Magnaporthe oryzae, the fungus responsible for rice blast disease, has invaded all rice growing areas. Virulent genotypes regularly (re)emerge, causing rapid resistance breakdowns. However, the world-wide genetic subdivision of M. oryzae populations on rice and its past history of invasion have never been elucidated.Inferring invasion routes and identifying reservoirs of diversity of plant pathogens are essential in proposing new strategies for their control. M. oryzae on rice, we analyzed the genetic diversity of 55 populations from 15 countries.In order to investigate the centers of diversity, origin and migration of M. oryzae populations on rice.Three genetic clusters were identified world-wide. Asia was the center of diversity and the origin of most migrations to other continents. In Asia, two centers of diversity were revealed in the Himalayan foothills: South China–Laos–North Thailand, and western Nepal. Sexual reproduction persisted only in the South China–Laos–North Thailand region, which was identified as the putative center of origin of all M. oryzae on rice that matches the past history of rice domestication. This study confirms that crop domestication may have considerable influence on the pestification process of natural enemies.Our results suggest a scenario of early evolution of  Increasing globalization of trade and climatic changes enhance the probability of (re-) emergence of invasive pests. These latter are usually difficult to manage without using pesticides, and represent unprecedented economic and food safety risks . UnderstMagnaporthe oryzae (Mo) is the Ascomycete fungus responsible for the most damaging rice disease world-wide: blast. This model species for the study of host–pathogen interactions , one of the host plants of Mo, probably originated from two independent domestication events involving wild rice O. rufipogon c. 7000 yr bp. These events occurred in two different regions that represent centers of diversification of cultivated rice, and resulted in two subspecies: Os ssp. japonica domesticated in southern China . However, the early history of Mo on cultivated rice, and especially the impact of rice domestication on Mo pestification, is still debated. How far human-mediated movements of infected materials are involved in inter-continental migration, especially between Asia and other continents, has seldom been investigated (Mo on cultivated rice has been suggested due to a single acquisition of pathogenicity (Setaria spp), probably in South China c. 10 000 yr ago where both rice and foxtail millet were domesticated and co-cultivated , we asked: What are the main genetic groups in world-wide Mo populations? Can we localize one  center(s) of genetic diversity? Based on the reproductive mode in the populations analyzed, can we infer the putative center of origin of the pathogen? Can we localize the geographic origin(s) of Mo migrations throughout the world?In sum these studies suggest that the origin of Mo rice strains isolated between 2000 and 2009  with 423, 422, 136 and 391 strains from Asia, Europe, the Americas and Africa, respectively. Fungal strains were obtained after monospore isolation, as previously described by We used 55 world-wide population samples of in vitro crosses as described by Mo requires strains of opposite type and at least one of the strains must be female-fertile (able to produce perithecia). Crosses were performed by confronting the tested strain to female-fertile strains for which the mating type is known (reference strains). Mat1 reference strains were IN1, TH12, CH999 and CH1003. Mat2 reference strains were GY11, TH16, CH997 and CH1019. Tested strains were classified as Mat1 when inducing or forming perithecia with a Mat2 reference strain (and conversely). Tested strains were classified as female-fertile when forming perithecia with reference strains. For 175 additional strains, the mating type was determined by PCR amplification with the primers specific of Mat1 and of Mat2  as the number of alleles that were present only in one population, averaged over the ten markers. The number of multilocus genotypes (MLG) and the index of association Multilocus v1.3 .For each population, the mean number of alleles per locus ty Hn.b.  were calcus v1.3 . The proadegenet package (v1.3-1) for the R software . We used the K-means procedure implemented in the function find.cluster to infer K, the optimal number of clusters, and let K vary between 1 and 60. K was determined using the Bayesian Information Criterion (BIC): if the function BIC = f(K) was U-shaped, then K was the abscissa of the minimum of this function; otherwise it corresponds to the point where the BIC decay rate abruptly changed  was performed after a 20 000 steps burn-in period. No a priori information was used on the assignments of individuals. We determined Ke, the optimal number of clusters, according to Ke−1 and Ke + 1. Individuals were assigned to a cluster if their probability of ancestry in this cluster was over the empirical cut-off of 0.7.We also used the Hn.b and the mean number of private alleles Np were assessed for each genetic cluster as described above.Unbiased gene diversity FST . We visualized the number of alleles shared between Asian clusters using a Venn diagram (package Venn.diagramm of the R software). At the world-wide scale, we calculated the number of MLG shared between different countries. We verified that these MLGs were real clones by calculating Psex, the probability that a genotype arose in several individuals within a population by independent reproduction events .An unrooted neighbor-joining tree of the world-wide populations was built from the pairwise Dindica or japonica) the strains were collected on, the strain mating type (Mat1 and Mat2) and the female fertility status .Chi-squared tests were performed to assess if cluster assignment was independent from the rice subspecies , in different distances classes. I ranges between −1  and 1 . Distances classes were manually selected, class limits being positioned at breakpoints in the range of pairwise distances. The significance of I values was assessed by performing 1000 permutations. Linear regressions of I against distance (or its logarithm), as well as the significance of the regression slopes, were also estimated; the intrapopulation classes  were not considered in these analyses.We evaluated spatial autocorrelation at different spatial scales in Asia using SPAGeDi v1.3 . We calcMagnaporthe oryzae (Mo), the first stage was to study the genetic structure over all populations. We first inferred population subdivision at the global scale and evaluated genetic differentiation between genetic groups using FST. Then we studied the distribution of genetic diversity with regards to genetic structure and geography, within populations and within clusters.In order to infer the centers of diversity of Mo rice strains from 15 countries , except for four individuals . With Structure, only 16 (among which 14 Asian strains) showed admixture signals (mixed ancestry in at least two clusters), but the DAPC assigned these 16 individuals to a single cluster.We genotyped 1372 strains from 55 population samples of FST = 0.41, 0.44 and 0.65 between clusters A and B, A and C, and B and C, respectively) when compared to the average differentiation in phytopathogenic fungi  calculated over all individuals within clusters, was at least two times higher in cluster A than in the other clusters . The mean number of private alleles (Np) was five times higher in cluster A compared to the other clusters .Gene diversity . Because sample sizes were highly different between some populations, and because gene diversity is known to be dependent on sample size (Hn.b. and Np in these 55 re-samplings (with five replicates of this re-sampling procedure). This confirmed that gene diversity was higher in Asian populations (Table S2).ple size , we randHn.b. and Np on these subsamples  had a gene diversity comparable to Asian subsamples but its number of private alleles was much lower. The most diverse subsamples belonging to cluster B and C also originated from Asia.Hence, the higher genetic diversity observed in Asian populations had two explanations: some Asian populations encompassed individuals from the three clusters e.g. CH2 and CH5), and others mainly encompassed individuals from the most diverse cluster, A (e.g. CH1 and LA1). To make the difference between these two potential causes, within each population we grouped strains belonging to the same cluster and calculated  and CH5,Mo compared to the other continents. To localize this center more precisely, we analyzed the genetic structure and the distribution of genetic diversity inside Asia.Hence, Asia was the best candidate to be, or include, the center of diversity of FST between genetic groups. We then studied the distribution of genetic diversity with regards to genetic structure and geography: within populations and within clusters.At the Asian scale, we also inferred genetic structure using assignments methods. We evaluated Structure was congruent with this result, and individual assignments to the four clusters were identical among the 10 replicates. 386 individuals could be assigned to a single cluster, the 37 remaining individuals showing admixture signal. The DAPC achieved to assign these 37 individuals to a single group. Individual assignments were identical with DAPC and with Structure, except for 12 individuals . Nine out of these 12 were assigned to cluster 1 by Structure and to cluster 4 by DAPC, or assigned to cluster 4 by Structure and to cluster 1 by DAPC.The DAPC performed on the 423 Asian individuals revealed that the region was organized in four genetic clusters . The pairwise DA chord distance calculated among the four Asian clusters confirmed that clusters 1 and 4 were more closely related to each other than to clusters 2 and 3 , and for the mean number of private alleles per locus . Hence, clusters 1 and 4 were more genetically diverse than clusters 2 and 3.Consistent with the three-cluster structure at the global scale, gene diversity was higher in clusters 1 and 4 than in clusters 2 and 3 , gave results similar to those obtained at the global scale , Laos  and Thailand (TH) where cluster 4 dominated, and a second region in Western Nepal (NP1 and NP3) where cluster 1 dominated. So, these results allowed us to define these two regions as two centers of diversity.Mo populations pathogenic on rice.We then wondered if one of these centers of diversity could correspond to the center of origin of In order to test whether clusters 2 and 3 could originate from clusters 1 or 4, we compared the number of shared alleles between clusters . We reasoned that any derived cluster should share more alleles with its cluster of origin than with other clusters. The number of shared alleles between clusters 2 and 1 was similar to the number shared between 2 and 4 . The number of shared alleles between clusters 3 and 1 was identical to the number shared between 3 and 4 (29). Thus, following this method, we could not figure out if clusters 2 and 3 are derived from cluster 1 or 4.In organisms that can reproduce both sexually and asexually, the ability to reproduce sexually is believed to be an ancestral state that can be lost in certain conditions. In this case, sexual reproduction is expected in the center of origin rather than in introduced areas . So, we G : N, the proportion of multilocus genotypes (MLG) discriminated, and G : N in populations was 54% ± 23% SD , intermediate in cluster 1  and lowest in clusters 2 and 3  and of female-fertile strains  significantly depended on the cluster of origin. The frequency of Mat1 and Mat2 strains in clusters 1 and 4 was relatively balanced and not significantly different from a random distribution , Laos and North Thailand – should be considered as a putative center of origin of pred.sup . All European/Mediterranean strains but one were assigned to cluster 2 . All the Psex values were highly significant (Table S4), indicating that all the MLG shared between countries were real clones.Twenty MLG were shared between different populations within and between countries. Shared MLG between countries belonged to cluster B and to cluster C Fig.. No MLG These results suggest long intercontinental migrations from Asia towards the other regions of the world, as well as intracontinental migrations. Furthermore, none of the MLG shared between continents were assigned to cluster A. In addition, only few strains assigned to cluster A were found out of Asia. This suggests that most of these migrations did not originate from the most ancestral genotypes, belonging to cluster A, but from more recent genotypes belonging to clusters B and C.Mo in Asia, we evaluated the spatial autocorrelation in this region using Moran's index I. When considering all Asian individuals . We never found any significant linear regression of I against distance (or its logarithm).In order to further address the migration capacities of uals Fig.a, signifster Fig.b, exceptA chord distance between pairs of populations  showed that populations were grouped according to their mosaic composition in the different genetic clusters  as well as at the population scale, except for Indonesian populations. Allelic and gene diversities, and the number of private alleles were (respectively) two, three and four times higher in Asian populations than in populations from Europe/Mediterranean Basin, the Americas or Madagascar. Asian strains formed four genetic clusters that did not strictly match a single Asian country or region. Rather, Asian populations were composed of ‘mosaics’ of these clusters, whose composition corresponded roughly to the geography. Such a mosaic structure, described for example for the fungus Venturia inequalis in its area of origin . Interestingly, the 69 Asian individuals also assigned to this cluster came mostly from two Chinese populations: 25 from CH4 and 26 from CH6. Moreover, we found common MLG between Hungarian strains and strains from CH3 and CH4 populations. There were also common MLG between Spanish strains and CH4 strains. Therefore, Yunnan, where CH3, CH4 and CH6 populations were collected, could likely represent the source of European/Mediterranean populations. A single introduction in the European/Mediterranean area is supported by the low genetic diversity observed in this region and by the fact that only one mating type (Mat1) is present there. The fungus was probably subsequently dispersed throughout Europe from a single, still undetermined entry.Indonesian and Chinese populations CH2 and CH7 had common MLGs, showing possible exchange between the two regions. Madagascan populations had a single origin and belonged to the same cluster as the Chinese population CH7 and Indonesian populations. In addition, a common MLG was detected between Madagascar and Indonesia. So, either the Indonesian and Madagascan populations originated from the same genetic pool independently, or the Madagascan strains migrated from Indonesia. We favour the second hypothesis because the diversity observed in Madagascar is lower than in Indonesia, and because it matches the history of human migrations in these areas. The first human groups arrived in Madagascar from Indonesia at least 2000 yr ago , and shared common MLGs with Asian populations: CL1 with CH2 and CH7 from Hunan province (China) and with ID1 and ID2 from Indonesia, and GY  with the Thailand population. So, The two North American populations obviously had different origins. All USA2 strains belonged to cluster B, like European strains, with two MLG shared with Spanish strains. So, this population might have originated either from the same Chinese genetic pool that migrated towards Europe, or directly from Europe itself. The USA1 population likely resulted from multiple introductions from different gene pools because it gathered strains belonging to two clusters (A and B): this suggests either two introductions from Asia, or one from Asia and one from Europe. Similar patterns of multiple introductions have already been described for other phytopathogenic fungi .The world-wide organization of genetic diversity agrees with stochastic human-driven migrations outside Asia. Inside Asia, spatial autocorrelation analyses did not reveal any significant deviation from random spatial pattern. Hence, inside Asia, whatever the distance range considered , and whatever the genetic origin of individuals, migration events were also probably purely stochastic, and closely linked to movements of human groups or to seed exchanges. This agrees with previous knowledge on the short-distance natural migration capacities of Mo populations. The higher diversity observed in clusters 1 and 4 may be explained by the higher diversity of the host in the Asian areas where clusters 1 and 4 are found. Indeed, both clusters encompassed mainly (179/226) strains collected on rice grown in upland conditions, where many traditional and diverse varieties were maintained (at least in the Asian regions sampled). Either the host diversity maintained directly pathogen genotypic diversity by selection, or indirectly by maintaining sexual reproduction.Our results support the hypothesis, also proposed by indica or japonica type in the area sampled. In Asia, cluster 3 was significantly over-represented in regions where indica rice is prevalent (Table S5a), and conversely all individuals but one from cluster 4 were sampled in regions were japonica rice is prevalent. World-wide, cluster B is prevalent in areas where japonica varieties – especially temperate – are grown (Table S5b), while cluster C is over-represented in areas where indica varieties are grown. Pathogenic specialization of blast on the different rice subspecies, suggested by indica and japonica rice subspecies were domesticated independently in two Asian areas, and our results show that population subdivision of blast in Asia matches this domestication process. Thus, following rice domestication, Mo possibly adapted independently to these two subspecies leading to differentiation in two clusters (B and C). This subdivision was maintained when these clusters spread into different countries because Mo was probably introduced with the rice varieties it was adapted to, and because different rice subspecies are used in different agrosystems, limiting the possibility of cross-adaptation between strains of the B cluster on indica rice varieties and of A cluster on japonica.We also found that membership to a particular genetic cluster was significantly associated with the prevalence of varieties of Mo could have evolved as a major pathogen on cultivated rice through a host-tracking process, following a host shift from an unknown plant towards wild rice. Host-tracking – that is, the coevolution of the host and the pathogen during domestication (Altogether, our results suggest that tication  – implieMo populations outside Asia derived recently from a limited set of founders. In addition, we found a nonrandom distribution of mating types and of female-fertile strains in the different clusters (χ2 = 441, P = 1.7 × 10−96, df = 2 and χ2 = 600, P = 5.1 × 10−131, df = 3, respectively; Table This study supports the hypothesis suggested by Mo/rice pathosystem, the independent domestication of indica and japonica rice subgroups led to the appearance of two genetic groups of the pathogen. Such a structure, probably accompanied by a specialization of the pathogen on the different rice subspecies, could be exploited to develop new strategies of deployment of resistance genes. Wild species have been proposed as a source of resistance genes to improve related crop species (Our study provides new insights on the native areas, diversity reservoirs and invasion routes of rice blast. We showed that several independent events of intercontinental migrations occurred which are likely linked with the transportation of infected materials. In a context of intense global exchanges, knowledge about these past events should lead to increased vigilance on the risk of introductions of new genotypes of the pathogen through the exchanges of rice seeds. Our work also exemplifies the role of plant domestication in shaping the population structure of plant pathogens. For the  species . A simil


The advent and democratization of next generation sequencing and genotyping technologies lead to a huge amount of data for the characterization of population genetic diversity in model and non model-species. However, efficient storage, management, cross-analyzing and exploration of such dense genotyping datasets remain challenging. This is particularly true for the bovine species where many SNP datasets have been generated in various cattle populations with different genotyping tools.http://widde.toulouse.inra.fr). As a first illustration, we hereby describe its first version dedicated to cattle biodiversity, which includes a large and evolving cattle genotyping dataset for over 750,000 SNPs available on 129 (89 public) different cattle populations representative of the world-wide bovine genetic diversity and on 7 outgroup bovid species. This version proposes an optional marker and individual filtering step, an export of genotyping data in different popular formats, and an exploration of genetic diversity through a principal component analysis. Users can also explore their own genotyping data together with data from WIDDE, assign their samples to WIDDE populations based on distance assignment method and supervised clustering, and estimate their ancestry composition relative to the populations represented in the database.We developed WIDDE, a Web-Interfaced Next Generation Database that stands as a generic tool applicable to a wide range of species and marker types  contains supplementary material, which is available to authorized users. Next Generation Sequencing (NGS) and genotyping (NGG) technologies have revolutionized variant genotyping and now allow cost-effective and genome-wide characterization of genetic diversity in a growing number of species including non-model species .In livestock species, based on low to high density SNP chips, a growing amount of genomic information on several dozens of local breeds have been generated as exemplified by cattle population studies –7 and stWe thus developed WIDDE, a Web accessible NoSQL Database, dedicated to the storage and management of dense genotyping datasets , coupled with various user friendly tools for (i) data selection, (ii) data exploration, (iii) export into various popular formats and (iv) population assignment. Via a web interface managing access to public (freely accessible) and private (accessible via login and password) data, users can therefore select (on a population and/or marker location basis) data subsets, perform basic data quality checking and standard population genetic analyses via a test for Hardy-Weinberg equilibrium and principal component analysis (PCA), and export the resulting datasets into various popular formats. Users can also jointly analyze their own genotyping data with WIDDE data subsets, in order to explore genetic proximity between populations by allele sharing distance (ASD) calculation, PCA and supervised clustering, to perform an estimation of ancestry composition of the samples and population assignment.WIDDE functionalities are illustrated on a large and evolving cattle dataset which is representative of the world-wide bovine genetic diversity.http://www.mongodb.org/) was chosen as an open-source solution supporting complex queries and easy scalability. MongoDB achieves relationship management by providing a concept of collections able to store documents with a flexible structure and that can be embedded in one another. Thereby, it uses BSON  as data storage format. Defining a data structure for use with NoSQL relies on preliminary analysis of the queries that the targeted application will need to execute. Therefore, WIDDE data structure was centered on variants . Opal Toolkit  allows submitting jobs to a computer cluster running Sun Grid Engine (SGE), to perform either PCA or individual assignment. The client interface was developed in JSP with the jQuery JavaScript library (http://jquery.com/), and relies on the D3.js library for PCA result display (http://d3js.org/).The server application was written in Java making use of the industry-standard Spring framework . We relied on a simplified version of the EM algorithm described in  and 14]14] to esand 4 in ) using dThe first WIDDE version includes bovine genotyping data, obtained with medium to high density Illumina SNP chips 54K and 770K), from different breeds arising from biodiversity studies 70K, from–18. GenoOnly cattle populations with at least 5 individuals were stored in WIDDE and those with at least 15 individuals were chosen as reference populations for population assignment. SNPchiMp was used to obtain consistent marker-lists and identify marker synonyms  and all At the time of writing, final data imports into WIDDE of any new population can be proposed by contacting administrators.The WIDDE application has four main functionalities: (i) storing high density genotyping data for hundreds to thousands of individuals each characterized by their population of origin and genotyping projects, (ii) selecting, filtering and exporting genotyping data subsets in several formats  for downstream analyses, (iii) exploring directly intra-species genetic diversity via PCA and (iv) exploring user-provided genotyped individuals with WIDDE individuals by assigning them to WIDDE reference populations. This latter step includes a visual assignment through PCA, a distance-based assignment without calibration and an estimation of samples’ ancestry composition relative to reference populations by supervised clustering –14. WIDDP < 0.001 by default). Last, a filter on Minor Allele Frequency computed over all populations of the selected dataset can discard poorly informative markers (MAF < 0.01 by default).The WIDDE website consists in five sections accessible from the homepage. The “Home” section provides a concise description of the database and gives general information about the tool. The “Tutorial” section contains a didactic step-by-step tutorial illustrated with several screenshots. The “Data sources” section lists the different references and sources of data included in the database and the “Contact us” section contains the name, affiliation and email addresses of the main people involved in database conception and maintenance. The “Cattle data” section gives access to the actual application dedicated to bovine species. At the top of the application’s screen, a logo and three icons allow respectively to (i) return to the homepage, (ii) visualize populations’ origin on a map, (iii) upload data to launch population assignment and user’s genotyping data exploration, and (iv) authenticate to have access to private genotyping data. At the middle of the screen, a user friendly Web interface contains three panels for individual selection, marker selection and quality filtering, successively appearing when previous selection is valid Fig. . Indeed,plink [, eigenstrat [Before or after quality filtering, genotyping data can be exported in popular formats .smartpca software [smarpca is then launched on a computer cluster. Individuals are then plotted by default on the first factorial plan in a new window allowing selection of other components  as well as smartpca output files  may be downloaded.Users may also explore genetic diversity via an online PCA performed with the software . The datnts Fig. . As thisplink format with nucleotide letters) with WIDDE public genotyping data. This process, which can be time consuming, is also detached from the web interface and runs on the mentioned computer cluster via Opal and SGE. Based on SNPs in common, user can choose (i) to perform a PCA of genotyping data combined with public genotyping data stored in WIDDE, and (ii) to assign these new individuals to populations of the public reference dataset based on ASD calculation and on estimation of ancestry proportions by supervised clustering [asd_summary.tsv and asd_results.tsv), (ii) a summary and the complete results of the supervised clustering (ancestry_summary.tsv and ancestry_results.tsv) and (iii) the merged dataset used in the analysis in eigenstrat format.By clicking on the assignment icon, users also have the option through an upload interface to analyze their own genotyping data , that were thoroughly selected and curated. These various local cattle populations are representative of the bovine genetic diversity and belong to the three main cattle groups, i.e. European (EUT) and African (AFT) taurine (Bos taurus) and zebus . Figure As an illustration of WIDDE functionalities, we hereby detailed the cattle module currently containing 783,640 SNPs and 3951 (2827 publicly available) individuals belonging to 129 (89 public) different cattle populations and 8 (7 public) populations of outgroup species . As a result, the assignment rate (for a given ancestry threshold t) was defined as the proportion of individuals assigned to a population and the misassignment rate corresponded to the proportion  that were assigned to a population different from their population of origin.A world reference dataset including all WIDDE public populations representative of the world-wide genetic diversity of the bovine species with at least 15 individuals was defined to assign user-uploaded individuals to WIDDE public populations. To illustrate this step, we estimated ancestry proportions of 2250 individuals from 45 public populations of the world reference dataset against the world reference dataset itself using supervised clustering. We started from 33K SNP , 10K SNP and 1K SNP randomly chosen within the 33K list, and considered different EM stopping criteria . Based on these supervised clustering results, the proportion of assigned individuals and the misassignment rate were then calculated for different ancestry thresholds ranging from 0 to 1  and Tarentaise  . Tarenta. The assignment method based on supervised clustering is especially accurate when the true population of origin is included in the reference dataset [Geneclass 2.0 software might be easy to generate and will be available shortly [Our results demonstrate the utility of WIDDE in assigning any individual to the genetically closest population and the ability to estimate the ancestry proportion of any individual to the WIDDE reference populations dataset . When th shortly .In summary, the NoSQL next generation database WIDDE represents a biodiversity database able to manage and explore a large amount of genotyping data, and to assign new individuals to populations stored internally. Thus, WIDDE is a generic tool applicable to a wide range of species and marker types. It is a versatile tool and further version of the database will include additional output formats and new tools dedicated to genetic diversity exploration. The first module, WIDDE-cattle, described here, represents the first database dedicated to cattle biodiversity and SNP genotyping data, which allows users to explore not only the WIDDE dataset but also their own genotyping data. It will be very useful for researchers interested in cattle genetic diversity and will be extended to other livestock species shortly.http://widde.toulouse.inra.fr for research and academic use.WIDDE is deployed on our institutional website at


Salmo trutta L.) populations exist in both metal-impacted and relatively clean rivers. We used microsatellites to assess the genetic impact of mining practices on trout populations in this region. Our analyses demonstrated that metal-impacted trout populations have low genetic diversity and have experienced severe population declines. Metal-river trout populations are genetically distinct from clean-river populations, and also from one another, despite being geographically proximate. Using approximate Bayesian computation (ABC), we dated the origins of these genetic patterns to periods of intensive mining activity. The historical split of contemporary metal-impacted populations from clean-river fish dated to the Medieval period. Moreover, we observed two distinct genetic populations of trout within a single catchment and dated their divergence to the Industrial Revolution. Our investigation thus provides an evaluation of contemporary population genetics in showing how human-altered landscapes can change the genetic makeup of a species.Humans have exploited the earth's metal resources for thousands of years leaving behind a legacy of toxic metal contamination and poor water quality. The southwest of England provides a well-defined example, with a rich history of metal mining dating to the Bronze Age. Mine water washout continues to negatively impact water quality across the region where brown trout ( The exploration for and exploitation of metals have played a crucial role in human history. Although metals are natural constituents of the earth's crust Wedepohl , their pNatural populations are predicted to experience changes in genetic diversity and population structure, especially through genetic drift, gene flow and/or selection, but these genetic shifts have been shown to be amplified by anthropogenic interference  populations are known to reside in rivers across southwest England, in both so-called clean and metal-impacted rivers. Due to the underlying geology  meaning that genetic substructure may exist not only between, but also within a single system  Is genetic diversity reduced in metal-impacted populations?; (ii) Can these patterns be explained by genetic evidence of population bottlenecks?; (iii) Do metal-impacted populations show distinct genetic structuring due to reduced gene flow and genetic drift?; (iv) Given the long history of mining in the region, what is the most likely genetic origin of contemporary metal populations?; and finally, (v) Can the genetic changes we observe be linked to periods of increased mining activity? With a potential tie to human-mediated evolutionary change, analysis characterizing a temporal component to these genetic changes in a historical context is vital  were included for study  were sampled as part of routine Environment Agency monitoring programs; briefly, fish were anesthetized using either clove oil or benzocaine (10 g/100 mL ethanol) diluted 1:2000 in river water prior to adipose fin clip removal. Fin clips were placed into 95% ethanol for storage.Twenty-two putative neutral microsatellite loci were screened: Ssa85 and Ssa197 , 3 μL RNase-free water, 5 μL of QIAGEN HotStarTaq Plus Master Mix, and 1 μL of primer mixture, in a total of 8 microsatellite multiplexes  was calculated using FSTAT 2.9.3 (Goudet HO) and expected heterozygosity (HE) were calculated using GENALEX 6.5 . BOTTLENECK was run for 10 000 iterations. To further explore evidence of population bottlenecks, we also calculated the M-ratio, which is the ratio of the number of alleles to the range in allele size. This is expected to be lower in bottlenecked populations due to the loss of rare alleles (Garza and Williamson E) between 50 and 100; theta (θ) thus varied from 0.1 to 0.2. We used a mutation rate of μ = 5 × 10−4 and used pg = 0.22 (pS = 0.78) and δg = 3.1, as suggested by Peery et al. . We also used the statistic lnRV (Schlotterer FST outlier simulations (Excoffier and Lischer FCT) with 100 demes simulated per group and 10 simulated groups. We considered a locus to be under selection if the P-value was <0.01.We used three tests to detect whether any of the microsatellite loci showed evidence of selection. We used Bayescan v 2.1  ranging from 1 to 20. Ten independent runs of the program were performed using the population admixture model and correlated allele frequencies. The most likely number of population clusters was determined using the ΔK statistic , based on the Cavalli-Sforza and Edwards chord distance, DE). DIYABC outputs averages for each event in generations. We converted generations into dates using a generation time for brown trout of 4 years  as implemented in the program DIYABC . Default minimum and maximum priors (10–10 000) were used for all parameters ; db—duration of bottleneck).Based on the results of the population structure analyses of this study, we conducted preliminary investigations based on simple scenarios comparing the general clean group to each metal group: (i) Clean & Hayle, (ii) Clean & Red River, and (iii) Clean & Crowlas/Trevaylor. For the River Hayle analyses, we specified two time (t)-splitting points, t1: the split within the River Hayle and t2: the split of the Hayle populations from the clean populations. Conditions were set so that t2 > t1. For the Red River and Crowlas/Trevaylor investigations, only one time-splitting point was specified (t1), defining the split between the metal-impacted group and the clean group. For each of these independent population comparisons, we passed two scenarios, each composed of a reference table consisting of 10Based on the outcome of the preliminary investigations, we constructed more complicated scenarios that included genotypes from all of the populations, organized into 5 population groups: (i) clean populations; (ii) RR1&2 (Red River); (iii) CRO/TREV1&2 (Crowlas and Trevaylor); (iv) HAY1&2 (downstream Hayle); and (v) HAY3&4 (upstream Hayle). We constructed 3 groups of hypotheses-testing scenarios, the details of the topology of these can be found in M, and two-sample summary statistics included FST and mean classification index. We used the default priors for the mutation model . We performed model checking using a PCoA, and posterior probabilities of each scenario were then calculated using a logistic regression of 1% of the simulated data. After bias and precision estimations, model checks were performed for each scenario, using the summary statistics of Guillemaud et al.  from each group with the highest posterior probability. These scenarios were then compared in a final run, simulating 106 datasets per scenario, using the same priors, summary statistics, and model checks as outlined above.For all investigations and scenarios, we used the summary statistics of Cornuet et al. : One-samd et al. , which iA total of 700 individuals from 15 brown trout populations were genotyped. Fifty-nine individuals (∽8.4%) were removed due to sibling effects within the sampling. The 24 primer sets amplified a total of 25 loci, with the primers for One102 amplifying two loci with nonoverlapping size ranges (designated One102a and One102b). There were potential genotyping errors at 14 loci. We removed locus CA053293, as these inaccuracies occurred in 7 of the 15 populations. Evidence of homozygote excess and null alleles was not consistently detected in any other loci.Indication of linkage disequilibrium (LD) between sasa-UBA & sasaTAP2B was statistically significant in 12 of the 15 populations. There is evidence that these loci are in fact physically linked . Allelic richness (AR) varied between 6.2 (HAY1) and 11.15 (CAM2). Similar patterns were observed for measures of heterozygosity: Expected heterozygosity (HE) varied from 0.61 (HAY2) to 0.78 (CAM2 & FAL) and observed heterozygosity (HO) ranged between 0.61 (HAY2) and 0.79 (CAM2). In particular, genetic diversity measures showed marked differences between the River Hayle and the River Camel, the mouths of which both flow out of north Cornwall; approximately 55 km from one another.Metal-impacted populations had lower genetic diversity in comparison with populations from the clean sites Table. These dAR = 0.005; HE = 0.001; HO = 0.001) but also to other metal-impacted populations . Sites downstream of the Godolphin region  had lower genetic diversity, compared to the two sites upstream , although these differences were not statistically significant. In particular, there was a marked difference in genetic diversity between the two sites straddling the metal region, which are separated by just 8 km; the upstream site (HAY3) had the highest genetic diversity , whereas the site immediately downstream (HAY2) had the lowest genetic diversity (for HE & HO).Across the metal populations, TREV1 had unusually high levels of genetic diversity, more similar to levels observed in clean populations. The River Hayle had very low genetic diversity when compared not only to the clean populations , and also for other metal populations  . Furthermore, these values were also lower than the critical value of 0.68 proposed by Garza and Williamson .ean Table. Across lliamson . This melnRV statistic . All pairwise FST values were statistically significant (P < 0.05), except for HAY1 and HAY2. Within the River Hayle, the global FST was 0.031. The highest FST was between the HAY2 and HAY4 sites, FST = 0.029 (P = 0.001), and the lowest was between HAY1 and HAY2, FST = 0.006 (P = 0.622). Across the Godolphin middle region, the pairwise FST between the downstream and upstream Hayle populations was 0.021 (P = 0.001).Global K = 3 : Group 1 (green) comprised CAM1, CAM2, GAN1, GAN2, FAL, TRES, RR1, and RR2; Group 2 (red) comprised the four Hayle populations (HAY1-HAY4); and Group 3 (blue) included the CRO, TREV1, and TREV2 populations . Using a generation time of 4 years, this split occurred approximately 960 years ago, c.1050 AD  of the Red River populations reduced by ∽34% and the Trevaylor and Crowlas populations actually showed a small (∽4%) increase in NE. The subsequent split within the Hayle (t1) occurred approximately 155 years ago, c.1860 AD . The River Hayle populations experienced a ∽70% reduction in NE. This reduction is the most severe of all population declines calculated , all population-group comparisons showed that the posterior probability of Scenario 2 was higher, and therefore, it was more likely that population bottlenecks occurred prior to the generation of each metal-impacted population. This is further confirmed by the measures of genetic diversity Table. Four scThe question of whether the genetic patterns of natural populations can be altered by human disturbance is a salient issue in modern ecology and conservation. We used a panel of microsatellites to investigate the capacity of metal mining to act as a driver of genetic change in brown trout populations, on both a local and a regional scale. We explored both the genetic diversity and differentiation of trout occupying metal-impacted rivers compared to trout from relatively unpolluted control sites. Using approximate Bayesian computation (ABC), we have placed time points on the demographical changes of these populations to elucidate how present-day genetic patterns have been affected by historical anthropogenic interference.The DIYABC analyses revealed several insights into the genetic makeup of contemporary trout populations in relation to increased historical mining activity. Firstly, our analyses revealed that the split between Hayle trout populations upstream and downstream of the highly contaminated Godolphin region of the river occurred approximately 155 years ago, c.1860. The peak exploitation of mines in the Godolphin region was from 1815 to 1850 Atkinson . AnalysiThe second identified split t2, Table, Table2 Tin streaming had a huge impact on environmental geochemistry , an M-ratio of 0.76 calculated i n other studies of salmonids , tests for demographic declines using BOTTLENECK showed that about half of populations had declined, whereas M-ratio values suggested all populations had suffered long-term declines as a result of hunting pressure  from two major mining regions in Canada showed distinct population structuring and reduced genetic variation that was correlated with cadmium concentration, but no relationship was found with levels of copper contamination , only 2.8% of loci were found to be under directional selection in Baltic populations  to undergo selective sweeps (Nosil and Feder The River Hayle provided an ideal system for identifying the effects of metal contamination on trout at a local scale. We revisit previous findings, which concluded that although Hayle water had a negative effect on metal-naïve fish, there were no reductions in genetic diversity and within-river variation was not structured on the basis of metal contamination (Durrant et al. Our analyses of the within-genetic structuring of the Hayle reveal that the upper and lower regions are highly spatially structured, despite being just 7.7 km apart. While olfaction is a critically important behavior in salmonids and is known to be negatively affected by metal ions (Hansen et al. Evolutionary change can be associated with human activities, and such disturbance history has been suggested to be influential on the genetic architecture of natural populations (Stockwell et al.


Aime-C, Aime-F, Aime-I, and Aime-L) and 8 microsatellites to infer patterns of genetic variation in the giant panda (Ailuropoda melanoleuca) and to further define conservation units.Evaluating patterns of genetic variation is important to identify conservation units  in endangered species. While neutral markers could be used to infer population history, their application in the estimation of adaptive variation is limited. The capacity to adapt to various environments is vital for the long-term survival of endangered species. Hence, analysis of adaptive loci, such as the major histocompatibility complex (MHC) genes, is critical for conservation genetics studies. Here, we investigated 4 classical MHC class I genes  from 218 individuals obtained from 6 populations of giant panda. We found that the Xiaoxiangling population had the highest genetic variation at microsatellites among the 6 giant panda populations and higher genetic variation at Aime-MHC class I genes than other larger populations . Differentiation index (FST)-based phylogenetic and Bayesian clustering analyses for Aime-MHC-I and microsatellite loci both supported that most populations were highly differentiated. The Qinling population was the most genetically differentiated.Overall, we identified 24 haplotypes (9 for The giant panda showed a relatively higher level of genetic diversity at MHC class I genes compared with endangered felids. Using all of the loci, we found that the 6 giant panda populations fell into 2 ESUs: Qinling and non-Qinling populations. We defined 3 MUs based on microsatellites: Qinling, Minshan-Qionglai, and Daxiangling-Xiaoxiangling-Liangshan. We also recommended 3 possible AUs based on MHC loci: Qinling, Minshan-Qionglai, and Daxiangling-Xiaoxiangling-Liangshan. Furthermore, we recommend that a captive breeding program be considered for the Qinling panda population. Evolutionary and conservation biologists are concerned with how genetic variation is maintained within populations of endangered species, especially within small and isolated populations . The assAiluropoda melanoleuca .The authors declare that they have no competing interests.SGF conceived the ideas for the study; YFG collected the samples; YZ and BY performed the experiments; YZ and QHW analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.ST (A) and G’ST (B) for MHC class I loci and microsatellites.Population pairwise FClick here for fileMean values of log probability of L(K) and delta K over 10 runs for each K value. (A) MHC class I loci and (B) microsatellites.Click here for fileLevels of genetic diversity estimated from the 8 microsatellite loci for 6 wild populations of giant panda.Click here for fileInformation on the giant panda samples analyzed in this study.Click here for fileAime-MHC class I genes.Locus-specific primers used to amplify exon 2, exon 3, and the longer exon 2–3 fragment from Click here for fileDetails of PCR conditions for MHC genes and microsatellites.Click here for filePrimers for the 8 polymorphic microsatellite loci used in this study.Click here for file


MDM2 promoter SNP285C is located on the SNP309G allele. While SNP309G enhances Sp1 transcription factor binding and MDM2 transcription, SNP285C antagonizes Sp1 binding and reduces the risk of breast-, ovary- and endometrial cancer. Assessing SNP285 and 309 genotypes across 25 different ethnic populations , the incidence of SNP285C was 6-8% across European populations except for Finns (1.2%) and Saami (0.3%). The incidence decreased towards the Middle-East and Eastern Russia, and SNP285C was absent among Han Chinese, Mongolians and African Americans. Interhaplotype variation analyses estimated SNP285C to have originated about 14,700 years ago . Both this estimate and the geographical distribution suggest SNP285C to have arisen after the separation between Caucasians and modern day East Asians . We observed a strong inverse correlation  between the percentage of SNP309G alleles harboring SNP285C and the MAF for SNP309G itself across different populations suggesting selection and environmental adaptation with respect to MDM2 expression in recent human evolution. In conclusion, we found SNP285C to be a pan-Caucasian variant. Ethnic variation regarding distribution of SNP285C needs to be taken into account when assessing the impact of MDM2 SNPs on cancer risk.The  MDM2 (Mouse Double Minute 2 homolog) gene plays a key role in cell cycle control as a regulator of p53 activity (the protein coded for by the TP53 gene). It also interacts with several other major proteins involved in cell cycle control and growth arrest, such as pRb and E2F1 ) [31,MDM2 promoter, respectively [While different studies have shown SNP309 and SNP285 to affect the risk for different cancer forms , 11, anyectively . FurtherMDM2 SNPs on cancer risk across different ethnic and geographic populations.In conclusion, detailed mapping of the prevalence of SNP285C in multiple populations indicates this variant to be spread through most populations of Caucasian origin. The status of this cancer risk reducing SNP needs to be taken into account when assessing the impact of other In the present project, we screened SNP285 and SNP309 genotypes in 5,937 healthy individuals, representing 21 different populations / cohorts. The populations and the number of individuals included in each of them are listed in Table MDM2 SNP285 and SNP309 status from genomic information from the 1000 Genomes project (www.1000genomes.org). Here, we retrieved SNP status from multiple ethnic groups mounting up to a total of 1,094 further individuals.In order to validate our findings, we performed data mining and extracted information regarding The Finnish, Estonian, Lebanese, German and Chinese samples were genotyped for SNPs 285 and 309 in their respective countries by PCR amplification and Sanger sequencing (the three former populations) or restriction fragment length analysis (the two latter populations), as previously described , 37-39. MDM2 SNP285 and 309 status using the LightSNiP technology (TIB-MOLBIOL) on a LightCycler 480 instrument (Roche) in our laboratory in Bergen, Norway. These amplifications were performed in a final reaction volume of 10 μl, containing 1 μl LightCycler® FastStart DNA Master HybProbe mix (Roche Diagnostics), 0.5 μl LightSNiP mix (TIB MOLBIOL), 3 mM MgCl2 and 10 - 50 ng DNA. The thermocycling and melt curve conditions were set according to the optimized protocol provided by TIB MOLBIOL: 10 minutes initial denaturation / activation at 95 °C, followed by 45 cycles of denaturation at 95 °C for 10 seconds, annealing for 10 seconds at 60 °C and elongation at 72 °C for 15 seconds. Subsequently, the high resolution melting (HRM) step was performed, starting with an initial denaturation at 95 °C for 30 seconds, followed by melting from 40 °C to 75 °C with a ramp rate of 0.19 °C/sec and a final cooling step at 40 °C for 30 seconds. The HRM curve profiles were then analyzed using the Melt Curve Genotyping module in the LightCycler® 480 software version 1.5. Both Light SNiP assays (for SNP285 and SNP309) had identical thermocycling conditions.The remaining samples were analyzed, for both For assay quality control, 3 % of the samples analyzed by LightSNiP technology had their genotypes validated by Sanger sequencing. The concordance between the two methods was 100%, assuring no bias in the data sets based on method of genotyping.MDM2 promoter P2 were genotyped by PCR amplification and size analyses of the amplified products. The STRs and the primers used for amplification are listed in MDM2 SNPs 285, 309 and 344.14 STRs surrounding the Haplotypes were imputed from the SNP and STR genotype data, using the Arlequin software v3.5 .21-(CA)13 at two STRs D12S1680 and D12S1693 for which LD with SNP285C was statistically significant was identified and used for an estimation of the age of SNP285C. We applied the formula g = log δ / log (1 - θ) [MDM2 gene and the “marker” is the two STRs with LD haplotype. To assess the degree of LD, we applied the formula δ = (PD – PN) / (1 – PN) [D is the frequency of the associated alleles on the diseased chromosome (bearing SNP285C in this case) and PN is the frequency of the same allele on the control chromosome (not bearing SNP285C). D12S1680 and D12S1693 are located in the same linked cluster on genetic Marshfield map and so the haplotype (CA)21-(CA)13 was used as a single associated allele in the formula. CI for δ was calculated as previously described [The only haplotype (CA) (1 - θ) , where g(1 – PN) , where Pescribed .www.1000genomes.org). We extracted SNP-data from 288 individuals, including all Caucasian individuals (possible carriers of SNP285C) except for Finns (where the frequency of SNP285C was expected to be very low). From these individuals, the genotypes of 49 SNPs , using SPSS software, version 18. All p-values given are two-sided, and p-values obtained by the Fisher's exact tests are given as cumulative.Comparisons of genotype distributions between population groups were performed by Fisher's exact tests (2x2 comparisons) and Chi-square tests (2x


Brassica rapa) before and after an extended drought in California. We found limited genetic differences between pre-drought ancestors and post-drought descendants, indicating that the two populations did not become more genetically similar, despite a greater overlap in reproductive timing after the drought. The findings of this study contribute to an understanding of the genetic impacts of climate change, which is critical in optimizing conservation efforts.Climate change can have widespread and devastating impacts, but little is known about the effects of this change on the genetics of natural populations. We examined the genetics of two populations of an annual plant ( Brassica rapa that evolved more synchronous flowering times over 5 years of drought in southern California. Using plants grown from seeds collected before and after the drought, we estimated genetic parameters within and between populations and across generations. We expected that with greater temporal opportunity to cross-pollinate, due to reduced phenological isolation, these populations would exhibit an increase in gene flow following the drought. We found low but significant FST, but no change in FST or Nm across the drought, in contrast to predictions. Bayesian analysis of these data indicates minor differentiation between the two populations but no noticeable change in structure before and after the shift in flowering times. However, we found high and significant levels of FIS, indicating that inbreeding likely occurred in these populations despite self-incompatibility in B. rapa. In this system, we did not find an impact of climate change on gene flow or population structuring. The contribution of gene flow to adaptive evolution may vary by system, however, and is thus an important parameter to consider in further studies of natural responses to environmental change.The impact of environmental change on population structure is not well understood. This study aimed to examine the effect of a climate change event on gene flow over space and time in two populations of  Climate change is a major environmental concern and one of the greatest threats to biodiversity .Climate change can act as a strong selective pressure in natural populations . SeveralGenerally, gene flow is expected to reduce local adaptation by homogenizing populations found in differing environments, or by spreading detrimental alleles across populations . HoweverBrassica rapa (field mustard). The drought, lasting from 2000 to 2004, corresponded to a shift to earlier mean flowering time in both populations, causing an abbreviated growing season and resulting in more synchronous flowering times between the populations following the drought  and June 2004 (immediately following the drought), at the end of the growing season for both populations in these years. A parent-offspring analysis from a greenhouse study demonstrated an additive genetic basis for this evolutionary change in flowering time .The current study focuses on the impacts of the change in flowering times on the genetics of these natural populations. We analysed the population genetics of this system before and after the drought to examine genetic structuring and gene flow across this phenological change. While previous studies have used population genetic data to infer historic levels of gene flow and genetic structure, this study is unique in using the ‘resurrection approach’ . Flowers are pollinated by many species of vagile insects, such as bees and flies, allowing for the dispersal of pollen across these insects' foraging range (The populations were located in upper Newport Harbor Back Bay (BB) and near the San Joaquin Marsh Arboretum (Arb), in Irvine, CA, USA Fig. . The twoSeeds collected from individuals in the Arb and BB populations in 1997 and 2004 were stored at 4 °C. Over 10 000 seeds were collected from individuals across both populations to obtain representative samples of the gene pools. Seeds from this stock were grown for tissue collection on light carts in the Franks laboratory at Fordham University. Plants were watered daily and fertilized weekly yielding 259 total samples  . DNA was extracted using a Qiagen DNeasy plant kit following manufacturer's DNeasy 96 Protocol. This extraction process yielded 200 µL of eluted DNA, which was stored at −20 °C.Supporting Information], M13F(-40) [D3] or M13R [D4]) corresponding to the tag sequence added to the 5′ end of the initial forward primer. This was included in a reaction with 0.5 µL of 1 : 100 diluted PCR product from the first PCR, along with RNase-free water and AmpliTaq Gold 360 Master Mix. This second PCR followed the same cycling protocol as the first. PCR products were run on 1 % agarose gel to confirm the presence of target microsatellite fragments. Fragment length was then analysed with a Beckman Coulter CEQ 8800 using Genetic Analysis System v.9.0 . Samples were run with 0.5 µL of Beckman Coulter 400 bp Size Standard and PCR product at volumes according to their fluorescent label: 2.5 µL of M13F(-20), 2.5 µL of M13F(-40) and 1.5 µL of M13R. Alleles were scored using GENEMARKER v.1.90 .He), observed heterozygosity (Ho) and inbreeding coefficient (FIS) for each locus. ARLEQUIN v.3.5.1.3  and FST (fixation), are used to describe the fixation of alleles for individuals in a population, and for populations in a metapopulation, respectively  across all loci, the number of private alleles (PA), A, ectively . Severaltellites . In addiated ϕST , which t related , we usedFST and ϕST, as well as AMOVA were performed in GENALEX. To determine whether levels of fixation and gene flow changed across this period, we compared FST values across these years. Significance was established using 95 % confidence intervals (CI) as determined through 1000 bootstrapping replicates to estimate FST using GDA v.1.1 . Out of 180 total locus pairs, 22 pairs across all populations showed LD. However, these locus pairs were not linked in each population. The estimated frequency of null alleles ranged from <1.0 × 10−4 to 0.3558 for all loci. The loci in HWE were found to have a lower frequency of null alleles. Therefore, supplementary analyses performed using only loci in HWE, also minimized the influence of allelic dropout.Across the populations, the loci analysed showed a mean polymorphism of 95 % per population Table . Betweenon Table . Of the Supporting Information][see . The greater proportion of individuals assigned to the genetic cluster represented by yellow in BB as compared with Arb indicates some genetic distinctiveness between these locations in both years. However, every individual sampled is estimated, in some proportion, to be assigned to each of these three clusters   (USA) to R.S.W., a Faculty Research Grant from Fordham and a grant (DEB-1142784) from the National Science Foundation (USA) to S.J.F. and by the New York Botanical Garden.Funding was provided by a All authors took part in designing the experiment, discussing the results and composing and editing the manuscript. R.S.W. carried out the research, analysed the data and drafted the manuscript.None declared.The following additional information is available in the online version of this article–Table S1. ‘HWE estimates of FIS’ provides estimates of FIS, and corresponding 95 % CI, from 1000 bootstrap replicates using only loci found to be in HWE. Loci used are listed for each population and generation.Table S2. ‘HWE estimates of FST’ provides estimates of FST, and corresponding 95 % CI (when available), from 1000 bootstrap replicates using only loci found to be in HWE. Loci used are listed for each generation.Table S3. ‘Hierarchical AMOVA tables using loci in HWE’ (FST) provides estimates of FST and the distribution of molecular variance for both generations, using only loci in HWE.Table S4. ‘Hierarchical AMOVA tables using loci in HWE’ (ϕST) provides estimates of ϕST and the distribution of molecular variance for both generations, using only loci in HWE.Table S5. ‘Loci in HWE’ provides a list of the loci estimated to be in HWE for each population and generation.Table S6. ‘Microsatellite loci characteristics’ lists allelic parameters calculated for each of the 10 loci used in this study.Table S7. ‘Hierarchical AMOVA tables’ provides estimates of ϕST and the distribution of molecular variance for both generations, using all 10 loci.Table S8. ‘Within population parameters’ provides estimates of standard deviation for genetic parameters of each population and generation.Table S9. ‘STRUCTURE likelihood outputs’ provides likelihood scores used to determine the optimal number of clusters comprising these populations.Table S10. ‘Within population parameters over time’ provides the results of Student's t-test comparing within population genetic parameters across generations.Table S11. ‘CI for within population fixation’ provides the 95 % CI corresponding to estimates of FIS from 1000 bootstrap replicates, for each population and generation.Table S12. ‘Estimates of FIS across populations’ provides global estimates of FIS for each generation, and across generations.


The expansion of Bantu-speaking agropastoralist populations had a great impact on the genetic, linguistic, and cultural variation of sub-Saharan Africa. It is generally accepted that Bantu languages originated in an area around the present border between Cameroon and Nigeria approximately 5,000 years ago, from where they spread South and East becoming the largest African linguistic branch. The demic consequences of this event are reflected in the relatively high genetic homogeneity observed across most of sub-Saharan Africa populations. In this work, we explored genome-wide single nucleotide polymorphism data from 28 populations to characterize the genetic components present in sub-Saharan African populations. Combining novel data from four Southern African populations with previously published results, we reject the hypothesis that the “non-Bantu” genetic component reported in South-Eastern Africa (Mozambique) reflects extensive gene flow between incoming agriculturalist and resident hunter-gatherer communities. We alternatively suggest that this novel component is the result of demographic dynamics associated with the Bantu dispersal. The genetic structure of African populations is the result of both ancient and more recent episodes of migration and admixture , 2014. Aa) or substantial gene flow into the arriving Bantu-speaking groups (fig. 1b). Alternatively, the reported Mozambique uniqueness could simply be the result of the dispersal process whose genetic signature would be expected to be shared with populations related to those living in Mozambique. This scenario might include or not some degree of gene flow from hunter-gatherer populations (fig. 1c).Previous investigations have focused on the demographic dynamics of the Bantu dispersal , 2014. Wfig. 1To test these hypotheses and further characterize the reported Mozambican component, we analyzed new genome-wide single nucleotide polymorphism (SNP) array data from 35 sub-Saharan African populations from 868 individuals (including 33 novel genotypes from Namibia and Lesotho) . Our resa). To further analyze these results, a second PCA was performed removing the East non-NC  populations. In this, the Khoisan-speaking groups and the NC-speaking populations are separated along the first component (PC1), the rainforest hunter-gatherers  located among the two (fig. 3b). RHG are separated from the other populations along the second component (PC2). The NC speakers are spread along the same axis, the Mandenka and Yoruba individuals on one side and various South-Eastern Bantu speakers on the other (fig. 3b). We then performed ADMIXTURE analysis, and obtained for K = 6 the most suitable number of ancestral components . Five groups emerge that we associate for simplicity to the linguistic group spoken by the majority of the populations characterized by the indicated component: Khoisan . These two components are present in all NC populations but their amount varies among groups. One of these components (red) dominates populations speaking West Bantu and other NC languages, whereas the other one (green) is more common among South-Eastern Bantu speakers (fig. 4a). The main component of the Mozambican sample (green) is similarly present in populations from Lesotho and South Africa . Notably, the Basotho from Lesotho and the amaXhosa/SE Bantu/SowetoBantu from South Africa also show a non-Bantu component (Khoisan) but additional components (Khoisan or others) are virtually absent in samples from Mozambique. The spatial distribution of these components was visualized by interpolation maps (fig. 4b–g). The two main components present in NC populations mapped on to different geographic regions, so that these two can be broadly identified as NC-West and NC-Southeast . The Afro-Asiatic and Nilo-Saharan components are mostly restricted to populations from Central-East Africa (fig. 4c and d) and the RHG component was geographically restricted to Pygmy populations (fig. 4e).Relationships among individuals were explored using principal component analysis (PCA) . The firfig. 3FST. The largest values are observed when the Khoisan component is compared with the East Africa ones, followed by the comparisons of the first with both components from NC populations. The two NC components (West and East) are the most genetically close, their FST value (0.042) at least two times smaller than any other comparison  Khoisan populations, as previously reported .Given the low number of markers analyzed, we performed a cross-validation (CV) FST population tree shows a separation of the main groups present in our data set, first with a group of Afro-Asiatic, Nilo-Saharan and linguistic isolates, then the Khoisan speakers  and the NC-speaking populations . The ADMIXTURE components present in the Mozambican samples are the same as those of other East and South-Eastern African populations from Kenya, South Africa, and Lesotho (a). The major component in Mozambique is also the major component in other populations from the South-Eastern region (green in a), and is present at lower frequencies in other NC speakers. Importantly, the closer affinity of this component to the other NC component (NC-West) suggests a more recent common origin for the two NC clusters than any of the others. The pairwise FST population tree confirms the clustering of all NC-speaking populations, with the South-Eastern Bantu-speakers placed within the variation of other Bantu-speaking populations (red and green branches in fig. 4a) in agreement with previous reports of Khoisan admixture in Southern African Bantu-speaking populations  and Bantu speakers in Mozambique. In fact, none of the tests in the form f3 gave significant or negative values. Furthermore, our CV analysis based on resampled data shows that the probability of having a false negative result is very low. Even though the absence of gene flow into the Mozambican sample here analyzed cannot be completely excluded given the low number of analyzed markers, we show that a high contribution from non-Bantu populations is highly unlikely in this population. We also noted that this might not be the case for all populations from this area as the analyzed sample is possibly not representative of the region as a whole. Sporadic highly divergent mtDNA and Y chromosome lineages have been reported in Zambia and Mozambique despite the lack of robust population signatures of admixture  and DNA was extracted according to manufacturer’s protocols. The samples presented here were collected during three field trips. The Basotho speakers were collected in Lesotho in 2009 , 2014; thttps://capelligroup.wordpress.com/data/).A total of 33 individuals  were genotyped using the Illumina Human 610-Quad BeadChip  (Basotho and Owambo) and the Human Omni5-Quad BeadChip (Illumina) . The analyzed data described in this article are available on CC’s group website fig. 2).r2 > 0.4 using a sliding window of 200 SNPs, shifted at 25 SNP intervals  filtering both SNPs and individuals. Each single population data set was initially filtered to remove SNPs and individuals with a call rate below 0.9. After the merge, an additional QC step was performed, removing SNPs and individuals with a call rate below 0.98. Related individuals up to the second degree were removed from our data set using the software KING . To overntervals .http://hapmap.ncbi.nlm.nih.gov/, last accessed July 30, 2015) (K = 4) were removed. The final data set was composed of a total of 868 individuals from 35 populations . Similar number of SNPs has been successfully used to recover evident and robust patterns of genetic diversity in African populations   were ini0, 2015)  with theulations  typed onulations .K) from 2 to 20. The most supported value of K was estimated using the CV procedure, as implemented in ADMIXTURE  was visualized by plotting the fraction of each component in the analyzed populations on a map with the interpolation plugin of the QGIS software, using the Inverse Distance Weighting method with a distance coefficient of 3  a significantly negative value of the statistic highlights a complex phylogeny of the Target population, that has a certain amount of ancestry from populations related to A and B. However, a positive f3 value does not necessarily imply the absence of admixture. For this reason, and given the low number of markers used in this analysis, we performed a f3 test on 100 simulated samples composed by Yoruba and Pop1, with proportion alpha and 1-alpha respectively, where Pop1 is represented by Juhoansi, Sandawe or Mbuti, and alpha ∈{0.05, 0.10, . . . , 0.95}.Given the fact that ADMIXTURE analysis is not a formal text of gene-flow, we carried out a f3 test  for windf3 test , and repent bias . BrieflySupplementary figures S1–S6 and tables S1–S3 are available at Genome Biology and Evolution online .


Sinopodophyllum hexandrum is an important medicinal plant whose genetic diversity must be conserved because it is endangered. The Qinling Mts. are a S. hexandrum distribution area that has unique environmental features that highly affect the evolution of the species. To provide the reference data for evolutionary and conservation studies, the genetic diversity and population structure of S. hexandrum in its overall natural distribution areas in the Qinling Mts. were investigated through inter-simple sequence repeats analysis of 32 natural populations. The 11 selected primers generated a total of 135 polymorphic bands. S. hexandrum genetic diversity was low within populations (average He = 0.0621), but higher at the species level (He = 0.1434). Clear structure and high genetic differentiation among populations were detected by using the unweighted pair group method for arithmetic averages, principle coordinate analysis and Bayesian clustering. The clustering approaches supported a division of the 32 populations into three major groups, for which analysis of molecular variance confirmed significant variation (63.27%) among populations. The genetic differentiation may have been attributed to the limited gene flow (Nm = 0.3587) in the species. Isolation by distance among populations was determined by comparing genetic distance versus geographic distance by using the Mantel test. Result was insignificant  at 0.05, showing that their spatial pattern and geographic locations are not correlated. Given the low within-population genetic diversity, high differentiation among populations and the increasing anthropogenic pressure on the species, in situ conservation measures were recommended to preserve S. hexandrum in Qinling Mts., and other populations must be sampled to retain as much genetic diversity of the species to achieve ex situ preservation as a supplement to in situ conservation. Sinopodophyllum hexandrum (Royle) Ying, family Berberidaceae, the only species of this genus in China, commonly known as Himalayan mayapple, is an endangered and medicinal perennial herb native to the Himalayan regions at elevations ranging from 2 700 m to 4 500 m S. hexandrum is a traditional Chinese medicine that has been used in folk medicine S. hexandrum contain large amounts of lignans. The most important lignan for human health is arguably the most active cytotoxic aryltetralin lignan, podophyllotoxin, with three times the podophyllotoxin levels compared to the American species Podophyllum peltatumS. hexandrum to the endangered species list of the Convention on International Trade in Endangered Species of Wild Fauna and Flora S. hexandrum was classified as an endangered species (grade 3) in 1987 by the Chinese Plant Red Book S. hexandrum populations in China has been noted to be very small and to be rapidly declining. The availability of podophyllotoxin from plants has become increasingly limited due to intense collection, habitat fragmentation, low natural regeneration rate, and the lack of organized cultivation. Wild S. hexandrum populations could become extinct without timely and effective protective measures, costing humans an ideal drug against cancer. Knowledge of the genetic diversity at intraspecific levels is an important prerequisite for species conservation and a rational exploitation program planning. However, previous studies have mainly focused on the identification and separation of the chemical components S. hexandrumS. hexandrum from the Northwestern Himalayan region are available S. hexandrum is grown, namely, western Sichuan Province and Himalaya–Hengduan Mt. region in China, were investigated to identify genetic diversity of this species through inter-simple sequence repeats (ISSR) and amplified fragment length polymorphism (AFLP) markers S. hexandrum populations had relatively high genetic diversity (He = 0.2944∼0.3377). However, there is a consensus that alpine plants are faced with pollinator restriction Calamagrostis porteri ssp. insperataAegiceras corniculatumSonneratia albaCoscinium fenestratumand Lilium pumilumCurrently, with the enhanced awareness of its medicinal value and superior efficacy in clinical applications, the wild S. hexandrum in Qinling Mts. are requisite because climate change and local overexploitation may cause unknown endangering mechanisms. The present study aims to establish management strategies for the conservation genetics of S. hexandrum by (1) examining the levels of genetic variability within and among S. hexandrum populations sampled from Qinling Mts. where historical records showed S. hexandrum having grown naturally, (2) assessing the possible factors that affect the genetic variation observed, and (3) comparing these within and among S. hexandrum populations with data published for itself or other plant taxa with similar characteristics.Molecular markers are very useful tools for genetic diversity studies. ISSR markers are molecular markers especially suited to genetic polymorphisms analyses of species without available sequence information The endangered species were collected, and research activities were scientifically conducted under the permits issued by the local forestry department. A detailed description of the experimental material collection and procedures is provided. The experimental procedures were approved by the Ethics Committee for Plant Experiments of Northwest A & F University and the State Forestry Administration, P. R. China. The names of the authorities that issued the permit for each location were listed in Table S1 in S. hexandrum, Qinling Mts. have unique environmental features which have high impact on the evolution of the species.This study was performed in the Qinling Mts. , which are located in central of China . The QinS. hexandrum distribution pattern and extent of in Qinling Mts. were investigated from 2010 to 2011. The S. hexandrum populations are distributed in small and scattered patches. According to the field survey information, a total of 32 wild S. hexandrum populations were sampled for DNA analysis between July 19, 2012, and September 17, 2012, to ensure collection period consistency . The extracted DNA was quantified by comparing with known DNA of standard quantity (Lambda DNA) through electrophoresis in ethidium bromide-stained 1.0% agarose gels , and the extracted DNA was diluted in TE buffer to a final concentration of 50 ng/µL and stored at−20°C before PCR amplification.Total genomic DNA was extracted from frozen leaves by using a plant genomic DNA rapid extraction kit (Spin-column) , and Taq DNA polymerase concentrations and annealing temperature, and reaction conditions. The optimization showed that 20 µL of reaction system is ideal. Each 20 µL amplification reaction consisted of 1×PCR buffer , 1.6 mmol L–1 dNTP mix, 0.6 µmol L–1 primer (UBC900 was used for preliminary test), 15 ng of template DNA, and 1.0 U Taq DNA polymerase , using a cycling profile of initial 5 min at 94°C, followed by 45 cycles of 30 s at 94°C, 45 s annealing at 50°C, and 90 s extension at 72°C, ending with a final extension of 7 min at 72°C.A total of 100 ISSR primers  were screened based on the primer set published by the Biotechnology Laboratory, University of British Columbia, Canada (UBC set No. 9) and the studies on Himalayan mayapple The optimized PCR experiment conditions were applied for primer screening in a PTC100TM Programmable Thermal Controller . Six populations  with observable variations  were selected to initially screen 100 primers by using 10 samples for each population. Primers that generated scorable bands and high levels of polymorphisms were selected by genotyping all populations. The amplification products were electrophoresed on 1.0% agarose gels buffered with 1.0×TBE for 2.5 h at 100 V and were detected through ethidium bromide staining, and the gels were imaged in the Gene Genius Bioimaging System. Band size was estimated from a 0.1 kbp DNA ladder . Each primer was amplified in triplicate to confirm reliability and reproducibility. A reaction without DNA was used as negative control.Amplification results were scored according to the positions of the DNA bands from electrophoresis, being labeled “1” for presence of the bands and “0” for absence of the bands in the data matrix. Only stable bands with repeatable differences were considered valid for polymorphism loci.o), effective number of alleles per locus (Ae), total gene diversity (Ht), the level of gene flow (Nm), Nei’s gene diversity (He), gene distance (GD), Shannon’s information index (Ho), within-population diversity (Hs), and mean coefficient of gene differentiation (Gst). An unweighted pair groups mean arithmetic (UPGMA) mean dendrogram was constructed using PowerMarker 3.23 to examine the genetic relationship at the species level 5 and 5×104 iterations. The “no admixture model” and independent allele frequencies were chosen for this analysis. The most likely number of clusters was estimated according to the model values (ΔK) based on the second order rate of change, with respect to K, of the likelihood function ab, was computed using the following formula: Lab = Arccos [cos(LATa)COS(LONGa)cos(LATb)cos(LONGb) + cos(LATb)sin(LONGb)cos(LATb)sin(LONGb) + sin(LATa)sin(LATb)]×R a, LATa and LONGb, LATb are the longitudes and latitudes (in radians) of sampling sites a and b, respectively; R indicates the radius of the earth, which is 6 378 km; and Lab represents the geographic distance between sampling sites a and b.The resulting ISSR phenotype data matrix (binary matrix from 0 to 1) was analyzed using the POPGENE software (version 1.31) S. hexandrum individuals from 32 populations, forty-eight ISSR primers amplified visible bands and were chosen from the initial set of 100 primers to screen for reproducible markers. PCR amplification results /Gst).For polymorphism testing of the 640  results  show thae ranged from 1.0182 (HJG) to 1.2643 (BG), with 1.1630 at the population level and 1.3732 at the species level. He within populations was lower (0.0621) than that of the species level (0.1434). Within each population, the He of most populations ranged from 0.0226 to 0.0971. Only in the population HG, a high He  and second (PCo2) principal coordinates . PCo1 exS. hexandrum populations. The correlation coefficient r of genetic distance and geographic distance was 0.212 (P = 0.287), and the correlation analysis diagram  genetic variance was expectedly observed among the populations and explained 63.27% of the total variance, supporting the results from the hierarchical and Bayesian clustering. Only 36.73% of the total genetic variance occurred within populations, indicating higher genetic differentiation between the populations than within each population and the emergence of genetic differentiation among populations. The Gst value (0.5823)   showed tS. hexandrum populations sampled in Qinling Mts. Populations are isolated given the population differentiation and clear clustering. The comparison of the average genetic diversity to that of S. hexandrum from Northwestern Himalayan region S. hexandrum populations of Qinling Mts. have low genetic diversity (average He = 0.0621). The S. hexandrum in the Northwestern Himalayan region e = 0.2944, 0.092, respectively). S. hexandrum is native to the Himalayan region, growing in valleys with secondary vegetation, or under shrubs or around trees S. hexandrum reproduces through vegetative reproduction and seeds. Insects and birds are limited in the high altitude regions, implying that S. hexandrum pollination is easier than in the Qingling Mts. The sizes of wild populations of S. hexandrum are very small and declines each year in Qinling Mts. because of habitat fragmentation and deterioration caused by human disturbance (overcollection due to economic interests). The rapid decrease in individuals in wild populations may also cause loss of the genetic diversity of this endangered species. Another Berberidaceae species, Dysosma versipellis, is an endangered species endemic to China and has been listed as a key protected wild plant in China due to habitat fragmentation or destruction. Qiu et al. found this species had high level of genetic diversity in China (He = 0.378) Dysosma pleiantha, a threatened medicinal plant species distributed in southeastern China, sexually and asexually reproduces. High He (0.364) was observed in this species D. versipellis and D. pleiantha than in the S. hexandrum populations. The He found in this study was 0.1434 at the species level, lower than those of some strictly self-pollinating soybean species (He = 0.1714) Oryza granulata (He = 0.210) S. hexandrum populations. Historical events are also responsible for the variation in genetic diversity The ISSR markers developed in this study effectively revealed low genetic diversity within the S. hexandrum in this high mountainous area. However, the genetic diversity of S. hexandrum is not much lower than other endangered species analyzed using ISSR markers, such as Leontice microrhyncha (Berberidaceae) (He = 0.021), which is a polycarpic perennial herb found in deciduous or coniferous forests in Korea and Northeast China L. microrhyncha, each pollinated flower produces an 8 mm berry and seed dispersal is restricted due to its heavy berry S. hexandrum was also higher than those of two other species, the endangered Pinus squamata (He = 0.020) Manglietia decidua (He = 0.0637) P. squamata and M. decidua are extremely rare and endangered tree species in China. The extremely low genetic diversity of this two species could have resulted from the severe bottleneck effect during their evolutionary process. The gene drift and inbreeding may further decrease their genetic diversity in the shrinking populations. The weak competitive ability against broad - leaved trees and human activities may also accelerated the decrease of genetic variation.Genetic diversity is affected by multiple factors, such as geographical distribution, mating system, life form, pollen and seed dispersal S. hexandrum is reasonably long lived because its rhizomes easily reproduce, which could slow down the loss of genetic diversity. Pollen dispersal is generally restricted to a small region due to the large pollen size, which limits gene flow to increase or maintain genetic diversity. S. hexandrum is native to the Himalayan region, including China, India, Nepal, and Myanmar. No other genetic diversity studies of S. hexandrum exist in other locations. Further research should include more populations in other regions of China and countries.High level of genetic differentiation and clear population structure was detected in this study. K = 3 in the Bayesian clustering as a meaningful value because group 1, 2 and 3 could be divided by detecting the within-group substructure. This result is also supported by distance-based clustering and PCoA. Groups 1 and 2 are genetically close but significantly different. Estimation of the number of clusters K should be treated with care because it is computationally difficult to obtain accurate estimates and the method merely provides an ad hoc approximation S. hexandrum populations distributed in the Qinling Mts. are clustered into three major groups. These methods consistently showed that high genetic differentiation existed among S. hexandrum populations, which is consistent with genetic variation studies in certain selfing species S. hexandrum should be a selfing species or a selfing predominant species, which is consistent with previous studies on S. hexandrum by Ma et al. m value is lower than 1.0. The Nm of S. hexandrum (0.3587) determined using the POPGENE software was lower than 1.0 in the present study, which suggested that some genetic drift may have emerged among the populations of this species. The distribution of S. hexandrum populations obviously tend to fragment based on the field investigation, which is consistent with the possibility of genetic drift.All sampled individuals were strongly assigned to their original populations, and all data strongly support the conclusion that the 32 S. hexandrum populations may be related to inbreeding of the species and limited seed propagation distance. Some studies have found that seed dispersal is the primary factor influencing variation of gene flow and population structure S. hexandrum usually drop to the ground because of rain or wind, settling some seeds in the soil, whereas others are dispersed by cattle, birds, or humans. Therefore, the short distance of seed dispersal of S. hexandrum probably resulted in limited gene flow among populations. Mountain ranges and rivers are possible barriers to either dispersal of pollen or rhizomes of S. hexandrum, reproductively isolating the populations. The restriction of gene flow associated with geographical distance is consistent with the results of previous studies on this species Migration of plant populations can occur through dispersal of pollen and seed S. hexandrum is a rare and threatened species S. hexandrum populations and genetic differentiation among populations was higher than within populations. Genetic diversity loss has deleterious effects on species fitness and threatens the population survival and could be the key reason that explains the endangerment of S. hexandrum in Qinling Mts. S. hexandrum conservation and its reasonable utilization. The results will help determine what to conserve and where and how to conserve this species.S. hexandrum reserves should be the primary method because the Qinling Mts. are situated in state forest conservation areas, where cutting and hunting are restricted. The management for the conservation of genetic variability in this species should aim to preserve not only large populations but also as many of the small populations outside nature reserves as possible. Reduced levels of genetic variation, especially in the smaller populations, will affect the species’ ability to adapt to changes in its habitat Gentiana pneumonantheArnica MontanaS. hexandrum protections must be emphasized, and related forest departments should be encouraged to undertake conservation through an integrated conservation strategy based on demographic, ecological, and genetic aspects.The field survey showed that the habitats of some populations have been destroyed by human disturbance for great medical value. Damage to natural habitats would led to a decrease in population sizes and probably a subsequent increase in inbreeding, decreasing its genetic diversity. In situ conservation effectively and sustainably prevents this problem. The establishment of S. hexandrum has favorable sexual reproduction. Each plant produces approximately 60 seeds, with a maximum of approximately 180 seeds S. hexandrum than other endangered species. Thus, a strategy involving extensive collection to ensure full sampling of genetic diversity, subsequent cultivation in a garden at least 1000 m above sea level S. hexandrum mainly grows wild on high altitude mountain ranges. For S. hexandrum populations in Qinling Mts., there are some preserved forest farms which could be used for relocation. However, ex situ conservation has many drawbacks because it is impossible to recreate the habitat as a whole. The new environment may have important ecological differences compared with the original habitat, and the approach is technically challenging and is often expensive. Therefore, ex situ conservation is recommended only to supplement in situ conservation or as a last resort. In vitro techniques are also proven to be an effective alternative means of propagation that facilitates the recovery of the rare and endangered S. hexandrumS. hexandrum. In vitro propagation may well be used as a means to rescue zygotic embryos for this species. In vitro techniques induce variability, but plants raised from tissue cultures may be screened for useful somaclonal variants and exploited to obtain plants or cultures with high podophyllotoxin contents, which possibly reduces the pressure on natural S. hexandrum populations.As a supplement to in situ conservation, ex situ conservation would also be feasible as underlined by other studies on endangered species S. hexandrum in Qinling Mts. must be guaranteed with these combined and sustained efforts.Most of the genetic diversity of the important medicinal and endangered species Text S1Description of the sampling procedures.(DOC)Click here for additional data file.Text S2Allele frequencies per locus.(DOC)Click here for additional data file.