast fragments and seven nuclear genes. A low level of genetic diversity at the population level was detected across its range, especially at the range margins, which was mainly attributed to the steep sea-level drop and associated climate fluctuations during the Pleistocene glacial periods. Extremely small effective population size (Ne) was inferred in populations from both eastern and western Malay Peninsula , mirroring the fragility of mangrove plants and their paucity of robustness against future climate perturbations and human activity. Two major genetic lineages of high divergence were identified in the two mangrove biodiversity centres: the Indo-Malesia and Australasia regions. The estimated splitting time between these two lineages was 3.153 million year ago (MYA), suggesting a role for pre-Pleistocene events in shaping the major diversity patterns of mangrove species. Within the Indo-Malesia region, a subdivision was implicated between the South China Sea (SCS) and the remaining area with a divergence time of 1.874 MYA, corresponding to glacial vicariance when the emerged Sunda Shelf halted genetic exchange between the western and eastern coasts of the Malay Peninsula during Pleistocene sea-level drops. Notably, genetic admixture was observed in populations at the boundary regions, especially in the two populations near the Malacca Strait, indicating secondary contact between divergent lineages during interglacial periods. These interregional genetic exchanges provided ample opportunity for the re-use of standing genetic variation, which could facilitate mangrove establishment and adaptation in new habitats, especially in the context of global climate changes.In this study, phylogeography of a mangrove tree S. alba. Our study highlights the fragility of mangrove plants and offers a guide for the conservation of coastal mangrove communities experiencing ongoing changes in sea-level.Phylogeogrpahic analysis in this study reveal that Pleistocene sea-level fluctuations had profound influence on population differentiation of the mangrove tree The online version of this article (doi:10.1186/s12862-016-0849-z) contains supplementary material, which is available to authorized users. Mangrove plants constitute a highly productive ecosystem of both ecological and economic importance that lies at the interface of terrestrial and marine environments . They prOver the last 3 million years, Earth has experienced multiple glaciation events associated with steep sea-level fluctuations and climate changes that are thought to have shaped the modern biogeography of both terrestrial and marine organisms –11. UnliRhizophora mangle [R. mucronata [During interglacial periods of the Pleistocene, the re-submergence of land shelves was thought to provide corridors for genetic exchanges between the oceanic regions isolated during glaciation . Using ma mangle  and R. mucronata , ocean cucronata . Thus, wSonneratia alba, a widespread mangrove species in the IWP region, is distributed from East Africa through Southeast Asia to southern Japan and northeastern Australia. Unlike species in the Rhizophoraceae family, which have viviparous propagules of 15 to 70 cm long, Sonneratia species produce small irregular seeds of 7 to 12 mm long [Sonneratia species, which are characterized by a separated calyx with exposed seeds, can float on sea currents and be carried some distance by sea currents before seed release [Sonneratia species may be different from that of Rhizophora species. A previous population genetic study of S. alba identified 12 highly divergent genes and 59 undifferentiated genes between two nearby populations in Hainan, China [S. ovata showed that drops in the Pleistocene sea level might lead to the loss of polymorphism in several populations from the South China Sea (SCS) [Sonneratia species. However, these studies were conducted over a relatively small area, and a more comprehensive phylogeoraphical study covering the entire range is required for Sonneratia species. mm long , 30. Mat release . Thus, tn, China , suggestea (SCS) . These fS. alba across its range  were identified in the chloroplast locus and produced seven chlorotypes. Only three populations, namely, those from Kuta, Indonesia (IKT), Cebu, Philippines (PCB) and Morotai, Indonesia (IBM), exhibited polymorphism. The nucleotide diversity (θπ) was 0.140, 0.050 and 0.140 per kb (/kb), respectively, while the nucleotide polymorphism (θW) was 0.250, 0.080 and 0.170/kb, respectively  and West Papua, Indonesia  had the highest and second highest diversity, respectively , which was nearly monomorphic in this region , while at the cpi and idr loci, the ADT population exhibited a closer relationship to the populations from the SCS.A similar but more complex genetic structure was observed at the seven nuclear loci Fig. . Consistion Fig. . The hapS. alba. Under the optimal clustering of K = 3 : 1.609–5.347 MYA, Fig. The divergence times between the major genetic lineages of rpl9, cpi, ppi, idr and nhx2), there was haplotype mixing between the SCS and the Indian Ocean lineages in the two populations near the Malacca Strait  hosted similar genetic composition with the Indian Ocean lineage  fell into a cluster with the SCS lineage  were highly divergent and fell into different clusters at two loci: hx2 Fig. e. The CQ2) by IMa2 analysis, the BEAST software was used to estimate the splitting time between these two oceanic regions  was estimated to be 0.070 (95% HPD: 0.000–9.650), which corresponded to 0.049 MYA (95% HPD: 0.000–6.791 MYA) under the assumed generation time of 20 years and an estimated nucleotide substitution rate of 1.616 * 10−9 s/s/y by BEAST  via the Malacca Strait were significantly greater than zero  from the TTN population in the SCS to the MKJ population at the Malacca Strait, while that from the MKJ population to the TPK population in the Indian Ocean was 0.142 . By contrast, the migration rate in the opposite direction was close to zero in both cases. These results suggest that secondary contact and asymmetric gene flow between the SCS and the Indian Ocean populations took place at the boundary region. The effective population sizes (Ne) of the TTN and TPK populations were estimated to be 44 (95% HPD: 0–748) and 396 , respectively, while it was estimated to be 2,155 (95% HPD: 660–6993) for the MKJ population , the two populations from the western coast of Thailand (TPK and TNG) hosted haplotypes from the SCS cluster, however, at cpi, one haplotype of the Indian Ocean cluster was identified in the Chanthaburi population from eastern Thailand  without median haplotypes between the two highly divergent haplotype groups Fig. . These rCeriops tagal populations on the two sides of Malay Peninsula to both historical gene flow via the leak of Kra Isthmus prior to the emergence of the isthmus (approximately 5.0 MYA) and the infrequent LDD detouring around the Malacca Strait. Such minor dispersal events may have a significant influence on the population dynamics of mangrove plants. In this study, the splitting time between the populations of S. alba from the SCS and the Indian Ocean was estimated at 1.145 MYA  differ in haplotype clustering at two of the seven loci (ST = 1) between the Qionghai and Sanya populations, although they are separated by only 100 km and are connected by ocean currents [S. alba, genetic divergence between the SCS and the Indian Ocean populations could accumulate in isolation stages, and interglacial gene flow could drive the homogenization of most genomic regions, except for those under selection. Some genetic variation may be neutral in one environment but adaptive in new habitats. This fitness advantages can drive these alleles to a high frequency over a short period of time, and can even reach fixation within a population. Moreover, local adaptation could also counteract the immigration of external alleles from other SCS populations, as suggested by Zhou et al. [As described in the Introduction, 12 of 71 genes were highly divergent . Furthermore, our results exhibited convincing evidence for interregional genetic exchanges during interglacial stages and addressed adaptive advance of standing genetic variations in facilitating mangrove establishment. These findings provide new insights into the conservation of mangrove plants and for future restoration efforts.Mangrove communities were highly impacted by Pleistocene sea-level fluctuations. In the present study, we propose that genetically distinct populations of S. alba were sampled across the IWP region. In the IWP region, there are two centres of mangrove biodiversity: Indo-Malesia (from India to Southeast Asia) and Australasia  [A total of 388 individuals from 22 populations of Pacific) . Within S. alba in this study. trnV - trnM, trnL - trnF and matK were amplified for 10 individuals from each population using the corresponding primer pairs  were designed based on the sequences of expression sequence tags (ESTs) from a leaf cDNA library of S. caseolaris [Genomic DNA of each individual was extracted using the CTAB method . Three cseolaris . Both cpπ) and DNA polymorphism (θW), were calculated for each locus using DnaSP.All sequences were aligned using Seqman v. 7.10  and consecutive indels were treated as one insertion/deletion event. The sequences of the three cpDNA fragments were concatenated as a single locus for further analysis. For both chloroplast and nuclear loci, haplotypes were inferred using DnaSP package v. 5.10 , and hapS. alba, 388 individuals were assigned into a putative number of clusters by the Bayesian clustering method using STRUCTURE v. 2.3.3 [6 Markov chain Monte Carlo (MCMC) with a burn-in of 2*105 under the assumed model of admixture and correlated allele frequencies. The most likely K was determined by the Delta K statistic according to [ST (fixation index) and KXY (average number of nucleotide differences between populations), were calculated between each pair of populations using DnaSP. Based on the result of KXY, a neighbor-joining (NJ) tree was constructed for all S. alba populations to visualize the genetic differentiation among populations using Mega v. 5.0 [ST values according to Monmonier’s algorithm in Barrier v. 2.2 [To identify the genetic structure of v. 2.3.3 . The maxrding to  using STrding to , and therding to . In paraa v. 5.0 . Moreover v. 2.2 .S. alba was estimated using BEAST v. 1.8.2 [Sonneratia species - S. caseolaris, S. apetala, S. ovata and S. griffithii - as outgroups. The topology of the tree prior among the five Sonneratia species was set based on the phylogenetic relationship inferred in our previous study [S. alba populations  was constructed according to the NJ tree for the 22 S. alba populations. Due to the lack of an accurate nucleotide substitution rate for Sonneratia species, we used the estimated divergence time between S. ovata, S. alba and S. caseolaris as three calibration points under a normal distribution prior with a mean of 7.0, 9.0 and 11.0 million years ago (MYA), respectively, and a standard deviation of 2.0, according to the genomic analysis of mangrove species .Divergence time among different genetic lineages of v. 1.8.2  with fouus study , while t−9 substitutions per site per year : 1.190–2.094*10−9 s/s/y) estimated by BEAST. According to the posterior distribution of preliminary simulations, the upper ranges for the parameter prior of population size (q), migration rate (m) and divergence time (t) were set at 10, 10 and 20, respectively. To confirm convergence, the simulation was repeated three times with different random number seeds. Each run was implemented at a 2*106 step burn-in under a geometric heating scheme (-hfg -ha0.96 -hb0.9), followed by an MCMC of 1*107 steps. Population parameters, including effective population size (Ne), inter-population migration rate (2NM) and population divergence time (t), were estimated by plotting the marginal posterior probability distributions of 100,000 genealogies, and the optimal estimations were obtained at the peaks of the posterior distribution curves. 95% HPD intervals were also estimated for each parameter, and the likelihood ratio test (LRT) [Furthermore, gene flow between populations from the two sides of the Malay Peninsula was estimated under an isolation with migration (IM) model using IMa2 software . Three pst (LRT)  was empl


Bretschneidera sinensis, a class-I protected wild plant in China, is a relic of the ancient Tertiary tropical flora endemic to Asia. However, little is known about its genetics and phylogeography. To elucidate the current phylogeographic patterns and infer the historical population dynamics of B. sinensis, and to make recommendations for its conservation, three non-coding regions of chloroplast DNA  were amplified and sequenced across 256 individuals from 23 populations of B. sinensis, spanning 10 provinces of China. We recognized 13 haplotypes, demonstrating relatively high total haplotype diversity (hT = 0.739). Almost all of the variation existed among populations , but that within populations was low . Strong genetic differentiation was detected among populations  with limited estimations of seed flow (Nm = 0.09), indicating that populations were strongly isolated from one another. According to SAMOVA analysis, populations of B. sinensis in China could be divided into five geographic groups: (1) eastern Yunnan to western Guangxi; (2) Guizhou-Hunan-Hubei; (3) central Guangdong; (4) northwestern Guangdong; and (5) the Luoxiao-Nanling-Wuyi -Yangming Mountain. Network analysis showed that the most ancestral haplotypes were located in the first group, i.e., the eastern Yungui Plateau and in eastern Yunnan, which is regarded as a putative glacial refugia for B. sinensis in China. B. sinensis may have expanded its range eastward from these refugia and experienced bottleneck or founder effects in southeastern China. Populations in Liping (Guizhou Province), Longsheng (Guangxi Province), Huizhou (Guangdong Province), Chongyi (Jiangxi Province), Dong-an (Hunan Province), Pingbian (Yunnan Province) and Xinning (Hunan Province) are proposed as the priority protection units. Bretschneidera sinensis Hemsl., a relict tree of the Tertiary tropical flora, occurs in evergreen broad-leaved or mixed evergreen and deciduous forests in montane areas at altitudes of 300–1700 m. Historically, B. sinensis was a heliophilous volunteer species and a long-term resident in the eastern Palearctic  and 2 min of extension at 72°C), with a final extension step at 72°C for 10 min. The products were visualized on 1.5% TBE-agarose gels stained with ethidium bromide. PCR products with single bright bands were purified prior to sequencing using an Axygen DNA gel extraction kit  and were sequenced using an ABI 3730xL automated DNA sequencer .To prepare the PCR work solution, 80 ng of template genomic DNA was combined in a volume of 40 μl, with 4 μl 10× PCR buffer, 100 mM of each of the four dNTPs, 10 μM of each primer, and four units of rTaq DNA Polymerase (Takara). The amplifications were performed in a Robocycler Gradient 96 instrument , with an initial denaturation step of 4 min at 94°C, followed by 35 cycles  h, haplotype diversity ; (3) π, nucleotide diversity; (4) hS, average within-population diversity; and (5) hT, total genetic diversity. The h and π indices were calculated for each population using Arlequin version 3.5 [hS and hT were estimated using PERMUT 2.0[Five indices were used to estimate cpDNA diversity: (1) Nsion 3.5 ; hS and ERMUT 2.0 with 200GST and NST, using PERMUT 2.0[NST matrix was calculated in DnaSP version 5.00.05 [To identify patterns of genetic variation within and between populations, we performed hierarchical AMOVA for all populations using Arlequin version 3.5 . In addiERMUT 2.0 with 200 5.00.05  for all www.fluxus-engineering.com/network_terms.htm) using the median-joining method [Carica papaya L. as outgroup. A neighbor-joining (NJ) tree was generated for haplotypes defined by all variable sites, and a maximum-parsimony (MP) tree was generated for haplotypes defined by variable sites excluding cpSSRs sites.We inferred genetic relationship among haplotypes  with Network 4.5.1.6  would be considered as priority units for protection.Where B. sinensis, four nucleotide substitutions and one 6-bp indel were observed in rps8-rps11, which was 832 bp in length after alignment. As to rps16-trnQ, one homopolymer repeat polymorphic site and two nucleotide variants were detected over the aligned length of 670 bp. Within trnT-trnL region, there were three substitutions, two indels, and two cpSSRs sites over an aligned length of 761 bp. The number of haplotypes recognized in rps8-rps11, rps16-trnQ and trnT-trnL were five, three, and ten respectively. In total, 13 haplotypes were defined by combining the sequences of the three regions.Across the 23 sampled populations of h) of all populations, excluding sites with gaps, was 0.645. We found a very limited number of haplotypes within each population, with only a single haplotype found in 19 of the 23 populations (−3) was found in the LS population of Guangxi province, followed by the HZ population. Total genetic diversity  was considerably higher than the average genetic diversity at the populational level.The haplotype diversity (ulations . Among tP < 0.001), and only 1.91% was distributed within populations. NST was significantly higher than GST , which indicated a phylogeographic structure of the haplotype distribution in B. sinensis. SAMOVA produced various sets of boundaries depending on the number of groups (K), which we arbitrarily set before analysis (FCT) was highest and the proportion of genetic variation between populations within groups (FSC) was lowest when K = 5, which indicated that the variation in cpDNA in B. sinensis could be divided into five geographical groups.According to our AMOVA result, the majority of variation existed among populations  tree topology, in which two major lineages can be identified . LineageD and Fu and Li’s D* and F* neutrality tests exhibited non-significant positive values at the specific level  and 10 derived haplotypes  were detected in this study . Within hT = 0.739) was demonstrated among the 23 populations of Bretschneidera sinensis in this study. High cpDNA diversity has been reported in other long-lived endemic or relict species, such as Eurycorymbus cavaleriei [hT = 0.834) and Taxus wallichiana [hT = 0.884). There are two possible explanations for this phenomenon. On one hand, relict plant species with a long evolutionary history may have accumulated a large amount of genetic variation [B. sinensis may represent a large effective population size from which significant polymorphism could have arisen.Comparatively high total haplotype diversity . In contrast, high diversity of B. sinensis was characterized by remarkable differentiation among populations (GST = 0.887). High GST is common in the cpDNA of angiosperm tree species [hT). While compared with the mean overall GST value for angiosperms (GST = 0.637) [B. sinensis is considerably greater. A similar result was produced by AMOVA analysis, which showed that almost all of the variation existed among, rather than within, populations. Low genetic diversity within populations, combined with strong population differentiation, revealed that the B. sinensis populations were highly isolated from each other. Severe bottlenecks and/or genetic drift associated with small effective population sizes for maternally inherited markers have usually been responsible for such phenomena [B. sinensis [Haplotype diversity within populations was quite low  , genetichenomena . The 23 sinensis , 53, 54,B. sinensis seeds may have further contributed to the high population differentiation observed in this species. The seeds of B. sinensis are large (mean diameter: 1.41–1.52 cm) [B. sinensis seeds; it is likely that its seeds are spread by gravity. Population differentiation of the chloroplast genome was found to be strongly influenced by the mode of seed dispersal in different ways, and species with seeds dispersed by gravity showed higher genetic differentiation than those with wind-dispersed seeds [B. sinensis.Short-distance dispersal in 1.52 cm)  and theied seeds , consistB. sinensis populations, especially in Yunnan province, is promising and planned for our next field trip.There are several criteria for the recognition of glacial refugia: they must have harbored both ancestral and endemic haplotypes ; they muB. sinensis. Populations in this area possess high levels of both nucleotide and haplotype diversity. In addition, all of the haplotypes found in this area  are endemic in the East Yungui Plateau  occur in this area and all are endemic. One possible explanation the high genetic diversity observed in this region may have resulted from genetic admixture after dispersal from adjacent regions.The Nanling Mountains and adjacent regions, where six populations of Bretschneidera sinensis may have the following history of population dynamics . Thus, we recommend these populations to be the priority conservation units for this species. These seven populations exhibit the following characteristics. First, some  are located in the inferred refugium areas, which retain ancestral haplotypes of the species. Second, some of these populations  possess private or rare haplotypes. If these populations become extinct, the diversity of B. sinensis would be diminished. Thirdly, several populations (HZ and CY) exhibit high genetic diversity. Protection of these populations could best preserve the diversity of this species to the maximum extent possible. Fortunately, most populations of B. sinensis are located in national nature reserves, including these seven priority populations. Finally, the seven priority populations can be used for ex situ conservation. Given the high degree of among-population differences in genotype, it is likely that genetic diversity will increase substantially by the exchange of genetic materials that takes place among those populations.Seven


The territory of present-day Vietnam was the cradle of one of the world’s earliest civilizations, and one of the first world regions to develop agriculture. We analyzed the mitochondrial DNA (mtDNA) complete control region of six ethnic groups and the mitogenomes from Vietnamese in The 1000 Genomes Project (1000G). Genome-wide data from 1000G (~55k SNPs) were also investigated to explore different demographic scenarios. All Vietnamese carry South East Asian (SEA) haplotypes, which show a moderate geographic and ethnic stratification, with the Mong constituting the most distinctive group. Two new mtDNA clades (M7b1a1f1 and F1f1) point to historical gene flow between the Vietnamese and other neighboring countries. Bayesian-based inferences indicate a time-deep and continuous population growth of Vietnamese, although with some exceptions. The dramatic population decrease experienced by the Cham 700 years ago (ya) fits well with the Nam tiến (“southern expansion”) southwards from their original heartland in the Red River Delta. Autosomal SNPs consistently point to important historical gene flow within mainland SEA, and add support to a main admixture event occurring between Chinese and a southern Asian ancestral composite . This admixture event occurred ~800 ya, again coinciding with the Nam tiến. It is one of the most populous countries in the world; according to estimates by the Vietnam General Statistics Office, in 2016 more than 94 million people lived in the country . Hanoi, the capital of the country, is located in the northern region and has around seven million inhabitants; it is the second most populated city in Vietnam after Ho Chi Minh (old Saigon), located in the southeastern most part of the country (>8.4million of inhabitants).The Republic of Vietnam, located in the easternmost end of the Indochinese Peninsula , is a country conformed by a rugged and mountainous strip of land with many coastal plains and deltas. It borders China and the gulf of Tonkin in the North, Laos and Cambodia in the West, and the gulf of Thailand in the South1, VGSO).Different populations and ethnic groups have influenced present-day Vietnamese communities. Thus, Vietnam is today a multi-ethnic country; its government recognizes 54 ethnic groups, of which the Kinh account for the vast majority of the country  and are widespread across the whole territory. The remaining 13% are divided into 53 other groups, officially recognized as ethnic minorities, that are dispersed over hilly areas (highlands cover three fourths of the Vietnamese territory) spreading from the North to the South. Among the minorities, the largest is the Tay group, conformed by 1.7 million people, followed by the Khmer Thai, Hoa, Mong, and Nung ethnic groups with a population of roughly one million each; the least populous are the Brau, Roman and Odu, conformed by only a few hundred people each2. According to Atkinson et al.3, roughly 60% of the global human population lived in SEA about 38,000 years ago; and Vietnam was one of the first world regions practicing rice agriculture4. Two main hypotheses have been proposed to explain the initial geographic/population sources contributing to present day SEA populations. The first one proposes that populations to the South of East Asia (EA) probably derived from the populations in SEA that migrated from Africa, possibly via mid-Asia following a coastal route5. The other hypothesis proposes at least two independent migrations: the same initial movement coming from Africa following a southern coastal route first, followed by a series of migrations along a more northern route that served to bridge European and EA populations6. According to the latter hypothesis, most Vietnamese ethnic groups today would be descendants from the ancient populations that spread from South of the Yangtze River towards Mainland SEA and the SEA islands1. However, numerous migrations and successive integration processes could have occurred over time, modeling the genetic composition of SEA5. For instance, Vietnam has been also involved in important commercial historical routes, such as the Silk Roads, and since more than 2,000 years ago, the Vietnamese coast has attracted merchants from as far away as the Middle East and Japan. These commercial exchanges have contributed much to the cultural7 and genetic exchange among Eurasian populations.South Asia (SA) was one of the first regions to have been peopled by modern humans; and this region has served as a major route of dispersal to other geographic areas, including SEA9. The remarkable (pre)-historical population growth of this region has paved the ground for the incubation of high genetic diversity in this area. Thus, SEA’s main mtDNA haplogroups  appeared over 50,000 years ago10, and they show a remarkable genetic variability within clades. Soares et al.11 suggested that global warming and sea-level rises at the end of the Ice Age, were the main forces driving human diversity in SEA.In recent years, SEA has been extensively explored in genetic studies. Some mitochondrial DNA (mtDNA) studies have suggested that this region constituted the starting point of the modern human expansions from Africa towards China, EA and Oceania12. One of the first studies aimed at unraveling the mtDNA composition of SEA countries was conducted by Oota and colleagues13. They analyzed a Vietnamese sample set collected from first generation South Vietnamese immigrants from California and compared it with sample sets collected from Han Chinese and Japanese. Their results showed that the Vietnamese sample had the highest levels of within-population mtDNA variation. Li and colleagues8 analyzed a Vietnamese and a Chinese sample set, and reported high frequency of haplogroups B, M7, F and R, which are common in SEA and key to understand the history of SEA populations. Irwin and collaborators14 analyzed a large sample set of Vietnamese mtDNA control region (CR) sequences with a predominantly forensic genetic focus. Their data indicated that the northern Vietnamese population has a high genetic diversity. Zimmerman et al.15 described high haplotype diversity in northern Thailand and reported that 14% of the Thai haplotypes were shared with other SEA populations. Bodner et al.12 analyzed the mtDNA composition of Laos, indicating that this population showed a mtDNA diversity pattern characteristic of SEA, where B4a, B5a, M7b1, F1a and R9 (the latter two referred in this article as R9’F) are the most frequent haplogroups. They also reported that many of the haplotypes found in the sample were shared with surrounding populations, mainly Thailand and Vietnam12. Zhang and colleagues16 analyzed an extensive mtDNA sample of aboriginal Cambodians and sequenced 98 mitogenomes; these authors suggest that Cambodian aborigines are descendants from ancient populations, and their results add support to the theory that modern humans initially settled in SEA and then dispersed towards China and the SEA islands. According to these authors, Cambodia might be the center of dispersal of B5a, F1a, M12b and B4c2 haplogroups to northern regions and mainland EA16. More recently, Summerer and collaborators9 investigated a Myanmar population sample set and sequenced 44 mitogenomes. They concluded that Myanmar displays a distinctive SEA haplogroup composition, but with northeast Asian and Indian influences. Their analyses indicated that the migration rates between Myanmar and Vietnam were approximately equal9.Despite the numerous studies carried out so far, knowledge about the mtDNA variability of many SEA countries such as Vietnam, Burma, or Cambodia is still very limited3. An important effort at characterizing the SEA area in terms of mtDNA variability has been carried out in the last decades16. The present project aims at contributing to improve our knowledge about the genetic variation of this region, given the importance of this region for the dispersion of modern humans in Asia. Although a few mtDNA studies have been conducted so far, the mtDNA data available from Vietnam is still scarce. To the best of our knowledge, the present study represents the largest sampling effort in Vietnam to date. A genome-wide analysis of a Vietnamese sample was also carried out to further test hypotheses suggested by the mtDNA data.A large number of studies have pointed to SEA as the cradle of present-day Asian populationsn = 113), Lao Cai (n = 115), Hanoi (n = 38), Hai Phong (n = 133), Da Nang (n = 135), and Ho Chi Minh (n = 88). For some analyses, the samples were collapsed into three geographic regions: North (n = 399), Center (n = 135) and South (n = 88). The samples were also analyzed according to their self-reported ethnic group adscription: Tay (n = 62), Kinh (n = 399), Hmong or Mong (n = 115), Hoa (n = 23), Nung (n = 21), Kho Me (n = 1) and Thai (n = 1). Table 18. Our newly generated data cover regions and ethnicities from Vietnam hitherto unexplored in the literature, such as Lao Cai, Da Nang and Cao bang, as well as some ethnic minorities .A total of 622 Vietnamese samples were recruited from six different locations: Cao Bang . Moreover, the study conforms to all applicable Spanish normative, namely the Biomedical Research Act (14/2007-3 of July), the Autonomy of the Patient Act (41/2002), Decree SAS/3470/2009 for observational studies and the Data Protection Act (15/1999).19. PCR was carried out in a 10 µL final reaction volume consisting of 3 µL QIAGEN master mix, 1 µL for each PCR primer (5 µM), and 2 µL of DNA (5 ng). Amplification was carried out in a GeneAmp PCR System 9700  using a hot start at 95 °C for 15 min and followed by 30 cycles at 94 °C for 30 s, 58 °C for 90 s, 72 °C for 90 s and a final extension at 72 °C for 10 min.mtDNA CR comprising hypervariable regions (HVS-I/II) were amplified using PCR primers previously reportedµ96 Plate 96-well device .Prior to the sequencing reaction, PCR products were checked by electrophoresis in polyacrylamide non-denaturing gel , and subsequently stained with silver nitrate. PCR products were then purified with a Multiscreen PCRThe Sequencing reaction was performed on both strands (when needed) in a GeneAmp PCR System 9700 (AB) using the BigDye® Terminator v3.1 Cycle Sequencing Kit (AB). The sequencing reaction was carried out using 0.5 µL of BigDye Terminator Kit, 2 µL of BigDye terminator Buffer, 1 µL PCR Forward or Reverse primer, 3.3 µL of PCR purified product in a final volume of 10 µL. The sequencing program consisted of a hot start at 96 °C for 3 min, followed by 25 cycles at 96 °C for 30 s, 50 °C for 10 s, and 60 °C for 4 min. Sequencing products were purified using EDGEBIO purification plates (Perfoma®V3 96-Well Shortplate), following the manufacturer protocol; capillary electrophoresis was done in ABI PRISM 3730 Genetic Analyzer (AB).The resulting data were analyzed with SeqScape Software 2.1 (AB) from position 16024 to16569 for HVS-I and from position 1 to 576 for HVS-II.20. New haplogroup labels were assigned to new phylogenetic branches when suggested by at least two mitogenomes, and when additionally supported by CR data; e.g. ref.21. In order to further investigate the phylogeny of haplogroups F1f and M7b1a1f, we further collected mitogenomes from the literature and GenBank belonging to these clades (n = 129). The mitogenomes used in the present study were compiled in Table With the aim of investigating in detail the phylogenetic characteristics of some of the mtDNA sequence motifs observed in our Vietnam samples, we additionally investigated the mitogenomes provided by The 1000 Genomes Project (1000G), and the literature. The 1000G dataset consists of 101 samples collected from Kinh donors from Ho Chi Minh City. The data was processed as reported before22. Indels were excluded from these calculations. Genetic structure and variation between individual and grouped population sets were carried out by means of Analysis of Molecular Variance (AMOVA) and RST genetic distances as implemented in Arlequin software v3.5.1.2, and using the CR sequences23.Diversity indices were computed on mtDNA sequences using DnaSP v.5 with the complete CR (16024–576) rangehttps://www.r-project.org/). We followed the commonly used ordinary Kriging method, implemented in the automatic interpolation package autoMap v. 1.0-14 (https://cran.r-project.org/web/packages/automap/index.html), for interpolating frequency values .Spatial geographic representation of haplogroup frequencies was carried out using R Project for Statistical Computing v. 3.3.1 25. Principal Component Analysis (PCA) was carried out with the haplogroup frequencies reported by Zhang et al.16 on 180 SEA populations, and using R software.Mitochondrial DNA haplogroup inference was done using Haplogrep2http://popart.otago.ac.nz) was used to generate phylogenetic networks of mtDNA haplotypes using the median-joining algorithm26, and considering the HVS-I region from position 16051 to 16400.The software popART , and three sequence partitions (as in e.g. ref.28). Hotspot mutations were excluded from the calculations. Mutational distances were converted into years using the corrected evolutionary rate proposed by Soares et al.29 and a generation time of 25 years.The time to the most recent common ancestor (TMRCA) for the phylogenies generated in the present study was estimated using the maximum likelihood (ML) procedure implemented in PAML 3.1330 were obtained using BEAST v1.845 for the CR sequences and mitogenomes analyzed in the present study as done previously28, in order to infer effective population size (Ne) through time and demographic changes from the data. We estimated the best fitting substitution model from our data using jModeltest31. The 1000 G F1f (n = 97) and M7b1a1f (n = 32) mitogenomes were aligned and divided in two partitions: the coding (positions 577 to 16023) and non-coding region (positions 16024 to 576). We used the mutation rate of 9.883 × 10−8 substitutions/nucleotide/year for the non-coding partition, and 1.708 × 10−8 substitutions/nucleotide/year for the coding region32. Three independent Markov chain-Monte Carlo runs of 50,000,000 steps each were performed, with samples taken every 1,000 steps. After inspecting Tracer (v1.6) outputs for distribution convergence, the runs were combined using LogCombiner v1.8.2, with 10% discarded as burn-in, for a final 135,000,000 total steps. The csv file from the combined runs was generated through the VDAanalysis/CSVexport functions in BEAST and plotted using R software. EBSP for the main ethnic groups was generated using the control region fragment 16051–16400 from all available sequences and a mutation rate of 1.62 × 10−7 32. Three independent Markov chain-Monte Carlo runs of 100,000,000 steps each were performed and subsequently combined. Demographic plots were obtained using the Java scripts from O’Fallon and Fehren-Schmitz33.Extended Bayesian Skyline Plots (EBSPs)34 to study inter and intra population gene flow in Vietnamese population. Mitochondrial DNA HVS-I segment from 16051 to 16400 was extracted from each population sample, and the ts/tv ratio and gamma shape parameter of the datasets were estimated with MEGA7 software35 through four gamma categories and HKY substitution model (most similar to the F84 model used by MIGRATE-n). We used all available sequences from Vietnam. We employed a Bayesian approach, which has been demonstrated to be more efficient to obtain reliable results with less effort than the ML procedure when using a single locus36. Firstly, we estimated the migration rate between the main language groups in Vietnam: Austroasiatic , Tai-Kadai , Hmong-Mien (Hmong) and Cham (the Chinese group represented by Hoa people was disregarded due to its low sample size). We compared the full migration model, which allows migration between all sub-population groups, to the panmictic model, which assumes that all subpopulations are very similar to each other due to a large gene flow between them through time. Subsequently, we studied the genetic exchange between the Kinh (which represent 86% of the population of Vietnam) and two available populations sets from neighboring countries, namely Cambodia (n = 1054) 16 and Laos (n = 214)12. We compared three different models with the population pairs Cambodia-Vietnam and Laos-Vietnam: the full migration model, the unidirectional migration models, and the panmictic model. For this purpose we calculated the model probability and log Bayes factor (LBF) of each model through the log marginal values using the thermodynamic integration method37. The best model will give the highest log mL and the model ranking is made comparing the LBF’s and probabilities with respect to the best model.We used MIGRATE-n software v3.6.11Theta). The parameter M expresses how much more important immigration is for the population compared to mutation. Additionally, we calculated the number of immigrants (Nm) in the receiving population as follows: xNm = Theta * M. For mtDNA data and assuming a sex ratio of 1:1 we can consider that the inheritance scalar x equals 1  and the mutation-scaled effective population size  values from the data using PLINK42. Multidimensional scaling (MDS) carried out on IBS values were built to discriminate clusters of genomic variation and employing cmdscale (library stats) from R software.SNPs from two main genome datasetsed Table . The preWe then used ADMIXTURE software to estimate maximum likelihoods of individual ancestries from multi-locus data, considering populations from mainland SEA, neighboring populations, and a further two reference populations, namely Europeans (CEU), and sub-Saharan Africans (YRI).f3-statistics was carried out by measuring allele frequency correlations between populations43. The f3-statistic values were obtained using f3 ; which formally test whether a target population  is admixed between the two source populations, namely CHS (southern Han Chinese population) as the main representative of the EA component and different populations from SEA (Y). We also computed the four-population D-statistics44 to formally test for admixture between Chinese and Malayans, given that these populations appear to be the best subrogates of the populations that contributed to the present pool of Vietnamese, as suggested by the other analyses. Given that this test provides statistical evidence of the direction of the gene flow, D-statistics were built as D and as D, which test for the significance of the contribution of different SEA populations (Y) to KHV and the significance of the CHS contribution under different SEA scenarios (Y), respectively. The weighted block jackknife procedure was used for the computation of D-statistics, using a block size of 5MB. The OUTGROUP was built using ancestral alleles at all sites of the genome45. Values below -2 are considered to be statistically significant.Computation of 46 was finally used to date admixture events in present-day Vietnamese, using the one-reference-population model.ALDERH) and nucleotide diversity (π) (Table H = 0.980) and nucleotide diversity (π = 0.0089) were found in the Mong population. Our Mong sample lived in Lao Cai province . Although the sample of Ha Noi is small (n = 38), it is worth highlighting the high values of diversity observed  . Molecular diversity indices in all locations and population samples analyzed were high for both haplotype (π) Table . The lowce Table ; Fig. 1BThere is no obvious correlation between sampling locations and molecular diversity as measured by statistical summary indices. However, when the samples were analyzed attending to main geographic regions , a clearer pattern of molecular diversity is revealed, suggesting that the diversity increases from North to South of the country Table ; Fig. 2ATypical SEA haplotypes predominate in the Vietnamese, mainly represented by haplogroups M , and haplogroup N  , and these frequencies were interpolated into geographic maps Fig. . The mapNetworks of CR sequences were built Figure  to inves48 found in Chinese samples the CR motif T16189C–T16217C–C16261T–T16357C, which was claimed to be exclusive of the Hmong-Mien speaking populations who live in the South of China. Our results support this hypothesis, as we have also found this motif in six samples of Vietnamese Mong people, which are known as Hmong/Miao in China.An interesting haplotype feature deserves particular mention. Wen and collaborators49. Mitogenomes added phylogenetic support and resolution to these new clades.Among the CR Vietnam sequences analyzed in the present study, we identified two distinctive motifs that had hitherto remained uncharacterized. The current version of the reference mtDNA haplogroup nomenclature (PhyloTree Build 17) does not contain these phylogenetic branches, and therefore new names were given to them. Confirmatory information of these sequence motifs could be obtained from the analysis of the mitogenomes obtained from 1000G and the literature, especially fromet al.24) and it has no mutational hits in Soares et al.29. A total of five mitogenomes carrying C332T were found in the 1000G project Vietnamese samples  . By inspecting other mitogenomes from the literature, we detected 14 additional M7b1a1f4 haplotypes sampled in the Indochinese Peninsula, including Cambodia, Malaysia and Indonesia. In addition, there are 13 other mitogenomes belonging to M7b1a1f that do not carry C332T but other extra motifs, thus constituting other minor sub-clades. Again, these other genomes were sampled in different locations of the Indochinese Peninsula. The analysis of control region sequences belonging to haplogroup M7b1a1f reveals roughly the same pattern observed for the mitogenomes, although indicating a higher prevalence of Vietnamese sequences within sub-clade M7b1a1f4. By revising the literature on CR sequences, we could detect only a few M7b1a1f4 members in populations from SEA ancestry and always at very low frequencies, most of them in Malay51. By examining control region sequences belonging to M7b1a1f, we observed that variant T16189C is not always present in M7b1a1f4 haplotypes carrying C332T. Moreover, a few M7b1a1f4 haplotypes, only present in Vietnam, carry T16324C . Transition C332T is very uncommon, e.g. it does not appear in the phylogeny from Phylotree (see Weissensteiner 79) Fig. . The fiv79) Fig. . The mit24C Fig. . From a 24C Fig. . In VietAnother singular sequence motif observed in our samples from Vietnam falls within haplogroup F1f Fig. . Thus, tAMOVA was conducted by classifying the sample by sampling locations, ethnic groups and broad geographic regions. As expected, within genetic variation accounted for most of the variation, ranging from 98.08–99.69% Table , indepenPCA based on haplogroup frequencies allows visualizing global patterns of variation between populations. We conducted a PCA based on mtDNA haplogroup frequencies of 180 Asian populations Fig. .Figure 4et al.52).The PCA displays an outstanding clustering of population samples by main geographic areas. The first component (PC1), which explains ~24% of the genetic variance, separates all the population samples in three main areas of the plot, with the North China population samples in one pole, and the opposite pole made up by population samples from the Indochinese Peninsula  and Eastern Asia (including a number of western Chinese samples plus Taiwan). A more geographically heterogeneous group of different populations from northern and Central Asia fall in between. Turning to the second component (PC2), which explains ~12% of the variance, the most remarkable feature is that it splits the SEA populations from those in western Asia Fig. . The patIn order to further investigate the patterns of haplogroup variations in Vietnamese populations we performed a second PCA Fig. , this tiF1f1 shows a population growth beginning at ~8 kya, followed by a slight decrease starting at ~2 kya but dramatically descending from ~1 kya onwards. M7b1a1f however, appears ~14 kya and it experiences a continuous growth until the present. The coalescent times obtained by EBSPs for lineages F1f1 and M7b1a1f coincide with TMRCA obtained using ML Fig. .Ne through time .EBSPs were also inferred from ethnic groups Figure . The mosNm) was estimated from the data. The Tai-Kadai family showed the highest migration rates towards the Austroasiatic family . The Hmong-Miao family showed substantial levels of migration towards the Tai-Kadai (Nm = 164). Finally, lower rates of gene flow were obtained from Austroasiatic and Tai-Kadai to Cham family . No maternal gene flow was detected between the other linguistic pairs analyzed .Secondly, gene flow was examined between Vietnamese and neighboring populations in Cambodia and Laos. The most probable gene flow model for the pair Cambodia-Vietnam was a unidirectional migration pattern from Vietnam to Cambodia , the KHV population has a main component that is most prevalent in the Chinese, and two minor components that find the highest frequencies in the Bidayuh from Malaysia and the Proto-Malay, suggesting a close genetic proximity between Chinese and Malaysians , indicates that the sample KHV can be explained by a mixture of Chinese and other populations from SEA, especially the Proto-Malay and other populations from Malaysia  and/or from Thailand (H’tin and Mlabri) , indicates that the Chinese (represented by CHS) have an unquestionable contribution to KHV, and this is statistically significant using almost all SEA populations as reference  suggest that only a few populations from SEA seem to statistically contribute to KHV; again the Malaysians  together with the Thai (represented by the Mlabri and the H’tin) are the samples that could be admixed with Chinese in the Vietnamese gene pool. The fact that the negative values are much higher in D than in D reveals that the Chinese component has a more clear presence in KHV. This result is in good agreement with ADMIXTURE.The ri) Fig. . The D-snce Fig. . Howeverad hoc z test . Haplogroup frequencies observed in previous studies fit well with the values observed in the present study. The analysis by Irwin et al.14 in Ha Noi City population indicated a high R9’F representation (>27%) followed by B and M7 (20%), and low frequency of haplogroups, N, A, C and D (<5%).The majority of Vietnamese people carried mtDNA haplotypes that clustered in clades M7 (20%) and R9’F (27%), two major maternal lineages dominating not only Vietnam, but SEA more generally. Other sub-haplogroups, such as A, B, C and D are represented in the Vietnam territory but with lower frequencies, with the exception of haplogroup B in Lao Cai, where it reaches 41% of the total. Haplogroup M is more prevalent in the North and in the Center of the territory than in the South. The high frequency of its sub-branch M7 is in good agreement with a previous study from 2002, which found this haplogroup only in the southern part of the East Asia, in countries such as Korea or Japanhttp://www.vietnamembassy.org.uk/population.html; accessed June 2017). Our data were sampled from different ethnic groups, and therefore it is possible to make inferences about genetic exchange between them and explore the population sub-structure of the country. AMOVA of Vietnamese groups detects only moderate population stratification, and the variation is lower within ethnic groups than within samples sorted by geographic region. Since inferences based on Wright’s FST alone can be misguided55, evaluation of stratification has to be examined from different angles. Thus, phylogenetic, phylogeographic, and MDS analyses of Vietnamese populations reveal the existence of more notable stratification in the country; at the same time, these analyses also provide evidence for historical gene flow between ethnic groups. Within this scenario, the most distinctive group is the Mong population (Lao Cai). Their mtDNA composition is quite different from those of neighboring ethnic groups from North Vietnam . The results find additional support in historical and anthropological data: archaeological evidence indicates that the early Mong were linked with the Neolithic cultures that settled in the Middle Reach of the Yangtze River and in Central-southern China, while linguistic evidence suggests that Mong occupied the same areas of southern China for at least the past 2,000 years56. Wen et al.48 indicated that most of Mong mtDNA lineages have their origin in southern China, although Mong populations seemed to have had contact with northeast Asians (Chinese Han).The fact that there are over 50 recognized ethnic groups distributed in the highlands has also contributed to the high genetic variability observed. The geographic and cultural characteristics of Vietnam configure a scenario that does not favor random mixing between ethnic groups; furthermore, some of these groups divide their population into social echelons that could restrict genetic exchange  from their original heartland in the Red River Delta (North of present day Vietnam), following the coast. Historical documents indicate that this expansion began in the 10–11th centuries and lasted about 700 years until the mid-18th century. The colonization process initiated by Vietnam was justified by geographic and demographic reasons. This expansion involved intense wars with the Chams and left the population of Vietnam dramatically reduced. It was during the 17–19th centuries that the Vietnamese penetrated the Mekong Delta in the South. Not all the ethnic groups analyzed in the present study show the same demographic pattern in EBSPs, which suggests that most likely the Nam tiến did not have the same impact in all Vietnamese. Estimates of migration rates indicate that the Cham neither were assimilated nor participated in gene exchange with other groups, overall indicating that the dramatic reduction of Champ Ne did not involve assimilation by other populations; see also17. Migration rates also indicate the existence of important assimilation of the Hmong-Miao people by the Tai-Kadai family group, both family groups coinciding geographically in the North of the country.Historical demographic events were also examined by way of EBSPs. The plot obtained from haplogroup F1f shows the existence of important demographic changes occurring about 1,000 years ago. Also, analyses carried out in ethnic groups indicate a similar demographic hallmark in the Cham ethnic group. Interestingly, the observed abrupt change of 58. Migration rates estimated from mtDNA data also agree well with historical records. The data show a unidirectional gene flow from Vietnam to Cambodia, a finding that fits with the historical incursions of Vietnamese into Cambodian territories already in the Dark Ages of Cambodia (from the 16th to 19th centuries), soon after the collapse of the Khmer Empire. These movements began with penetration of the Vietnamese Kinh into the southern Mekong Delta, displacing the Khmers59, and giving way to a period in which Cambodia was alternately controlled by Thailand (previously named Siam) and Vietnam. Nowadays Vietnamese people represent the second largest minority in Cambodia, concentrated mostly in the country.We have examined phylogeographic and gene flow patterns with other neighboring countries. PCA based on mtDNA haplotypes suggests a close genetic proximity between different Vietnamese subpopulations and other Southern China and SEA populations. Phylogeographic characteristics of M7b1a1f and F1f1 suggest that genetic exchange has been common in ancient but also more recent times.et al.12. These authors did not find significant differences in mtDNA patterns between these populations, suggesting intense gene flow by migration between them. These movements may have been favored by their geographic proximity as well as by the partly shared political history of both countries. Our analysis shows a panmitic migration model is the most likely, hence adding further support to this hypothesis. In addition, the Vietnamese language (Kinh ethnicity) belongs to a branch of the Austroasiatic language family with an unexpected similarity with that of the Laotian population, despite the fact that Laos is mainly dominated by Daic language family (Tai-Kadai family)12. This might indicate that a great proportion of Austroasiatic maternal lineages were assimilated by the Laos population.The high genetic similarity between Laotians and Vietnamese was previously noted by Bodner F3-statistics and D-statistics give statistical support to this two-way admixture. A rough estimate based on genome-wide data fits surprisingly well with the results obtained from mtDNA data and a different set of samples from Vietnam, by dating the time of admixture to the Nam tiến expansion.With the aim of contrasting phylogenetic and demographic patterns observed with mtDNA data, we also investigated genome-wide SNP patterns using data from Vietnam and neighboring countries. MDS analysis indicates a close proximity of the Vietnamese Kinh (KHV) with Chinese, Thai and Malaysians. This relation is also evident when examining admixture patterns, which indicate that the presence of the Chinese component is more prevalent in Vietnamese. 60. Haplogroup frequencies estimated from this sampling strategy are also key for the interpretation of biomedical studies, such as case-control studies where undetected population structure could lead to undesirable false positive of association with diseases62. From an anthropological point of view, the overall scenario is that present-day Vietnamese have a dual ethnic origin: a major component coming from South of China, superimposed to a minor component originating from a Thai-Indonesian composite. The Nam tiến has probably been key for the configuration of the genome architecture of present-day Vietnamese.The results of the present study confirm the existence of high molecular variation in Vietnam, as expected from a region considered as one of the main cradles for Asian settlements. Notwithstanding the moderate genetic differences existing between regions and ethnic groups in Vietnam, there is evidence for important gene flow within the country and more broadly within the Indochinese Peninsula. The data presented in the present study is the largest dataset generated to date; it is of interest not only in anthropological studies, but also in other applied fields of medical research; e.g. forensic genetics, where databases of mtDNA data are needed to estimate the weight of the evidence in casework and kinship analysisTable S1Table S2Table S3Table S4Supplementary Data