Homo sapiens, evolved within a single population and/or region of Africa. The chronology and physical diversity of Pleistocene human fossils suggest that morphologically varied populations pertaining to the H. sapiens clade lived throughout Africa. Similarly, the African archaeological record demonstrates the polycentric origin and persistence of regionally distinct Pleistocene material culture in a variety of paleoecological settings. Genetic studies also indicate that present-day population structure within Africa extends to deep times, paralleling a paleoenvironmental record of shifting and fractured habitable zones. We argue that these fields support an emerging view of a highly structured African prehistory that should be considered in human evolutionary inferences, prompting new interpretations, questions, and interdisciplinary research directions.We challenge the view that our species,  Homo sapiens evolved from a single region/population within Africa has been given primacy in studies of human evolution.The view that However, developments across multiple fields show that relevant data are no longer consistent with this view.Homo sapiens evolved within a set of interlinked groups living across Africa, whose connectivity changed through time.We argue instead that Genetic models therefore need to incorporate a more complex view of ancient migration and divergence in Africa.We summarize this new framework emphasizing population structure, outline how this changes our understanding of human evolution, and identify new research directions. Homo sapiens probably originated in Africa at least ∼500 thousand years ago (ka) H. sapiens fossils do not demonstrate a simple linear progression towards contemporary human morphology. Instead, putative early H. sapiens remains exhibit remarkable morphological diversity and geographical spread. Together with recent archaeological and genetic lines of evidence, these data are consistent with the view that our species originated and diversified within strongly subdivided  populations, probably living across Africa, that were connected by sporadic gene flow H. sapiens and more divergent hominins , Omo Kibish (∼195 ka) and Herto (∼160 ka), both in Ethiopia, are morphologically diverse H. helmei’, using the binomen given to the Florisbad partial cranium in 1935 H. sapiens idaltu because they fall outside the variation of recent humans.Extant human crania are characterized by a combination of features that distinguish us from our fossil relatives and ancestors, such as a small and gracile face, a chin, and a globular braincase. However, these typical modern human features emerge in a mosaic-like fashion within the  Kibish ∼95 ka andd Herto ∼0 ka, botH. sapiens as an evolving lineage with deep African roots, and therefore prefer to recognize such fossils as part of the diversity shown by early members of the H. sapiens clade. The full suite of cranial features characterizing contemporary humans does not appear until fairly recently, between about ∼100–40 ka H. sapiens fossils, together with their geographic distribution across Africa, suggests that evolution may at times have progressed independently in different regions, in populations that were often semi-isolated for millennia by distance and/or ecological barriers, such as hyperarid regions or tropical forests.However, we view H. sapiens populations can be gained from more recent forager populations in Africa, which were also strongly structured. For example, Later Stone Age (LSA) human remains highlight both the retention of ‘archaic’ traits and the maintenance of considerable morphological diversity into the terminal Pleistocene Holocene, the skeletal record becomes much richer, but there remains considerable spatial variation in morphology. Variation between populations in different regions and environments of Africa may have been shaped by isolation-by-distance and local environmental adaptations Further insights into the geographic extent and potential habitat diversity of early H. sapiens anatomy remain incompletely understood. However, the data do not seem to be consistent with the long-held view that human ancestry is derived predominantly from a single African region hosting a panmictic population. Instead, H. sapiens likely descended from a shifting structured population , each exhibiting different characteristics of anatomical ‘modernity’. The discovery that the primitive-looking H. naledi dates to between ∼335 ka and 236 ka Homo heidelbergensis skull may date to ∼300–125 ka H. sapiens, raising the possibility of African archaic interbreeding. Future research should attempt to determine which features evolved before the appearance of our species and which primarily developed within the evolutionary history of our species. Another key area concerns understanding the extent to which different processes shaped observed changes. For example, the narrowing of the pelvis may reflect different processes including neutral genetic drift, adaptation to ecological variation, and life-history variation.Ultimately, the processes underlying the emergence of any ‘package’ of derived features diagnostic of early large cutting tools such as handaxes, and an increased emphasis on prepared core technologies and hafting, marked a profound technological reconfiguration of hominin material culture. These technological changes, which define the transition to the Middle Stone Age (MSA), seem to have occurred across Africa at a broadly similar time; for example at ∼300 ka both at Jebel Irhoud, where they are found with early H. sapiens fossils H. sapiens fossils, but both H. naledi and H. heidelbergensis probably persisted into the late Middle Pleistocene.Across Africa, the virtual abandonment of handheld Late Pleistocene, grassland and savannah expansion in North Africa led to dense human occupation associated with specific regional technological features such as tanged implements  Although geographical differences are clear at the continental scale, localized spatial patterning is harder to discern. Similarities between regions may have been produced by occasional contact or by convergent adaptation to common environmental conditions. In East Africa, for example, although there is certainly some variation, there appears to be underlying continuity in material culture throughout much of the MSA e.g., . In manyBox 1IBD is the expectation that genetic differences correlate positively geographic distances as a consequence of the fact that mating is more likely to occur at shorter than longer distances. This concept, although well established in genetics, is rarely applied to Pleistocene archaeological and human fossil material, despite its potential value as a null model for observed cultural or morphological differences between materials from different sites Because archaeological problems are concerned with identifying the processes that generate observed patterns of cultural variation over time and space, the lack of equivalent null models is particularly problematic. Processes generating cultural variation constitute a complex balance between patterns of inherited knowledge, local innovation, modes of cultural transmission, local adaptation, and shifting population dynamics  unraveling the relative contributions of different African regions/habitats to recent human evolution; (ii) understanding shifting patterns of population structure through the differential appearance, expansion, contraction, and disappearance of regionally distinct artefact forms ; and , whereas in tree models geographic location may be implicit but is not explicitly modeled. For example, two populations that are geographically close may be assumed to share a more recent splitting event than populations that are more distant. Other models  may incorporate geographic space explicitly by connecting demes via gene flow only with their spatial neighbors.Population genetic modeling for demographic inference often assumes panmixia, wherein all individuals in a population have an equal chance of mating with one another. In such cases there is often an implicit assumption that panmixia occurs at the whole-species level. However, real populations rarely meet this condition, owing to the existence of spatial structure at the species level or other stratification of individuals within the population. Population structure and its consequences for genetic data can be modeled in various ways. For example, Wright’s n-island models have no temporal aspect. Conversely, tree models may generate very different results depending on the timing of sampling in relation to the splitting events and gene flow between populations. More complex models allow demographic expansions or contractions in space, often based on simulations Another distinction among model types is whether they incorporate change over time. The stepping-stone and a priori by secure information, or fitted to substantial quantities of conditioning data, more complex models can be more wrong, not more realistic. The past two decades have also seen the development of many different inference methodologies, the results of which are often difficult to compare. This is because they often make different assumptions , explain different aspects of the data , and can be computationally demanding – challenging interpretation, explanatory power, and validation, respectively. Models are valuable tools for understanding and interpreting data, but we should not be surprised if a single family of models is unable to explain all patterns of human genetic diversity.This diversity in range of structured population demographic models, with varying levels of complexity, leads to some arbitrariness in which models are chosen. Because we know that the human past was complex, it is often assumed that more complex models of that past are more realistic. However, more complexity means more parameters and more ways for a model to differ from reality. This means that unless informed Alt-text: Box 2The starting point for most genetic studies of human origins has been to investigate the depth of present-day diversity between and within African populations. Most studies have used simple ‘tree-like’ demographic models to infer population split times, neglecting or simplifying population structure, even if sometimes considering a degree of gene flow between branches . Such steffective population size (Ne) may result from changes in connectivity between ancient populations rather than from, or in addition to, changes in census population size Ne and census population size is not straightforward, and may even be counterintuitive when structure exists Models incorporating more complex population structure can be considerably more parameter-rich and therefore more difficult to test computationally, particularly with limited data. However, they do offer a more generalized and flexible view of past demography – one that can accommodate, but not be limited to, more traditional population tree models. Furthermore, the increased availability of genomic data and developments in analytical methodologies now permit inference under more complex and realistic models. These developments have shown that structure cannot be neglected, and can cause patterns in genetic data that are similar to those generated by other forms of demographic change e.g., , 67. ForAlthough modern genomic data have been shaped by, and thus contain large amounts of information on, past demography, these data can be explained by many different models of population history . Moreover, all such models are necessarily abstractions and simplifications of the true population histories, and the discrepancies involved may be particularly problematic for certain questions about the past (mis-specification). This means that structure can be difficult to unambiguously detect, and even harder to reconstruct. For example, several studies on African populations have identified genes with coalescence times on the order of 1 million years, which could be interpreted as indicating admixture with archaic hominins Ancient DNA (aDNA) data can provide additional resolution, and studies on Holocene individuals recently revealed extensive structure and migratory activity during that period H. sapiens evolved in highly structured populations, probably across many regions of Africa. Elucidating the degree of and mechanisms underlying population structure will require consideration of Middle and Late Pleistocene environmental variability in both space and time , the strength and location of which varies according to changes in insolation that are driven primarily by precessional aspects of Milankovitch forcing. Consequently, parts of tropical Africa that are currently humid likely experienced numerous episodes of extreme aridity in the past The genetic, fossil, and archaeological data discussed above indicate that me e.g.,  . Did key diagnostic morphological characteristics emerge in one region and become elaborated with subsequent dispersals? Or did the transition from ‘archaic’ to ‘modern’ – whether indicated by morphology or material culture – occur gradually, and in a mosaic-like fashion across the continent? If this was the case, did African archaic hybridization also play a role? How does the existing evidence for structure affect our understanding of the history of population size changes and dispersals? Similarly, we have no firm grasp on the concordance that might exist between morphological and cultural structuring. Regional cultural signatures are apparent, raising the possibility that spatially distinct forms of material culture reflect similar patterns of population isolation and aggregation. Filling in these knowledge gaps requires us to reconsider paleoanthropological species concepts which are challenged by the view of deep population structure with sporadic gene flow/admixture.Ultimately, reconstructing the demographic history of human populations in its full complexity is beyond the power of population genomics alone, necessitating an interdisciplinary approach. In the past this has been achieved by geneticists working with archaeologists and paleoanthropologists to define a narrow set of simplified hypotheses whose genetic outcomes can be compared to identify the models that best explain the data. While such an approach has met with considerable success, a more complete picture will require integrating different data types  using the same or analogous models of population structure, size change, and dispersal. This represents a major challenge for ancestral demographic inference over the coming years.Fully characterizing the nature of this apparent ‘African multiregionalism’ also requires rejecting numerous longstanding, if implicit, assumptions, and formulating new questions. For example, the chronological lag between genetic estimates of population divergence times and morphological changes in the fossil record is not well understood, and should not be assumed to be short – particularly because inferences from genetic data are profoundly influenced by the models or families of models used. For instance, the estimates of population split times that are sometimes published may become less appropriate or relevant in our understanding of human evolution if models of spatial structure are to be used.H. sapiens, can it be effectively characterized for application to the fossil record? We emphasize that H. sapiens is a lineage with deep and likely diverse African roots that challenge our use of terms such as ‘archaic H. sapiens’ and ‘anatomically modern humans’. Unless they can be operationalized with more clearly defined traits, such categories will have declining value. Diagnostics of H. sapiens must reflect trajectories of evolution rather than static views of our species – which has changed, and continues to change, at various scales.Similarly, while a globular braincase does seem to represent a synapomorphy of extant Outstanding QuestionsH. sapiens emerged in one region and/or population of Africa. Instead, new data suggest that a variety of transitional human groups, with a mosaic of primitive and derived features, may have lived over an extensive area from Morocco to South Africa between >300 ka and 12 ka.In the conventional view, H. sapiens? Many aspects of the delicate H. sapiens facial shape may not be derived but instead be primitive retentions from an ancestor with a generalized facial shape. It therefore seems possible that H. sapiens did not evolve from the African forms of H. heidelbergensis , but from a more primitive H. antecessor or H. erectus-like ancestor, beginning at ∼0.5 Ma H. sapiens and the character of ancestral populations represents a crucial first step in understanding the emergence of the morphological features that diagnose our species during the later Middle Pleistocene.Three outstanding questions emerge from this view. First, within the African ‘multiregional’ paradigm, which species best fits as the ancestor(s) of H. sapiens? Did adjoining areas of western Asia also play a part? It seems possible that early humans followed the same ecological partitioning and subspeciation patterns that are seen among continentally distributed African mammals, many of which emerged at the same time as the genus Homo. The Sahara may have played a particularly important role in this respect. Other areas, such as regions of forest, may also have supported populations who remained semi-isolated from those in grasslands and savannahs. Addressing the challenges of research in deserts and rainforests will be difficult, but is likely to be rewarding.Second, how many populations, environments, and geographical areas of Africa played a role in the origins of H. naledi and H. heidelbergensis confirms the late survival of at least two archaic species in Africa. The size and environmental diversity of Africa, particularly the poorly investigated forested regions, may have permitted the late survival of more archaic species as well as of early forms of H. sapiens. These discoveries have fuelled speculations that H. sapiens may have interbred with archaic species in Africa itself. Distinguishing admixture between species from the reintegration of diverse H. sapiens lineages represents a major challenge, with significant taxonomic implications.Finally, were some of our anatomical traits inherited from transitional African forms before they became extinct? The range of dates for The next decade of research will be crucial to resolving these emerging research themes. Contemporary human genomes are now available from across the African continent, together with an increasing number of ancient genomes. Our understanding of paleoecology is also improving thanks to biogeographic reconstructions premised on the genomes of African fauna. Paleoclimate reconstructions are increasingly precise, with rapidly growing proxy data and better models covering key periods. Finally, the expansion of paleoanthropological investigations into neglected areas of Africa will undoubtedly reveal new data that will significantly refine the parameters of recent human evolution.


Hungarians who live in Central Europe today are one of the westernmost Uralic speakers. Despite of the proposed Volga-Ural/West Siberian roots of the Hungarian language, the present-day Hungarian gene pool is highly similar to that of the surrounding Indo-European speaking populations. However, a limited portion of specific Y-chromosomal lineages from haplogroup N, sometimes associated with the spread of Uralic languages, link modern Hungarians with populations living close to the Ural Mountain range on the border of Europe and Asia. Here we investigate the paternal genetic connection between these spatially separated populations. We reconstruct the phylogeny of N3a4-Z1936 clade by using 33 high-coverage Y-chromosomal sequences and estimate the coalescent times of its sub-clades. We genotype close to 5000 samples from 46 Eurasian populations to show the presence of N3a4-B539 lineages among Hungarians and in the populations from Ural Mountain region, including Ob-Ugric-speakers from West Siberia who are geographically distant but linguistically closest to Hungarians. This sub-clade splits from its sister-branch N3a4-B535, frequent today among Northeast European Uralic speakers, 4000–5000 ya, which is in the time-frame of the proposed divergence of Ugric languages. One of the main results of the study by Tambets et al.10 was that the Hungarians differ from the majority of Uralic-speakers – they do not show any specific link with their linguistic relatives compared to their non-Uralic neighbours in Central Europe. These results are in line with the earlier observations of anthropologists who have suggested that since the forefathers of Hungarians arrived in the Carpathian Basin at the end of the 9th century18, the population has changed significantly during the demographic processes that homogenized different ethnic groups in the area within the last 1100 years19. Anthropological differences detected among ancient Hungarians from different macro-regions in the Carpathian Basin were interpreted as a possible reflection of their heterogeneous geographical origin and relatively recent admixture of ancient Hungarian groups20. Archaeogenetic studies also confirm the admixed genetic background of the early Hungarians. Comparing the Hungarian Conqueror mtDNA dataset to a large modern-day population dataset and archaeogenetic database, researchers found strong genetic affinities towards modern populations of Inner Asia, North and East Europe, Central Russia, and Late Bronze Age populations of the Baraba region, situated between the rivers Ob and Irtis21. Also, most researchers agree that the size of the Avar population, who resided in the Carpathian Basin in the 8–9th century AD, was unequivocally greater than the number of putative Hungarian ancestors22. It is notable, that the mtDNA gene pool of Avar “commoners” and mixed Avar-Slavic cemeteries showed significantly lower genetic distances toward medieval European populations than Asian populations, indicating that the genetic imprint of the Inner Asian Avar elite through their mtDNA was rather weak in those populations24. In addition, an ancient DNA study dealing with 6th century barbarian migrations shows that Y-chromosomes of 21 ancient individuals from a Szólád cemetery (Hungary) belong to predominantly Central and Southern European haplogroups 25. Considering the possibility of sometimes intensive migration to Carpathian Basin from the neighbouring regions26, it is reasonable that present-day gene pool of Hungarians has become very similar to neighbouring populations. However, it is an open issue if there is any trace in the recent Hungarian gene pool reflecting their possible homeland in the East.Despite of the eastern roots of the Hungarian language the present-day gene pool of Hungarians is very similar to the neighbouring non-Uralic speaking Central Europeans according to autosomal27. So far, hg N3 has not been reported for Indo-European speaking populations in Central Europe30, but it is present among Hungarians, although the proportion of hg N in the paternal gene pool of present-day Hungarians is only marginal (up to 4%) compared to other Uralic speaking populations27. It has been shown earlier that one of the sub-clades of hg N – N3a4-Z1936 – could be a potential link between two Ugric speaking populations: the Hungarians and the Mansi31. It is also notable that some ancient Hungarian samples from the 9th and 10th century Carpathian Basin belonged to this hg N sub-clade32: Three Z1936 samples were found in the Upper-Tisza area  and two in the Middle-Tisza basin cemeteries (Nagykörű and Tiszakécske). The haplotype of the Nagykörű sample is identical with one contemporary Hungarian sample from Transylvania that tested positive for B545 marker downstream of N3a4-Z193632. Similar findings come from the maternal gene pool of historical Hungarians: the analyses of early medieval aDNA samples from Karos-Eperjesszög cemeteries revealed the presence of mtDNA hgs of East Asian provenance21.Certain chr-Y lineages from haplogroup (hg) N have been proposed to be associated with the spread of Uralic languages33 who are a Turkic speaking population residing in the Volga-Ural region. Another study reported excessive identical-by-descent (IBD) genomic segments shared between the Ob-Ugric speaking Khantys and Bashkirs but a moderate IBD sharing between Turkic speaking Tatars and their neighbours including Bashkirs34. According to this study the gene pool of Bashkirs is a mixture of Turkic, Ugric and Indo-European contributions. The complexity of genetic composition of Bashkirs was shown by Yunusbaev et al.35, both in autosomal and in chrY data.Archaeogenetic studies based on mtDNA haplotypes have shown that ancient Hungarians were relatively close to contemporary BashkirsIn this study we use, for the first time, the chrY high coverage sequencing data of Hungarian samples together with samples from the populations from regions of the Ural Mountains and West Siberia. We refine the phylogeny of hg N, estimate the divergence times of sub-clades of hg N3a4 and, together with a large genotyping dataset, reveal the spatio-temporal distribution pattern of hg N3a4. This lineage is the genetic link between present-day Hungarians and populations from the Ural region and West Siberia, the proposed region of origin for the Hungarian language.To reconstruct the phylogeny of hg N3a4-Z1936 and refine its inner structure we used 33 high-coverage chrY sequences, including those of 5 Hungarians Fig. . All varPhylogenetic tree of hg N3a4 has two main sub-clades defined by markers B535 and B539 that diverged around 4.9 kya . Inner sub-clades of N3a4-B539 (defined by markers B540 and B545) split 4.2 kya (95% CI = 3.0–5.6 kya). Further sub-clades of B540, one containing Y-chromosomes of a Khanty and a Mansi and other containing chrY’s of Hungarians and Bashkirs Fig.  split arAlthough, the frequency of hg N among modern Hungarians is only marginal, this small, but very intriguing from historical point of view, portion of paternal lineages belongs to N3a4 clade.31, we analysed the genotyping data of about 5000 Eurasian individuals, including West Siberian Mansi and Khanty who are linguistically closest to Hungarians  and not found among Lithuanians. Towards east its frequency is from 1–9% among Eastern European Russians and populations of the Volga-Ural region such as Komis, Mordvins and Chuvashes, but it is absent among Tatars and Bashkirs with an exception of Burzyansky District Bashkirs where the frequency is up to 5% , but the resolution level used in this study does not allow us to specify further sub-clades. In other Hungarian sub-groups, the frequency of the B540 clade is around 1%. The B545-clade is absent in Sekler Hungarians and is less than 1% in other Hungarian sub-groups , their neighbouring Indo-European populations (0%) and Southern Ural/West Siberian populations (13%). For that we implemented 3 models: A, B and C historical phase of Hungarians points to West Siberia, East of the Ural Mountains48 52 and the succeeding Karayakupovo cultures (9th‒10th centuries CE)54, with sites mainly found in present-day Bashkortostan, Tatarstan and in the Chelyabinsk area of the Trans-Ural region8. Based on the archaeological evidence, a portion of the Hungarians moved to the west in the middle of the 9th century and appeared near the lower reaches of the Dnieper River in present-day Ukraine, where Hungarians are mentioned in historical sources8. However, archaeological sites of Chiyalikskaya culture (11th–13th) support the survival of Hungarians in the Ural region56. Furthermore, other sources confirmed the survival of Hungarians in the Ural region until the 13th century46. Also, the eastern Hungarian homeland called Magna Hungaria (Great or Ancient Hungary) is referred in the early Hungarian chronicles57.The comparison of genetic and cultural history of human populations has excited scientists for decadess48 Fig. , but the 46 Fig. . The fir58, a phenomenon observable also in our study of N3a4 clade (ranging in frequency from 1–59%)  at the outer borders of distribution zone in different Hungarian sub-populations is expected 61. The time-frame is the same for the cooling climate in West Siberia with its peak at the 9th and 8th CE which could have resulted in the movements of several West Siberian populations62.The split between of B540 and B545 subgroups within Baskhirian, Tatar and Hungarian populations started around 2700–2900 yBP Fig.  that is th-early 10th century cemeteries32. This frequency is quite similar to Z1936 > B539 frequencies found among various Khanty, Mansi and Bashkir groups  in the archaeologically richest Hungarian late 954. Further studies of chrY and autosomal diversity in ancient samples of the representatives of those cultures could also provide new insight into the demographic history of the Hungarians.The homeland of ancient Hungarians around the Ural Mountain region, and the Hungarian affinities of Kushnarenkovo and Karayakupovo cultures is widely accepted among researcherset al.63, 6 sequences published in Ilumäe et al.27, 2 sequences published in Wong et al.64, and 17 new sequences from this study. Two N3a2 and six N3a4 sequences were generated with Complete Genomics (Mountain View) technology at 40x coverage. Six published and 17 samples from current study were sequenced at Gene By Gene by using the commercially available “BigY” service. The complete chrY sequences have been deposited to European Nucleotide Archive with the accession numbers ERS2768175 to ERS2768191 (study accession PRJEB28776).For reconstructing the phylogeny of N3a4 clade we included 8 whole chrY sequences published earlier in Karmin 65 and the human reference hs37d5. Read duplicates were removed using Picard (v2.0.1)66 followed by realignment around known indels and base quality score recalibration (BQSR) using GATK (v3.5)67. Variant calling was performed with GATK tool HaplotypeCaller. Filtering of the raw VCF files produced by GATK was done using bcftools (v1.4)68. We merged both the Illumina and Complete Genomics filtered data sets using CombineVariants from GATK (v3.8)67. We extracted the effective overlap between the two data sets by masking out all the positions with 5% or higher proportion of missing genotypes in either the Illumina or the Complete Genomics data sets. We also masked out regions with poor mappability as described in Karmin et al.63, resulting in a final total of 9.7 Mb of sequence analyzed.Mapping of fastq files was done using BWA-MEM (v0.7.12)69 to reconstruct the hg N3a4 phylogenety and to estimate coalescent times by using two N3a2 samples as an outgroup. We used a Bayesian skyline coalescent tree prior, the general time reversible (GTR) substitution model with gamma-distributed rates, and a relaxed lognormal clock. The results were visualized in Tracer v.1.4. As a calibration point for coalescent time estimation we used an age for hg N3a2′6 of 7113 years 27. The N3a4 phylogenetic tree and the mutation list . Data used for generating the maps is presented in Supplementary Tables We used data from 16 chrY STRs genotyped in 128 samples in this study and merged with previously published datasets to construct the phylogenetic network . All the procedure was performed using python, numpy version 1.14.3 and pandas 0.20.381. We set the effective population size of all populations 5000 individuals and two “alleles”: one haplogroup N34-B539 denoted as 1 and all other haplogroups denoted as 0. In every generation, we randomly resampled (with replacement) N (N = effective population size) number of haplotypes from the previous generation (using numpy.random.choice). In order to simulate single migration event or admixture, a randomly selected α proportion of alleles from one population was moved into another. At final generation, we randomly selected 2677, 95, 957 samples, reflecting the sample size of Indo-European, Hungarian Sekler and Southern Ural/West Siberian, respectively.To test if a scenario with no gene flow or admixture is sufficient to explain the observed frequency of haplogroup N3a4-B539 in Hungarian Sekler, neighbouring Indo-European and Southern Ural/West Siberian populations we implemented a resampling approach, similarly to the one implemented in Barbieri Finally, we have evaluated the frequency of haplotype “1” (N34-B539) and retained the simulation as successful (which will be later used for the success rate of a given model) only if the simulated “1” frequency was within 2SD of the observed one, estimated as follows:/n + 1.When the observed frequency was  = 0, we set it to 1We implemented the following three models A, B and C , Hungarian Sekler (4%) and Southern Urals/West Siberians (13%) can be the result of genetic drift alone. In doing so, we simulated three populations which diverged 100 generations ago  with a starting frequency for N3a4-B539 randomly generated from a uniform distribution with min = 0 and max = 1. As in model A, our null hypothesis is that the haplogroup frequencies of N3a4-B539 can be explained by random drift alone, we retained only those simulations which have final haplogroup frequency within two standard errors of the one observed in Siberia . We then calculated the success rate as mentioned above to know if the null hypothesis can be rejected.In Model B, we want to check if the lack of N3a4 haplogroup observed in Indo-European, but not Hungarian populations might be the result of genetic drift after having the same amount of genetic contribution from an external source . We simulated Southern Ural/West Siberian and Indo-European populations separately. Southern Ural/West Siberian population already has haplogroup N3a4 (with unknown proportion as above) whereas both Indo-European and Hungarian Sekler populations do not. We simulated one single admixture event from Southern Ural/West Siberian to both Indo-European and Hungarian Sekler populations occurring 30 generations ago, with same unknown amount of admixture [using numpy.random.random_sample] and then randomly drifted for 30 generations. Here our null hypothesis was a single admixture event from Siberia in European populations (both Indo European and Hungarian) can explain the haplogroup frequencies. Thus, we chose only those events which have the final frequency of 13% in Southern Ural/West Siberia with Hungarian frequency of 4% (within tolerance limit). We then calculate the success rate of model B.To compare between models, we chose such simulations 100 times for every model. We then calculated at how many times the populations have similar haplogroup frequency as modern observed populations. We repeated this process 100 times to get mean and standard deviation .To provide possible parameters explaining the dynamics resulting the observed N3a4 haplogroup frequencies, we simulated an additional model.In Model C, we wanted to calculate the parameters and the confidence interval to see how much power we have for our accepted model. This model is similar to Model B but instead of same admixture proportion from Southern Ural/West Siberia to Indo-Europeans and Hungarians, we put two independent amounts. We only chose those simulations where final frequency is within two SDs from the observed (thus the success rate is 100 percentage). We repeated the procedure 1000 times to calculate the mean and confidence interval for those unknown parameters.Supplementary informationSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Table S7


Axis porcinus) is threatened by habitat alteration, fragmentation, and poaching, which have led to a drastic decline of its wild population. Two subspecies of A. porcinus have been described from its distribution range. A. p. porcinus is reported to occur from Pakistan along the Himalayan foothills through Nepal, India and Myanmar, and A. p. annamiticus is found in Thailand, Indo-China, Laos, Cambodia, and Vietnam. However, the current distribution range of A. p. annamiticus is still unclear. We used the partial control region (CR) of mitochondrial DNA (mtDNA) and seven microsatellite loci to investigate the intra-species structure, differentiation, and demographic history of hog deer populations from three landscapes, the Terai Arc, Northeast, and Indo-Burma , Manipur, India) landscapes. We also carried out divergence time estimation using the complete mitogenome. The level of variation was ~4%, and the time of divergence of the KLNP population and the other Indian populations was about 0.22 Mya, i.e., during the last glaciation periods of the Late Pleistocene/Early Holocene. The KLNP haplotypes of the control region were shared with the Southeast Asian subspecies, A. p. annamiticus. The results of the investigations of the microsatellite loci supported the mtDNA results unambiguously. Two genetically distinct lineages are found in India: one is found from the Terai Arc to Assam (A. p. porcinus) and the other in Manipur (A. p. annamiticus). The genetic diversity in KLNP was low and exhibited a higher degree of genetic differentiation compared with major Indian populations. The Bayesian skyline plots indicated that after a long phase of historic demographic stability, the populations of both the lineages of hog deer suffered pronounced declines during the period from ~800 years BP to 5000 years BP. In summary, our finding provided evidence that the KLNP population is probably a prime, isolated and sustaining stock of A. p. annamiticus and should be managed as evolutionarily significant units (ESUs).The hog deer ( Axis porcinus), is an endangered species in the IUCN Red List and is protected under Schedule I of the Indian Wild Life (Protection) Act, 1972. Historically, it was distributed from Pakistan, in the west, to non-Sundaic Southeast Asia4. It is a very primitive cervid, reported to have been present since the Pliocene and Pleistocene, when forests were interspersed with open areas across Europe and Asia5. At the beginning of the 20th century, it was widely distributed throughout the Southeast Asian countries. However, in recent decades, continued depletion of its habitat have resulted in a population decline, and it is confined to a few localities in its historical distribution range7. It is supposed to be extinct in 35 localities of Southeast Asia where it was found6. However, the population of hog deer in Kaziranga National Park (KZNP) is persistent8. Keibul Lamjao National Park (KLNP), Manipur is a part of the Indo-Burma biodiversity hotspot, which holds a small population of hog deer along with the sympatric Eld’s deer (Rucervus eldii eldii).The hog deer (A. p. porcinus (western race) is distributed from Pakistan9 and the terai grasslands  through Nepal to Myanmar and in the floodplains of the rivers Ganges and Brahmaputra10. It has been re-introduced in Thailand and is being managed in captivity and semi-wild conditions4. According to the IUCN Red List, A. p. annamiticus (eastern race) is found in Thailand, Indo-China, Laos, Cambodia, and Vietnam14. The historical distribution of the eastern race of the hog deer (A. p. annamiticus) is still unknown15. A small and fragmented population of A. p. annamiticus is known to be found in Cambodia. This is presumed to be its last known wild stock17. Due to the bleak distribution of A. p. annamiticus, the western limit of its range could not be identified properly. Such isolated and small populations are always of concern as they are prone to loss of genetic diversity, resulting in their inability to adapt to a changing environment19.Two sub-species of the hog deer have been reported from its range. 25. One phylogenetic study recommended a change in the taxonomy hog deer21; however, it was subsequently found that the hog deer sample used was a case of mistaken identity23. Hence, reliable samples are essential for resolving phylogenetic relationships. In spite of the vulnerability of the hog deer to changing environmental conditions and anthropogenic pressure, very few studies have been conducted on the systematics, phylogenetic status, and ecology of this species. It is believed that the genetic diversity of the hog deer population is eroding24. Overall, the population boundaries and the genetic structuring of the hog deer remain unclear, and the classification of A. porcinus subspecies is still under dispute. An appropriate conservation and management plan for an endangered species can be prepared by incorporating reliable knowledge about the existing genetic diversity and population structure from across the geographic range of the species. Due to the maternal mode of transmission, mitochondrial DNA (mtDNA) provides information about female-mediated gene flow, whereas microsatellite markers are widely used to investigate the genetic variation and genetic structuring of populations26. Investigation of the genetic pattern across the distribution range of this little studied species, the hog deer, will allow insights into its evolutionary relationships and enable better conservation planning. Herein, we aimed to assess the genetic structure and differentiation among the extant hog deer populations to answering the key question of whether all Indian populations of the hog deer belong to a single evolutionary unit. We used complete mitogenome sequence data to estimate the divergence time; the partial control region (CR) to assess the genetic diversity, demography, and phylogeographic patterns; and microsatellite loci to address the genetic variation, differentiation, and population genetic structure. On the basis of genetic data, we discuss the relevance of our results to the biogeographical changes that took place during the Late Pleistocene in Northeast India.Several studies have been conducted on the genetic variations and phylogeny of cervidsA. p. annamiticus. We obtained 38 variable sites among the CR sequences. Of these, 14 polymorphic sites were singletons, and 24 were parsimony informative. These sequences were grouped into 26 haplotypes, and there were different numbers of individuals in each haplotype. Of these, 13 haplotypes (Hap 1 to Hap 13) were observed in Indian samples, and these were submitted to GenBank (MH392156 to MH392168). Haplotypes (Hap) 1 to 3 were observed in the samples from Corbett National Park (CNP), and Hap 1 was shared with Delhi Zoo (DZ). Haps 4 and 5 were found in Dudhwa National Park (DNP), and the unique Hap 6 was noted in the Chandigarh Zoo (CHZ) sample. Haps 7 to 10 were from KZNP, Assam, and Haps 11 to 13 were observed in KLNP, Manipur. Haps 12 to 26 were detected in the Southeast Asian hog deer (eastern races). Interestingly, Haps 12 and 13 were common in the KLNP population and Southeast Asian samples  than to the western race, A. p. porcinus , and 16 clustered in lineage 2 (KLNP and Southeast Asian origin). The haplotypes (h) and nucleotide diversity (π) of lineage 1 were 0.831 ± 0.043 and 0.031 ± 0.001, and those of lineage 2 were 0.66 ± 0.059 and 0.002 ± 0.0003. The overall mean intra-population genetic distance of lineage 1 (0.018 ± 0.004) was much greater than that of lineage 2 (0.004 ± 0.003). The genetic distance between the KLNP population and the rest of the Indian population was 0.028 ± 0.006. The average genetic distance between the groups indicated that the KLNP population was genetically closer to the eastern race, A. p. annamiticus. The median-joining network of all the haplotypes from different locations (India and Southeast Asia) could be classified into lineage 1 and lineage 2. All the haplotypes of India except those of KLNP clustered in lineage 1 (haplogroup A), and the haplotypes of KLNP clustered along with the sequences of A. p. annamiticus in lineage 2 (haplogroups B)  consists of the major haplotypes of the western race hog deer , and lineage 2 consists of the eastern race hog deer and the KLNP population , we found an ~4% sequence variation between KLNP and the rest of the Indian populations of the hog deer.Axis species occurred in the Late Pliocene, around 2.6 Mya (95% HPD = 1.5–4.1). Within Axis porcinus, the split between the western lineage (CNP and DNP) and the eastern lineage (KLNP) was estimated to have occurred at around 0.22 Mya (95% HPD = 0.11–0.38)  of the bovids and cervids at 16.6 ± 2 Mya (95% high posterior density [HPD] = 11.7–19.8). On this basis, we estimated that the split between the S were observed in both the lineages of hog deer. Fu and li’s d and f tests also indicated no significant departure from neutrality (P > 0.10). The multimodal and ragged-shaped graphs in lineage 1 suggest strong population subdivision and a stable population size, which are indications of a demographic equilibrium, whereas the mismatch distribution plot for the eastern lineage was smooth and unimodal, indicating a population expansion. To assess the fitment of our data, we calculated the SSD and raggedness statistic under the demographic expansion model for each population. However, these values were not statistically significant, which indicates that neither the selective neutrality test nor the mismatch distribution test supported the hypothesis that the populations of the hog deer have passed through population expansions. Thus, the demographic pattern estimated in the BSPs and the findings of the mismatch distribution curve support each other.The Bayesian skyline plots (BSPs) simulated the fluctuation of the populations over time. After a long phase of demographic stability and relatively constant population size over the last 8000–40,000 years, both the lineages of the hog deer appeared to experience a pronounced decline in effective population size over the period from ~800 years BP to 5000 years BP  ranged between 0.54, for KLNP, and 0.76, for CNP, with a mean of 0.755 ± 0.072. Thus, the population of KLNP showed low genetic diversity compared with the populations of CNP, DNP, and KZNP (Table IS) estimates across all populations ranged from 0.126 to 0.226.The genetic diversities of the different hog deer populations were calculated using seven microsatellite loci Table . The selThe Bayesian clustering analysis under the admixture model identified a high ∆K (mean likelihood of K (mean LnP [X/K] = −1263.550) value at K = 2 Fig. .Figure 5FST for genetic differentiation of hog deer populations demonstrated significant genetic differentiation between lineage 1, of DNP, CNP, and KZNP (0.072–0.092) and lineage 2, of KLNP (>0.110)  and the major geographical distance between the populations of the hog deer.The spatial genetic analysis detected a significant correlation between the pairwise genetic and geographical distances for the entire study area , through Myanmar and Thailand up to Cambodia. It possibly extends up to Vietnam, and this needs further investigation. The structure, DAPC, and FCA analyses efficiently identified the two major clusters or ideal populations of the hog deer. The structure analysis (K = 2), supported by the other mentioned analyses, showed that the hog deer from CNP to KZNP represent one lineage and those from KLNP are from a second lineage  generally exhibited a high level of genetic diversity, with a large number of haplotypes diversity and numbers of alleles. In contrast, a relatively low level of genetic diversity was observed in the KLNP population. The comparatively high levels of genetic diversity in the major Indian populations might be due to the historical gene flow, whereas the KLNP population has been losing its potential connectivity with the source population of the hog deer after the glacial periods. The phylogenetic relationship and network revealed the presence of two mtDNA lineages of the hog deer in India. Interestingly, the sequences of the control region (Hap1 to 10) were found in the major Indian populations, whereas the sequence of KLNP (Hap 11 to 13) exhibited a pattern similar to that of age Fig. . BesidesAxis separated from the Cervus clade during the Late Pliocene30. During the Late Pliocene, numerous species split due to the various climatic and geographical changes that acted as a driver of speciation in Southeast Asia, e.g., Muntiacus31; the Asian golden cat and the bay cat32; C. perrieri and C. cusanus33; and Macaca species34. The Bayesian skyline plot analysis strongly indicates that the hog deer population has been demographically stable over the last 8000–40,000 years and also provides evidence of a slight reduction in the effective population size over the last ~800–5000 years BP  and the KLNP (Manipur) population was found. Several methods were used in our analyses to obtain evidence for the genetic differentiation. The complete mitogenome was sequenced from individuals of the major Indian and KLNP populations. A sequence variation of around 4% was found, and therefore these sequences were used to estimate the divergence time along with that of other cervid and bovids species to detect the TMRCA. The major population of the hog deer in India split from the KLNP population around 0.22 Mya, and our data provide the first divergence time evidence. This divergence estimate is supported by previous molecular studies and found that the genus 35. Deterioration of the forest cover due to the new settlement of humans in Southeast Asia during the late Holocene might be one of the major factors for the reduction in the effective population size of the hog deer. The genus Axis generally occurs in a mixture of forest and open grassland36, and such vegetation may have extended the foothills of the Himalaya to Southeast Asia during the early Pleistocene. However, the dynamic influences of the monsoon, climatic variation, and vegetation changes during the Late Pleistocene and early Holocene might have affected the demography and population structure of the hog deer.A study on the vegetation and climate in the Eastern Himalaya showed that around 20,000 years BP the eastern parts were fully covered with open grassland and with mixed forest. Due to warm and moist climatic conditions that prevailed between 18,000 and 12,000 years BP, the forest cover was replaced with grasslandA. p. annamiticus of Southeast Asia was very low. The main factor for the separation of KLNP from the rest of the Indian population could be geographical barriers, in the form of the major Purvanchal (Northeast) mountain ridges. The Purvanchal ranges consist of the Garo, Khasi, Jalatia, Naga, Barail, and Mizo hills and might be acting as a geographical barrier between these two subspecies (A. p. porcinus and A. p. annamiticus). This theory can be well supported by specialized habitat uniqueness and isolation by distance using mantel test analysis on hog deer. Thus, the present genetic features of the KLNP population are the consequence of long-term geographical isolation and adaptation to the local environment. However, this assumption needs to be supported by additional ecological and genetic studies.The limited gene flow in the KLNP region could be an indication of genetic drift due to the restricted nature of the habitat in a geographically isolated area. Interestingly, the genetic distance between the KLNP population and the recognized A. p. annamiticus. It indicated that the western limit of A. p. annamiticus is Manipur and not central Thailand as suspected earlier4. Similarly, KLNP (Manipur) is also known for the last remaining population of Eld’s deer (Rucervus eldii eldii) in eastern India, whereas the other two subspecies of Eld’s deer (R. e. thamin and R. e. siamensis) are distributed in a few localities of Indo-China and southern China, and hence this area is an ecological hotspot for similar cryptic populations. Therefore, on the basis of our mtDNA and nuclear data, we recommend that both the distinct lineages of Indian hog deer should be managed as evolutionarily significant units (ESUs). The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) has listed one subspecies of hog deer (A. p. porcinus) in Appendix III, whereas it has listed the other subspecies (A. p. annamiticus) in Appendix I. In a recent assessment of A. p. annamiticus in Southeast Asia, it was indicated that an IUCN criteria-based evaluation would justify its place in the Critically Endangered category of the IUCN Red List under criterion C117. Recently, the status of the Indian hog deer was also upgraded from Schedule III to Schedule I in the Indian Wildlife (Protection) Act, 1972, which forbids hunting by more stringent rules. As the hog deer has lost ground in other countries4, and KLNP holds about 100 adult individuals37, this genetically distinct and evolutionarily significant population becomes important for the conservation of this subspecies (A. p. annamiticus). Considering the low population size and low genetic diversity compared with other hog deer populations and recognizing the need to conservation attention, as for the Cambodian population, it would be appropriate to upgrade the status of the hog deer (A. p. annamiticus) of KLNP, Manipur from Appendix III to I under CITES. The findings of this study can also be used to differentiate the two subspecies at the genetic level for management and wildlife forensics practices.This study confirmed the existence of a small population of hog deer in Manipur that genetically resembles 38 method and from the antlers and fecal pellets using the Gu-HCl-based silica-binding method39. The complete mitogenome of five hog deer individuals (one each from CNP and DNP and three from KLNP) was amplified and sequenced. The samples were selected on the basis of the quality of the gDNA. The samples were collected from the remains of dead animals or non-invasively. Hence, the approval of the animal ethical committee was not required for the experiments. The samples were obtained from the field with the permission of the Chief Wildlife Warden and Park Manager of the state and field. We confirm that all the experiments were performed in accordance with the relevant guidelines and regulations.We collected 77 hog deer samples comprising shed antlers, fresh fecal pellets, and tissues from dead animals Table . We pres2, 0.2 mM of each dNTP, 3 pmol of each primer, and 0.5 units of AmpliTaq Gold DNA polymerase (Applied Biosystems). We performed PCR amplification using 23 overlapping fragments of the complete mtDNA40 and variable portions of the control region primers, Cerv.tPro, and CervCRH41. The thermal cycling conditions for both the primer pair was as follows: an initial hot start at 95 °C of 10 minutes, followed by 35 cycles at 95 °C for 45 seconds, 55 °C for 45 seconds and 72 °C for 1 minute, with a final extension of 72 °C for 15 minutes. The efficiency and reliability of the PCR reactions were monitored using control reactions. The PCR products were electrophoresed on 2% agarose gel and visualized under UV light in the presence of ethidium bromide dye. The amplified PCR products were treated with Exonuclease-I and Shrimp Alkaline Phosphatase  for 15 minutes each at 37 °C and 80 °C, respectively, to remove any residual primer. The purified fragments were sequenced directly in an Applied Biosystems Genetic Analyzer 3500XL from both primers set using a BigDye v3.1 Kit.PCR amplification was carried out in 20-μl volumes containing 10–20 ng of the extracted genomic DNA, 1 × PCR buffer (Applied Biosystems), 2.0 mM MgCl48. Multiplex amplifications were carried out in 10-μl reaction volumes containing 5 μl of QIAGEN Multiplex PCR Buffer Mix (QIAGEN Inc.), 0.2 μM labeled forward primer (Applied Biosystems), 0.2 μM unlabeled reverse primer, and 20–100 ng of the template DNA. PCR was performed under the following conditions: initial denaturation at 95 °C for 15 min, followed by 35 cycles of 95 °C for 45 seconds, 55 °C for 1 minute and 72 °C for 1 minute, with a final extension of 60 °C for 30 minutes. Alleles were resolved in an ABI 3500XL Genetic Analyzer (Applied Biosystems) using the LIZ 500 Size Standard (Applied Biosystems) and analyzed using GeneMapper version 3.7 (Applied Biosystems).Seven microsatellite loci  were used for genetic analysis of the hog deerA. p. annamiticus. Sequences derived from the forward and reverse directions were aligned and edited using SEQUENCHER® version 4.9 . These sequences were aligned separately using the CLUSTAL X 1.8 multiple alignment program49, and the alignments were checked by visual inspection. DnaSP 5.050 was used to analyze the haplotype diversity (h), nucleotide diversity (p), and polymorphic sites (s). The numbers of nucleotide substitutions per site were estimated for multiple substitutions using the Tamura-3 parameter method in MEGA751. Insertion–deletion (INDEL) sites were excluded from the sequence analysis. For the genetic distance, we used the Tamura-3 parameter using the discrete Gamma distribution (TN92 + G) with lowest BIC score value. Phylogenetic analyses were conducted in BEAST ver 1.752. One sequence of Elaphurus davidianus (accession number AF291894) was used as an out-group for better insights into the phylogenetic relationships. The spatial distribution of the haplotypes was visualized using a median-joining network, which was created using the PopART software package53.In addition to 77 sequences of hog deer generated in-house, weobtained 19 hog deer sequences from GenBank (NCBI). Twelve of these sequences had been generated from the eastern subspecies, 52. The temporal trends in the effective population size of the hog deer over time/generations were estimated using a coalescent BSP. We used HKY and empirical base frequencies models under a strict molecular clock and a stepwise skyline model with a substitution rate of 0.118 × 10–6 substitutions/site/year56.A Bayesian skyline plot (BSP) was constructed using the Monte Carlo Markov Chain (MCMC) method using BEAST ver 1.750 to infer if the hog deer had experienced a demographic expansion, equilibrium, or bottleneck. Different neutrality statistical approaches were used to investigate the demographic history of each sample and each population. Tajima’s D57, Fu’s Fs58 and Fu and Li’s F and D59 are appropriate statistics for short DNA sequences and were used to test whether the sequences conformed to the expectations of neutrality using DnaSP 5.050. In general, the significant negative value of Tajima’s D and Fu’s Fs test is an indication of population demographic expansion. Besides, the goodness-of-fit of distribution with expected distribution using a model of population expansion by estimating the sum of squared deviations (SSD) and Harpending’s raggedness index (Rg) by assessing significance with 1000 bootstrap replicates were calculated to test for demographic changes in the hog deer populations using Arlequin version 3.5.1.260.Mismatch distribution graphs were plotted using DnaSP 5.0A. porcinus (JN632600) was used as a reference to map the generated sequences. To estimate the divergence times of the hog deer populations, we inferred genealogies using a relaxed lognormal clock model in BEAST v.1.752. We performed the analysis using 10 sequences downloaded from the NCBI database, including the sequences of nine species of the family Cervidae, i.e., the swamp deer , sambar , chital , fallow deer , red deer , Eurasian elk , European roe deer , water deer , and mule deer  and the sequence of one species of the family Bovidae, i.e., the banteng . The TMRCA of bovids and cervids was set as a calibration point to 16.6 ± 2 million years ago (Mya) with a normal prior distribution61. We used a Yule-type speciation model and the HKY + I + G substitution rate model for tree reconstruction. We subsequently used the TMRCA estimate of the Cervus-Axis split as the tree prior for the calibrations within our dataset. We conducted two independent analyses, using MCMC lengths of 10 million generations, logging every 1000 generation. All the runs were evaluated in Tracer v. 1.6. The final phylogenetic tree was visualized in FigTree v.1.4.2. (http://tree.bio.ed.ac.uk/software/figtree/).The complete mitochondrial genomes of five individuals were sequenced (MH443786 to MH443790), and the sequences were used to date the divergence of the hog deer populations. The complete mtDNA sequence of 62. The linkage disequilibrium was tested using the statistical software package GENEPOP 3.463. The allelic richness (Ar), mean inbreeding coefficient (FIS)64, and pairwise FST values (gene flow) among the populations were estimated using FSTAT, version 2.9.365. We checked all the loci at the population level for deviation from the Hardy–Weinberg equilibrium (HWE) using an exact test in GENEPOP 3.463. CONVERT 1.3166 was used to change the input file into the required formats. The existence of population genetic structure in hog deer, the number of genetic clusters (K) to all individuals were estimated by model-based Bayesian assignment method using Structure 2.3.267. In this method, independent assessments are made for each cluster without prior information about the origin of the population. The log-likelihood data [Ln Pr (X/K)] were estimated for the given K between 1 and 10 with ten independent runs set by 5,00,000 Markov chain Monte Carlo (MCMC) iterations, followed by a burn-in period of 50,000 iterations. The highest hierarchical level of Delta K was determined by comparing the log-likelihood [Ln Pr (X/K)] estimates at different values of K using Structure Harvester68. Populations were separated by factor correlation analysis (FCA) using the GENETIX 4.05 software package69.Based on the amplification success of the microsatellite loci, we selected 54 hog deer samples for microsatellite analysis Table . The pol70. DAPC is a multivariate approach that maximizes the genetic differentiation between groups with unknown prior clusters, thus improving the discrimination of populations. The pairwise FST values (gene flow) among the populations were estimated using FSTAT, version 2.9.365.We carried out assignment and clustering of the populations using the DAPC method and the ADEGENET package in R71 was used to correlate the pairwise genetic and geographic distances by detecting the pattern of IBD between the disjointed areas with hog deer according to Mantel’s test.Alleles in Space 1.0Supplementary Tables and Figures


E-27, and a combined power of discrimination (CPD) of 99.99999999%. Off-ladders and tri-allelic variants were observed in various samples at D12S391, SE33 and D22S1045 loci. Additionally, pairwise genetic distances based on FST were calculated between Bahraini population and other populations extracted from the literature. Genetic distances were represented in a non-metric MDS plot and clustering of populations according to their geographic locations was detected. Phylogenetic tree was constructed to investigate the genetic relatedness between Bahraini population and the neighboring populations. Our study indicated that the twenty-one autosomal STRs are highly polymorphic in the Bahraini population and can be used as a powerful tool in forensics and population genetic analyses including paternity testing and familial DNA searching.Bahrain’s population consists mainly of Arabs, Baharna and Persians leading Bahrain to become ethnically diverse. The exploration of the ethnic origin and genetic structure within the Bahraini population is fundamental mainly in the field of population genetics and forensic science. The purpose of the study was to investigate and conduct genetic studies in the population of Bahrain to assist in the interpretation of DNA-based forensic evidence and in the construction of appropriate databases. 24 short-tandem repeats in the GlobalFiler PCR Amplification kit including 21 autosomal STR loci and three gender determination loci were amplified to characterize different genetic and forensic population parameters in a cohort of 543 Bahraini unrelated healthy men. Samples were collected during the year 2017. The genotyping of the 21 autosomal STRs showed all of the loci were in Hardy-Weinberg Equilibrium (HWE) after applying Bonferroni’s correction. We also found out no significant deviations from LD between pairwise STR loci in Bahraini population except when plotting for D3S1358-CSF1PO, CSF1PO-SE33, D19S433-D12S391, FGA-D2S1338, FGA-SE33, FGA-D7S820 and D7S820-SE33. The SE33 locus was the most polymorphic for the studied population and THO1 locus was the less polymorphic. The Allele 8 in TPOX scored the highest allele frequency of 0.496. The SE33 locus showed the highest power of discrimination (PD) in Bahraini population, whereas TPOX showed the lowest PD value. The 21 autosomal STRs showed a value of combined match probability (CMP) equal to 4.5633 These islands are Bahrain, Muharraq, Umm and Nasan and Sitra. Bahrain is positioned in the Arabian Gulf. To the southeast of Bahrain is the State of Qatar, and to its west lies the Kingdom of Saudi Arabia, with which it is connected by a 25-kilometer causeway. To the north and east of Bahrain lies the Islamic Republic of Iran .Bahrain is one of the most densely populated countries in the world, with a total landmass of 760 square kilometers. Mid-2014, estimates of Bahrain’s population stood at 1,314,562 persons. Of these, 568,399 are Bahraini citizens (46%) and 666,172 are expatriates (54%) , trade goods from the Persian Gulf made its way into Europe through Antioch . This made Bahrain an important port city, a metropolitan hub where different cultures met .Standing between the most substantial focal points of the ancient world–the Far East, the Indus Valley, Fertile Crescent, the Red Sea and the Coast of East Africa . Iranians and migrants of Iranian heritage constituted the largest groups of migrants who were Muslim and ethnically not Arab . Indian and Iranian migration boomed in the early and mid-20th century, as the Bahrain Petroleum Company sought a workforce for the oil that was discovered in the island .Because of the geographic location of Bahrain, the diversity of the population had been affected. This could be explained by the migration flows from several areas regionally, and eventually internationally ,9,10. This geographical and social organization might be expected to have an effect on patterns of a genetic diversity .Population is mainly divided into four main ethnic groups: Arabs, Baharna and Persians  Genetic studies on Bahrain to date are very limited and knowledge of any such structure is important in the interpretation of the significance of DNA-based forensic evidence and in the construction of appropriate databases. This present study is the first to characterize genetically the Bahraini population, using Globalfiler amplification kit. Twenty-four autosomal short-tandem repeats (STRs) in GlobalFiler PCR Amplification kit  were studied to characterize different forensic and genetic population parameters in 545 Bahraini males. The 6-dye GlobalFiler PCR Amplification kit  was designed to incorporate 21 commonly used autosomal STR loci  and three gender determination loci  which have been proven to provide reliable DNA typing results and enhance the power of discrimination (PD).Five hundred and forty-three (543) blood samples were collected on Nucleic-Cards  from non-relatives’ Bahraini males. The research study was announced publicly through different social media channels such as Twitter and Instagram. Participants who wished to participate contacted the corresponding author for establishing meetings and arrived to the General Directorate of Criminal investigation and Forensic Science–Kingdom of Bahrain to submit their blood samples for the research. Age of the participants ranged from 20 to 55 years old.In each case, males with ancestry  from four different geographical subdivisions of the country  were sampled. Ethical review for analysis was provided and approved by the Research and Research Ethics Committee (RREC) (E007-PI-10/17) in the Arabian Gulf University. All participants provided informed consent for contribution their blood samples.DNAs were punched and amplified from Nucleic-Cards  blood-spot samples using a fully automated workstation, starting from 1.2-mm diameter punches produced using the easyPunch STARlet system .The samples were directly amplified using GlobalFiler  according to manufacturer’s recommendation. 15μl of low TE Buffer (pH 8.0) was added to the MicroAmp Optical 96-Well Reaction Plate  prior to the addition of 10μl of GlobalFiler master mix. A total of 24 loci were amplified, including 21 autosomal STR loci and three gender determination loci.The PCR products (1μl) were separated by capillary electrophoresis in an ABI 3500xl Genetic Analyzer  with reference to the LIZ600 size standard v2  in total of 9 μl of LIZ600 standard and Hi-Di formamide  master mix. GeneMapper ID-X Software v1.4  was used for genotype assignment. DNA typing and assignment of nomenclature were based on the ISFG recommendations.. Fisher’s exact tests to evaluate the Hardy–Weinberg equilibrium (HWE) by locus and linkage disequilibrium (LD) between pair of loci were estimated with STRAF—A convenient online tool for STR data evaluation in forensic genetics . Phylogenetic tree was constructed from allele frequency data by using the neighbor-joining method via web version of POPTREEW  It is used to compare between different genetic structure of the populations with Bahraini population using the minimum available loci for different populations. The tree was constructed with allele frequency data of fifteen STR loci  for all populations.Allele frequencies, Minor allele frequencies (MAF) and different parameters of forensic efficiency—such as power of discrimination (PD), random matching probability (PM), power of exclusion (PE), polymorphism information content (PIC), typical paternity index (TPI), and heterozygosity (He)—were estimated for each locus using GenAlEx software V.6.503  to investigate the populations structure between Bahraini population and the abovementioned populations based on FST’s genetic distances.Also, Multidimensional scaling (MDS) analysis was done using IBM SPSS Statistics 21.0 Tables –5. After Bonferroni’s correction was applied (p > 0.000092), all of the samples were in HWE. Full dataset of Bahraini population is shown in S1 Table. The study also showed no significant deviation from LD between pairwise STR loci after Bonferroni’s correction (p > 0.000092) in Bahraini population except for the following loci; D3S1358-CSF1PO, CSF1PO-SE33, D19S433-D12S391, FGA-D2S1338, FGA-SE33, FGA-D7S820 and D7S820-SE33 when plotted. The highest pairwise LD was 1.00 when plotting CSF1PO- D19S433, D21S11-FGA and FGA- D1S1656. The marker D22S1045 did not show any probability. This lack of probability correlated with the off-ladder cases observed in D22S1045 and which may be the reason for the null probability value. D22S1015, SE33 and D21S11 loci also revealed evidence of a rare variant and off-ladders .In the present study no significant deviation from HWE was observed (p> 0.05) except for three markers; D3S1358, D19S433 and D5S818 Tables –5.In the studied population, the number of allele (Na) per locus was ranged from 7 for markers D16S539, TPOX and THO1 to 48 for SE33, the mean number of alleles per locus was 14, and a total number of alleles observed was 288. The most polymorphic locus was SE33 Tables –5.The probability that two randomly chosen person have the same unspecified genotype at a locus is the sum squares of the frequencies of all genotypes at that locus. Some alleles show very high frequencies in the Bahraini population; allele 8 in locus TPOX scored the highest frequencies of 0.496 followed by allele 15 in D22S1045 with frequency of 0.417 and the lowest allele frequency was 0.00092 for 35 different alleles. Tables –5.Generally, the polymorphism degree of a specific locus can be measured by two distinct parameters–the heterozygosity and the Polymorphism Information Content (PIC). We have found out that the observed heterozygosity (Ho) was ranged from 67% for locus TPOX to 92% for locus SE33. PIC values for all STR loci were highly informative (PIC≥0.6) with an average of 78.3%. The means for (Na) and (He) designate the high levels of genetic diversity in the population studied. These high informative values support the heterozygosity values indicating the high degree of genetic polymorphism.Tables –5.The random matching probability (PM) was ranged from 0.006 for SE33 to 0.156 for TPOX. The Power of exclusion (PE) was ranged from 0.384 for locus TPOX to 0.838 for locus SE33. The SE33 locus showed the greatest (PD) in Bahraini population, whereas TPOX showed the lowest. The higher the discrimination power of a locus, the more efficient it is in discriminating between members of the population The PD values for most of the tested loci was above 0.9; the highest value was observed for SE33 (0.994) whereas the least value was observed at TPOX (0.844). The combined power of discrimination (CPD) and combined matching probability (CMP) for all the 21 STR loci were 99.999999% and 4.5633E-27 respectively., Kuwaiti population , Iraqi populations , Iranian populations , Egyptian population , Bengali population , Sri Lankan population , Indian population , Emirati population  and Saudi population  based on fixation index FST and Nei’s genetic distances respectively. The comparison with published data showed that the populations in this study had similar pairwise FST values with those populations that are geographically most. As shown in Fig 2, Bahraini and Saudi populations share the most genetic relatedness among the other populations, followed by Emirati, Kuwaiti, Iranian, and Qatari in the same cluster. On the other hand, Sri Lankan, Bengali and Indian showed relatedness with each other and in distant genetic structure from Bahraini population. Sample bias corrected FST distances were obtained and were represented multidimensional scaling (MDS) plots  with sizes of 207.67 bp, 213.64 bp and 217.78 bp  respectively and it was not observed and reported in STRbase (http://strbase.nist.gov/index.htm) . Sample# 520 showed 3 variants in D2S441  with sizes of 85.03 bp, 89.15 bp and 93.30 bp respectively  and it was observed and previously reported in STRbase (http://strbase.nist.gov/index.htm) .Different samples showed off ladder (OL) in 10 various cases; two allelic ladder variants were detected at the D12S391, Sample#5 indicated OL  in 238.69 bp and sample#511 showed OL  in 238.64 bp. Two allelic ladder variants were detected at the SE33, Sample#288 indicated OL in  in 320.61 bp and Sample#538 with OL  in 359.20 bp. Six allelic ladder variants were detected in D22S1045, sample#309 showed OL  in 99.44 bp, sample#331 showed OL  in 99.51 bp, sample#487 showed another OL  in 99.45 bp, sample#516 indicated an OL  in 99.44 bp, sample#524 showed OL  in 99.45 bp and sample #549 showed OL  in 99.44 bp . The PD in correlation with PM supports the high degree of polymorphism between Bahraini individuals.The observed deviation from HWE (neglecting the Bonferroni’s correction) could be a result of the diversity of the Bahraini population or caused by high polymorphism at the same loci investigated loci. This observation are likely to reflect the high level of inbreeding with consanguinity rates in Bahrain, with intra-familial unions accounting for 20–50% of all marriages compared to other Arab countries , 22. As the above-mentioned populations share the most informative and polymorphic locus is SE33 and the least informative locus is TPOX. The least polymorphic was locus D16S539 for UAE population  whereas THO1 for both Bahraini and Saudi Arabian populations . Allele 8 in locus TPOX scored the highest frequency for Bahraini, Kuwaiti, Saudi Arabian, Iraqi, Egyptian and Iranian populations , 22 whereas the highest frequency for Indian and Bangladeshi populations is allele 12 in CSF1PO . Regarding the phylogenetic tree construct, the data from the ten populations are consistent with other population data from the region , 23, 24 based upon the FST values obtained. The obtained FST value of Bahrain is 0.006 which is less than the recommended value for casework statistics of FST < 0.01 .We have compared Bahraini population data to the nearest available populations using the accessible loci. It is shown that the Bahraini population shares similar results with the study conducted of Saudi Arabia and UAE populations using the GlobalFiler STR loci As expected, the diversity between the data obtained in this study compared to the neighboring data populations varies, as the populations become more geographically separated.Once more studies of Arab populations in the region become accessible, it may be more probable to develop a greater understanding of the genetic associations between the different populations for the Arabian Peninsula.In conclusion, we have reported the allele frequencies and forensic statistical parameters of the GlobalFiler STR loci in Bahraini population to be indicated in literature for the first time. The polymorphism of the 21 autosomal markers observed in this study such as SE33 marker indicates its usefulness for paternity testing, forensics and familial DNA searching in the population of Bahrain.Overall, these parameters indicated the general utility of this STR loci panel for forensic personal identification and paternity testing in the Bahraini population, thereby further confirming of its efficacy for forensic practice also in Bahraini sub-populations and other populations' genetics and diversity studies.S1 Table(XLSX)Click here for additional data file.


Handroanthus impetiginosus (Bignoniaceae), sampling 75,838 SNPs by sequence capture in 128 individuals across 13 populations. We found evidences for local adaptation using Bayesian correlations of allele frequency and environmental variables (32 loci in 27 genes) complemented by an analysis of selective sweeps and genetic hitchhiking events using SweepFinder2 (81 loci in 47 genes). Fifteen genes were identified by both approaches. By accounting for population genetic structure, we also found 14 loci with selection signal in a STRUCTURE-defined lineage comprising individuals from five populations, using Outflank. All approaches pinpointed highly diverse and structurally conserved genes affecting plant development and primary metabolic processes. Spatial interpolation forecasted differences in the expected allele frequencies at loci under selection over time, suggesting that H. impetiginosus may track its habitat during climate changes. However, local adaptation through natural selection may also take place, allowing species persistence due to niche evolution. A high genetic differentiation was seen among the H. impetiginosus populations, which, together with the limited power of the experiment, constrains the improved detection of other types of soft selective forces, such as background, balanced, and purifying selection. Small differences in allele frequency distribution among widespread populations and the low number of loci with detectable adaptive sweeps advocate for a polygenic model of adaptation involving a potentially large number of small genome-wide effects.The role of natural selection in shaping patterns of diversity is still poorly understood in the Neotropics. We carried out the first genome-wide population genomics study in a Neotropical tree,  The investigation of the relative contributions of demography and natural selection to the spatial patterns of genetic variation has been a recurring theme in evolutionary biology. Spatial variations in the pattern of natural selection can lead to local adaptation and genetic differentiation among populations. Adaptation to spatially varying selective pressures is evident in the geographical distribution of many traits in plants   Mattos  is a Neotropical hardwood tree with a widespread distribution throughout the seasonally dry tropical forests (SDTFs) of South and Mesoamerica. It ranges from Northeast to Southwestern Brazil, Bolivia, and is also scattered throughout the fragments of SDTFs in Central Brazil and on the slopes of Andes in Peru , rot-resistant wood. It is the second most expensive timber and the most logged species in Brazil  with a genome size estimated at 2C c. 1Gb by flow cytometry  internal transcribed spacer (ITS)  and expected heterozygosity Nei, . These pf) using Arlequin 3.5  and inbreeding coefficient (FIS), across all SNPs, using analysis of molecular variance implemented in Arlequin 3.5  and inbreeding coefficient  < 0.1 (see results below) in different genome scaffolds to minimise linkage disequilibrium. We also accessed the genetic structure of the populations using the 200 putatively neutral loci to obtain the most likely number of genetic clusters  with a spatial resolution of 30” (0.93 × 0.93 = 0.86 km2 at the equator) were obtained for the 13 populations sampled. We performed a factorial analysis with Varimax rotation and selected four variables with low collinearity. These four variables explain 90.4% of the total environmental variation among the 13 populations (Supplementary File http://www.fao.org/docrep/018/aq361e/aq361e.pdf). Using Varimax factorial analysis, we selected three soil variables that explained 77.5% of the variation among populations . Loci with adaptive selection signal were selected with the following criteria, following the directions of Günther and Coop .Putative SNP loci under selection were annotated with SNPEff  and for the EOC (2100) using the RCP 4.5 (rising radioactive forcing pathway leading to 4.5 W/m2 in 2100) from the RCP database v 2.0.5 (http://tntcat.iiasa.ac.at/RcpDb). We assumed the soil variables constant through time.For the loci under selection, we applied co-kriging to the Neotropics using ArcGIS 10.2 to obtain a spatial interpolation of allele-frequency estimates based on their autocorrelation with the climatic and soil variables. We also hindcasted the allele frequency for loci under selection in the LGM and forecast how future climate changes might affect the distribution in allele frequencies. We obtained the same environmental variables used in Bayenv2 analysis  for the LGM from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database  of 2.02 and an average missing genotype of only 6.6% in the sample of 128 trees. The distribution of minor allele frequency (MAF) showed the expected “L” shape distribution with a larger proportion of low-frequency SNPs. A vast majority of SNPs (75.1%) had an MAF < 0.15, and the median MAF was equal to 0.071. These SNPs were found in coordinates of 4,862 distinct targeted loci across 2,341 scaffolds in the genome assembly. The resulting average read depth at SNP coordinates across the samples was 69.6 x ± 41.7 x, and the minimum depth of aligned reads to call a heterozygous genotype for a sample was 3 with the median equal to 14. Coordinates of these loci are within or in close proximity (≤ 5kb upstream/downstream of transcription initiation) to 6,489 of the 30,271 predicted protein-coding genes.Sequence capture with 14,135 sequence probes targeted 11,026 distinct loci across the genome assembly generating a VCF file with 304,488 SNPs. After the removal of sites with more than 20% missing data, a total of 75,838 high-quality polymorphic SNPs with a call rate > 88.3% were detected. The average per-sample call rate across all 128 individuals was 93.4%. The We estimated the density of SNPs across the genome assembly, partitioned in 10,802 virtual bins of 10kb. As expected for sequence capture-based SNP genotyping, a large proportion (63.5% or 6858 regions) of the bins had no SNP identified in the sampled individuals. The average density of SNPs  and no significant inbreeding . Populations POF, ALT, and MOC had the highest genetic diversity (He) over all loci  when using only putatively neutral loci. Bayesian clustering supported four independent genetic clusters (K = 4), showing high genetic differentiation, but with admixture in some populations  was the most likely model among the four tested to predict the observed genetic parameters of H. impetiginosus populations was high (FST = 0.513), the threshold for the detection of selection signals was also high. Because of the high differentiation among populations, we performed an additional genome-scan analysis using Outflank for individuals belonging to cluster C2, the only cluster with a sufficient sample size to warrant acceptable statistical power for the analysis. Cluster C2 included 34 individuals from populations BAG, CAC, MIM, NIQ, and POT . Using only samples from these populations, Outflank found 14 loci with the selection signal. These outlier loci had FST values ranging from 0.63 to 0.73.The genome scan based on Outflank detected no outlier loci  to 17 genes. Most genes  had at least one SNP within their transcribed regions  in the FUNC program , and members of the ATP-binding cassette (ABC) superfamily of transporters. Although highly conserved during evolution, these complexes are large connected systems that are thought to have diversified into functional distinctive groups. Examples in vertebrates include the proteasome system in adaptive immunity Tanaka, , and memH. impetiginosus was found to be enriched for genes involved in metabolic processes and catalytic activity in comparison with other species of the Lamiids, such as Erythranthe guttata and Olea europaea H play a central role in this process, rapidly diffusing throughout the cell to the sites where energy is used for catalytic activities  when measured at short distances tends to be underestimated due to gene conversion  and chloroplast (FST = 0.891) intergenic spacer sequences. Bayesian clustering of populations based on neutral SNPs showed an east to west differentiation in allele frequency , Eastern Amazonia (population POF), and the eastern most region of Brazil along the Atlantic forest (SEC), despite the environmental differences. On the other hand, a large difference in environmental conditions over time is expected to have occurred for H. impetiginosus, from the LGM to the present day, potentially changing the patterns of the expected allele frequencies, as forecasted by our simulations. We acknowledge, however, that our simulation is a simplification of the evolutionary process, because of the arbitrary assumptions that selection is constant through time, and only environment variables change.Contrasting environments for bioclimatic variables showed little difference in allele-frequency distribution in The small differences in allele frequency distributions among contrasting environments for many loci suggest the potential ability of the species to cope with the impact of climate changes. It is also reasonable to suggest that the species will potentially be able to track environmental changes or will be largely unaffected due to its phenotypic plasticity Agrawal, . The smaOur results are somewhat in contrast with those reported in population genomics studies of forest tree species in temperate regions. Typically, distinct adaptive signals can be detected for photoperiod-responsive and dormancy- or temperature-related traits that show strong latitudinal clines and are critical for species survival  analysis of the expected allele frequency across the SDTFs indicated that changes in environmental landscape in the future might impose adaptive challenges to H. impetiginosus. These results, in turn, are consistent with the concept that adaptive traits are largely under polygenic control in natural populations (Pritchard and DiRienzo, In conclusion, what we have carried out, to the best of our knowledge, is the first genome-wide population genomics study of a Neotropical tree species. Our results point to the likelihood of local adaptation at a few specific loci that were within the detectable level of our sample size and confounding genetic structure background. Being aware of the limits of our experiment, it is also worth noting that other softer selective forces such as background, balancing and purifying selection, largely undetectable, cannot be dismissed at this point, as the key components in shaping local adaptation in Additional data are provided as supporting information in the online version of this article.Supplemental File S1Supporting Information S1 - TablesSupporting Information S2 - TablesSupporting Information S3 Figures