Scots pine is one of the most widely occurring pines, but future projections suggest a large reduction in its range, mostly at the southern European limits. A significant part of its range is located in the Caucasus, a global hot-spot of diversity. Pine forests are an important reservoir of biodiversity and endemism in this region. We explored demographic and biogeographical processes that shaped the genetic diversity of Scots pine in the Caucasus ecoregion and its probable future distribution under different climate scenarios. We found that the high genetic variability of the Caucasian populations mirrors a complex glacial and postglacial history that had a unique evolutionary trajectory compared to the main range in Europe. Scots pine currently grows under a broad spectrum of climatic conditions in the Caucasus, which implies high adaptive potential in the past. However, the current genetic resources of Scots pine are under high pressure from climate change. From our predictions, over 90% of the current distribution of Scots pine may be lost in this century. By threatening the stability of the forest ecosystems, this would dramatically affect the biodiversity of the Caucasus hot-spot. However, the genetic components of our trees should be a conservation priority as well2,3. Three perspectives of biodiversity that mirror the evolutionary legacy of the species are genetic diversity , phylogeographic diversity (intraspecific evolutionary lineages) and phylogenetic diversity . All these facets of genetic biodiversity should be more explicitly embodied in the conservation priorities that have been recently articulated by the scientific community4.Since the beginning of the industrial era we have lost 32% of the world’s forest, and over 8,000 of the world’s 60,065 tree species are threatened with extinctionPinus sylvestris L.) is the most widely distributed pine species, yet its occurrence is seriously threatened by climate change, especially in the isolated and relic populations at the southern fringes of its range6. In the South Caucasus and Asia Minor Scots pine is a Tertiary relic with a patchy distribution in mountainous areas separated from the current main boreal range by a distributional gap of over 1,000 km. By the end of 2080, approximately one-third of the current distribution of Scots pine in Europe might be lost, with southern stands being most affected6. If this decline happens, it will greatly impoverish the genetic, phylogeographic and phylogenetic diversity of Scots pine, that may ultimately affect adaptive potential of the species.Scots pine , the Pontic Mts. in Turkey and the Hyrcanian Forest in the Alborz Mts. of Iran20. Numerous studies have documented how different factors have shaped the distribution and the genetic patterns of Scots pine in different part of the range, including Asia Minor29. However, none of these studies included a comprehensive representation of populations from the South Caucasus (Georgia), despite this region representing the largest part of the species range in the Caucasus ecoregion. The most recent paper relevant for the region focused on populations located in the Northern Caucasus (Russia)30. Based on cytoplasmic markers, Semerikov et al.30 suggested a probable time of the divergence between European and the Caucasian lineages at ca. 1 Mya but only partly reconstructed the possible migration routes in the region due to insufficient sampling.Locations of glacial refugia, colonization pathways and intraspecific divergence patterns are important because they can give insight into the possible genetic consequences of a range shift in the context of climate change. The Caucasus has regularly been highlighted as a potential Last Glacial Maximum (LGM) refugial area for different organismsP. sylvestris var. hamata Steven from Asia Minor and the South Caucasus to obtain insight into the key demographic and biogeographical processes  governing the species evolutionary history in the region. Specifically, we asked several questions: (1) Did Scots pine in Asia Minor and the Caucasus survive the last glacial period in a single refugium or in multiple refugia and where were they located? (2) Is the present genetic diversity geographically structured? (3) Does the structure reflect the vicariance process in isolated refugia? (4) What theoretical model describes best the spatial patterns of genetic diversity and differentiation? (5) Does geographical marginality represent the climatic marginality of Scots pine in the Caucasus ecoregion? (6) What are the possible changes in species distribution in the Caucasus ecoregion according to different climatic scenarios?In this study, we applied species distribution models (SDMs) and the analysis of the mitochondrial DNA sequences (mtDNA) and nuclear microsatellite markers (nSSRs) in 31.Needles were collected from 28 populations  in the natural species range covering Georgia (18 populations) and Turkey (10 populations). More specifically, we sampled seven populations in the Lesser Caucasus (LC), 10 populations in the Greater Caucasus (GC), a single population in the Gombori range that links the Greater Caucasus and the Lesser Caucasus (GR), five populations in West Anatolia (WA), and five populations in East Anatolia (EA) Figs. . Genomicnad7 and nad1 which have proved to be polymorphic in Scots pine and interpretable in terms of glacial refugia26. PCR reactions were performed according to Dering et al.26. The results of genetic analysis conducted in the Caucasus were compared with mtDNA data presented in Dering et al.26 which covered the European and Asiatic range of the species with a single population located in the Caucasus.We used two mtDNA regions 33 and combined in three multiplex PCRs: Multiplex I—Psyl57, Psyl25, Psyl17, Psyl36,Multiplex II—Psyl44, Psyl42, Psyl19, Psyl2, Psyl16 and Multiplex III—SPAC7.14 SPAC11.4 SPAC11.8 SPAC12.5. Details of the reactions’ conditions are presented in Supplementary file Thirteen nuclear microsatellite markers (nSSR) were selected for the study34 was used to determine the mean number of alleles (A) and the number of private alleles (PA). Observed (HO) and expected (HE) heterozygosity per population were calculated with INEST v. 2.035, whereas allelic richness (AR) was computed using FSTAT v. 2.9.336, and null alleles (Null) in FreeNA37 (using the Dempster method). The difference among geographic regions  in mean allelic richness, gene diversity and FST was tested in FSTAT with a permutation test (103 permutations). INEST was also used to estimate the inbreeding coefficient robust to null allele presence. The calculations were run using a total of 5 × 104 MCMC iterations with every 200th updated and 5 × 103 of burn-in. The Deviance Information Criterion (DIC) was used to choose the best model by comparing the inbred population model  with the random mating model . The Excluding Null Alleles (ENA) correction implemented in the FreeNA software was used to adjust Wright's fixation index (FST) for the null alleles presence. The significance of FST was tested using the bootstrapping method.GenALEx v. 6.538. The analysis was based on the admixture model and correlated allele frequencies model. Ten independent runs for each K value were applied, ranging from 1 to 29 (i.e. the number of sampling locations + 1) and burn-in of 1 × 104 steps, followed by 2 × 104 MCMC iterations. Genotype clustering is a challenging task but finding the optimal number of clusters, called the K number, is an even more complicated issue, and there is no dominant approach in population genetics39. In this paper, we took advantage of an approach proposed by Puechmaille40 that has been shown to define K with a higher accuracy in comparison to commonly used ΔK41 or LnP(D)38. To do this, we used STRUCTURE SELECTOR42.The spatial genetic structure was examined in STRUCTURE v.2.343. The matrix of pairwise genetic dissimilarities was calculated over the triangular grid that covered the region of the South Caucasus and Anatolia (300 demes). The effective migration and diversity rates were estimated in five independent runs with 4 × 106 MCMC iterations, 106 of burin-in and thinning set to 9999. We checked for convergence and mixing, and visualized migration and diversity surfaces using the rEEMSplots package43. To provide a graphical summary of the observed genetic dissimilarities, the estimated rates were interpolated across geographical space. The effective migration and genetic diversity rates were presented on a log10 scale, hence log(m) = 1 means that the effective migration is tenfold faster than the average.Due to the presence of natural barriers to gene flow in the study area, the genetic differentiation pattern may reflect population connectivity that is more a function of landscape features than a geographic distance. Therefore, we used the EEMS method (Estimated Effective Migration Surfaces) to investigate whether the observed population genetic structure is a function of migration rates that are shaped by the existing barriers and corridorsM-ratio method implemented in INEST was used. The critical M-value  under the TPM model (two-phased mutation model) assuming a proportion of one-step mutations (ps) of 0.22 and mean size of multi-step mutations (Δg) of 3.1. The equilibrium M-value was estimated as a mean across 104 coalescent replicates. The Wilcoxon’s signed-rank test (markers treated as independent replicates) was applied to test for significant difference between the observed and the equilibrium M-value.To test whether the studied populations reveal signs of a genetic bottleneck, the 45. The investigated populations were assembled into five groups revealed by STRUCTURE (see Results): West Greater Caucasus (GC_01-GC_08), West Lesser Caucasus (LC_01-LC_05), East Caucasus , West Anatolia  and East Anatolia (EA_02-EA_05). Four scenarios were focused on divergence events (Scenarios 2–5), and one on an admixture (Scenario 1) . These scenarios were built based on suggestions about the location of refugial areas in the Caucasus, results of population pairwise ST and STRUCTURE clustering. In the initial analysis, it was difficult to satisfactorily discriminate between scenarios according to their fit to the observed data. Thus, we decided to split loci into two groups based on their polymorphism. The details of the DIYABC procedure are given in the Supplementary file  that included published locations, the GBIF , and our data from the Caucasus, was used for SDM  with a resolution of 250 m from the SoilGrids51. The correlation between climatic variables was evaluated with the raster.cor function from the ENMTools package in R 3.4.353. To reduce collinearity between variables, bio11, bio14, and bio17 were excluded from further analyses. Four analyses were performed: 1) for LGM and 2) for the current conditions with and 3) ignoring the soil raster, and 4) for the future according to three Representative Concentration Pathways (RCPs): RPC 2.6 (an increase of average temperature by 1 °C before the year 2100), RCP 4.5 (+ 1.8 °C before the year 2100), and RCP 8.5 (+ 3.7 °C before the year 2065)54. The bootstrap procedure with 100 replicates with a 'random seed' option was used, 20% of data were used as test points for model evaluation. Output was set to a logistic, convergence threshold to 0.00001 and maximum iterations were set to 10,000. Area Under the Curve (AUC) was used to evaluate the final model performance55. Results were visualized in QGIS 3.4.8 'Madeira' . Suitable area and average altitude in the theoretical range of species were calculated in SAGA GIS software56.The theoretical range of 57, and are also predicted to change in adverse manner under climate changes, we used variables related to temperature and precipitation to better characterize the species niche in the region. We chose annual mean temperature (bio1) and annual precipitation (bio12) from CHELSA, and annualPET , aridityIndexThornthwaite (aridity index) and climaticMoistureIndex (relative wetness to aridity) from the ENVIREM database58. All raster layers used in the analysis were characterized by a 30 arc-sec resolution. The results were visualized as two-dimensional ecoplots presenting the Anatolian and Caucasian sites plotted against all remaining sites from the whole European natural range. The species' occurrence points for the analysis were downloaded from the chorological database provided by59 with a 30 arc-sec resolution. Additionally, we explored the species autecology by comparing several bioclimatic parameters  among different distributional domains. Those geographical domains were defined as follows: the Alps, the Balkans, Central Europe, the Pyrenees, East Europe, Scandinavia, Scotland, the Caucasus and Anatolia . In this analysis, we focused on the driest and hottest parts of the Caucasus ecoregion, so the North Caucasus (the Russian part) was excluded. We used the variables that gave the highest contribution for the South Caucasus and Anatolia in the analysis of the current and the predicted LGM distribution. Since precipitation and temperature are the two major limiting factors of plant growth and reproduction979–2013.prcomp’ function in R and visualised using the ggbiplot package in R package52.Additionally, the environmental differences between the four major geographic subregions determined by climatic differences in which the species was sampled were verified by PCA. These subregions were West Anatolia, East Anatolia, the Greater Caucasus and the Lesser Caucasus. The analysis was performed on 28 populations using the five bioclimatic variables that were most important in the MAXENT model (see Results). PCA was performed using the ‘d  was noted again in a population in the West Anatolia  and the lowest in the population in the Gombori range . The highest number of private alleles (PA = 13) was present in populations from the Greater Caucasus, especially in those in the western part (Table PA = 10). The low number of P A characterized populations from the Lesser Caucasus—only three unique alleles were detected. The regional-level analysis for the presence of private alleles indicated that the highest PA is in the Greater Caucasus (19), next in West Anatolia (15), East Anatolia (7) and the lowest number of regional P Ais noted in the Lesser Caucasus. Heterozygosity was at a very similar level across all populations, but inbreeding varied greatly, ranging from 0.026 (GC_05) up to 0.280 (EA_03). In populations with a high FIS value, inbreeding was inferred as the likely cause of homozygosity excess  and lowest in GC_07 from the Greater Caucasus (5.23). The highest allelic richness  has a relative contribution close to 50% in all models  to moderate (up to 45%). Habitat suitability was also partially reduced in the eastern part of the range covering the eastern Greater Caucasus (Dagestan area) .Prediction of the current theoretical range of ata Fig. . The mairy high 7% to modeca. 30%.During LGM, the theoretical range of Scots pine was much reduced, and the suitability of the South Caucasus was particularly low Fig. . Three m2  to 4922 km2 which is just ca. 6% of its current theoretical area . There is less pronounced climatic variability within the Lesser Caucasus—here the most extreme position is occupied by population LC_01 located in the most humid area in Adjara.The PCA analysis performed for 28 studied populations based on the five most relevant bioclimatic variables indicated significant differentiation among them  West Pontic Mts., and 2) East Pontic Mts. along with the Adjara  and 14,025 , years BP, respectively. This suggests that the present genetic structure of P. sylvestris in the Caucasus ecoregion is a result of recent postglacial history rather than vicariance in isolated refugia as it is in other European tree species68. Recently, the Holocene fragmentation was demonstrated as the significant factor shaping the spatial genetic structure in P. nigra in Europe69. The pollen records from the Caucasus suggest that the Early Holocene was climatically adverse to Scots pine and to other conifers, leading to a reduction in their abundance in the region64. In this period the upward migration of Scots pine probably occurred in response to warming noted in the Caucasus. This likely contributed to further fragmentation and genetic differentiation due to reduced gene flow in a complex mountainous landscape. Additionally, Scots pine is less competitive in comparison to other conifers growing in the Caucasus which could also reduce the overall probability of establishment and increased fragmentation. Pioneer and light-demanding Scots pine is outcompeted by shade-tolerant Abies nordmanniana and Picea orientalis, and pushed onto rocky and open sites of mostly southern slopes70. Those populations that survived during the Early Holocene in upper altitudes were likely the local foci of the much wider expansion of the species noted in the Late Holocene64. Similar to other conifers, the species reached its maximum abundance just ca. 4000 years ago thanks to the cooling of the climate (cooler winters), natural fires and, partly, human activity that promoted conifers62.Assuming the generation time for Scots pine to be 20–25 years, the divergence within the Anatolian-South Caucasian genetic pool might have taken place at the transition between the Pleistocene and Holocene, between ca. 3700–4600 years BP. The high aridity of the region 6000–3000 years BP, with a few extremely dry episodes lasting a few hundred years each, could be a possible factor in range fragmentation and further isolation71. Also, this genetic pattern of differentiation might be driven by the impact of local adaptation since the West Anatolian populations are very different from the East Anatolian in terms of climatic conditions. Orsini et al.72 argue that local adaptation may profoundly shape the pattern of neutral genetic differentiation.The demographic analysis dated the split between both Pontic genetic pools (West–East) relatively recently, 73 demonstrated the convergence in the evolutionary history of the herbivorous beetle and its host tree, Picea abies. Such an approach would be also welcomed for other trees, including Scots pine in the Caucasus.The evolutionary history of Scots pine we have tried to reconstruct in this paper is based mostly on the demographic analysis performed in DIYABC which has some constraints. The first relates to the markers used in demographic estimations. The mutational properties of microsatellites are mostly unknown for particular species and the mutation rates almost certainly do not reflect those of the genome as a whole. This may bias the final dating results. Additionally, estimation of the divergence time is expressed as the number of generations. Routinely, this parameter is subsequently converted into calendar years. Thanks to this, it is possible to make a direct reference to the geological event or time period that likely induced the process. However, the critical issue is the proper delimitation of the effective generation time. In the case of organisms with non-overlapping generations there is more certainty than in long-lived trees. In trees, researchers use different surrogates for generation time such as the age at which a species starts reproduction or total life span, the first being the most frequently used. This may be a source of apparent inconsistencies among different studies because it may lead to a different reconstruction of the evolutionary history of the species. One possible solution may be comparing the most probable evolutionary scenario of long-lived organisms to one reconstructed for a co-occurring organism  which has clearly distinguishable generations. Recently, Goczał et al.7. However, our study has demonstrated that, on average, the allelic and gene diversity of Scots pine populations in the South Caucasus and Anatolia is comparable to the core populations in the boreal forest25 and to populations on the southern margins of the European range76. Indeed, the Caucasian-Anatolian populations are even more genetically variable than populations from Central and Northern Europe77, but less than in isolated Alpine populations78. Consequently, the pattern of genetic diversity revealed for populations of Scots pine in the South Caucasus and the Asia Minor vs. populations in the core range is not consistent with the assumptions of the central-marginal hypothesis80 and the rear-edge concept provides a better explanation81. Furthermore, there is a growing agreement that the rear-edge and relic tree populations may display high regional genetic diversity, mostly due to their complex history84.The Caucasian range of Scots pine has a marginal and relic character. In the past, such populations were undervalued due to their genetic impoverishment and unpredictable demographic trends86.However, despite overall high genetic diversity, high inbreeding levels have been detected in some Anatolian populations. Additionally, gene flow was shown to be severely reduced among populations in the West Pontic Mts., and half of the Anatolian populations experienced a bottleneck. Although the exact time of the bottlenecks have not been inferred, it could be a genetic echo of the Mid-Holocene arid episodes that could contribute to the differentiation between Anatolian lineages. The impact of more recent fragmentation by humans on diversity loss cannot be ruled out. The low level of differentiation might even favour this hypothesis, because the effects of habitat fragmentation and population size reduction in trees can be buffered by their longevity77. The latest expansion in the Late Holocene might have contributed to an intensification of gene flow. Similar levels of differentiation to the Anatolian-Caucasian stands were reported for mountainous populations in the Carpathians and Apennines78. However, we noted that the level of regional-level FST varied considerably and was highest in the Greater Caucasus (FST = 0.074). Though located within the same mountain range, the Greater Caucasus populations are subjected to different local climatic conditions, which have been suggested in our ecological analysis. The western Greater Caucasus receives much more precipitation but summer is relatively cool while the eastern part is warmer and with significantly less precipitation87. Even geographically close populations within Svaneti range (GC_02 vs. GC_01 and GC_03) displayed very distinct climatic profiles that could induce adaptation to specific habitats. In this case, isolation by adaptation might explain the pattern of differentiation72. In summary, the exceptional genetic differentiation in the Greater Caucasus appears to have a solid ecological background.The overall genetic differentiation among populations was medium, though higher than that typically noted in the European rangeCastanea sativa88 and Abies nordmanniana89. In a study by Wójkiewicz at.25 that included limited number of stands from Turkey, with western and eastern populations grouped together, only the results of PCoA suggested some distinctiveness between them. More comprehensive sampling in the region would allow us to gain insight into this subtle genetic structure.The clustering of Anatolian populations of Scots pine separates western stands from the eastern stands. A similar spatial pattern of genetic structure was presented for 92. The main barrier for gene flow retrieved with the EEMS analysis encompasses mostly the western part of the South Caucasus , two others from the Lesser Caucasus (LC_05 and LC_06) and, to some extent, the Gombori range (GR), is puzzling considering their geographic locations. However, the issue becomes clearer if the landscape-level pattern of wind currents is considered  adaptive potentialP. sylvestris cannot be defined as purely ecologically marginal, although some populations occupied an extreme position . However, the Caucasus does not seem to be typically marginal but rather an isolated geographic domain that allowed large effective populations to survive. Genomic adjustments were likely necessary for species during its postglacial range formation in a complex net of Caucasian habitats and changing climate during the Holocene. Comparing the current and the LGM distribution of Scots pine in the Pontic Mts., an eastward range shift is evident in the Holocene. Currently, the West Pontic Mts. region receives lower rainfall during spring and summer compared to the East. Consequently, the north-eastern part of the Pontic Mts. possesses a Eurosiberian character while the north-western part is more Mediterranean, suggesting that the species was able to follow the changing environmental conditions in the past. Along with a range reorganization, Scots pine likely produced local adaptations which allowed survival under changed climate. Currently, precipitation and temperature are predicted to be the two main drivers of local adaptation in a changing climate96.Populations in the South Caucasus showed great variability in terms of the selected climatic factors. Our ecoplots showed that the studied populations overlap significantly with those representing the wide European range. Thus, being geographically marginal, the South Caucasian range of The Asia Minor Peninsula represents the most arid and warmest place within the Scots pine niche. These are genuinely ecologically marginal populations. Unfortunately, these populations, frequently of low-density, are dispersed in a landscape profoundly changed by human activity. The excess of inbreeding detected in Anatolian populations might be the first signal of the adverse genetic processes that take place in small populations in harsh conditions.97. The wide autecological variability of the South Caucasian populations of Scots pine and the climatic marginality of the Anatolian range are likely signs of the local adaptations that should be preserved. Intriguingly, the spatial scale of adaptation in the Caucasus was very small compared to the main European range. Such a local, or microgeographic, adaptation suggests that the selective pressure on the species genetic pool was probably strong.The future projection for the Caucasus region clearly show that the Scots pine range may be profoundly reduced or will no longer support the species. The South Caucasian-Pontic genetic pool of Scots pine is thus at high risk of extinction that would deprive us of the valuable and adaptive genetic diversity. We stress that SDMs does not consider the adaptive potential stored in the populations. Including this variable in the modelling may deliver more detailed information about the species response to climate change98 and acts as polygenic selection99. In this context, the relatively high genetic diversity in the South Caucasian and Pontic populations deserves special conservation measures. The current health state of Turkish forests shows some negative impact of ongoing climate change. For example, the level of defoliation of trees in most Turkish forests reached 25% during 2010 to 2018, and climatic factors, particularly drought stress, are described as the main drivers (foresteurope.org). According to EUFORGEN there are 21 Genetic Conservation Units (GCU) designed for Scots pine in Turkey. Our sampling covers the areas in where the GCUs are located so the obtained results are the most up-to-date source of population genetic structure trends. Scots pine forests in Georgia are protected within the network of national parks and nature reserves that covers ca. 10% of the country’s forest area70. Based on our results we are convinced that populations in the Svaneti range (western Greater Caucasus) should be immediately incorporated into a national network of protected areas since they represent a very unique genetic pool. Ideally, the result of our investigation could serve as the basis for setting the Conservation Units (CUs) for P. sylvestris var. hamata in Georgia.The evolution of new adaptations in trees is based on existing rather than de novo genetic variationSupplementary Information 1.Supplementary Information 2.


Bufo bufo) in Sweden and present evidence suggesting that these processes have resulted in two separate lineages of common toad, which colonised Sweden from two directions. Using ddRAD sequencing data for demographic modelling, structure analyses, and analysis of molecular variance (AMOVA), we provide evidence of a contact zone located between Uppland and Västerbotten in central Sweden. Genetic diversity was significantly higher in southern Sweden compared to the north, in accordance with a pattern of decreased genetic diversity with increasing distance from glacial refugia. Candidate genes under putative selection are identified through outlier detection and gene–environment association methods. We provide evidence of divergent selection related to stress response and developmental processes in these candidate genes. The colonisation of Sweden by two separate lineages may have implications for how future conservation efforts should be directed by identifying management units and putative local adaptations.Clinal variation is paramount for understanding the factors shaping genetic diversity in space and time. During the last glacial maximum, northern Europe was covered by glacial ice that rendered the region uninhabitable for most taxa. Different evolutionary processes during and after the recolonisation of this area from different glacial refugia have affected the genetic landscape of the present day European flora and fauna. In this study, we focus on the common toad ( One of the most challenging tasks in modern population genetics is to distinguish between migration, selection, and drift when studying divergence among recently separated populations and species Hahn . PatternDuring the last glacial maximum (LGM), 23–18 Kya, northern Europe was covered by glacial ice that rendered the region undesirable, and most European animal and plant species had retreated to glacial refugia at lower latitudes in Europe and western Asia  sequencing protocol to obtain genome-wide single-nucleotide polymorphism (SNP) data from the different populations. Using an outlier-based method as well as gene–environment associations, we identified candidate SNPs under putative selection. Using neutral SNPs, we used population genomic methods to unravel the postglacial evolutionary history of these populations. We also investigated patterns of genetic diversity found along the latitudinal gradient. Through demographic model selection, we obtained the best fitting model describing the recolonisation of the Scandinavian peninsula. If the recolonisation of Scandinavia was as a result of a single southern postglacial colonisation route, in accordance with Scandinavian common toads belonging to the central European complex, we expect to find a pattern of decreasing genetic diversity from the south to the north along the latitudinal gradient  were collected from 12 different populations in Sweden along a 1400 km long latitudinal gradient in April–May 2015 , based on the visual inspection of the electrophoresis gel making sure that sample DNA was not fragmented.Adult common toads . The DNA product from the gel extraction was purified through magnetic beads cleaning (AMPure XP). The library was sequenced at SciLife Laboratory in Uppsala on an Illumina NovaSeq 6000 SP flow cell with a paired end read length of 150 bp.The raw reads were processed with the program STACKS 2.41 , which explain a proportion of variation in the dataset from multiple SNPs in a single dimension. Population structure corresponds to differences in shared genetic variation between individuals; individuals who are more related have more shared genetic variation amongst them relative to individuals who are less related. As each PC is a single dimension, SNPs correlated to each other are closer on the PC and can therefore be used to infer population structure. Pcadapt utilizes this by calculating vectors of z-scores for each SNP derived from regressions between j SNPs and k PCs -based method pcadapt, implemented in R . The LFMM function was run using the ridge penalty and an FDR of 0.05.Gene–environment associations were also used to find putatively selected SNPs with latent factor mixed models (LFMM) . Summary statistics were calculated for the neutral dataset to investigate connectivity between populations, calculations of summary statistics were included in the STACKs pipeline . Allelic richness was calculated using the R package PopGenReport  and a distance matrix of the natural logarithm of the geographic distances in kilometres between each population (Rousset Isolation by distance was tested with a Mantel test. The Mantel test was performed using a pairwise- Rousset . The tes Rousset .K) using the neutral SNP dataset; the cluster algorithm was repeated 50 times for each value of K and averaged across runs. The best number of ancestral populations was chosen based on the cross-validation score plots  each SNP needed to be represented in to reduce the presence of missing data when running Fastsimcoal 2.6. We constructed models of demographic histories for the establishment of each region from an ancestral population. By using regions instead of populations for the demographic histories, we reduce the number of parameters needed to be estimated, which makes interpretation of the models easier while also reducing computation time. Using regions instead of populations is valid as the PCA plots and TESS3r cluster analysis all show the populations from each region being clustered together. The python module easySFS was used to generate the observed mSFS (Overcast https://github.com/isaacovercast/easySFS).We generated the observed mSFS from 14,285 SNPs that were obtained from STACKS by running the populations module using the parameter set -M 3, -n 2, -p 12 while also removing the SNPs found to be under putative selection in pcadapt and LFMM. We decided to increase the number of populations  and a logarithmic uniform distribution was used for the population resizing parameters. The migration and time since divergence were given as complex parameters, leading to migration being a function of population size, and time to divergence was estimated relative to previous time splits in the model. A generation time of 3 years was used for the simulations and a mutation rate of 7.7e−10 as estimated for the western clawed frog Xenopus tropicalis and High Himalaya frog Nanorana parkeri  calculations were used for model selection between the best replicates of each model; complex parameters were not scored multiple times when calculating AIC.AMOVA was carried out to test for differences between the northern and southern regions. The test was implemented in the R package poppr . A visualisation of the association is presented in Fig The outlier analysis on the first four e) were lower for the populations in Norrbotten and Västerbotten (Welch two-sample t-test. π: p < 0.001 (df = 8.21), He: p < 0.001 (df = 8.28), Fig. Fis) close to 0, there was no significant difference between expected and observed homozygosity . Summary statistics for observed and expected homozygosity and heterozygosity, nucleotide diversity, inbreeding coefficients, and allelic richness are available in Table FST was highest between the northern and the southern populations, the highest difference being found between Skan1 and Nbot1  and expected heterozygosity (HK); the TESS3r analysis revealed the best number of ancestral populations to be in the range of 2–5 distinct genetic clusters  separated from the other populations in that region , vascular endothelial growth factor receptor kdr-like (kdrl), DnaJ heat shock protein family (Hsp40) member C28 (dnajc28), death-associated protein 3 (DAP3), and nuclear factor of activated T cells 5 (NFAT5). K = 2, and best fitting demographic model, M4, being a bidirectional colonisation scenario. The study found that genetic diversity was lower in the northern regions, as nucleotide diversity and expected heterozygosity were lower relative to southern regions. This lower diversity in the north is in accordance with a negative relationship between genetic diversity and distance to refugia as the southern lineage probably used a more western refugium, while the refugium for the northern lineage was located further to the east  compared to Skåne and Uppland (southern populations), which is to be expected from a longer postglacial colonisation route utilised by the populations now inhabiting the northern regions. In the northern regions, Västerbotten shows slightly lower nucleotide diversity compared to Norrbotten (dark grey regression line in Fig. FST between the Uppland populations and the northern populations support the theory of increased gene flow. The population Upla3 had slightly lower difference in pairwise FST with the northern populations as compared to Upla1 and Upla2 (Fig. Vipera berus, where a contact zone was found between Västerbotten and central Finland (Carlsson et al. The Uppland populations did not create a distinct single regional cluster in the PCA Fig. , insteadla2 Fig. . There iRana arvalis (Knopp and Merilä K = 2 (Fig. The demographic analysis indicate that the putative hybrid zone lies between Uppland and Västerbotten, as has been shown in studies of = 2 Fig.  also sup= 2 Fig. .PC axes. Outliers that are related to PC1 divergence are SNPs that are associated with the clustering between the northern and the southern regions. Outliers mapped to PC2 and PC3 are associated with the population clustering within the northern region and within the southern region, respectively. Lastly, outliers mapping to PC4 are associated with the clustering of populations within Uppland. In the BLAST results, we found the Znf341 and vascular endothelial growth factor receptor kdrl genes to be associated with PC1, thus being under putative selection between the northern and southern regions. Znf341 is a central regulator of immune homoeostasis in humans and may have a similar function in the amphibians (Frey-Jakobs et al. Drosophila melanogaster (Qiu et al. The BLAST results for the reads that contained differentiation outliers were mapped to their related As two major genetic clusters of common toads are present in Sweden, presumably as a result of different postglacial colonisation routes, an argument can be made for these clusters to be seen as separate conservation units. Per definition, separate conservation units need to be significantly diverged at nuclear loci and reciprocally monophyletic for mtDNA alleles Moritz . With thR. arvalis, V. berus, and Ursus arctos (e.g. Taberlet et al. The results in this study suggest that after the LGM, two separate lineages of common toad colonised Sweden from two directions. This type of bidirectional colonisation of the Scandinavian Peninsula have been found in other animal taxa such as the Supplemental MaterialTable_S3Table_S4Table_S5


Arvicola, a group of complex evolution with fossorial and semi-aquatic ecotypes. To do this, we first estimated mutation rates of ddRAD loci using a phylogenetic approach. An IM model was then used to estimate split times and other relevant demographic parameters. A set of 300 ddRAD loci that included 85 calibrated loci resulted in good mixing and model convergence. The results showed that the two populations of A. scherman present in the Iberian Peninsula split 34 thousand years ago, during the last glaciation. In addition, the much greater divergence from its sister species, A. amphibius, may help to clarify the controversial taxonomy of the genus. We conclude that this approach, based on ddRAD data and an IM model, is highly useful for analyzing the origin of populations and species.Molecular dating methods of population splits are crucial in evolutionary biology, but they present important difficulties due to the complexity of the genealogical relationships of genes and past migrations between populations. Using the double digest restriction-site associated DNA (ddRAD) technique and an isolation-with-migration (IM) model, we studied the evolutionary history of water vole populations of the genus  However, several difficulties arise when trying to obtain accurate split times, specially at shallow divergencies2, which has slowed down the adoption of appropriate models for these estimates in populations of non-model species. First, the analysis of divergence occurring at shallow levels is especially problematic due to the effects of coalescence, incomplete lineage sorting, and migration between populations4, which requires appropriate models for the inferences. Among the most powerful methods for estimating population split times and other demographic parameters are isolation-with-migration (IM) models, which consider coalescence and migration while taking into account mutation rates of the sequence markers used7. The use of simpler models may lead to important biases in time estimation, particularly of recent splits9.Estimating diversification times using genetic data is crucial for analyzing the evolutionary history of species and populations. Divergence time information, in combination with other population parameters, can help not only to understand the origin of biodiversity, but also to delimit species and evolutionary significant units of conservation interest11. Reduced-representation genome sequencing methods such as the ddRAD technique12 have been widely used in different phylogeographic and fine-scale population structure studies14. The popularity of this approach is due to the fact that it allows hundreds or thousands of loci to be obtained from a large number of individuals at a moderate cost. The sequence fragments obtained are relatively short , and they are generally used only for single nucleotide polymorphism (SNP) discovery, but full ddRAD sequences have enormous potential for using them with IM models. However, they have not so far been combined with these methods and it is not yet clear whether, being so short, they can help produce robust estimates, especially between recently diverged populations and species.Second, the paucity of nucleotide differences between populations makes it necessary to use large sequence datasets in the estimates16 and along the genome19. Therefore, directly extrapolating mutation rates from distant species or from different genomic regions may lead to deviations in the split-time estimation if they are not properly selected. Since mutation rates of ddRAD markers have not been investigated so far, it is necessary to estimate them, for example, with phylogenetic methods that use molecular clock models21.Third, the mutation rates of the markers used should be properly estimated to calculate divergence times. The estimation of rates of novel markers is challenging for several reasons including the variability of mutations rates across different lineagesArvicola is a group of Eurasian rodents with a rich evolutionary history and several taxonomic aspects currently under debate25, which would greatly benefit from the use of an accurate methodology to estimate population split times and other demographic parameters. The target taxa of this work are the montane water vole, A. scherman  (formerly A. terrestris), and the Eurasian water vole, A. amphibius . Although these two taxa and their populations have a particularly problematic taxonomy, here we follow the reference of Musser and Carleton22, which considers them as two species. These two species form a monophyletic group, while other species of the genus, including two recently described species, are outgroup lineages28. Arvicola scherman is a fossorial species that inhabits lowland and upland grasslands across the main mountainous region of south-western and central Europe29. Because of their relatively-high population growth and frequent multiannual fluctuations of density, this species is considered a harmful agricultural pest in many areas31. In the Iberian Peninsula, A. scherman has two geographically isolated populations recognized as subspecies, different from the subspecies of central Europe32. Specimens from the Cantabrian region (A. scherman cantabriae) show differences in skull morphology and have significantly lower body size than that from Pyrenean specimens (A. scherman monticola)33. The close geographic proximity of the two A. scherman populations within the Iberian Peninsula makes them an ideal model for studying recent divergence times between populations. Due to the presence of several mountain ranges, it was hypothesized that the Iberian Peninsula was not a single homogeneous refuge during the glaciations and rather that important levels of population structure were generated in different isolated refugia within the peninsula34. This refugia-within-refugia hypothesis may be especially true for species of low dispersal capacity such as amphibians, reptiles and small mammals. Obtaining accurate population split times such as those between the Cantabrian and Pyrenean populations of A. scherman may be key for testing the refugia-within-refugia hypothesis and understanding whether these populations became isolated due to the effects of glaciations or if their divergence occurred more recently.The genus Arvicola amphibius populates aquatic habitats both in lowlands and mountains from most of Europe (excluding the Iberian Peninsula) to northwestern China22. Although most populations are semi-aquatic, there are also fossorial ecotypes35. These two ecotypes are geographically separated but may coexist in some areas of central Europe and cannot be distinguished with mitochondrial data24. This species therefore displays a remarkable ecological variability. Paleontological data and analyses of ancestral states and ontogenetic trajectories suggested that the ancestral ecological state of the Arvicola genus was aquatic or semi-aquatic while the origin of the fossorial forms was supposed to be relatively recent37. Due to their ecological versatility and a low mitochondrial divergence between the two lineages, some authors have recently proposed that the populations of A. scherman and A. amphibius belong to a single species24.Arvicola using ddRAD data with an IM model, with special emphasis on the analysis of the Iberian populations of A. scherman. Thus, we study the phylogeography of the Cantabrian and Pyrenean populations of A. scherman and test whether the divergence between them was associated with the glaciations or occurred in more recent times. Additionally, we try to shed some light on the evolution and taxonomic issues of the genus by analyzing the split time and migration rates between the most divergent populations of A. scherman as well as between this species and A. amphibius. In a first step of the dating procedure, we estimated specific mutation rates for the different ddRAD markers using orthologues from other rodent species. We then employed these rates with an IM model to estimate divergence times, population sizes and migration rates. We show that this framework based on ddRAD data and an IM model allows us to estimate reliable divergence times and other parameters and thus achieve a more in-depth understanding of the generation of phylogeographic patterns and the speciation process.In this work, we estimate several important dates and other demographic parameters for the evolutionary history of Arvicola were analyzed. They included 32 samples from the two Iberian populations of A. scherman (19 from the Cantabrian region and 13 from the Pyrenees), 1 sample of the same species from central Europe and 6 samples of A. amphibius , which was the best supported model, separated A. scherman and A. amphibius. The clustering outcome for K = 4 showed a coherent subdivision in the four populations considered here  performed using 2877 SNPs grouped the samples according to both species and the geographical distribution of the populations: it corroborated the genetic separation of ere Fig. .Figure 2A. amphibius appeared as the most external group and highly divergent from A. scherman. At the fine-scale level, specimens were grouped according to their locality, indicating the low overall dispersal of the species and the high resolving power of the ddRAD genomic data.The genome phylogeny based on the average genetic distances between the 3361 loci grouped individuals according to the population of origin  genome. To try to ensure a 1:1 orthology, we considered only sequences with a single hit and an E-value of less than 10–40, generating 118 orthologues. Using the sequence coordinates from the mouse sequences detected, 114 mammalian orthologues were downloaded from the ENSEMBL database41. Only alignments that included house mouse (M. musculus) and brown rat (Rattus norvegicus) were kept, leaving a total of 86 orthologues. From these, we selected the available sequences from Muroidea . We then added the corresponding A. scherman sequence to each set of rodent orthologues and realigned them. After filtering out one alignment that was invariant, alignments of 85 loci across 9 rodent species remained , based on fossil data42. The calibrated phylogeny indicated that all the species diverged 18 Myr ago  ago, with a 95% C.I. of 17–57 Kyr . The estimated effective sizes of the Cantabrian, Pyrenean and central European populations of A. scherman, and of the A. amphibius population, were 51,000, 51,000, 187,000 and 59,000, respectively  and the mean number of polymorphic positions (2.29) were small compared to other loci generally used with IM models45. This resulted in a small number of loci generating parameters with large confidence intervals or not converging properly, as we observed in initial runs. After increasing this number in successive IMa3 runs, we found that a set of 300 ddRAD loci resulted in reasonably good mixing and convergence  compared to the relative rates of all loci (1.34) due to the difficulties in detecting orthologues for the most accelerated loci. Accordingly, the demographic population sizes and split times should be ~ 11% lower than the values estimated here. Moreover, we do not have a reference genome in this group of species, so it was not possible to efficiently filter loci of sex chromosomes or with linkage disequilibrium56. All these factors in the estimation of mutation rates and their application to the IM model should be taken into account and make it necessary to be cautious with the estimates of split times and other demographic parameters. In this sense, it is convenient that the hypothesis being tested does not refer to a very specific time point but rather to broader time periods.To introduce mutation rates for the divergence-time estimation, we calibrated the ddRAD loci within a phylogenetic framework, obtaining a mean mutation rate for 85 loci of 2.6 × 10eny see “”. A moreArvicola. Thus, the ddRAD data allowed us to determine the most likely periods of population and speciation splits from the last 2 million years of Pleistocene glaciations  , indicating a low homogenization between the two populations59, in line with the strong structure found . If we assume that A. scherman is mainly fossorial, this date marks the minimum age for the origin of this ecotype37. It is also interesting that a small amount of gene flow was found from the A. scherman ancestral populations . Different P1 Illumina adapters for each sample (each with a different 5-nucleotide barcode), up to a maximum of 24, and one P2  were used. When there were more than 24 samples, different PCR indexes were used. A PCR of 20 cycles was performed with primers annealing over the adapters (or 24 cycles in the case of weak PCR products). Two more PCR reactions were performed to homogenize the coverage of loci. When there was no PCR product, the samples were removed before pooling. The three PCR products from each sample were pooled and visualized in a gel. To construct the final library, the samples were pooled with proportions that depended on the product intensity observed in the gel. After initial bioinformatic analyses to estimate the coverage of each sample, some tissues and most of the skull samples were reprocessed in subsequent libraries to increase coverage. The libraries were sequenced using the NextSeq Sequencing System (Illumina) with the 150-cycles Mid Output kit and single-read runs, at the Genomics Core Facility of the Pompeu Fabra University.DNA extraction from tissues and skull samples was carried out as described in Balmori-de la Puente et al.38. First, process_radtags was used to filter the sequences, assign sequences to the different samples according to the sequenced barcode, and truncate them to 145 bp with the recovery option (r). As bone samples of A. scherman contained a large proportion of exogeneous sequences, they were filtered using the tissue samples14. For this, a database including the sequences of the tissue samples of this species was constructed with the bowtie-build tool from Bowtie 2.3.061. We then performed a Bowtie search of the bone sequences against the tissue database with parameters "‐‐score‐min L,‐0.6,‐0.6", retaining only bone sequences that gave at least one hit for further analysis. After this step, sample reads of the same specimen from different libraries were merged by concatenating the sequence files. Using ustacks from the Stacks package, the initial minimum coverage (m) was set to 3 and the maximum differences between stacks (M) to 6. A catalog of loci from all the samples was constructed, allowing for a number of differences between loci from different samples (n) of 6 in cstacks. After testing different values, this set of parameters was found to be optimal for loci assembly. Finally, the populations program from the Stacks package was used with a minimum coverage (m) of 6 and a minimum proportion of individuals (r) of 1, i.e., only loci that were present in all individuals were selected. The 145 bp sequences were saved in FASTA format (with the command "--fasta_strict") and the first SNP of each variable locus was saved in PLINK format ("--write_single_snp --plink"). The numbers of SNPs and variable loci in FASTA format were not exactly the same , as they were generated with different statistical models. The heterozygosity rate of each individual was calculated by counting the proportion of variable positions along all loci in the FASTA file.The library sequences were analyzed using different programs in the Stacks 1.35 package62. The results revealed no major deviations in the assembled loci, with only 4 possible outliers  out of the 2877 SNPs.With the aim of detecting any potential bias in the assembly, we performed an Fst analysis for each locus using the SNPs dataset with BayeScan 2.18. To summarize the divergence of the two separate alleles of each locus, a pairwise distance matrix was calculated by estimating genetic distances between all possible combinations of alleles from a pair of individuals using Equation 8.1 in Freedman et al.63. Then, the resulting matrix of pairwise distances was used to construct a tree with the Fitch program of the Phylip package64. Mid-point rooting was used to represent the tree.A genomic tree of the individuals was constructed using all loci, following Igea et al.65.A PCA applied to the SNPs dataset was performed using the program SNPRelate available in R, using the genetic covariance matrix39 was applied to the same SNPs. An admixture and uncorrelated allele frequency model with the ancestry prior, recommended when the sampling is unbalanced, was used. The number of populations, K, analyzed ranged from 2 to 6, and the initial Dirichlet parameter for degree of admixture was defined for each K as 1.0 divided by the number of populations. The optimal K value was estimated using the method of Evanno66. The number of iterations was set to 500,000 with a 10% of burn-in. Ten independent replicas for each K were performed. Replicas with different patterns were found for K = 4, 8 of the 10 being the same. CLUMPP 1.1.267 was used to summarize the results.STRUCTURE 2.3.4st distances between the different populations were estimated using the SNPs with the Weir and Cockerman (1984) method in the genet.dist function of the hierfstat R package68. Using boot.ppfst from the same package, 95% confidence intervals were calculated with 100,000 replications bootstrapping over loci. Any intervals that did not overlap zero were inferred to be significant.Pairwise FArvicola ddRAD sequences and estimate their mutation rates, as depicted in Fig. A. scherman . Only loci with single hits and reported E-values lower than 1e−40 were considered to ensure as much as possible 1:1 orthology (118 orthologues). Chromosome number and sequence coordinates of each hit were annotated. Mammalian orthologues of each mouse sequence were then downloaded from the EPO suite of the ENSEMBL database . This database includes complete genomes of vertebrate species, with one genome used as reference per species, and multiple genome alignments of these species from which orthologous regions can be obtained41. For downloading the orthologous sequences, we entered the chromosome number and coordinates of the mouse sequence fragments found in the script dna_getAncestralSequences.pl from the ENSEMBL Compara API (http://www.ensembl.org/info/docs/api/compara/index.html). Sequences with more than 100 bp and less than 10 unknown nucleotides were kept. In addition, only alignments that contained mouse and rat sequences were considered . From these alignments, sequences from Muroidea species were retained: Mus pahari, Mus caroli, Mus spretus, Mus musculus, Microtus ochrogaster, Cricetulus griseus, Rattus norvegicus, and Peromyscus maniculatus. The number of species per alignment ranged from 4 to 9; 93% of alignments had at least 6 species and 41% had all 9 Muroidea species. After adding one A. scherman sequence to each locus . If any sequence in this set was not orthologous or was too divergent, it could be detected in a phylogenetic tree as an anomalous branch length. We therefore reconstructed a maximum-likelihood phylogenetic tree from each alignment using RAxML version 8.0.1971. The trees were visually inspected to ensure that no trees with large branch lengths were present in the final set.A pipeline of several bioinformatic steps was designed to detect orthologues of 21, using the calibrating point of the mouse-rat split at 10.4–14.0 Myr20. Unlinked site models (HKY + I) across loci, unlinked clocks (strict clock) and linked trees (Yule) were selected. A strict clock was chosen because the analysis converged better than with a relaxed clock in initial runs, as expected when the mutation rate variation between lineages is low72. The calibrated node was modeled using a lognormal prior distribution with minimum and maximum constraints in real space; the offset and the soft maximum were set to 10.4 and 14.0 Myr, respectively, to coincide with the 95th percentile of the probability density distribution with a standard deviation of 1. A total of 75 million generations were run, sampling each 1000 generations. The Tracer v1.7.1 program73 was used to check convergence and retrieve the mutation rate of each locus. The TreeAnnotator program from the BEAST package was used to calculate the consensus tree with median heights and 10% burn-in.A tree was constructed from the 85 rodent alignments with BEAST version 2.5.243. Four populations were considered: the Cantabrian and Pyrenean populations of A. scherman from the Iberian Peninsula, a sample from the central European distribution of this species, and the A. amphibius samples. In this configuration, the population topology was, according to the genomic tree: , A. scherman central European), A. amphibius). After different tests, the final analysis was carried out using 300 randomly selected loci that included the 85 calibrated loci. The mutation rates estimated previously using BEAST2 were included in the corresponding loci of the IMa3 input file after scaling them per alignment, as required by the program. To calculate the population size in demographic units, the generation time was set to 1 year, a value that can be considered adequate for a short-lived species like Arvicola sp. However, it should be noted that the generation time does not affect the estimation of divergence times5. The infinite sites model was used for all loci. Seven loci that did not pass the four gametes test were trimmed, with the longest fragment being selected74. The priors in the IMa3 model were adjusted to short loci and recently split populations and species, and they were selected according to their convergence in initial runs: maximum population size mutation rate 4Nµ (q) = 1.5; maximum split time tµ = 0.5; and maximum migration rate m/µ = 2. Note that all parameters are scaled by the mutation rate µ. Runs were performed with 420 chains (a large number due to the considerable number of loci) on 28 processors . Burn-in was set to ~ 500,000 steps and 15,000 genealogies, sampled every 100 steps, were saved. To ensure proper mixing and convergence, plots of parameter trends and marginal posterior probability distributions of the parameters were checked, and estimates of the first and second halves of the sampled genealogies were compared. Parameter estimates reported were the histogram bins with the highest value and confidence intervals were the 95% highest posterior density intervals (HPD). The IMfig program45 was used to prepare the figure with the schematic representation of the generated isolation-with-migration model. Significance of migration rates was based on the log-likelihood-ratio test of the null hypothesis of zero migration3.To estimate divergence times in an IM analysis we used IMa3Supplementary Information.


Orobanche foetida Poiret is a holoparasitic plant that lacks chlorophyll and totally depending on its host for its growth. Orobanche foetida parasitizes host plant roots and extract nutrient and water via a haustorium. Although O. foetida distributes in the Mediterranean region as a wild plant parasite, it parasitizes faba bean causing serious damages which may reach 90% yield losses in Tunisia. Analysis of genetic diversity of the parasite is important to better understand its evolution and spread, remained largely unknown. In this work, we present the first study on genetic diversity and population structure using the robust technique Restriction-site-Associated DNA sequencing (RADseq) for Orobanche spp. We collected 244 samples of O. foetida from 18 faba bean fields in the north of Tunisia including 17 populations from the north-west and one population form the north-east. To overcome the difficulty of SNP discovery in O. foetida genome as a non-model and tetraploid plant, we utilized three different informatics pipelines, namely UNEAK, pyRAD and Stacks. This study showed that genetic differentiation occurred in the Tunisian O. foetida emphasizing the isolation by distance effect. However, no strong population clustering was detected in this work basing on the three data sets and clustering methods used. The present study shed the light on the current distribution and the genetic variation situation of the fetid broomrape in Tunisia, highlighting the importance of understanding the evolution of this parasite and its genetic background. This will aid in developing efficient strategies to control this parasite and its expansion in Tunisia and worldwide. Orobanche is the largest genus among the holoparasitic members of Orobanchaceae with about 170 species (Orobanche foetida Poiret (common name: fetid broomrape) is one of the most virulent species of Orobanche; it is an obligate holoparasite, devoid of chlorophyll, parasitizing plant roots and totally depending on its host for its growth. It establishes a connection at host root level via a haustorium to extract nutrient and water. Poiret described the fetid broomrape for the first time in North Africa during his surveys on 1783 . At that period, the main constraint on faba bean crop in Tunisia was caused by O. crenata, while O. foetida was observed with insignificant damage. Currently, O. foetida is representing a real threat causing severe damages in Tunisia mainly on faba bean. In fact, O. foetida populations are more aggressive on faba bean than on other food legume crops , but never on faba bean, even when this legume crop was in close proximity, or even in the same O. foetida-infested field , grass pea (Lathyrus sativus), lentil (Lens culinarus), vetch (Vicia sativa) and fenugreek (Trigonella foenum-graecum)  . The lonO. foetida was obtained by the application of Glyphosate at budding stage and 15 days later , Spain (VF172), ICARDA (BPL2210) were used in crosses to release new faba bean varieties with good tolerance to O. crenata and O. foetida such as Giza 429, Giza 674, Giza 843, etc. from Egyptian breeding programs   is a proO. foetida populations collected from 18 natural infested faba bean fields using RADseq. To deal with the tricky SNP calling task, we used three different pipelines previously applied for polyploidy species; pyRAD . Size selection for fragments of approximately 350 base pairs (bp) was carried out using the E-Gel size select . We conducted a single-end sequencing with a read length of 51bp with the Illumina HiSeq 2500 platform .DNA was extracted from fresh floral buds (180 mg per sample) using innuPREP Plant DNA Kit  following the manufacturer’s instructions. Restriction-site-Associated DNA (RAD) libraries were prepared following the protocol according to O. foetida. We used three different software or workflows for comparison; (1) The Universal Network-Enabled Analysis Kit (UNEAK) implemented on TASSEL v3.0 software characterized by its ability to resolve the paralog, repeat issues and to analyze ploidy organisms  Starganisms , and (3)rganisms , which arganisms .m = 3), the nucleotide mismatches between loci within a single individual (M = 2), and the mismatches between loci when building the catalog (n = 1). The SNP genotype for each individual was exported with a minimum read depth of 6, using the ‘populations’ program implemented in Stacks. Finally, we set the following parameters for pyRAD workflow; 0.95 similarity, MinCov = 108, MaxSH p.50 and ploidy = 4.We started our analysis by trimming raw reads with TRIMMOMATIC v.0.32 software . For the UNEAK pipeline, Tag counts were generated from FastQ files with the FastqToTagCountPlugin, then were merged with the MergeMultipleTagCountPlugin, the minimum count of a tag that must be present was defined to five. Tag pair was identified for SNP calling with UTagCountToTagPairPlugin and the error tolerance rate was specified as 0.03. Then, UTagPairToTBTPlugi and UTBTToMapInfoPlugin were applied before generating the HapMap file with UMapInfoToHapMapPlugin by fixing minimum and maximum allele frequency on mnMAF = 0.05 and –mxMAF = 0.5,–mnC = 0.5 and –mxC = 1. For Stacks, we set the minimum number of identical reads required to create a stack  to explore genetic structure in the genetic data; fastSTRUCTURE, a non-spatial method and TESS3 a spatial admixture analysis . FirstlyHo), the expected heterozygosity (He), the inbreeding coefficient (FIS) and the fixation index (FST). The analysis of molecular variance (AMOVA) was carried out to examine patterns of genetic variation and to estimate variance components for the 18 collected populations using “poppr” v2.8.2  and the other Groups (north-west) using the three datasets , secondly, between Group 2 and Group 4 using pyRAD (FST = 0.0109) and UNEAK (FST = 0.0080) data sets as shown in FST values among the four potential groups using the three data sets.Additionally, we calculated the pairwise datasets . Whereasp = 0.001; Stacks: Mantel’s r = 0.558, p = 0.018; pyRAD: Mantel’s r = 0.596, p = 0.007).Furthermore, the IBD analysis  revealedAr, Ho, He and FIS) are similar among the 18 populations for each data set. Although, differences arise when comparing these parameters among the three data sets. However, UNEAK data set revealed higher values compared to those from pyRAD and Stacks data sets. Moreover, the mean values of allelic richness (Ar) among the 18 populations were similar using pyRAD and Stacks data sets (1.06), while it was slightly higher using UNEAK data set (1.28). Furthermore, the observed heterozygosity (Ho) was lower than the expected heterozygosity (He) for the three data sets excepting for Pops 6, 8, 12, 13 using UNEAK data set and Pop 13 using Stacks data set. The Ho values were ranged from 0.192 to 0.348 with a mean value of 0.253 for UNEAK data set, from 0.044 to 0.059 with a mean value of 0.048 for Stacks data set and varied from 0.032 to 0.046 with a mean value of 0.039 for pyRAD data set. While the He mean values were 0.28, 0.062, and 0.058 for UNEAK, Stacks and pyRAD, respectively. Regarding the inbreeding coefficient (FIS), UNEAK data set generated FIS values ranged from −0.131 to 0.240 , while for Stacks, FIS values were comprised between −0.017 and 0.259  and they were ranged from 0.092 to 0.279  for pyRAD data set.The genetic diversity within and among populations was estimated basing on the 18 collected populations. According to p < 0.05) using the three data sets. At populations level, the molecular variance within populations was comprised between 96.29% and 96.74%, whereas among populations it varied from 3.25% to 3.70%.The AMOVA analysis revealed a significant level of genetic differentiation  collected from five crops  using 10 RAPD primers, revealing 75 polymorphic bands. Due to the absence of specific markers and a reference genome for O. foetida, we chose the RADseq as a technique that did not require a gene set reference. To the best of our knowledge, this is the first study using RADseq technique to evaluate the genetic diversity of one of the most harmful Orobanche spp. in Tunisia, which represent a real threat worldwide.In the present study, we used RADseq technique to investigate the genetic diversity and population structure of the Tunisian de novo assembly approach due to the absence of a reference genome, but the choice of the pipeline remains a relevant decision. Indeed, pipelines differ in the applied algorithms and the possibility to change some parameters. Besides for several pipelines, some intermediate files are not accessible; these files are temporary and deleted in the end of the analysis, which does not allow to track the evolution of the workflow and to detect some possible issues. For that purpose, we used three different software pipelines UNEAK, pyRAD and Stacks, which generate different numbers of SNP as expected.In the present study, we adopted a O. foetida populations.The number of the detected SNPs varied among the three data sets. Indeed, Stacks generated the highest number  followed by pyRAD , however, UNEAK detected only 206 SNPs. This last result could be explained by the ability of UNEAK to eliminate paralogs, which are highly present in polyploidy species. Differences noticed in our results when applying different pipelines for the same dataset are also reported in various studies. In fact, the work of Regarding the population structure analysis for the studied populations, the distribution and the number of the clusters revealed by the DAPC analysis varied among the three pipelines, but even that, a common cluster characterizing the population 7 was detected using the three data sets. TESS3 revealed a clear genetic structure using Stacks and pyRAD data sets comparing to fastSTRUCTURE which was not able to detect any meaningful clusters using these two data sets. Regarding UNEAK data set, we detected an informative clustering pattern only with fastSTRUCTURE. In fact, TESS3 was not able to detect a clear one.FST among the four groups showed very low values in The population structure analysis was conducted using different clustering methods. Among them, TESS3 and fastSTRUCTURE, which are two Bayesian clustering models in population genetics that compute allele frequencies in each gene pool and estimate the proportions of an individual genome that originate from multiple ancestral gene pools (ancestry coefficients) . In the FST was observed between Group 1 and the other groups. This finding is consistent with a study on Striga hermonthica from Ethiopia . Furthermore, about 60% of the collected populations are classified in Group 2 from Beja Governorate, known as the main growing area of faba bean in Tunisia which might reflect a narrow genetic background. In fact, the close geographic distance among these populations could be explained by the spread of high number of long-lived tiny seeds of Orobanche through this area. Indeed, seeds dissemination is highly facilitated by different factors as wind, machinery, cultivation practices, as well as the use of contaminated faba bean seed stocks (Striga hermonthica is monitored by geographic isolation.The population 7 (Group 1) from the north-east of Tunisia was the most genetically divergent one in comparison to the other three assigned groups representing populations from the north-west of the country, using the three data sets , and four populations structure methods . Additionally, the highest pairwise Ethiopia , in whicd stocks . All theO. foetida in Tunisia according to the variability parameters Ar, Ho, He and FIS. These parameters were higher using UNEAK data set comparing to Stacks and pyRAD data sets. We recorded an average of 1.28 regarding the allelic richness for UNEAK data set and an average of 1.06 for Stacks and pyRAD data sets as reported in FIS value for the majority of the studied populations indicating an inbreeding behavior of the O. foetida populations in Tunisia. Similar results were also recorded among O. cumana populations from Turkey , Ho (0.04), and He (0.33) (Ho (0.25) and He (0.277), this could be explained by the co-dominance nature of markers used in both studies. Nevertheless, comparison among diversity studies is more conclusive using the same species and the same type of markers. Indeed, O. cumana populations from Turkey. They reported a variation regarding mean values of Ne (1.67), Ho (0.21), He (0.34) and the Fixation index (F) (0.34) and were able to compare their results with those of O. cumana from Spain, Bulgaria and Turkey. For the case of O. foetida, up to now, few genetic diversity researches were performed, and the molecular markers used were dominant such as RAPD and AFLP markers  . These v markers . These kO. foetida populations parasitizing wild plants and crop in Morocco, 86.63% of the variation was due to within-population variations, whereas only 13.75% was due to among-population variations. Moreover, in a study performed on fetid broomrape populations using RAPD markers by The results of AMOVA analysis were consistent among the three data sets showing a high genetic variation within the 18 populations (from 96.29% to 96.74%) while the remaining part was assigned to interpopulation variation. Consistently, in the study of O. foetida may be an autogamous species. Furthermore, Striga gesnerioides populations parasitizing the wild legume Indigofera hirsuta L. and cultivated cowpea . In the same context, O. minor var. minor and O. minor spp. Maritiama which were classified in distinct clusters using SCAR markers.The high genetic variation observed within populations lead us to consider that d cowpea . By contBulgaria . Accordion vetch , while i broteri  while poO. foetida on legumes and wild relatives was assumed for several years as an adaptation event .The original contributions presented in the study are publicly available. This data can be found here: NCBI’s Sequence Read Archive (Accession: AB, YI, MB, and KS conceived and designed the experiments. AB, YI, and AN performed the experiments. AB and SS analyzed the data. AB, YI, SS, and MB wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


IKZF1 is a relevant gene associated with the pathogenesis of acute lymphoblastic leukemia, and the rs4132601 (T>G) and rs11978267 (A>G) polymorphisms have been associated with the development of this disease in several populations. The aim of this study was to determine the allelic and genotypic frequencies of the rs4132601 and rs11978267 polymorphisms in two indigenous Mexican groups (Cora and Huichol) and Mestizo populations from Nayarit, Mexico, and compare them with the frequencies of both polymorphisms in other populations of the world.One hundred, 116, and 100 subjects from the Mestizo, Huichol, and Cora populations, respectively, all of them residents of the state of Nayarit, Mexico, were analyzed. The frequencies of rs4132601 and rs11978267 were determined by allelic discrimination using TaqMan assays.The allelic frequencies of rs4132601 were as follows: Mestizo group T = 0.74, G = 0.26; Cora T = 0.745, G = 0.255; and Huichol T = 0.47, G = 0.53. In the case of the rs11978267 polymorphism, the allelic frequencies were Mestizo A = 0.745, G = 0.255; Cora A = 0.735, G = 0.265; and Huichol A = 0.457, G = 0.543. For each population, both polymorphisms were in Hardy–Weinberg equilibrium.rs4132601 and rs11978267 polymorphisms.The Huichol population from Nayarit presented the highest frequencies of the risk allele reported to date in the whole world for both  An indigenous Mexican population presented the highest frequencies of the risk allele reported to date in the whole world for both rs4132601 and rs11978267 polymorphisms. The number of individuals analyzed from this population, represents the 1% of the total population from Nayarit. IKZF1 encodes the protein IKAROS, an anti‐leukemic transcriptional factor that is highly conserved and essential for the differentiation of the B lineage in hematopoietic stem cells and as a tumor suppressor in B‐cell ALL , and partial or complete gene deletions producing haploinsufficiency  and ~40% in Ph‐like‐chromosome‐positive individuals ) in European, European‐American, Afro‐American, and Hispano‐American populations. Specifically, both polymorphisms increase the risk of BCP‐ALL in European populations , 100 from the Cora population  for both polymorphisms, 116 from the Huichol population  for rs11978267 and 100 from the Huichol population  for rs4132601. Mestizo subjects were defined as those born in Nayarit, Mexico, who spoke Spanish, had Mexican parents, and did not belong to any native group. The indigenous Mexican subjects spoke their native language and had three generations belonging to the ethnic group. All subjects received an explanation of the features of the study prior to inclusion.Male and female Mestizos >18 years old from Nayarit and the Amerindian populations from the region of “Huicot Region or El Gran Nayar,” Nayarit were included. DNA samples were taken from 316 selected subjects: 100 subjects from the Mestizo population  , according to the manufacturer's guidelines. Samples were quantified using an Eppendorf BioPhotometer® D30 spectrophotometer, and the DNA concentration was established at 2.5 ng/μl for PCR analysis. Samples were stored at –80°C until genotyping analysis.DNA was extracted from peripheral blood using the QIAamp DNA Blood Mini Kit (QIAGEN2.4® SNP Genotyping Assays: C__26019772_10 and C_199413_10 (Applied Biosystems™), respectively. Briefly, the PCR reactions were carried out in a total volume of 10 µL, 5 µL of TaqMan™ Genotyping Master Mix (Applied Biosystems™), 0.25 µl of the predesigned TaqMan® SNP Genotyping Assay (C__26019772_10 or C_199413_10), 1 µl of DNA sample (2.5 ng/µl), and 3.75 µl of nuclease‐free water. The reactions were carried out in an ABI 7500 Fast Real‐Time thermocycler using the standard conditions recommended by the manufacturer: 95°C for 10 minutes and 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. The data were analyzed by means of an allelic discrimination assay using the SDS software (Applied Biosystems™). The samples were analyzed out in duplicate, and positive and negative controls were used in each genotyping analysis.Genotyping of the rs4132601 and rs11978267 polymorphisms was carried out by Real‐Time PCR, using predesigned TaqMan2.5p < 0.05 was considered as statistically significant.To compare the genotypic and allelic frequencies in Mestizo, Cora, and Huichol populations, the Minitab 17 software was used. The allelic and genotypic frequencies from Mestizo versus Cora, Mestizo versus Huichol, and Huichol versus Cora were compared. Also, the allelic frequencies from Mestizo, Cora, and Huichol populations were compared with the global allelic frequency. The SNPs analyzed were tested for Hardy–Weinberg equilibrium in the Mestizo, Cora, and Huichol populations. A confidence level of 95% was used and 33.1p > .05). There was linkage disequilibrium (LD) between the two polymorphisms evaluated in IKZF1 gene for Cora  and Huichol population . The distribution of the IKZF1 rs4132601 and rs11978267 polymorphisms among the three populations is shown in Table The three populations analyzed were in Hardy–Weinberg Equilibrium (HWE) , while the frequencies of the G (0.53) and G (0.543) alleles, respectively, were statistically higher (p < .001), when compared with the frequencies reported in the global population , which are genomic loci that regulate the levels expression of the mRNA of the neighbor gene FIGNL1, an important gene involved in maintenance of genomic stability, in DNA double‐strand break (DBS) repair, via homologous recombination and cancer prevention. Likewise, this gene regulates the osteoblast proliferation and differentiation, and recently, has been associated with BCP‐ALL (Jayaram Laurynenka et al., IKZF1 deletions are present.The rs4132601 and rs11978267 are not located on the promoter or enhancer region of The frequencies of the allele G of both polymorphisms were higher in Huichol than in Cora and Mestizo populations from Nayarit Table . These rThere are several reports describing the variability of genotypic and allelic frequencies of different kinds of polymorphisms of clinical and pharmacogenetic interest among indigenous Mexican groups, specifically the Huichol population (Gordillo‐Bastidas et al., A few studies have determined the frequency of these polymorphisms in Mexican HapMap,  and NatiThe presence of the polymorphisms rs4132601 and rs11978267 has been associated with ALL development in several populations (Dai et al., The differences between Huicholes versus Meztizos and Coras can be attributed to they have a different origin. The Huicholes have been reported to be more related to the Tarahumara tribe in comparison with Coras and Mestizos (Páez‐Riberos et al., Moreno‐Estrada et al.,  reportedrs4132601 and rs11978267 polymorphisms, and also, they were in LD. It is necessary to pay attention to the results obtained, and from these, propose a strategy for monitoring the indigenous populations, which are considered the most vulnerable populations in Mexico with respect to access to specialized health systems, and thus they could receive early detection and medical attention and treatment for ALL.In summary, the individuals analyzed in the present study represent the 1% of the total Huichol population from Nayarit, and presented the highest frequency of the risk allele reported so far in the whole world for both All authors declare that they have no conflicts of interest to disclose.Briefly, J.G.F. and M.F.A.P. performed the genotyping analysis and carried out the analysis of results, M.J.D.A. and N.V.P did the recruitment of the subjects, took the blood samples, and also carried out the analysis of results. D.E.S.J, D.G.B.A, and J.S.M. performed DNA extraction and Genotyping analysis. H.J.P.G., J.F.Z.Z., and J.M.A.C., participated in the analysis and interpretation of results. A.V.R. designed, conceptualized and supervised the study, was responsible for acquiring the funds and performed the genotyping analysis. All the authors did a literature review and contributed to writing and editing the manuscript and provided important comments.