ST = 0.39, FLK = 21.93, FDR p-value = 0.10) that also displayed genome wide significance  during GWAS. A total number of 6 positional candidate genes  were identified within 500 kb flanking regions around the significant marker. In addition, two QTLs related to fat tail deposition are reported in genomic regions 800 kb downstream the significant marker. Based on gene ontology analysis and literature evidence, the identified candidate genes possess biological functions relevant to local adaptation that worth further investigation.In Greece, a number of local sheep breeds are raised in a wide range of ecological niches across the country. These breeds can be used for the identification of genetic variants that contribute to local adaptation. To this end, 50k genotypes of 90 local sheep from mainland Greece  were used, as well as 147 genotypes of sheep from insular Greece , Lemnos, n = 36 and Lesvos, n = 90). Principal components and phylogenetic analysis along with admixture and spatial point patterns analyses suggested genetic differentiation of ‘mainland-island’ populations. Genome scans for signatures of selection and genome-wide association analysis (GWAS) pointed to one highly differentiating marker on OAR4 (F Ovis aries) have spread and distributed over a wider geographic range around the globe and adapted to a variety of diverse, often harsh, environments. Following the global trend, a number of indigenous sheep breeds are found in Greece, in a range of ecological and environmental conditions )The above local sheep present valuable genetic resources and are critical for preserving local genetic diversity and ecosystem integrity, as well as food chain productivity and resilience. With regard to the latter, milk from these ewes is used to produce famous, high quality dairy products, of which some cheeses have been granted protected designations of origin, such as Feta in Epirus and Peloponnesus, Ladotyri in Lesvos (PDO), Kalathaki (PDO), and Melichloro in Lemnos.Local sheep breeds traditionally raised under extreme environments are an excellent model for studying genetic adaptation . Under tIn sheep, SS have been detected in a variety of wild and domestic sheep populations –17). Put. Put17])Driven by the diversity of the ecological niches  across the country, this study was primarily designed to identify plausible genetic loci associated with local adaptation (LA) in sheep. To this end, mainland sheep populations (Epirus and Peloponnesus) were compared to insular ones , assuming environmentally driven selection throughout: i) has been the primary driving force for LA, and ii) has affected the genomes of sheep raised in similar habitats in the same way.In the present study, first we examined whether mainland-island present genetically different entities and then conducted genome scans and genome-wide association analysis in attempts to address two questions with regard to local adaptation (LA). Specifically: a) does LA leave detectable differential footprints in sheep genome? and b) can genetic differentiations lead to detection of functionally relevant genes to LA?All applicable ethical guidelines for the care and use of animals were followed and animal blood samples were collected by trained personnel under strict veterinary rules. All samples and data in our study were collected under the consent of the sheep breeders. The protocol of the present study was approved by the Research Ethics and Ethics Committee of the Agricultural University of Athens (no 54/2020 and no 18/2021) according to the article 23 of law 4521/2018 of the Greek government.−5 and linkage disequilibrium (LD) r2 levels  and autosomal mapped SNPs. Application of QC criteria at the marker level resulted in a total number of 38,893 autosomal mapped SNPs retained for further analyses. QC was performed using the SNP & Variation Suite software (version 8.9.0). The distribution of samples across regions is shown in A total number of n = 311 sheep blood samples were collected from 17 herds across 5 regions (3 island and 2 mainland) of Greece see . All anielatives ) was useDetection of genetic structure was assessed using various approaches. First, the genomic relationship matrix (GRM) between all pairs of individuals using genotypes of the 38,893 SNPs was calculated. Next, Principal Component Analyses (PCA) of animals’ genetic relationships was conducted and the first two PCs were plotted on the two dimensional space to resolve genetic patterns in the samples. The above analyses were performed using SNP & Variation Suite software (version 8.9.0).https://forge-dga.jouy.inra.fr/projects/hapflk, [Pairwise Reynold’s genetic distances between regions were automatically obtained by the hapFLK software  and the/hapflk, .http://clumpak.tau.ac.il/ [Admixture analysis (AA) was also applied to infer assignment probabilities for mainland or island membership for individual samples. To this end, the ADMIXTURE software  was emplu.ac.il/ ).A spatial point patterns analysis (SPPA) was performed using the elements of the first PC of animals’ genetic relationships as the events (observations) to investigate whether genomic relationships are distributed randomly or follow a specific pattern (indicative of structure) across the geographical landscape of sampling sites. A Gaussian kernel (or covariance function) was employed to fit the first-order intensity (number of observations per unit area) of a spatial point pattern using procedure SPP in SAS (ver.9.4). Spatial patterns were then resolved by constructing plots of the nearest neighbor distance and the smoothed kernel density of animals’ relationships on the geographical landscape with X and Y representing longitude and altitude of the sampling sites, respectively. The global Moran’s index (I)  and the First, LD scores for each of the 38,893 markers using two maximum windows sizes of 500 kb and 1000 kb were calculated. Windows were always centered around the marker being scored, unless the window was at the beginning or the end of a region. This estimation was carried out for the island and mainland populations. Finally, the Wilcoxon Rank Sum test as implemented in procedure NPAR1WAY in SAS (ver.9.4) was applied to test the hypothesis that the two LD score distributions differ with respect to their medians.iy is the estimated id is the inter-marker distance (in kb) for marker pair i, β is the coefficient that describes the decline rate of LD with distance  and ie is a random residual with ie~. A maximum inter-marker distance of 2,500 kb was considered here. This analysis was carried out with procedure NLMIXED in SAS (ver. 9.4) using r2 values of the markers that had no negative or 0 values (because of correction for sample size). This selection resulted in a total number of 35,911 and 37,276 r2 values for mainland and island sheep, respectively. Estimation of LD scores and LD r2 values was performed with SNP & Variation Suite software (version 8.9.0).Second, the decline rate of LD with inter-marker distance (in kb) within genes (using the OAR4.0 annotation) was estimated by fitting the following equation:obertson  r^′2= bound of the empirical FST distribution were considered as outliers. As individual FST values may be subject to genotyping errors, we also estimated genome-wide smoothed FST estimates, by averaging FST values across 500 kb windows. Windows  with less than four SNPs were discarded, which resulted in on an average of 7.82 (SD = 2.14) SNPs per window . Here, smoothed FST values exceeding the 99.999th percentile (0.1357) bound were considered as outliers pointing to genomic regions under positive selection.Signatures of positive selection were detected using various approaches, as described below. First, the FST ,29) stat statST . Here, a marker with FDR p-value below or equal to 0.1 was considered significant.For each SNP, the FLK estimate and its p-value were obtained using the hapFLK software . As inp/hapflk, ). To idehttp://www.cmpg.unibe.ch/software/bayescan/, [A Bayesian framework was additionally applied to evaluate whether each locus is affected by selection using a reversible-jump Markov chain Monte Carlo (MCMC) approach and the BayeScan 2.0 software . Duringyescan/, , we usedST, genetic diversity within (estimated as the average observed Heterozygozity (Ho) of the two populations) and between populations  in 1 Mb flanking genomic regions surrounding the FST outlier(s) marker. Then, for the three parameters, non-parametric loess smooth curves obtained via the LOESS procedure in SAS (ver.9.4) were estimated and plotted to detect characteristic patterns after ‘removing’ local noise. Finally, following Booker et al. [First, we estimated summary statistics of Fr et al. , a visuaA single locus mixed (additive) model GWAS using the EMMAX algorithm  was applhttps://www.ensembl.org/Tools/VEP, [Ovis aries. Positions of QTLs were remapped from Oar_v3.1 to Oar_v4.0 assembly using the Genome Remapping Service from NCBI database [First, we searched within 500kb downstream and upstream flanking regions of each significant marker for positional candidate genes using the NCBI database  and theols/VEP, ) tool todatabase .http://cytoscape.org/, [Ovis aries. Each time, the input genes were compared with a reference list of ovine genes using the following settings: minimum number of genes per BP = 1, minimum percentage of genes included in GO BP = 3%, minimum GO levels = 3, maximum GO levels = 8 and minimum kappa score for GO BP network connectivity = 0.4. Here, GO BPs with FDR p-values lower than 0.05 for the right-sided hypergeometric test were considered as significantly enriched. The CluePedia V1.5.7 [http://cytoscape.org/, [Positional candidate genes were subject to Gene Ontology (GO) Biological Process (BP) enrichment analysis using the ClueGO V2.5.7  plug-in pe.org/, ). Duringa V1.5.7  plug-in pe.org/, ) was alsThe two-dimensional plot of the first two PCs derived from PCA on animals’ genomic relationships is shown in Results of SPPA of animals’ genomic relationships (accounted here by the first PC) are depicted on β coefficient estimates obtained from nonlinear regression analysis with β estimate higher for island (is)  = 0.1154–0.1191 when contrasted to its respective counterpart for mainland (m) sheep  = 0.1044–0.1080) (results not shown). Note that as the CI(95%) of the two β estimates do not overlap, the two estimates are statistically significantly different. As β coefficients describe the decline rate of LD by distance, high and low β estimates imply low and high LD persistency, respectively. Thus, both LD score and β coefficient estimates indicate lower extent of LD (or lower LD persistency) for the island compared to mainland sheep.ST and FLK along with results of GWAS. Only one marker i.e. s25289.1  had a raw FST value (0.3909), exceeding the 99.999th bound (0.3554)  and had a pFDR-value  = 7.7260) lower than the 0.05  = 5.890) threshold denoting genome wide significance during GWAS  exceeded the 99.999th bound (0.1357) of the empirical window-based FST distribution and this window included marker s25289.1. Results of FST outlier(s) detection were also confirmed with Bayescan with a posterior probability (prob) equal to 0.9946 .(0.3554) . The saming GWAS . With reing GWAS . With reST, genetic diversity within (avHo) and between populations (dHo)) in 1 Mb genomic regions surrounding the FST outlier marker are shown in ST values (orange line) at marker position is accompanied by a decrease in avHo (purple line) and a notable peak of dHo (green line). The latter, which is typical of long-term local adaptation, excludes the scenario of global adaptation and genetic drift . All. AllST, ZNF804B, C4H7orf62 or TEX47, STEAP4, SRI and ADAM22) and one pseudogene (LOC106990429) were detected within the searched genomic areas around the significant marker (s25289.1) on OAR4 (LOC106990429 while ADAM22 gene was located furthest away from the marker (494 kb). In addition, two published QTLs  related to tail fat deposition are reported, distanced ~800 kb downstream the marker. Note that at a more distant location (~1.62 Mb) upstream the marker resides another member of the STEAP family genes, namely STEAP2 .A total number of 6 positional candidate genes i.e., 5 protein coding genes . The maximum number of enriched GO BPs (n = 32) were detected for SRI gene while TEX47 gene presented the minimum number of GO BPs (n = 4). With regard to the number of most significantly enriched GO BP terms, 3 GO BPs were revealed for TEX47 gene while one GO BP was found for STEAP4 and SRI genes  showed the lowest FDR p-value (0.007) while the most significantly enriched GO BP for SRI gene was the negative regulation of cardiac muscle contraction (GO:0055118) with FDR p-value as high as 0.006.All genes were recognized by ClueGO. As seen in RI genes . SpecifiX47 gene . For STEThe current study sought to answer the intriguing question of whether LA leaves distinct genome footprints manifested in terms of LD structure. The current findings provided an answer to this question, indicating that mainland sheep have higher LD persistency than island sheep. In both populations, LD decays fast that is typical for local breeds experiencing weak artificial selection in contrast to ‘commercial’ breeds where LD extends for larger genomic distance . Given tThe genetic patterns discovered here corroborate the genetic similarity of Lemnos and Lesvos sheep, probably due to past gene flow . FurtherIntuitively, the genetic variation of the studied populations herein is interpreted in terms of environmental adaptation. Yet, as the studied populations are placed on an east-west geographic axis, genetic variation may equally fit a geographical axis instead of an environmental axis. In line with such a scenario, results of population structure and SPPA are supportive of an east-west geographical gradient with Skyros sheep positioned in the middle of the two geographical extremes (Lemnos—Lesvos and Epirus). As geography overlaps with climatic zones in the way that (east) insular sheep are kept on semi arid areas and (western) mainland sheep in rainy, humid and high altitude areas, it is impossible to identify the exact source of genetic differentiation. In general, regional origin has been identified as the main determinant of genetic differentiation of numerous European sheep breeds with three Greek breeds  placed intermediate on an east–west cline between the fat-tailed Asian and European sheep . Since ts25289.1 on OAR4. Genomic regions with such outlier markers are intuitively interpreted as evidence of LA. Nonetheless, apart from LA, extreme FST outliers can also be generated by global adaptation and genetic drift [ST, genetic variation within and between populations in genomic regions surrounding FST outliers, may aid in determining which type of selective processes has occurred . Fol. Fols252ccurred  have been associated with excess fat accretion in tails [With regard to eostasis . In humaeostasis –57, whil [STEAP4  and othein tails . The latin tails . Apart fin tails ,62) thatin tails . Finallyin tails . All of ADAM22 is a member of the ADAM  gene family that are involved in a broad range of biological processes such as cell fusion, spermatogenesis and development [With regard to rest genes with no detected enriched GO terms, elopment  and in telopment . The genelopment  and withelopment .ZNF804B is found to be located in genomic regions with overlapping SS and Copy Number Variations that were associated with selective adaptation in cattle [Finally, n cattle .TEX47, STEAP4, SRI, ADAM22 and ZNF804B, that warrant further investigation.In conclusion, here we report a clear genetic differentiation of mainland vs. island Greek indigenous breeds that can be explained either by longitudinal or environmental factors or both. Furthermore, a genomic region (OAR4: 72.4–73.3Mb) associated with LA was identified. Consistently with previous findings , this reS1 Fig(TIF)Click here for additional data file.S2 Fig10 obtained from the single locus mixed model and the red line represents the expected values for the null hypothesis under no association.Blue dots denote the −log(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.


F. culmorum and F. avenaceum. This increased disease pressure and provoked genotypic differentiation. To normalize effects of variable weather conditions across consecutive seasons, we used a disease ranking of the genotypes based on quantification of fungal DNA contents and multiple Fusarium toxins in harvested grain. Together, this allowed for assessment of stable quantitative FHB resistance of barley in several genotypes. Fungal DNA contents were positively associated with species-specific Fusarium toxins in single years and over several years in plots with soil inoculation. In those plots, plant height limited FHB; however, this was not observed after spray inoculation. A multiple linear regression model of recorded weather parameter and fungal DNA contents over five years identified time periods during the reproductive phase of barley, in which weather strongly influenced fungal colonization measured in mature barley grain. Environmental conditions before heading and late after anthesis showed strongest associations with F. culmorum DNA in all genotypes, whereas for F. avenaceum, this was less consistent where we observed weather-dependent associations, depending on the genotype. Based on this study, we discuss aspects of practical resistance breeding in barley relevant to improve quantitative resistance to FHB and associated mycotoxin contaminations. Epidemiology of Fusarium Head Blight (FHB) of spring barley is relatively little understood. In a five-year study, we assessed quantitative resistance to FHB in an assortment of 17 spring barley genotypes in the field in southern Germany. To this end, we used soil and spray inoculation of plants with  Ramularia collo-cygni) recently became the most dominant disease in barley in many countries worldwide -3ADON was used as the internal standard, and the enniatins were calculated using 15[N3]-Enniatin A1 as the internal standard. For analytes without a stable isotope-labelled standard , matrix-matched calibration was performed using a mycotoxin-free starch as blank matrix. After LC-MS/MS measurements, matrix-matched calibration curves were obtained by plotting peak areas against concentrations and performing linear regression. Linearity was confirmed for all analytes by applying Mandel’s fitting test [For LC-MS/MS measurements, chromatographic separation was performed on a Shimadzu Nexera X2 UHPLC system  using a Shim-pack Velox PFPP column  with a Shim-pack Velox PFPP guard-column  at 30 °C; the binary gradients are listed in the ing test .To balance season effects of FHB severity and to evaluate FHB resistance of each genotype, a ranking list was calculated as described by . A mean


Grapevine cultivars are clonally propagated to preserve their varietal attributes. However, genetic variations accumulate due to the occurrence of somatic mutations. This process is anthropically influenced through plant transportation, clonal propagation and selection. Malbec is a cultivar that is well-appreciated for the elaboration of red wine. It originated in Southwestern France and was introduced in Argentina during the 1850s. In order to study the clonal genetic diversity of Malbec grapevines, we generated whole-genome resequencing data for four accessions with different clonal propagation records. A stringent variant calling procedure was established to identify reliable polymorphisms among the analyzed accessions. The latter procedure retrieved 941 single nucleotide variants (SNVs). A reduced set of the detected SNVs was corroborated through Sanger sequencing, and employed to custom-design a genotyping experiment. We successfully genotyped 214 Malbec accessions using 41 SNVs, and identified 14 genotypes that clustered in two genetically divergent clonal lineages. These lineages were associated with the time span of clonal propagation of the analyzed accessions in Argentina and Europe. Our results show the usefulness of this approach for the study of the scarce intra-cultivar genetic diversity in grapevines. We also provide evidence on how human actions might have driven the accumulation of different somatic mutations, ultimately shaping the Malbec genetic diversity pattern. Grapevine (Vitis vinifera L.) cultivars are perennial crops, displaying highly heterozygous genotypes originated from sexual crossing and clonally propagated to preserve their productive traits2. Grapevine is among the top five fruit crops in terms of tons produced worldwide3 and it has a rather relatively small genome size (~ 480 Mb)4. These features make this species an attractive model for studying the impact of somatic mutations on the genetic diversity of clonal crops7. In this regard, there are many well-documented cases of somatic mutations affecting traits that are interesting from a productive standpoint, mainly related to berry color10, berry aroma11, cluster shape13 and reproductive development15. However, somatic mutations do not always have qualitative consequences, and quantitative effects have also been reported among clones17, even with the mutation identified at the nucleotide resolution level18. Nonetheless, somatic mutations might not have known phenotypic consequences; with these so-called ‘silent’ mutations still constituting a valuable source of genetic diversity6. Silent mutations are also relevant and can be used in marker-assisted selection programs20, or as genetic markers to study anthropically influenced processes shaping cultivars’ current genetic diversity patterns22.Clonal propagation is a common practice in perennial crops. In this kind of growing systems, the expected degree of genetic variability is low among clones within a given cultivar. However, intrinsic genetic events such as somatic mutations still occur and accumulate over time24. However, studying the genetic diversity at the intra-cultivar level is more challenging. This difficulty is due to the low extant variability and because traditional markers, like SSRs and SNPs selected from inter-cultivar polymorphisms, have shown low efficiency in such approaches28. Recently, the increased accessibility to genome-wide scale sequencing technologies, has made possible to address the study of clonal genetic diversity more accurately30.Genetic variation in grapevines is notorious if different cultivars are compared31. This cultivar has for long been well-appreciated for the elaboration of high-quality red wines32. According to the genetic evidence33 and historical records34, Malbec was originated from the outcross of Prunelard and Magdeleine Noir des Charentes cultivars in Southwestern France (Cahors region). Malbec was then introduced in Argentina (Mendoza province) during the 1850s34. In fact, this South American region has produced the largest amounts of Malbec wine for the past two decades35. Malbec presents a remarkable clonal phenotypic diversity36 and a great adaptation capacity, being successfully introduced under a wide range of agroecological conditions across Argentina37. However, little is known about Malbec clonal genetic diversity. Tracing back the clonal propagation history of this cultivar, human-driven processes that could have shaped the current pattern of genetic diversity can be detected. In first place, the single seedling that was selected to be cultivated after the above-mentioned outcrossing. In second place, the “bottleneck effect” suffered when Malbec grapevines were initially introduced from France to Argentina. Finally, the accumulation of different somatic mutations on both sides of the Atlantic Ocean, as consequence of clonal propagation under different environmental pressures and selection criteria.Herein we focused on Malbec cultivar, which prime name is CotIn this work we surveyed Malbec intra-cultivar genetic diversity, with emphasis on how anthropic actions might have driven the accumulation of different somatic mutations, propelling the origin of clonal lineages. We implemented a whole-genome resequencing (WGR) approach to detect single-nucleotide variants (SNVs). We focused specifically on SNVs, because we aimed at designing a high throughput genotyping experiment based on this type of genetic markers. After a stringent variant calling and validation process, a reduced set of SNVs was selected to custom-design a genotyping chip. The results obtained with this experiment provided information on Malbec clonal genetic diversity across an extensive sampling.Vitis vinifera L. reference genome PN400244 (hereafter: PN40024), covering ~ 78% of its length, with a mean read depth of ~ 30× ; this is, each clone had a genotype that was different from the other three. On the other hand, 57 SNVs were shared (hereafter: Sh-SNVs); this is, two clones had the same genotype and different from the other two clones. Genotypes for SC-SNVs were classified in relation to the reference genome as heterozygous , reference , and homozygous  .We then studied the phylogenic relations among the 14 clonal genotypes identified on the basis of the 41 SNVs. This analysis included a unique sequence representing each of the nine genotypes (A–I) and the five singletons. The resulting phylogenetic tree displayed the existence of two divergent clonal lineages, named Group-Ar (Argentina) and Group-Fr (France) Fig. . FinallyV. vinifera ssp. sativa) have retained most of the genetic diversity present in their wild counterpart, ssp. sylvestris38. This genetic diversity is evidenced through the great variability observed among cultivars24. However, as expected, genetic variation is strongly reduced at the intra-cultivar level. In this work we applied a stringent procedure to identify a reliable set of single nucleotide variants, that were employed to build a high throughput genotyping experiment for the assessment of the clonal genetic diversity in Malbec grapevines. We detected several clonal genotypes and found evidence on how human actions might have driven the accumulation of different somatic mutations, ultimately influencing the existence of two clonal lineages.Grapevines that are currently cultivated . Moreover, the experimental corroboration of the identified variants was successfully performed by means of two alternative technologies, i.e. Sanger sequencing  identified different numbers of SNVs, ranging from the few thousand in Chardonnay30 and Nebbiolo5, to several thousands in Zinfandel6. A reason for the low number of SNVs reported in this work might be that, assuming the presence of putative false negatives in the variant calling procedure, we employed a stringent filtering to yield reliable markers for subsequent identification of genotypes. Nonetheless, comparing the number of variants reported in different works is challenging, because final results might be influenced by technical and biological factors, as well as by the final scope of the analysis48. We also observed that SNVs affected preferably the intergenic regions  that have longer remained in Europe and close to the origin of this cultivar in France. Group-Fr also included all the other genotyped samples that have never been grown in Argentina, or were introduced in this country over the past 30 years. On the other hand, genotypes from Group-Ar are closely related to the resequenced accessions with a longer time span of clonal propagation in Argentina (MB53 and MB59), suggesting a closer link to those first plants introduced from France during the 1850s. Among the analyzed samples, we can easily identify those that have never been grown or which have recently been introduced in Argentina (< 30 years). However, we cannot accurately determine the exact time span of clonal propagation for those accessions that have remained for more than 70 years in Argentina. In particular, some of the latter accessions were included in Group-Fr  in the 1850s51. In this sense, custom genotyping for grapevine cultivars has proved to be a valuable tool with applications that are also relevant for the wine industry; including nurseries, wineries and producers. For instance, a WGR genotyping approach was employed in Chardonnay cultivar to fill in the historical gaps of commercially relevant clonal accessions51. On the other hand, in Nebbiolo cultivar TaqMan genotyping has proved useful for clonal geographic traceability5 and for wine authentication purposes53.The set of markers included in the chip proved useful for the identification of clonal genotypes and unveiled the existence of two clonal lineages. In fact, genotyping as few as four Sh-SNVs would be enough to determine if a Malbec plant is closely related, either to ancestors that were early introduced in Argentina or to those that remained longer in Europe. SC-SNVs were also essential for the detection of all the reported clonal genotypes. This observation supports the importance of combining single clone and shared variants to enhance the sensitivity of genotyping methods in clonal genetic diversity studiesIn this work we set-up a stringent workflow to identify a reliable set of SNVs that were employed to design an informative genotyping chip. We were able to detect several clonal genotypes within Malbec, and provided evidence on the processes that might have driven the accumulation of different somatic mutations, influencing the existence of two clonal lineages. Our findings further support the usefulness of high-throughput genotyping methods in grapevines to better understand cultivars’ history, and as a valuable tool for the wine industry. Future studies should determine if the identified clonal lineages in Malbec could be associated to phenotypic differences affecting traits of productive interest.To perform whole genome resequencing, we obtained young leaves and shoot tips from four Malbec clonal accessions. Two accessions, Malbec-501 (MB53) and Cot-ENTAV-598 (C225), were sampled at Mercier Argentina nursery (hereafter: Mercier) collection . The other two accessions, Malbec-059 (MB59) and Cot-143 (C143), were sampled at Mercier Granata vineyards and “Finca El Encín” ampelographic collection , respectively. The two accessions labeled as Malbec (MB53 and MB59) represent plants with long history of clonal propagation in Argentina, meaning that they have been propagated in this country for more than 70 years (Mercier records). We also included two accessions labeled as Cot (C225 and C143), having short and null histories of clonal propagation in Argentina. More precisely, C225 was introduced into Argentina from France (ENTAV-INRA) during the 1990s (Mercier records). C143 arrived to “Finca El Encin” in 1951 from INRA Vassal-Montpellier (France), and has never been grown in Argentina.. We sampled 70 Malbec clonal accessions (including the four resequenced ones) belonging to: (a) the National Institute of Agricultural Technology (INTA Mendoza) collection (28 clones), (b) Mercier collection (37 clones), (c) Mercier Granata vineyards (three clones) and (d) “Finca El Encin” (two clones). The time spans of clonal propagation in Argentina were obtained from INTA Mendoza and Mercier records. We also obtained 30 samples from five different Mercier mass selections , located at Granata vineyards. Further details about mass selections and sample origin is available in Supplementary Tables For the genotyping analysis, shoot tips and young leaves were obtained from 219 plantsWhole genomic DNA from the four Malbec accessions  was isolated using the DNeasy Plant Mini Kit (Qiagen), including an RNase treatment according to manufacturer’s recommendations. DNA quantification and quality checks were performed with a NanoDrop 2000 spectrophotometer and 5% agarose gel electrophoresis. The preparation of the library and sequencing were performed at the Center for Genomic Regulation . Paired-end reads 125 bp long were generated using the HiSeq 2000 Illumina technology with the Sequencing v4 chemistry.fastq files were performed with FastQC54. Raw reads were pre-processed following the GATK Best Practices workflow with the GenomeAnalysisTK-3.3-0 toolkit41. After marking Illumina adapters with Picard toolkit v2.9.455, sequences were aligned to the Vitis vinifera L. reference genome PN400244. We employed the Burrows–Wheeler algorithm as implemented in BWA-MEM v0.7.12-r103956 to align our reads to the reference genome. Mapped reads were thoroughly filtered also with the Picard toolkit55 allowing only non-duplicates, unique and concordant alignments with a maximum read edit distance of 1 per 25 nucleotides of query sequence57. Filtered alignments were used as input for variant calling, using three different tools operating in the multi-allelic mode and with default parameters: GATK UnifiedGenotyper41, BCFtools call v1.942 and and VarScan2 mpileup2cns v2.3.943. gVCF files generated for each accession were intersected and only those SNVs identified by all three callers were retained, while INDELs and structural variations were not considered in this study. Bioinformatics procedures were adjusted using a set of SNPs between Malbec and PN40024, retrieved from Vitis18kSNP array results58. Only confident identified raw variants were retained on the basis of WGR recommendations of total depth (DP), variant allele frequency (VAF), strand bias and distance bias . Cut-off values for these parameters were: DP = [15–150], VAF(Ref) ≤ 0.025, VAF(Het) = [0.25–0.75], VAF(Hom) ≥ [0.95]. Significance levels were: P-value (strand bias) ≤ 0.0001 and P-value (distance bias) ≤ 0.0001. Variant allele frequency ranges were particularly adjusted to reduce—at the minimum possible—the presence of spurious variants. Chimeric mutations are frequent in grapevines, occurring differentially between the L1 and L2 cell layers of the developmental tissue from the apical meristem49. The L1 layer gives rise to the epidermis and represents a smaller proportion of the total tissues conforming a plant (nearly 30%)50. With the VAF filters employed we expected to detect most of the chimeric mutations occurring in the L2 cell layer . Whereas chimeric heterozygous mutations occurring only in the L1 would be mostly excluded. With the aim of reducing the number of false positive results, we assumed these variants loss as a trade-off. The distribution of variants across the genome, the genomic regions affected by these variants and the transitions to transversions ratio were estimated with SnpSift59.Standard quality checks of 60 to manually corroborate a subset of the SNVs identified and to isolate a ~ 600 bp sequence containing the target SNVs in the mid-region. These sequences were used as templates for primer design to perform PCRs and Sanger sequencing of amplicons. In order to avoid both, primer annealing and later genotyping problems, we checked for the absence of variable sites on the 5′- and 3′-regions of the sequence and in the proximities of the SNV target position. Primers were designed using the Primer BLAST tool61 with an average annealing temperature of 60.3 °C (range 58.8–62.5 °C) and an average amplicon length of 447 bp (range 300–582 bp). More details are available in Supplementary Table vcf file and in the IGV genome browser. For example, for a particular heterozygous SC-SNV, the clone for which the variant was identified must be heterozygous and for the other three clones must be homozygous as the reference genotype for the target position.We employed IGV v2.3.97DNA extractions were performed employing the NucleoSpin Plant II Mini kit (Macherey–Nagel). The quantification of the isolated DNA was performed using NanoDrop 8000 Spectrophotometer (Thermo Fisher Scientific) and the Qubit 2.0 Fluorometer .https://www.fluidigm.com/) and perform the genotyping analyses. Each experiment allowed the simultaneous genotyping of 48 samples using 48 SNVs in a two-step reaction. In the first step, the target region containing the position to be genotyped is amplified using two pre-amplification primers (locus-specific primer and target-specific amplification). In the second step, an additional PCR amplifies a portion of that SNVs target region using the locus-specific primer and two fluorescent allele-specific primers, which are internal primers containing either the first or the second allele, respectively. Finally, the genotype is determined by measuring the fluorescence intensity of both alleles using the Fluidigm genotyping analysis software.To build the genotyping chip, only heterozygous alternative variants were selected from the deep-filtered list. We included 42 SC-SNVs and 6 Sh-SNVs, based on their capacity to discriminate among the four resequenced clones. For the SC-SNVs, equivalent number of variants for each clone were chosen. Sh-SNVs were picked for their capacity to differentiate between the resequenced accessions with a long history of clonal propagation in Argentina and those with a short or null history of propagation in this country. The latter was because we were particularly interested in identifying genetic markers that could consistently resemble that historical aspect across our samples. The SNVs chosen were distributed across different chromosomes to better represent the genomic diversity. In total, 48 sequences containing each SNV of interest  to detect potential groups of genotypes closely related to each other. Finally, an AMOVA was performed to estimate the proportion of the total molecular variance that could be explained by the variance observed between groups. We obtained the PhiPT parameter recommended for distances obtained from co-dominant genotypic data. The p-value was obtained after 900 bootstrap iterations. Both PCoA and AMOVA were also performed with the GenAlEx v6.5 software.To assess the degree of diversity across all the genotyped samples a median-joining networkSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.


Carex bigelowii s.lat. focussing on Norway. This species complex is considered a candidate for seeding, but presents considerable morphological, ecological, and genetic variation. The genetic structure of 132 individuals of C. bigelowii s.lat., including Carex nigra as an outgroup, was explored using ordinations, clustering analyses, and a genetic barrier algorithm. Two highly divergent clusters were evident, supporting the recognition of two taxonomic units “C. dacica” and C. bigelowii “subsp. bigelowii”. Previously defined seed‐sourcing regions for C. bigelowii s.lat. did not consider the known taxonomic complexity, and therefore interpreted the overall genetic structure as seed‐sourcing regions, not taxa. We estimated genetic neighborhood sizes within each taxon to be 100–150 km and 300 km, respectively, indicating species‐specific delimitations of local seed‐sourcing regions. Frequent hybrids, local genetic distinctiveness, and suggested ecotypes add complexity to the discussed seed‐sourcing regions. Our results show how genomic screening of diversity and structure in a species complex can alleviate the taxonomic impediment, inform practical questions, and legal requirements related to seed‐sourcing, and together with traditional taxonomic work provide necessary information for a sound management of biodiversity.There is a growing demand for ecological restoration using suitable seeds following international standards or national legal demands for local seed‐sourcing. However, before selecting the appropriate geographic origin of seeds, it is vital to explore taxonomic complexity related to the focal taxa. We used ddRAD‐seq to screen genomic diversity within  Carex bigelowii s.lat. can alleviate the taxonomic impediment, inform practical questions and legal requirements related to seed sourcing, and together with traditional taxonomic work provide necessary information for a sound management of biodiversity.There is a growing demand for ecological restoration using suitable seeds following international standards or national legal demands for local seed sourcing. However, before selecting the appropriate geographic origin of seeds, it is vital to explore the taxonomic complexity related to the focal taxa. Genomic screening of diversity and structure of the complex case of  Rebuilding ecosystems can be done by ecological restoration, which is the process of assisting the recovery of an ecosystem that has been degraded, damaged, or destroyed  with associated regulations (https://lovdata.no/dokument/SF/forskrift/2015‐06‐19‐716), which instruct the use of plant material of local provenance without defining what local means. Based on AFLP markers, general seed transfer zones for commercial seed production of a set of common and widely distributed alpine plant species in Norway have been proposed ; Jørgensen et al., C. bigelowii s.lat. is a taxonomically complicated species complex presenting considerable morphological, ecological, and genetic variation, and it is known to frequently hybridize within Scandinavia and elsewhere  T. V. Egorova , and plants growing in a dryer habitat (ridges) with a sessile staminate spike and more pointed pistillate scales  containing a southern Norwegian population , a southern group belonging to a European cluster (C. bigelowii subsp. rigida), but also an admixed population in between  Reichard were included as outgroup. Because C. bigelowii s.lat. is known to reproduce clonally , C. bigelowii subsp. dacica (20 sites), a hybrid between the two subspecies (three sites), or even a hybrid with C. nigra  following the manufacturer's protocol, and quantified on a Qubit 2.0 using the HS Assay kit (Thermo Fisher Scientific), before ddRAD‐seq libraries were prepared following the protocols described in Westergaard et al. . One sam2.2process_radtags component of STACKS v. 2.3e, before de novo assembly of a catalogue of loci, and calling of SNPs and genotypes with the component denovo_map  that would require an allele to be present in more than one individual, that is, min‐maf 0.01. For a more detailed description of the SNP calling and filtering, including a MAF value sensitivity analysis showing the effect on downstream analyses, see Appendix Fst values were calculated for all Norwegian sites using populations, excluding single individual sites for mean Fst values.Altogether, 10 individuals collected from 10 different sites were excluded from the final analyses as they had more than 50% missing data , 140 PCs were kept, and K = 3 was chosen based on the “elbow” of the Bayesian Information Criterion (BIC) curve. The dapc function was run on the three clusters identified, keeping 100 PCs and two discriminant analysis functions. Second, we ran STRUCTURE v.2.3.4  using dataset B and the TESS3r R‐package  with Euclidean distance using the dartR package , the “gl.filter.pa” function was applied with the dartR package using dataset A to calculate fixed (homozygote AA in one group and homozygote aa in the other group) and private alleles (heterozygote Aa in one group and homozygote aa) separating C. bigelowii s. lat in Norway, the Monmonier maximum‐difference algorithm  using the package adegenet in R. Two main genetic clusters were recognized within C. bigelowii s. lat.  were considered. The genetic neighborhood size and spatial autocorrelation of the fine‐scale genetic structure within the two main Norwegian genetic clusters  and same settings as Carvalho et al. , and the two individuals from site 33 Båtsfjord with unknown origin/taxonomy for K = 3 and K = 4, each with 10 replicates. The “elbow” entropy values were considered to evaluate the genetic composition of site 33 Båtsfjord.All our results showed Site 33 Båtsfjord as being highly deviant. To see if this site is better explained as a distinct cluster or an admixture of two or three clusters, we performed a test with sNMF allowing admixture, similar to what was performed using STRUCTURE by van Hengstum et al. . To avoi33.1C. dacica”) were highly separated from individuals in northern Norway, Canada, and Greenland (hereafter referred to as C. bigelowii “subsp. bigelowii”) at the first axis , however, intermediate individuals were found in between all three main clusters. DAPC analyses indicated three clusters: one for “C. dacica,” one for C. bigelowii “subsp. bigelowii,” and one comprising C. nigra and the intermediate individuals apparent in the PCoA . The two individuals were morphologically determined as C. bigelowii subsp. bigelowii, but seemed genetically admixed involving all three taxonomic entities when K = 3 . Both the PCoA ; hence, it was recognized as a relatively uniform group. Apart from the genetically distant site 33 Båtsfjord, the sites from northern Norway were also more or less uniform .In general, genetic distance was high between sites in northern and southern Norway  further blurs taxon limits in this section ; to avoid damage to biodiversity (§6); and apply a precautionary principle when there is little knowledge on the effects of a measure. Our genomic results show that C. bigelowii “subsp. bigelowii” and “C. dacica” are highly separated, supporting a growing body of literature on the recognition of the two taxonomic units belonging to different species. Therefore, seed‐sourcing regions must be discussed separately within each taxon. The essential difference between our northern and southern Norwegian clusters, and the geographically similar population clusters reported by Jørgensen et al. , non‐local seeds will work just as well as local seeds for restoration  zones, or local provenance areas refer to regions that have similar environments in which natural genetic exchange occurs so that transfer of plant material within the zones should have little or no negative impact  for ecological restoration purposes. For the southern “C. dacica,” a stronger spatial autocorrelation was found, and the genetic neighborhood size was estimated to be 100–150 km. Although a major genetic cluster prevailed, some local genetic distinctiveness not interpreted as hybrids was evident within “C. dacica” ; Data curation ; Formal analysis ; Funding acquisition (lead); Investigation (lead); Methodology ; Project administration (lead); Resources ; Software (supporting); Validation ; Visualization ; Writing‐original draft (lead); Writing‐review & editing (lead). Magni Olsen Kyrkjeeide: Conceptualization ; Data curation ; Formal analysis (supporting); Investigation ; Methodology ; Project administration ; Resources ; Software (supporting); Validation ; Writing‐original draft ; Writing‐review & editing (supporting). Marie Kristine Brandrud: Data curation ; Formal analysis ; Investigation ; Methodology ; Software (lead); Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing (supporting).Supplementary MaterialClick here for additional data file.


Molgula manhattensis. We revealed that most invasive populations exhibited significant genetic differentiation, low recent gene flow, and no signal of significant population bottleneck. Based on three genome scan approaches, we identified 109 candidate loci potentially under environmental selection. Redundancy analysis and variance partitioning analysis suggest that local environmental factors, particularly the salinity‐related variables, represent crucial evolutionary forces in driving adaptive divergence. Using the newly developed transcriptome as a reference, 14 functional genes were finally obtained with potential roles in salinity adaptation, including SLC5A1 and SLC9C1 genes from the solute carrier gene (SLC) superfamily. Our findings confirm that differed local environments could rapidly drive adaptive divergence among invasive populations and leave detectable genomic signatures in marine invaders.Elucidating molecular mechanisms of environment‐driven adaptive evolution in marine invaders is crucial for understanding invasion success and further predicting their future invasions. Although increasing evidence suggests that adaptive evolution could contribute to organisms’ adaptation to varied environments, there remain knowledge gaps regarding how environments influence genomic variation in invaded habitats and genetic bases underlying local adaptation for most marine invaders. Here, we performed restriction‐site‐associated DNA sequencing (RADseq) to assess population genetic diversity and further investigate genomic signatures of local adaptation in the marine invasive ascidian,  We investigated genomic signatures of rapid local adaptation in the highly invasive ascidian, Molgula manhattensis. We found that local environmental factors, such as salinity‐related variables, represented crucial evolutionary forces in driving adaptive divergence during biological invasions. We also identified functional genes responsible for salinity adaptation, such as SLC5A1 and SLC9C1 genes from the solute carrier gene (SLC) superfamily. In addition, some biological characteristics, such as high fecundity, rapid growth rate, and short life cycle, can facilitate this ascidian to rapidly colonize in novel environments  dataset, we firstly assessed population genetic variation and gene flow patterns. Subsequently, we combined population differentiation (PD) and environmental association (EA) tests to search for loci potentially under selection. We further conducted multiple analyses to investigate the effects of environmental variables in driving putatively adaptive genetic variation. Finally, with the aid of the transcriptome sequencing, we annotated a set of functional genes potentially related to environmental adaptation.Using the RADseq on 22.1. manhattensis individuals were collected from eight sites along the Chinese coast in 2013 (Table www.nodc.noaa.gov/OC5/SELECT/woaselect/woaselect.html), including three sea surface salinity matrices: annual average salinity (AveS), the highest monthly average salinity (MaxS), and the lowest monthly average salinity (MinS), and three sea surface temperature matrices: annual average temperature (AveT), the highest monthly average temperature (MaxT), the lowest monthly average temperature (MinT) . The quality and quantity of isolated genomic DNA were examined by a NanoDrop 2000 spectrophotometer . The 2b‐RAD library construction was conducted as described by Wang et al.,  with minprocess_radtags” module in STACKS v.2.1  in ustacks, maximum number of two mismatches allowed between stacks (M = 2) in ustacks, two mismatches allowed between sample loci within a catalog (n = 2) in cstacks and the default for sstacks to match stacks of all samples in the population against the catalog , retained bi‐allelic SNP per locus with minor allele frequency (MAF) > 0.05 to reduce false‐positive SNP calls  and 75% of individuals in a population (r = 0.75). Thirdly, we performed the “write_single_snp” option with only the first SNP per RAD tag retained in the final set to avoid physical‐linked loci . Another PD approach, OUTFLANK, which estimated the distribution of FST for neutral loci based on the trimmed distribution of FST values, was adopted to detect FST outliers using the “OutFLANK” package in R  at putatively neutral loci. Genome‐wide population genetic diversity, including observed heterozygosity (Ho), expected heterozygosity (HE), and nucleotide diversity (Pi), was calculated with the Populations program in STACKS. We further tested whether these populations had experienced recent genetic bottleneck using BOTTLENECK v.1.2.02  in the R package “ADEGENET”  to infer the best‐supported number of genetic clusters. We used the “optim.a.score” function to determine the number of retained principal components (PCs) and discriminant functions (DAs). To complement the DAPC analysis, we also performed one admixture analysis with the Bayesian clustering in STRUCTURE v.2.3 , with 5,000,000 Markov chain Monte Carlo iterations preceded by 500,000 burn‐in steps. The optimal K value was identified using the ΔK method of Evanno et al., .We estimated the recent migration rate between populations based on all neutral loci using the modified BayesAss analysis “BA3‐SNPs”  of putatively adaptive and neutral loci calculated in PLINK v.1.90, respectively. The MAF data were detrended with the Hellinger method implemented in “vegan” package in R. We identified significant explanatory variables using “forward.sel” function in R. The partial RDA (pRDA) and variance partitioning analysis (VPA) were then used to distinguish the effect of environmental variables from spatial factors. The pRDA analysis was implemented with the R package “vegan”. Variance partitioning analysis (VPA) was performed to assess the pure effects of environments and geography as well as their joint effects using the “varpart” function with 1,000 permutations.Redundancy analysis (RDA) and variance partitioning analysis (VPA) were used to investigate the relative contribution of spatial distribution and environmental factors to both putatively neutral and adaptive genetic variation among populations. Because both genetic variation and environmental factors may be spatially autocorrelated . The RAD tags containing candidate adaptive loci were blasted against the complete transcriptome of M. manhattensis using BLAST v.2.5.0+ (McGinnis and Madden 2004) with an e‐value threshold of 1.00E‐05 and a maximum of two nucleotide mismatches. A set of functionally genes were yielded, and their functions were assessed by GO annotation terms. To further characterize whether these SNPs were synonymous or nonsynonymous changes, we firstly translated contig sequences containing candidate loci into codons in the six reading frames with the program BIOEDIT v.7.1 and subsequently translated the codon containing the SNP into an amino acid based on the location of the start codon  ranged from 0.248 to 0.285, expected heterozygosity (HE) varied from 0.226 to 0.272, and nucleotide diversity (Pi) ranged from 0.238 to 0.279 : one cluster with four populations , one cluster with two populations (ND and XIM), and the other two clusters with two different populations LAZ and QJZ , 3,616 candidate neutral SNPs with Hardy–Weinberg equilibrium were finally retained, constituting a rather reliable and conservative panel for subsequent analyses of neutral genetic patterns. Based on 3,616 neutral SNPs, we revealed a similar level of genetic diversity across all eight sampling sites. The observed heterozygosity  in the RDA analysis. This parsimonious RDA model was significant (P‐model = 0.001) with an adjusted coefficient of determination (Adj.R2) of 0.282 (Table p‐PC1‐S = 0.039). Moreover, salinity‐related variables (PC1‐S) provided a higher proportion than temperature‐related variables (PC1‐T) in explaining putatively adaptive genetic variation  analysis, two vectors V1 and V2) were retained as representative spatial distribution variables. Four variables, including the first two principal components retained by PCA analysis of environmental factors  and two spatial vectors (V1 and V2), were firstly considered as the primary explanatory variables in the RDA model. PC1 and V2 were then selected as the significant predictors of the putatively adaptive genetic variation according to the forward selection  subfamily, the solute carrier family 5 member 1 (SLC5A1). This gene can get involved in sodium ion transport, glucose transmembrane transport, and intestinal hexose absorption  and the key role of environmental factors in affecting putatively adaptive genetic variation. Similar genotype–environment matching patterns have been recently uncovered in a variety of marine organisms, including the American lobster  protein to regulate intracellular pH and acid–base homeostasis. Multiple studies also indicated the crucial role of sodium/hydrogen exchanger genes in osmoregulation in a variety of teleost species  gene superfamily. Adaptive response to salinity stressors of marine species requires the effective ion transport and osmoregulatory processes ; Formal analysis (lead); Methodology (lead); Software (lead); Validation (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Yangchun Gao: Formal analysis ; Methodology ; Software ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Xuena Huang: Data curation ; Formal analysis ; Investigation ; Methodology ; Software ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Shiguo Li: Data curation ; Formal analysis ; Investigation ; Methodology ; Software ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Aibin Zhan: Conceptualization (lead); Formal analysis ; Funding acquisition (lead); Investigation (lead); Methodology ; Project administration (lead); Resources (lead); Software ; Supervision (lead); Validation ; Visualization ; Writing‐original draft (lead); Writing‐review & editing (lead).Supplementary MaterialClick here for additional data file.


The Grand Rapids Arch Collapse Classification system was devised in 2011 to assist physicians’ and patients’ understanding of the mechanisms underlying arch collapse. Five types of arch collapse are described, based on which part of the foot or ankle is affected. The purpose of this study was to determine the inter- and intrarater reliability of this classification system when used by physicians with various levels of training.A senior author identified a stratified selection of 50 patients (10 per classification type) who presented with foot/ankle pain and suitable radiographs. A survey was designed using prediagnosis radiographs and clinical synopses of the patient’s chart. The survey consisted of a description of the classification scheme and the 50 cases in a randomized order. Eight weeks later, they repeated the test to analyze for intra-rater agreement.Of the 33 physicians who received the survey, 26 completed the first round . Overall, there was substantial agreement among raters in all five types. Kappa scores for each type were 0.72, 0.65, 0.72, 0.70, 0.63, respectively. The combined kappa score was 0.68. After 8 weeks, 13 of the 26 participants repeated the study. A Kappa analysis was once again performed for the 13 respondents, which produced a substantial level of agreement with a value of 0.74 for intrarater reliability.The Grand Rapids Arch Collapse Classification system was designed to provide an accessible mechanism for physicians to consistently describe arch collapse, its effects, and the conditions associated with it. The utility of this system is wholly reliant on the repeatability among clinicians. This study has demonstrated that the classification system has substantial rates of reliability among physicians of different levels of experience and training.Level IV. In their model, there were 3 stages: stage 1 has no fixed deformity of the foot and ankle; stage 2 has a dynamic deformity of the hindfoot; finally, stage 3 has a fixed hindfoot deformity. Myerson later added a fourth stage to account for valgus tilt of the ankle. Since this model was developed back in 1989, foot and ankle surgeons have developed a better understanding of not only posterior tibial tendon dysfunction, but also the pathophysiology underlying many common conditions associated with arch collapse. With this deeper understanding, there comes a need for a more updated model to explain arch collapse and the conditions associated with it. The senior authors have published many studies delving into the conditions associated with arch collapse. The Grand Rapids Arch Collapse Classification System accounts for many common degenerative conditions associated with arch collapse.In 1989, Johnson and Strom proposed that posterior tibial tendon dysfunction led to degenerative arch collapse.The Grand Rapids Arch Collapse Classification (GRACC) was devised to describe the gradual progression of arch collapse due to tensile failure of the plantar apparatus of the foot. The classification system was designed around the premise that one of the major forces behind degenerative arch collapse is gastrocnemius contracture—defined as <10 degrees of ankle dorsiflexion with the knee in full extension. A type II deformity results from progressive medial column incompetence with weight bearing, leading to elevation of the first ray with an overload of the lesser metatarsal heads. These conditions can lead to the processes  to evaluate interrater reliability. Of these 26 physicians, 13 completed the second round 8 weeks later in order to evaluate intrarater reliability. Reliability was determined both for inter- and intrarater observations for all 5 deformity types.An independent statistics group determined the reliability rating of each type of deformity for the GRACC using Cohen’s kappa coefficient analysis. The level of agreement for each kappa [κ] value was determined based on the recommendations by Cohen and Landis .9,19,22The interobserver reliability average for all five deformity types showed substantial agreement with a κ value of 0.6839. The κ value for each deformity type was 0.7164, 0.6510, 0.7219, 0.7013, and 0.6291, respectively . The intResults of this study showed substantial reliability among physicians of varying experience for interrater reliability. This reliability supports the authors’ hypothesis that the GRACC would be easy to adopt and implement. The consistency of agreement in this study demonstrates the consistent application that is crucial to the utility of any classification system. Furthermore, there was substantial intrarater reliability, indicating the ability of this classification to monitor a patient’s deformity progression over time. Future work can focus on measuring the validity and responsiveness of this classification scheme. In this case, validity would require comparing responses to gold standard responses. Responsiveness would be measured by observing how well this classification system works in patients whose deformity type changes over time.The surgical and nonsurgical treatment options depend on the arch collapse type. Patients with a type I deformity can be treated with physical therapy, orthotics, gastrocnemius recession, and/or tenoachilles lengthening. Type II deformities can be treated with physical therapy, orthotics, midfoot fusion, hammertoe correction, and/or shortening of long metatarsal bones. Type III deformities can be treated with physical therapy, orthotics, and/or midfoot joint fusions. Type IV deformities can be treated with orthotics, Arizona Brace, foot reconstruction with tendon transfers, subtalar joint fusion, and/or a triple fusion. Finally, type V deformities can be treated with orthotics, Arizona Brace, ankle joint replacement, and/or ankle joint fusion. Surgical templates have been created for each deformity type to streamline surgical decision making . TreatmeThis study was limited by its relatively small sample size of physicians. Because of the low sample size, a greater number of patients than initially anticipated were required to achieve sufficient power. The large number of clinical vignettes that each physician had to work through could have led to fatigue by the end of the survey; randomization of the order of cases for each physician was used in an attempt to account for this bias. The study is further limited by its retrospective nature and any bias introduced by the creation of the clinical vignettes and image selection. These were minimized by having an inexperienced medical student extract the cases and using the same radiograph views for each patient. Recall bias was minimized for intrarater reliability by using a standardized 8-week delay in repeating the test. Finally, this study is limited by physical exam findings being given to the clinicians as opposed to evaluating themselves in the clinic. Ideally, clinicians would be able to independently evaluate each patient to obtain objective information; however, this was not possible in a study of this nature. Factors such as first ray mobility and, to a much lesser degree, gastrocnemius equinus can be somewhat subjective on clinical exam and likely represent a spectrum as opposed to a binary variable. However, for the purposes of this assessment, they were treated as binary variables to facilitate classification.In summary, this study has demonstrated that the GRACC has substantial reliability among physicians with proper training on the classification system. This classification offers potential as a beneficial tool in guiding treatment and decision making for management of patients with adult acquired arch collapse.Click here for additional data file.Supplemental Material, FAO834531-ICMJE for Statistical Validation of the Grand Rapids Arch Collapse Classification by David Burkard, Daniel Patton, Michelle Padley, John David Maskill, Donald Raymond Bohay and John Gregory Anderson in Foot & Ankle Orthopaedics


Succisa pratensis, a key species of nutrient poor semi natural grasslands. We genotyped 180 individuals from 12 populations of S. pratensis and characterized AMF composition in their roots, using microsatellite markers and next generation amplicon sequencing, respectively. We also investigated whether the genetic makeup of the host plant species can structure the composition of root-inhabiting AMF communities.Ecosystem restoration is as a critical tool to counteract the decline of biodiversity and recover vital ecosystem services. Restoration efforts, however, often fall short of meeting their goals. Although functionally important levels of biodiversity can significantly contribute to the outcome of ecosystem restoration, they are often overlooked. One such important facet of biodiversity is within-species genetic diversity, which is fundamental to population fitness and adaptation to environmental change. Also the diversity of arbuscular mycorrhizal fungi (AMF), obligate root symbionts that regulate nutrient and carbon cycles, potentially plays a vital role in mediating ecosystem restoration outcome. In this study, we investigated the relative contribution of intraspecific population genetic diversity, AMF diversity, and their interaction, to population recovery of Our analysis revealed that population allelic richness was strongly positively correlated to relative population growth, whereas AMF richness and its interaction with population genetic diversity did not significantly contribute. The variation partitioning analysis showed that, after accounting for soil and spatial variables, the plant genetic makeup explained a small but significant part of the unique variation in AMF communities.Our results confirm that population genetic diversity can contribute to population recovery, highlighting the importance of within-species genetic diversity for the success of restoration. We could not find evidence, however, that population recovery benefits from the presence of more diverse AMF communities. Our analysis also showed that the genetic makeup of the host plant structured root-inhabiting AMF communities, suggesting that the plant genetic makeup may be linked to genes that control symbiosis development.The online version contains supplementary material available at 10.1186/s12862-021-01928-0. Terrestrial biodiversity is increasingly threatened by land-use change, climate change, pollution and invasive species –3, potenOne such important but often overlooked facet of biodiversity in a restoration context is within-species genetic diversity , 13. GenIt is well known that also belowground biodiversity can mediate restoration success, as it shapes aboveground biodiversity, ecosystem functioning and services , 22. KeyAs a plant’s unique combination of genes, i.e. its genetic makeup, can shape key physiological and morphological plant traits, it can also be expected to affect the function and composition of microorganisms associated to their roots. The genetic makeup of the host plant can indeed alter the composition of rhizosphere fungal communities . InverseSuccisa pratensis, a characteristic plant species of nutrient-poor semi-natural grasslands. We sampled 180 individuals of S. pratensis from 12 populations. Plants were genotyped and AMF communities in their roots were characterized using microsatellite markers and next generation amplicon sequencing, respectively. We hypothesized that both plant genetic diversity, AMF diversity and their interaction contribute to population recovery. Additionally, we hypothesized that the genotype of the host plant drives the AMF community composition in plant roots.In this study, we investigated the relative contribution of intraspecific population genetic diversity, AMF diversity, and their interaction, to population recovery from habitat fragmentation and degradation of Succisa pratensis Moench is an AMF-dependent diploid long-lived perennial rosette herb with a short vertical rhizome [S. pratensis is self-compatible, insect-pollination by bees, bumblebees and hoverflies promotes the seed viability. The seeds fall close to the mother plant and can germinate immediately, but can be dispersed over long distances by epi- and endozoochory. Seeds only survive for a short time and thus do not form a persistent seed bank. Next to sexual reproduction, new rosettes are sporadically formed clonally at the end of short stolons. Historically, S. pratensis occurred widely throughout the temperate zones of Eurasia in nutrient-poor acidic and calcareous grasslands, heathlands, unfertilized hay meadows and calcareous fens [S. pratensis in the Netherlands declined by 74% since 1935 [ rhizome . It flow rhizome . As the  rhizome . Althougous fens . Due to nce 1935 . The rem2010) [2020) was determined by counting all the individuals in the population. Per population, a pooled topsoil sample (0–10 cm) consisting of ten soil cores was collected with an auger of 2 cm diameter. In each population, 15 individuals were randomly selected for sampling. For each individual, a leaf sample and pooled root sample, mainly consisting of fine roots, was collected. Care was taken to leave a sufficient amount of roots and leaves of the plant unharmed to ensure its survival. Leaf and root samples were stored at − 20 °C until further analysis and soil samples were stored at 4 °C for maximum one week to prevent nitrogen loss. In total, 12 soil, 180 leaf and 180 root samples were collected.This study was conducted in the Hageland region , which has a maritime mesothermic climate with significant precipitation in all seasons, with an average annual precipitation and temperature of 792 mm and 11.0 °C, respectively. On the basis of known population sizes in 2010 (N2010) , we sampFor each soil sample, soil pH was quantified using a glass electrode in a 1:10 soil/water mixture. As a measure of plant-available soil inorganic nitrogen, ammonium and nitrate were quantified by shaking 10 g of soil in 200 mL of 1 M potassium chloride solution for one hour. Total soil inorganic nitrogen was calculated as the sum of soil ammonium and soil nitrate. As a measure of plant-available soil phosphorus, Resin P values were quantified by shaking 3 g of soil in 30 mL water for 16 h with anion exchange membranes and subsequent colorimetric analysis of the extracts using the Malachite Green reaction. Extracts were analyzed colorimetrically using the Evolution 201 UV–visible Spectrophotometer . Moisture content was determined by the weight loss of 10 g of fresh soil after evaporation of water content in an oven at 105 °C for 1 day. Organic matter was quantified by the weight loss of 10 g of dry soil after combustion of organic matter at 700 °C.S. pratensis, DNA was extracted from 50 mg of leaf material with the DNA Isolation Plus Kit  following the manufacturer’s protocol. The genetic variation of S. pratensis was assessed using microsatellite analysis , as they are widely applied for genotyping plants, highly informative and cost-effective [Per leaf sample of ffective . Twelve ffective . Three mPer root sample, fresh roots with a diameter of 3 mm or less were mixed and DNA was extracted from 100 mg of root material using the UltraClean Plant DNA Isolation Kit . All DNA extracts were PCR amplified using the sample-specific barcode-labelled versions of the primers AMV4.5NF and AMDGR . This prfastq_mergepairs command. Next, quality filtering of the reads was performed with the fastq_filter command, allowing a maximum expected error of 0.5 for the individual sequences. Then, sequences were dereplicated and sorted by abundance. Sequences occurring only once in the entire dataset were removed prior to clustering as this has been shown to improve the accuracy of diversity estimates [Sequences from the Illumina run, obtained as de-multiplexed FASTQ files, were clustered into operational taxonomic units (OTUs) using USEARCH (v. 11) following the recommended pipeline . First, stimates . Sequencstimates . OTUs westimates . Taxonom2010-N2020)/N2010 where N2010 and N2020 represent the population size in 2010 and 2020, respectively. To assess the S. pratensis intraspecific population genetic diversity, allelic richness and expected heterozygosity were calculated based on the 12 SSR markers. Allelic richness per population was calculated as the average number of alleles across all loci using the allel.rich function (R package PopGenReport) [Hs function (R package adegenet) [The relative population growth was calculated as (NnReport) . Expectedegenet) .iNEXT function (R package iNEXT) [We calculated AMF richness as the number of AMF OTUs per sample. To prevent bias due to different sequencing depth, resampling techniques to the smallest number of reads per sample are often used. In our AMF dataset, however, the number of reads per sample (median = 23 808) was not related to observed AMF richness . Nevertheless, to obtain a reliable and comparable measure of AMF OTU richness and avoid any bias due to sequencing depth, AMF richness was extrapolated or interpolated to 4 000 reads per sample using the e iNEXT) . PopulatTo test the first hypothesis, whether population genetic diversity and AMF diversity positively affect population recovery, a linear model was built using JMP (v15.1) to relate relative population growth to population allelic richness, AMF richness and their interaction. As allelic richness and expected heterozygosity were highly correlated , only the former variable was included in the model. The soil variables  were also included to account for effects of these variables on the relative population growth. The Akaike Information Criterion (AIC) was used to select the most parsimonious model (i.e. with the lowest AIC) out of a range of reduced models compared with the full model including all explanatory variables, also allowing an interaction effect between population allelic richness and AMF richness. The interaction between population allelic richness and AMF richness was visualized in a contour plot, where the relative growth of the population was represented by the contour curves.rda function of the R package vegan. To determine which alleles significantly explained variation in the AMF communities, forward selection was performed using the ordistep function . To test the significance of the selected alleles in the final model, permutation tests on the individual terms (1000 permutations) were performed using the anova.cca function. Second, to investigate the relative contribution of plant allelic composition compared to geography and soil chemical variables to explain the total variation of AMF communities, variance partitioning was performed using the varpart function of the R package vegan. Three explanatory matrices were used: geography , soil data  and allelic composition. For the geographical matrix, a set of spatial predictors from the geographical coordinates were calculated by principle coordinates of neighbor matrices (PCNM) using the pcnm function of the vegan package [ordistep function), in each of the three matrices were included in the variance partitioning. The venneuler package in R was used to create Venn diagrams to visually present the results.To test the second hypothesis, whether the genotype of a plant can affect AMF community composition, redundancy analysis (RDA) and variation partitioning were performed. First, AMF community composition was directly related to allelic composition using canonical redundancy analysis (RDA) using the  package , 52. OnlThe relative growth of the sampled populations as measured between the year 2010 and 2020 ranged from − 0.55 to 8.21 with an average relative growth of 3.09. The microsatellite analysis found a total of 71 alleles across all individuals and the twelve loci. All loci were polymorphic, with the number of alleles ranging from 3 to 13 (mean 5.91) per locus , while the full model had an AIC of 275.70. The parameter estimate of the relationship between relative population growth and population allelic richness was 3.75, indicating a positive relationship between both variables. The average population AMF richness and its interaction with population allelic richness were not included in the final model , while the allelic composition explained a small but significant part of the unique variation in AMF communities   and the spatial predictors (R2 adjusted = 6.14%).The RDA with forward selection selected 12 alleles significantly explaining 18.1% variation in AMF communities .Additional file 2: Table S2. Microsatellite genotype data based on twelve loci.Additional file 3: Table S3. Sample X OTU matrix with all environmental variables.


Phymatolithon calcareum, a maerl‐forming red algal species, whose geographical distribution spans the north‐east Atlantic, from Norway to Portugal. Our results, using 14,150 genome‐wide SNPs (single nucleotide polymorphisms), showed that P. calcareum maerl beds across the north‐east Atlantic are generally structured geographically, a pattern likely explained by low dispersal potential and limited connectivity between regions. Additionally, we found that P. calcareum from the Fal Estuary, south‐west England, is genetically distinct from all other P. calcareum sampled, even from The Manacles, a site located only 13 km away. Further analysis revealed that this finding is not the result of introgression from two closely related species, Phymatolithon purpureum or Lithothamnion corallioides. Instead, this unique diversity may have been shaped over time by geographical isolation of the Fal Estuary maerl bed and a lack of gene flow with other P. calcareum populations. The genomic data presented in this study suggest that P. calcareum genetic diversity has accumulated over large temporal and spatial scales, the preservation of which will be important for maximizing the resilience of this species to changes in climate and the environment. Moreover, our findings underline the importance of managing the conservation of maerl beds across western Europe as distinct units, at a site‐by‐site level.Maerl beds are vital habitats for a diverse array of marine species across trophic levels, but they are increasingly threatened by human activities and climate change. Furthermore, little is known about the genetic diversity of maerl‐forming species and the population structure of maerl beds, both of which are important for understanding the ability of these species to adapt to changing environments and for informing marine reserve planning. In this study, we used a whole genome genotyping approach to explore the population genomics of  Our second aim was to investigate the hypothesis that introgression has occurred from a closely related species into the Fal Estuary using a subset of individuals and SNP loci. Specifically, we tested for evidence of introgression into P. calcareum from another maerl species belonging to the same genus, P. purpureum, and from Lithothamnion corallioides, a maerl species that, like P. calcareum, also forms extensive maerl beds in the Fal Estuary . In comparison to studies based on microsatellite markers, in which typically only tens of markers are isolated, whole genome genotyping can isolate thousands to tens of thousands of genetic markers across the genome of an organism, thereby providing unprecedented resolution into patterns of genetic diversity and population structure. In the present study, therefore, our first aim was to better understand population genetic structure in 22.1Lithothamnion corallioides identified in previous studies , is that all SNPs in the population sample can be identified and the cost is comparable to RADseq when working with small genomes. Genomic DNA was prepared in a skirted 96‐well plate and sent on dry ice to SNPsaurus (Oregen) for whole genome genotyping. Genomic DNA was first fragmented with Nextera reagent , which also ligates short adapter sequences to the ends of the fragments; the Nextera reaction was scaled for fragmenting 5 ng of genomic DNA. The libraries were sequenced on a paired‐end 2x150 bp NovaSeq 6000 S4 lane.Red macroalgae genomes published to‐date vary in size from 43 Mbp  with default parameters using ABySS 2.0  investigating introgression. To generate a P. calcareum data set, first non‐P. calcareum samples and P. calcareum individuals which failed sequencing were removed. Second, SNPs were filtered such that remaining SNPs (i) were biallelic, (ii) had a minimum quality score of 30, (iii) had a minimum depth of 7, (iv) had a maximum depth of 100, (v) had no more than 5% missing data, (vi) had a minor allele count greater or equal to 5 and (vii) did not depart from Hardy–Weinberg equilibrium . Third, where pairwise comparisons of SNP loci had a r2 value of >0.50, only one locus was retained to mitigate the effects of linkage disequilibrium. Fourth, in the R programming environment  assessing population genetic structure in 2.4de novo. Prevosti genetic distances were computed, and a heatmap and a histogram were visualized to assess the frequency of low genetic distances. The number of private alleles, allelic richness, observed heterozygosity and the inbreeding coefficient (FIS) were then calculated using the R packages poppr 2.8.6 , the presence of clones was assessed 2.5P. calcareum and Lithothamnion sp. sequences downloaded from GenBank as initial seeds . Second, the target reads were assembled using Unicycler 0.4.9b . For each sample, the organelle genome sequence was used as input to GeSeq  because only partial sequences of these CDS are present on Genbank (whereas the complete CDS are captured in the organelle genomes extracted here). The cox1 and psbA CDS were chosen because they are the most commonly used markers in DNA barcoding and phylogenetic studies of maerl‐forming species  an alignment containing all individuals from each species.As a comparison to the results gained from nuclear‐derived SNPs, mitochondrial and chloroplast genomes were assembled from the raw sequencing data. First, target organelle reads  were extracted using GetOrganelle 1.6.4 , P. purpureum (Sco), all individuals of P. calcareum from the Fal Estuary  and The Manacles (Man), and one or two individuals of P. calcareum from each of Milford Haven (Mil), Morlaix (Mor), Norway (Nor), La Rochelle (Roc), Trévignon (Tre) and Zara Shoal (Zar). Second, SNPs were filtered such that remaining SNPs (i) were biallelic and polymorphic, (ii) had a minimum quality score of 30, (iii) had a minimum depth of 3 and (iv) had no more than 10% missing data. A PCA was then performed using the same method as described previously. Lastly, snapclust . Three individuals of P. calcareum  were discarded as they had less than 20 high‐quality reads and zero alignments to the reference.After removing low‐quality reads, 1.1 billion reads were retained across all samples Table . The Phy3.2P. calcareum after filtering contained 71 individuals genotyped at 14,150 biallelic SNPs. No outliers were detected in this data set using OutFLANK. In addition, no individuals were removed due to clonality because the distribution of Prevosti genetic distances did not provide clear evidence of clones in the data set, though a few pairwise distances were close to zero  Zara Shoal and The Manacles, (ii) the Fal Estuary, (iii) Morlaix, (iv) Trévignon, (v) Bornalle and (vi) Illa de Ons  plastome assemblies were circular and complete , while four plastomes were assembled, of which three were circular and complete . In contrast, 23 CDS were predicted for P. purpureum mitogenomes, while 25 CDS were predicted for L. corallioides mitogenomes  and/or evolutionary divergence. Nevertheless, out of these SNP loci, 803 loci had no missing data so at least this number of loci was genotyped across all three species. A PCA using these SNPs showed clear separation at a species level, with Fal Estuary individuals all grouping with P. calcareum , sea lochs (e.g. western Scotland) and fjords (e.g. Norway) also exhibit these characteristics.Distinct genetic diversity was detected in  Figures , 2, 3. S Figures , but one Foster, , this su Foster, , two sit Foster,  and 2 suP. calcareum and not with P. purpureum or L. corallioides . Interestingly, all Fal Estuary samples in the microsatellite study of Pardo et al. (N = 6191 SNPs), the mean number of allelic differences among individuals was 2320 (range: 1738–2796). We did, however, find higher mean heterozygosity and more loci with negative FIS values in the Fal Estuary, which could be interpreted as a signature of asexual reproduction, thereby supporting a hypothesis of clonality in this population. At present, autopolyploidy cannot be ruled as a potential explanation because we do not yet know the true ploidy of the Fal Estuary samples, but this could be explored in future research by estimating the genome size and ploidy of individuals using flow cytometry techniques.In this study, we also tested the hypothesis that individuals from the Fal Estuary are putative hybrids produced by introgression from a closely related species, as postulated by Pardo et al. . A PCA ss Figure , and thet Figure  showed to et al.  reportedo et al.  were alsP. calcareum and L. corallioides are listed in Annex V of the EU Habitats Directive and the maerl beds they form are listed as a key habitat within the Annex I habitat definitions, ‘large shallow inlet and bays’ and ‘sand banks which are slightly covered by seawater at all times’  or possibly from an unsampled source population related to Zara Shoal. Therefore, conservation management within England should consider P. calcareum maerl beds from the Fal Estuary and The Manacles as separate and distinct populations.In comparison to the Fal Estuary, the nearby Manacles maerl bed has a relatively restricted distribution which is comprised primarily of dead maerl  and Zara Shoal (Northern Ireland), a spatial scale similar to Bornalle and Morlaix, which may suggest that P. calcareum is indeed able to take advantage of infrequent events of extreme weather and ocean dynamics to disperse its gametes, spores or clones to distant environments  managed and monitored as independent units across the north‐east Atlantic.In conclusion, the results of our whole genome genotyping study reinforce management guidelines advocated in previous reports on maerl‐forming species, that is, the genetic and species diversity of maerl bed ecosystems has likely accumulated over a large temporal scale and that maerl beds in western Europe should be considered a nonrenewable resource (Barbera et al., The authors declare that there is no conflict of interest.Figures S1–S8Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.