Mytilus galloprovincialis, and a morphologically indistinguishable and taxonomically contentious native Australian taxon, Mytilus planulatus. Using transcriptome‐wide markers, we demonstrate that two contemporary M. galloprovincialis introductions into south‐eastern Australia originate from genetically divergent lineages from its native range in the Mediterranean Sea and Atlantic Europe, where both introductions have led to repeated instances of admixture between introduced and endemic populations. Through increased genome‐wide resolution of species relationships, combined with demographic modelling, we validate that mussels sampled in Tasmania are representative of the endemic Australian taxon (M. planulatus), but share strong genetic affinities to M. galloprovincialis. Demographic inferences indicate late‐Pleistocene divergence times and historical gene flow between the Tasmanian endemic lineage and northern M. galloprovincialis, suggesting that native and introduced taxa have experienced a period of historical isolation of at least 100,000 years. Our results demonstrate that many genomic loci and sufficient sampling of closely related lineages in both sympatric  and allopatric (e.g. northern hemisphere Mytilus taxa) ranges are necessary to accurately (a) interpret patterns of intraspecific differentiation and to (b) distinguish contemporary invasive introgression from signatures left by recent divergence histories in high dispersal marine species. More broadly, our study fills a significant gap in systematic knowledge of native Australian biodiversity and sheds light on the intrinsic challenges for invasive species research when native and introduced species boundaries are not well defined.Introduced species can impose profound impacts on the evolution of receiving communities with which they interact. If native and introduced taxa remain reproductively semi‐isolated, human‐mediated secondary contact may promote genetic exchange across newly created hybrid zones, potentially impacting native genetic diversity and invasive species spread. Here, we investigate the contributions of recent divergence histories and ongoing (post‐introduction) gene flow between the invasive marine mussel,  Here, we use transcriptome‐wide population genomic analyses with ABC inferences to test alternative hypotheses regarding the origins of Australian Mytilus mussels  and resolve the contributions of past and ongoing (post‐introduction) gene flow with introduced M. galloprovincialis in south‐eastern Australia. This study represents the first transcriptome‐wide investigation of demographic history and introgression between introduced and Australian endemic Mytilus species.To date, taxonomic delineation of the Australian endemic taxon has been hampered by a limited number of reliable loci that has also precluded detailed investigations of its recent evolutionary history. In particular, high levels of ancient incomplete lineage sorting among 22.1Mytilus californianus, n = 3; Mytilus trossulus, n = 3; M. edulis n = 3) were collected from known contemporary allopatric ranges to minimize the possibility of sampling hybrid individuals. We collected Atlantic (n = 5) and Mediterranean (n = 10) M. galloprovincialis belonging to two genetically divergent lineages separated by the Almeria‐Oran front, including a population east of the Siculo–Tunisian Strait dividing the eastern and western Mediterranean  and a second location , to extend previous sampling efforts across the eastern coast of Australia. We additionally included samples from Tasmania (n = 5), where high frequencies of the divergent southern mitochondrial haplotypes have been reported  mitochondrial marker COIII using primers from Riginos, Hickerson, Henzler, and Cunningham . Different subsequent analyses required different filtering schemes  and filtered in VCFtools  of demographic history.RNA‐seq reads from Australian samples and four northern Appendix  using BoAppendix , and PCRs Figure : for gen2.3M. galloprovincialis. Principal component analysis and genomic clustering analyses using the program ADMIXTURE  and directions to determine whether incorporating admixture events improves the likelihood of the tree given the genetic data. Mytilus edulis was used as an outgroup to focus inferences on recent admixture events and we accounted for linkage disequilibrium by performing analyses on windows of 100 variants. We examined the residual plot of pairwise population genetic covariances to infer the possibility of gene flow, where negative residual standard error values suggest closer relationships between populations than compared to the population tree with no migration events. We then modelled 1–10 migration events sequentially to see whether adding migration edges to the phylogeny improved the likelihood fit to the data. We calculated the standard error of migration events with the –se option without sample size correction (option –noss). We used a stepwise comparison Akaike information criterion (AIC) between sequential migration models to determine whether adding a migration edge significantly improved the likelihood of the population tree. We calculated AIC values as (−2(ln(likelihood)) + 2K), where K is the number of free parameters in the model. We did not consider additional migration events when the difference between nested models was less than two (∆AIC < 2).Second, to test for introgression and to validate potential sources of gene flow, we performed joint analyses of migration and historical population relationships in TreeMix v1.12  test of admixture , which is determined through a jackknifing procedure over 100‐SNP windows.Third, the three‐population , M. edulis or M. trossulus) to the nuclear species tree using a heuristic topology weighting analysis in TWISST . We also excluded the most distant outgroup, M. californianus, to limit comparisons to three possible topologies. Consensus haplotypes for each locus were analysed individually; we inferred locus‐specific genealogies with the R package ‘Ape’ using the neighbour‐joining method and F84 distances  have experienced an independent evolutionary history from northern M. galloprovincialis. We evaluated six alternative models of Australian Mytilus origins representing a spectrum of divergence histories between northern and southern taxa: panmixia (pan), divergence in isolation (div), isolation with migration (im), divergence with ancient gene flow (divAGF), divergence with recent (invasive) secondary contact (divSC) and divergence with ancient gene flow and recent secondary contact (divAGFSC) , model‐based inferences of isolation and migration are robust to small sample sizes (i.e. n = 5–10)  implementing an existing ABC pipeline . The resulting empirical data sets consisted of 1,362 loci (Mediterranean‐Tasmania) and 1,539 loci (Atlantic‐Tasmania). For the simulated data, we used msnsam to generate one million multilocus simulations under each demographic model, for each population pair  among loci and homogeneous migration rates (m) every generation. Each simulation was parameterized by model‐specific demographic parameters  on the model identity and summary statistics of the posterior samples  to estimate demographic parameters. We varied the initial Ne and m values for a certain proportion of loci by either (a) decreasing the initial parameter value (hetero1) or (b) allowing loci to have a lower or higher parameter values than the initial draw (hetero2). Finally, demographic parameters were estimated for each population pair using the posterior distribution approximated by accepted simulations under the best inferred demographic model.For initial ABC comparisons, simulated demographic models assumed genome‐wide homogeneous 33.1M. galloprovincialis, explaining 7.31% (PC1) and 6.55% (PC2) of variance among individual genotypes  for individual migration edges, although the direction of the migration edges should not be interpreted at face value  and migration from the Atlantic M. galloprovincialis population into Batemans Bay (w = 40%), suggesting contemporary admixture between native and introduced populations. Results did not provide evidence for migration into Tasmanian mussels. The slight signal of admixture between Batemans Bay and M. edulis suggests shared genetic elements are likely a result of secondary contact with Atlantic M. galloprovincialis populations that share ancestry with M. edulis through past and ongoing introgression  is consistent with small proportions of M. edulis ancestry observed among Atlantic M. galloprovincialis individuals in clustering analyses . Similarly, slight evidence of admixture between the eastern Mediterranean and Batemans Bay likely indicates allele sharing with Mediterranean M. galloprovincialis through introgression with Atlantic populations prior to introduction; however, this migration edge was not strongly supported  for almost all population combinations involving either Sydney Harbour or Batemans Bay as the admixed population, with M. planulatus (Tasmania) and northern M. galloprovincialis as putative ancestral populations  and Mediterranean M. galloprovincialis (Mediterranean‐West) genetic backgrounds. Three‐population tests involving Tasmania as an admixed population did not yield significant values for any population combinations, supporting the hypothesis that samples in this region are representative of the endemic lineage.For 3.2Mytilus outgroup taxa constructed from 12 mitochondrial genes and 2,620 nuclear contigs revealed discordance between the mitochondrial tree and the average nuclear tree. Individuals carrying the Australian  mitotype formed a distinct divergent clade  with M. galloprovincialis, suggesting high levels of ongoing incomplete lineage sorting in Tasmanian samples or genetic exchange, although we did not recover evidence supporting introgression in this population in any analyses. Topologies grouping M. planulatus with M. edulis and M. trossulus showed mean weightings across contigs of 21% and 23%, respectively , compared to private polymorphisms in M. planulatus (average private polymorphic sites = 2.87–4.07). Levels of nucleotide diversity at synonymous sites (averaged across loci) were similar for all populations based on Tajima's pi  and 0.026 (Atlantic) for northern M. galloprovincialis. Pairwise comparisons indicated low population differentiation evidenced by few fixed variants (0–0.02 averaged across contigs) and low FST or absolute (dxy) and net (Da) divergence values between Tasmania and Mediterranean  or Atlantic  M. galloprovincialis, indicating that population differentiation is largely driven by the presence of private alleles. Departures of the site frequency spectrum measured as mean Tajima's D values  and greater negative values in M. galloprovincialis , indicating an excess of rare alleles due to population expansions, gene flow from unsampled populations or signatures of directional selective processes.The majority of biallelic polymorphic sites were shared between  Tajima,  ranging  Tajima,  varied b3.3.2M. planulatus and M. galloprovincialis using an ABC framework. We first inferred the best demographic model by comparing models with homogeneous among‐locus parameters. The model of divergence with ancient gene flow (divAGF) received the highest posterior support  for both population pairs (Table div) provided the second highest posterior probability in most comparisons. We observed consistent rejection of the isolation‐with‐migration (im) model suggesting that historical gene flow was followed by divergence in isolation. Model comparisons returned no support for panmixia and weak support for all models that included recent gene flow associated with contemporary introductions < 600 years ago.We compared six models of divergence between Tasmanian divAGF) and models including recent genetic exchange . The divAGF model had the highest posterior probability in 59% (Atlantic) and 61% (Mediterranean) of model comparisons using PODs generated under the same model . Measures of robustness in the accuracy of model discrimination indicated a minimum threshold for model probability ≥ 83% required to yield a robustness of 95% or greater for the divAGF model, corroborating initial model choice inference  and including  ongoing gene flow since divergence, suggesting that M. planulatus and M. galloprovincialis have experienced a period of historical isolation.Model choice validation indicated that we could discriminate between the best inferred model  allowing among‐locus variation in Ne and m provided an improved model fit to the observed genetic data when compared to the divAGF model with homogeneous parameters  and where m was either homogeneous (Mediterranean‐Tasmania) or heterogeneous (Atlantic‐Tasmania) outperformed other models (Ne homo and Ne hetero1) with a cumulative posterior probability > 90% for both population comparisons. Substantial support for hetero2 models suggests that heterogeneity in Ne is better captured with models incorporating both extremes of variation across loci; processes leading to both low levels of variation , including the time of onset of ancient gene flow since the present (Tnc), were poorly resolved and could not be estimated with accuracy.For the best heterogeneous 4Mytilus mussels in Australia. Unexpectedly, we find that contemporary introductions of M. galloprovincialis into south‐eastern Australia originate from genetically distinct northern hemisphere lineages, implicating at least two independent introductions into this region. Contingent on resolving species relationships, we also provide evidence that both introductions are associated with recent admixture with the native Australian taxon, M. planulatus. Through increased genome‐wide resolution of species relationships, combined with demographic modelling, we validate that M. planulatus sampled in Tasmania are representative of the endemic Australian lineage and have been isolated from northern M. galloprovincialis for at least 100,000 years. Taken together, our study demonstrates the utility of genomic data for detangling the contributions of contemporary invasive hybridization from signatures left by historical gene flow and recent divergence histories in high dispersal marine species.Detecting species introductions and documenting cases of recent introgression are notoriously challenging when native and introduced species inhabit the “grey zone” of the speciation continuum, where species boundaries are often contentious  habitats relative to Sydney Harbour, where Mediterranean M. galloprovincialis predominate. However, there is no evidence to date suggesting differences in temperature tolerance among M. edulis‐introgressed Atlantic mussels and those of purely Mediterranean origins. Similarly, whether post‐introduction admixture between divergent M. galloprovincialis lineages has enhanced the success of introduced populations will require additional investigations examining these synergies  likely obtained through post‐introduction admixture with introgressed Atlantic M. galloprovincialis (rather than direct gene flow with M. edulis). These findings are consistent with previous investigations implicating asymmetric introgression of M. edulis genes into Atlantic M. galloprovincialis populations in south‐western Europe  and allopatric ranges (i.e. M. edulis outgroup) is imperative for accurate interpretations of intraspecific genomic differentiation and signatures of introgression associated with marine introductions.While genetic studies in marine systems have not explicitly investigated whether introgression directly promotes successful introductions, it is evident that hybridization can influence both invasive and native species in a number of ways  suggests ongoing incomplete lineage sorting  between native and introduced taxa.Discordant species relationships between mitochondrial and nuclear genomes are expected to arise due to the fourfold difference in effective population size between genomes, with stronger genetic drift and faster lineage sorting in mitochondrial genes . Estimates are also consistent with previous studies proposing separation times between northern and southern hemisphere Mytilus species ~0.5 to 1.3 million years ago based on mitochondrial loci  and M. platensis in South America and the Kerguelen Islands which is more related to M. edulis in the northern hemisphere  and M. galloprovincialis are mostly due to recent divergence histories possibly associated with historical gene flow  database. Strong sequence similarities and permeable barriers to gene flow with M. galloprovincialis, however, support previous proposals to assign the endemic Australian lineage with a regional subspecies status  will therefore require further investigation.Based on the genomic data presented here, we infer that shared polymorphisms and modest signals of genome‐wide differentiation between 4.3Mytilus mussels are well‐known ecosystem engineers, and changes in their abundance and range could impose significant alterations to intertidal communities . In such cases, surveys for high gene flow marine invasive species using environmental DNA  indicates significant contributions of introgression to the genomic composition of Australian mussels, likely during the early stages of the introductions. Future research in Mytilus and other marine invasive species should focus on temporal and spatial sampling of greater numbers of individuals to assess the rate and direction of introgression and the potential impacts of introgression swamping of native genomes on ecologically relevant timescales (Glotzbecker et al., Knowledge regarding the potential ecological consequences associated with introduced populations will therefore be essential for monitoring and minimizing the spread of introduced lineages (Braby & Somero, d Pogson  recentlyNone declared. Click here for additional data file.


Hopea hainanensis Merrill & Chun (Dipterocarpaceae) is an endangered tree species restricted to Hainan Island, China and a small part of Northern Vietnam. On Hainan Island, it is an important indicator species for tropical forests. However, because of its highly valued timber, H. hainanensis has suffered from overexploitation, leading to a sharp population decline. To facilitate the conservation of this species, genetic diversity and population structure were assessed using 12 SSR markers for 10 populations sampled across Hainan Island. Compared to non-threatened Hopea species, H. hainanensis exhibited reduced overall genetic diversity and increased population differentiation (AMOVA: FST = 0.23). Bayesian model-based clustering and principal coordinate analysis consistently assigned H. hainanensis individuals into three genetic groups, which were found to be widespread and overlapping geographically. A Mantel test found no correlation between genetic and geographical distances . The observed genetic structure suggests that long-distance gene flow occurred among H. hainanensis populations prior to habitat fragmentation. A recent population bottleneck was revealed, which may cause rapid loss of genetic diversity and increased differentiation across populations. Based on these findings, appropriate strategies for the long-term conservation of the endangered species H. hainanensis are proposed. Earth’s biodiversity is rapidly declining as a consequence of agricultural expansion, overexploitation, deforestation, pollution and climate change –3. ApproTree species from a single family, the Dipterocarpaceae, are key community members in Asian tropical forests, accounting for 20–50% of forest basal area and more than 50% of canopy trees , 8. ManyHopea species, H. hainanensis Merrill & Chun and H. reticulata Tardieu, are found there. H. hainanensis grows in the tropical lowland forests of Hainan Island and Northern Vietnam. It is a large evergreen tree with a height up to 20 meters, and it blooms and fruits almost every year. This tree species serves as an indicator for tropical forests on Hainan Island [Hopea [H. hainanensis. The size of H. hainanensis populations on Hainan Island has been greatly reduced due to the overexploitation and habitat loss for rubber tree plantations and agriculture [H. hainanensis was assessed as endangered in the IUCN Red List of Threatened Species [H. hainanensis has hampered the effective conservation and management of this endangered species.Dipterocarp forests flourish in the Malay Peninsula, Sumatra, Borneo, Java and wetter parts of the Philippines, and stretch to the northern limits of tropical Asia . The spen Island , 12 and d [Hopea , 14. Altd [Hopea . Neithericulture , 16. Theiculture . H. hain Species . This sp Species . HoweverIn view of the ecological and silvicultural importance of the Dipterocarpaceae, there have been many studies of dipterocarp trees that have investigated patterns of genetic diversity, fine-scale spatial genetic structure, mating system and gene flow among populations –22 The mH. hainanensis were performed using 12 microsatellite markers newly developed for this species. The goal of the study was to quantify the amount of genetic variation in H. hainanensis populations on Hainan Island, and compare this to the genetic diversity of non-threatened Hopea species. Then, the geographic pattern of genetic variation was revealed and discussed. The effect of habitat fragmentation on genetic differentiation across populations was also evaluated. Our studies on genetic diversity and population structure should facilitate the long-term conservation of this endangered species.In this study, population genetic analyses of H. hainanensis is very rare in tropical forests of Hainan Island [H. hainanensis is primary or secondary tropical lowland forest, at altitudes ranging from 200 to 1000 meters [H. hainanensis were deposited in the Herbarium of Hainan University, Haikou, China.Totally 76 individuals were collected from 10 natural reserves for population genetic analyses, as n Island  ; the 20 μL final reaction volume contained 2 μL gDNA (at least 50 μg/mL), 0.2 μL of each primer (50 μM) 10 μL 2× Taq PCR MasterMix , and ddH2O. The following cycling program was used: an initial five minute denaturation at 94°C; followed by 32 cycles of denaturing at 94°C for 20 s, annealing at 59–63°C for 20 s, and extension at 72°C for 60 s; with a final extension of seven minutes at 72°C. PCR products were mixed with Hi-Di Formamide loading buffer  and GeneScan 500 LIZ Size Standard , and then genotyped on an ABI 3730XL DNA Analyzer (Applied Biosystems) owned by the TIANYI Biotechnology Company . Alleles were scored using the GeneMarker software .Total genomic DNA was extracted from the silica gel-dried leaves using a DNeasy Plant Mini Kit  following the manufacturer’s instructions, with the addition of a step to remove high levels of polysaccharides in the initial extracts. The concentration and quality of the extracted DNA were measured with a NanoDrop 2000 spectrophotometer . Using second generation sequencing and computational screening, twelve polymorphic SSR primer pairs were developed to genotype H. hainanensis , allelic dosage was determined based on the ratios between peak intensities following the MAC-PR method [Na), effective number of alleles (Ne), and observed (Ho) and expected (He) heterozygosity were then calculated. The heterozygosity-based Gis statistic was used to test for deviations from Hardy-Weinberg equilibrium (HWE). Another challenge posed by autopolyploidy is polysomic inheritance, under which double-reduction may occur, biasing the results of standard population genetic analyses [In view of the autopolyploid nature of R method   and expected (He) heterozygosity, polymorphic information content (PIC) and inbreeding coefficient (Gis) were estimated for each population and locus. Differentiation between populations was assessed using GST [FST was adopted as the measure of genetic distance [The observed  was impsing GST  with 9,9distance . Principdistance , which hdistance .K) varied from one to ten, and for each value of K, ten independent replicates were run with 100,000 burn-in iterations followed by 1,000,000 MCMC (Markov chain Monte Carlo) iterations. The best K was inferred using STRUCTURE Harvester [K, and a graphical representation of the population structure was generated with Distruct version 1.1 [H. hainanensis individuals using the software package MEGA 5.0 [A model-based clustering method, STRUCTURE v. 2.3.4 , was imparvester  followinarvester . The Grearvester  was usedsion 1.1 . A neighMEGA 5.0 .H. hainanensis [Vateriopsis seychellarum [Shorea leprosula [S. macrophylla [A qualitative graphical method was performed to identify potential population bottleneck in nanensis . Allele nanensis . For comhellarum , and twoeprosula  and S. mrophylla , were fuHopea hainanensis populations on Hainan Island, a total of 45 alleles were detected, for an average of 3.75 alleles per locus; the minimum number of alleles detected per locus was two  and the maximum was six (Hha7 and Hha8). The polymorphism information content (PIC) varied from 0.205 (Hha10) to 0.707 (Hha7), with an average of 0.477. A large number of loci were found to deviate from HWE (P < 0.05) across the study populations (Ne) ranged from 1.550 in DL to 2.351 in JF, averaging 1.964 alleles per population. The expected heterozygosity (He) as estimated by GenoDive ranged from 0.265 (DL) to 0.525 (JF), while the He estimated by Polygene ranged from 0.233 (DL) to 0.495 (JF). The inbreeding coefficients (Gis) were all greater than zero, ranging from 0.138 (FJ) to 0.408 (JF), and averaging 0.243  between the genetic distance (as measured by Slatkin's linearized FST) and the natural logarithm-transformed geographical distance among populations  was found to increase from K = 1 to 10 and ΔK was maximized at K = 3, suggesting that there are three distinct genetic clusters in H. hainanensis. The results of the individual assignment indicated that only a few individuals were found to have mixed ancestry of more than one genetic cluster  are much more abundant than those at intermediate frequency (0.101–0.200) in the other two non-threatened Shorea species. Such a mode-shift distortion of allele frequency distribution is a characteristic of bottlenecked populations, revealing that H. hainanensis on Hainan Island may have undergone a recent population bottleneck.The distribution of allele frequencies in  species . The numShorea species have been found to have high levels of genetic variation  [Hopea dryobalanoides is a non-threatened species widely distributed in Malaysia, Sumatra and Borneo [H. dryobalanoides was collected from the Pasoh Forest Reserve in Peninsular Malaysia, and five SSR markers were used to evaluate its genetic diversity. Expected heterozygosity (He) was estimated at 0.678 in H. dryobalanoides, which is comparable with that found for a number of Shorea species [He for H. hainanensis populations from Hainan Island was 0.409 as estimated by Polygene and 0.427 as estimated by GenoDive, which is markedly lower than the He measured for H. dryobalanoides [H. hainanensis (Na = 2.46) versus H. dryobalanoides (Na = 5.60). In most cases, reductions in the effective population size and shifts of mating system from outcrossing to selfing cause the loss of genetic diversity within species [H. hainanensis is most likely due to a severe demographic bottleneck, as this species has undergone approximately a 70% population reduction over the last three hundred years [Eucalyptus plantations, as well as urban expansion [H. hainanensis populations would shrink proportionally, perhaps even more, due to the high quality of its timber.Species in the family Dipterocarpaceae are generally characterized by moderate to high levels of genetic diversity and low differentiation among populations . This par locus) , 47–49. d Borneo . A singl species , 22, 49.lanoides . Further species , 52. Theed years . On Hainxpansion , 53. TheH. hainanensis populations  to the number of alleles at intermediate frequency (0.1–0.2) is 11.35 for S. leprosula and 7.10 for S. macrophylla. However, the ratios are 3.86 and 1.0 for V. seychellarum and H. hainanensis, respectively. Obviously, many low-frequency alleles have been lost during recent bottlenecks in the populations of V. seychellarum and H. hainanensis. A higher proportion of low-frequency alleles are maintained in V. seychellarum than in H. hainanensis, which may arise from different sample sizes used for the two species. Since the graphical method requires only samples of 5 to 20 polymorphic loci and approximately 30 individuals, the SSR data used in this study should be adequate [H. hainanensis populations on Hainan Island may have experienced a recent demographic bottleneck, resulting in relative fewer alleles at low frequency and lower genetic diversity detected in this species. The genetic diversity of H. hainanensis in Northeast Vietnam was recently evaluated using 10 SSR markers [He = 0.382, allelic richness = 2.08) and Hainan Island have similar levels of genetic variation . In this study, Gis ranged from 0.138 to 0.408, higher than the Gis estimates for many other dipterocarps [H. hainanensis and resulting population fragmentation likely produced small and isolated populations, in which enhanced inbreeding would be expected [H. hainanensis populations on Hainan Island are typical of endangered species, and are likely the consequence of intensified inbreeding and genetic drift acting in small H. hainanensis populations.When the size of a population is drastically reduced, genetic drift may override natural selection and act as the main evolutionary force, leading to a loss of genetic variation . Moreoveerocarps –50. The H. hainanensis using both Bayesian model-based clustering and principal coordinate analysis (PCoA)  and geographical distance  Figs  and 3. ToA) Figs . The throA) Figs . In addidistance , suggest species , 57. Neoed 400 m . In addi species . In summGST across adult populations of the endangered V. seychellarum is 0.20 [Hopea species. A moderate level of differentiation between two geographically adjacent populations was revealed in H. bilitonensis , which is an extremely rare and predominantly selfing dipterocarp in Peninsular Malaysia [Hopea species were also reported to have low to moderate levels of genetic differentiation  [Hopea species, H. hainanensis showed higher overall genetic divergence (GST = 0.229). The AMOVA analysis also revealed high differentiation among populations (FST = 0.23). Population genetic theory predicts that under Island model of complete isolation, reduction in population size could promote divergence among populations [H. hainanensis. However, other factors, such as the number of generations after population isolation (t), geographic distance between sampled populations, mating system and evolutionary history also influence the level of population divergence [H. hainanensis populations.Endangered dipterocarp species are generally highly differentiated across populations based on the analysis of SSR markers. For example, the overall  is 0.20 . Habitat is 0.20 . GeneticMalaysia . Two throdorata) . Compareulations , which ivergence . In summH. hainanensis populations, BW, FJ and DL, were found to have relatively lower levels of genetic variation. These three populations may therefore be more vulnerable to abiotic and biotic stresses and should be the focus of special conservation attention. Meanwhile, the BL, JF, JX, LM and QW populations had relatively higher levels of genetic diversity and contained more than one genetic subpopulation. These five populations could therefore serve as seed sources for the propagation of seedlings and saplings, to be used in the restoration of previously logged lowland rainforests on Hainan Island. The natural regeneration of H. hainanensis populations is challenging: seedlings and saplings grow very slowly and often fail to establish in the highly shaded understory. Therefore, selecting populations with high genetic diversity  to produce seedlings may facilitate the restoration of endangered H. hainanensis populations on Hainan Island.Because the loss of genetic variation is a major threat to the survival of endangered species, an important conservation action is to preserve and restore genetic variation , 60. ThrS1 File(XLSX)Click here for additional data file.S1 TableTwo options, “Consider negative PCR” and “Consider selfing”, were combined along with optimal model selection.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 FigK = 3.Colored bars represent individual assignments by STRUCTURE when (TIF)Click here for additional data file.


A sustainable breeding program requires a minimum level of germplasm diversity to provide varied options for the selection of new breeding lines. To maximize genetic gain of the North Dakota State University (NDSU) flax breeding program, we aimed to increase the genetic diversity of its parental stocks by incorporating diverse genotypes. For this purpose, we analyzed the genetic diversity, linkage disequilibrium, and population sub-structure of 350 globally-distributed flax genotypes with 6200 SNP markers.H = 0.22 and I = 0.34). The pairwise Fst comparison revealed a great degree of divergence (Fst > 0.25) between most of the combinations. A whole collection mantel test showed significant positive correlation (r = 0.30 and p < 0.01) between genetic and geographic distances, whereas it was non-significant for all sub-populations except P4 and P5 . In the entire collection, the mean linkage disequilibrium was 0.03 and it decayed to its half maximum within < 21 kb distance.All the genotypes tested clustered into seven sub-populations (P1 to P7) based on the admixture model and the output of neighbor-joining (NJ) tree analysis and principal coordinate analysis were in line with that of structure analysis. The largest sub-population separation arose from a cluster of NDSU/American genotypes with Turkish and Asian genotypes. All sub-populations showed moderate genetic diversity (average Fst > 0.50). In contrast, low genetic differentiation between P5 and P2 may enhance the accumulation of favorable alleles for oil and fiber upon crossing to develop dual purpose varieties. As each sub-population consists of many genotypes, a Neighbor-Joining tree and kinship matrix assist to identify distantly related genotypes. These results also inform genotyping decisions for future association mapping studies to ensure the identification of a sufficient number of molecular markers to tag all linkage blocks.To maximize genetic gain, hybridization between NDSU stock (P5) and Asian individuals (P6) are potentially the best option as genetic differentiation between them is highest ( Linum usitatissimum L.) is an ancient crop, grown throughout the world to be sold at market. Domestication events have separated flax into two broad types: seed flax and fiber flax [Flax  and association mapping (AM) approaches are commonly used. QTL mapping is done by tracking the cosegregation of QTL and marker loci in biparental mapping populations and reveals low resolution regions due to the relatively low recombination rates of a single cross. AM reveals marker trait association by utilizing linkage disequilibrium (LD) of germplasm collections . AlthougIn this study, we genotyped 350 flax germplasm accessions using 6200 informative SNP markers. The objectives were (1) to explore genetic diversity and differentiation among the genotypes, (2) to investigate the potential of the collection as parental resource and (3) to assess the suitability of the collection for marker-assisted breeding.Fis), fixation index (F) and observed heterozygosity (Ho) of all the markers were 1, 1 and 0 respectively as all were homozygous. The Shannon’s information index (I) of all markers ranged from 0.03 to 0.70 with a mean value of 0.34.The selected 6200 SNPs were distributed across 15 chromosomes with an average marker density of 1 per 51.17 kb Chromosome Lu1 and Lu4 contained highest  and lowest  number of SNPs, respectively. The SNP density was lowest on chromosome Lu4 66.34 kb) and was highest on chromosome Lu13 (36.95 kb) (Table 4 kb and He) ranged from 0.08 to 0.53 with a mean value of 0.30. The polymorphic information content (PIC) ranged from 0.07 to 0.47 with a mean value of 0.24  of pure accessions, whereas P4 contained the lowest percentage (18%) Table . We alsoH) of the seven sub-populations ranged from 0.12 (P6) to 0.28 (P3) with an average of 0.22. The Shannon’s information index (I) ranged from 0.21 (P6) to 0.44 (P3) with an average of 0.34. Likewise percentage of polymorphic loci and diversity, it was highest in P3 and lowest in P6. The Tajima’s D value ranged from − 0.85 (P6) to 1.50 (P3) with an average of 0.52 (Table r) among individuals within sub-population was significant (p < 0.01). The P3, P5 and P1 showed lower (< 0.1) r values and it increased for P2 (0.10), P4 (0.11), P7 (0.12) and was highest for P6 (0.34) .In all sub-populations the percentage of polymorphic loci was greater than 60%. It was highest in P3 97.53%) and lowest in P6 (62%). The diversity .The AMOVA revealed that variance among sub-populations covered 28% of total variation whereas the remaining 72% of total variation accounted for variance among individuals within sub-populations Table  with a FFst > 0.25) [Fst > 0.50 was observed between P2 and P6, P5 and P6, P7 and P6  whereas it was not significant in other sub-populations  was revealed by mantel test.Individuals of P4 and P5 showed significant positive correlation between geographic and genetic distance  at short distance bin (0–1 kb) and declined with increasing bin distance (Table Sr2 = 0.50), and was lowest in P4 (r2 = 0.39). In P6, highest proportion (28.22%) of total loci pair was linked, whereas it was very low (1.08%) in P3 (Table The linkage disequilibrium (LD) pattern was investigated across the entire collection, each sub-population and chromosome-wise. LD = e Table S. In the r2 = 0.3. In P6, LD persisted the longest in chromosome Lu1 35.42 kb) and Lu3 (34.40 kb). The decay distance was shortest in chromosome Lu13 13.71 kb) and Lu8 (14.68 kb)  for each marker. The PIC determines the usefulness of any marker for linkage analysis whereas He determines the diversity of haploid markers [He value for all markers was always greater than PIC value as PIC value become closer to He with more alleles and with increasing evenness of allele frequencies [We used a total of 6200 homozygous SNP markers for diversity analysis of 350 genotypes. The used SNPs were well distributed throughout the genome. The transition SNPs were more frequent than transversion SNPs, indicating that transition mutations are more tolerable to natural selection . A simila sativa , Camellisinensis , Hevea biliensis  and Brasca napus , 31, thaca napus . We also markers . We foun markers . Other r markers , 37, ric markers  and maiz markers . Bi-alle markers  restrictquencies .H = 0.22), which is in line with our expectation as flax possesses an autogamous reproduction system. A similar level of diversity was found in one study [r). The negative correlation between diversity indices (H and I) and relatedness indicates that inbreeding and genetic drift play a significant role in reducing genetic variability in the studied population which results in increased differentiation among sub-populations. Similar phenomenon was also found in Arapaima gigas species [Selection of diversified materials is crucial for widening the genetic base of a breeding germplasm collection. In our study, based on the identified SNP markers, the different sub-populations exhibited moderate diversity  populations [The success of any breeding program usually depends on the right choice of parental groups at the inception. The NDSU flax breeding program is comparatively old. The program already has developed some high yielding and high oil content varieties as well as considerable amount of advanced breeding lines. To enrich the parental stock of the on-going program, the genetic diversity of 350 flax germplasms comprising NDSU released varieties and advanced breeding lines were analyzed in this study. We partitioned the whole collection to its maximum i.e. seven sub-populations based on structure, PCoA and NJ-tree analysis though cluster number was less , 50, 51 than one . It was  procera , in Nelubo lutea  and flaxbo lutea . Sub-popbo lutea . The prebo lutea . The resbo lutea . Howeverbo lutea  and in Lulations .Fst is a good indication of the degree of divergence among populations. Both high and low pairwise Fst value is good for parent selection depending on the objectives. In the present study, we identified statistically significant large and small pairwise Fst values. Similar results were also found in previous studies [Fst between them is highest (Fst > 0.50) These sub-populations also contain different released varieties. For creating dual purpose transgressive segregants, we will choose parents from pair P2 and P6 (Fst > 0.50) as P2 contained mainly fiber type and P6 contained oil type genotypes. For quick fixation of both fiber and oil contributing alleles in single individuals, crosses between genotypes of P2 and P5 will be more effective as pairwise Fst < 0.20. Within sub-populations, crossing among genotypes will also be useful as AMOVA reveals variance among individuals within sub-population covered a larger portion of total variation than variance among sub-population. This result is in line with the previous findings [h > 2.30). All sub-populations contained both pure (non-hybrid) as well as admixed genotypes. For parent selection, the pure genotypes will be prioritized. However population diversity tends to inflate the real differentiation between any two pair of individuals as it exploits the alleles that not necessarily come from the same parent or ancestor. IBS coefficients are good to decide what individuals will be crossed to combine positive alleles that historically never have been combined. The molecular kinship or coancestry in self-pollinated crops tends to be higher than that in cross-pollinated crops as heterozygosity reduces the probability of two alleles at a locus of being identical by state [Hybridization among genotypes from divergent populations will usually produce more diversity, transgressive segregation, and heterosis resulting in higher genetic gain. Pairwise  studies , 60. To findings , 58, 61,findings , 63. In by state . In our Fst with high but similar level of LD will reduce the number of required individuals and markers for AM analysis. However, population structure and cryptic relatedness also affects AM analysis by increasing the false positive rate [Most of the economically important traits are quantitative in nature. To develop markers for quantitative traits, association mapping (AM) is used and knowledge of linkage disequilibrium (LD) is useful to determine the number and density of markers and experimental design needed to perform the analysis. Although low LD requires more markers for high resolution, it increases the predictive power of each one . We founive rate , 70. To ive rate .In the present study, we used highly informative SNP markers which were developed through GBS analysis. The identified SNPs provide a clear picture of genetic structure, diversity, relatedness and linkage disequilibrium of the studied population which leads to higher precision in parent selection for a need-based future breeding program. These markers will also facilitate QTL mapping, association mapping, to allow us to utilize marker-assisted and genomic-selection breeding tools for multiple traits breeding.A core collection of 350 flax germplasm accessions originated in 38 countries of 6 continents were collected from North Central Regional Plant Introduction Station (NCRPIS), Ames, Iowa, USA, North Dakota State University (NDSU) released varieties and advanced breeding lines, varieties developed by different institute of USA and Canada Fig. .Fig. 6DApekI enzyme was used for GBS library preparation [Young leaves were collected from 30 days old plants and flash-frozen in liquid nitrogen. Tubes were stored at -80̊ C until lyophilized. The lyophilized leaf tissue was ground in tubes with stainless beads using a plate shaker. DNA was extracted using Qiagen DNeasy Kit  from lyophilized tissue following the manufacturer’s protocol. DNA concentration was measured using a NanoDrop 2000/2000c Spectrophotometer (Thermofisher Scientific). The paration . Sequencftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/224/295/GCA_000224295.2_ASM22429v2) using Bowtie 2 (version 2.3.0) alignment tool [TASSEL 5 GBSv2 pipeline  was usedent tool . After pent tool  was usedThe collection was divided into genetic groups using STRUCTURE v2.3.4  softwareFst and relatedness (r) values were calculated using GenAlex v6.5 [https://github.com/YinLiLin/R-CMplot). The polymorphism information content (PIC) of markers was calculated using software Cervus [r) was correlated with Shannon’s information index (I) and diversity (H) in R v3.5.2 [Analysis of molecular variance (AMOVA) was done to partition the genetic variance among the groups identified by STRUCTURE in Arlequin3.5 . The avelex v6.5 . GenAlexe Cervus . Tajima’e Cervus . The levR v3.5.2 . We perfR v3.5.2  within eR v3.5.2  and kinsR v3.5.2 . LinkageR v3.5.2 .Additional file 1: Table S1. List of the genotypes analyzed in this study.Additional file 2: Table S2. Marker diversity parameters.Additional file 3: Table S3. Sub-population wise marker diversity parameters.Additional file 4: Table S4. a Percentage of variation explained by the first 3 axes, b Eigen values by axis and sample Eigen vectors.Additional file 5: Table S5.Fst values at loci level.Additional file 6: Table S6. Mean LD values according to distance.Additional file 7: Table S7. Chromosomewise LD decay rate (Kb) within each sub-population.Additional file 8: Table S8. Kinship (IBS) matrix.Additional file 9: Figure S1. Mantel test output for whole collection and each sub-populations.Additional file 10: Figure S2a-b. Chromosome-wise LD decay rate considering whole collection.Additional file 11: Figure S3. Chromosome-wise SNP distribution pattern.Additional file 12: Figure S4. Heatmap of kinship matrix.Additional file 13: Figure S5. Histogram of IBS coefficients.


Phoxinus lagowskii is a freshwater fish that is widely distributed in China. In this study, a comparative analysis of the mtDNA control region (D-loop) was performed to analyze the natural population structure and genetic diversity of 54 individuals from eleven locations  which was divided with reservoirs. The estimated haplotype and nucleotide diversity were 0.734 and 0.03514, respectively. An AMOVA indicated that 79.78% of the total variation originated from individual populations and 20.22% came from variation within the 11 geographic populations, which showed high genetic differentiation among the 11 geographic groups. A test of neutral evolution and mismatch distribution indicated that historical expansion occurred in these populations. However, the findings of low genetic diversity and high genetic differentiation demonstrated that the reproduction isolated by reservoir has showed a certain effect for the development of the populations, and the results should provide new information for the conservation and exploitation of this species. Phoxinus lagowskii), a member of the family Cyprinidae, subfamily Leuciscus, is a common edible fish that is abundant and widely distributed in the Yangtze River, Qingjiang River, Hanjiang River and northern Yellow River in China , 100 mM ethylenediaminetetraacetic acid (EDTA) (pH 8.0), 1% SDS) and 5–8 ml 20 mg/ml proteinase K for 4 h at 50 °C with constant gentle mixing. After centrifugation at 12,000 rpm for 10 min, the supernatant (DNA extract) was collected in a new 1.5-ml microcentrifuge tube and was sequentially extracted with 300 ml each of phenol and chloroform. The total DNA was recovered by overnight precipitation in 1 ml volumes of frozen absolute ethanol and washed twice by 75% ethanol at 12,000 rpm. The total DNA was standardized to a concentration of 50 ng/ml and stored in ddH2O.Total genomic DNA was extracted from the fin tissue samples by a modified phenol/chloroform extraction method  using the forward primer DL1: 5'-caa cat gcc ggg cgt tca tg-3' and the reverse primer DL2: 5'-gcg atg gct aac cgt agc tc-3'. Amplifications were carried out in 30 μl reaction volumes containing the following components: 4 μl of genomic DNA, 23.2 μl of golden mix , 1.2 μl of each primer, 0.4 μl of Taq polymerase . The PCR conditions were as follows: 95 °C for 3 min followed by 35 cycles of denaturing at 94 °C for 30 s, 55 °C for 30 s and 72 °C for 1 min, and a final extension at 72 °C for 10 min. All of the amplified products were purified with a Gel Extraction Kit (Generay Biotech (Shanghai) Co., Ltd., Shanghai, China) following the manufacturer’s instructions. The PCR products were sent to Shanghai Generay Biotech Co. Ltd.  for sequencing.The sequences were proofread, edited and aligned with BioEdit version 7.0.5.3 . The polymorphism was assessed using DnaSP version 5.0 . Arlequin software version 3.11  was used to estimate the genetic differentiation and Fst values with an analysis of molecular variance (AMOVA) model    were obth = 1.000), followed by the T5 population (h = 0.833), the T4 and T6 populations (h = 0.8), the T10 and T2 populations , with the T3 population exhibiting the lowest haplotype diversity. The total haplotype diversity of the T11 population was 0.734. The T7 population exhibited the highest level of nucleotide diversity (π = 0.08142) among the 11 populations.The estimates of haplotype diversity and nucleotide diversity are shown in The AMOVA model Meirmans  was usedp > .05) among the 11 populations; the topology of the resulting neighbour-joining (NJ) tree  indicated a positive value (in population T8), but there was no significant difference (NJ) tree  revealedPhoxinus lagowskii is mainly distributed in the Heilongjiang River, Tumen River, Liaohe River, Yellow River and the middle reaches of the Yangtze River in China. Phoxinus lagowskii is the dominant carp species in the Liaohe River of Liaoning Province , as a common haplotype among the nine populations, supports the fact that these populations are descended from a common ancestor, whereas the other twenty-four haplotypes separately belong to different populations. All twenty-five haplotypes were associated with the dispersion and expansion of the population . Therefoh = 1.000), whereas the T7 population exhibited the highest nucleotide diversity (π = 0.08142). The Phoxinus lagowskii in the Liaoning River exhibit high genetic diversity. Nevertheless, this rare fish resource should be protected from the degeneration of biodiversity.The analysis of the genetic structure of the eleven populations revealed that haplotype and nucleotide diversities were an important index of population genetic diversity. The haplotype and nucleotide diversities of the 54 individuals collected from the eleven sampling points were 0.7340 and 0.03514, respectively. The T7, T8 and T9 populations exhibited the highest haplotype diversity (Phoxinus lagowskii have suffered severe population declines; in the past 40 years, some populations have totally disappeared due to increases in the local water temperature and excessive fishing by humans (Liang Phoxinus lagowskii in the Liaohe River has adapted to changing environmental and demographic events (Sun et al. In recent years, populations of ns Liang .The distPhoxinus lagowskii conformed with the results of the AMOVA revealing that variation within the population accounted for 79.78% of the total variation and variation among populations was only 20.22%; therefore, genetic variation does not occur primarily in individual populations. Thus, it was concluded that the genetic differentiation among the eleven populations was low. Despite the low level of genetic differentiation, the results of the present study also support substantial differences in geographic structure associated with different population drainages. As above, the results of this research demonstrated that the reproduction isolated by reservoir has showed a certain effect for the development of Phoxinus lagowskii populations, and the results should provide some new information to the government and scientific researchers for the conservation and exploitation of this species.The differentiation of haplotypes in


Anopheles funestus (s.s.) is a primary vector of the malaria parasite Plasmodium falciparum in Africa, a human pathogen that causes almost half a million deaths each year. The population structure of An. funestus was examined in samples from Uganda and the southern African countries of Malawi, Mozambique, Zambia and Zimbabwe.An. funestus from 13 representative locations across five countries. These were comprised of four sites from Uganda, three from Malawi and two each from Mozambique, Zambia and Zimbabwe.Twelve microsatellites were used to estimate the genetic diversity and differentiation of e) ranged from 0.65 to 0.79. When samples were pooled three of the 12 microsatellite loci showed Hardy–Weinberg equilibrium. Unsupervised Bayesian clustering analysis of microsatellite data revealed two clusters with An. funestus samples from Mozambique, Uganda and Zambia falling into one group and Malawi and Zimbabwe into another. The overall genetic differentiation between the populations was moderate (FST = 0.116). Pairwise differentiation between the pairs was low but significant. A weak but significant correlation was established between genetic and geographical distance for most populations.All loci were highly polymorphic across the populations with high allelic richness and heterozygosity. A high genetic diversity was observed with 2–19 alleles per locus and an average number of seven alleles. Overall, expected heterozygosity (HAn. funestus populations. Further research on the population dynamics of An. funestus in east and southern Africa is essential to understand the implications of this structuring and what effect it may have on the efficient implementation of mosquito vector control strategies.High genetic diversity revealed by the loci with low to moderate differentiation, identified two clusters among the  Anopheles funestus (sensu stricto) is present throughout most of sub-Saharan Africa and is one of the four most important vectors of human malaria parasites [An. gambiae complex [An. funestus due to its strong association with humans and human habitations [arasites –3. In pa complex , 4. The itations , 2. Howeitations , 6 has sitations –9. This itations , 11. To An. funestus have been carried out in Uganda [An. funestus [Mosquito vector surveillance covers the monitoring of all aspects of mosquito biology, from geographical distribution to the more complex genetic diversity within and between populations. Studies of population genetics provide insight into gene flow between mosquito populations and through that, the likelihood of the spread of genes (e.g. those conferring insecticide resistance) across geographical regions. In east and southern Africa, studies focused on the population structure of n Uganda –14, Malan Uganda , 15–17, n Uganda , Tanzanin Uganda , Mozambin Uganda , 16, 17 funestus –19.An. funestus was examined using specimens from one country in east Africa (Uganda) and four countries in southern Africa . Twelve microsatellite markers distributed across all five chromosome arms were used to address the questions: (i) Is there any evidence for population genetic subdivision within this collection of An. funestus? and (ii) What is the maximum amount of differentiation observed within each selected site? Population structure analysis quantifies the amount of genetic exchange that occurs between sample sets and provides insight on how these underlying genetic components may be used to address biological phenomena such as insecticide resistance, parasite transmission and dispersal of the mosquito.Understanding the gene flow dynamics of populations of malaria vector mosquitoes is important for strategic planning of vector control interventions. In this study, the population structure of Preserved mosquito samples from selected sites Table  in five In Mozambique, samples were collected from Maciana and Matola, near Maputo in southern Mozambique. Matola is the largest suburb of Maputo and lies on the coastal plain north of Espirito Santo estuary. In Matola, found 42 m above sea level, the climate is characterized by a warm and wet season from December to March, and a dry and cold season from June to August. Precipitation averages 1050 mm (41 inches) per year, with abundant rainfall from December to March, and rare rains from May to October. Temperature varies between 15–34 °C and relative humidity ranges between 28–89%. Maciana, at 68 m above sea level is characterized by a warm and wet season from November to April, and a dry and cold season from May to October. Precipitation averages 815 mm (32 inches) per year, most of which falls from November to March. The rainiest month is January (170 mm). Temperature varies between 11–34 °C and relative humidity ranges between 59–67%.Uganda experiences a warm tropical climate with heavy rains in March–May and September–November for the South. In northern Uganda, the wet season extends from April to November with peaks in April and August. Samples were obtained from Agule, Apac, Lira and Kamuli districts in North and central Uganda. Agule and Apac were originally one district so do not have much variation in climate characteristics. Agule lies 1034 m above sea level. Temperature varies between 20–34 °C and relative humidity varies between 28–88%. Apac lies on 1040 m above sea level with a tropical climate, over the course of the year, the temperature varies between 18–35 °C. Temperatures are highest in February, with an average of around 25.1 °C, whilst July is the coldest month of the year with an average of 22.1 °C. Precipitation over this area, averages 1305 mm. Humidity varies between 21% and 69%. Lira lies at 1091 m above sea level and has an average temperature of 23.2 °C and a yearly average precipitation of 1376 mm. The wet season runs from April to mid-November and with two dry seasons (December to February and June to August). February is the warmest month of the year with an average temperature of 33 °C. July has the lowest average temperature, with an average of 21.6 °C. Overall, the average humidity for Lira is 58%. On the other hand, Kamuli lies 1185 m above sea level and temperature varies between 15.5–32 °C. March is the warmest month of the year, with an average temperature of 22.7 °C, whilst July has the lowest average temperature of 20.7 °C. There is a great deal of rainfall in Kamuli, even in the driest month (January) with 63 mm of rainfall. April is the wettest with an average of 193 mm. Average rainfall is 1325 mm over the year. Humidity varies between 62–95%.Zambian collections were from Nchelenge and Namwala. Namwala is located in the north-western corner of the southern province next to Kafue National Park. Zambia has a tropical climate, which experiences wet and dry periods. In Namwala, the wet season lasts from mid-November to March and the dry season from end of November to April. The cool season is from May to August with temperatures varying between 10–27 °C and the dry season from September to November with temperatures around 26 °C to 37 °C and humidity between 19–75%. Some days are particularly dry where humidity can be as low as 0%. Nchelenge district is located near the Democratic Republic of Congo next to Lake Mweru. For Nchelenge, the wet season lasts from September to May and the dry season from end of May to September. The cool season is from December to April with temperatures varying between 15–27 °C. The hot season is from September to October with temperatures around 22 °C to 34 °C and humidity varying between 22–89%. In Nchelenge, humidity can also reach a low of 0%.In Zimbabwe, samples were obtained from Honde and Mangwanda. Both sites are located within Mutasa district. In Zimbabwe, Honde, from late October to around the end of April, the weather is hot and humid. Temperatures rise up to 28 °C and is when most of the convectional rainfall is received. Average rainfall is 1150 mm. From May to the beginning of July, the temperatures are very low, up to 2 °C, whilst August is very windy. During September to October very hot temperatures are recorded, where the maximum temperature may average 30 °C. Humidity varies between 11–45%. In Mangwanda, the climate is warm and temperate. The summers have much more rainfall with an average of 809 mm. From May to August the temperatures are very low, varying between 9–16 °C, while August is very windy. From September to December, it is very hot and the temperatures range between 16–28 °C. The average humidity is 73%.Specimens were collected using indoor aspirations for all mosquitoes mainly in the living/sleeping area of the houses (huts), but for two sites human landing catches were carried out Table . AnophelAn. funestus mosquitoes using a DNA extraction kit prepGEM®Insect  following the manufacturer’s instructions. In short, a leg of an individual mosquito was homogenized with reagents, incubated for 15 min at 75 °C and at 95 °C for 5 min. A 10 µl elution of genomic DNA was obtained for each sample from the extraction.Genomic DNA extractions were performed on the legs of the individual An. funestus sequence data [2 (Takara Bio Inc), 0.2 mM of each dNTP (Takara Bio Inc), 10 pmol of each primer, 0.1 U of Taq DNA polymerase (Takara Bio Inc). The forward primer was labelled at the 5′-end with either PET, HEX or 6-FAM fluorescent markers to allow multiplex electrophoresis. Amplification was carried out with a T100™ thermal cycler , under the following conditions: an initial denaturation step at 94 °C for 2 min; 36 cycles of 30 s at 94 °C, 30 s at 54 °C, 30 sec at 72 °C; and a final elongation step of 10 min at 72 °C. Fragment analysis was performed commercially at Macrogen  with an ABI PRISM 377 . Alleles were sized relative to an internal size standard using Peak Scanner v1.0 . Output files (*.fsa) generated by the autosequencer were loaded into Peak Scanner™ software. Determination of fragment size was performed using default settings, with GS500LIZ specified as size standard and ‘PP - Primers Present’ in the ‘Analysis Method’ column. Peak information showing height, area and size were labelled and scored. The resulting fragment sizes were used for further analyses.Twelve microsatellite loci were selected from previously published nce data –25, baseIS). Linkage disequilibrium between pairs of microsatellite loci was assessed using FSTAT 2.9.3 [ST [ST between sampling groups and their significance was assessed using the randomization approach implemented in FSTAT 2.9.3 with Bonferroni-adjusted P-values.Microsatellite allele and genotype frequencies were determined using Arlequin 3.5  and FSTAAT 2.9.3 . The lev.9.3 [ST . PairwisST was assessed by estimating Wright’s F-statistics, calculated according to Weir & Cockerham [https://www.ghostscript.com). As an estimate of gene flow, the number of migrants per population per generation (Nm ≈ (1 − FST)/4FST was conducted [ST/(1 − FST) on the logarithm (ln) of geographical distance, in GenALEx 6.503 [The significance of genetic differentiation between populations based on allelic distribution across populations was examined using a Fisher exact test with FSTAT 2.9.3 . The paiockerham . A locusockerham  was perfockerham , 30. Thiockerham , 30. Basockerham  followedockerham  and DISTockerham . The visonducted . The corEx 6.503 , 36.An. funestus samples. PCoA was conducted based on mosquito genotypes. The genotypes at each site were transformed with natural log (ln (x + 1)). Then, the sites on the PCoA ordination map were marked as groups from the cluster analysis extracted from the collected sites. PCoA is an indirect gradient analysis method for seeking the strongest linear correlation structure among variables [For the majority of the time, principal coordinates analysis (PCoA) uses Euclidean distance between two points that are being compared. To visualize the genetic relatedness among individuals, we performed PCoA after all the preliminary analyses using the GenALEx 6.503 , 36 progariables , 36 and ariables , 36.An. funestus individuals were identified from the 13 localities. Both Clades I and II were found  compared with previous studies. Overall, the mean observed heterozygosity (Ho) values ranged from 0.26 to 0.42 and expected heterozygosity (He) from 0.65 to 0.79 . A summary of the mean variation of microsatellite loci from each country is presented in Table n = 2) and AFUB11 the greatest number of alleles (n = 19).Levels of microsatellite polymorphism were moderate to high for all the twelve loci. One site from Malawi and two from Uganda had small sample sizes , three loci  of the twelve showed significant deviations from the Hardy-Weinberg equilibrium (HWE). This may have been due to significant heterozygote deficiency or due to the sub-structuring within the population. When these were removed no significant deviation was observed.ST ranged from low to moderate. Values between 0.00–0.05 indicate little divergence, 0.05–0.15 moderate divergence, 0.15–0.25 high divergence and over 0.25 a very high degree of divergence [ST between pairwise population comparisons for all loci varied from 0.006 to 0.396 across populations. These were significant within countries (P < 0.05) for Mozambique (Maciana and Matola), Uganda  and Zambia   and the Zimbabwe sites (Honde and Mangwanda) but these were not significantly different (P > 0.05). Sites in Malawi varied between 0.029 (Likoma) to 0.054 (Karonga), 0.12 between Maciana and Matola, 0.013 between Mangwanda and Honde and the highest divergence was between all the sites in northern Uganda and Kamuli. Over all populations studied, the FST estimate was 0.116 indicating that there was some genetic structure in the dataset (P < 0.05). This suggests that there are levels of genetic differentiation among populations within the selected countries.Genetic divergence between the sampling groups estimated by Fvergence , 38, 39.a) Table . The higST results, the most likely number of genetic clusters in the dataset was two. However, when STRUCTURE was re-run with only 8 loci, three clusters were obtained for Malawi and Mozambique, and the rest of the countries retained the original two clusters  increased from K = 1 to K = 2 until it reached a maximum value at K = 2, after which values decreased gradually for all countries  . The AMOVA of all twelve microsatellites confirmed the differentiation and structure analysis with the variation between individuals, within populations, and among populations, being 38%, 50.24% and 0.21% (Table s) Table . Only foP = 0.007; for Mozambique, Maciana and Matola villages, Rxy = 0.257, P = 0.07; for Zambia, Nchelenge and Namwala, Rxy = 0.026, P = 0.007; for Zimbabwe, Mangwanda and Honde Rxy 0.071, P = 0.005; and for Uganda, Kamuli, Apac, Agule and Lira, Rxy = 0.15, P = 0.023. A low but significant correlation for Malawi, Zambia, Zimbabwe and Uganda was observed. No significant difference was observed for Mozambique (P < 0.05). The estimates of gene flow among An. funestus populations suggests that there is a range of gene flow between country level collections (Nem ranging from 0.03 to 41), with some populations more reproductively isolated than others with Nem country averages of 6, 2, 8, 18 and 14, for Malawi, Mozambique, Zambia, Zimbabwe and Uganda, respectively  hypothesis was performed separately for each country using GenAlEx 6.501 to test for the occurrence of a positive correlation (Rxy > 0) between the genetic matrix and the geographical distances. For Malawi, the villages of Karonga, Likoma and Majete showed an Rxy of 0.083, An. funestus microsatellite loci, which would be the threshold required for statistical significance in PCoA  or the spatial pooling of individuals originating from different houses or focal points [An. funestus populations [Low to moderate differentiation of the 13 n Africa , 39–42 a species , 43–45. a et al. , Michel a et al. , who foul points . Changesulations , 48. HWEulations , 48. Theem values of gene flow for An. funestus populations have been reported to vary from 17 to 483 [em for An. funestus in this study were much lower [The rate of gene flow over time is considered suitable for estimating exchange of genes between populations . Nem val7 to 483 , 18, 39.ch lower , probablch lower , Michel ch lower  and OgolRxy = 0.083, P = 0.007), not indicating environmental or other barriers to gene flow. Only the two sites in Mozambique  gave negative results for isolation by distance. Genetic/geographical distance correlations are variable with previous studies showing these to be weak but significant [A study on upregulation of insecticide resistance metabolic enzyme genes provided evidence of population structuring between northern and southern sites in Malawi . The Mannificant , 22, weanificant , or non-nificant .versus Uganda/Zambia/Mozambique. These groups do not coincide with the mitochondrial Clades I and II of Choi et al. [An. funestus from Zambia, the Democratic Republic of Congo and Tanzania, showed that there were two strong maternal lineages in Zambia and Tanzania. Whether the PCoA results illustrated in Fig. Using genetic structure analysis, the populations studied here were clustered into two groups, Malawi/Zimbabwe i et al. , both ofi et al.  based onAn. funestus with populations from Mozambique, Uganda and Zambia forming one group and Malawi and Zimbabwe the second group. However, almost all genetic diversity (88%) was partitioned within populations, indicating small differences, except for the PCoA of the Zambian samples which were clearly divergent. The variable effective population sizes, climate and different collection time points, may be some of the factors affecting the differentiation. Future research should investigate changes in mosquito populations over time, especially as insecticide use and their coverage evolve, new interventions are rolled out, and climate/land use changes.Two genetically distinct clusters were revealed in Additional file 1: Table S1. Population comparisons, HW proportions and independence of loci .Additional file 2: Figure S1. Structure clusters by population in each of the countries, Malawi, Mozambique, Uganda, Zambia and Zimbabwe with eight microsatellites. The X-axis corresponds to the population codes. The Y-axis presents the probability of assignment of a population to each cluster.


With high consanguinity rates on the Arabian Peninsula, it would not have been unexpected if the population of the United Arab Emirates (UAE) was shown to be relatively homogenous. However, this study of 1000 UAE nationals provided a contrasting perspective, one of a relatively heterogeneous population. Located at the apex of Europe, Asia, and Africa, the observed diversity could be explained by a plethora of migration patterns since the first Out-of-Africa movement. A strategy to explore the extent of genetic variation of the population of the UAE is presented. The first step involved a comprehensive population stratification study that was instructive for subsequent whole genome sequencing (WGS) of suitable representatives (which is described elsewhere). When these UAE data were compared to previous smaller studies from the region, the findings were consistent with a population that is a diverse and admixed group of people. However, rather than sharp and distinctive clusters, cluster analysis reveals low levels of stratification throughout the population. UAE emirates exhibit high within-Emirate-distance/among-Emirate distance ratios. Supervised admixture analysis showed a continuous gradient of ancestral populations, suggesting that admixture on the south eastern tip of the Arabian Peninsula occurred gradually. When visualized using a unique technique that combined admixture ratios and principal component analysis (PCA), unappreciated diversity was revealed while mitigating projection bias of conventional PCA. We observe low population stratification in the UAE in terms of homozygosity versus separation cluster coefficients. This holds for the UAE in a global context as well as for isolated cluster analysis of the Emirati birthplaces. However, the subtle clustering observed in the Emirates reflects geographic proximity and historic migration events. The analytical strategy used here highlights the complementary nature of data from genotype array and WGS for anthropological studies. Specifically, genotype array data were instructive to select representative subjects for WGS. Furthermore, from the 2.3 million allele frequencies obtained from genotype arrays, we identified 46,481 loci with allele frequencies that were significantly different with respect to other world populations. This comparison of allele frequencies facilitates variant prioritization in common diseases. In addition, these loci bear great potential as biomarkers in anthropological and forensic studies. Comprehensive reference panels of genetic variation for entire populations are paramount for the interpretation of the medical and functional relevance of DNA sequence changes. The Arabian peninsula is notoriously understudied with only 0.08% of human genetic sequences in public databases from populations of a region . This stThe UAE is located in the south eastern corner of the Arabian peninsula. Apart from the UAE, the Arabian Peninsula comprises Kuwait, Bahrain, Oman, Qatar, Saudi Arabia, Yemen, as well as parts of Iraq and Jordan. It is an ethnically diverse area that has arisen from a series of social and cultural influences. Collectively, the inhabitants are referred to as “Arabs”; a major pan-ethnic group that inhabits Western Asia, North Africa, and parts of the Horn of Africa. Arabs are so defined as they share common linguistic, cultural, religious, and political traditions. Countries such as Kuwait and Saudi Arabia have land borders with Asian counterparts, hence are influenced by overland trade routes from the east, and the north, respectively. In contrast, the UAE in the south east is relatively isolated with land borders with Saudi Arabia and Oman. Migration from Asia over the relatively short distance that is the Straits of Hormuz was convenient and did occur. The efforts described here provide genetic information that complements and adds to the body of knowledge relating to immigration, providing an understanding of the peopling of the south east region of the Arabian peninsula.The Arabian peninsula sits at the crossroads of significant human migration between the African, European, and Asian continents. Mitochondrial DNA (mtDNA) analyses have been widely used as a tool for human migration studies . ArcheolThe Neolithic Revolution in Mesopotamia, the productive land around the Tigris and Euphrates rivers, around 10,000 years ago gave rise to milestones in human development, such as the invention of agriculture, writing, mathematics, and astronomy. Mesopotamia’s impact along the eastern coast of the Arabian Gulf is evidenced by numerous settlements  that occurred after the last substantial sea level rise around 6000 to 5000 BCE. They exhibit long distance trade with Mesopotamia and the Indus Valley, pottery, animal husbandry, and permanent stone houses . IntriguThe eventual development of trade routes  in more The original inhabitants of the region led a nomadic existence and moved throughout the peninsula, in search of favorable grazing areas , resulting in settlements that were the crossroads for trade and cultural exchange. At these ports, admixture probably took place between intermingling populations. Historical texts show that the oversea crossing to Central and South Asia across the Arabian gulf was possible and in fact did take place into the south eastern region of the peninsula, which now includes the UAE. In other parts, populations coalesced at overland ports that connected North Africa and Central Asia. In the northern perimeter, populations would have intermingled with Levantines, and in the south west corner, there were exchanges across the Red Sea with Sub-Saharan Africa. Within the peninsula, there were interconnected routes between these settlements, providing opportunities for further diversification. While evidence for the gene flow between Africa, Southern, and Central Asia during prehistoric times is complex and multi-layered including back migrations , more reAs mentioned, the influence from Sub-Saharan Africa into the southern regions of the peninsula including the UAE has arisen by crossings from the Horn of Africa into Yemen . In addiMigration patterns are never unidirectional and back migrations are a common trend of human movement throughout history . Being jContemporary UAE was formed by the union of seven emirates or sheikhdoms in 1971 led by Sheikh Zayed bin Sultan Al Nahyan. The Trucial states that formed the UAE were once located on the southern routes of human migration within the peninsula. To understand the establishment of settlements in the UAE, the genome of its nationals has to be sequenced and analyzed. Of the approximate 10 million population of the UAE, some 15% are citizens of the country. The majority of the residents of the UAE are expatriates, with approximately 30% being South Asian in origin.With the UAE joining the genome fraternity relatively recently, this study describes the results of a genome analysis pipeline that was optimized to meet our needs, using gene array data to guide the selection of individuals for WGS to study the UAE population.Prior to enrollment, the 1000 participants provided their written informed consent that had been approved by the Institutional Ethics Committee of Mafraq Hospital in Abu Dhabi, UAE. Subjects were also given a questionnaire to collect their historical and demographical information. The inclusion criteria for the study were that the subjects had to be an adult (>18 years old) citizen of the UAE who understood their contribution to the study and was subsequently able to give consent.®L2P system  was used to extract genomic DNA from buccal cells in the saliva samples. The extracted DNA aliquots were quantified using the DS 11 FX Fluorometer  and the integrity of each was checked by electrophoresis on an agarose gel.Saliva samples were collected from the participants using the Oragene OGR 500 kit . The prepITThe samples were genotyped on the Illumina Omni5 Exome Bead Chip v.4.0.1  which contains 4.6 million single nucleotide polymorphism (SNP). Illumina recommended the standard protocol was used for sample hybridization and scanning using the Illumina iScan platform.r2 threshold of 0.4 (plink parameters –indep-pairwise 1000 10 0.4). In addition, we exclude SNPs that fail the Hardy Weinberg Equilibrium test with a significance of 0.001, minor allele frequency < 1%, and missingness > 1%. This step retained 2.3 Million quality controlled loci. In a similar fashion, set AB was merged with 1043 samples from the Human Genome Diversity Project (HGDP)  and 488 (dataset B) samples yielding dataset AB. The data were merged using PLINK v1.90 . For most (HGDP)  on the bfreq option, resulting in. frq file in addition to PLINK’s bed/bim/fam files. A custom parser for gnomAD’s (gnomad.broadinstitute.org) extracted allele frequencies for all available populations at all biallelic loci. Moreover, the script extracted the overall allele frequencies from the Greater Middle East (GME) Variome project , Central/South Asia, Sub-Saharan Africa, North Africa, Oceania, Native-America, East Asia, and Europe were used. The command “admixtureAdditionally, phylogenetic analysis has been conducted to contextualize the UAE genomes in comparison to other world populations. This is achieved by calculating the intergenomic distance between a subset of 104 UAE individuals—for visualization purposes—of the UAE samples and the HGDP samples. We use the identity by state (IBS) distance measure from PLINK to calculate the mutual intergenome distances. The resulting distance matrix was subjected to neighbor joining and the phylogenetic tree was visualized using iToL .1-ibs) for samples was generated using PLINK’s distance parameter. The distance matrix was then subjected to hierarchical clustering with average linkage [also referred to as unweighted pair group method with arithmetic mean (UPGMA)] using scipy  was run on the previously described dataset ABH using the PLINK’s A Python program that generates novel, feature rich visualizations for genotyping data was developed. It parses output from PLINK (PCA results), ADMIXTURE (supervised admixture components), and geographic information for HGDP context samples. Based on matplotlib, the script plotPCA.py was used to generate a PCA plot, but in addition to conventional uni-colored dot plots of two to three components, each individual sample also contained admixture information in form of a pie chart, i.e., with pie-sections proportional to their respective components that contribute to a sample’s genotype. We further developed PCA plots with only selected populations  that were deemed most impactful based on the ADMIXTURE calculation of K with minimal cross validation error. For those PCA plots, we down-sample each population to 100 samples.https://github.com/henschellab/populapy.The visualization software is available on ibs dissimilarity) of samples translates into dendrogram proximity. Further, for comparison purposes, the dendrogram was consistently colored according to population membership of samples. Again, the Python scripts have been made available in the abovementioned repository.The plotting of admixture bar plots is conventionally arranged either by preconceived groupings or by lexicographic ordering of the individual admixture components. In contrast, the bar plots were arranged here according to the dendrogram derived from hierarchical clustering. This way, the genetic makeup of clusters becomes immediately apparent, as genetic similarity (as measured through 1-ibs) distances of UAE and HGDP samples. We then use Pandas and Python to calculate the average within-population distance  and among-population distances (separation) for all populations. Formally, the within-population distance is given by the average of all distances of pairs of an individual in a population. The pairwise separation between two populations is calculated by averaging over all pairs with one being a member in one population and the other being member in the other population. The separation of one population is then the average of separation values to all other populations. For the sake of an unbiased separation value for the UAE, we omit HGDP Middle East. We then calculate compactness of a population as a measure for how well it clusters, by dividing heterogeneity with separation. Compactness can be interpreted as a measure of population stratification. We down-sample populations by randomly selecting 100 individuals per population . This process is repeated 100 times to estimate variance in separation, heterogeneity, and compactness. The (pairwise) separation values yield a new 8 × 8 distance matrix (seven HGDP populations plus the UAE) with pairwise distances between populations, to which we apply hierarchical clustering with average linkage. In addition, for each population, we calculate heterogeneity (average within-population distance), separation (average among-population distance), and the separation/heterogeneity ratio, which balances within-cluster homogeneity against between-cluster separation. We perform the same steps for six UAE Emirates separately, with cluster labeling derived from the samples’ respective birth places. We finally combine all HGDP populations and individual Emirates, to put inter-Emirate differences into global perspective. The Jupyter notebook that reproducibly details all steps and calculations is provided in the abovementioned repository.We use PLINK (plink–cluster–genome) to perform population stratification. The first step produces a 2043 × 2043 distance matrix containing all pairwise IBS  is used to estimate the UAE population differentiation using molecular markers from the seven UAE subpopulations (grouped by their birth place). The analysis was conducted using the Arlequin  command The genotype array analysis yielded the allele frequencies for 2,382,081 exonic loci after the quality control step. In the PCA that is In Additional PCA has been performed by removing the continental populations that have only marginal contribution to the ancestry of UAE citizens  . Those pIn Approximately 10% of the samples have >99% of their genotype assigned to the Middle Eastern model population from HGDP see , the remThe admixture cross validation for different numbers of ancestral populations yielded a minimal error for K = 5 see . We use The bar plot generated by the ADMIXTURE Software tool shown in Given this more comprehensive picture of the subpopulations and in order to present the diversity within the population of the UAE, four individuals were selected from distinct parts of the PCA plot for WGS. These four individuals were centrally located within their respective population groups in the PCA plot. Representatives with predominantly Middle Eastern (UAE S011) and Central/South Asian ancestry (UAE S013) were selected. The remaining two representatives (UAE S012 and UAE S014) had different admixture ratios with influences from Sub-Saharan Africa, North Africa, Middle East, Central/South Asia, and Europe. The influence from Oceania, native Americans, and East Asia was minimal (no more than 1.08%) in all four representatives. WGS for these four individuals has been completed and the results of their analysis are unpublished.The comparison of the UAE seven subpopulations (grouped by their birthplace in the emirates), using AMOVA revealed that only 0.24400% of the total variation was found among subpopulations  while the rest (96%) was within subpopulations’ individuals scoring a fixation index (Fst) value of (0.00244) see . This sha) 12 groups  six groups (UAE and five HGDP), and (c) two groups (UAE and HGDP) has shown higher “among population” diversity in the populations structures (a) and (c)  . The pop and (c)  distanceIdentity by state-based pairwise distances between all samples showed that the UAE samples have a comparatively high heterogeneity. ST values are shown in ST values are closest to Central/South-Asia, Middle East, Europe, and North Africa, whereas remarkably, all Emirates exhibit very low FST values amongst each other in comparison to global FST values.The color coded Fp-value of 6.3 × 10–5) when compared to allele frequencies reported for gnomAD and the GME populations. Nine hundred and six of these were annotated by the following high impact SnpEff functional assignments: Non-synonymous Coding (880), Start-Gained (six), Stop Gained (five), and Splice Site Region (15), as shown in We identified 46,481 variant loci with significantly different allele frequencies . Had there been clear distinct clusters, it would reflect in higher cluster coefficients with high separation and low within-cluster heterogeneity—for example, in Oceania or Native America (both 1.38). The impact of respective Middle Eastern, Central/South Asian, and African influences can be appreciated in Nevertheless, the gradual admixture that was observed here in the UAE study was enhanced by a novel visualization tool, which graphically combines admixture information in the form of a pie chart with PCA. The use of both PCA and admixture analyses is commonly used, for example, in the Indian study showing the genetic influence from Africa  and FiguThe general results compare well to previous studies from the region, specifically from Qatar  and KuwaWe only observed very subtle stratification effects and admixture, for example, individuals with substantial Sub-Saharan component were born in nearly all the seven Emirates of the UAE. This is found to be in agreement with the preformed AMOVA analysis results .The diversity observed in the contemporary UAE population could have arisen in two possible ways. The generally held understanding of the peopling of the world has origins in Sub-Saharan Africa, migrating northward, crossing into the Arabian Peninsula, and radiating eastward throughout Asia, across the Bering Strait which now separates Russia from Alaska, followed by the colonization of North and subsequently the South American continent . The curAlternatively, the dispersed distribution of the UAE subjects, when considered in concert with the ancestral composition, shows that the contemporary population of the country was historically influenced by populations from surrounding regions . It is wThe Emirati population is thus less stratified than what would have been expected according to the Kuwaiti  and QataAnother important result from the genotyping is the provision of allele frequencies for 2.3 million variant loci. Hitherto statistically underpowered, this study is a timely effort and a statistically significant estimation derived from over 1000 independent random samples. Note that databases such as ExAC and gnomAD  do not pn + 1” local sequencing effort. Even though the population at hand is small, due to its diversity, the choice of a suitable reference is not a trivial undertaking. However, at least one of the genomes is likely a more adequate match than the conventional reference genomes  in terms of read mapping percentage and variant calling.By combining the statistical strength from array genotyping for 1000 samples and the high coverage of WGS for four representatives, the data in this study provide a rich base for future genomic studies in the UAE and in the Arabian Peninsula, which has yet to be studied. This analysis has established a reference data collection that has breadth and depth. The large set of comprehensive genotype arrays has not only facilitated the mapping of the local genetic landscape in a global context within the major world populations, it has also provided a rich set of country specific allele frequencies. On the other hand, the four genomes will work as a more suitable reference for the next “In summary, efforts here show that affordable genotyping using arrays helps in generating a comprehensive population overview, which in turn provides guidance in choosing adequate representatives; in terms of their centrality in the subpopulation and their admixture content; for more in depth study . Of the 2.3 million variants identified, there were 46,481 variants (approximately 2%) with allele frequencies that were significantly different, when compared to other populations. These variants were relatively evenly distributed across all 22 chromosomes with the MHC on the short arm of chromosome 6 containing the dominant number of variations , as expeEGAS00001004362.The Data described in this article is deposited at the European Genome Archive (EGA) under the accession number The studies involving human participants were reviewed and approved by Mafraq Hospital in Abu Dhabi, United Arab Emirates (UAE). The patients/participants provided their written informed consent to participate in this study.HA obtained the funding for this study. HA and GT designed the study. GT, GD, and AH analyzed the data and prepared the manuscript. GT, HA, GD, and AH reviewed and edited the manuscript and contributed to the discussion.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.