Cornus florida L.) using genotyping by sequencing (GBS). This species is ecologically important to eastern US forests but is severely threatened by fungal diseases. We analyzed subpopulations in divergent ecological habitats within North Carolina to uncover loci under local selection and associated with environmental–functional traits or disease infection. At this scale, we tested the effect of incorporating additional sequencing before scaling for a broader examination of the entire range. To test for biases of GBS, we sequenced two similarly sampled libraries independently from six populations of three ecological habitats. We obtained environmental–functional traits for each subpopulation to identify associations with genotypes via latent factor mixed modeling (LFMM) and gradient forests analysis. To test whether heterogeneity of abiotic pressures resulted in genetic differentiation indicative of local adaptation, we evaluated Fst per locus while accounting for genetic differentiation between coastal subpopulations and Piedmont‐Mountain subpopulations. Of the 54 candidate loci with sufficient evidence of being under selection among both libraries, 28–39 were Arlequin–BayeScan Fst outliers. For LFMM, 45 candidates were associated with climate (of 54), 30 were associated with soil properties, and four were associated with plant health. Reanalysis of combined libraries showed that 42 candidate loci still showed evidence of being under selection. We conclude environment‐driven selection on specific loci has resulted in local adaptation in response to potassium deficiencies, temperature, precipitation, and  disease. High allele turnover along ecological gradients further supports the adaptive significance of loci speculated to be under selection.Discovering local adaptation, its genetic underpinnings, and environmental drivers is important for conserving forest species. Ecological genomic approaches coupled with next‐generation sequencing are useful means to detect local adaptation and uncover its underlying genetic basis in nonmodel species. We report results from a study on flowering dogwood trees ( There is little debate abiotic and biotic stressors can result in local adaptation and lead to evolutionary divergence of populations via isolation by adaptation (IBA)  using a population landscape genomic approach with genotyping‐by‐sequencing (GBS) data. Cornus florida is threatened by fungal pathogens, especially by powdery mildew  may not have true adaptive value  that contain DNA fragments of specific target sizes to uncover loci with single nucleotide polymorphisms (SNPs)  What genetic predisposition does C. florida possess to adapt to ongoing climate change in North Carolina? (5) And how does repeated GBS experimentation influence final results? The latter question is of utmost importance to researchers incrementally expanding sequencing‐based investigations across increasing portions of a taxon's range, and as such, we primarily report findings within North Carolina as part of a broader effort to characterize adaptive variation throughout the flowering dogwood range.In this study, we addressed the major concerns of the GBS method  using a combination of methods to more reliably identify loci under selection. First, we incorporated replication of sampling design into our sequencing strategy. Second, we isolated candidate loci that were detected by two 22.1C. florida comprises distinct and heterogeneous environments—spanning as far as north as Maine and occurring as a disjunct subspecies along the Sierra Madre Oriental; as such, various biotic and abiotic stressors have varied effects on the species in different ecoregions. Although ongoing research is underway to capture the full range of adaptive variation in C. florida, North Carolina is well suited for initial study as it encompasses three ecoregions with distinct environments spanning a range of longitudinal–elevational gradient similar to conditions of northern and southern portions of the species range  and were recorded at sites during sample collection . Additional environmental data  were obtained via GIS . We note further in Supporting Information that the size of our environmental dataset was reduced for certain analyses, namely GF analysis. As collinearity among variables and its effect on the random forest algorithms (that GF is an extension of) are not fully understood, we safeguarded against any possible problems by reducing the number of collinear pairs of environmental variables. Prior to reduction of collinearity for GF , our environmental dataset consisted of 12 variables (Table S1 Appendix), excluding 15 soil core measurements and soil types from the USGS soil classification scheme.2.3Two functional plant traits, plant health and leaf osmotic potential, were measured in this study. Plant health was measured during plant collection. We measured the health condition of every sampled tree using a visual estimation method , which is indicative of plant drought tolerance  was available. GBS libraries were prepared for two DNA libraries of 96 and 85 individuals separately, according to the double‐digest RAD‐seq/GBS method  reads were processed separately for each of the two libraries . GBS barcode splitter, other custom Perl scripts, and FASTX‐Toolkit were used to sort samples by barcode, trim PE1 reads to 90 bps, and remove sequence reads that had more than 5% of their bases with a quality score below 20. Bowtie 2 .Missing data were also important factors to consider for processing steps. A common practice is to use >20% missing data criterion as an arbitrary cutoff to exclude loci in datasets  using ten replicate analyses each with a burn‐in of 100,000 and 100,000 subsequent iterations. The same procedure was carried out on the combined library of 1,171 putatively neutral SNPs in Hardy–Weinberg equilibrium .To identify loci strongly deviated from the general population genetic structure and strongly associated with environmental differences, we first characterized individuals’ membership to biological clusters. Using a dataset of uncorrelated SNPs not in linkage disequilibrium for our two libraries (first occurring SNP per RAD‐tag), STRUCTURE , identified with the Evanno method for STRUCTURE analysis , was incorporated in LFMM. For LFMM, we ran the analysis with 50,000 sweeps for each pairwise test with a burn‐in of 12,500 sweeps because repeated tests of parameters showed a precise consensus in regard to SNPs being detected as highly associated with environmental and functional traits.Significant genotype–environment association (GEA) was investigated using latent factor mixed modeling  had compelling evidence for being under selection, defined as being detected by multiple methods across libraries  and were analyzed in GF using the regression model, which was a standard implementation of the gradientForest R package. Remaining parameters to fit GF models were selected according to Fitzpatrick and Keller  to provide control for isolation by distance while detecting the significant correlations between overall genetic and environmental distances . Full and partial mantel tests were carried out independently for each environmental–functional trait.Partial mantel tests were performed with R ade4 and ecodist packages  between at least two of six populations compared (Table S1 Appendix). Several soil features, however, did not show significant differences among sampled locations . Tukey–Kramer tests (or Dunnett's modified Tukey–Kramer tests for violations of homoskedasticity) supported observations that environmental features vary along the gradient of mountain, Piedmont, and coastal regions (Table S1 Appendix). Higher elevation populations were wetter per month and shorter in growing period compared to those of populations at lower elevations (Table S1 Appendix). Humidity in Piedmont locations was slightly lower than in coastal locations. Coastal populations were environmentally differentiated from other populations in soil features, especially for density (mg/dm3) of sodium, potassium, manganese, and soil weight to volume ratios (Table S1 Appendix).Results of one‐way ANOVA  indicated the majority of environmental features were significantly different , but leaf osmotic potential varied to a lesser extent (Table S1 Appendix). Results of Tukey–Kramer tests confirmed mountain populations had significantly lower plant health ratings than other populations . In contrast, population differences of leaf osmotic potential were relatively modest, showing a west–east gradient of osmolality (mmol/kg) from lower values in mountain populations to higher values in coastal populations.3.2Before removing contaminant reads aligned to fungal genomes and low‐quality sequences, libraries one and two had a total of 82,697,746 and 99,062,919 paired sequences, with an average of approximately 861,435 and 1,179,320 PE1 sequences per individual, respectively  group . Differences between coastal and mountain‐Piedmont groups accounted for approximately 3% of total genetic variation for both library one and two results, which is marginally insignificant with a two‐tailed statistical test . This suggests extensive genetic mixture within regions and frequent gene flow or weak genetic differentiation between coastal and mainland populations. STRUCTURE and AMOVA results from analyses for a less probable hierarchical population structure of mountain, Piedmont, and coast division are available in Supporting Information .AMOVA results Table  showed a3.4Fst values estimated by Arlequin from a coastal‐mainland hierarchical structure shows a majority of loci have a Fst value below 0.25, and a very small portion have a Fst value of 0.25–0.8 corresponding closely to the ninety‐ninth significance percentile . Among these loci, 54 were consistently detected in both libraries. Analyses using BayeScan found 43 and 37 outlier loci from library one and two, respectively, passing a q‐value cutoff of 0.1. Two loci identified as Fst outliers in BayeScan results were common to both libraries and also matched significant Arlequin results from each library  with one or more of the following: 12 environmental variables, 15 soil properties, leaf osmotic potential, two scales of plant health, and three reduced principal components of environmental distances. Z scores of 4 correspond to p‐values ≤ .01 with a Bonferroni correction for 1,000 loci. Q‐Q plots from LFMM  and 133 (library two) RAD‐tags had at least one SNP correlated  sequence repository. The conservative subset of 54 SNPs had extensive evidence to support adaptive significance and was defined by having three or more overlaps from Figure The rate of consistently genotyping a locus across libraries was 75%–82%, whereas the rate of consistently identifying SNPs as candidates under selection by at least one method ranged between 23%–32% when considering any genome scan analysis of library one and two. See Supporting Information (Defining Consistency) for our definition of consistency. A smaller subset of candidates under selection were identified as highly interesting as they were detected between both libraries by multiple C. florida , our chosen subset of 54 candidate loci was parsed to 43 in order to conduct GF on the combined dataset. The 43 candidate SNPs for GF analyses differed strongly from our chosen 1,171 reference SNPs  in respect to overall patterns of allele turnover along ecological gradients of frost‐free period, mean July precipitation, and soil densities of potassium, sodium, manganese, phosphorus, and sulfur . In addition, mantel tests of collection sites for both reference and candidate SNP datasets supported that frost‐free period and levels of potassium were the top two ecological variables for explaining patterns of genetic differentiation, albeit isolation by distance was also strongly correlated (Table S3). For GF, individual allele functions of candidate SNPs were plotted and highlighted in Figure 4C. florida might have evolved local adaptation in response to a heterogeneous landscape of ecological pressures within mountain, Piedmont, and coastal regions of North Carolina. While further study along the entire range of C. florida is ongoing, our conclusions of the adaptive variation in North Carolina may be related to findings of the broader species range. North Carolina has long been noted to contain a large variety of plant communities , which are similar in species composition to communities at more northern and southern latitudes  autocorrelated with soil measures. We speculated soil leaching pressures and plant adaptations for efficient regulation of osmoticum might be critical factors to explain the high turnover of alleles for this SNP along a potassium gradient , and the transcript was predicted to encode a probable glycerol‐3‐phosphate dehydrogenase  (Table The SNP on locus B332 (B332_14) was one of only nine genomic locations  to meet five of the six criteria for being considered a candidate of selection. It is repeatedly identified as a high 6) Table  and late6) Table , the eff6) Table . The GF XM_00648094). While the function of this particular locus is speculative, leucine‐rich repeat receptor‐like kinases have previously been implicated in pathogen recognition .Locus B1350 is predicted to be a leucine‐rich repeat (lrr) receptor‐like serine threonine–protein kinase . Of the 43 candidate loci consistently showing evidence of selection among the two libraries after additional SNP validation , all but one of the candidates (locus 5B) still showed evidence of selection when the combined library was reanalyzed Figure B. The diFst outlier tests, GEA tests, and novel methods like GF.In regard to library quality, we minimized biases using high‐quality DNA of the same amount for each sample, and we selected fragments of 300 ± 36 bps for sequencing. Although sampling size might influence results, the sample size of our subdivided populations was still sufficiently large to accurately make population genetic inferences, according to simulated study  and temperature during the growing season were important drivers of ecological and genetic divergence of the species. The study identified 54 putative candidate loci under selection for local adaptation. A few had annotated functions in biological processes that might have adaptive roles such as increased hardiness to drought and disease resistance. Several of these loci will serve as candidates for a broader scale and more thorough analysis to further support roles for genes of interest. We concluded high genetic variation within populations and significant allelic differences among ecologically heterogeneous regions readily predisposed C. florida for local adaptation to ongoing climatic shifts. We expect some independent local adaptations to occur in other areas outside this pilot study's scope, but the genetic and ecological patterns detected here have provided hypotheses for an expanded study that will assess large‐scale environmental gradients and genetic structure across the species range.Our GBS study of six populations of None declared.10.5061/dryad.h55q8. GBS datasets for each sequence library along with adaptor–barcode information are available as separate files in Supporting Information (Data S1–S3) in addition to metadata corresponding to analyses of each locus (Table S1).Environmental and physiological data available in Dryad Digital Repository: doi: Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Charadrius pallidus is a Near‐Threatened shorebird species endemic to mainland Africa. We examined levels of genetic differentiation between its two morphologically and geographically distinct subspecies, C. p. pallidus in southern Africa (population size 11 000–16 000) and C. p. venustus in eastern Africa (population size 6500). In contrast to other plover species that maintain genetic connectivity over thousands of kilometres across continental Africa, we found profound genetic differences between remote sampling sites. Phylogenetic network analysis based on four nuclear and two mitochondrial gene regions, and population genetic structure analyses based on 11 microsatellite loci, indicated strong genetic divergence, with 2.36% mitochondrial sequence divergence between individuals sampled in Namibia (southern Africa) and those of Kenya and Tanzania (eastern Africa). This distinction between southern and eastern African populations was also supported by highly distinct genetic clusters based on microsatellite markers . Behavioural factors that may promote genetic differentiation in this species include habitat specialization, monogamous mating behaviour and sedentariness. Reliance on an extremely small number of saline lakes for breeding and limited dispersal between populations are likely to promote reproductive and genetic isolation between eastern and southern Africa. We suggest that the two Chestnut‐banded Plover subspecies may warrant elevation to full species status. To assess this distinction fully, additional sample collection will be needed, with analysis of genetic and phenotypic traits from across the species’ entire breeding range.The Chestnut‐banded Plover  Genetic differentiation most commonly emerges between populations divided as a result of geographical barriers. Over evolutionary time, this process can result in the formation of distinct species from a single ancestral population via allopatric speciation Mayr . In the Charadrius) are shorebirds and include several species that exhibit high gene flow on a continental scale. For example, the Kentish Plover Charadrius alexandrinus of Eurasia remains genetically homogeneous across 10 000 km , and includes approximately 6500 individuals , Lake Magadi in Kenya  and Lake Manyara in Tanzania . The distance between the Kenyan and Tanzanian sampling sites was approximately 150 km, whereas the distance between Namibian and Kenyan/Tanzanian sampling sites was more than 3000 km.We sampled Chestnut‐banded Plovers at three locations Fig. b: Mile 4et al. μL of blood to an Eppendorf tube for storage in 1 mL Queen's Lysis Buffer . To extract DNA from these samples, we used an ammonium acetate precipitation method , sealed with mineral oil to prevent evaporation. We carried out PCR amplification under the following conditions: 95 °C for 15 min, 35 cycles of 94 °C for 30 s, 56 °C for 90 s, 72 °C for 60 s, and finally 60 °C for 30 min. We visualized amplicons on an ABI 3730 automated DNA analyser and scored fragment lengths using genemapper software version 4.1 . Full genotype data are included in Table We conducted multiplex PCRs for all study individuals on a DNA Engine Tetrad 2 Peltier Thermal Cycler  in 2‐cervus v3.0.3  to check consistency in allele scoring. Scoring was fully consistent across markers. We assessed heterozygosity in each population using relate .Although sampled individuals were putatively unrelated based on field observations, evaluation of microsatellite data in ML‐FST, GST′ and D; Weir & Cockerham r  and assessed pairwise fixation indices (FST) using arlequin 3.5.2  for 30 s, 72 °C for 30 s and a final extension of 72 °C for 10 min. We tested a 3‐μL aliquot of the PCR products on a 1.5% agarose gel to determine amplification success. We purified PCR products using 2 μL 10× diluted ExoSAP‐IT  according to the instructions of the manufacturer. Cycle‐sequencing was performed by GenePool Laboratory, Edinburgh, UK, on an ABI 3730 DNA analyser (Applied Biosystems) using the BigDye Terminator v3.1 cycle sequencing kit (Applied Biosystems).We conducted PCRs for three individuals from each sample site. Reactions of 10 codoncode aligner 3.7.1  using the ClustalW algorithm. We submitted sequence data to GenBank . amova attributed 38.93% of molecular variance at microsatellite loci to differences between the Namibian individuals and those of Kenya/Tanzania, whereas there was no molecular variance detectable between Kenya and Tanzania  corresponding to southern and eastern African populations. Individuals were assigned with high confidence to each cluster . Similarly, these two genetic clusters were clearly identifiable based on PCA, with the first principal component (PC 1) explaining 34.87% of total variance and the second (PC 2) explaining 7.56% of variance at microsatellite loci  but low and not different from zero for the within‐East African sample site comparison . Inbreeding coefficients FIS for each location were: Namibia 0.078 (P = 0.115), Kenya 0.142 (P = 0.017), Tanzania 0.092 (P = 0.162).Global fixation indices based on microsatellite loci indicated substantial genetic differentiation among populations  and Namibia at mitochondrial loci , but not at nuclear loci . In total, 3100 sites were analysed across four nuclear loci and just three substitutions were present among Tanzanian individuals. Average sequence divergence at mitochondrial loci was 2.36%  compared with < 0.01% at nuclear loci.Results of phylogenetic network analyses based on mitochondrial sequence data supported assignment of Namibian individuals to a distinct genetic cluster from those of Kenya and Tanzania Fig. . This clC. p. venustus of eastern Africa and C. p. pallidus of southern Africa, based on microsatellite genotyping and mitochondrial sequence analyses. Genetic structure and estimated levels of inbreeding can be affected by including related individuals in the analysis (Rodríguez‐Ramilo & Wang FIS) indicated a significant heterozygote deficit among Kenyan individuals, suggesting that levels of inbreeding may be higher at the Kenyan site in contrast to the Namibian and Tanzanian sites.We identified strong genetic divergence between the two currently recognized subspecies of Chestnut‐banded Plover, et al. Charadrius species , and similar to that between the Kittlitz's Plover and Madagascar Plover Charadrius thoracicus , but also comparable to subspecies‐level divergence among island and mainland populations of the Three‐banded Plover Charadrius tricollaris and White‐fronted Plover Charadrius marginatus . In line with clear morphological differences, our analyses of microsatellite loci also confirmed that the two Chestnut‐banded Plover subspecies belong to distinct genetic clusters, with genetic differentiation between subspecies far greater than between populations of the same subspecies.The classification of subspecies does not always directly correspond to genetic differences Zink . A recenet al. C. p. pallidus and C. p. venustus. Our current results indicate that dispersal of Chestnut‐banded Plovers between eastern and southern Africa is limited and, with continued isolation, these taxa are likely to diverge further. This strong genetic divergence is surprising given that for most other Charadrius species studied to date, dispersal and gene flow between populations occur over much greater continental distances . In addition, these wetlands are bordered by some of the highest mountains in Africa, as well as some of the deepest freshwater lakes. To understand the evolutionary history of Chestnut‐banded Plover populations and how they reached their current state of isolation, it is important to consider the influence of long‐term climatic and geographical changes.With a behavioural tendency towards sedentariness, geographical isolation is likely to decrease population connectivity over a smaller scale than for more widely dispersing species. Subspecies et al. et al. et al. et al. et al. et al. et al. et al. et al. C. p. venustus. In contrast, south‐western Africa, home to C. p. pallidus, has remained relatively stable both geologically and tectonically for the last 3.5 million years, providing refugia for many taxa . This study was funded by the NERC as part of the PhD research of N.dR. C.K. was supported by a Marie Curie IEF Postdoctoral Fellowship. Permits for fieldwork and collection of samples were provided by the National Council for Science and Technology, Kenya, the Namibian government, Ministry of Environment and Tourism, and the Tanzania Commission for Science and Technology (COSTECH) and Tanzania Wildlife Research Institute (TAWIRI).Table S1. Microsatellite genotype data for all individuals  and GenBank accession numbers for sequence data .Click here for additional data file.


Human-derived environmental pollutants and nutrients that reach the aquatic environment through sewage effluents, agricultural and industrial processes are constantly contributing to environmental changes that serve as drivers for adaptive responses and evolutionary changes in many taxa. In this study, we examined how two types of point sources of aquatic environmental pollution, harbors and sewage treatment plants, affect gene diversity and genetic differentiation in the blue mussel in the Baltic Sea area and off the Swedish west coast (Skagerrak). Reference sites (REF) were geographically paired with sites from sewage treatments plant (STP) and harbors (HAR) with a nested sampling scheme, and genetic differentiation was evaluated using a high-resolution marker amplified fragment length polymorphism (AFLP). This study showed that genetic composition in the Baltic Sea blue mussel was associated with exposure to sewage treatment plant effluents. In addition, mussel populations from harbors were genetically divergent, in contrast to the sewage treatment plant populations, suggesting that there is an effect of pollution from harbors but that the direction is divergent and site specific, while the pollution effect from sewage treatment plants on the genetic composition of blue mussel populations acts in the same direction in the investigated sites. Anguilla anguilla) and American eel (Anguilla rostrata)  .Mytilus) and the adaptation to brackish water has caused morphological and genetic differentiation between the blue mussels off the Swedish west coast and blue mussels in the Baltic Sea Fig. 1). ltic Sea . The gen mussels . Clear d mussels , attribus genome . The blu genomes . The gen genomes  and can  genomes , and the genomes .M. edulis trossulus), is a keystone species in the Baltic Sea ecosystem serving several ecological important features such as filtering the water and providing a food-chain link between the benthos and the pelagic (The blue mussel ( pelagic . The blu pelagic . It is a pelagic .Elliptio complanata) (Rutilus rutilus) (E. complanata)  (Gasterosteus aculeatus)  . Wastewaplanata) , feminizrutilus)  and up-rplanata) , marine ncialis)  and sticuleatus) .Patiria miniata) in the Southern California Bight, the genetic structure as estimated by mtDNA and microsatellites was correlated with runoff from sewage treatment plants and storm water. The runoff from sewage treatment plants and storm water was proposed to cause larval mortality and act as a barrier to larval dispersal, resulting in genetic differentiation among populations. In a study of roaches (R. rutilus) living in rivers highly polluted by sewage effluents, Hamilton and colleagues . These are often highly affected by high concentrations of heavy metals, hydrocarbons, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, polychlorinated benzodioxins, polychlorinated dibenzofurans, polybrominated biphenyls and dioxins . These srocesses . High gerocesses .Contamination of aquatic environments from point sources like sewage treatment plants and harbors typically include a range of pollutants that have different effects on organisms, therefore a complex selective response including multiple genes is expected . NeutralThe aim of this study was to explore how two types of point sources of environmental pollution (STP and HAR) affect gene diversity and genetic differentiation in the blue mussel in the Baltic Sea and the Swedish west coast (Skagerrak). By using a combination of a nested sampling scheme , with a high-resolution marker (AFLP), we examined if pollution from sewage treatment plants and harbors could (i) affect the gene diversity at polluted sites, (ii) cause genetic differentiation between polluted sites and reference sites, and (iii) cause differentiation that is consistent in direction in sites within pollution types or if the direction of differentiation is divergent between individual polluted sites. We also aimed at evaluating the effect of the species introgression pattern on the genome wide genetic structure and effects in combination with pollution type.Mussels were sampled during June 2012 and 2013 from five locations in the Baltic Proper (BP) and off the Swedish west coast (WC): Trosa/Nynäshamn Archipelago (east coast of Sweden) (ASK), Tvärminne in the Gulf of Finland (TVA), Karlskrona (Southern Sweden) (KAR), the Gulf of Gdańsk, Poland (GDA) and Kristineberg  (KRI) , Table 1At each of 15 sites, 30 blue mussels , with small modification of the manufacturers protocol. In short, tissue was incubated overnight at 56 °C in 250 µl ML1 lysis buffer and 25 µl proteinase K followed by a chloroform:isoamyl  extraction step with 3 min centrifugation . DNA suspended in the aqueous phase was carefully separated from the interphase (200-300 µl) and 300 µl MBL buffer with 10 µl RNAse was added, and samples were incubated for 10 min at 70 °C. The DNA was precipitated from the solution with 99.7% ethanol, transferred to DNA HiBind spin columns and centrifuged to remove any traces of alcohol. Purified DNA was eluted with 50 µl 10 mM Tris–HCl buffer (pH 8.5) (preheated to 70 °C). DNA quality was visually estimated using agarose gel 1.5% electrophoresis  stained with  SYBRSafe (Life technologies).M. edulis and M. trossulus in the Baltic Sea and the west coast, and extensive mixing of the M. edulis and M. trossulus genomes , 1 µl BSA, 10 mM dNTP (Thermo Scientific Molecular Biology), and100 µM of each primer. Forward primer (ME15) 5′-CCAGTATACAAACCTGTGAAGA-3′ 5′-end was labeled with fluorescein and reverse (ME16) 5′-TGTTGTCTTAATAGGTTTGTAAGA-3′ (MWG Biotech). The amplification cycle consisted of an initial denaturation for 5 min at 94 °C followed by 38 cycles at 94 °C for 30 s, 55 °C for 30 s and 72 °C for 45 s, followed by elongation for 10 min at 72 °C. DNA fragments were separated on an ABI-3730XL capillary electrophoreses unit at Uppsala Genome Center with separation medium POP7™ Polymer (Applied Biosystems), size standard GeneScan™ 500 ROX™ (Applied Biosystems), injection time 15 s (1.6kV), run time 1,600 s and array length 50 cm.The Glu-5′ PCR fragments were amplified in 10 µl reactions containing: 20–40 ng DNA, 25 mM MgCl2O. Followed by ligation for 3 h at 37 °C, in; ligation buffer (10X), 100 mM E-adaptor 5′-CTCGTAGACTGCGTACC-3′, 3′-CATCTGACGCATGGTTAA-5′ (MWG Biotech), 100 mM M-adaptor 5′-GACGATGAGTCCTGAG-3′, 3′-TACTCAGGACTCAT-5′  (MWG Biotech) and 10 U/ml T4 ligase (Thermo Scientific Molecular Biology).Markers were generated as described by 2, 2 µl PCR-buffer (10x), 0.8 µl BSA, 1 mM dNTP, 5U Taq polymerase and ddH2O. Pre-amplification PCR conditions included an initial denaturation step for 2 min at 94 °C followed by 20 cycles of 94 °C for 30 s, 56 °C for 30 s and 72 °C for 60 s, and a terminal step at 72 °C for 10 min.Pre-amplification was performed in 20 µl reactions containing 10 µl DNA template , 100 µM E-primer 5′-GACTGCGTACCAATTCA-3′ and 100 µM M-primer 5′-GATGAGTCCTGAGTAAC-3′, 25 mM MgClCTG_EAGC and MCTT_EACT, showed polymorphic bands, with a congruent pattern in duplicated individuals. The PCR reaction for selective amplification was conducted containing; 2.5 µl of the diluted pre-PCR template, 25 mM MgCl2, 1 µl 10x PCR-buffer, 1 µl BSA, 1 mM dNTP, 100 mM E-primer 5′-GACTGCGTACCAATTCNNN-3′ with a fluorescein 5′-end, 100 mM M-primer 5′-GATGAGTCCTGAGTAANNN-3′ and 5U Taq polymerase and ddH2O to a volume of 10 µl. The selective amplification cycle consisted of denaturation for 2 min at 94 °C, 12 cycles at 94 °C for 30 s, 56 °C with a touch-down temperature −0.7 °C for 30 s and 72 °C for 60 s, followed by 23 cycles at 94 °C for 30 s, 56 °C for 30 s, 72 °C for 60 s and a terminal step at 72 °C for 10 min.For selective amplification eleven primer combinations were evaluated using a subset of individuals from all the geographical regions and habitats. The two chosen combinations MThe DNA fragments (AFLP) were separated on an ABI-3730XL capillary electrophoresis unit at Uppsala Genome Center with separation medium POP7™ Polymer (Applied Biosystems), size standard GeneScan™ 500 ROX™ (Applied Biosystems), injection time 15 s (1.6kV), run time 1,600 s and array length 50 cm.CTG_EAGC: 250–500 bp, (Primer MCTT_EACT: 230–500 bp), bin width 1.5 bp, peak detector 170 rfu, normalization using sum of signals from all individuals within one primer combination and no smoothing. For error rate analysis 7–14% of the samples were randomly chosen and duplicated from extraction and/or ligation step. The duplicates were manually checked for congruency and the recorded duplicated loci where used to calculate error rate i.e., the number of inconsistent loci per primer combination divided by total number of loci resulting in the percentage of error. The two genotype-matrices were merged into one dataset used in all subsequent analyses.To generate a genotype matrix for each primer combination, AFLP data from the two primer combinations were analyzed separately in Genemapper 4.0 (Applied Biosystems). Analyses settings: primer MM. galloprovincialis , M. trossulus (165 bp) or M. edulis (177 bp) Twenty percent of the samples were duplicated to check for congruency.To score the target allele length at the Glu-5′ locus, GeneMarker 2.5.2 (SoftGenetics LCC) software with size standard GS500 and analysis type Fragment  was used. In total, 364 individuals were scored, by the allele lengths as HE) and between sites (FST) were obtained using the software AFLP-SURV 1.0  and excluding the west coast sites , were performed as ANOVAs in the Rcmdr package   each pollution type was analyzed separately in AFLP-SURV including each location  calculations (FDR = 0.05) (www.sdmproject.com/utilities/?show=FDR) for 105 pairwise comparisons, 10 locations comparisons and 2 pollution type comparisons .Genetic differentiation, FLP-SURV  using diere made . To examt sites) . To examlocation . The sig = 0.05)  using thThe large-scale geographical component in the sampling scheme, and the complex species introgression pattern in the studied area made it necessary to take these components into account when analyzing possible effects of pollution type. As the genetic difference between the west coast and the Baltic Proper locations is expected to be large  and to gTo understand the general species effect on the genetic structure  a cPCoA was conducted using all sites  both including and excluding the west coast, with species identity as explanatory variable.http://www.sdmproject.com/utilities/?show=FDR) FDR = 0.05.To test the effect of pollution type on the genetic structure  cPCoA models including location, species identity, pollution type and the interaction between pollution types and species were constructed. Here location, species identity and pollution type were used as explanatory variables in an order dependent ANOVA model performed by using the setting “by terms” as implemented in the vegan package . The terA different way to perform the cPCoA is to remove the effects of variables, in this case location and species identity, using a condition  which maThree additional constrained ordination analyses were performed, using only sites from Baltic Proper, where differentiation between locations for each pollution type was analyzed separately.To identify alleles at AFLP loci associated with differentiation between the pollution types four approaches were used; two of them, DFDIST  and BayeFST than the expected value may be considered to be under directional selection while loci with lower FST values than expected may be considered to be under balancing selection. Here the application Mcheza  in support of selection over neutrality for each locus (The Bayesian approach implemented in BayeScan  uses thech locus . In BayeThe package varSelRF  in R 3.2http://www.sdmproject.com/utilities/?show=FDR) was used to correct for 354 pairwise comparisons (one/locus), FDR = 0.1.The Cochran-Mantel-Haenszel test for repeated independence  was usedCTG_EAGC: 195 loci, MCTT_EACT: 159 loci). For error rate analyses 14% of duplicated samples for MCTG_EAGC and 7% of duplicated samples for MCTT_EACT were randomly chosen and yielded a congruent pattern and an error rate of approximately 6% and 7% respectively.A total of 396 individuals were used in the analyses and the two primer combinations yielded 354 polymorphic loci (MHE) for each site and mean gene diversity for each pollution type is presented in FST = 0.027, P-value <0.01). No significant difference in mean gene diversity between the pollution types was found; including the west coast sites  and excluding the west coast sites meanREF = 0.128, meanSTP = 0.130, meanHAR = 0.124, ANOVA: df = 2, F = 0.387, P-value = 0.690). Significant FST pairwise differentiation  was found between the Baltic Proper sites and the west coast sites, and between two pairs of STP and REF sites (ASK_STP/GDA_REF and ASK_STP/KAR_REF) and between one pair of HAR sites (ASK_HAR/KAR_HAR), within the Baltic Proper (Gene diversity (c Proper .FST) between the pollution types STP and REF was observed, both including and excluding the west coast sites  between the five locations, ASK, TVA, KAR, GDA and KRI  for each of the three pollution types showed a significant difference after FDR corrections (FDR = 0.05) between all the Baltic Proper locations and KRI and one significant comparison within the Baltic Proper i.e., between ASK and KAR for HAR , M. trossulus (165 bp) or M. edulis (177 bp). Twenty percent of the samples were duplicates and all of the duplicated pairs of samples had the same allele combination. Three individuals contained M. galloprovincialis alleles, all in heterozygotes, two from KRI_REF and one from KRI_HAR, but since our analyses are based on variation between and within groups, a group of three is far too small to give reliable results and these three individuals were excluded from subsequent analyses. The remaining individuals were assigned a species identity coded as homozygous M. trossulus (2), homozygous M. edulis (0) or heterozygotes between the two (1) and species identity was used in subsequent analyses to explore and account for any differences in species identity , but not when analyzing only the Baltic Proper sites . These results showed a general species identity effect between the west coast and the Baltic Proper, but not within the Baltic Proper.To test the species identity effect on the overall genetic structure a constrained principal coordinate analysis (cPCoA) was used, using both the whole data set and a data set excluding the west coast sites. A significant effect of species identity on the genetic structure was found when including the west coast sites  were used to assess the significance of the explanatory variables (constraining variables) location, pollution type and species identity. cPCoA analyses including order dependent, permutation based ANOVAs were conducted on different aspects on the dataset. The cPCoA including all STP and REF sites, using location, species identity and pollution type as constraining variables, was tested with the order dependent ANOVA and showed a significant structuring of the genetic variation both for location and pollution type after FDR correction but not for species identity or the interaction between species identity and pollution type . The cPCThe conditioned cPCoA, using pollution type, STP and REF as constraint and location and species identity as conditions also showed a significant effect of pollution type on the genetic structure in the Baltic Proper and was used to visualize the effect of pollution type , Fig. 2.In contrast to the STP and REF comparisons, the cPCoA for the HAR and REF data showed no genetic differentiation between the pollution types. When including all sites  a significant effect (after FDR correction) of location was found, but not for species identity, pollution type or the interaction between the two . No effedf = 3 F = 1.05, P-value = 0.274) or the STP sites  could be found, while there was a significant difference between the HAR sites .To compare the genetic differentiation between the locations for each pollution type independently, three additional cPCoAs were conducted using only sites from Baltic Proper. No significant genetic differentiation between the REF sites  where a significant correlation between storm- and wastewater and genetic structure was found . Multiple studies of marine organisms have shown a range from significantly lower HE (HE (HE) between STP and REF sites or between HAR and REF sites, most likely reflecting high connectivity and high gene flow among the sites.Anthropogenic habitat changes can lead to increase or decrease in gene diversity  correlated with major sources of sewage effluents and storm water. The bat star has a similar life history to the blue mussel with large populations and strong gene flow caused by larval dispersal over large distances. Despite this strong gene flow FST) could be explained by sewage water causing a barrier to larval dispersal.Despite the low general genetic differentiation within the Baltic Proper a significant differentiation between pollution type STP and REF was detected. This was shown by several analyses such as the cPCoA , Fig. 2,analyses . This efanalyses . Sewage A. anguilla) and European eel (A. rostrata) (The significant differentiation between STP and REF sites despite strong gene flow is in line with studies finding significant genetic differentiation among populations when subjected to high gene flow . Taking ostrata) .The AFLP method, employed in the present study, has previously been shown to identify specific marker loci linked to adaptive variation among habitats . This poElliptio complanata)  and indirect stressors , likely reflecting the complexity of sewage effluents. The stressors have included immunological , genotoxplanata)  and up-rmplanata , and in incialis . The expincialis  and in tincialis , and as M. edulis and M. trossulus as reference individuals is needed but this was beyond the scope of the present study.As the blue mussels in the Baltic Proper show a strong introgression pattern  the effeIn contrast to the genetic differentiation found between STP and REF sites, we did not find any results suggesting a congruent effect on the genetic differentiation between the HAR and REF sites. However, the cPCoA show significant differentiation among HAR sites in the Baltic Proper. This pattern may be interpreted as a signature of divergent local selection among HAR sites and is in contrast to a study of Mediterranean mussels where no significant genetic differentiation between harbor sites was found . A possiSimilar to what was found for the STP pollution type, no significant effects of species identity were found, and again this result indicates that species identity is not an important part of the pollution type effect on genetic differentiation.In conclusion, this study identified genetic differentiation in the Baltic Sea blue mussel associated with exposure to sewage treatment plant effluents. This study adds to the growing body of literature showing genetic effects associated with exposure to sewage treatment plant effluents on wild populations. Blue mussels from HAR sites were more genetically divergent than mussels from STP and REF sites, indicating possible divergent selection caused by different pollution regimes in different harbors or by inter- and intra- Baltic shipping routes. This putative within-generation footprint of local selection in blue mussels despite extensive gene flow through pelagic larval dispersal suggests that in other organisms, with other life history characters that promote e.g., local recruitment, pollution may direct evolutionary processes to an even higher degree. The results imply that evolutionary effects of pollution on wild populations are important for ecosystem function and biodiversity conservation.10.7717/peerj.2628/supp-1Table S1Name of Sewage treatment plant, size of the municipal area , type of waste water process. Record of eutrophication (N and P), main pollutants . NA = no available data.Click here for additional data file.10.7717/peerj.2628/supp-2Table S2Name and type of harbor, traffic (number of boats/year). Record of main pollutants . NA = no available data.Click here for additional data file.10.7717/peerj.2628/supp-3Table S3Click here for additional data file.10.7717/peerj.2628/supp-4Supplemental Information 1Click here for additional data file.10.7717/peerj.2628/supp-5Data S1REF representing a subset of all individuals sampled from the reference sites (REF) and sewage effluent affected sites (STP) in the total study area (ALL) and the Baltic Proper (BP). HARREF representing a subset of all individuals sampled from the reference sites (REF) and harbor sites (HAR) in the total study area (ALL) and the Baltic Proper (BP).The raw data presented including all individuals analyzed, with sample name, sampling site, sampling location, pollution type, species identity coded as Mytilus trossulus (T), heterozygotes (H), Mytilus edulis (E), species identity coded as Mytilus trossulus (2), heterozygotes (1), Mytilus edulis (0), AFLP data with each marker coded with name and number. The data is divided into subsets, ALL representing the total data set, BP representing a subset of all individuals sampled from the Baltic Proper. STPClick here for additional data file.10.7717/peerj.2628/supp-6Supplemental Information 2Click here for additional data file.


Puccinia striiformis f. sp. tritici, has seen the emergence of new races that are adapted to warmer temperatures, have expanded virulence profiles, and are more aggressive than previous races, leading to wide-scale epidemics. Here, we used field-based genotyping to generate high-resolution data on P. striiformis genetics and carried out global population analysis. We also undertook comparative analysis of the 2014 and 2013 UK populations and assessed the temporal dynamics and host specificity of distinct pathogen genotypes. Our analysis revealed that P. striiformis lineages recently detected in Europe are extremely diverse and in fact similar to globally dispersed populations. In addition, we identified a considerable shift in the UK P. striiformis population structure including the first identification of one infamous race known as Kranich. Next, by establishing the genotype of both the pathogen and host within a single infected field sample, we uncovered evidence for varietal specificity for genetic groups of P. striiformis. Finally, we found potential seasonal specificity for certain genotypes of the pathogen with several lineages identified only in samples collected in late spring and into the summer, whereas one lineage was identified throughout the wheat growing season. Our discovery of which wheat varieties are susceptible to which specific P. striiformis isolates, and when those isolates are prevalent throughout the year, represents a powerful tool for disease management.Recent disease outbreaks caused by (re-)emerging plant pathogens have been associated with expansions in pathogen geographic distribution and increased virulence. For example, in the past two decades’ wheat yellow (stripe) rust,  Puccinia striiformis f. sp. tritici (PST), that is present in all wheat growing areas of the world. The resulting disease can cause high yield losses, even in countries where resistant wheat varieties and/or fungicides are used (P. striiformis races adapted to warmer temperatures and showing expanded virulence profiles have caused widespread epidemics (P. striiformis strains (PstS1 and PstS2) were present at epidemic sites across five continents in 2009, reflecting the most rapid and extensive spread of any important plant pathogen to date emerging plant pathogens have frequently been associated with expansions in pathogen geographic distribution and increased virulence of known pathogens. One example is the wheat yellow (stripe) rust pathogen, are used . In the pidemics . As a ca to date . The emecontinue .P. striiformis has re-emerged as a major constraint on wheat production; races recently arising in Europe have overcome many of the major resistance genes in European germplasm. For instance, in 2011, two new P. striiformis races collectively known as Warrior  and Kranich (based on the virulence of one variant to the wheat variety Kranich) emerged as serious threats to wheat production  as described previously . Quality and quantity of extracted RNA were assessed using an Agilent 2100 Bioanalyzer  and cDNA libraries prepared using an Illumina TruSeq RNA Sample Preparation Kit (Illumina). Libraries were sequenced on the Illumina HiSeq 2500 machine at the Earlham Institute, United Kingdom. The FASTX-Toolkit (version 0.0.13.2) was used for adapter and barcode trimming and quality filtering. The 101-bp (HiSeq) pair-end reads were aligned to the PST-130 assembly as described previously  for all isolates from 2014, which indicated that the number of positions that fall within the 0.8 and 0.9 boundaries represent just 0.59% of the total number of positions considered. Read frequencies were calculated for biallelic heterokaryotic SNP sites and plotted using ggplot2 in R ) of each K value was calculated. However, as STRUCTURE is sensitive to asexual reproduction . The genotypic diversity was then calculated corresponding to the interindividual diversity within a genetic group and the genetic distance (AD and XYD) of these genetic groups using the Egglib software.FST statistic. This was carried out for all 2013 and 2014 European isolates combined. To further characterize the different P. striiformis genetic groups, 427 genes with at least 80% breadth of coverage for all isolates  were selected, and their sequences concatenated using an in-house Python script. The excess of heterozygotes (FIS) and nucleotide diversity (Π) were calculated for all genetic groups using Egglib version 2.1.2  reached 5%. An average of these assessments was then taken to represent disease progression throughout the season. The seedling tests were also extended to include the same variety set used in the adult plant tests under controlled environmental conditions. A subset of differentials from the initial pathotype screening was also included to check consistency between tests. Varieties were tested and assessed as described earlier.P. striiformis-infected wheat sample matched the SNP site for a particular variety, the position was scored as 1, and if the site partially matched, the score was 0.5. Otherwise, the position was scored as 0. This analysis was carried out for all SNP markers in a sample, and the cumulative score for all varieties was determined. The final score per variety represents the similarity between the P. striiformis-infected sample and a particular variety. The highest score corresponds to the most similar variety across varieties where marker data were available.From the set of ∼90,000 SNP markers genotyped using the wheat Illumina iSelect array , 21,505 P. striiformis across Europe in the 2014 growing season, we collected 246 P. striiformis-infected wheat, triticale, and rye samples from December 2013 to August 2014 from fields across 16 European countries , determined from alignment against the PST-130 genome, as described previously  via a maximum-likelihood model , Australasia (2006–2012), South America (2014), and Africa (2014) that were classified as belonging to the emergent P. striiformis population based on phylogenetic analysis  an additional subgroup of Group 1, which gave rise to Group 5-1. We also sought to confirm the number of groups independently using the average log probability (LnP(D)) of each K value in the model-based clustering program STRUCTURE. The average log probability stabilized at K = 6  via a maximum-likelihood model (P(D)) in the program STRUCTURE, which supported the division of the combined 2013 and 2014 P. striiformis isolates into five genetic groups (P. striiformis genetic group (Group 5-1). Pair-wise comparisons of the five genetic groups generated a range of FST values, with Groups 3 and 1 being the most distantly related (FST 0.65) and Groups 3 and 4 being the most closely related (FST 0.20) , indicating a smaller excess in heterozygosity than the other genetic groups, with the smallest number of SNPS/kb  A. To detkb 0.79,  online. AD distance; XYD; AD: 1,100.5, 1,037.8, and 1,102.5, respectively; XYD: 1,158.9, 1,132.7, and 1,149, respectively), with an average AD of 793.1 (295.0±SD) and XYD of 885.3 (286.7±SD) for all pairwise comparisons  and between Groups 1 and 5-1  and compared it with the nucleotide diversity calculations  of P. striiformis isolates in Group 4 when comparing isolates between 2013 and 2014. These findings suggest that Group 3 may have (at least in part) been displaced in 2014 in the United Kingdom and that Group 4 isolates increased in prevalence (44% in 2013 vs. 84% in 2014) and diversity during this time  were inoculated on a series of 46 differential wheat varieties. Disease severity was recorded in seedling tests at 16- to 20-day postinoculation  with virulence on a broader set of varieties at both the seedling and adult plant stages  and a maximum-likelihood model for the phylogenetic analysis  to a particular genetic group within the European data set. A total of 29 representative isolates from 2013 and 2014 were selected for analysis of P. striiformis 14/106, which represented an average of five isolates from each of the five genetic groups identified previously (P(D)) )  online. y (P(D))  online. roup 5-1 . This suP. striiformis-infected samples from December 2013 to August 2014. Isolates belonging to Groups 1, 2 and 5-1 were identified only in samples collected in May, June, and July  led to their predominance in the United Kingdom.In the 2014 growing season, we collected and July A, whereag season A. This re season  online. P. striiformis genotypes showed any preference for wheat varieties that they infected in the field. Where possible, we used our data sets and a set of 1,831 differential wheat SNP markers to confirm the wheat variety in a particular P. striiformis-infected sample, as described previously , where in multiple cases the variety was distributed across different geographic regions (P. striiformis-infected samples for which the wheat variety was reported and (where possible) confirmed. We found that 98% of the 56 wheat varieties harbored P. striiformis isolates from a single genetic group. Only the Kranich variety harbored genetically distinct P. striiformis isolates . However, a previous analysis of isolates collected pre-2008 from East Africa and South America showed that these areas harbored races that were genetically different to the European lineages at that time  of harmful genotypes , diseaseh” races . In thisn region . This fihat time . TherefoP. striiformis populations in Europe is the unusual scale of inter- and intradiversity  rapid diversification and expansion of Group 4 between 2013 and 2014 in the United Kingdom, 2) a new exotic incursion of P. striiformis isolates with a similar genetic background and/or 3) the small sample size in 2013 not being truly representative of the existing P. striiformis diversity. Regardless, such high levels of diversity within Group 4 may reflect differences in life history strategies of the new P. striiformis lineages, such as an increase in the rate of somatic hybridization, which would enhance genetic variability.Traditionally, the pre-2011 European yellow rust population propagated asexually, with mutation driving race variability within the confines of Europe, thereby leading to a single clonal genetic group . Howeveriversity . FurtherP. striiformis lineages identified in the United Kingdom (FST range 0.20–0.65) is consistent with the notion that the P. striiformis population recently arose from multiple exotic incursions from distinct geographical sources  space partitioning, 2) time partitioning, and 3) resource partitioning . Of thesaptation . AlternaP. striiformis into Australia (P. striiformis genetics. This analysis revealed that races of P. striiformis that recently emerged in Europe are in fact similar to populations identified on a global scale. Furthermore, we uncovered the possibility of seasonal and varietal specificity in genetic groups of P. striiformis. Knowing which wheat varieties are susceptible to specific P. striiformis isolates prevalent at certain times of the year could help agronomists fine-tune disease management strategies accordingly. The scale and frequency of emerging diseases will continue to increase, making the integration of high-resolution genotyping into international surveillance programs important as emergent plant pathogens become increasingly unpredictable. As global trade continues to expand and people increasingly vacation in more remote locations, the spread of pathogens into new territories is inevitable. For wheat yellow rust, we have already witnessed the devastation caused by the human-assisted introduction of ustralia . TherefoGenome Biology and Evolution online.Supplementary InformationsClick here for additional data file.


Caenorhabditis elegans has been one of the primary model systems in biology since the 1970s, but only within the last two decades has this nematode also become a useful model for experimental evolution. Here, we outline the goals and major foci of experimental evolution with C. elegans and related species, such as C. briggsae and C. remanei, by discussing the principles of experimental design, and highlighting the strengths and limitations of Caenorhabditis as model systems. We then review three exemplars of Caenorhabditis experimental evolution studies, underlining representative evolution experiments that have addressed the: (1) maintenance of genetic variation; (2) role of natural selection during transitions from outcrossing to selfing, as well as the maintenance of mixed breeding modes during evolution; and (3) evolution of phenotypic plasticity and its role in adaptation to variable environments, including host–pathogen coevolution. We conclude by suggesting some future directions for which experimental evolution with Caenorhabditis would be particularly informative.The hermaphroditic nematode  The main organismal models to which EE has been applied are mice, fruit flies, yeast, and bacteria  or a control group (differentiation). Using numerical simulations, we show in Because the development of evolutionary theory has far outpaced the generation of relevant data (genome-sequence data being a singular exception), using EE to confront long-standing problems has the potential to illuminate our understanding of evolution for decades to come provided a few qualifications are appreciated. The most important considerations are the potential difficulty in balancing simplicity and realism in contrived laboratory experiments , the uncDoing EE well requires careful planning and organization, and the commitment to spend at least several hours transferring individuals every few days for months, or even years, on end. Why would anyone take on such a thing when they could start today on a comparative study in which nature has already done most of the work? Since students of evolution are primarily driven by “why” questions . And whiCaenorhabditis species did not begin in earnest until the 1990s with the work of johnson and colleagues on the evolution of aging .We now understand that natural populations of  is rare , with C.Caenorhabditis somewhat odd also make it ideally suited for laboratory approaches to studying evolutionary questions . Of course, the real power here is the ability to capitalize on decades of hard work on the genetics and functional biology of C. elegans that has been provided by the broader C. elegans research community , respectively. This long-term laboratory selection has also modified allocation among N2’s life-history traits via modification of chromatin state by the NURF chromatin remodeling complex , and as a method for characterizing their phenotypic consequences and evolutionary dynamics  lead to predictable differences in eN: the greater i is, the smaller eN will be , genotype-by-environment, and epistatic interactions, the usefulness of the concept of al e.g., . But ign selfing .eN being generally lower than N . Promisingly, CEs are beginning to be used for gene discovery and understanding the genetic basis of adaptive traits in so-called “evolve and resequence” experiments .Competition experiments (CEs) are the best approach for measuring natural selection . CEs areC. elegans populations of 1000 individuals, finding that these new mutants were frequently lost even when adaptive, and, conversely, tended to linger within populations at frequencies higher than the deterministic expectation when they had deleterious effects, in keeping with the classical theory of stochastic population genetics  . In one genetics .C. elegans have taken the opposite approach—“reverse EE”—by initially perturbing the genetics of the population and then observing how they respond to their ancestral environment, or to environments that allow the recovery of ancestral states by selection. In this fashion, one can assess if and how selection overcomes evolutionary history , and is thought by some to be the primary driver of long-term evolutionary patterns . In general, selfing reduces effective population sizes, particularly if background selection of deleterious recessive alleles is present, potentiating genetic drift and the loss of neutral diversity. When considering multiple loci, however, neutral diversity can be maintained under selfing by hitchhiking with (positive) selected alleles and/or because of “identity disequilibrium” generated by genotypic correlations. Understanding selection under selfing is highly difficult but we do know that it fundamentally depends on the degree of dominance within loci and epistasis between loci.Evolution from standing genetic variation can occur through the sorting of extant genotypes by segregation or through a combination of segregation and recombination between existing genotypes. For C. remanei and C. elegans have employed hybrid ancestors constructed from crosses of several wild isolates  to selection, but organisms are, of course, not merely collections of atomized traits. Multivariate evolutionary responses will depend on how pleiotropy, linkage disequilibrium, and inbreeding/assortative mating have shaped genetic variances and covariances among traits [summarized in the genetic variance-covariance matrix G;  loci can reveal if inbreeding depression is due to strongly deleterious recessive alleles that are purged during the inbreeding experiment . In conthttp://datadryad.org/ doi: 10.5061/dryad.bg08n.R code for simulation of EE is deposited at Why so much genetic variation? The discussion surrounding that question will be familiar to most readers of GENETICS; it suffices to say that the poles of the issue are “Mutation + Drift” at one end, and “Balancing Selection” at the other. In recent decades, the debate has centered on variation at the molecular level, but arguments long antedates the molecular era.  unbiased estimates of the rate and spectrum of spontaneous mutation unencumbered by selection (Appendix 3 in File S1). Direct estimates of the per-nucleotide, per-generation mutation rate, µ, in model organisms provide a critical reality-check on indirect estimates of µ from sites putatively free from natural selection  because many tests of non-neutral molecular evolution assume there is a class of neutrally evolving sites that can serve as a reference , and reported that 2–3% of the genome appeared to harbor ancient segregating variation (“ancient” meaning an estimated average time of divergence on the order of 106 generations), which they attributed to long-term balancing selection. Taken at face value, these two studies lead to the seemingly odd situation of most of the genome being genetically depauperate, but a dispersed fraction carrying ancient polymorphisms.The question “Why so Why so much genetic variation? applies equally to emergent phenotypic traits. If mutation and random genetic drift are the only forces at work, at mutation-drift equilibrium the standing genetic variance for the trait should equal GV = 2eVMN (eN is the effective population size (Box 1) and MV the mutational variance (Appendix 1 in File S1). If an estimate of eN is available, as it would be from segregating nucleotide variation and an independent estimate of the per-nucleotide mutation rate (because θ = 4eµN), a value of GV << MV implies the trait is subject to purifying selection and can be used as an approximate estimate of the strength of selection acting against mutations that affect the trait (MV and GV for hundreds of gene transcripts in four MA lines and five wild isolates of C. elegans (including N2). Small sample sizes notwithstanding, the results were impressive: of ∼3700 genes investigated, not one had a MV/GV ratio greater than the neutral expectation, from which it could be concluded that transcription is under strong and ubiquitous stabilizing selection.The question of  = 2NeVM , where Nhe trait . For exaa priori to be under different types of selection (MV explains a large fraction of the variance in GV (∼90%), and the ratio MV/GV is well below the neutral expectation. That finding is entirely consistent with genetic variation in C. elegans being largely modulated by mutation and purifying selection , with the between-population component of genetic variance of phenotypic traits (QST) , QST is predicted to be >FST. To our knowledge, this type of test has yet to be applied in Caenorhabditis. C. elegans is probably not conducive to QST/FST comparisons due to its unusual population structure, but C. briggsae, with its hierarchical population structure  . For traGV and MV have not turned up compelling evidence for balancing selection, several lines of evidence support the conjecture that balancing selection may play a role in maintaining genetic variation in natural C. elegans , one route to selfing involves restrictions on outcrossing between males and females . Hermaphrodites able to self autonomously will in this case be favored because they provide reproductive assurance and guarantee population survival  where outcrossing was restricted. Adaptation was not due to the loss of males, but rather to the replacement of females by hermaphrodites, as shown by the lower adaptive rates of androdioecious populations, which similarly lost males and reproduced exclusively by selfing by the end of EE.  do not maintain males  whose break-up by outcrossing will lead to outbreeding depression. Comparison of crosses between wild isolates in several C. remanei has provided convincing evidence for such genetic assimilation, in line with classic work in Drosophila  for 48 generations  decreased in the coevolved host and pathogen populations compared to the non-coevolved controls.Box 3and in the presence of non-coevolving pathogens, and, similarly, pathogens must be evolved in the presence of coevolving hosts and with non-coevolving hosts  species may depend on genotype frequencies in the other species, and that the effects of alleles in coevolved host populations confer high(er) fitness in the presence of the coevolved pathogen genotype. A straightforward prediction is that some salient property(s) of coevolved hosts and pathogens will differ from hosts evolved in the presence of non-coevolving pathogens, and vice versa, if coevolution has played an important role in the evolution of either partner. To unambiguously demonstrate such a difference, hosts must be evolved in the presence of coevolving pathogens, ng hosts , and theCaenorhabditis provide a uniquely powerful model system with which to experimentally investigate coevolution because they are so readily cryopreserved. C. elegans can host a wide spectrum of bacteria, fungi, protozoa, and viruses, as well as combinations thereof , often revealing genes that explain a significant fraction of the genetic variation. Most investigations of host–pathogen interactions involving C. elegans have understandably focused on host traits, but there is also a significant body of work focusing on pathogen or parasite traits  differs from evolution in the presence of nonevolving pathogens (or hosts)  genotype and developmental stage.How the composition of microbial communities can generate variable and fluctuating selection is, at present, unknown, and much work is needed in order to characterize these communities and their potential effects on Caenorhabditis EE has begun to touch on many aspects of population and quantitative genetics, its potential use in addressing questions related to evolution in structured populations remains largely untapped. An exception is the study of npr-1 locus could lead to differences in dispersal rates and the maintenance of polymorphism driven by behavioral characteristics. Another exception is the recent study of srx-43 locus could lead to density-dependent selection. But few studies have used the systematic manipulation of migration rates to examine, say, the influence of local adaptation on rates of genomic change, or the evolution of dispersal traits  more such variants. Current studies frequently utilize thousands of individuals within a single population. Future experiments performed using liquid culture of Caenorhabditis or novel rearing approaches should allow populations to be maintained at sizes in the millions or more, making EE more typical of natural populations. While still perhaps not at yeast or bacteria population sizes, moving in this direction—especially in sexual species—has the potential to qualitatively change the way that experiments are conducted.In many cases, simply observing trait changes over time is sufficient for addressing the question at hand. Nevertheless, one of the strong appeals of using such well-developed model systems as C. elegans. Perhaps more interestingly, the ability to make identical allelic substitutions in different genetic backgrounds opens up a new world of potential experiments. Chief among these will be the ability to more precisely test for epistatic effects between loci within different genetic backgrounds. One can readily imagine using CRISPR to manipulate the genetic backgrounds of different base populations that serve as the basis of later evolutionary experiments (Once putative genetic changes have been identified, application of new genetic transformation methods will allow functional hypotheses to be more readily tested, especially outside of eriments . How depCaenorhabditis is still largely lacking (C. elegans’ tolerance to various forms of environmental challenges likely to be encountered by natural populations, such as pathogen exposure, or osmotic, thermal, and oxygen stress, have started to be dissected (e.g., e.g., Clearly, there are major gaps to be filled by future work aimed at identifying and characterizing the genetic and phenotypic bases of adaptation and by “translational” studies to connect such results to evolution in nature. Although functional genetic information for Caenorhabditis is that these nematodes are relatively easy to manipulate via a variety of methods, allowing high-throughput, high-precision phenotyping to be applied (e.g., microfluidics (Caenorhabditis allows their cellular and developmental structure and function to be examined with exquisite detail in an EE context (Caenorhabditis as a central player (Finally, the fundamental problem of evolution is the problem of the phenotype. One advantage of fluidics , has notl player . The rolCaenorhabditis species have come of age as models for EE. EE with these nematodes have provided significant insights into the origin and evolution of reproductive modes, adaptation to changing environments and into mutation rates, their genomic context and fitness effects. Use of Caenorhabditis allows unprecedented control over the properties of standing variation, population sizes, transgenerational effects, and degree of sexuality for a metazoan. Coupled with our extensive understanding of their genetics, and their cellular and developmental biology, future EE studies with Caenorhabditis promise to unravel many of the outstanding problems of evolutionary biology.www.genetics.org/lookup/suppl/doi:10.1534/genetics.115.186288/-/DC1.Supplemental material is available online at Click here for additional data file.


Recent genomic studies have begun to reveal the genetic architecture of psychiatric disorder and to give important insights into the relationship between the psychiatric syndromes that form the basis of current taxonomy. These studies have demonstrated the highly polygenic nature of psychiatric disorders, and have indicated that many individual genetic associations are shared across multiple disorders in a way that points to extensive biological pleiotropy and challenges the biological validity of existing diagnostic approaches.I will present genomic data, predominantly from the study of rare variants, that support the idea of a neurodevelopmental continuum, in which schizophrenia and bipolar disorder, together with childhood neurodevelopmental disorders, such as ID, ASD and ADHD represent the diverse range of outcomes that follow from disrupted or deviant brain development and furthermore that, within the neurodevelopmental continuum, severe mental illnesses occupy a gradient of decreasing neurodevelopmental impairment as follows: ID, ASD, schizophrenia and bipolar disorder. I will also present findings indicating that common genetic variation modifies the outcome of neurodevelopmental impairment explaining in part the diversity of psychiatric outcomes. Finally, I will explore how genetic data might be used to inform novel approaches to patient stratification which will be informative for prognosis and treatment response and facilitate the identification of novel drug targets.Finally, despite the undoubted complexity and the fact that much of the genetic risk remains unaccounted for at the DNA level, there are encouraging signs that the genes implicated in schizophrenia converge onto sets of plausible biological processes. In particular, the data point to synaptic function and histone modification and implicate mechanisms involved in brain plasticity that are important in development and in learning and cognition. While these are almost certainly not the only processes involved, they provide robust entry points for clinical and basic neuroscience research.N/A


Cynara cardunculus germplasm and suggest to the preference of using accessions from different gene banks to run an association map.Understanding the distribution of genetic variations and accession structures is an important factor for managing genetic resources, but also for using proper germplasm in association map analyses and breeding programs. The globe artichoke is the fourth most important horticultural crop in Europe. Here, we report the results of a molecular analysis of a collection including globe artichoke and leafy cardoon germplasm present in the Italian, French and Spanish gene banks. The aims of this study were to: (i) assess the diversity present in European collections, (ii) determine the population structure, (iii) measure the genetic distance between accessions; (iv) cluster the accessions; (v) properly distinguish accessions present in the different national collections carrying the same name; and (vi) understand the diversity distribution in relation to the gene bank and the geographic origin of the germplasm. A total of 556 individuals grouped into 174 accessions of distinct typologies were analyzed by different types of molecular markers, i.e. dominant (ISSR and AFLP) and co-dominant (SSR). The data of the two crops (globe artichoke and leafy cardoon) were analyzed jointly and separately to compute, among other aims, the gene diversity, heterozygosity , fixation indexes, AMOVA, genetic distance and structure. The findings underline the huge diversity present in the analyzed material, and the existence of alleles that are able to discriminate among accessions. The accessions were clustered not only on the basis of their typology, but also on the basis of the gene bank they come from. Probably, the environmental conditions of the different field gene banks affected germplasm conservation. These outcomes will be useful in plant breeding to select accessions and to fingerprint varieties. Moreover, the results highlight the particular attention that should be paid to the method used to conserve the  Cynara cardunculus is a diploid species with 2n = 2x = 34 belonging to the Asteraceae family. A recent botanical classification grouped two vegetable crops and their wild progenitor, which were previously considered distinct species, under C. cardunculus. The three taxa within the species are: wild cardoon (C. cardunculus var. sylvestris (Lamk) Fiori), globe artichoke (C. cardunculus var. scolymus (L.) Fiori), and cultivated or leafy cardoon . In line with this classification, the updated CPVO/UPOV protocol (entered into force on 27.02.2013) to tests on distinctness, uniformity and stability, has become the same for both the globe artichoke and the leafy cardoon [Globe artichoke germplasm can be classified according to different criteria, the most important of which are (i) the harvesting time and (ii) the head shape. (i) The first divides the globe artichoke into (a) autumn and spring (early or “re-bloom” varieties) or (b) only spring (late or “spring” varieties). (ii) The second criterion classifies the globe artichoke on the basis of the morphology of its commercial part, the immature flowers (buds) called “capitula” or “heads”. In particular, the characteristics taken into consideration are: shape, color, and the presence of spines. It is therefore possible to identify four groups: "Spiny" with long sharp spines on both bracts and leaves, "Violet" with violet-colored capitula, "Romanesco" with spherical or sub-spherical non-spiny capitula, and "Catanese" with relatively small, elongated non-spiny capitula . Spiny aThe globe artichoke is cultivated all around the world, but it is particularly well adapted to the different pedo-climatic conditions of the Mediterranean . It is wThe center of origin of the globe artichoke is the Mediterranean Basin, probably southern Italy. Evidence based on its diversity and on morphological and molecular data indicates that the globe artichoke was possibly domesticated in Sicily at the beginning of the first millennium –8, whileC. cardunculus germplasm collections are held by Italy, France and Spain, where traditional globe artichoke cultivars are predominant. However, cultivations of single uniform varieties have expanded with a consequent reduction in diversity compared with the original. Indeed, in spite of the huge germplasm diversity available in Italy, only a few varieties are cultivated over large areas: ‘Violetto di Sicilia’, ‘Brindisino’, ‘Violet de Provence’ , ‘Romanesco’ , and ‘Spinoso Sardo’. In France, only about five or six varieties are commonly cultivated. Almost all French production is based on the globe artichokes ‘Camus de Bretagne’, ‘Gros Vert de Laon’, ‘Blanc Hyerois’, ‘Violet du Gapeau’, ‘Castel’ and the small cylinder globe artichoke ‘Petit Violet de Provence’. In Spain, the ‘Blanca de Tudela’ ecotype represents 90% of the whole production.The main C. cardunculus genetic diversity it is important to know the propagation system, which differs between globe artichoke and cardoon. They are both allogamous plants, but the first is mainly propagated vegetatively by means of basal shoots or semi-dormant shoots with a limited root system [C. cardunculus conservation and characterization is that the local varieties are often named on the basis of the area where they are cultivated [C. cardunculus is essential for its correct conservation and utilization.In order to understand and preserve t system , while tt system , 12, 13;ltivated , regardlltivated –19). ConMoreover, it is sometime difficult to define globe artichoke varieties since, after cultivation over several decades in various geographical areas, they might be subjected to divergent selection. Thus, the accessions stored in gene bank field collections need to be rationalized by improving core collections and avoiding duplications –22.a priori DNA sequence information, and co-dominant markers, for which sequence knowledge is necessary. The dominant markers include RAPD , AFLhic DNA, , or spechic DNA, , Romaneshic DNA, ,30,31, Vhic DNA, –35, globhic DNA,  and cardhic DNA, . Among chic DNA, , 38–42 ahic DNA, .The association between genotype and phenotype can be achieved either by controlled biparental crosses (linkage mapping) or via association mapping, controlling the linkage disequilibrium, i.e. the non-random association of alleles between loci, regardless of their position across the chromosomes . The assC. cardunculus characterization were addressed at specific accession typologies and used limited collections belonging to restricted geographic areas. The only exception is our previous paper, which reported preliminary data using only some of the dominant markers in the European collection [C. cardunculus European collections, using both dominant and co-dominant markers, (ii) to determine the population structure, (iii) to measure the genetic distance between accessions; and (iv) to cluster the accessions according to the molecular data. Moreover, two other questions are addressed: (v) are the accessions present in the different national collections and carrying the same name really the same material? and (vi) how is diversity distributed in relation to the geographic origin of the germplasm?Previous works on llection . The aimA total of 556 individuals belonging to Italian 264) , French (162)  and Spanish (130)  collections, and representing 174 accessions were jointly analyzed. The accession list is reported in 64  and 1U Taq polymerase. Amplification conditions were 94°C/5 min, followed by 35 cycles of 94°C/1 min, 43–59°C  were used.For 19 SSR markers developed by Acquadro et al.   or a CEQ 8800 (Beckman Coulter) sequencer. The detected bands were checked for reproducibility even if the visualization by sequencer showed high sensitivity and precision. For the co-dominant (SSR) markers, each allele was scored in accordance with its molecular weight in bp, while for the dominant (AFLP and ISSR) markers, 0–1 matrices were obtained, without knowing the allelic relationships. In this case, each possible band was considered as a locus with 2 possible alleles, 0 (absence) or 1 (band presence). In some cases, the 0–1 matrix was considered as haplotype, while in others it was converted into a co-dominant matrix with 1 as dominant homozygote (A/A) and 0 as the other homozygote (a/a).The gene diversity index was calculated for each locus and population according to Nei , using tputed as .F-statistics are based on the expected level of heterozygosity. The measurements were computed for the different levels of the accession structures, such as the variance of allele frequencies within accessions (FIS), variance of allele frequencies among accessions (FST), inbreeding coefficient within individual total diversity (FIT), variance among accessions within types (FSC) and variance permuting accessions among groups (FCT), which are related to the degree of heterozygosity at various levels of the accession structure. The terms mentioned above are related through the formula: 1-FIT = 1-FIS + 1-FST, where I indicates the individual, S the sub-accession and T the total accession; FIT refers to the individual compared with the total; FIS is the individual compared with the sub accession; and FST is the sub accession compared with the total. The total F, indicated by FIT, can be partitioned into FIS (or f) and FST (or θ). FST can be computed using the formula: FST = (HT-HS)/HT, where HT is the proportion of the heterozygotes in full accessions and HS the average proportion of heterozygotes in sub-accessions. The F statistic was also used in the AMOVA  to measure the partition of variation among typologies, among accessions within typologies, among individuals within accessions, and within individuals.To compare differences among and within accessions and groups Wright’s fixation indices were used . The F-sHe) and genetic identity (J or Ho) were also used to estimate the genetic distance. IfThe genetic diversity , both K = 3 and K = 6 were adopted and are presented below. Individuals were assigned to subgroups by the “No admixture model”. The output reports the subsequent probability that individual i is from accession k. The prior probability for each accession is 1/K. This model is appropriate for studying fully discrete accessions. The “admixture model” was also run, but no better resolution was observed with this model (data not shown). Linkage disequilibrium between loci, which measures the deviation from random association between alleles at different loci [P values of χ2 with 1000 permutations), was also computed.The obtained distance was then used to cluster the accessions according to different clustering methodologies such as the UPGMA algorithm. The clustering was also performed by K-mean, which is a non-hierarchical method of classification that partitions a set of samples into the most appropriate number of clusters decided in advance . Run lenent loci , and itsIn some cases, the co-dominant and dominant data typologies were analyzed separately, since it was not possible to compute some parameters for the dominant markers. In addition, analyses were run both jointly and separately for globe artichoke and cardoon accessions.To compute the above mentioned parameters, GenAlEx ,57, PoweThe identified co-dominant alleles were 147 in total, from the 556 individuals analyzed. For each co-dominant marker, the alleles ranged from 1 to 15 with an average of 7.4 alleles per marker . NeverthThe gene diversity computed as expected heterozygosity (He) and the polymorphism information content (PIC) provide information of a marker’s ability to determine polymorphism. In the present study, He values for the dominant markers were quite uniform, with an average of around 0.26 and 0.28 for AFLP and ISSR, respectively . For SSRFIS), within individuals (FIT), and among accessions (FST) was quite different from one marker to another. The average values for the FIS, FIT and FST were -0.66, 0.16 and 0.51, respectively  and the Wright fixation indices by considering both He and Ho. The observed heterozygosity ranged from 5% for CLIB-02II to 99% for CMAL-25, with an average of 44% . As a coectively . Generalectively  Tables. ectively  except fThe accessions analyzed had quite different levels of diversity, as detected using both dominant and co-dominant markers, as shown in The structure of the 174 accessions was analyzed by means of a Bayesian based approach in the STRUCTURE program, considering only the co-dominant loci. According to the Evanno  calculatThe first sub group (SG1), labeled in green in Geographical localization of some accessions was provided by the gene banks, which collected them. For the accessions geographically localized with certainty, the average proportions of the Q values obtained by STRUCTURE analysis were positioned on the geographical map . Even whIndividual distance was computed using Nei genetic distance  or EucliPrincipal Coordinates Analysis (PCA) via Covariance matrix, based on co-dominant markers only, distributed the 556 individuals into a two-dimensional scatter plot. The first two PCA axes accounted for 32.39 and 23.60% of the genetic variation among accessions, respectively . Also inF statistic at all SSR loci revealed that the genetic variation among accessions within the same geographical group (FSC) was 0.227, while among geographical groups (FCT) it was 0.155, and among accessions across the entire study area (FST) it was 0.347. All the values were highly significant even if most of the variation was not among typologies.The hierarchical distribution of co-dominant molecular variance was partitioned among three levels: typology, accessions, and individuals. For typologies, seven groups, including the unassigned one, were used. The analysis revealed that only 15.49% of the total variation was among typologies, 19.22% was among accessions, but the greatest part, 65.29%, was within accessions . CalculaAn assessment of genetic diversity is essential for understanding which germplasm should be conserved and/or what is being lost or could be in danger of extinction. The genetic diversity indicates how to build up a core collection, maximizing the variation among accessions and minimizing accession repetition. This is particularly important considering that cultivation strategies tend to concentrate on few varieties, with a consequent reduction in the cultivation of many local landraces. Moreover, a knowledge of population structure in a collection is a prerequisite for making association analyses to attribute allelic variation to important agronomic traits. Molecular markers may or may not correlate with the phenotypic expression of a genomic trait, but, whatever the case, they offer several advantages over conventional, phenotype-based alternatives. In particular, they are stable regardless of cultivation practices, environment, phenological phases or tissue. A structured collection analyzed with molecular markers is the basis for association studies involving phenotypic variation and biological function.The germplasm analyzed here mainly derives from its center of origin , so it iC. cardunculus evaluation since it was one of the most variable markers  per marker and a maximum of 15 alleles per marker . An averPagnotta  analyzinPagnotta  in wild It is important to point out that despite the many alleles identified in the entire germplasm , very feFIS, FIT, and FST were -0.66, 0.16, and 0.51, respectively (FST) detected here is higher than the average values found in wild cardoon [C. cardunculus [e-Ho) ÷ He = 1-(Ho ÷ He), its values ranged from -1 to +1. Values close to zero are expected with random mating, while substantial positive values indicate inbreeding or undetected null alleles. Negative values, as were here found, indicate an excess of heterozygosity due to negative assortative mating or selection for heterozygotes, which is not surprising in a highly outcrossing species like C. cardunculus. This is particularly true for globe artichoke , while for leafy cardoon  the values were among the highest, indicating random mating. This difference can be explained with the different propagation system, clonal for globe artichoke, by seed for leafy cardoon as for wild cardoon [The average values for the ectively . The var cardoon , 60 or idunculus . This is cardoon .It is interesting to note that C3 was the accession with the lower level of observed heterozygosity. C3 clone has come to be widely cultivated in Italy in the last 10 years since micro-propagation was started, enabling its multiplication at a high rate . It shouC. cardunculus accessions belonging to single collections. Not surprisingly, they generally revealed good discrimination among the three C. cardunculus taxa, separating the globe artichoke from the leafy cardoon and/or the wild cardoon [versus leafy cardoon accessions. The globe artichoke and the leafy cardoon displayed morphological and genetic differences, so it is surprising that, according to UPOV, the two taxa are bulked in the same description protocols. Moreover, the two taxa are differentiated by their reproductive habits [All the previously published works analyzed  cardoon –40, 43. e habits  and for C. cardunculus was run only in a collection that mainly included wild cardoon (43 accessions) and a limited number of leafy cardoon (10 accessions) and globe artichoke (16 accessions) [In a previous study, the STRUCTURE analysis in essions) . In the essions) , the Romessions) , only thThe resulting geographical distributions obtained by mapping the Q values, only for the globe artichoke accessions with a defined cultivation area, showed no clear pattern. Even if in some geographic areas some colors seemed to predominate . Most ofThe dendrogram obtained with the Nei  genetic Within the groups, the leafy cardoon accessions (CL1) were clearly sub-grouped into leafy cardoon from France and leafy cardoon from Spain, in agreement with previous works, in which leafy cardoon accessions were placed in distinct groups according to their geographical origin , 37, 62.C. cardunculus showing a higher variation within populations (accession) Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableNumber of bands or alleles in the case of SSR, frequency of the principal allele (MAF), Gene diversity and Expected Heterozygosity.(DOCX)Click here for additional data file.S4 TableNumber of bands or alleles in the case of SSR, frequency of the principal allele (MAF), Gene diversity and Expected Heterozygosity.(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 TablePolymorphism (P), expected heterozygosity (He), observed heterozygosity (Ho), Fixation Index (F), Number of alleles with frequency higher than 5% (Na), Shannon's Information Index (I), and number of less common Alleles (< = 25%) (No).(DOCX)Click here for additional data file.S7 TableNumber of alleles (Na), Number of effective alleles (Ne), Shannon's Information Index (I), expected heterozygosity (He), Unbiased Expected Heterozygosity (UHe), and amplicons in the case of dominant markers.(DOCX)Click here for additional data file.