Homo sapiens and has the earliest evidence for symbolic behaviour and complex technologies. The best-attested early flowering of these distinctive features was in a glacial refuge zone on the southern coast 100–70 ka, with fewer indications in eastern Africa until after 70 ka. Yet it was eastern Africa, not the south, that witnessed the first major demographic expansion, ~70–60 ka, which led to the peopling of the rest of the world. One possible explanation is that important cultural traits were transmitted from south to east at this time. Here we identify a mitochondrial signal of such a dispersal soon after ~70 ka – the only time in the last 200,000 years that humid climate conditions encompassed southern and tropical Africa. This dispersal immediately preceded the out-of-Africa expansions, potentially providing the trigger for these expansions by transmitting significant cultural elements from the southern African refuge.Africa was the birth-place of  Homo sapiens. Although evidence suggests limited gene flow with archaic species outside Africa1, this statement remains valid on genetic, palaeontological and archaeological grounds. However, one aspect of early modern human evolution where evidence is still scarce is precisely where in Africa Homo sapiens emerged. That question might not be answerable using genetic evidence2, and the question itself may even be mis-conceived, but eastern, southern and central Africa have in fact all been proposed as the cradle of humanity. Southern Africa has been suggested on the basis of the genome-wide variability of its indigenous “Khoe-San” groups3, recently shown using ancient DNA to have extended as far north as Malawi in the Early Holocene4; central Africa based on a range of features such as overall mtDNA diversity6, deep Y-chromosome ancestry7 and parasitology and palaeoclimatology8; and eastern Africa, mostly on palaeontological grounds11. A pan-African view13, or “multi-regionalism within one continent”15, has also gained currency recently, especially given that Homo sapiens remains at Jebel Irhoud in Morocco, with similarities to early Homo sapiens remains in both eastern and southern Africa, have now been dated to more than 300 ka 18.Africa was the birth-place of 20. This split cannot, in itself, distinguish between an origin in the north or the south, but it does establish the existence of two groups of humans early in the evolution of our species. For example, the mitochondrial DNA (mtDNA) genetic clock suggests a coalescence time for all human mtDNA sequences  soon after 200 ka21, and two deep branches in the tree: haplogroup L0 in southern Africa, and the L1′6 branch present in central and eastern Africa . Genome-wide analyses show the same separation19, although Y-chromosome sampling and analysis on the current genomic level22 are still required for any solid inferences to be drawn.A consistent feature that recurs in several distinct lines of genetic evidence is a deep split between hunter-gather/herder “Khoe-San” groups in southern Africa and all other present-day humans (including outside Africa)5, re-emphasising the emerging picture of deep sub-structuring of early modern human populations within Africa14. It is intriguing that the appearance of these three major phylogenetic groupings matches the time at which the use of hafted stone tools (“Mode 3” technologies) became more prominent across the continent23. A hypothetical origin for these industries in tropical Africa24 could also suggest a major source of modern humans in central Africa before 150 ka6, although eastern Africa may also be possible25. Such areas would display little gene flow between them, at least until the early Holocene, and drastically later with the Bantu expansion from central Africa27. The first episodes of Middle Stone Age (MSA) genetic exchange between areas detected at the mtDNA level are the emergence of the L2 branch in central/western Africa derived from the deeper eastern African L2′6 haplogroup, ~85–100 ka26, the appearance of southern African haplogroup L0 subclades in eastern Africa some time before 70 ka6 and the spread of eastern African haplogroup L3 after ~70 ka across eastern Africa, into central Africa and beyond the continent29.Furthermore, mtDNA analysis suggests a split within the northern side of the tree (L1′6) between central/western Africa (L1) and eastern Africa  dating to 150–135 kaHomo sapiens and the appearance of distinctively modern behaviour and social features, marked by an increase in symbolic activities and technological complexity in the archaeological record30. , with dates of ~100–70 ka, including layers associated with the Still Bay (SB) industry, characterized by distinctively symmetrical, extensively shaped (using pressure flaking on heat-treated silcrete), and bi-facially worked ‘leaf-point’ forms33, and an ochre-processing toolkit at ~100 ka34. Even earlier occurrences of engraved red ochre are recorded from sites in the Pinnacle Point region (further east on the South African coast), dated to at least 100–150 ka35.In parallel with the genetics, there has been much discussion in recent years over the relationship between the origins of Homo sapiens32: beads similar to those seen in southern Africa have been found in Morocco at ~82 ka36 and even earlier in the Levantine coastal sites of Skhul and Qafzeh, and there are even examples amongst European Neanderthals38. However, these sporadic occurrences41 might reflect early, but isolated, elements of cultural modernity which could have been “trumped” by demographic processes, in which these features were subsequently lost due to drift and population extinctions42, during times of adverse climate that prevailed in tropical and southern Africa until ~90 ka, and in eastern Africa until ~80 ka8. Moreover, there is an underlying taphonomic problem with excavated archaeological sites across Africa: whilst southern Africa is dominated by cave sites, elsewhere open-air sites predominate; and symbolic artefacts, frequently made on organic materials, suffer disproportionately from poor preservation.These are not completely unique to this region or even to 43. A recently reported 78,000-year-long archaeological record at the Panga ya Saidi cave site in Kenya detects the earliest bead between 67–63 ka, followed by a (markedly non-linear) accumulation of novel symbolic and technological behaviors44. Furthermore, there is a possibility that southern Africa may have been the source of microlithic technology45, established promptly after the Toba eruption ~74 ka46, and potentially reflecting the appearance of bow-and-arrow technology and the associated capacity to kill game at a greater distance than possible with purely hand-propelled hunting missiles48. This appears in the “Howiesons Poort’ (HP) industries, which in stratified archaeological sequences invariably overlie the SB industries, with dates spanning the range from ~67–58 ka49. The increased indications of symbolic behaviour are best reflected in the geometrically engraved ostrich eggshell fragments from the site of Diepkloof and Klipdrift Shelter, potentially  reflecting marks of personal ownership of egg-shell water containers52.The archaeology of eastern Africa for this period lacks similar evidence in the same time frame, but MSA eastern African reconstructions derive primarily from open-air sites, where such evidence is unlikely to be preserved. Nevertheless, some indicators, such as beads and incised ostrich shell, become more common in eastern Africa towards the end of the MSA53 and ESR dates of between 62.5 ± 5 and 59.5 ± 5 for the Naisiusiu sites54 – broadly contemporaneous with the South African HP sites43. A stratigraphic intrusion of backed microlithic forms (predominantly crescents), recorded in layer 11 and 12 at the Kenyan coastal site of Panga ya Saidi cave referred to above and dated broadly to between 58.3 to 44.5 ka44, could add further support to the dating of the similar HP-like microlithic industries at the Mumba and Naisiusiu sites. Microliths, defined in the broadest technological terms, were clearly reinvented at several different times and locations within the later  time range (as for example in some of the Gravettian and related Kebaran industries in Europe and western Asia). But these later occurrences of microlithic technology in the Eurasian archaeological record show few if any resemblances to the highly distinctive range of microlithic shapes  which consistently define the much earlier HP-like technologies in both southern and eastern Africa31.Unlike the preceding SB technologies, HP-like microlithic technologies appear further north in East Africa , and with associated OSL dates of 56.9 ± 4.8 and 49.1 ± 4.3 for the Mumba industry55 Since the 95% error ranges overlap substantially, it is not possible to point to direction purely on the basis of the dates, and it remains possible  that they arose independently. In the future, residue, use-wear and macro-fracture analysis could more directly test this suggestion. However, we can test the possibility of a migration with genetic evidence. In fact, though, to date no migration between southern and eastern Africa has been detected in this timeframe28.The roughly simultaneous appearance of these distinctive HP-like microlithic industries in both southern and eastern Africa suggests that the two phenomena may have been connected by migration, perhaps facilitated by the onset of more humid climatic conditions across tropical Africa ~70–60 kaHere, therefore, we reassess the phylogeography of mtDNA L0 lineages in southern Africa and the rest of sub-Saharan Africa, in order to refine our view of early gene flow between continental regions in the MSA. We also analyse genome-wide data for over 1 M SNPs (single-nucleotide polymorphisms) to test the extent to which autosomal evidence can corroborate the mtDNA picture and examine its limitations. We identify, for the first time, a clear signal of dispersal of modern humans from southern to eastern Africa in the time frame of 70–60 ka.56.This signal survives only in the maternal record, despite the fact that we show that the overall genetic sub-structuring within Africa indicated by the genome-wide analysis is closely reflected in the mtDNA phylogeny, suggesting that the mtDNA patterns are not misleading. This shows the continued value of modern mtDNA analysis to discern very ancient migration events that can no longer be recovered by genome-wide analysis, and have not yet proven amenable to investigation by means of ancient DNA  (aDNA), due to preservation issues at high time depths in the tropics. This is analogous to the way in which signals in Y-chromosome variation have revealed male-dominated  dispersals in the mid- to late Holocene that have left no signal in the rest of the genome21 and an aDNA calibration that yielded an estimated mutation rate of 2.69 × 10−8  substitutions per site per year. Ancient DNA calibration with a set of very recent fossil L0 sequences (dating between 1.4 and 6.1 ka) can lead to serious underestimates for ancient divergences such as the ones analysed here, but nevertheless offers an independent calibration and so lower bound of the time of ancient splits. The new data, namely the new structuring of the tree, allowed us to significantly re-evaluate earlier inferences on the timing of the dispersal of L0 from south to north6.We constructed a full phylogeny of haplogroup L0 Table  coupled 57 and aDNA data4) and, more importantly, an entirely new branch, labelled L0g, in southern Khoe-San groups57. This shifts the time at which the southern African indigenous haplogroup L0 is restricted to southern Africa from an earlier estimate of ~120–100 ka6 to ~70 ka in the new analysis  with the remainder concerning the period after 60 ka. While descriptive statistics were unable to clearly distinguish between gene flow from Khoe-San to eastern Africa only and bidirectional gene flow, all simulation models without migrations from Khoe-San to eastern Africa were rejected. Overall the demographic simulations placed an emphasis on migrations between 75–60 ka with obligatory Khoe-San to eastern Africa ancient migrations. However, we should take into account that simulations do not necessarily indicate the best or most probable model but rather they can allow us to reject specific models based on the descriptive statistics, and that here they do allow our hypothesis to stand. Analysing both the simulations and the phylogeographic signal together, it becomes evident that the phylogeography indicates the direction of the migration (southern to eastern Africa) through the polarity of the phylogenetic tree, pointing clearly to a directional movement of L0a′b′g and L0f lineages currently present in eastern Africa but securely nested within a much deeper southern Africa ancestry.We also performed coalescent simulations, in order to test a range of alternative migration scenarios. They fully rejected models of isolation and gene flow between southern and eastern Africa before 75 ka Table , ruling 59. Here we envision the dispersal and relocation of a group within Africa, with only a mild demographic expansion.We investigated signals of possible population expansions associated with haplogroup L0 in the BSP (Bayesian Skyline Plot) patterns for southern and eastern populations Fig. . For the61, who had a very divergent pattern whose drifted status was evident in the PCA and ADMIXTURE analyses, and we also excluded the “Coloured_Colesberg” and the “Coloured_Wellington” samples, which corresponded to very heterogeneous groups. In the PCA, we identified and removed six outliers – three Oromo samples and three Somali individuals which have undergone excessive amounts of drift and might therefore skew the analysis. We analysed this modified dataset again using ADMIXTURE, sNMF, PCA and TreeMix.We next compared the mtDNA patterns with genome-wide patterns across Africa. Following a preliminary analysis of the overall dataset we excluded the FulaniK = 4, which is thus the hypothetical ideal value of K. The computed component proportions were consistent between ADMIXTURE and sNMF across all values of K. As expected, K = 2 separates southern Africans from the rest, with variable input from the north as expected in groups impacted by the Bantu expansions27, and K = 3 clearly separates southern, eastern and central/western components, while the hypothetical optimum K = 4 splits the southern component into two  but no signal of ancient migration from southern Africa into eastern Africa of any kind is evident.Following an initial reconstruction with TreeMix, most of the migration vertices that were added were related to gene flow from central to eastern and southern Africa, representing most likely recent (Bantu) migrations. In order to maximize the detection of earlier migrations, we excluded populations that were a clear mixture of central/western and southern components (mixture of the Khoe-San component with the Bantu component) and a few individuals within populations that showed clear evidence of admixture . We observed that the populations were successfully clustered according to their geographical location within the continent. More striking was the population tree structure of an initial split between central/western/eastern Africa and southern Africa, with the former then splitting into central/western and eastern Africa – respectively mimicking the splits between mtDNA haplogroups L1′6 and the southern “Khoe-San” L0 and the split of L1′6 into central/western haplogroup L1 and eastern L2′6 in the mitochondrial tree . Most recently, Blombos has yielded the earliest evidence for drawing32. These activities initially appeared sporadically, rather than as a package. However, it has been argued that the southern Cape was a refuge throughout the glacial Marine Isotope Stage 6, and a greater intensity seems evident after the end of Stage 6, and especially after 90 ka, as parts of southern Africa likely became wetter and more hospitable and the southern coasts were settled66. As a result, some have suggested that all later modern humans must, in some sense, descend from a small refugial population of modern humans who lived in southern Africa during Marine Isotope Stage 6, ~200–130 ka68.In the last two decades, remarkable evidence from (in particular) Pinnacle Point and Blombos Cave at the southern Cape of South Africa has accumulated for signs of symbolic behaviour stretching back more than 150 ka. This evidence includes an intensification of selection of red ochre for potentially ritual-based body decoration, sophisticated technological operations such as heat-treatment of lithic raw materials, optimising shell-fishing by tracking lunar phases, and use of shell beads for ornamentation28. Thus, at least two Stage 6 refuges contributed to modern human mtDNA diversity, but by far the larger contribution was from eastern Africa.The deep genetic division between Khoe-San groups and other extant humans shows that the idea of a southern refuge is highly plausible – but also that it was not a major genetic source for the rest of humankind. Genetically, present-day modern humans do not descend primarily from southern Africans who lived before ~70 ka – modern African diversity has much deeper roots. In particular, extant mtDNA diversity coalesces around the transition from the inter-glacial Marine Isotope Stage 7 and the glacial Marine Isotope Stage 6, at ~190 ka, and comprises two deep branches, both dating to ~150 ka, one of which arose amongst the ancestors of present-day hunter–gatherer/herder (“Khoe-San”) southern Africans and the other amongst the ancestors of present-day central and eastern Africans. These two deep branches separated between ~190–150 ka, and the northern cluster then split further into a central and an eastern branch. It is from the eastern branch that much African  diversity descends, in the wake of unprecedented demographic expansions, which fuelled dispersals of mtDNA haplogroup L3 both across Africa and out of Africa, after ~70–60 kaHomo sapiens inhabitants of both of these regions would have been cognitively modern – in other words, they would not seem out of place if they were raised in any twenty-first century society. Shea69 has argued, for example, that early MSA eastern Africans were as behaviourally flexible as other MSA population where symbolic activities are more evident. It nevertheless remains intriguing that the most impressive evidence for symbolic activities and technological complexity is seen in southern Africa before ~70 ka, and appears in eastern Africa only after 70 ka. This was the time at which microlithic technology appears in both regions almost simultaneously, and was also the only time in the last 135 ka that the climate was simultaneously humid across the whole of the sub-Saharan Africa, so that dispersals became more feasible between what had generally been isolated regions8, following the end of the “megadrought” that had kept the tropics largely arid since ~135 ka44. Did the southern refuge somehow play a role in the dramatic expansions that led to the peopling of the world?It is widely agreed that the If this was the case, contact between the two regions at this time may have left a trace in a migration from south to east. Here, indeed, we have described the discovery of a small stream of migrants who moved from southern to eastern Africa around 70–60 ka, during precisely the window of opportunity provided by the climate. Our results suggest it was only during this pan-African wet phase that connections were finally established between southern and eastern Africa with dispersals from south to east.26 . This supports the deep L0 southern African ancestry and, consequently, the presence of more recent L0 subclades in eastern Africa can only be explained by gene flow from southern to eastern Africa.Such deep admixture events would be most readily detected in regions that did not undergo large-scale recombination, and this is where the non-recombining mtDNA and Y-chromosome variation come into their own. Detailed high-resolution genomic Y-chromosome data for eastern Africa and southern Khoe-San populations are still lacking, although we must note that, in any case, Y-chromosome results will not necessarily match mtDNA patterns, as males and females can have very different populations histories – and deep ancestry seems to be more often over-written on the male line of descent, further exacerbating the problem of limited sampling74. It is therefore tempting to speculate that the migrants at 70–60 ka might have transmitted innovations developed in the southern refuge, such as microlithic technology or even novel symbolic activities, to eastern Africa – innovations that may have had a role in the subsequent unprecedented expansions of eastern African populations. The number of migrants from the south was probably not very large, as reflected in the present-day frequencies of L0 in eastern Africa of 5–15% on average5. Technologies and culture are readily transmitted horizontally, and a relatively minor genetic influx  may have sufficed to transmit the cultural innovations to eastern Africans ~70–60 ka, and seed the populations that migrated outside the continent. In genetic terms, the earliest out-of Africa populations might even have carried a low level of southern African ancestry, but the out-of-Africa bottleneck was very drastic, with the maternal lineage of all humans outside Africa eventually deriving from a single (and likely random) eastern African sequence, the root of haplogroup L3, and any hypothetical genome-wide South African ancestry was diluted through recombination.Several archaeologists have proposed that developments during the Still Bay and Howiesons Poort phase of the MSA maybe have contributed to the out-of-Africa expansionsHomo sapiens. A cluster of Homo sapiens populations, living in a glacial refuge area on the southern coast of Africa, developed a sophisticated repertoire of complex technological and symbolic behaviours over many tens of thousands of years76. This was already evident ~165 ka at Pinnacle Point and reached a zenith at sites such as Blombos by ~80–70 ka. These developments were far from unique : similar developments took place at other sites across the range of early Homo sapiens in North Africa and the Near East, who likely had similar cognitive capacities. However, these were lost or dispersed southwards due to the onset of harsher climatic conditions in the north after 75 ka41 – there may even have been a “corridor” connecting these regions via the eastern African coastline prior to this time66. Populations may well have moved rather than simply becoming extinct – indeed, population shifts seem to characterise the MSA66. At the same time, however, favourable climate conditions in the tropics and the south and demographic expansion led to the innovations that emerged in southern Africa being fixed in an expanding population, resulting in them being transmitted to other human groups in, ultimately, eastern Africa. Thus demography likely played a critical role, re-shaping cognitive capacities that may have been in place for perhaps half a million years37.It is possible that both the dispersal south to north and the out-of-Africa expansion are independent by-products of the shift in climate after 70 ka, and that the simultaneous appearance of the microliths is a coincidence or an artefact of the scanty archaeological evidence in eastern Africa. On the other hand, if the connections were causal, it would imply that a seemingly minor re-evaluation of the mtDNA tree in Africa may have significant implications for the evolution of 43. As well as the appearance of microlithic tools, the earliest bead in a 78,000 year-long archaeological record in Kenya appears at ~67–63 ka alongside a shift in toolkits44. This migration from southern to eastern Africa after 70 ka was closely followed by a major demographic expansion that is clearly visible in both the genetic and archaeological records77, marked by the decreasing reliance on diagnostic MSA technologies characteristic of the MSA–LSA transition. While the microlithic evidence for a southern source into eastern Africa can be contested on statistical grounds, the putative scenario described for the migration of people and behavioural elements from southern to eastern Africa at the 70–60 ka time frame would be consistent with the concomitant transmission of a technology which clearly appears in both regions at this time. This spread likely involved a small group of migrants from southern Africa, leading not to population replacement or an immediate acculturation across indigenous eastern African populations after 70 ka, but creating a patchwork distribution in both time and space, consistent with the heterogeneity of the archaeological record44.Thus the use of beads, incised ochre, heat treatment and possibly microlithic technologies, might then potentially have been transmitted to eastern Africa after 70 ka, when – even considering a sampling bias for previous periods – some of these elements become more common in the archaeological record74 must have been widespread across both southern and eastern Africa, and have been carried out of Africa. The inference from various aspects of the record to “symbol use” and from there to “fully syntactical language” is far from straightforward74. However, the fact that a phenomenon is imperfectly understood does not mean it should be dismissed78, especially when it as potentially significant as charting the emergence of people who potentially were beginning to communicate in ways approaching present-day humans. Indeed, linguists and archaeologists have begun to address the theoretical challenges74, and it is possible to sketch out plausible trajectories from courtship rituals shaped by sexual selection into symbolic activity and language, over a likely timeframe of hundreds of thousands of years81.By 60,000 years ago, however, a fully “syntactic” language akin to those used todayEven so, there has been growing scepticism amongst archaeologists in recent years against the possibility that human symbolic behaviour radiated outwards from a single source in southern Africa at such a recent date. By providing novel genetic evidence for a dispersal at this time, we show that a transmission mechanism existed. If symbolic behaviour first gained a foothold primarily in the south, this dispersal could have transmitted it to the rest of Africa by migration and subsequent acculturation.The populations in eastern Africa that resulted from the arrival of new groups from the south were the starting point for the greatest expansion ever undergone by modern humans, not only back across Africa but also out of Africa, along the Indian Ocean into South Asia, Southeast Asia and Australasia, and ultimately to the rest of the globe. Interpreted in this way, the archaeological and genetic evidence concur that southern Africa is a plausible candidate for the cradle of modern humankind.82 to build an initial network that was further manually checked and refined using the relative mutation rate of the different mtDNA positions21. We selected 204 further African sequences to represent the rest of the African mtDNA tree5 57, L0d1c (I4468), L0d1b2b (I4427), L0a1′4 (I1048), pre-L0k2 (I4421), L0k1 (I4422) and pre-L0f3 (I4426)4, all dating between 1.4 and 6.1 ka. Some L0 sequences from the work by Skoglund and collaborators4 were excluded, due to large gaps present in the sequences. External sequences included a pre-N sequence (Oase1) dating to 37.8 ka85, a Neanderthal sequence (KC879692) dating 50.3 ka86 and a Denisovan sequence (FR695060) dating to ~50 ka87. We built BSPs, using the same aDNA calibration parameters, for the southern African L0 data (excluding L0a sequences that are most likely related with the later Bantu migration into the south of the continent)26, and by using the L0a′b′f data from the eastern African specific clades. The first dataset, the southern sequences, approximates population data, as no other clades have been detected to date that could trace into MSA southern Africa.We estimated ages of the overall African tree using maximum-likelihood88, in order to test the validity of the model proposed here. We took several models into account, including total isolation following the split between eastern and southern Africa, models that consider both unidirectional and bidirectional gene flow in the periods between 60 and 75 ka, in the last 60 ka, and before 75 ka. Simulations were run in the cluster SEARCH from the University of Minho. For populations in the simulations we used the ancient southern L0 data as an approximation of ancient Khoe-San mtDNA gene pool and the 1000 Genomes Project Luhya data to represent eastern African population. We estimated changes in effective population size through time as Bayesian skyline plots in BEAST84. We describe details of the analysis in the Supplementary material text  and Table We performed coalescent simulations using the software ms15 with 161 individuals spanning 13 Sahelian groups60. Both datasets contained genotype data for over 2.2 million SNPs, of which 2,032,247 were found to overlap. We included an additional 170 samples from the five African populations of the 1000 Genomes Project. Upon excluding SNPs showing strand inconsistencies, we kept over 1.5 M SNPs  in the merged dataset. The aim of the analysis was to test if we could identify signals of MSA gene flow on the genome-wide level using TreeMix72 with the support of ADMIXTURE89 and PCA90 for data characterization.For the selection of the dataset, in order to investigate deeper evolutionary patterns in African populations, we opted for maximizing the number of SNPs in the analysis Table . We init89 and sNMF91 for values of K between 2 and 10 with the calculation of cross-validation errors in ADMIXTURE, and performed a PCA for 10 major principal components (using EIGENSOFT 6.1)90. We assessed population splits and possible migrations using TreeMix. We used 100 bootstrap replicates to generate a consensus tree based on the African populations, to be used as scaffold during a posterior run in which six migration edges were added. In all cases, we performed standard error calculations using blocks of 100 SNPs. For TreeMix, we aimed to maximize ancient admixture processes in terms of migration edges, by minimizing clear recent processes of admixture supported by the three types of analyses. To that end, we excluded groups that were recently established from two ancestral populations as well as some individuals that showed recent double ancestry within otherwise non-mixed groups, in a similar fashion to that performed before15.We carried out ancestry analysis by running ADMIXTURESupplementary materialSupplementary dataset 1


Phrynosoma cornutum) inhabits much of the southern Great Plains of North America. Since the 1950s, this species has been extirpated from much of its eastern range and has suffered declines and local extinctions elsewhere, primarily due to habitat loss. Plans are underway to use captive breeding to produce large numbers of Texas horned lizards for reintroduction into areas that were historically occupied by this species and that currently have suitable habitat. We used mitochondrial markers and nuclear microsatellite markers to determine levels of genetic diversity and population structure in 542 Texas horned lizards sampled from across Texas and some neighboring states to help inform these efforts. Texas horned lizards still retain high genetic diversity in many parts of their current range. We found two highly divergent mitochondrial clades (eastern and western) and three major genetic groupings at nuclear microsatellite loci: a west group corresponding to the western mitochondrial clade and north and south groups within the eastern mitochondrial clade. We also found some evidence for human-mediated movement between these genetic clusters that is probably related to the historical importance of this species in the pet trade and as an iconic symbol of the southwestern United States. We do not know, however, if there are fitness costs associated with admixture  or if there are fitness costs to moving these lizards into habitats that are distinctly different from their ancestral areas. If present, either one or both of these fitness costs would decrease the effectiveness of reintroduction efforts. We therefore recommend that reintroduction efforts should maintain current genetic structure by restricting breeding to be between individuals within their respective genetic clusters, and by reintroducing individuals only into those areas that encompass their respective genetic clusters. This cautionary approach is based on the strong divergence between genetic groupings and their correspondence to different ecoregions.The Texas horned lizard ( The loss of suitable native habitat to agriculture and urbanization, and the overexploitation of populations have been the largest drivers of the decline of many species . As a rePhrynosoma cornutum) belong to a specialized group of lizards (Phrynosoma) that are endemic to North America. They have a variety of adaptations for living in dry environments and for specializing on a diet of large venomous ants  which can prey on the eggs and young of horned lizards, the loss of harvester ants (Pogonomyrmex spp.) due to widespread use of insecticides and competition with fire ants, and over-collecting for the pet and curio trades  Ecoregions of the US—Level III) and anecdotal accounts suggest it was abundant in many areas . Declineo trades . Currento trades .Widespread interest by private landowners and the Texas Parks and Wildlife Department has led to a plan in which Texas zoos will captive breed large numbers of individuals to be reintroduced into areas that historically had Texas horned lizards and that currently have suitable habitat. To aid in this effort, we used mitochondrial markers and nuclear microsatellite markers to determine levels of genetic diversity and population structure of Texas horned lizards across Texas and some neighboring states. Species like Texas horned lizards, which occur over large geographic areas, inhabit a range of habitats, and have relatively low dispersal capabilities may have an increased chance of developing regional adaptations . We therhttps://www.epa.gov/eco-research/ecoregions).A number of volunteers collected 542 Texas horned lizard tissue samples across Texas, New Mexico, and Colorado between 2009 and 2017 . While mWe extracted DNA by incubating swabs and tail or toe clips overnight in 300 µl lysis buffer and 15 µl of Proteinase K (20 mg/ml) at 55 °C. The following day, a half volume of 7.5 M ammonium acetate was added to precipitate proteins. Then 0.7 volume of isopropanol was added to the supernatant and the samples were placed at −20 °C overnight to precipitate the DNA. Finally, DNA was pelleted and washed in 70% ethanol, air dried, and resuspended in 100 µl 10 mM Tris pH 8.5. reported in PcGATA 49) and so are not included in this study. PCR reactions contained 10–50 ng DNA, 0.2 µM of each primer, 1× Qiagen Multiplex PCR Master Mix with HotStarTaq, Multiplex PCR buffer with 3 mM MgCl2 pH 8.7, and dNTPs. Reactions were cycled in an ABI 2720 thermal cycler . The cycling parameters were one cycle at 95 °C for 15 min; followed by 35 cycles of 30 s at 94 °C, 90 s at 60 °C, 90 s at 72 °C; then a final extension at 60 °C for 30 min. Following amplification, all reactions were diluted with 200 µl dH2O. For each sample, 0.5 µl of diluted product was loaded in 10 µl HIDI formamide with 0.1 µl LIZ-600 size standard  and electrophoresed on an ABI 3130XL Genetic Analyzer . Genotypes were scored and binned using Genemapper 5.0 . We reamplified a subset of samples  to estimate the genotyping error rate.We amplified 10 microsatellite loci in three multiplexes using 10 µl polymerase chain reactions PCR; . Four lo2 pH 8.7, and dNTPs. Reactions were cycled in an ABI 2720 thermal cycler. The cycling parameters were one cycle at 95 °C for 15 min; followed by 35 cycles of 30 s at 94 °C, 15s at 55 °C, 30 s at 72 °C; then a final extension at 72 °C for 5 min. Reactions were cleaned enzymatically with ExoI and rSAP using the manufacturer’s protocols . Products were sequenced in both directions using PCR primers and internal primer ND4#2-TACGACAAACAGACCTAAAATC from MK100594 –MK100710).We amplified a 353 bp fragment near the 5′ end of the mitochondrial control region using primers PcCR F—CTTATGATGGCGGGTTGCT and PcCR R—GGCTGTTAAATTTAT CCTCTGGTG for all 542 individuals. We also amplified the mitochondrial NADH dehydrogenase subunit 4 (ND4) and the tRNAs Histidine, Serine, and Leucine region using the primers ND4—ACCTATGACTACCAAAAGCTCATGTAGAAGC and Leu—CATTACTTTTACTTGGATTTGCACCA from O) and expected heterozygosity (HE) and FIS. We used HP-RARE (R) at each site to standardize comparisons across sample sizes. We tested for heterozygote excess and deficiencies and genotypic linkage disequilibrium using GENEPOP 4.0  which utilized all samples.The cryptic nature and low density of Texas horned lizards in most places made it difficult to find large numbers of individuals from single localities. To calculate traditional genetic diversity metrics, however, we grouped 449 of the 542 horned lizards into 16 sampling sites with ≥10 individuals in each , Fig. 2. HP-RARE  to calcuEPOP 4.0 . We usedEPOP 4.0  was usedEPOP 4.0  eq1. As 4 iterations and ran the MCMC (Monte Carlo Markov Chain) for 106 iterations. STRUCTURE can give misleading results both for the number of populations and individual ancestry if there is uneven sampling across clusters  before values started to plateau (q) was ≥ 0.10 in each of two or more clusters. We chose this value since similar cut-off values have been used in a number of other studies  and because simulation studies have found only a slight reduction in the power of assignment tests in the presence of null alleles  of the control region for the 16 sampling sites mentioned above. We used CONTRIB 1.02 (HR) at each site. We compared mtDNA population structure for these 16 sites to the nuclear microsatellite structure by calculating ϕPT for both data sets and conducting AMOVAs in GenAlEx.We used GenAlEx to calculate the frequency of haplotypes and haplotype diversity  was used to root the ND4 gene tree and Phrynosoma blainvillii (NC_036492.1) was used to root the control region tree. Bayesian analyses using MrBayes 3.2  in MEGA. The resulting models were HKY + G for ND4 and HKY + G + I for the control region. These models were then used to create maximum likelihood trees in MEGA using the nearest-neighbor-interchange and a strong branch swap filter. Bootstrap values were calculated using 1,000 replicates. ayes 3.2  were alsh), nucleotide diversity (π), and θ (Watterson’s estimator). We also conducted neutrality tests in DnaSP, including Tajima’s D  as τ = 2 µkt, where t is the time in generations, µis the mutation rate per site per generation, and k is the sequence length  identified in the gene trees had experienced past population expansion or were stable, using all samples sequenced for the ND4 region and a subset of samples for the control region. We used DnaSP 6.11.01  to calcujima’s D  and Fu’sa’s D  that had a significant heterozygote deficit after sequential Bonferroni correction. In addition to this one comparison, MICRO-CHECKER also identified five other loci/site comparisons that had null alleles . There wHE = 0.83 ± 0.006, AR = 8.47 ± 0.18, n = 16 sampling sites) and similar across sites, with the exception of the population on Matagorda Island which had lower heterozygosity and allelic richness than mainland sites . Population differentiation was modest but significant with a global FST of 0.044 (95% CI [0.033–0.061]) and pairwise FST ranging from 0.003 to 0.115  and two other more separated sets of sites  and  and for the 11 sampling sites that formed the tight cluster in the PCA .Genetic diversity was generally high  also occurred in the Chihuahua Deserts ecoregion in Texas. There were two individuals that had high (q > 0.90) ancestry assignment in the north population but were found in the South Texas Plains, and one individual with high ancestry assignment in the south population that was found in the Central Great Plains ecoregion.The west population is found within the Chihuahua Deserts and Madrean Archipelago ecoregions and the south population is found south of the Balcones Escarpment in the Southern Texas Plains, East-Central Texas Plains, and Western Gulf Coastal Plain ecoregions , Fig. 1.n = 41 of 488 individuals) was more common than between west and north populations . The remaining two individuals were admixed between all three populations; one was found in southern Texas whereas the other was found in northern Texas. Admixed individuals with north and west ancestry were found in Brewster Co. where the north and west populations meet  and in widely separated areas including Colorado (n = 4), Seminole Canyon SP (n = 3), Matador WMA (n = 1), Yoakum Dunes WMA (n = 1), CMA (n = 1), and Starr Co. (n = 1). Admixed individuals with north and south ancestry were found south of the Balcones Escarpment (n = 14) in the south population and far north of the Escarpment (n = 27) in the north population.Most individuals  were strongly assigned to a single population. The remaining individuals  had evidence of admixture. Admixture between the south and north populations (h) and richness was more variable between the 16 sites than microsatellite diversity . Population subdivision was considerably higher for the mtDNA control region than for the microsatellite loci with 32% of the variance between sites for the control region  and 8% of the variance between sites for the microsatellite loci .Haplotype diversity at the control region was high with a total of 86 haplotypes found across all 542 individuals. Haplotype diversity (iversity  and 3. FP. cornutum differed from P. blainvillii by 12.40% (range: 11.31–13.74%). The western haplotypes mainly belonged to individuals with high microsatellite ancestry in the west population , with the exception of two individuals that had high north ancestry (q = 0.99) and were found in Brewster Co. Within Brewster Co., there were also 21 individuals with high west ancestry (q > 0.90) that had eastern haplotypes, indicating that this region is an area of admixture between the western and eastern mitochondrial clades. We found no evidence for separate mitochondrial clades corresponding to the north and south populations detected by STRUCTURE. Individuals with the eastern haplotypes had microsatellite ancestry within the north, south, and west populations. Control region haplotypes were, however, largely unique to the geographic regions encompassed by the three main nuclear microsatellite populations  as indicated by the colored symbols in h = 0.90), followed by the west (h = 0.88), and then the north region (h = 0.72). To compare the three regions, we used the standardized estimate of ϕPT using the method of Hedrick to ensure that ϕPT = 1.0 when populations have non-overlapping sets of haplotypes (ϕPT was 0.97 (P = 0.001) among the three regions, reflecting the low sharing of haplotypes between them.The control region gene tree revealed two clades between haplotypes found in western areas (New Mexico and far western Texas) and more eastern areas . The wesplotypes . ϕPT wasP. cornutum differed from P. asio by 16.40% (range: 15.80–16.80%). Similar to the control region, the western clade comprised individuals with high microsatellite ancestry in the west population. Similar to the control region, we did not find separate clades for the ND4 gene that corresponded to the north and south populations detected by STRUCTURE.The ND4 gene tree also revealed two well-supported clades between haplotypes found in western areas (New Mexico and far western Texas) and more eastern areas . Both thS was significantly negative (P < 0.05) relative to neutral expectations for both western and eastern clades of ND4 and the control region. Tajima’s D was significantly negative (P < 0.05) only for the ND4 data , haplotype diversity and sequence diversity were higher for the eastern clade than the western clade . This reND4 data . Using tWith the exception of the Matagorda Island population, the Texas horned lizard sampling localities included in this study harbor high levels of genetic variation and would therefore provide suitable source individuals for captive breeding and reintroduction efforts. Across the surveyed area, Texas horned lizards exhibit two major groupings at mtDNA loci (western and eastern) and three major genetic groupings at nuclear loci . These major genetic patterns should be used to inform both captive breeding and reintroduction strategies for this species.The major genetic groupings were found in ecoregions which differ from each other in patterns of precipitation, temperature, and vegetation . The wes2 in southern New Mexico, far western Texas, and Mexico has been hypothesized as the barrier that originally separated these two clades  within the eastern clade could also be considered separate management units MUs; , based oϕPT was higher for the mitochondrial locus than for nuclear microsatellites. Stronger population structure at mtDNA compared to nuclear loci may also be an indication that populations have recently become fragmented and isolated. Mitochondrial loci might be expected to reveal the effects of drift first since they have a lower effective population size than nuclear loci. The relatively low mtDNA haplotype diversity seen at some sampling sites  are currently unknown for Texas horned lizards, but radio-telemetry studies, anatomy, and life history characteristics indicate that the species is generally sedentary and probably has limited long-distance dispersal capabilities. Typical home ranges are 0.4–7.0 ha for adult horned lizards and daily movement distances are 0–247 m, although distances up to ∼800 m over several days of travel have been detected in a few cases . Horned ng sites  may be iTexas horned lizards are unique among lizards in Texas because of their nostalgic and symbolic status in historical accounts and folklore . AnecdotThe widely separated and isolated occurrences of some admixed individuals is consistent with human-mediated movement from anecdotal accounts. If the admixture we observed was due simply to natural dispersal, we would have expected to find admixture primarily in regions where the microsatellite clusters come into contact. For instance, the west and north populations come into contact in the Chihuahua desert ecoregion in Brewster County, TX where we found evidence for admixture from the microsatellite and mitochondrial loci. We were not able to determine if there was evidence for admixture along the Balcones Escarpment where the north and south populations might be expected to come into contact, as this species has been extirpated from most of the eastern and southern border of the escarpment and only a few horned lizards were sampled directly north of the escarpment in the Edwards Plateau. The presence of some west ancestry in widely separated individuals within the geographic range of the north and south microsatellite populations, as well as the presence of south ancestry in northern Texas  or north ancestry in southern Texas  seems to be at odds with natural dispersal patterns.Reintroductions of the Texas horned lizard are only planned for areas that historically had horned lizards, currently have suitable habitat, and are within the geographic ranges encompassed by the north and south populations. We recommend that breeding facilities at Texas zoos keep individuals from the three microsatellite genetic clusters separate and return their offspring to regions that correspond to their microsatellite genetic cluster. This strategy is based on the indication that these lizards may be regionally-adapted. Assuming reintroductions are successful, there may also be nearby populations that will eventually come into contact with these introduced individuals. Avoiding the mixing of differentiated clusters would therefore be advisable to reduce the chances of outbreeding depression. Nonetheless, recent reviews have suggested that fears of outbreeding depression may have been over-emphasized in some conservation programs . This maOn the other hand, one might argue that genetic contamination has already occurred between these populations given our evidence of admixture in unexpected areas. Selection is expected to eventually remove the deleterious effects of outbreeding depression , and it 10.7717/peerj.7746/supp-1Table S1O is observed heterozygosity; HE is expected heterozygosity; FIS is the inbreeding coefficient; Null is the frequency of null alleles (α = 0.05). For sampling site names, SP = state park, WMA = wildlife management area, CMA = Cross Bar Management Area, Co. = county, RPQRR = Rolling Plains Quail Research Ranch.N is the number of individuals; NA is the number of alleles; H alleles  eq1, * iClick here for additional data file.10.7717/peerj.7746/supp-2Table S2ST has been corrected for the presence of null alleles using the ENA correction method of P ≤ 0.0004) except for those values shaded in grey (P > 0.0004). For sampling site names, SP = state park, WMA = wildlife management area, CMA = Cross Bar Management Area, Co. = county, RPQRR = Rolling Plains Quail Research Ranch.FClick here for additional data file.10.7717/peerj.7746/supp-3Table S3For sampling site names, SP = state park, WMA = wildlife management area, CMA = Cross Bar Management Area, Co. = county, RPQRR = Rolling Plains Quail Research Ranch.Click here for additional data file.10.7717/peerj.7746/supp-4Figure S1N = 10 runs) for K clusters, and bottom panel—ΔK for Kclusters (using the method of Top panel—mean (±SD) LnP (D)  for an individual lizard with the colors representing the cluster or population identified in STRUCTURE. Individual lizards in the south top panel are organized by geographic sampling location, starting with the Matagorada Island WMA individuals and then the mainland. Individual lizards in the west bottom panel are organized by longitude. The tan bars are predominately in individuals from west Texas  and especially individuals 29–49 from Brewster Co. near the Elephant Mountain WMA (EMWMA). Individuals 50–52 were found in Colorado and Seminole Canyon SP.Each vertical bar in the two panels indicates the proportion of ancestry , mitochondrial control region haplotypes are identified by numbers corresponding to numbers in alignment file, latitude and longitude are in decimal degrees/WGS 84, ancestry values are proportion of membership in each of 3 genetic clusters  as defined by STRUCTURE (see text). The site column indicates the samples which were grouped into 16 sites and analyzed for genetic diversity, population differentiation and isolation-by-distance (the coordinates used for these 16 sites are given in the “coordinates for 16 sites” worksheet).Click here for additional data file.10.7717/peerj.7746/supp-7Data S2Click here for additional data file.10.7717/peerj.7746/supp-8Data S3Click here for additional data file.


Western hemisphere goats have European, African and Central Asian origins, and some local or rare breeds are reported to be adapted to their environments and economically important. By-in-large these genetic resources have not been quantified. Using 50 K SNP genotypes of 244 animals from 12 goat populations in United States, Costa Rica, Brazil and Argentina, we evaluated the genetic diversity, population structure and selective sweeps documenting goat migration to the “New World”. Our findings suggest the concept of breed, particularly among “locally adapted” breeds, is not a meaningful way to characterize goat populations. The USA Spanish goats were found to be an important genetic reservoir, sharing genomic composition with the wild ancestor and with specialized breeds . Results suggest goats in the Americas have substantial genetic diversity to use in selection and promote environmental adaptation or product driven specialization. These findings highlight the importance of maintaining goat conservation programs and suggest an awaiting reservoir of genetic diversity for breeding and research while simultaneously discarding concerns about breed designations. Globally, goats tend to be raised in low input production systems and generally lack high levels of artificial selection, suggesting their genetic composition may be less structured than other species3.Unlike other livestock species, goats are unique in terms of their function and environments where they are utilized. Their body size, levels of production, dietary preferences, and low cost of investment make them a pliable species for livestock producers to use4 from 9,900 to 10,500 YBP. The Bezoar ibex (Capra aegagrus) is thought to be the only living wild progenitor of the goat5. Upon domestication, goats accompanied human migration and trade, thereby developing subpopulations and breeds differentiated by various selection factors and genetic drift6.Goat domestication occurred in the Fertile Cresent7. These populations have become well adapted to low input agricultural environments typically found in northeastern Brazil, west Texas, and southern Argentina (Patagonia)9, creating locally adapted breeds. Further, multiple waves of importation to the western hemisphere have occurred and included product-specialized breeds, such as dairy , fiber (Angora) and meat (Boer). However, western hemisphere breeding lags behind other livestock species, in part due to their low economic return10.During the colonization of the western hemisphere, settlers brought goats potentially from the Iberian Peninsula and west AfricaIn general, local goat breeds may be largely panmictic, due to multiple importation waves, unsupervised crossbreeding and the lack of strong artificial selection. In this work, we used genotypic data (50 K SNP) from 12 goat breeds found in the Americas, augmented by genotypes from South Africa, Iran, Morocco and Bezoar ibex . Brazilian breeds had the highest number of monomorphic SNPs and inbreeding coefficients  from the New World samples. Compared to Angora_USA, Angora_SA had a higher inbreeding coefficient . The Spanish breed had the lowest inbreeding coefficient (0.01), while Saanen_CR and C. Neuquino_AR also had low inbreeding levels , Iran (42.2%), Spanish_USA (26.2%), Morocco (12.1%) and all Argentinean breeds (11.6%). The dairy breed cluster  was observed in Spanish_USA (31.0%) and C. aegagrus (30.4%). The cluster that represented 80.2% of genomic composition of C. Llanos_AR was observed in Spanish_USA (11.0%) and other nondescript Argentinean breeds . Likelihood estimates indicated 6 to 9 migration events best fit the model  identified by the genetic structure analyses (PCA and ADMIXTURE). Pairwise Fst suggested high differentiation of Brazilian breeds, Angora_SA and Boer_USA  on chromosomes 6, 7, 13, 18 and 25 Fig.  were ide13. HapFLK analyses used the same breed groupings except that Bezoar ibex was used as the root. Five regions with reduced haplotype diversity were identified and considered selection signatures for these groups ps Table .Table 2S13. To improve the haplotype estimation process, five different hapFLK runs were performed  highlighted a selection sweep signal in Caninde_BR and Moxoto_BR , suggesting the progenitors were from both Spanish and Portuguese populations which were pivotal sources of genetics exported to the western hemispherents Fig.  are cons20.A weak genetic structure was observed Fig.  for Span23. Carvalho et al.23 reported the concept of breed for meat goats might not be relevant for goat production, reinforcing our perspective that many so called breeds are actually landraces at best and panmixia predominates in these genetic reservoirs. In addition, Lenstra et al.10 suggested pure genetic ancestry was not a prerequisite for goat breeds. The Spanish goat raised predominately in the southern USA seems to typify such an assessment25. Our results demonstrate how this population shared genomic components (>10%) with dairy breeds (Saanen and Lamancha), Argentinean, Morocco, Iran and C. aegagrus. It is known that no gene flow has occurred recently between the populations mentioned due to geographic distances, except to Lamancha. Therefore, this genomic sharing represents old (400–500 years ago) admixture events that remain conserved in the populations. Given these levels of admixture with old world populations suggests the Spanish breed is a genetic diversity reservoir in the western hemisphere.Previous studies also showed a weak structuring of goat breeds26. Conversely the USA had imported South African Angora in the 1980’s which likely decreased inbreeding and is evident in the clustering analysis  were previously observed as a selection signature for hot and arid environments29. Another region in chromosome 6 (86.6–94.9 Mb) harbors two genes (PPEF2 and SHROOM3) previously associated with platelet distribution width, mean corpuscular volume and mean corpuscular hemoglobin concentration in swine30, which is also related to heat tolerance and parasite resistance31.Five significantly selected regions were observed in Moxoto and Caninde, the main breeds raised in Northeast Brazil noted for high temperatures and low humidityCCSER1, CPEB2, NMUR2, SPARC, DNMT3B),37. Angora goats have been intensely selected for increased mohair production while compromising body weight and potentially lowering their adaptability to sub-optimum conditions40. South Africa and United States populations shared selected regions (chromosome 17 and 6), which was previously observed as a selection signature for arid environment29 and crimp in wool41. The region on chromosome 17  has been associated with cytokine receptors and cell proliferation42 suggesting regions involved with mohair production and environmental adaptability. Therefore, this region can be simultaneously linked to the selection for mohair production and for harsh environments, or also can be a genetic hitchhiking based on the selection for one of the traits.Angora populations showed five genes within selected regions that were associated with body size, average daily gain, longissimus muscle area and carcass weight . Selection for fiber production among Angora_USA during 60’s and 2000’s was substantial43. These animals are able to continue producing mohair fiber even during periods of feed shortage or nutrient restriction47. Therefore, we expected to find a higher number of strong selection signatures in this breed than in local breeds that did not undergo any artificial selection. However, the number of selection signatures found was not very different from other genetic groups or populations. This could be related to the high polygenic nature of the fleece traits, which did not leave strong selection signals in the genome48. Moreover, this reinforced the different picture of goat genetic structure in comparison to other livestock species.Angora goats have been bred for mohair production in the United States since the introduction of these animals from Turkey in 184949. Three regions identified in Boer  have genes related to meat traits also found in Australian and Canadian Boer50. Another region selected on Boer (CHI13) harbors the bone morphogenetic protein 2 (BMP2) gene, which plays a role in skeletogenesis, osteoblastic differentiation and limb patterning51.Boer have the largest body size of the studied populations and had strong selection signals for traits associated with size and muscularity similar to cattle and sheepet al.50 associated this region with ear size selection on Lamancha animals (short ears). Here, Boer (long ears), Caninde_BR (average ears) and Lamancha showed selective sweep on this region. Interestingly, these three breeds have contrasting ear phenotypes of Brito et al.50, validating this region as related to ear morphogenesis.Ear structure in goats is variable with implications for adaptation to heat stress. A selection signature in chromosome 7 was identified in two hapFLK analyses. Brito 5. The Fst approach is more sensitive to bias by genetic drift in populations52. The hapFLK is only slightly affected by migration and is not affected by bottlenecks5. Depending on the time scale of selection, the causative SNP eventually became fixed while genetic drift gradually reduces the signal-to-noise ratio53, which compromises the Fst approach. The two Brazilian breeds (high inbred and drifted populations), for example, did not have any selection sweep identified by Fst, while the hapFLK analyses identified five regions under selection.The Fst and hapFLK analysis showed different selected regions probably due to the known differences in these approacheset al.13 The Argentinean breeds, for example, did not have any selection signature in the runs with groups and 16 populations. In the run with 12 populations, a first soft signal for C. Neuquino was detected. Then, in the run with only Spanish and Argentinean local breeds, the region in chromosome 6 was confirmed as selected on C. Neuquino  with the domestic goat (Capra hircus) suggested that the population bottleneck associated with the domestication process was not as severe as for other domesticated species48. Goat domestication occurred multiple times, which provided a high diversity to the species54. Goat populations presented seven mitochondrial haplogroups until Neolithic era. Modern goat populations, otherwise, have predominant mitochondrial haplogroup (haplogroup A) in the world, which also confirms this history of gene flow across different geographical regions56. A study using wild and domestic goats and sheep showed that the average relatedness was 0.859 and 0.823 for sheep and Asiatic mouflon, respectively, while the average relatedness was around 0.915 between the domestic goats, and 0.916 between Bezoar ibex5. Therefore, goat populations are more related to each other than are sheep populations.The comparison of the genome of the wild ancestor Bezoar ibex  raised in the western hemisphere Samples with call rate <0.90 were removed. SNP with call rate <0.90 or with MAF = 0.0 were removed and only autosomes SNPs were used. The final number of SNPs after quality control was 48,442 SNP.www.goldenhelix.com). One animal of each pair with a genomic relationship higher than 0.25 was removed, reducing the dataset to 267 animals.In order to remove highly related animals within breeds, a genomic relationship matrix for each breed was calculated using SNP & Variation Suite v8.7  consisting of 7 samples from Capra aegagrus, 9 samples from Iran population and 30 samples from Moroccan goat population were also used. The filtering criteria for this data were less stringent as the objective was to use them as the root and outgroup (reference population) in different analyses. The autosomes SNP were filtered based on SNP call rate (<0.8) and sample call rate (<0.9), yielding 49,051 SNPs. The two datasets were merged, resulting in a final dataset with 313 animals and 48,203 SNPs (Table Raw data (50 K SNP chip) of NextGen consortium , resulting in 46,214 SNPs.Three analyses  were applied to evaluate the genetic diversity in these goat populations. For these analyses, a stringent filtering criteria were applied to avoid bias related to linked markers. SNP with call rate lower than 0.95 and MAF < 0.05 were removed. Moreover, LD pruning was applied using a window size of 50 SNPs, an increment of 5 and rwww.goldenhelix.com) and the plotting of the first three components were performed in R 3.4.2 using the scatterplot3D package. The parameters for PCA analysis were set to find the first 10 components, normalizing each marker data by their actual standard deviation, using an additive model and outlier removal up to 5 times, which was considered as more than 6 standard deviations, from 5 components.Principal component analysis (PCA) was carried out in SNP & Variation Suite v8.7  was executed to estimate prediction errors for each K value (from 2 to 22). The value of K that minimizes the estimated prediction error represented the best predictive accuracy. Individual coefficients of membership to each K cluster produced by ADMIXTURE were visualized using the on-line CLUMPAK server with the feature DISTRUCT for many K’s.12. The program was run on the dataset with animals classified in 17 populations, using Capra aegagrus as the root. A variable number of migration events (M) from 0 to 20 were tested and the log-likelihood was used to determine the most predictive model.The tree-based approach was used to reconstruct historical relationships between the analyzed populations and to test for the presence of gene flow using the TreeMix softwareCapra aegagrus and Iran populations were used in selection signature detection as a reference group (outgroups).The selective sweeps were identified by two different approaches, Fst and hapFLK statistics. The main groups identified in genetic structure analyses were used further in the selection signatures. The populations were grouped as: Fiber specialized (Angora populations from South Africa and USA), Meat specialized (Boer), Milk specialized (Saanen and LaMancha), Spanish breed, Brazilian local breeds (Moxoto and Caninde), Argentinean local breeds . Angora_AR was not used due to their recent formation and crossbreeding observed in the previous analyses. Moroccan population was not used for selection signature detection as this was not an objective of this study. 50. The hapFLK is a haplotype FLK based test that identifies selection signatures among hierarchically structure populations13. It differs from Fst in order that takes into account the hierarchical structure of the sampling, allowing genetic drift to differ for each population5.The two tests used are able to detect different selection signature. The FST indicates a difference among groups of individuals in each marker that could be caused by different selection events. FST test detects highly differentiated alleles, where positive selection in a given genome region causes exaggerated frequency differences between populationsC. aegagrus and Iran population) were also performed.Each group had three Fst results from the comparison with: all other groups combined, all specialized breeds  and the meat and milk specialized breeds. For Brazilian breeds, since they were placed in different clusters in the ADMIXTURE results, we chose to run the Fst analysis for each breed separately as well. Fst comparison between breeds group and wild populations (www.goldenhelix.com) considering the mean asymmetric method. Smoothing process consider a moving average of a certain number of markers. This process is an approximate method when looking for regions where selection is apparent over multiple markers, rather than one-off high values50. The number of SNP to be included in the smoothing window in each comparison were determined based on the number of monomorphic SNP in each group and aiming a false discovery rate lower than 0.5 according to Ramey et al.60  to be used further, several runs of fastPHASE were performed to register the likelihoods. The point where the increase in number of clusters represent a small increase in log-likelihood was selected as the K to use in the hapFLK analysis. Then, the five hapFLK analyses  were run using K equal 70, 80, 35, 60 and 20, respectively. The hapFLK statistic was computed as the average across 20 expectation maximization (EM) runs to fit the LD model (–nfit = 20).https://forge-dga.jouy.inra.fr/projects/hapflk) was used to estimate the hapFLK chi-squared density, standardize hapFLK values and calculate the corresponding p-values of hapFLK results. The plots were generated using the R packages ape and qqman. The significant regions  were identified and local population trees and haplotype clusters of each regions were plotted. The local population trees used only those SNPs located within the regions of signatures of selection identified to show the breeds undergoing selection.The hapFLK software was run chromosome by chromosome and the results merged to a single file. A python script  softwarehttp://www.ncbi.nlm.nih.gov/). The genes were identified based on the Annotation Release 102 and ARS1 genome assembly.The causal variants identified with CAVIAR and the significant SNP observed in Fst comparisons were used to identify genes in each region using the Genome Data Viewer in the NCBI platform (http://www.pantherdb.org/)  was executed to identify phenotypes known to be affected by variation in the genes located in each significant genomic region.The biological functions and pathways in which these genes are involved were assessed using PANTHER (Supplementary material


Anopheles gambiae. Accurate estimation of mosquito migration is crucial for efforts to combat malaria. Modeling and cage experiments suggest that mosquito gene drive systems will enable malaria eradication, but establishing safety and efficacy requires identification of isolated populations in which to conduct field testing. We assess Lake Victoria islands as candidate sites, finding one island 30 km offshore is as differentiated from mainland samples as populations from across the continent. Collectively, our results suggest sufficient contemporary isolation of these islands to warrant consideration as field‐testing locations and illustrate shared adaptive variation as a useful proxy for connectivity in highly polymorphic species.Documenting isolation is notoriously difficult for species with vast polymorphic populations. High proportions of shared variation impede estimation of connectivity, even despite leveraging information from many genetic markers. We overcome these impediments by combining classical analysis of neutral variation with assays of the structure of selected variation, demonstrated using populations of the principal African malaria vector  Large population sizes result in high levels of polymorphism in the genome and impede accurate estimation of connectivity Waples,  and discAnopheles gambiae Giles, 1902 sensu stricto (henceforth An. gambiae) is among the most genetically diverse eukaryotic species  were included in further analyses. All mosquitoes were preserved with silica desiccant and transported to the University of Notre Dame, Indiana, USA, for analysis.Mosquitoes were sampled from five of the Ssese Islands in Lake Victoria, Uganda , and four mainland sampling localities  at varying distances from the lake in May and June 2015. Sampling took place between 4:40 and 8:15 over a 30‐day period as follows: Indoor resting mosquitoes were collected from residences via mouth or mechanical aspirators and subsequently identified morphologically to species group. Female mosquitoes assigned to the 2.2An. gambiae s. s. N = 116 mosquitoes was extracted from the whole body via phenol–chloroform extraction . This yielded 33.1 million SNPs. Three individuals sequenced to lower coverage (4.3–5.3×) were included to maximize sample size, and the following filtering steps were applied to remove sequencing errors. Individuals and variants with high levels of missingness (>10%) and variants that were not biallelic or exhibited values of Hardy–Weinberg equilibrium (HWE) that were likely due to sequencing error (p < .00001) were excluded from further analysis  For use in population structure inference, the SNP dataset was further pruned for linkage disequilibrium by sliding a window that is 50 SNPs long across the genome in 5 SNP increments and recursively removing random SNPs in any pairs with 2r > .5 using PLINK  of 1,000,000 , and window size of 2 Mb.After realignment around indels with GATK's IndelRealigner, variants were called using GATK's UnifiedGenotyper  collected between 2000 and 2012 from Angola, Burkina Faso, Guinea‐Bissau, Guinea, Cameroon, Gabon, Uganda, and Kenya  SNP set with a recently published dataset of 2r > .01 in sliding windows of size 500 SNPs and with a stepsize of 250 SNPs. For each replicate, we ran ADMIXTURE for five iterations in fivefold cross‐validation mode for values of k clusters from 2 to 10. This resulted in 50 estimates for each value of k. We assessed these results using the online version of CLUMPAK with default settings to ensure the stability of the resulting clustering  and excluding laboratory crosses from the Ag1000G dataset , we assigned individuals’ genomes to ancestry components using ADMIXTURE to better understand population structure in the LVB  with PLINK  between‐locality samples for An. gambiae using the unbiased estimator of Hudson  and heterochromatic regions. We also computed z‐scores via block jackknife, using 42 blocks of size 5 Mb. We tested for isolation by distance, or a correlation between genetic and geographic distances, with a Mantel test  using PLINK  estimated with the method of moments, minor allele frequencies , and a measure of linkage disequilibrium (2r) using VCFtools  was estimated with the method of moments as implemented in VCFtools /(Ni − Ei), where, for individual i, Oi is the total observed number of loci which are homozygous, Ei is the total expected number of loci homozygous by chance, and Ni is the total number of genotyped loci. For 2r, we computed the measure for all SNPs (unpruned for linkage) within 50 kb of a random set of 100 SNPs with MAF > 10% and corrected for differences in sample size by subtracting 1/n, where n equaled the number of sampled chromosomes per site, after  to them. We also inferred runs of homozygosity using PLINK , requiring 10 homozygous SNPs spanning a distance of 100 kb and allowing for three heterozygous and five missing SNPs in the window. Runs of homozygosity were inferred using LD‐pruned SNPs outside of inversions or heterochromatic regions. We tested the significance of differences in these statistics between island and mainland categories using a two‐sided Wilcoxon rank‐sum test.We computed numerous diversity metrics to test the hypothesis that island and mainland sites differed in these key measures for vector control. Grouping individuals by site , we calculated nucleotide diversity (π) and Tajima's 2.6Ne for each site, we inferred regions of IBD from unphased data with IBDseq . We assessed the goodness of fit visually using the residuals of the comparison between model and data frequency spectra , for time were T ∈ , for migration were m ∈ , and for genotyping uncertainty were pmisid ∈ . Parameters were perturbed before allowing up to 1,000 iterations for optimization. We estimated parameter uncertainty using the Fisher information matrix and 100 bootstrap replicates of 1 Mb from the dataset. If the Hessian was found to be not invertible when computing the Fisher information matrix, the results of that iteration were excluded from the analysis. For population size change parameters, ν, optimized values for one or both populations were often close to the upper limit. Due to this runaway behavior, common in analyses of the SFS  demographic model with a Figure . Using tδaδi‐ and stairway plot‐based estimates of Ne and time to individuals and years respectively, we assumed a generation time of 11 per year and a mutation rate of 3.5e‐9 per generation  and evidence of differing selective sweep histories (as inferred from XP‐EHH). Excluding the mainland‐like portion of Bugala , we identified putative locality‐specific sweeps (H12 over 99th percentile in one population), island‐specific sweeps , or LVB mainland‐specific sweeps . To place these putative sweeps in their continental context, for the region of each putative locality‐, island‐, or LVB mainland‐specific sweep, we determined whether the H12 values of each of the Ag1000G populations  indicated little differentiation among mainland samples in the first two components and varying degrees of differentiation on islands, with Banda, Sserinya, and Bukasa the most extreme Figure . As bothFST between‐sampling localities (range: 0.001–0.034) was approximately 0 (≤0.003) for mainland–mainland comparisons and was highest in comparisons involving the small island Banda , islands displayed slightly lower nucleotide diversity  and often severely fluctuated, particularly in the last 250 generations (~22 years). Mainland sites Wamala and Kaazi had island‐like recent histories, with Wamala abruptly switching to an island‐like pattern around 2005.To test islands for isolation and demographic independence from the mainland, we inferred the population history of LVB samples by estimating long‐term and recent trends in δaδi, in which an ancestral population splits into two populations, allowing exponential growth and continuous asymmetrical migration between the daughter populations  indWe would expect independent sweeps on novel mutations (i) to exhibit differences in genetic background between the two populations, evidenced by distinct haplotype clusters, each comprising near‐identical haplotypes separated by individual haplotypes lacking signatures of a selective sweep. In both other scenarios (ii–iii), we would instead expect haplotypes with the sweep to group together when clustered by genetic distance. By itself, haplotype information does not differentiate sweeps targeting standing ancestral variation (ii) from those targeting adaptive variants spread through gene flow (iii). However, additional information such as geographic distance between the populations, estimates of gene flow inferred from other regions of the genome, and assessment of gene flow between other nearby populations, may suggest that one of these scenarios is the more likely.While the sharing of a sweep may indicate migration between populations, the inverse would be suggestive—though not conclusive—of barriers to gene flow. A lack of sharing of a selective sweep signal between two populations may indicate no migration is occurring. However, it would also be consistent with the occurrence of migration that is subsequently countered by the local effects of selection or lost to genetic drift.FST , concordant with increased isolation of the islands Table . Sweeps We next reasoned that continent‐wide selective sweeps, with broadly distributed selective advantage, would be the most likely to be shared via gene flow. Widespread sweeps that were absent or at extremely low frequency on the islands would be a strong suggestion against contemporary gene flow , and those that conversely were present on the islands would be indicative that gene flow had occurred, if the alternative scenarios could be excluded as outlined above. To identify these regions, we intersected our set of sweeps with those under putative selection in populations across the continent . For instance, the large genomic region spanning the cluster of insecticide resistance‐associated cytochrome P450s (Cyp6p) on chromosome arm 2R, including Cyp6p3 which is upregulated in mosquitoes with permethrin and bendiocarb resistance , and elevated haplotype homozygosity (H12) within the LVB populations  indicated low differentiation between LVB localities, as expected for a continent‐wide sweep  was found in all assayed Ag1000G populations and LVB mainland sites, but found on no island but Sserinya (Table Gste1‐Gste7), including one sweep specific to Uganda. This Ugandan sweep was similarly confined largely to the mainland in the LVB. These sweeps at targets of selection throughout the continent that are largely restricted to the mainland are suggestive of strong barriers to gene flow to the islands, either due to lack of connectivity or the countering effects of selection or drift. Other sweeps had colonized the islands incompletely. The sweep targeting cytochrome P450 gene Cyp9k1, likely linked to pyrethroid resistance . Low‐diversity haplotypes formed a single cluster including LVB haplotypes overwhelmingly from the islands Figure . Transfe4Although the perfect field‐testing site for gene drive mosquitoes would lack any outward migration, mosquitoes’ massive dispersal potential renders the existence of such a site doubtful. However, a genetically modified construct created to induce population suppression would be expected to reduce the transgenic and overall mosquito population to low levels or absence in a period of years  The tsetse‐mediated arrival of sleeping sickness in 1902 brought “enormous mortality"  in Mityana, the district containing Wamala , or the stochasticity of genetic drift is difficult. Instead, we must rely on estimates of the strength of selection in the two locales to inform our conclusions. For example, we would expect that an insecticide sweep found over a large region in Africa would spread in island mosquito populations with insecticide treated bed nets, despite the considerable effect of genetic drift in small populations. As insecticide treated bed net usage is present on the islands  Although these distributions confirm that past migration from the mainland to islands has occurred and we are unable to exclude low levels of contemporary gene flow, taken together our data are consistent with potentially high degrees of gene flow restriction on contemporary timescales for some islands of the Ssese archipelago.We found two sweeps on insecticide‐related genes that are common targets of selection elsewhere but which have incompletely colonized the Ssese Islands: one on cytochrome P450 monooxygenase L and encompassing many genes, haplotypes with sweeps in distinct genetic backgrounds across Africa suggest the region has been affected by multiple independent convergent sweeps. In Uganda, most individuals with the sweep are from the mainland, suggesting a local origin and spread via short‐distance migration. The putative target of the second sweep is diacylglycerol kinase (AGAP000519) on the X chromosome, a homolog of retinal degeneration A (rdgA) in Drosophila. The gene is highly pleiotropic, contributing to signal transduction in the fly visual system  dispersal (James et al., The authors declare no competing financial interests.C.M.B., J.K.K., and N.J.B. designed the study; C.M.B., M.L., R.M.W., and J.K.K. collected biological samples; C.M.B. analyzed the data; C.M.B., M.C.F., and N.J.B. wrote the manuscript; M.C.F., J.K.K., and N.J.B. supervised the research; C.M.B., M.L., R.M.W., M.C.F., J.K.K., and N.J.B. edited the manuscript. Click here for additional data file.


Aedes aegypti Linnaeus was first detected in three cities in central California . It has now been detected in multiple locations in central and southern CA as far south as San Diego and Imperial Counties. A number of published reports suggest that CA populations have been established from multiple independent introductions.In the summer of 2013, Ae. aegypti based on individual, field collected whole genome sequences. We analyzed 46 Ae. aegypti genomes to establish genetic relationships among populations from sites in California, Florida and South Africa. Based on 4.65 million high quality biallelic SNPs, we identified 3 major genetic clusters within California; one that includes all sample sites in the southern part of the state (South of Tehachapi mountain range) plus the town of Exeter in central California and two additional clusters in central California.Here we report the first population genomics analyses of Ae. aegypti into California from multiple, genetically diverged source populations. Our data reveal signs of hybridization among diverged populations within CA. Genetic markers identified in this study will be of great value in pursuing classical population genetic studies which require larger sample sizes.A lack of concordance between mitochondrial and nuclear genealogies suggests that the three founding populations were polymorphic for two main mitochondrial haplotypes prior to being introduced to California. One of these has been lost in the Clovis populations, possibly by a founder effect. Genome-wide comparisons indicate extensive differentiation between genetic clusters. Our observations support recent introductions of The online version of this article (10.1186/s12864-019-5586-4) contains supplementary material, which is available to authorized users. Aedes aegypti has a short flight range, usually not actively moving more than 200 m from their breeding source . Variants in the mitochondrial genome were called with Freebayes as described for the nuclear genome, but set to single ploidy. Mitochondrial coverage was on average 160 times greater than the nuclear genome coverage with a minimum of 25-fold difference  was used for generating plots. Inkscape (https://inkscape.org/) version 0.92 was used to edit images.QGIS version 2.18 was used to create maps. Python matplotlib version 3.0.2 Additional file 2:Table S2. Pairwise FST values based on nuclear genome SNP data. (XLSX 15 kb)Additional file 3:Figure S1. Genome-wide comparison of Ae. aegypti populations comparing GC2 vs GC3, GC2 vs GC4, GC3 vs GC4, and GC1 vs GC1-B (Vero Beach and Exeter). (PDF 352 kb)Additional file 4:Appendix S1. Comparison of Evans et al. 2015 SNPchip probe sequences and genotypes with our data. (DOCX 554 kb)


Red Junglefowls (RJFs), the wild progenitor of modern day chickens (DCs), arebelieved to be in genetic endangerment due to introgression of domestic genesthrough opportunistic matings with domestic or feral chickens. Previous studiesfrom India reported rare hybridization of RJFs in the wild. However, RJFpopulation genetic structure, pattern of gene flow and their admixture with DCpopulations are poorly understood at the landscape level. We conducted thisstudy with a large sample size, covering the predicted natural distributionrange of RJFs in India. We documented strong evidence of directional gene flowfrom DCs to free-ranging wild RJFs, with the Northeastern RJF populationexhibiting the most genetic variants in their nuclear and mitochondrial genomes,indicating it to be the ancestral population from which early radiation may haveoccurred. The results provide evidence that landscape features do not act as abarrier to gene flow and the distribution pattern could not be explored due tophysical sharing or exchange of wild birds in the past when forests werecontinuous across RJF range in India. Gallus gallusdomesticus) is a reason to speculate that gene flow between RJFs and DCs is widespread and more frequentthan supposed by previous studies [etal. [Kauai chicken through invasive geneticsand further raised the issue of 'domestication in reverse'. Ingeneral, hybridization in the absence of reproductive isolation is an inevitablephenomenon and cannot be avoided in cases where domestic and wild congenerics aresympatric [et al. [etal. [The polyphyletic origins of Domestic Chickens . We scrutinized these loci based on their wide coverage on the genomeand high degree of polymorphism ,26. The 5′-AGGACTACGGCTTGAAAAGC-3′) as theforward primerand H547 (5′-ATGTGCCTGACCGAGGAACCAG-3′) as reverse primerwere used. This primer pair amplified a 550 bp fragment between sites 16750 according to themanufacturer’s protocol. Direct sequencing of a HV1 segment of the D-loop regionwas performed using two internal primers CR-for(5′-TCTATATTCCACATTTCTC-3′) and CR-rev(5′-GCGAGCATAACCAAATGG-3′). Sequencing was done usingthe BigDye Terminator (ver. 3.1) Cycle Sequencing Kit  and the purified sequencing products were electrophoresedon the ABI 3130 Genetic Analyzer.To amplify mitochondrial DNA D-loop hypervariable region (HV1), specific primersL16750  and 547_001323; ). The PCGenetic variability, inbreeding and populationdifferentiation. Genetic diversity estimatesi.e. observed (Na) andeffectivenumbers of alleles (ne), observed (Ho), expected (He) and unbiased expectedheterozygosity (UHe) and pair‐wiseFST were estimatedusing GENEALEX (ver. 6.5) [R), which is anestimate of an independent sample size for each population and thepolymorphic information content (PIC)were estimated using FSTAT(ver.2.9.3.2) [FIS following Weir andCockerham [er. 6.5) . The mea2.9.3.2) and Cerv2.9.3.2) , respect2.9.3.2) , 33.Theockerham  methodaPopulation genetic structure. Population geneticstructure of RJF in India was inferred using the Bayesian clusteringalgorithm implemented in program STRUCTURE (ver.2.3.3) [K populations (e.g. 1 to 10). We selected anadmixture model with a burn‐in period of 50,000 and 500,000 Markov chainMonte Carlo repetitions as well as a model of correlated allele frequencieswithout prior sampling location information. Twenty independent runs at eachK value were performed. The most appropriateK value was determined by calculating ad hoc quantity(ΔK) as proposed by Evanno et al. [i.e. q ≥ 0.80, following Mukeshet al. [http://clumpak.tau.ac.il/index.html), aweb server that provides a full pipeline for clustering, summarizing andvisualizing the STRUCTURE results.r.2.3.3)  followino et al.  and eachhet al. , 37,38.Spatial genetics and barrier detection. As samplingwas done across the species distribution in India, we performed a spatialBayesian clustering analysis in GENELAND (ver.) 4.0.3 [i.e. latitude and longitude) of eachindividual along with their multi-locus genotypes for specificallyidentifying genetic breaks while generating maps of population ranges. Weemployed the spatial model to infer the most likely number of clustersindicated as ‘K’ with either correlated or uncorrelated allele frequencymodel. In these analyses, we conducted 30 independent runs for each Kranging from 1 to 10, with 1,000,000 iterations and 1,000 thinning. Otherparameters were set to default values . The top three runs showing the highest averagelogarithm of the posterior probability were selected and post-processing wasconducted using 100 x 100 pixels in the spatial domain with a burnin periodof 200..) 4.0.3  to detecGenetic variability and demographic history. Wechecked all raw sequences using Sequencher ver. 4.7 (www.genecode.com) and any ambiguity, ifencountered, was manually corrected. The cleaned sequences were alignedusing CLUSTAL W-multiple sequence alignment algorithm, implemented inBioEdit version 7.2.5 (http://www.mbio.ncsu.edu/BioEdit/bioedit.html). Using DnaSP(ver.5.10) [i.e. polymorphic sites(S), number of haplotypes (H), nucleotide diversity (π), haplotype diversity(Hd), average number of nucleotide differences (K) and mismatch distributiontest for demographic expansion, equilibrium or bottleneck [i.e. Tajima’s D [er.5.10) ,diversittleneck  were comjima’s D , Fu’s Fsjima’s D  and Fu ajima’s D  werecarhttp://www.fluxusengineering.com). In NETWORK, we assignedequal weights to all variable sites and applied default values for theepsilon parameter (epsilon = 0). The microsatellite and sequencing dataphylogenetic trees constructed were used to investigate the recent andlong-term admixture and the genetic relationship between wild RJF and DCpopulations. For microsatellite data, a genetic distance matrix wascalculated using Program GENALEX (ver. 6.5) [Program STRUCTURE with the same parameters as described above was used withthe inclusion of DC samples collected in the vicinity of RJFs to assign theadmixed individuals. To evaluate the direction of gene flow, haplotypesshared, and their frequencies of occurrence between RJFs and DCs wereexamined using the phylogenetic network based on the median-joining method implemener. 6.5) , and a per. 6.5) . Forseqer. 6.5) .Among the 57 RJF and 79 DC samples collected, there was 68 (32 RJFs and 36 DCs)samples from North, 25 (9 RJFs and 16 DCs) from East, 5 (2 RJFs and 3 DCs) fromCentral, 5 (4 RJFs and 1 DC) from South East and 33 (10 RJFs and 23 DCs) fromNortheast of India. Since, we can trap only a few wild RJFs in Central and Southeastzones; we pooled samples of these zones to create a Cent-Southeast population forfurther analysis. During analysis, two birds- RJ1 (Cent-Southeast) and RJ9(Northeast) were excluded due to uncertainty in the GPS locations of their samplepoints.i.e. LEI0192, MCW0014, MCW0183 and MCW0284exhibited a considerable amount of missing values for few a samples even aftermultiple repetition, so we removed them from further analysis. We manually checkedallelic data, and found no indication of any genotyping error .We genotyped all samples three times or repeated the process until we generatedconsensus genotypes for all samples. However, four lociR ranged from 2.59±0.13 in Cent-Southeast to 3.04±0.07 in Northeast population.Total count of NP was the highest in North  but thelowest in Cent-Southeast  RJF populations. TheFIS estimates in the fourRJF populations were significantly positive (P<0.001), suggesting that allthe populations were slightly inbred. The number of loci deviated from HWE(P<0.05) ranged from four in Cent-Southeast population to 22 in Northpopulation with the majority of them showing a heterozygote deficit.Nevertheless only two loci (MCW0295 and MCW0123) deviated from HWE across thefour populations  to 9.69±0.76 (North) and from 3.11±0.27 (Cent-Southeast) to5.02±0.35 (North), respectively. The Ho ranged from 0.52  to 0.58  and did not differsignificantly among the four populations. The mean PIC values were greater than0.5 for all the four RJF populations. The UHeranged from 0.67±0.04 inCent-Southeast to 0.81±0.02 in Northeast population, and the averageA P<0.05; . Several P<0.05; , but noni.e.38 wild RJF  and 62 DC collected in the vicinity of the RJF.Wild RJF yielded 37 polymorphic sites which formed 31 haplotypes with an averageof 6.48 nucleotide differences. All analyzed samples yielded 68 haplotypes,among which 37 were found in DCs. Most haplotypes were present in one sampleeach. Haplotypes # 1, 12 and 13 were shared among the individuals of the samepopulation while haplotype 3 was shared between North and Northeast populations were inferred in 20 out of 30 runs 66.66%; . In contK as eachpeak breaking out quite clearly at K = 2 and K= 4  were assigned to any of these two clusters while atK = 4 nine RJFs were admixed .The results indicated a multi-modal value of delta nd K= 4 . Individ4  tree obtained from the microsatellite data formedseveral groups of possible hybrids between the RJFs and DCs , between RJF populations (Hap#6) and between DC populations . Among three haplotypes shared between RJFs and DCs, Hap#14 and19 were predominant in DCs at 60% and75%, respectively. While Hap#10 was sharedby 50% in RJFs or DCs. In the second group, Hap#6 which was shared between RJFpopulations of North (Uttarakhand) and Northeast (Assam) might be the ancestralhaplotype reflecting historic gene flow between these two populations.Interestingly, it was well supported by the results of STRUCTURE , tended to represent most of its genetic diversity across theentire range. Thus our study warrants that Northeast RJF population would be thewild progenitor due to its abundance of genetic variants in both nuclear andmitochondrial genomes from where early radiation of RJFs throughout India would haveoccurred.All the 26 loci were polymorphic and informative for population genetic analysis asthe majority of loci showed a highPIC value > 0.5. Several loci were deviatedindividually from HWE in different populations e.g. four loci in Cent-Southeast and22 loci in North RJF populations due to heterozygote deficit following possibleinbreeding at regional level. Two loci were deviated from HWE across all four RJFpopulations attributed to the significant heterozygote deficit. None of the locuspairs showed significant LD, and independent assortment across the four RJFpopulations. The diversity indices, m et al.  reportedcluster-I; Duars region of North Bengal and the Terairegion of Nepal and India. However, the GENELAND clustering pattern showed fourclusters c ,S2 (Bihar), S3 (Chhattisgarh), S4 (Odisha), S5 (Assam) and S6 , reflecting this cluster-IV to be a genetic reservoir and perhaps a sourceof radiation either due to historic gene flow or physical exchange of the birds.The Bayesian analysis of population assignment grouped all the samples into twoclusters and the shared assignment of Northeast RJFs with North RJFs  to be shared between RJFs and DCs, supporting alonger-term admixture of the species. Among these three haplotypes, frequencies ofHap#14 and 19 were 60% and 75% in DCs suggesting gene flow between RJFs and DCs.This trend, however was supported by a small number of samples probably derived frommale RJFs crossing with DC hens. This explanation was highly reasonable since wehave encountered the mixing behaviour of RJF males with DC hens even in the presenceof DC cocks [et al.[Kauai chickens whose behaviour and morphology overlap withthose of DCs and RJFs, implying these chickens to be feral G.gallus descendent from recent invasion(s) of DCs into anancient RJF reservoir.Interestingly, RJF samples from the State of Uttar Pradesh  were assignedto cluster-IV and RJF samples from the State of Uttarakhand were assigned to cluster-I, irrespective of their physical proximity. There may beseveral explanations for this unusual observation, one possiblity might be therestricted exchange of wild RJFs within the region by the locals for upgrading theirDCs or the other possible reason could be the anthropogenic activities in thisregion, particularly in and around Terai East and West along the Gola river whichmight have affected the movement of free-ranging RJFs within the region, as alsodemonstrated for other animal species such as tigers and leopards . After iividuals ; Table 4 history . To recoet al.[To infer on the threat status of the species undergoing hybridization, we believe itis necessary to sample across the species range as the phenomenon may alter with thecompromised samples or the sampling locality. Kanginakudru et al. concludeRJF wild populations across the range are under extreme pressure of poaching forlocal consumption, habitat loss and genetic hybridization with domestic/feralchickens, it is necessary to monitor the presence/absence and abundance of thisspecies in different parts of the distribution range within as well as outsideProtected Area network. Conservation breeding of ‘genetically pure’ RJFs may beinitiated with inclusion of genetic parameters and captive stocks are required to bescientifically managed for future use such as research and reintroductions.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOC)Click here for additional data file.S5 Table(DOC)Click here for additional data file.S1 Fig.(TIF)Click here for additional data file.S2 FigA, North; B, East; C, Central-Southeast and D,Northeast.(TIF)Click here for additional data file.S3 FigAnalysis was conducted under the spatial model with uncorrelated allelefrequencyincorporating latitude/longitude.(TIF)Click here for additional data file.