Gadus morhua, using genome wide expressed sequence derived single nucleotide polymorphisms in 466 individuals sampled across the range. Outlier tests identified elevated divergence at 5.2% of SNPs, consistent with directional selection in one-third of linkage groups. Genomic regions of elevated divergence ranged in size from a single position to several cM. Structuring at neutral loci was associated with geographic features, whereas outlier SNPs revealed genetic discontinuities in both the eastern and western Atlantic. This fine-scale geographic differentiation enhanced assignment to region of origin, and through the identification of adaptive diversity, fundamentally changes how these populations should be conserved. This work demonstrates the utility of genome scans for adaptive divergence in the delineation of stock structure, the traceability of individuals and products, and ultimately a role for population genomics in fisheries conservation.As populations diverge, genomic regions associated with adaptation display elevated differentiation. These genomic islands of adaptive divergence can inform conservation efforts in exploited species, by refining the delineation of management units, and providing genomic tools for more precise and effective population monitoring and the successful assignment of individuals and products. We explored heterogeneity in genomic divergence and its impact on the resolution of spatial population structure in exploited populations of Atlantic cod,  The presence of adaptive diversity is a principle consideration in the management and conservation of exploited species . DespiteThe ability to resolve genomic regions associated with adaptation e.g.  providesGadus morhua), with linkage mapping information to demonstrate the utility of genome scans to resolve adaptive divergence and to inform management and conservation of exploited marine species. First, we use tests for selection and linkage information to explore the genomic distribution of outliers identified as potentially experiencing directional selection. Second, we examine the subsequent impact of adaptive variation on the resolution of spatial population structure and assignment success in Atlantic cod. We build on a previous study (n = 40) and populations (n = 14). Here, we examine the distribution of divergence and its consequences for the resolution of spatial structure, providing critical insight into the geographic scale of population dynamics in exploited populations of Atlantic cod.Our study integrates SNP-based genome scans for elevated divergence in a marine fish, Atlantic cod (us study  that exaN = 466) from 23 locations throughout the North Atlantic from 1996 to 2007 , and tests for Hardy–Weinberg equilibrium were performed using ARLEQUIN  consistent with multilocus genetic data. This approach uses a stochastic optimization procedure rather than MCMC to identify the number of groups. We ran BAPS with the predefined number of clusters = 2–25 and replicated five times to ensure the stability of results. Assignment power of the SNP panel was evaluated using the proportion of successful identifications to the population of origin identified using GeneClass2.0  were significant at α = 0.01. Locus-specific FST values were as high as 0.60 for some loci; the median and maximum pairwise population FST values were 0.13 and 0.50, respectively.Although we attempted genotyping of the 23 population samples  for 1536 previously identified informative loci, the failure of some assays reduced successful loci to 1405. Observed (expected) heterozygosity overall was 0.307 (0.301) and ranged from 0.222 (0.220) in the Baltic Sea sample to 0.385 (0.360) for the Gulf of St. Lawrence and Georges Bank (See Table S1). The percentage of polymorphic loci in each sample varied from 69.0% in the Baltic to 99.3% in the Cape Sable sample (Table S1). Tests for departures from Hardy–Weinberg equilibrium indicated that approximately 1% of population/locus combinations , we identified 73 loci potentially experiencing directional selection at a false discovery rate of 1% A. In comFST per LG among nonisolated western populations varied by more than an order of magnitude  with eight LGs containing loci identified as putatively experiencing selection . Average posterior odds (BAYESCAN) per LG was above neutral expectations for the seven of the eight LGs that possessed loci identified as potentially experiencing selection (The number of SNPs that mapped to particular linkage groups (LGs) among the 23 identified LGs ranged from fewer than 40 , but the removal of outlier SNPs from the analysis emphasized the influence of the three distinct groups . Bayesian clusters in BAPS were consistent with the PCoA groups. Clustering after removing outliers revealed five clear groups consistent with the isolation of the Arctic lakes, Gilbert Bay, and East and West Atlantic  for the panel with outlier loci included in the nonisolated populations.Principle coordinate analysis (PCoA) using all SNPs produced a two-dimensional representation with the first two axes explaining 62% of the variance in the complete dataset A. Five dAtlantic A. ClusteAtlantic B; howeveAtlantic C. These Atlantic . AssignmThe successful management of capture fisheries necessitates the representation of adaptive diversity in conservation units , the abiGenome scans can potentially resolve adaptive genetic variation and inform management or conservation strategies in species, which are difficult if not impossible to study through other means . From a The utility of genomic resources for the monitoring of marine populations requires a clear understanding of the function of SNPs or identified genes. Although the functional relationships among these outlier genes remain undetermined, observations are consistent with a previous study identifying temperature associations in a subset of these outliers and populations. The observed signals of fine-scale geographic differentiation at these gene-associated, high-divergence SNPs also provide the opportunity for accurate assignment of individuals and products to populations or regions of origin. Our study indicates very high success of assignment to cluster, but also that inclusion of outlier loci significantly improved support for assignments. Overall, we identified 5.2% of our surveyed SNPs as outliers, a pattern consistent with past studies that indicated 5–10% of a genome may display signatures of selection ; though,The roles ascertainment bias or temporal variation among samples may play in the observed spatial patterns, or potential assignment success remains unclear. Ascertainment bias is evident in the present dataset as a decline in diversity from west to east associated with distance from the location of the ascertainment panel . We evalMus musculus) where divergent regions accounted for 7–8% of the genome and associated with eight genomic regions , which is widely distributed across the Atlantic, a dominant genetic break separates populations to the southwest and east of western Greenland with some eastern affiliations of populations along the northern Grand Banks off Newfoundland , longstanding genetic isolation occurs in the south on either sides of the Atlantic, but again Newfoundland and Labrador consistently contain populations characterized by eastern Atlantic genotypes . Previous studies report similar trans-Atlantic trends in genetic structure and the presence of eastern Atlantic genotypes in the northwest Atlantic for both marine and anadromous fishes. In Atlantic wolffish (oundland . Similarenotypes . TogetheGenomic regions associated with adaptation may directly inform conservation efforts in exploited marine species, by delineating management units, and providing genomic tools for population monitoring and individual assignment. We observed significant variation in the genomic and geographic scale of differentiation in Atlantic cod, using a panel of EST-associated SNPs. Genomic islands of divergence were associated with several LGs and in several instances clustered significantly, supporting the hypothesis that divergent regions co-occur in response to strong selection or low recombination. Outlier SNPs, likely associated with adaptive divergence, resolved fine-scale structure not evident at neutral loci and provide ample power for successful individual assignment to the region of origin. Geographic population differentiation in Atlantic cod appears to be distinguished by a few discrete islands of genomic divergence, although finer-scale sequence analysis and selection experiments are needed to further resolve the genomic scale of divergence. This work demonstrates the utility of genomic regions of adaptive divergence in the delineation of stock structure, traceability of individuals and products, and ultimately a role for population genomics in the conservation of marine fisheries.


Acer mono var. mono, an economically and ecologically important maple species, is extensively distributed in Northeast China (NE), whereas it has a scattered and patchy distribution in South China (SC). In this study, the genetic structure and demographic history of 56 natural populations of A. mono var. mono were evaluated using seven nuclear microsatellite markers. Neighbor-joining tree and STRUCTURE analysis clearly separated populations into NE and SC groups with two admixed-like populations. Allelic richness significantly decreased with increasing latitude within the NE group while both allelic richness and expected heterozygosity showed significant positive correlation with latitude within the SC group. Especially in the NE region, previous studies in Quercus mongolica and Fraxinus mandshurica have also detected reductions in genetic diversity with increases in latitude, suggesting this pattern may be common for tree species in this region, probably due to expansion from single refugium following the last glacial maximum (LGM). Approximate Bayesian Computation-based analysis revealed two major features of hierarchical population divergence in the species’ evolutionary history. Recent divergence between the NE group and the admixed-like group corresponded to the LGM period and ancient divergence of SC groups took place during mid-late Pleistocene period. The level of genetic differentiation was moderate  among all populations, but significantly higher in the SC group than the NE group, mirroring the species’ more scattered distribution in SC. Conservation measures for this species are proposed, taking into account the genetic structure and past demographic history identified in this study.Knowledge of the genetic structure and evolutionary history of tree species across their ranges is essential for the development of effective conservation and forest management strategies.  Northeast China contains the country’s most important forest areas, but since the late 19th century forests in this region have been severely disturbed by wars, fires, deforestation, overexploitation and changes in land use to agriculture and grazing. Consequently, many tree species are now threatened Phellodendron amurense (AFLP) Quercus mongolica  Tilia amurensis (ISSR) Fraxinus mandshurica (SSR) Q. mongolicaF. mandshuricaet al.F. mandshurica populations arose from northward expansion of the forest biomes in the mid-Holocene from a single refugium located in eastern China during the last glacial maximum (LGM) The genetic diversity and population structure of various tree species in Northeast China have been evaluated using several types of molecular markers, for example Acer mono (mono maple) is a common deciduous tree species that is widely distributed in east Asia, where there are many varieties A. mono is mainly distributed in Northeast China A. mono have decreased, and almost disappeared, at some sites due to: (1) harvesting of its wood for timber or fuel; (2) inappropriate forest management activities such as removal of all undergrowth species during thinning; and (3) of most importance recently, transplantation from natural forests to urban areas for use as ornamental, shade, or landscape trees. The conservation and sustainable management of natural populations of A. mono, as well as breeding and artificial planting of the species, are therefore now receiving more attention. However, few molecular ecology studies have addressed the genetic diversity and structure of natural populations of A. mono in China, although several have examined the reproduction and mating patterns of natural populations of the species in Japan A. mono using nuclear SSR markers to: 1) evaluate the wide-range genetic structure of 56 populations selected to cover its distribution in northeastern and southern China, 2) infer its population demographics using an ABC approach and 3) formulate conservation and management strategies for this natural genetic resource in Northeast China, taking into account its current genetic structure and inferred evolutionary history.We therefore conducted large-scale genetic analysis of Sampling and study sites” section), in which the sampling activities were permitted by the relevant authorities. The field studies did not involve disturbance of any endangered or protected species.No specific permission was required for sampling activities in most populations because we sampled in the wild and only a few leaves were collected from each tree, except for some populations located in National Nature Reserves , and eight in the rest of China , where there are scattered, low-density populations (We collected leaves of ulations .The NE populations were distributed in two major mountain ranges (numbers in brackets indicate assigned designations): (i) the Xiaoxing’anling Mountains (1–12), and (ii) the Changbai Mountains (13–48) including the main range , branches such as the Zhangguangcailing (13–24) and Laoyeling Mountains , and ranges extending outwards such as the Wandashan , Longgangshan , and Qianshan Mountains (48) . The indA. monoAcer species DNA was extracted using a modified CTAB method 2O, and 0.5 µL template DNA by PCR  using a TaKaRa TP600 Thermal Cycler. All PCR products were genotyped using an ABI 3130 Genetic Analyzer and the results were analyzed using GeneScan analysis software and Genotyper software version 3.5 (Applied Biosystems).Primers were mixed to a final concentration of 2 µM, except for primer Am258 (4 µM). The target sequences were then amplified in 5 µL reaction mixtures containing 2.5 µL of 2× Qiagen Multiplex PCR Master Mix, 0.5 µL of the primer mixture, 1.5 µL HAN), range of allele size (ASR), allelic richness (RA), expected heterozygosity (EH), and fixation index (ISF). In addition, significant deviations from Hardy-Weinberg equilibrium, as indicated by deviations of FIS from zero, and genotypic disequilibrium for all locus pairs in each population, were tested by randomization using FSTAT. Rare allelic richness  and geographical gradients . Since clear genetic differentiation was detected between the NE group (Populations 1–48) and the SC group (Populations 51–56) in the NJ tree and by STRUCTURE analysis  STG′) was then calculated following Hedrick The genetic differentiation among populations was evaluated by calculating FST/(1−FST)) and the natural logarithms of direct minimum geographic distance between populations. The genetic relationships among populations were evaluated by generating a Neighbor-Joining (NJ) tree based on the DA genetic distances et al. K) ranging from 1 to 20. All runs involved 100,000 Markov Chain Monte Carlo (MCMC) generations, after a burn-in period of 100,000 iterations according to Hubisz et al. et al. F-model) described by Falush et al. K. The optimum number of K was evaluated by calculating ΔK values K were summarized using CLUMPP software Isolation by distance (IBD) DIYABC v1.0.4.39 and v2.0 software STF = 0.029, To simplify scenarios for ABC analysis, we defined three populations based on the results of STRUCTURE analysis: Pop1 (Northeastern populations 1–48), Pop2 (admixed populations 49 and 50) and Pop3 (Southern populations 51–56). All sampled individuals of Pop2  and Pop3  were used for analysis. However, for Pop1, 200 individuals were randomly selected from the pool of 48 NE populations, as genetic structure among them was quite weak ((isolation with admixture model): Pop2 was generated by admixture of Pops 1 and 3 at t1, then Pop1 merged with Pop3 at t2. Based on the results of STRUCTURE analysis, this scenario was expected to be the most likely one.Scenario 1 : Northern Pop1 merged with Pop2 at t1 and subsequently with southern Pop3 at t2.Scenario 2 : Southern Pop3 merged with Pop2 at t1 and subsequently with Northern Pop1 at t2.Scenario 3 (simple split model): all three populations diverged at the same time, t2.Scenario 4 EH) and number of alleles (AN) were used as the summary statistics for each of the three populations, while for summary statistics for each pair of populations from the three groups we used EH, AN, genotype likelihood and STF. A million simulations were performed for each scenario. After the total of three million simulations, the most-likely scenario was evaluated by comparing posterior probabilities using logistic regression. The goodness of fit of the scenario was assessed by the option “model checking” with principal component analysis (PCA) in DIYABC, which measures the discrepancy between model and real data.In all scenarios, Pop2 was specified as the population to be traced back to an ancestral population . In addition, we assumed a bottleneck in the past at t3, which we positioned before the divergence point in all scenarios. We assumed past population growth and set the ancestral population size (Na in scenario 1 and Nb in scenarios 2–4) to be smaller than that of other populations. In DIYABC, we employed the higher mutation rate in the generalized stepwise mutation model (GSM) ΔK was highest at K = 4 and detected clear genetic structure , RA from 4.824 to 16.778 (10.867±3.981 on average), and EH from 0.587 to 0.861 (0.737±0.110 on average) (ISF) for individual loci ranged from –0.116 to 0.223 and the average over all loci was 0.053. No significant genotypic linkage disequilibrium for pairwise-loci was detected within any population (P>0.05).In total, 223 alleles were scored over the seven loci among the 1702 sampled average) . The fixAN, AR, AP, RA, RRA, RPA and EH ranged from 39 to 84, 8 to 55, 0 to 7, 5.181 to 11.210, 1.002 to 6.031, 0 to 1.293 and 0.535 to 0.840, respectively  and geographical gradients  were evaluated, significant positive correlation was detected only between EH and latitude , while both RA and EH increased together with latitude within SC populations  ranging from 0.056 to 0.093 and 0.051 to 0.1, respectively, and 66.35% of pairwise-STFvalues were significant (P<0.05). The overall value of STF corresponded to a standardized genetic differentiation (STG′) of 0.278, suggesting moderate population structure. Separate analysis of the genetic variation in NE and SC populations, based on results of the STRUCTURE analysis and the NJ tree (see below), showed that EH and RRA values were significantly higher for NE than SC population , but no significant differences in RA was detected  and there was no significant differentiation between them  in the SC group while the proportion was much lower (56.2%) in the NE group. Moreover, separate analysis of the pattern of genetic diversity within the groups showed that genetic variation decreased with increasing latitude in NE populations, but increased in SC populations (P = 0.01). Mantel tests indicated that there was significant isolation by distance in all populations , but not within either the NE or SC populations .The detected . The FIeen them . The G′Sulations . AnalysiK up to K = 8, where it started to plateau . However, with values of K = 3 and 4, although genetic structure was not clear over all NE populations, these two populations were assigned to a specific cluster. The NJ tree showed similar genetic structure to that detected by the STRUCTURE analysis, with two main groups corresponding to the NE and SC populations . Moreover, separate STRUCTURE analysis of NE populations detected weak genetic structure among populations associated with the mountain ranges, notably the 12 populations located in the Xiaoxing’anling Mountains (1–12) were almost entirely dominated by a single cluster (K = 2).According to the whole-range STRUCTURE analysis, the probability of the LnP(D) data increased progressively with increases in  plateau . However K was 2 , in the o groups , K = 2. ulations . Populato groups . In addio groups , indicatoups  was estimated at 6480 generations ago . However, the posterior distribution pattern suggested that this estimate was not robust  and number of alleles (AN) values for any population – or EH, AN, genotype likelihood and FST values for any population-pair – estimated using the acquired posterior distributions significantly differed from observed values, suggesting that scenario 2 was a good fit  was expected to be the most likely. However, the highest posterior probability was found for scenario 2 , and the value  was much higher than those for scenario 1 (0.2557), 3 (0.0032) and 4 (0.0071) . In scent robust . Estimatectively . None ofgood fit . This wagood fit .K = 4) found the highest posterior probability for the hierarchical population split scenario 3 , e.g. Fraxinus mandshurica (EH = 0.746±0.051) Juglans mandshurica (EH = 0.756) EH) and latitude was detected over all the 56 populations  than among SC populations . For the NE populations, the relatively short geographic distances between populations and the continuous distribution of mountains provide a more uniform landscape that probably facilitated gene exchange among populations F. mandshuricaK = 2). More specifically, the clustering pattern shown by the barplots indicated that the 12 populations located in the Xiaoxing’anling Mountains (1–12) were almost entirely dominated by a single cluster. Thus, the weak genetic structure in NE populations was probably due to the mountain ranges limiting gene flow, as they often do for tree species A. mono is an insect-pollinated species Betula maximowicziana), which was examined by Tsuda et al. K = 2, but they appeared as an additional, independent genetic group in the results with K = 3 and 4. Since the isolation by admixture scenario was not supported by the ABC analysis, we propose that these two populations constitute an independent group.The A. mono over a long time scale. The estimated timing of t3  was not robust , and their estimated divergence time was estimated at 20,430 a BP. The Northeast Plain was covered by coniferous forest/steppe during the early late Pleistocene and broad-leaved forest during the middle late Pleistocene, but at the end of the Pleistocene , because of the colder and drier climate, most of the Northeast Plain was covered by sand, and the vegetation changed to open forest/steppe The ancient divergence of Pop3 at t2, may have been caused by the Taihang Mountains acting as a barrier. These Mountains have a north-south orientation  and form a major natural boundary in eastern China. The southern Taihang Mountain marginal fault zone has been a major boundary of neotectonic deformation since the mid-late Pleistocene, dividing northern China latitudinally, and the south region containing the deformation system that includes the south Qinling Mountains ountains  [63], buSTRUCTURE analysis divided SC populations (Pop3) into four subgroups. The strong genetic structure in these populations was probably caused by a combination of the greater geographic distances separating them Fagus sylvaticaAcer saccharumA shelter wood system of forest management has been found to have little or no impact on genetic diversity and mating in A. mono in China are under increasing pressure due to its use as a source of wood and various other materials. Hence, specific natural varieties are being selected for artificial planting and silviculture in Northeast China and elsewhere. We only used neutral genetic markers in the present study, but the information obtained is potentially highly informative for the breeding, conservation and silviculture of A. mono, especially for the designation of conservation units. Since clear genetic differentiation was found between NE and SC populations, these two groups should be treated as independent conservation units. Although admixture was found for Populations 49 and 50 in the STRUCTURE analysis when K = 2, the ABC-based analysis suggested that these two populations were not generated by the admixture of NE and SC populations. Thus, populations in this region of northern China located between NE and SC populations should also be treated as an independent conservation unit. Seeds and nursery stocks of local provenance should be used in reforestation efforts in each area to avoid nonlocal introductions and genetic homogenization ex situ conservation efforts, which could involve collecting seeds from populations in this area, may be useful. Since Populations 51 and 52 showed high levels of diversity and more private alleles than other populations, natural reserve areas should be established in these regions. In contrast, the NE populations maintain relatively high levels of genetic diversity and densities of individuals within populations, but low genetic differentiation among populations, implying that the species should be able to recover without assistance (if further disturbance is avoided) in this region. Selection of elite trees and the development of varieties for specific purposes are likely to be more important than provenance selection for conserving A. mono in Northeast China. To obtain the thorough overview required to develop an effective conservation strategy for this species further studies are required, including evaluation of the extent of its adaptive genetic variation.Natural resources of Figure S1K = 1–20, and distributions of ΔK  for K = 1–20.Mean values of ln P(D) and standard deviations obtained from 10 runs for each value of (TIF)Click here for additional data file.Figure S2K = 6, in which the clustering corresponded to each of the six SC populations; B, K = 2 (according to the highest ΔK) for the 48 NE populations.Results of additional runs of STRUCTURE analysis focused on the six SC and 48 NE populations. A, (TIF)Click here for additional data file.Figure S3Prior and posterior distributions for each parameter obtained by DIYABC analysis of populations across the whole range.(TIF)Click here for additional data file.Figure S4Principal Component Analysis (PCA) score plot obtained from DIYABC analysis of populations across the whole range.(TIF)Click here for additional data file.Figure S5Prior and posterior distributions for each parameter obtained by DIYABC analysis of in South China populations.(TIF)Click here for additional data file.Figure S6Principal component analysis (PCA) score plot obtained from DIYABC of South China populations.(TIF)Click here for additional data file.Table S1Prior distributions of the parameters used in the whole-range DIYABC analysis.(DOC)Click here for additional data file.Table S2Prior distributions of the parameters used in the South China DIYABC analysis.(DOC)Click here for additional data file.Table S3Demographic parameters obtained from the whole-range DIYABC analysis.(DOC)Click here for additional data file.Table S4Comparison of summary statistics for the observed data set and posterior simulated data sets.(DOC)Click here for additional data file.Table S5Demographic parameters obtained from DIYABC analysis of South China populations.(DOC)Click here for additional data file.Table S6Comparison of summary statistics for the observed data set and simulated posterior data sets.(DOC)Click here for additional data file.


Rhodiola dumulosa (Crassulaceae) is a perennial diploid species found in high-montane areas. It is distributed in fragmented populations across northern, central and northwestern China. In this study, we aimed to (i) measure the genetic diversity of this species and that of its populations; (ii) describe the genetic structure of these populations across the entire distribution range in China; and (iii) evaluate the extent of gene flow among the naturally fragmented populations.R. dumulosa were collected, covering as much of the entire distribution range of this species within China as possible. Population genetic diversity and structure were analyzed using AFLP molecular markers. Gene flow among populations was estimated according to the level of population differentiation.Samples from 1089 individuals within 35 populations of R. dumulosa was high but decreased with increasing altitude. Population-structure analysis indicated that the most closely related populations were geographically restricted and occurred in close proximity to each other. A significant isolation-by-distance pattern, caused by the naturally fragmented population distribution, was observed. At least two distinct gene pools were found in the 35 sampled populations, one composed of populations in northern China and the other composed of populations in central and northwestern China. The calculation of Nei's gene diversity index revealed that the genetic diversity in the northern China pool (0.1972) was lower than that in the central and northwestern China pool (0.2216). The populations were significantly isolated, and gene flow was restricted throughout the entire distribution. However, gene flow among populations on the same mountain appears to be unrestricted, as indicated by the weak genetic isolation among these populations.The total genetic diversity of  Habitat fragmentation is a significant threat to the maintenance of biodiversity in many terrestrial ecosystems Fragmented distributions of plant populations are caused not only by human activity but also by natural factors, such as long-term, large-scale climate oscillations, topographical changes, the isolation of suitable habitats, or other ecological changes. Studies of the genetic diversity of naturally fragmented populations may not only reveal the ecological consequences of population fragmentation over long periods of time but also provide a frame of reference for predicting the consequences of habitat fragmentation by human activities Rhodiola dumulosa (Crassulaceae) is a perennial plant species that is found in northern, central and northwestern China. R. dumulosa is suggested to be a diploid plant R. dumulosa from different mountains across its entire distribution in China. We then used these results to address the following questions: (1) How much genetic diversity is maintained in these naturally fragmented populations of R. dumulosa? (2) How is the genetic diversity spatially structured across the entire distribution range of this species in China? (3) Is there any gene flow among these naturally fragmented populations?In this study, we used AFLP analysis to assess the genetic diversity, differentiation and structure of isolated populations of Four AFLP primer combinations were used to analyze 1,089 individual genotypes, resulting in 225 markers, 221 (98.22%) of which were polymorphic.r = 0.801; P<0.01) across the whole sampled region, indicating significant isolation-by-distance. We also conducted separate Mantel tests on the Donglingshan and Heyeping populations, in which populations were collected according to altitude, to determine whether isolation-by-distance also occurred on a smaller geographical scale. These results did not indicate any significant correlation between genetic differentiation and geographical distance within these populations .The Mantel test  revealedHierarchical cluster analysis (UPGMA) showed that populations sampled from northern China and central China were grouped separately according to their geographical distribution . ClusterTo further test this population structure, a model-based clustering method was implemented in the program STRUCTURE P<0.001) with 50.08% within populations and 49.92% among populations, indicating that the total genetic variation was almost equally divided between intrapopulation and interpopulation variations. This approximately equal partitioning could have been the result of significant differentiation among the populations in northwestern China and frequent gene flow among some populations in northern China; we sampled more closely distributed populations in northern China than in northwestern China. Therefore, we randomly chose one population from each locality  and repeated the analyses (P<0.001) with 54.25% of variation occurring among populations, only slightly higher than the variation within populations. Therefore, we conclude that the differentiation among groups was significant, but the genetic variation within populations was maintained.Analysis of Molecular Variance (AMOVA) based on the two genetic clusters indicated that majority of genetic variation (43.49%) occurred within populations, while the variation between the two clusters was 23.68% . AMOVA banalyses . After er = −0.478, P = 0.004) across the entire range of R. dumulosa. A regression analysis of Nei's genetic diversity and altitude using SPSS 15.0 revealed that population diversity decreases with increasing altitude . The estimated gene flow, Nm (Nm = 0.5(1− Gst)/Gst), was 0.5695. The results of this analysis reveal that the genetic differentiation among populations throughout the entire distribution area is significant and that gene flow is restricted. Furthermore, the populations in northern China always congregated together as a cluster . The genetic differentiation, Gst, among all northern populations was 0.3155, and the Nm was 1.0847. Thus, the gene flow among populations in northern China was much higher than that across the entire range.The genetic differentiation among 35 populations of On some mountains, more than one population was sampled. For instance, there were five populations in Donglingshan and four populations in Heyeping . To inveR. dumulosa has maintained a high overall genetic diversity (Ht = 0.2473), similar to that of the two other species of Rhodiola but much higher than that of other perennial plants  in some localities. Moreover, the gene flow among populations on the same mountain was high (Gst and Nm for Donglingshan and Heyeping populations). Another explanation could be that this high genetic diversity is a reflection of high historic genetic variability, which is quite common in long-lived perennial plant species Our analysis of AFLP molecular markers indicated that l plants . Thus, tR. dumulosa is thought to breed by facultative xenogamy, which requires pollinators for outbreeding R. dumulosa concluded that populations in open fields, such as those near peaks, received more frequent pollinator visits than those at lower altitudes R. dumulosa populations in high-altitude montane regions, whereas the populations in lower altitude areas with more favorable climate conditions survived. During interglacial periods, some offspring of the surviving low altitude populations recolonized the higher altitude. Thus, populations in higher altitude sites, derived from only a few individuals from lower altitude populations, would exhibit less genetic variation. Greater genetic diversity at in situ survival areas than at recolonized areas was also found in a number of phylogeographic studies, especially in the Alps Analyses of the correlation between population genetic diversity and geographic altitude showed a weak but significant negative correlation. The lower genetic diversity of populations at higher altitudes might be the result of the smaller populations of pollinators in these areas; R. dumulosa in China. This pattern suggested that the dispersal of this species might be constrained by distance such that gene flow is most likely to occur between neighboring populations R. dumulosa across its range in China. However, we also found that the gene flow among populations on the same mountain is relatively unrestricted, and the genetic differentiation among these populations is weak. For example, the Gst and Nm of the five populations in Donglingshan were 0.1125 and 3.9438, respectively, while the Gst and Nm of the four populations in Heyeping were 0.0728 and 6.6390. Furthermore, Mantel tests conducted on these two groups of populations revealed no significant correlations between genetic and geographical distances. Thus, our AFLP data suggest that although an isolation-by-distance pattern may be detected across the whole range of R. dumulosa, the gene flow and the relationship between geographical and genetic distances have different patterns at different spatial scales. Similarly distinct patterns at different spatial scales were also found for some other plant species A strong correlation between genetic and geographical distances  revealed a pattern of isolation-by-distance across the distribution range of R. dumulosa could be split into two clusters, one in northern China (NC) and the other in central and northwestern China (CNWC). In particular, the populations collected from northern China always clustered together regardless of the approach used for genetic structure analysis. AMOVA within the northern populations also clearly indicated that the genetic variation within populations was higher than that among populations . Thus, we treated the northern populations as a reasonable cluster. However, when the populations from central and northwestern China were pooled together, AMOVA indicated that there was greater genetic variation among populations than within populations (56.90% and 43.10%), and the variation between NC and CNWC clusters was only 23.68%. Thus, the central and northwestern populations may not form a good cluster. More data may help resolve the genetic relationship between populations in these two areas. Moreover, when AMOVA was conducted based on different grouping approaches, we always found high genetic variation within populations , were sampled from 19 localities across the entire distribution of this species in 2005, 2006 and 2007. The sampling area ranged from the northernmost population “DQ” in the Inner Mongolia Autonomous Region (40.838°N) to the southernmost population “SNJ” in Hubei province (31.446°N) and from the easternmost population “WL” in Hebei province (117.461°E) to the westernmost population “DT” in Qinghai province (101.691°E) (Thirty-five populations of 1.691°E) , Table 2Leaf material was stored in zip-lock plastic bags with silica gel until DNA extraction. Sample vouchers were deposited in the collection at Beijing Normal University. Genomic DNA was extracted from silica gel-dried leaf material using a plant DNA extraction kit  according to the manufacturer's protocol, with some modifications.et al.EcoRI-ATG, MseI-CTG, MseI-CAA, MseI-CAC and MseI-CAG) were used for selective amplification. Selective amplification products were separated using an ABI 3100 sequencer (Applied Biosystems) with a GeneScan ROX 500 internal size standard. Electropherograms were then analyzed using GENEMAPPER 3.7 software (Applied Biosystems). To create a binary matrix, amplified fragments of 80–500 base pairs were scored visually as having present (1), absent (0), or ambiguous (?) peaks in the output traces. Only distinct peaks were scored as present, and the manual scoring procedure was repeated twice on separate occasions to reduce scoring errors.AFLP fingerprinting was performed according to the original protocol presented by Vos The unweighted pair group method with averages (UPGMA) clustering analysis, derived from the Nei's minimum distance matrix Table S1Membership of each pre-defined population in each of the two clusters generated by CLUMPP based on the STRUCTURE v2.2 analysis (K = 2).(DOC)Click here for additional data file.Table S2Population genetic diversity of R. dumulosa. .(DOC)Click here for additional data file.


P = 0.001) is observed, while gene pool I is clearly separated from the other two. We propose that these three gene pools represent genetic signatures of separate events in the history of the continent that relate to the arrival and dispersal of village chickens and humans across the region. Our results provide new insights on the history of chicken husbandry which has been shaped by terrestrial and maritime contacts between ancient and modern civilizations in Asia and East Africa.Unravelling the genetic history of any livestock species is central to understanding the origin, development and expansion of agricultural societies and economies. Domestic village chickens are widespread in Africa. Their close association with, and reliance on, humans for long-range dispersal makes the species an important biological marker in tracking cultural and trading contacts between human societies and civilizations across time. Archaezoological and linguistic evidence suggest a complex history of arrival and dispersion of the species on the continent, with mitochondrial DNA (mtDNA) D-loop analysis revealing the presence of five distinct haplogroups in East African village chickens. It supports the importance of the region in understanding the history of the species and indirectly of human interactions. Here, through a detailed analysis of 30 autosomal microsatellite markers genotyped in 657 village chickens from four East African countries , we identify three distinct autosomal gene pools . Gene pool I is predominantly found in Ethiopia and Sudan, while II and III occur in both Kenya and Uganda. A gradient of admixture for gene pools II and III between the Kenyan coast and Uganda's hinterland ( Across Africa, domestic village chicken are raised under free-range scavenging conditions and show large variations in qualitative and quantitative traits  , including two layers (White Leghorns from Europe (n = 26) and USA (n = 35)), one broiler (Chunky (n = 35)) and one multipurpose breed (Barred Plymouth Rock (n = 16)) were also genotyped. These were chosen to represent the main commercial breeds usually imported into the study area for crossbreeding with local flocks to improve egg and meat production . All genotypes were double-blind scored independently by five people conversant with the scoring of microsatellites.We genotyped 657 birds from 15 populations of village chickens from four countries in East Africa , giving an average sample size of 43 birds (range = 28–54) per population a, b. Theet al.  and are m of each primer, 2.5 mm of each dNTP, 1.5 mm MgCl2 and 1 unit of Taq DNA polymerase (Promega). All amplifications were carried out on an Applied Biosystems 9700 Cetus thermal cycler and involved an initial denaturation at 95 °C (5 min), 35 cycles of denaturation at 95 °C (1 min), primer annealing at temperatures varying between 58 and 62 °C (1 min) and extension at 68 °C (1 min). A final extension step at 72 °C (1 min) completed the PCRs. Genotyping was carried out on an ABI PRISM 3100 automated capillary sequencer using the GS400HD Rox internal lane size standard. Allele size calling and binning were carried out with GeneMapper v3.5 using the 3rd Order Least Squares regression method (Applied Biosystems). All loci were examined for technical artefacts with the software Micro-Checker  products were obtained in 10 μL multiplexed reactions containing 10 ng template DNA, 1X Buffer (Promega), 10 pHe) and observed (Ho) heterozygosity) were estimated from allele frequencies with FSTAT 2.9.3.2  was partitioned into within (FIS) and among population (FST) components following Weir & Cockerham , allelic richness, polymorphic information content (PIC), effective number of alleles) and genetic diversity  to vary between 1 ≤ K ≤ 15, using a burn-in of 50 000 followed by 100 000 Markov Chain Monte Carlo (MCMC) iterations. Ten simulations were carried out for each K assuming four scenarios: (i) populations are admixed and allele frequencies correlated; (ii) populations are admixed and allele frequencies independent; (iii) populations are not admixed but allele frequencies are correlated; and (iv) populations are not admixed and allele frequencies are independent. To estimate the most optimal K, we used three approaches. First, we used the best log-likelihood score resulting in the highest percentage of membership coefficient (q) to each cluster  was plotted against ΔK = m|L”(K)|/s|L(K)| and the optimal number of clusters identified by the largest change in log-likelihood (L(K)) values between the estimated number of clusters  implemented in GENETIX 4.05 (Belkhir Tv) for each marker; the contribution of markers to the construction of the reference typology, which is equal to the product of the variance (Var) multiplied by the congruence with the consensus Cos2   (Chessel & Hanafi T2-test with the modified two-phase mutation model (TPM) (Garza & Williamson T2-test), detects recent bottlenecks on the principle that a reduction in effective population size leads to an exponential decay in heterozygosity and allele numbers at polymorphic loci and that reduction in allelic diversity is more pronounced and faster than the decline in heterozygosity  and the inter-locus variance test (g-test) , or the combination of the variance and kurtosis  of negative and positive k-values. The g-test, on the other hand, compares the observed and estimated values of the variance in allele sizes across loci. In stable populations, the variance is highly variable among loci, whereas in expanding populations, it is much more even. For this test, low variances in allele sizes may be taken as evidence of expansion, and we used the cut-off values given in et al. (k- and g-tests were performed using the Macro program ‘kgtests’ (Bilgin Demographic history of the populations was investigated by assessing whether or not East African village chicken populations are at mutation-drift equilibrium (MDE). We searched for signals of population expansions or contractions using four statistical approaches. Using the program Bottleneck  of above 0.2 for the commercial cluster were regarded to be influenced by commercial breeds. The possibilities of non-random associations between genetic differentiation, measured as [FST/(1-FST)] (Rousset http://ibdws.sdsu.edu). Geographic distances between populations were calculated using the MapCrow Travel Distance Calculator (http://www.mapcrow.info/) as the distance between the central most towns within each sampling locations. To investigate the ecological specificity of any genetic clusters generated by STRUCTURE, FCA and PCA as an indirect indicator of adaptation to different ecological zones (eco-zones), we tested whether any of the genetic clusters were associated with any of the eco-zones . Using MICROCHECKER, we did not detect any consistent evidence for occurrence of null alleles across populations. Further details regarding the microsatellite loci and their allele sizes are presented in Supplementary information S1.P < 0.001; Wilcoxon rank-sum test corrected for multiple comparisons) less variable  = 7.61 ± 4.02; He = 0.61 ± 0.02) than Kenyan ones  (P > 0.05), although a marked difference between Ho and He can be observed in all the populations analysed.The MNA was 9.50 ± 5.56 and the effective number of alleles was 3.40 ± 1.59 across village chicken populations . The Tes ± 0.02) . Overall ± 0.02) . HoweverK varying from 1 to 15 assuming different models of population admixture and allelic correlations . All scenarios tested gave similar results ’ was first applied but no distinct plateau of the estimates of the LnP(D) was observed  against K  indicating two distinct clusters. These two clusters group together Sudan and Ethiopia chicken on one hand and Kenya and Uganda chicken on the other. However, K = 3, there is a further separation of the populations, which decay when K is increased beyond four clusters for the Kenyan and Ugandan chickens but the cluster incorporating Ethiopian and Sudanese chickens remains distinct. We further performed another STRUCTURE run using only Kenyan and Ugandan chickens. As expected, the delta K approach revealed an upper ΔK value of two groups lending further support to the existence of two genetic groups in Kenyan and Ugandan chicken as seen in STRUCTURE, FCA and PCA analyses reveal the genetic partitioning of the observed microsatellite diversity at the individual and population level. We ran STRUCTURE with K = 3 , II (green) and III (purple) , and our previous knowledge on the mtDNA D-loop haplogroup pattern in these populations (Mwacharo (purple) a, b.The three gene pools occur in the four countries in different proportions. In Ethiopia, gene pool I occurs with the highest frequency (95.9%), while the other two are observed at very low frequencies (II at 1.65% and III at 2.45%), and in only four birds which show a mixed genetic ancestry of the three gene pools. Gene pools I (99.9%) and III (0.1%) are found in Sudan, with III occurring in a single bird of mixed genotype. All three gene pools are observed in Kenya and Uganda where gene pools II and III are the most frequent. Interestingly, gene pool II occurs with a high frequency in populations found in the Eastern side of Kenya, while gene pool III is predominantly found in populations from the Western side of Kenya and in Uganda. Gene pool I, which is mainly found in Ethiopia and Sudan, occurs in Kenya and Uganda with a maximum population frequency of 0.59% and 0.05%, respectively, and in individuals showing a mixed genetic make-up between the three gene pools. The spatial geographic distribution of Gene pool II and III overlap to reveal an East–West genetic cline, II decreases in frequency from the Eastern side of Kenya to the hinterland of Uganda and the opposite is observed for III a, b.To further understand the genetic differentiation between gene pools, we excluded the 235 admixed individuals from the data set, leaving a total of 423, and reanalysed the data. We observe 274 alleles out of which 162 (59.12%) are now shared between the three gene pools  and re-ran STRUCTURE. In all cases, we ended up observing the three gene pools in the study populations with identical geographic distribution and similar pattern of genetic diversity  between the 15 village chicken populations  for each population and across groups of populations (P ≤ 0.05) indicating a deficiency in heterozygotes. Excluding one individual from each homestead and recalculating the FIS values returned similar results indicating that this was not an effect of the sampling strategy but possibly of long-term mating between closely related birds and/or small effective population sizes. Overall, low FST and high FIS values were observed, and it is most likely that the effects of inbreeding are counterbalanced by genetic intermixing due to extensive exchange and movement of genetic stocks among local farmers.The global estimate of ulations . Removinulations  were posK ≤ 5 when commercial breeds are included in the analysis and assuming no admixture between populations and no correlation among allele frequencies. Although Evanno et al. . It excludes the possibility of any gene pool to be significantly influenced by commercial introgression.o et al.  approachb = −0.869; R2 = 0.630; P = 0.001) between geographic distance and the proportion of gene pool II and a significant but positive one  between geographic distance and the proportion of gene pool III (FST/(1−FST)] . This therefore means that there has not been enough time for population movements to obscure the genetic signatures of the initial colonization of the region by the three distinct gene pools.Regression analysis between pairwise geographic distances (expressed in km) from the Kenyan coastal town of Mombasa against the proportions of gene pools II and III across Kenyan and Ugandan populations reveals a significant negative regression coefficient  indicating no relationship between the eco-zones and the three gene pools. Indeed, the distribution of the three gene pools cut across different ecological zones. For instance, all three gene pools are found in eco-zone II and gene pool I occurs in eco-zones II, III and V and across different countries  or excess (0.251 ≤P ≤ 0.978). However, when all the 15 populations were pooled together, the T2-test revealed significant heterozygote deficiency (P = 0.001). We also found a heterozygote deficiency (0.001 ≤ P ≤ 0.046) when the three gene pools were analysed individually or when taking into account all the three gene pools together (P = 0.004). On the other hand, the mode shift indicator test did not detect any shift in allele frequency distribution across populations, at individual population levels, across gene pools and for individual gene pools. Both the intra (k)- and inter (g)- locus tests revealed evidence of demographic expansions across populations and across the three gene pools. Across populations, the k-test revealed 9 of 30 loci to have a positive kurtosis, a number which was significantly different from the expected binomial distribution (P = 0.014). Across gene pools, 10 of 30 loci showed a positive kurtosis, a number that differed significantly from the expected binomial distribution (P = 0.035). Similarly, six, ten and nine loci showed a positive kurtosis for gene pools I, II and III respectively, and these were also significantly different from the expected binomial distribution (P ≤ 0.05). The g-test revealed a g-ratio of 4.826 across populations, 7.095 across the three gene pools, 7.005 for gene pool I, 7.201 for gene pool II and 7.672 for gene pool III. From et al. , and between gene pool III and mitochondrial haplogroup D  in Kenya and Uganda  across village chicken populations within the East African region a, b. Ourvice versa gradients of admixture observed  and fat-tailed sheep (Ovis aries) (Hanotte et al. et al. It is worth mentioning that linguistic and archaeological evidences are supporting the presence of more than one gene pool of domestic chicken on the African continent. More particularly, linguistic evidences suggests at least three separate introductions of domestic chickens to West Africa; two across Central Africa from the East coast of Africa and one from the North across the Sahara (Williamson et al. et al. et al. et al. et al. et al. In the absence of comparable detailed microsatellite information across the geographic range of domestic chicken in Asia, we can only speculate about the Asiatic origins of the autosomal gene pools I, II and III, their entry points in the Horn of Africa and subsequent dispersion. In this respect, it is worth mentioning that we do observe a positive correlation between the proportions of autosomal gene pools and mtDNA haplogroups in the same populations (Mwacharo et al. et al. et al. et al. In conclusion, our study provides the first detailed empirical assessment of the spatial distribution of genetic diversity and structure of indigenous village chickens in East Africa. The results indicate that there is moderate-to-high genetic diversity among East African village chickens that can be attributed to three distinct gene pools. The spatial distribution of the three gene pools independent of any eco-climatic influence, selection pressure and/or introgression of exotic commercial blood is compatible with the arrival of three independent waves of domestic chicken in Africa. Our results underline the complex history of the species in the Horn and the East African region and illustrate the important role of the later for understanding the agricultural history of the continent. Our findings also support an intricate web of interactions between Asia and the East African region along maritime and terrestrial corridors but also within the Horn of Africa. Last but not least, along with other studies (Hanotte


The level of expected evolutionary change depends on the level and distribution of genetic variation of TBB. While numerous experimental studies have highlighted the role of divergent selection in promoting clinal TBB differentiation, we explored whether the observed patterns of variation could be generated by the joint effects of assortative mating for TBB and gene flow among natural populations. We tested this hypothesis using an Our simulations showed that genetic clines can develop even without divergent selection. Assortative mating in association with environmental gradients substantially shifted the mean genetic values of populations. Owing to assortative mating, immigrant alleles were screened for proximal or distant populations depending on the strength of the environmental cline. Furthermore, we confirmed that assortative mating increases the additive genetic variance within populations. However, we observed also a rapid decline of the additive genetic variance caused by restricted gene flow between neighboring populations resulting from preferential matings between phenologically-matching phenotypes.We provided evidence that the patterns of genetic variation of phenological traits observed in forest trees can be generated solely by the effects of assortative mating and gene flow. We anticipate that predicted temperature increases due to climate change will further enhance genetic differentiation across the landscape. These trends are likely to be reinforced or counteracted by natural selection if phenological traits are correlated to fitness. Monitor growth , (ii) itcundity . The ada values . Additio values . Finally values .in situ observations of TBB show congruent patterns across species: bud burst in southern latitudes or lower altitudes occurs earlier than in northern latitudes or higher altitudes [e.g. populations from southern latitudes flush earlier than populations from northern latitudes, when assessed under the same conditions in common gardens [Regardless of species, TBB follows strong geographic clinal patterns of variation, either altitudinal, latitudinal or longitudinal. Phenotypic clines revealed by titudes -12, becatitudes . Genetictitudes ,15. Co-ggardens ,17. In tgardens ,19.Clinal variations, either co- or counter-gradient, have usually been interpreted as consequences of divergent selection among populations by either abiotic or biotic selection pressures. For example, late-flushing trees will not suffer the detrimental effects of late frosts  or may ain silico the evolution of TBB under contrasting levels of assortative mating and environmental clines.A number of theoretical studies have dissected the effects of assortative mating on the evolution of quantitative traits under polygenic inheritance, beginning with the early investigations by Fisher (1918)  and WrigVW and VB) as well as the differentiation among populations as measured by QST, which are standard genetic measurements used in quantitative genetics. Our main objective was to track components of genetic variation in phenology-related traits in a subdivided population that would mimic extant ecological settings. We were primarily interested in assessing the within- and between-population genetic variances   and KremQST) and has only a minor impact on differentiation at QTLs  matings between individuals sharing similar genetic values, which would correspond to positive assortative mating and (2) matings between individuals likely to have different genetic values resulting from gene flow. In the extreme case, these matings may result from negative assortative mating. The shift of the genetic value is therefore driven by the level of effective gene flow kE = 1, kE = 2 and kE = 3.We simulated the evolution of 55 populations of 500 individuals each spread homogeneously on a 5 × 11 grid depicted in Figure ρ = 0.3 and ρ = 0.8 to model moderate and strong assortative mating, respectively. Random mating was considered as well with ρ = 0.Recent observations in oak populations suggested that assortative mating for TBB is substantial . Indeed,Nm = 5.1 and Nm = 10.2. These values fit the range of variation of FST values (2.4% to 4.7%) observed in natural oak populations [mp and ms) were then inferred from Nm values and introduced in the simulations, assuming further that mp = 100∗ms . Indeed, under assortative mating, genetic variances increased rapidly during the early generations, then they very rapidly dropped below even the level of genetic variance reached under random mating. As for covariances, there was a strong effect of the level of assortative mating and only a minor effect of the environmental cline. The decrease in genetic variance due to assortative mating could be dramatic after 400 generations. Furthermore, the final heritability for the trait was divided by a factor 2.5 at generation 500. As expected without selection in large populations, genetic variance was maintained under random mating and extensive gene flow.The variations in θBincreased during the early generations and was maintained at higher values through the 1000 generations, in contrast to θW values. There was a stronger impact when environmental clines were steeper. For example, under strong assortative mating, the maximum value of θB was 2.7 when kE = 2vs 2.5 when kE = 1. The initial phase of increase lasted longer under moderate assortative mating than under strong assortative mating: 500 generations vs 230 generations when kE =  1 , which was very close to differentiation at neutral markers 0.024) (data not shown). Overall, between-population genetic variances exhibited strong differences between moderate and strong assortative mating and also between low and strong environmental clines . Consequently, phenological matches between trees from different populations were limited, thus increasing the filtering of incoming genes to proximal populations , no significant change was observed in the slopes of the clines. However, additional investigations indicated that lower migration rates decreased the slopes of the genetic clines and induced higher QSTvalues, owing to an important drift effect [QST and VW . Figure Our simulations demonstrated that genetic clines could be established in the absence of divergent selection. We showed that the combination of assortative mating and pre-existing environmental clines resulted in population genetic differentiation along the environmental cline. We also confirmed that assortative mating increased the within-population genetic variances in the early stages of the evolutionary scenarios. However, assortative mating was also responsible for the severe decline in genetic variation in later generations.Δ obtained at the extreme northern latitude under the strongest assortative mating intensity and across the steepest environmental cline.These patterns resulted in a positive covariance between genetic and environmental population values and corresponded to what has been called co-gradient variation ,15. We dWe can subdivide the evolutionary process into three main phases, illustrated in Figures Δremains limited: 0.0798 at the allelic level for northern populations.(1) In the very early generations (05), the mean genetic value is 0 for all populations, there is no within-population allelic covariance, and alleles are randomly spread over the landscape. During this period, assortative mating will generate phenotypes with extreme genetic values in each population. Hence the genetic variance within populations increases as predicted by previous analytical models ,34 and nθW. The genetic variance between populations continues to increase steadily. During the second phase, the Δvalue tends to be larger (0.14) as a result of more divergent alleles imported by distant gene flow. A similar effect that symmetrically decreases the Δvalue of incoming gene flow within southern populations is expected to take place at the same time. As a result, the mean genetic values of the population shift strongly, leading to the progressive formation of the genetic cline.(2) From generation 5 to about 30, because the increase in within-population genetic variance has now produced phenotypes with more extreme values, gene flow tends now to import alleles from more distant populations Figure . The fra(3) After generation 30, most of the alleles have been spatially redistributed by gene flow constrained by assortative mating at the landscape level. Allelic covariations within populations have been exhausted and the genetic variance has now reached its maximum. Assortative mating within populations tends now to become a selective factor favoring phenotypes following the shift of the mean genetic values. Furthermore, gene flow again becomes strongly restricted to neighboring populations that share fewer divergent alleles than distant populations. Restricted gene flow therefore reinforces the decrease in the genetic variance. Overall, phase 3 is characterized by a continuous decrease in genetic variance and the reaching of an asymptotic mean genetic value in populations; the genetic cline is establishing. We further advocate that restricted gene flow, together with within-population assortative mating, now constrains effective population sizes, accelerating the decrease in genetic variance due to drift. A similar decrease was observed by Devaux and Lande  in a sinThese three phases were observed for all of the simulation settings we used. The lengths of the two first phases extended over longer periods, populations differentiated more rapidly, and genetic clines were shaped faster under strong assortative mating. By dissecting the evolutionary process, we showed that the screening of immigrant alleles due to assortative mating triggers shifts in the genetic values of populations Figures  and 9. kE values (kE = 3).Increasing the slope of the environmental cline generated more genetic variance and higher genetic differentiation as well. According to equation 15, each generation steeper environmental clines increase the expected divergence between mates from distinct populations. However, the divergence is constrained by the necessary overlap of parental flowering times. If long distance pollen flow is restricted by large phenological differences among populations, then assortative mating will favor matings between proximal populations, and the shift in genetic values will be limited. In our simulations, the latter case occurred when we explored very large Δand considering the features of the stepping-stone migration model, limited Δ values are expected owing to pollen flow from adjacent latitudes. However, incoming alleles from neighboring northern populations balance with incoming alleles from neighboring southern populations. As a consequence, the shift induced within populations by southern gene flow is systematically canceled by the one caused by northern flow, resulting in a null contribution to the Δ values. Finally, because under the stepping-stone migration model, incoming gene flow is latitudinally unbalanced at the northern and southern margins of the grid, the genetic values of populations can be shifted by assortative mating at these latitudes. These results suggest that the spatial configuration of the populations in combination with the migration model may also contribute to the building of the genetic cline. Any combination that increases an asymmetry in gene flow between northern and southern populations will enhance the genetic cline, while symmetry will tend to even out the effects of northern and southern gene flow.A similar outcome was observed under the stepping-stone migration model. In this case, populations do not differentiate except at the northern and southern edges of the landscape Figure . This rekE varying between 1 and 3), when long distance pollen flow is possible, and when the patterns of incoming pollen flow at population level are unbalanced regarding the environmental cline. Interestingly these criteria are met under realistic situations. Taking oaks as an example, flushing dates may vary over 5 weeks from southwestern to central France [To summarize, the construction of a genetic cline as a result of the combined effects of gene flow and assortative mating can only be met under certain circumstances when there is a balance between the intra-population and between-population phenotypic variance , island migration model and steep environmental cline (kE = 2). Brown line: Nm = 10.2, red line: Nm = 5.1, green line: Nm = 1, green dashed line: Nm = 0.5 and green dotted line: Nm = 0.1. Each line represents the mean of 50 independent replicates for each evolutionary scenario.Figure S2. Variations in mean population genetic values at different latitudes under a range of migration rates. The value for each latitude is the average of the five mean genetic values for the populations concerned at generation 300. All scenarios were conducted under strong assortative mating , island migration model and steep environmental cline (kE = 2). Brown line: Nm = 10.2, red line: Nm = 5.1, green line: Nm = 1, green dashed line: Nm = 0.5 and green dotted line: Nm = 0.1. Each line represents the mean of 50 independent replicates for each evolutionary scenario.Figure S3. Click here for file


Helicoverpa armigera is an important pest of cotton and other agricultural crops in the Old World. Its wide host range, high mobility and fecundity, and the ability to adapt and develop resistance against all common groups of insecticides used for its management have exacerbated its pest status. An understanding of the population genetic structure in H. armigera under Indian agricultural conditions will help ascertain gene flow patterns across different agricultural zones. This study inferred the population genetic structure of Indian H. armigera using five Exon-Primed Intron-Crossing (EPIC)-PCR markers. Nested alternative EPIC markers detected moderate null allele frequencies (4.3% to 9.4%) in loci used to infer population genetic structure but the apparently genome-wide heterozygote deficit suggests in-breeding or a Wahlund effect rather than a null allele effect. Population genetic analysis of the 26 populations suggested significant genetic differentiation within India but especially in cotton-feeding populations in the 2006–07 cropping season. In contrast, overall pair-wise FST estimates from populations feeding on food crops indicated no significant population substructure irrespective of cropping seasons. A Baysian cluster analysis was used to assign the genetic make-up of individuals to likely membership of population clusters. Some evidence was found for four major clusters with individuals in two populations from cotton in one year (from two populations in northern India) showing especially high homogeneity. Taken as a whole, this study found evidence of population substructure at host crop, temporal and spatial levels in Indian H. armigera, without, however, a clear biological rationale for these structures being evident. Helicoverpa armigera (Hübner) on a wide range of wild and crop hosts across different ecological zones, its highly variable life-history traits  and seasonal abundance present a unique challenge for ecological and evolutionary studies. The number of generations possible per year is directly influenced by temperature, rainfall and presence of suitable hosts H. armigera is an important pest of cotton, legumes, cereals and vegetables, and presents a unique challenge to those studying its population genetic structure.The polyphagous nature of the Old World cotton bollworm H. armigera at any given time of the growing season H. armigera to move between hosts and geographic areas throughout the year. Furthermore, the presence of three major cropping scenarios in India  are influenced by the pattern of the monsoons H. armigera all year round in any given region, although cotton represents the main host crop on which this pest species completes three out of possible seven to eight generations annually in 11 states H. armigera populations, between March and May H. armigera in central India is likely to occur at the end of the cropping season (December–January), while rains prolong the growing season in northern and southern India, with the resulting adult migration in these regions typically occurring around March–April The farming landscape in India is predominantly characterised by small farms and mixed cropping systems. The cropping patterns in India normally ensures the presence of five to six different host crops in different proportions for H. armigera could be categorized into races based on host-feeding preferences and limited inter-mating  preference The only India-wide major polyphagous crop pest thoroughly examined for genetic diversity is the whitefly H. armigera population genetics based on different DNA markers such as random amplified polymorphic DNA H. armigera. In Australia, studies have revealed small genetic distances between widely separated populations based on isozymes H. armigera. Endersby et al. H. armigera populations collected from the southern and western regions of Australia and found no significant patterns of population substructure. The conflicting findings of Scott et al. Studies of H. armigera, effective and reliable molecular genetic markers must demonstrate efficiency in PCR amplification in individuals from within and between populations within a country, and between populations from different countries. Although less likely to be affected by TE-induced PCR failures as seen in various lepidopteran microsatellite markers , 5 populations of pigeonpea , 4 populations of chickpea  and one of eggplant . Larvae were collected by direct sampling from different host plants, either directly into ethanol until needed for gDNA extraction, or kept on artificial diet until the pupal stage. Some of these were taken as samples only after they had emerged into adult moths. Male moths from Nagpur_1 and Nagpur_2 were collected by pheromone traps ; followed by 35 cycles that consisted of template denaturation  – primer annealing at specific temperature for 1 minute 2O; post-PCR desalting was undertaken according to the protocol provided by GAF. Genotypes were scored manually with the help of marker panel set options implemented in the program GENETIC PROFILER 2.2 (Amersham Biosciences). All genotypes were scored unambiguously and where necessary allele peaks were corrected manually. Individuals which amplified for one locus but failed to amplify in PCR for other loci, were repeat amplified for up to a further two times. If a sample failed to amplify across all loci in at least one of the three rounds of PCR, it was considered as a DNA-extraction failure and discarded from subsequent analyses.The forward primer of each EPIC-PCR marker was labelled with a fluorescent dye  to allow detection during electrophoresis. All amplifications were performed in a 15 µL reaction volume consisting of 7.5 µL of 5× GoTaq® Green Master Mix (Promega # M7122); 1.8 mM MgClTo estimate EPIC-PCR markers null allele frequencies we designed nested  EPIC-PCR forward and reverse primers for the EPIC-PCR primer pairs used . For altFISFIS, an inbreeding coefficient, measures the reduction in heterozygosity of an individual due to non-random mating within its sub-population. Observed (Ho) and expected (He) heterozygosity were estimated and departures from Hardy-Weinberg equilibrium (HWE) were tested using the probability test as implemented in GENEPOP version 3.2 P values derived by a Markov chain method . The significance values for multiple significance tests were set using the sequential Bonferroni procedure FST estimates Basic statistics for the EPIC-PCR data  were excluded because of the unknown host crops. Erode, the only cotton population in season 1 (2004–05), was also excluded from these analyses.The geographic partitioning regime used by Kranthi et al. tructure v2.3.2 K’) through the assumption of known source populations and HWE at all loci K we evaluated all possible K's  using simulation of 20 iterations, with each iteration consisted of 50,000 ‘burnin’ followed by 500,000 Markov Chain Monte Carlo (MCMC) replications, with default settings for both the Ancestry Model (Admixture Model) and the Frequency Model . The ΔK method of Evanno et al. K value, although the log probabilities of data (Ln P(D)) for K were also evaluated. The proportions of an individual's genome belonging to particular K population clusters are given a ‘Q’ score which enables Structure to assign individuals  to a particular cluster The program SH. armigera population genetics structure. As we were unable to estimate the null allele frequency of RpL12, analyses of population substructure patterns of our Indian samples were performed by both including or excluding RpL12 , reduced in seven populations , and increased in the remaining 16 populations. Taken as a whole, the inclusion of the RpL12 locus lead to lower and/or no change in FIS estimates in 19 of the 26 populations studied, and did not drastically lower FIS estimates in cotton populations, suggesting that this marker is unlikely to have harboured an excessively high frequency of null alleles.Of the five sets of nested EPIC-PCR markers, we failed to design an alternative RpL12 EPIC primer due to the short exon sequence available, and the null allele frequency for this marker was therefore not estimated. Based on the alternative EPIC-PCR markers alt_RpS2, alt_RpL29, alt_RpL3 and alt_RpS6, null allele frequencies for the original EPIC-PCR markers were estimated at 9.4%, 6.5%, 6.3%, and 4.3% respectively which were considered as being at a moderate level ng RpL12 . FIS esH. armigera. The most polymorphic marker was RpS6 , followed by RpL29 , RpL3 , RpS2  and RpL12 . The mean number of alleles and the mean observed (Ho) and expected (He) heterozygosities for each population are shown in A total of 155 alleles were scored from the five loci  in 26 populations (n = 786) of H. armigera. This significant deviation from Hardy-Weinberg equilibrium was due to an excess of homozygotes at all loci, and is further reflected by the FIS values , host plants  or cropping seasons . However significant genetic structures were detected among populations within geographic regions , within host plants   and within cropping seasons . Large variations (FST>95%) were found within populations in each of three model structures  contrasted with those observed in cotton populations and were generally non-significant (except Coimbatore) . However, using the Evanno et al. K value (614.37) at K = 2 followed by the second highest ΔK value of 4.06 at K = 4  towards more plausible scenario of presence of population substructure . The large and positive ΔK value for K = 4  optimum K = 8. Between K = 2 and K = 9, the Bharuch (pop. 4) and Surat (pop. 5) populations in central India appeared highly homogeneous, while all other populations showed higher levels of admixture  of 20 simulations for each at K = 4 , Fig. 3.or K = 4  was therresented  to help dmixture . Detaileat K = 4 , which dH. armigera samples using EPIC markers clearly indicated no obvious population substructure with geographic region, year or crop, and indicated significant genetic differentiation between northern and central/southern Indian cotton-feeding populations in the cropping season of 2006–07. Within the populations HW non-equilibrium was detected, with a likely contributing factor being the presence of null alleles within our DNA marker system. Null allele frequency estimates for four of these EPIC-PCR markers were moderate (4.3% to 9.4%). The conclusion on population substructure patterns remained overall unaltered regardless of the inclusion or exclusion of the untested RpL12 locus from analysis. In the population genetic study involving predominantly Australian H. armigera populations, Endersby et al. FST estimates were obtained when null alleles in marker systems were not excluded , although within populations with substantial gene flow , FST estimates were less biased than in populations with limited gene flow .Population genetic analysis of Indian H. armigera population genetic markers are relatively common, despite attempts to minimise their occurrence by designing markers at conserved gene coding regions. The effort in designing EPIC-PCR markers has other advantages such as enabling nested PCR markers to be developed for retesting of populations at the same loci, as well as the overall lower null allele frequencies as compared with null allele frequencies from random SSR markers which may be associated with TE's H. armigera populations tested, further indicating only low to moderate levels of null alleles affecting our EPIC markers.Our study further demonstrated that null allele events in FIS, may further affect interpretations of population structure of H. armigera in India. The sample size of n = 786 allowed us to detect significant departures from HWE for all five EPIC markers. Although the Structure analysis assumed that all potential source populations were sampled when assigning genetic clusters to individuals, our study was aimed at understanding broader H. armigera population patterns in Indian's highly heterogeneous agricultural landscape rather than inferring individuals' specific origins. The lack of interpretable and biologically meaningful Structure results as well as the general patterns of low population substructure in our study could potentially be due to our markers being in non-HWE .Departures from HWE due to homozygous excess may represent true biological phenomena in H. armigera is exposed to may also be important underlying factors that contributed to population substructure differences. Host crops with short flowering periods  generally support no more than one or two H. armigera generation; while hosts such as cotton with prolonged flowering periods are capable of supporting≥three consecutive generations H. armigera populations varies greatly, with relatively small populations on only one or a few crops at some times of year in some regions. In northern India, H. armigera is known to occur initially on food crops  during February to July prior to feeding on cotton from August  prior to switching over to food crops. Central and southern Indian H. armigera populations are therefore likely to experience less consistent insecticide/Bt protein selection pressure approaching the end of each cropping season as host crops change from cotton to food crops. This may promote gene exchange between populations as they move between crops. This scenario is the reverse of that in northern India, where populations sampled near the end of the cropping season might be expected to show more differentiation, as selection by insecticides and Bt toxins in cotton on such populations potentially operates over several generations with reduced migration, creating a mosaic of genetically different populations across the various cotton types and management systems. This may be part of the explanation for the significant pairwise FST values from cotton populations    suggestiH. armigera populations showed a high degree of differentiation between collections within cropping seasons. Based on FST values the observed genetic variation was most marked with populations collected from cotton in cropping season 3 (2006–07). The genetic differentiation revealed by pair-wise estimates of FST values suggested that there existed seasonal and geographical variation within H. armigera populations collected from cotton . Possible reasons contributing to such strong genetic differentiation may include different H. armigera generations being collected . It is also possible that some of the diversity/structure observed may be associated with the mosaic of Bt versus non-Bt fields, and this would warrant further study. Pairwise FST values between seasons 2 and 3 in the cotton crop broadly indicated population substructure differences between northern and central/southern Indian, and may reflect underlying differences in cropping patterns .Overall estimates of pairwise nificant . The excH. armigera populations and could result in genetic differentiation among local populations, but this assumption is only valid when there is substantial genetic isolation between populations. In India, H. armigera population substructure has been further suggested based on feeding preferences H. armigera were found to be associated with rainfall and humidity in Australia  and shows some shift of genetic profile (H. armigera, P. xylostella, C. pomonella, H. armigera populations analysed in this study do not cover all desired sampling locations and hosts for all seasons, and the patterns seen may also be influenced by factors such as sampling errors. In order to explain this intra-seasonal or host crop-associated genetic differentiation, further analysis of samples collected several times from the same host crops and sites over multiple cropping seasons will be needed.If the ideas presented above are correct, patterns of population structure analysed in one season should be reflected in the analysis of subsequent seasons. Consistent patterns observed between seasons, hosts and regions would thus support there being host- and/or region-associated micro-population structuring in Indian  profile . A similH. armigera populations from cotton may be further advanced with research on the detoxification capabilities and ecological aspects of this highly polyphagous pest insect species. Variables such as insecticide usage, Bt/non-Bt cotton, different hybrids of cotton, different hosts, climatic conditions, and diapause should be considered separately to better ascertain their importance to the population genetic structure of cotton-feeding H. armigera in India.Understanding the observed genetic structuring in Indian


The geostrategic location of North Africa as a crossroad between three continents and as a stepping-stone outside Africa has evoked anthropological and genetic interest in this region. Numerous studies have described the genetic landscape of the human population in North Africa employing paternal, maternal, and biparental molecular markers. However, information from these markers which have different inheritance patterns has been mostly assessed independently, resulting in an incomplete description of the region. In this study, we analyze uniparental and genome-wide markers examining similarities or contrasts in the results and consequently provide a comprehensive description of the evolutionary history of North Africa populations. Our results show that both males and females in North Africa underwent a similar admixture history with slight differences in the proportions of admixture components. Consequently, genome-wide diversity show similar patterns with admixture tests suggesting North Africans are a mixture of ancestral populations related to current Africans and Eurasians with more affinity towards the out-of-Africa populations than to sub-Saharan Africans. We estimate from the paternal lineages that most North Africans emerged ∼15,000 years ago during the last glacial warming and that population splits started after the desiccation of the Sahara. Although most North Africans share a common admixture history, the Tunisian Berbers show long periods of genetic isolation and appear to have diverged from surrounding populations without subsequent mixture. On the other hand, continuous gene flow from the Middle East made Egyptians genetically closer to Eurasians than to other North Africans. We show that genetic diversity of today's North Africans mostly captures patterns from migrations post Last Glacial Maximum and therefore may be insufficient to inform on the initial population of the region during the Middle Paleolithic period. Subsequent migrations of Arab populations followed, in particular the 10th century saw considerable movement of Bedouins to North Africa The peopling of North Africa is particularly interesting for anthropologists and human population geneticists due to North Africa's strategic location at a crossroad between Europe, the Middle East and the rest of Africa. The area has been characterized by shifting patterns of human settlements with human movements constrained by the Mediterranean Sea and the Sahara Desert, which might have limited migrations into an east-west direction. However, recent studies have suggested that these barriers might have not been totally impermeable to human movements. Diverse migration and admixture processes appear to have played a pivotal role in shaping the peopling of North Africa since the Middle Paleolithic period. Archaeological data suggest that the earliest modern humans arrived to North Africa around 160,000 years ago (ya) Early genetic studies have identified an Upper Paleolithic component in current northern African populations, and suggested that the Neolithic transition occurred through cultural diffusion Analysis of uniparental markers have found two Y-chromosome lineages (E1b1b1a-M78 and E1b1b1b-M81) at high frequency in North African populations, although the origin and emergence of these lineages have been controversial, with some studies suggesting a Paleolithic component In this study, we complement our previous findings on the maternal lineages by analyzing Y-chromosome and genome-wide markers in North Africans. We analyze Y-chromosome markers in more than 3,000 samples from African and Eurasian populations including 302 new samples from Libya and Morocco. In addition, we explore recently published genome-wide data from North Africa, the Middle East, and Europe using new methodologies to infer on populations' relations. We ask specific questions relating to past demographic processes to reconstruct a comprehensive description of the evolutionary history of North Africa populations: 1- Do female and male lineages show similar patterns of admixture and gene flow or they have contrasting histories similar to the contrast seen in neighboring regions Written informed consent was obtained from the participants and analyses were performed anonymously. The present project (2010/3746/I) obtained the ethics approval from the local Institutional Review Board, Comitè Ètic d'Investigació Clínica – Institut Municipal d'Assistència Sanitària (CEIC-IMAS) in Spain.We have genotyped 302 unrelated males belonging to the general population of Libya (215) and Central Morocco (87). Genealogical information of the donors was recorded for a minimum of two generations to ascertain their paternal ancestry. All samples were procured with informed consent following the ethical guidelines specified by the Institutional Review Board of the Comitè Ètic d'Investigació Clínica-Institut Municipal d'Assistència Sanitària (CEIC-IMAS) in Barcelona, Spain.For comparative purposes, additional published samples  from Africa, the Middle East and Europe were included in the analyses . The YCCDNA was extracted from blood samples using a standard phenol/chloroform protocol lSTR® Yfiler® PCR Amplification Kit (Applied Biosystems) and a 3130xl Genetic Analyzer (Applied Biosystems).Samples were additionally genotyped for seventeen Y-chromosome STRs using the AmpA graphical representation (contour map) of the geographical distribution of Y-chromosome haplogroups frequencies  was plotThe phylogenetic relationship between haplotypes belonging to E1b1b1b-M81, E1b1b1a E-M78, J1-M267 and J2-M172 haplogroups was inferred through reduced-median networks using Network 4.5.0.1 ST distances between populations computed by Arlequin on DYS19, DYS389I, DYS389b, DYS390, DYS391, DYS392, DYS393, DYS437, DYS438, DYS439. A principal component analysis (PCA) To study the genetic diversity within populations, we calculated haplotype and haplogroup frequencies, haplogroup and haplotype diversity, and mean number of pairwise differences (MPD), using Arlequin 3.5 In order to examine the potential signals of population structure in North African populations, a hierarchical analysis of molecular variance (AMOVA) was carried out grouping the populations according to geographical criteria. The main null hypothesis tested by AMOVA was the non-differentiation of Western and Eastern North African populations. Detailed grouping designs are shown in We have used BATWING Samples from North Africa TreeMixTreeMix was also used for inference of population admixture implementing three-population tests PCA was performed using smartpca, part of the EIGENSOFT 3.0 package The paternal lineage distribution in North African populations was compared to neighboring European and Levantine groups  using 30We have studied the main haplogroups further by constructing reduced-median networks from haplotypes found in each population. The E-M81 network  is charaE-M78 network  reveals Network analysis of the J-M267 included 448 haplotypes, mostly from Middle Eastern populations . J-M267 Haplogroup J-M172 was frequent in Middle Eastern groups (73.9%), and less in Europeans (18.5%) and North Africans (7%) . J-M172 Comparison of the studied populations was first carried out using principal component analysis (PCA) on haplogroup frequencies shown in ST) using Y-STR haplotypes. The MDS  and test by increasing m sequentially up to m = 20. The initial tree structure remains mostly unchanged when migration edges are added. All North Africans except Tunisians appear admixed from an ancestral population to Yoruba. For figure clarity, we show plot m = 6 and the migration edges weights  implies that population A derives from at least two different groups that are related to B and C. We constructed trees that infer population relationships using  weights . When m> weights . PositivThe anthropological interest in North Africa as a crossroad between three continents and as a stepping-stone outside Africa has led to numerous studies describing the genetic landscape of the human population in this region. These studies used paternal, maternal, and biparental molecular markers to investigate population structure in North Africa. However, information from these markers which have different inheritance patterns has been mostly assessed independently, resulting in an incomplete description of North Africa populations. In this study, we analyze uniparental and genome-wide markers proved informative for inferring population origin and history. We explore our populations by examining similarities or contrasts in the results from these markers and consequently provide a thorough description of the evolutionary history of North Africa populations, trying to avoid the bias that might result by analyzing one single genomic region.Our results from the maternally inherited mtDNA genome Although recent cultural expansions from the Middle East, like the Islamic expansion, could have introduced new lineages to North Africa and facilitated admixture between populations from both regions, our results show that the North African component mostly formed much earlier. This is shown in the admixture tests where Basque and Lebanese Christians but not Lebanese Muslims formed potential source populations to North Africans. In particular, Lebanese Christians were shown to have been isolated for at least the last 2,000 years and were proposed to be genetically close to the ancestral population of the Levant region from which current Europeans diverged ∼15,900–9,100 ya between the last glacial warming and the start of the Neolithic The gradual termination of the African Humid Period started ∼6,000 ya establishing today's North Africa desert ecosystem ∼2,700 yaAlthough most North Africans appear as an admixture of populations from the surrounding regions, the Tunisian Berbers show long periods of genetic isolation, allowing a distinctive genetic component to evolve. Unlike other North Africans, our admixture tests propose that Berbers diverged from surrounding populations without subsequent mixture. We show that coalescence time estimate from paternal lineages are pushed back ∼15,000 years when Tunisians  are included in the analyses suggesting an early upper Paleolithic ancestral population with most North Africans .Homo sapiens focused on the fossils of East Africa. Recent studies of hominin fossils from northwestern Africa present strong evidence of resemblances and possible evolutionary connections with fossils representing migrations out of Africa between 130,000 and 40,000 ya There has been recent interest in North Africa as a source for modern human migrations after most early research studying the origins of Figure S1Map of populations' location. Map shows the geographical distribution of the analyzed populations(TIF)Click here for additional data file.Figure S2Y-chromosomal phylogenetic chart. Hierarchical phylogenetic relationships and absolute frequencies of the Y-chromosomal haplogroups observed in Libyan and Moroccan populations. Nomenclature is according to Karafet et al. (2008).(PDF)Click here for additional data file.Figure S3Median joining (MJ) networks. Plotted are MJ networks of Y-STR haplotypes within haplogroups A) E-M78, B) E-M81, C) J-M172, and D) J-M267. The circle sizes are proportional to the haplotype frequencies. The smallest area is equivalent to one individual. Branch lengths are proportional to the number of mutational steps separating two haplotypes.(TIF)Click here for additional data file.Figure S4Inferred population tree with mixture events. A) Tree of population relationships inferred by TreeMix allowing six migration events. Horizontal branch lengths are proportional to the amount of genetic drift that has occurred on the branch. B) Residual fit from the maximum likelihood tree. Positive residuals indicate populations where the fit might be improved by adding additional edges.(TIF)Click here for additional data file.Table S1Populations selected for the Y-chromosome analyses.(DOC)Click here for additional data file.Table S2Y-chromosome haplogroup frequencies in populations selected for the present study.(DOC)Click here for additional data file.Table S3Analyses of Molecular Variance (AMOVA) in North African and Middle Eastern samples based on Y-STR haplotypes and Y-SNP haplogroups. Acronyms are listed in (DOC)Click here for additional data file.Table S4Y-chromosome haplogroups and haplotypes in individuals from Libya and Morocco.(XLS)Click here for additional data file.Table S5BATWING results showing times of demographic factors for Y-chromosomes from North Africans.(DOC)Click here for additional data file.Table S63-population test showing gene flow to North Africans.(DOC)Click here for additional data file.