Structure and k-means clustering algorithm divided the panel into three groups. The old accessions formed a distinct subpopulation. The cluster analysis further distinguished the modern releases according to the geographic region and genealogy. Gene exchange was evidenced mainly between the subpopulations of contemporary cultivars. The achieved understanding of the genetic diversity and structure of the Bulgarian wheat population and distinctiveness of the old germplasm could be of interest for breeders developing cultivars with improved characteristics. The obtained knowledge about SNP informativeness and the LD estimation are worthwhile for selecting markers and for considering the composition of a population in association mapping studies of traits of interest.Genetic diversity and population structure are key resources for breeding purposes and genetic studies of important agronomic traits in crops. In this study, we described SNP-based genetic diversity, linkage disequilibrium and population structure in a panel of 179 bread wheat advanced cultivars and old accessions from Bulgaria, using an optimized wheat 25K Infinium iSelect array. Out of 19,019 polymorphic SNPs, 17,968 had a known chromosome position on the A (41%), B (42%) and D (11%) genome, and 6% were not assigned to any chromosome. Homoeologous group 4, in particular chromosome 4D, was the least polymorphic. In the total population, the Nei’s gene diversity was within the range 0.1–0.5, and the polymorphism information content ranged from 0.1 to 0.4. Significant differences between the old and modern collections were revealed with respect to the linkage disequilibrium (LD): the average values for LD ( Triticum aestivum, L.) is an important strategic crop for food security globally. In Bulgaria, bread wheat is the major cereal crop grown on 1.0 to 1.3 million ha land area, with an average annual production of 5.1 million tonnes, and an average grain yield of 4.2 tonnes/ha . Bread wheat  and plant adaptability alleles for vernalization response, Vrn; photoperiod response, Ppd, and frost tolerance, Fr) [The early breeding activities in Bulgaria date back to the beginning of the 20th century when the relatively homogeneous selections made within landraces and, later, crosses involving local and foreign accessions have brought up the first cultivars. In the 1960s, the science-based wheat breeding was launched with the introduction of alleles for reduced height or semi-dwarfism  along with microsatellites are currently the most abundant class of DNA markers and the most valuable genomic tools in plants ,20. For Characterization of molecular genetic diversity and population structure analysis in the existing Bulgarian bread wheat germplasm have enormous significance for supporting breeding efforts, association genetic studies of important agronomic traits, and resources preservation. For the present study, a panel of 179 accessions, comprising 128 most representative modern semi-dwarf cultivars, 44 historic tall cultivars and 7 landraces has been assembled. The aim was to provide an update of the molecular genetic variation available in the bread wheat genepool in Bulgaria: to characterize the genetic diversity, population structure, and linkage disequilibrium in the collection of contemporary and old accessions of bread wheat using a new optimized 25K SNP genotyping array.The analysis of the reduced number of SNPs  showed a wide range of Nei’s gene diversity (GD) and polymorphism information content (PIC) values across the whole population . The valThe 19,019 SNPs were well distributed across all 21 wheat chromosomes within the set of 179 accessions. A total of 17,968 SNPs had known positions on the A-, B- and D-genome chromosomes , and 105The contribution of SNPs number scored on the A and B genomes was similar, 41% and 42%, respectively, while the D genome chromosomes had the least coverage (11%). The number of SNPs ranged from 108 on chromosome 4D to 1443 on chromosome 5B A. ConsidAfter the removal of SNP markers with minor allele frequency (MAF) less than 5% as well as those markers with more than 20% missing values, we estimated the SNP number, GD, PIC and linkage disequilibrium (LD) patterns for each chromosome of the 128 modern cultivars and the 51 old ones .From the total number of 19,019 markers, 15,289 (80%) were polymorphic in both collections, 2077 (11%) markers were polymorphic only in the group of old accessions, while 1653 (9%) were polymorphic only in the modern cultivars. The number of polymorphic markers with a known chromosome position was 16,942 in the set of old accessions, while in the modern collection this number was higher . From the total of 17,968 polymorphic markers with a known chromosome position, 1026 (6%) markers were polymorphic only in the old accessions, 602 (3%) only in the modern cultivars, and 16,340 (91%) were polymorphic in both collections. In both sets of accessions, the D genome was characterized with the lowest SNP number, while A and B genomes had much higher coverage B, more e2, the percentage of locus pairs having significant LD at p < 0.001, and LD decay) were estimated for individual chromosomes and genomes  characteristics (square of marker correlations r genomes . 2r) ranged from 0.45 (chromosome 7D) to 0.71  with an average of 0.64, while in the modern set, LD (2r) ranged from 0.34 (chromosome 4D) to 0.56 (chromosome 6D) with an average of 0.43. Both selections (old and modern) had lowest LD (2r) in the B genome . The old cultivars had highest value of LD (2r) in the A genome—0.65, while the modern cultivars had highest LD (2r) value in the D genome—0.47. LD (2r) mean values for each chromosome and genome, and the average one were significantly higher in the old germplasm.In the group of old cultivars, LD (p < 0.001 ranged from 6% (chromosome 4A) to 27% (chromosome 6A) with an average value of 16%, while in the modern collection this parameter ranged from 10% (chromosome 7D) to 45% (chromosome 1B) with an average value of 30%. The lowest percentage of markers with significant LD at p < 0.001 was in the D genome—12% and 21% for the old and modern sets, respectively. The old cultivars had highest percentage of locus pairs with significant LD at p < 0.001 in the A genome (19%), while in the group of modern ones this statistic had highest value in the B genome—39%  to 6 cM . In the modern collection, LD decay ranged from 2  to 8 cM . On average, the LD decay was higher in the modern collection (4.1) compared to the LD decay value (3.3) in the old germplasm. Following the Evanno method in STRUCTURE Harvester software, the optimal number of subpopulations (SP) (ΔK) was 3 A. The thThe k-means clustering algorithm was applied using the common SNP markers between the old and modern accessions. This approach successfully discriminated the two germplasm types. The resulting neighbor-joining dendrogram  consisteTwelve of the accessions in branch CS1.2 corresponded to the STRUCTURE SP2, 6 to SP1 and 1 (Sadovo 552) was classified as admixed. Branch CS1.3 was the most diverse one, including eight modern cultivars; all of them but one (Levent) coming from Southern Bulgaria. These eight accessions belonged to all STRUCTURE subpopulations: SP1 (4), SP3 (2), SP2 (1), and one (Pavlinka) was classified as admixed. Branch CS1.4 had 14 members, all of them from the modern collection belonging to the STRUCTURE SP1. All of them were from Northern Bulgaria, except for two (Hrabrets and Nova Zvezda), coming from the Southern breeding centres. All 29 accessions placed in branch CS1.5 were classified by STRUCTURE as SP1, the majority (27) being modern releases, and only two were old accessions (Erythrospermum 19-16 and Ferrugineum-2), all of them originated from the Northern breeding region. Branch CS1.6 had 14 modern members classified as SP1 from the STRUCTURE software. Most of them (10) originated from the Northern breeding region, two (Murgavets and Yana) were releases of the Southern breeding centre and other two (Gladiator-113 and Sredets-68) were created by the Western breeding institute. The last branch CS1.7 contained 27 entries revealed as SP1 according to STRUCTURE, 23 of them being modern and four old accessions . Cultivar Kiten in this branch was classified by STRUCTURE as admixed; seven accessions were from Southern breeding centres, one (Altimir-67) was from Western Bulgaria, and the remaining 19 were coming from the Northern region. The second-largest cluster CS2  corresponded to SP2, including exclusively the old cultivars and landraces, and only three modern releases. This cluster can be divided into two branches: CS2.1, consisting of 22 entries, of which 15 from the Northern region, five from the Southern and two from the Western part of the country. In this branch, the cluster analysis placed one modern cultivar (Rada). Branch CS2.2 comprised 19 accessions, of which two (Druzhba and Trapezitsa) were modern ones. The distribution according to the geographic region was: Northern—11, Southern—six and Western—two. The smallest cluster CS3  corresponded entirely to SP3. Ten entries in this cluster were contemporary ones and only two (Nova Sadovka and Yubilejna-3) belonged to the old germplasm. Most of the members of the old group (84%) fell into SP2, 12% belonged to SP1 and only two cultivars representing 4% were placed in SP3 A. The grThe relationship between the old germplasm and modern releases was also analyzed by principal component analysis (PCA) as a complementary approach to illustrate the clustering mode. In compliance with the results obtained with STRUCTURE and cluster analysis, the first two components of the PCA clearly distinguished the old cultivars from the modern ones A. The twTH of the 179 Bulwheat accessions was 0.3586 and ranged among the three subpopulations (SPs) from 0.3289 for SP3 to 0.3419 for SP2 , suggesting higher gene exchange between the modern subpopulations, SP1 and SP3. The total genetic diversity  for SP2 . The valSTD) between SPs was low (0.0242) and this led to a low mean value of the coefficient of genetic differentiation STG (0.0675) among the SPs  indicates that there is a certain level of gene exchange between the subpopulations. Comparison between the SPs showed that both statistics STD and STG had similar values in the pairs SP1-SP2 and SP2-SP3, while STD and STG values in the pair SP1-SP3 were considerably lower . Correspondingly, the gene flow estimator Nm had highest value in the pair SP1-SP3 (12.66), whereas the pairs SP1-SP2 and SP2-SP3 had significantly lower Nm values  genes were introduced into then high-productive cultivars thus resulting in a grain yield outbreak known as the Green Revolution. However, the practice of modern intense breeding was suggested to cause inevitably a reduction in the genetic variation available for crop improvement [Genetic diversity is a main source of biodiversity, which represents any measure that quantifies the magnitude of genetic variability within a population . In brearovement . The conrovement  discusseIn this study, we described aspects of DNA genetic diversity, population structure and linkage disequilibrium using the currently adopted markers of choice (SNPs) in a bread wheat panel comprising both old accessions and contemporary cultivars from Bulgaria. Nei’s gene diversity (GD) and polymorphism information content (PIC) are important guidance for breeding programs. Genetic diversity at DNA level is a reflection of similarities and differences in the genes of individuals. The gene diversity estimator GD lies between 0 (no differentiation in the population) and 1; higher GD values are associated with higher population differentiation. In our current study, the GD values ranged from 0.1 to 0.5. The PIC, a statistic used to measure the informativeness of a genetic marker for linkage studies varies between 0  and 1 ; a gene or marker with only two alleles has a maximum PIC = 0.375, and PIC value 0.44 is considered to be moderately informative . In thisHowever, the obtained results in the current study are in agreement with findings for other wheat populations. In a study of 250 Nebraska winter wheat accessions with GBS-derived SNPs, Elather et al.  reportedHe  differed significantly among the contemporary cultivars and the two groups of landraces. A very recent study in durum wheat from Ethiopia by Alemu et al. [per se genetic variability of the old germplasm. The observed differences in the genetic diversity between old and modern collections could be also attributed to some anthropogenic factors such as seed exchange, and use of various parental sources in breeding programs. The interest of scientists towards the old germplasm (landraces and old historic cultivars) has been driven by its intrinsic wide genetic variability thus being an important genetic resource for enhancing modern crops with new alleles. This nearly extinct germplasm could play a buffer role in case of adverse environmental conditions and needs characterization and evaluation ,24. Our u et al.  using hiIn the current study, we also observed strong differences in terms of proportion of SNPs with known chromosome assignment between genomes and homoeologous groups. The D genome was the least polymorphic (11%), while A and B genomes displayed almost equal coverage , and group 4 chromosomes had the smallest number of SNPs (7.9%) of those with known chromosome location. This observation completely agrees with the outcome of the whole hexaploid wheat genome resequencing from a number of European, Australian, Chinese and Israeli accessions . They alTriticum turgidum, with the D-genome progenitor, the diploid wild goat grass Aegilops tauschii) using nearly 36,000 high-quality SNPs [The outcome of our study about the least D genome polymorphism extent is also in lineage with a comparative study between elite bread wheat cultivars and synthetic hexaploid wheats  were significantly different between the old and modern collections. The LD (2r), the percentage of the locus pairs in LD calculated for intra-chromosomal loci and LD decay were 0.64, 16% and 3.3 for the old germplasm, and 0.43, 30% and 4.1 for the modern releases, respectively (Linkage disequilibrium (LD) is the nonrandom association of alleles at different loci that reflects their proximity and correspondingly, the probability of recombination breaking the haplotype on which they are found . LD is aectively . The higAnalysis of population structure is worthwhile for understanding genetic diversity and for association mapping studies. The Bulwheat old and contemporary accessions were allocated to three subgroups, which was confirmed by the two applied approaches—STRUCTURE and k-means clustering algorithm. Not surprisingly, the vast majority of the old germplasm (landraces and historic cultivars) formed a distinct group using both methods. The outcome of the PCA also supports the results obtained by STRUCTURE and the cluster analysis with respect to the differentiation between the modern and old collections A. HoweveNm values) then the differentiation of the populations is low and they are considered as a single effective population [STG and, respectively, higher value for the gene flow estimator Nm compared to the corresponding values in the old collection, which is suggestive for higher levels of gene migration within the set of modern releases. These results are in agreement with the population structure and the neighbor-joining dendrogram. The STRUCTURE-derived subpopulation SP2 and the corresponding cluster CS2 in the dendrogram included mostly historic cultivars and landraces. The known information about the pedigree of some old accessions from SP2  suggests some extent of gene exchange between the representatives of the old germplasm. Subpopulations SP1 and SP3 and the corresponding clusters CS1 and CS3 consisted mainly of modern releases and a few old accessions. The existing high gene exchange between these two groups is evidenced by low value of the genetic differentiation estimator STG (0.0380) and high value of the gene flow statistic Nm  accessions originated from Bulgaria (Bulwheat population). The Bulwheat population included 128 modern semi-dwarf cultivars and 51 representatives of the old germplasm of tall stature . For genomic DNA extraction, a bulk sample of leaf tissue was taken from ten young seedlings for each accession. The DNA extraction was performed using an in-house CTAB-based protocol with chloroform extraction and isopropanol precipitation, followed by ethanol washing and resuspending the dried pellet in pure water. To generate SNPs, an optimized wheat 25K Infinium iSelect array was used. The array contains 24,145 SNPs combining 17,229 markers from the Illumina 20K array, 6916 new markers from the 135K Axiom array, and additional trait- and gene-specific markers. SNP discovery was performed with the GenomeStudio Project software package . The reference genome v1.0 of the Chinese Spring genome assembly from the International Wheat Genome Sequencing Consortium (IWGSC_WGA_v1.0)  were useTH, and mean diversity within each subpopulation SH) were estimated according to Nei’s formula [TH and SH were used in calculations of the basic diversity statistics for population differentiation  [STG) [STD and STG were calculated using R software package diveRsity [The MAF and polymorphism information content (PIC) were deliberated using PowerMarker software V 3.25 . PIC val formula . Designao et al. ): Jost’sl. [DST)  and coefT) [GST) . DST andiveRsity .STD) was calculated by the formula: TH is the total genetic diversity in a population, SH is the mean diversity within each subpopulation, and k is the number of subpopulations that are sampled.The Jost’s index of population differentiation  with a sliding window of 50 cM at a significance level of p < 0.001. For each genome, the critical value of 2r was estimated as the mean 2r. To determine how fast LD decays for each chromosome, the critical value of marker correlation (2r) was estimated as the mean 2r for the chromosome, and the 2r values were plotted against the genetic distance. Then, the LOESS curve [2r. The projection of this point of intersection on the coordinate axis of the distance determines where LD becomes approximately equal to zero. The R package ggplot2 was used to plot the LOESS curves [To estimate linkage disequilibrium (LD), the software package TASSEL v.5.0 was used . LD was SS curve  was fittS curves .FactoMineR package available in R was used. In addition, Principal Component Analysis (PCA) was applied to the data to identify and visualize subpopulations using the R software package FactoMineR [factoextra package was used to extract, visualize and interpret the results obtained by the model-based clustering algorithm and PCA.The genetic structure of the population was analyzed using the Bayesian clustering algorithm in STRUCTURE 2.3.4 software. From one to six subpopulations were suggested to analyze the Bulwheat population structure and the admixture model with 10,000 burn-in iteration, and 100,000 Monte Carlo Markov chain replications after burn-in was used . The EvactoMineR . The facThe current study explored the extent of genetic diversity, linkage disequilibrium (LD) and the presence of genetic structures in a panel of 179 bread wheat old accessions and advanced cultivars collected or developed in the Eastern European country Bulgaria using SNP markers. Slight differences were noted between the old and modern collections regarding Nei’s gene diversity and PIC, and significant differences were revealed with respect to LD. The three implemented approaches—STRUCTURE, k-means clustering algorithm and PCA, partly revealed the geographical pattern of accessions distribution. The inferred structure for the modern cultivars reflected the different strategies adopted by the breeding centres. The role of the old germplasm in the contemporary breeding programs had been low. The detailed understanding of the genetic diversity and population structure of the Bulgarian old and modern bread wheat cultivars provided in this study could be of interest for motivating development of new cultivars with improved characteristics, especially by using the unrealized potential of the old germplasm. The obtained knowledge on SNP informativeness and population LD is a pre-requisite for further association mapping studies of important agronomic traits.


Arabidopsis thaliana accessions. We show that a combination of geography, environmental parameters, demography and different genetic processes all combine to influence the specific chemotypes and their distribution. This showed that causal loci in specialized metabolism contain frequent independently generated alleles with patterns suggesting potential within-species convergence. This provides a new perspective about the complexity of the selective forces and mechanisms that shape the generation and distribution of allelic variation that may influence local adaptation.Plants produce diverse metabolites to cope with the challenges presented by complex and ever-changing environments. These challenges drive the diversification of specialized metabolites within and between plant species. However, we are just beginning to understand how frequently new alleles arise controlling specialized metabolite diversity and how the geographic distribution of these alleles may be structured by ecological and demographic pressures. Here, we measure the variation in specialized metabolites across a population of 797 natural  Since plants cannot move, they have evolved chemical defenses to help them respond to changes in their surroundings. For example, where animals run from predators, plants may produce toxins to put predators off. This approach is why plants are such a rich source of drugs, poisons, dyes and other useful substances. The chemicals plants produce are known as specialized metabolites, and they can change a lot between, and even within, plant species. The variety of specialized metabolites is a result of genetic changes and evolution over millions of years.Evolution is a slow process, yet plants are able to rapidly develop new specialized metabolites to protect them from new threats. Even different populations of the same species produce many distinct metabolites that help them survive in their surroundings. However, the factors that lead plants to produce new metabolites are not well understood, and it is not known how this affects genetic variation.Arabidopsis thaliana, which is widely studied. The investigation found that many factors affect the range of specialized metabolites in each variant. These included local geography and environment, as well as genetics and population history (demography). Katz et al. revealed a pattern of relationships between the variants that could mirror their evolutionary history as the species spread and adapted to new locations.To gain a better understanding of this process, Katz et al. studied 797 European variants of a common weed species called These results highlight the complex network of factors that affect plant evolution. Rapid diversification is key to plant survival in new and changing environments and has resulted in a wide range of specialized metabolites. As such they are of interest both for studying plant evolution and for understanding their ecology. Expanding similar work to more populations and other species will broaden the scope of our ability to understand how plants adapt to their surroundings. Continuous and dynamic change in a plant’s habitat/environment creates a complex system to which a plant must adapt. Central to this adaptation are the production and accumulation of different metabolites ranging from signaling hormones, primary metabolites to a wide array of multi-functional specialized metabolites . The comSignificant advances have been made in recent decades to identify genetic sources contributing to metabolic variation. A common finding of these studies is that the metabolic variation within and between species is the result of structural variation at the enzymes responsible for the chemical structures, or variation at the expression levels of these enzymes, which contributes to the quantitative variation in specialized metabolism . These sIf selective pressures are sufficiently non-linear, it is possible to have repeated and independent generation of structural variants creating the same metabolic variation in processes that are akin to parallel and convergent evolution are used to describe interspecific variation. Specifically, parallel and convergent evolution describe independent evolution of the same trait that differs depending on the beginning state of the organisms. In parallel evolution, the lineages begin from the same state and in parallel evolve to the same new state, while in convergent evolution the lineages start at different states and independently converge on the same new state . In thisArabidopsis thaliana)  pathway as a model. GSLs are a diverse class of specialized metabolites that display extensive variation across the order Brassicales, which includes the model plant Arabidopsis  . GSLs cohaliana) . The Arahaliana) . These mhaliana) . Beyond haliana) . The pubGS-Elong, GS-AOP and GS-OH. The specific alleles at these three loci combine to determine a predominant chemical structure and define chemically distinct aliphatic GSL chemotypes. In addition to these large-effect loci, there is a large suite of loci that can quantitatively alter the total accumulation and relative concentrations of GSLs within each chemotype . The elongation of the side chain by one methylene group is the result of one cycle that includes three steps: deamination of the methionine to create a ω-methylthio-2-oxoalkanoic-acid, condensation of the ω-methylthio-2-oxoalkanoic-acid with acetyl-CoA, and then isomerization and oxidative decarboxylation. The one carbon longer outcome can then undergo additional cycles of elongation . The AOP2 enzyme removes the MSO moiety leaving an alkenyl sidechain, while AOP3 leaves a hydroxyl moiety. Previous work has suggested three alleles of GS-AOP: the OHP allele that expresses only AOP3 and accumulates terminal OH containing GLS in the leaves and seeds; an alkenyl allele expressing AOP2 in the leaf and AOP2 and AOP3 in the seed leading to solely alkenyl GLS in the leaf and both alkenyl and OH aliphatic GLS in the seed; and a final allele containing a null mutation in the AOP2 gene that accumulates MSO GLS in the leaf and enhanced MSO and OH GLS in the seed. , MAM2 (that is not present in Col-0 plants) and MAM3 (AT5G23020) genes.To test which of the major causal loci are detectable in this collection and to identify new genomic regions that are associated with the observed GSL variation, we performed GWA  analyses using the PC1 and PC2 values. This collection of natural accessions presents a dense variant map that is 3× larger than previous GSL GWA mapping populations and includes 6,973,565 SNPs. In spite of the large population size, both PC1- and PC2-based analyses identified the same two major peaks covering two of the known causal gene clusters controlling GSL diversity . The larGS-AOP locus containing the AOP2 and AOP3 genes . Applying a more permissive cutoff did not result in the detection of any other related genes , we created a linear model to further check the interaction between each environmental condition to geography in respect to the carbon chain length. As was shown by the chemotypes models, most of the environmental parameters  significantly interacted with geography, showing again that the relationship of environment to GSL alleles changes across Europe .GS-AOP, GS-Elong and GS-OH loci, the causal genes creating Arabidopsis GSL chemotypes, and use the available genomic sequences in all these accessions to investigate the allelic variation in these genes to map the allelic distribution and test the potential for convergent and/or parallel evolution within each locus.The presence of different GSL chemotype to environmental relationships across Europe raises the question of how these chemotypes are generated. Are these chemotypes from locally derived alleles or obtained by the intermixing of widely distributed causal alleles? Further, if there are multiple alleles, do they display within-species convergent or parallel signatures? We focus on the GS-Elong: Because the variation in the GS-Elong locus is caused by complex structural variation in MAM1 and MAM2 that is not resolvable using the available data from short-read genomic sequence, we used the MAM3 sequence within this locus to ascertain the genomic relationship of accessions at the causal GS-Elong locus  and colored the tree tips based on the accessions-dominant chemotype.ng locus . We aligMAM2) and five clades expressing the C4 (MAM1). The use of MAM3 clades as proxy for C3/C4 GLS chemotypes is supported by prior genomic sequencing of the GS-Elong region from 15 accessions , and a second with a local duplication of a MAM1 pseudogene in clade 2  that may have influenced their distribution. In combination, this suggests that a complex demography is involved in shaping the chemotype’s identity with some regions, Iberia, showing evidence of local selection while other regions, Central Europe, possibly showing a blend requiring further work to delineate  and hydroxyalkyl (AOP3 expressed) accessions. Alkenyl expressing accessions like Cvi-0 with an expressed copy of the AOP2 enzyme formed a single continuous cluster (AOP3 expressed) accessions clustered into two separate groups with one group of 3OHP-dominant accessions partitioning from the rest of the accessions at the most basal split in the tree (AOP2 tree). This haplotype is marked by having an inversion swapping the AOP2 and AOP3 promoters as shown in bacterial artificial chromosome sequencing of the Ler-0 accession  were identifiable in all the major clades on the tree  and between-species (GS-AOP and GS-Elong) variation between the closely related Arabidopsis sister species, A. lyrata, A. petraea and A. halleri. However, the underlying genetic basis is independent events at the AOP and MAM loci showing that the variation did not go through the speciation boundary. Instead, this suggests that this variation has been recreated repeatedly in these species This raises the possibility that there may be a class of loci that are being repeatedly sampled by pangenomic variation across species within a family. To test this possibility would need a deeper phylogenetic sampling within and between species, particularly for understanding the intersection of ecology and evolution  is caused by a rare allele causing an extreme lesion phenotype. It is not yet clear what selective pressures influence ACD6 genetic variation  and alleles that possibly arose locally . Further, the chemotypes found in any one region appear to be created by a combination of alleles as a result of a gene flow across the continent, local generation of new polymorphisms and local selective pressures.The allelic diversity at the GSL loci illustrates the benefit of simultaneously tracking the phenotype and genotype when working to understand the distribution of trait variation. For example, the Iberian Peninsula and the Mediterranean Basin had low variability in aliphatic GSL chemotypes, which show strong geographic structure. By contrast, Central/North Europe had high aliphatic GSL diversity with chemotypes showing overlapping geographic distributions. At first glance, this contrasts with previous work showing that the Iberian Peninsula and the Mediterranean Basin are genetically diverse. However, this discrepancy was caused by one of the causal loci. Specifically, the t Europe . Thus, tGS-OH. Maximizing the number of genotypes and the SNP marker density was unable to overcome the complications imposed by the complex pressures shaping the distribution of these traits, potentially due to unequal dense sampling from the different areas. In this system, the optimal path to identifying the causal polymorphisms has instead been a small number of Recombinant Inbred Line populations derived from randomly chosen parents. In complex adaptive systems, the optimal solution to identifying causal variants is likely a blend of structured mapping populations and then translating the causal genes from this system to the GWA results and tracking the causal loci directly.Another challenge potentially caused by allelic heterogeneity and differential selective pressures, as displayed within this system, is detecting the known and validated causal natural variants within a population. Specifically, the GWA with this collection of 797 accessions was unable to find 80% of the known causal loci including one of the three major effect loci, In this work, we combined different approaches to uncover some of the parameters shaping the aliphatic GSL content across Europe. Widening the size of the population will enable us to deepen our understanding on the evolutionary mechanisms shaping a phenotype in a population.A. thaliana) genotypes were obtained from the 1001 genomes catalog of A. thaliana genetic variation (https://1001genomes.org/). All Arabidopsis genotypes were grown at 22°C/24°C (day/night) under long-day conditions (16 hr of light/8 hr of dark). Two independent replicates were performed, each of them included the full set of genotypes. The replicates obtained from independent maternal plants were grown in randomized fashion. In the analyses, only accessions from Europe and around Europe were included  with the RStudio interface (http://www.rstudio.com/). For each independent GLS, a linear model followed by ANOVA was utilized to analyze the effect of accession, replicate and location in the experiment plate upon the measured GLS amount. Broad-sense heritability  of short-chained aliphatic GSLs.MAM enzymes: The total amount of three carbon GSLs and four carbon GSLs was calculated for each accession. Three carbon GSLs include 3MT, 3MSO, 3OHP and Allyl GSL. Four carbon GSLs include 4MT, 4MSO, 4OHB, 3-Butenyl and 2-OH-3-Butenyl GSL . Accessions with high amounts of 2-OH-3-Butenyl GSL were classified as GS-OH functional. Accessions with high amounts of 3-Butenyl GSL were classified as GS-OH non-functional. The accessions were plotted on a map based on their original collection sites  and from the Arabidopsis CLIMtools  were obtained from the 1001 genomes website  or the chemotypes (Allyl and 2-OH-3Butenyl) ~ Genomic group + Geography (north versus south)+ Max temperature of warmest month + Min temperature of coldest month + Precipitation of wettest month + Precipitation of driest month + Distance to the coast.The phenotypes for GWA studies were each accession value for PC1 and 2. GWA was implemented with the easyGWAS tool  using thMAM3 – AT5G23020, AOP2 – Chr4, 1351568 until 1354216, AOP3 – AT4G03050.2, GS-OH – AT2G25450 and MYB37 – AT5G23000 were obtained using the Pseudogenomes tool (https://tools.1001genomes.org/pseudogenomes/#select_strains).Genomic sequences from the accessions for A. lyrata’s functional orthologue or closest homologue.Multiple sequence alignment was done with the msa package (default settings) in R using the ClustalW, ClustalOmega and Muscle algorithms . PhylogeMAM3 bootstrap analysis, the accessions with low-quality sequencing were excluded.Bootstrap analyses (Bootstrap = 100) was done with ‘ape’ package in R , with thAmino acid phylogenies: Sequences were taken from A. thaliana Col-0 genome and the MAM2 amino acid sequence 1006452109 from the Arabidopsis Information Resource (TAIR) database. Alignments were run using MAFFT (ng MAFFT  and cleang MAFFT . RAxML wng MAFFT  with thePacBio long read-based de novo genome assemblies of the relevant accession were generated as part of the 1001 Genomes Plus project. The genomes were assembled with Canu (v1.71)  and poli In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The manuscript by Katz and colleagues makes key advances in understanding how glucosinolate chemotype variation across Arabidopsis ecotypes is linked to the underlying genetic variation. It demonstrates the complexity of the interplay between genetic and environmental factors and gives insights into how complex traits could evolve in natural populations. This article reveals the complexities involved in conducting genome-wide association studies on geographically structured populations which cannot be eliminated even in a very well studied system. It thus bears important general lessons for researchers in the genomic era.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Arthur Korte (Reviewer #1); Juergen Kroymann (Reviewer #3).Thank you for submitting your work entitled "Genetic variation, environment and demography intersect to shape Arabidopsis defense metabolite variation across Europe" for consideration by eLife. However, the reviewers and editors agree that both the topic and the general approach are of interest. eLife would consider a substantially revised version of the manuscript which addresses the points raised by the individual reviewers and summarized below. Because we expect that these revisions represent substantial work, we would consider a revised manuscript as a new submission.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in Arabidopsis thaliana, as this indicates fundamental complexities which cannot be remedied by large volumes of data alone, but require well-informed approaches to analyze the data and in some cases, perhaps, cannot be resolved. Thus although we are not convinced that any one aspect of this study is very novel, we do think that the study as a whole presents an unusually holistic perspective and thus could be a very valuable contribution. Nevertheless, we also agree that there are substantial gaps and pitfalls in the analyses as they are currently presented, and these must be remedied in order for the conclusions of the study to be well supported:Specifically, the reviewers and editors agree that the study could provide an important new insight into patterns and processes of evolution. In the words of Reviewer 1, it is valuable "to present the complexity of the interplay between genetic and environmental factors and its contribution to trait evolution in natural populations." It is perhaps especially valuable to do so in such a well-studied system as 1. We are concerned that MAM3 is not a good proxy for the GS-Elong locus, and there is evidence of recombination within MAM3; and that resequencing of this locus in the accessions studied is required to support the conclusions that the authors wish to draw in this study. Please see detailed comments from Reviewer 3.2. Several sections of the study require deep revision in terms of writing and analysis. See major comments from Reviewer 2, as well as the other reviewers.Reviewer #1:The authors present a detailed and well-written analyses of the variation in the glucosinolate chemotype of different Arabidopsis ecotypes. These differences were linked to the underlying genetic variation. They could show many complex patterns that are related to the distinct chemotypes. The analyses highlights the complexity of the interplay between genetic and environmental factors and gives insights into how traits evolve in natural populations.I only have one point that I was wondering about.23 different GSL have been detected and quantified and GWAS has been performed on PCs that summarize the GSL variation. The GWAs on PC1 and PC2 do only identify the two known loci AOP and NAM and no additional loci are significant. As this first two PCs explain only 33 % of the total phenotypic variation, I wonder if GWAS on the individual GSL concentration, especially as all of these show a high heritability, could identify additional associations? It would be nice to have an overview of these potential associations.Reviewer #2:Arabidopsis thaliana across Eurasia. To this end, the authors carried out a very extensive characterization of different GLSs in a large collection of natural Arabidopsis accessions. Genetic analyses of these data show that the epistasis underlying causal loci hampers the identification of new loci. More importantly, authors compare these data with environmental factors to show significant environmental associations, which support a differential adaptive role of this variation across Europe. In addition, thorough genetic analyses of the major underlying genes identified multiple independent mutations in several duplicated genes, which demonstrate a repeated evolution pattern with differential geographic histories. Overall, this study shows a complex adaptive history of Arabidopsis plant defenses mediated by GLSs at a continental scale and brings forward our current understanding of adaptation of plant defense mechanisms.This manuscript describes the contribution of environment and demography to shape the genetic variation underlying the glucosinolate (GLS) composition of Although most sections of the manuscript are written in a comprehensive manner, several sections of results and discussion are rather confusing due to lack of information on particular analyses. The following sections should be revised:1. The section entitled "Geography and environmental parameters affect GLS variation" (pages 13-14) is very confusing due to an unclear distinction between demography and environment. Given the strong geographic structure of Arabidopsis genetic diversity across Europe , most genetic diversity can be expected to correlate with environment due to Arabidopsis demographic history but not reflecting true adaptive association. For this reason, to distinguish true environmental associations from correlations related with demographic history, analyses must take into account the genetic structure (genetic or genomic relationships among accessions). Usually, the effect of demographic factors on the distribution of genetic diversity is inferred by the genetic structure. According to Supp. Figure 7 and Materials and Methods, authors seem to have included such genetic structure in their MANOVA analyses, which is referred to as genomic group. However, authors should describe what are those genomic groups, and if they were included also in Figure 4B. As shown by Supp. Figures 7A and 8, the genetic structure explains most of the diversity in MANOVA and Random forest models, indicating that most of the geographic patterns can be explained by the demographic history. However, both Figures also show that there is still a significant association with environmental factors, which supports that the environment contributes to maintain the genetic diversity of chemotypes. The text describing these results should be carefully rewritten to explain how the genetic structure (demography) has been taken into account and separating clearly demographic and environmental components. The genetic structure is only mentioned in line 259 where it is confusingly named as "ancestral state".2. The section describing the genetic diversity of MAM3 across Europe (lines 302-326) shows certain overinterpretation of the action of natural selection, which should be revised and rewritten in a softer tone. For instance, the sentence in line 306 stating "If the latter is true, this would argue for a selective pressure shaping this C3/C4 divide". The two scenarios described in paragraph of lines 302-306 could be explained by natural selection or demography, and one should be cautious interpreting geographic patterns. In particular, the geographic pattern observed in Iberia is just showing that this region contains a large diversity for C3/C4 chemotypes and MAM3, as shown previously for most genes in this region (The 1001 Genomes Consortium). Therefore, the sentence in lines 310-313  or lines 324-326  are not fully supported by data. In addition, this section on MAM3 could include the recent study on the genetic diversity of Europe, which suggests that Iberia could have also been colonized from central Europe after the last glaciation .3. The section describing GS-OH gene and Table 1 (lines 377-390) is rather confuse. In line 377 is stated: "We could not identify the causal LOF in the other six accessions due to sequence quality". However, Table 1 shows the structure of GS-OH gene with apparent location of six mutations. It is unclear which are those six accessions of Table1, what is represented in the structure of the genes, and the meaning of symbols on those genes . Are those accessions of Table 1 the same described in the text? It should also made clear the type of mutations of those six accessions instead of the confusing term "Independent mutation". Furthermore, this section refers to Supplementary Table 2 (line 383), but I could not find such table.Reviewer #3:eLife its current state.The manuscript is based on an impressive data set of (seed) glucosinolate profiles across Europe and beyond, with a focus on methionine-derived glucosinolates. However, the manuscript reads in many aspects like a 'best of' of previous research, with limited net gain in insight. In addition, there are substantial weaknesses (in particular relating to GS-Elong) that prevent me from recommending this manuscript for publication in 1. The representation of the MAM reaction is misleading and incorrect (lines 101ff). MAMs catalyze condensation of 2-oxoacids with acetylCoA. Two further reaction steps, catalyzed by an isomerase and a dehydrogenase, are necessary to complete chain extension. This extension consists formally in the addition of a methylene group per reaction cycle. With MAM2, the net result is an extension by one (not two!) methylene group, with MAM1 it is mostly two; note that methionine has already two methylene groups in the side chain! I suggest replacing the reference Abrahams et al., 2020, with Graser et al., 2000 . Furthermore, Kroymann et al., 2001 , Benderoth et al., 2006 and Textor et al., 2007  should be added as original references for the MAM1, 2 and 3 reactions.2. The use of MAM3 as a proxy for inferring the configuration of the GS-Elong locus is highly debatable. Even though recombination appears to be suppressed at and around GS-Elong, an additional gene  upstream of GS-Elong should be used to construct (a) gene tree(s) and compare with the MAM3 gene tree for congruency. In any case, without sequencing of the MAM genes at the GS-Elong locus, it is not possible to conclude that 'local' configurations of MAM1/MAM2 have not arisen in Iberia (or elsewhere).A. lyrata ortholog of Arabidopsis MAM3 is MAMb (not MAMc). The MAM3 gene tree does not provide any statistical support for the branching order. Therefore, it remains unclear whether the presented tree reflects the true branching order and whether the 'most basal' clade in the tree is indeed the most basal. Note also that the Cal-0 gene at the MAM2 position is actually mostly MAM2, except for a converted stretch towards the 3' end of the gene; see: Figure 2 in Kroymann et al., 2003. Hence, the most parsimonious interpretation of available data is that the ancestral state of GS-Elong in Arabidopsis is indeed: MAM2, MAM1 and MAM3. Note also that the Arabidopsis genes in Abrahams et al., 2020, are from TAIR 10, i.e. from Col-0 . Because Col-0 does not have MAM2, this gene is not included in the gene trees presented by Abrahams et al., 2020.3. The 4. Despite the use of an impressive number of accessions, sufficiently dense sampling appears to be only available for the Iberian peninsula, the southern coast of Sweden and the south-western coast of Italy. In this regard, the authors overstate their findings .5. While the link between glucosinolates and biotic stress is well documented, evidence for a major role of abiotic factors in shaping glucosinolate profiles is less well substantiated. Now, the authors seem to find that certain chemotypes are favored by high precipitation in 'the North' but that the same chemotypes are favored by low precipitation in 'the South'. This is interesting, but what could explain this unexpected pattern? Any idea? Furthermore, with additional sampling on the eastern coast of Italy and the eastern coast of the Adriatic, would you expect to obtain similar patterns?6. Please, make sure that the citation style is uniform. Please also make sure that the tense is uniform throughout the manuscript. Please, simplify the introduction in a way that it is accessible by non-specialists. Please improve Figure 5D – this is a mess.eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Meredith Schuman as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Arthur Korte (Reviewer #2).Thank you for submitting your article "Genetic variation, environment and demography intersect to shape Arabidopsis defense metabolite variation across Europe" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential Revisions:While we find the overall story to be insightful and advancing the field, we do think that some of the analyses still need to be redone, while the eco-evolutionary framework needs to be better defined.1. We have serious concerns regarding the environmental association analyses. Since there is a general correlation between genetic/genomic/phenotypic diversity and environmental factors due to Arabidopsis demographic history, the overall genetic structure needs to be included in the statistical models testing the associations between traits and environmental factors. You should produce tables similar to that of Supplementary Figure 8A for the chemotypes of Figure 4 and derive statistical significances from these tables, in order to provide convincing evidence of the differential climatic patterns in both European regions.2. The conceptual framework of repeated evolution that differentiates between parallel and convergent evolution was traditionally developed for interspecific evolution, not for intraspecific evolution where it refers to parallel evolution by multiple independent mutations in the same gene. The differentiation between parallel and convergent evolution for intraspecific evolution is not so straightforward. Therefore we want you to provide a more precise definition of intraspecific convergent and parallel chemotype evolution and outline the role of different or the same genes from a tandem repeat cluster, as well as the role of epistasis between duplicated genes, therein. To this end, the LD analysis between MAM genes and flowering genes should also be removed from the argumentation, because this analysis clearly shows that LD between these genes is not larger than average LD between random pairs of genes. Therefore, the conclusion drawn from this LD analysis is not supported.We are optimistic that regardless of the outcome of the corrected environmental analysis (point 1), this study represents a valuable contribution, especially if the revised framework for intraspecific repeated evolution – helping to conceptually unite studies of population and functional genetics in an apt system – can be articulated and illustrated more clearly in the revised analysis (point 2).Reviewer #1 (Recommendations for the authors):In this new version of the Manuscript by Katz et al., authors address numerous important questions raised in the previous version, and rewrite many of the paragraphs that were confusing or incorrect. In this version authors provide better support for some of their conclusions in a more comprehensive manuscript. However, this Reviewer still has serious concerns regarding the environmental association analyses carried out in this study to support their conclusions. In addition, for clarity and comprehension I also suggest addressing an important conceptual issue, as well as the numerous editorial points described below.A. thaliana there is a general correlation between genetic/genomic/phenotypic diversity and environmental factors due to Arabidopsis demographic history. For this reason, the overall genetic structure needs to be included in the statistical models testing the associations between traits and environmental factors. Authors now provide more detailed description of the statistical tests applied for such associations. However, as stated in Materials and methods, the statistical models used to build Figure 4 and Supplemental Figure 8B do not include the genomic groups. Therefore, significant associations described in these Figures and throughout the manuscript are mainly reflecting the association of genomic groups (demographic history) with climate. These two figures are the main support for the differential environmental associations in Northern and Southern Europe, but they just show that different genomic groups are present in each geographic area. It seems that such differential association between climate and chemotypes might be true, as shown by the table of Supplementary Figure 8A, where authors include the genomic groups to test the differential association specifically for C3 and C4 status. However, in this table the variable "Precipitation for the driest moth" appears as non-significantly associated. In spite of this, authors choose this variable in Supplementary Figure 8B  to illustrate that this climatic variable is associated when applying a simple t-test to compare both groups. This test does not support such environmental association because it is not controlling for the genomic group (demographic history). By contrast, the table of Supplementary Figure 8 shows a stronger differential association in Northern and Southern Europe for minimum temperature, which is associated only in the south but not in the north. Tables similar to that of Supplementary Figure 8A should be provided for the chemotypes of Figure 4, and statistical significances should be derived from them, in order to provide convincing evidence of their differential climatic patterns in both European regions.1A. As explained in previous Reviewer 2 report to Authors, in 1B. Related with the environmental associations, authors include random forest analyses. However, in the way these analyses are presented, they are hard to interpret because variables with the lowest mean decrease Gini (the probability of the variable of being wrong according to Authors) mostly have the lowest mean decrease accuracy (suitability of the variable as predictor).2. A major goal of this manuscript is the distinction between convergent and parallel evolutions in GSL chemotypes, which are described as two different patterns of repeated evolution. However, these concepts have been classically developed to characterize interspecific evolution, and their translation into the intraspecific variation is not straight forward, thus requiring a precise definition of both alternatives. Authors define parallel and convergent evolution as traits derived from either the same or different "genetic backgrounds". In this definition, the concept of genetic background is very vague and it is unclear what is meant. Authors apply these two concepts to the particular case of repeated evolution of GSL chemotypes evolved specifically from two tandem duplicated genes (MAM and AOP loci). Given the epistasis between these pairs of duplicated genes, the same trait can be derived from the same or different gene within. Then, it seems to this Reviewer that Authors mean by parallel and convergent evolution, the evolution occurring from the same or different gene of the cluster, respectively. In my opinion, this dissection of repeated evolution for the specific case of duplicated genes with epistasis is a major contribution of this study, but it is hard to understand. The manuscript and the audience would benefit by making these concepts clearer. I suggest that Authors add this in discussion, (e.g. paragraph of lines 550-554), explaining the role of different or the same gene from tandem repeat cluster, as well as the role of epistasis between duplicated genes for the distinction of their concepts of parallel and convergent evolution.Reviewer #2 (Recommendations for the authors):The authors present a substantial revised version of their manuscript that improved in clarity. All my concerns and suggestions are addressed in this version and I don't have any further comments. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]1. We are concerned that MAM3 is not a good proxy for the GS-Elong locus, and there is evidence of recombination within MAM3; and that resequencing of this locus in the accessions studied is required to support the conclusions that the authors wish to draw in this study. Please see detailed comments from Reviewer 3.2. Several sections of the study require deep revision in terms of writing and analysis. See comment from Reviewer 2, as well as the other reviewers.We have worked extensively to address both of these concerns in the revised manuscript. There are more details below about the specifics. In summary, we have worked on the entire manuscript to work on the wording and clarification in all noted sections and in the entire manuscript. Specifically, we have tried to make the holistic aspect of the study clearer. For the MAM locus, we have redone the analysis with the flanking gene to test for within locus recombination and how this may influence the analysis. Further, we have obtained 11 new sequences for this locus to further test the use of MAM3 as a proxy. Both approaches support the use of the MAM3 as a proxy to derive general conclusions. Finally, we have reworked all discussion on this locus, the enzymes chemistry and its potential evolution to reflect what is and is not supported by the phylogenetics.Reviewer #1:The authors present a detailed and well-written analyses of the variation in the glucosinolate chemotype of different Arabidopsis ecotypes. These differences were linked to the underlying genetic variation. They could show many complex patterns that are related to the distinct chemotypes. The analyses highlights the complexity of the interplay between genetic and environmental factors and gives insights into how traits evolve in natural populations.I only have one point that I was wondering about.23 different GSL have been detected and quantified and GWAS has been performed on PCs that summarize the GSL variation. The GWAs on PC1 and PC2 do only identify the two known loci AOP and NAM and no additional loci are significant. As this first two PCs explain only 33 % of the total phenotypic variation, I wonder if GWAS on the individual GSL concentration, especially as all of these show a high heritability, could identify additional associations? It would be nice to have an overview of these potential associations.To test the reviewer’s hypothesis, we have performed GWAS using the accumulation of each of the 23 individual GSL to test if this identifies more associations both with known GSL genes and unknown genes. This analysis did find a few additional SNPs in unknown genes, it failed to detect additional GSL related genes similar to the use of principal components. We added a supplemental figure of the 23 Manhattan plots showing it, and a supplementary table that summarize the number of SNPs in each of the expected GSL genes .Reviewer #2:Arabidopsis thaliana across Eurasia. To this end, the authors carried out a very extensive characterization of different GLSs in a large collection of natural Arabidopsis accessions. Genetic analyses of these data show that the epistasis underlying causal loci hampers the identification of new loci. More importantly, authors compare these data with environmental factors to show significant environmental associations, which support a differential adaptive role of this variation across Europe. In addition, thorough genetic analyses of the major underlying genes identified multiple independent mutations in several duplicated genes, which demonstrate a repeated evolution pattern with differential geographic histories. Overall, this study shows a complex adaptive history of Arabidopsis plant defenses mediated by GLSs at a continental scale and brings forward our current understanding of adaptation of plant defense mechanisms.This manuscript describes the contribution of environment and demography to shape the genetic variation underlying the glucosinolate (GLS) composition of Although most sections of the manuscript are written in a comprehensive manner, several sections of results and discussion are rather confusing due to lack of information on particular analyses. The following sections should be revised:1. The section entitled "Geography and environmental parameters affect GLS variation" (pages 13-14) is very confusing due to an unclear distinction between demography and environment. Given the strong geographic structure of Arabidopsis genetic diversity across Europe , most genetic diversity can be expected to correlate with environment due to Arabidopsis demographic history but not reflecting true adaptive association. For this reason, to distinguish true environmental associations from correlations related with demographic history, analyses must take into account the genetic structure (genetic or genomic relationships among accessions). Usually, the effect of demographic factors on the distribution of genetic diversity is inferred by the genetic structure. According to Supp. Figure 7 and Materials and methods, authors seem to have included such genetic structure in their MANOVA analyses, which is referred to as genomic group. However, authors should describe what are those genomic groups, and if they were included also in Figure 4B. As shown by Supp. Figures 7A and 8, the genetic structure explains most of the diversity in MANOVA and Random forest models, indicating that most of the geographic patterns can be explained by the demographic history. However, both Figures also show that there is still a significant association with environmental factors, which supports that the environment contributes to maintain the genetic diversity of chemotypes. The text describing these results should be carefully rewritten to explain how the genetic structure (demography) has been taken into account and separating clearly demographic and environmental components. The genetic structure is only mentioned in line 259 where it is confusingly named as "ancestral state".We apologize for not making it clearer that we had included population structure in these analyses and we have worked to clarify this and to ensure that it is clear for each analysis how population structure/demography was included. For the MANOVA and Random Forest models, population structure was included using the genomic groupings as previously defined in The 1001 Genomes Consortium, 2016.2. The section describing the genetic diversity of MAM3 across Europe (lines 302-326) shows certain overinterpretation of the action of natural selection, which should be revised and rewritten in a softer tone. For instance, the sentence in line 306 stating "If the latter is true, this would argue for a selective pressure shaping this C3/C4 divide". The two scenarios described in paragraph of lines 302-306 could be explained by natural selection or demography, and one should be cautious interpreting geographic patterns. In particular, the geographic pattern observed in Iberia is just showing that this region contains a large diversity for C3/C4 chemotypes and MAM3, as shown previously for most genes in this region (The 1001 Genomes Consortium). Therefore, the sentence in lines 310-313  or lines 324-326  are not fully supported by data. In addition, this section on MAM3 could include the recent study on the genetic diversity of Europe, which suggests that Iberia could have also been colonized from central Europe after the last glaciation .We rewrote this section, to both soften and better explain our hypothesis. We emphasized the fact that though there is high diversity in the haplotypes across Iberia, there is a clean delineation of the two opposing chemotypes controlled by these haplotypes. Further, the haplotypes within a chemotype are randomly distributed across the portions of Iberia containing that specific chemotype. This suggests that the haplotypes within a chemotype are more random across the landscape than the chemotype providing evidence supportive of selective delineation but not definitive. We also added the necessary citation.3. The section describing GS-OH gene and Table 1 (lines 377-390) is rather confuse. In line 377 is stated: "We could not identify the causal LOF in the other six accessions due to sequence quality". However, Table 1 shows the structure of GS-OH gene with apparent location of six mutations. It is unclear which are those six accessions of Table1, what is represented in the structure of the genes, and the meaning of symbols on those genes . Are those accessions of Table 1 the same described in the text? It should also made clear the type of mutations of those six accessions instead of the confusing term "Independent mutation". Furthermore, this section refers to Supplementary Table 2 (line 383), but I could not find such table.We prepared a new version for table 1. In this table we presented a clearer description of what the mutations are (and which ones were not identifiable from the sequence), we added the genes structure, and emphasized that six accessions with a loss of enzyme activity had an unidentified lesion due to sequence quality for this locus. We added an additional sup table presenting the frequency calculations of the mutated accessions.Reviewer #3:The manuscript is based on an impressive data set of (seed) glucosinolate profiles across Europe and beyond, with a focus on methionine-derived glucosinolates. However, the manuscript reads in many aspects like a 'best of' of previous research, with limited net gain in insight. In addition, there are substantial weaknesses (in particular relating to GS-Elong) that prevent me from recommending this manuscript for publication in eLife its current state.1. The representation of the MAM reaction is misleading and incorrect (lines 101ff). MAMs catalyze condensation of 2-oxoacids with acetylCoA. Two further reaction steps, catalyzed by an isomerase and a dehydrogenase, are necessary to complete chain extension. This extension consists formally in the addition of a methylene group per reaction cycle. With MAM2, the net result is an extension by one (not two!) methylene group, with MAM1 it is mostly two; note that methionine has already two methylene groups in the side chain! I suggest replacing the reference Abrahams et al., 2020, with Graser et al., 2000 . Furthermore, Kroymann et al., 2001 , Benderoth et al., 2006 and Textor et al., 2007  should be added as original references for the MAM1, 2 and 3 reactions.We apologize for the error and have rewritten this section, corrected the mistakes, and updated the citations.2. The use of MAM3 as a proxy for inferring the configuration of the GS-Elong locus is highly debatable. Even though recombination appears to be suppressed at and around GS-Elong, an additional gene  upstream of GS-Elong should be used to construct (a) gene tree(s) and compare with the MAM3 gene tree for congruency. In any case, without sequencing of the MAM genes at the GS-Elong locus, it is not possible to conclude that 'local' configurations of MAM1/MAM2 have not arisen in Iberia (or elsewhere).To address this concern, we have generated an additional phylogenetic tree using the sequences of MYB37, the gene on the opposite end of the GS-Elong locus (At5g23000), from all the 797 accessions. To test for within locus recombination that may be confusing us, we compared this tree to the MAM3. The vast majority of accessions showed the same major clade memberships in both the MAM3 and MYB37 trees arguing against a high level of within locus recombination. We also reanalyzed our analysis using just the 637 accessions with perfect agreement in the two trees and these showed the same message. Thus, our overarching patterns are not confounded by recombination shuffling this locus.We realized that we had been inaccurate when discussing a local Iberian haplotype as we’d meant to mean a single haplotype explaining all C3/C4 diversity Iberia. The reviewers comment led us to search for possible true local haplotypes by using the few accessions that have an incongruency in the MYB37/MAM3/C3/C4 data. This identified a collection of 6 accessions in a very narrow region of Iberia that appear to have developed a new local haplotype that requires further sequencing. We added a supp. Figure that describes the detection of these accessions and added this comment to the manuscript. Additionally, we have obtained new genomic sequences of the MAM locus for 11 accessions that resample the existing haplotypes and sample new haplotypes from the clades previously not sampled. These sequences support the classification of the accessions to the clades based on the MAM3 sequences, and the use of the MAM3 gene as a proxy to the MAM1/MAM2 haplotypes. In combination this suggests that while there is at least one local haplotype in Iberia, the vast majority of accessions in Iberia don’t have unique events, and the presence of these few accessions will not change the overall message.3. The A. lyrata ortholog of Arabidopsis MAM3 is MAMb (not MAMc). The MAM3 gene tree does not provide any statistical support for the branching order. Therefore, it remains unclear whether the presented tree reflects the true branching order and whether the 'most basal' clade in the tree is indeed the most basal. Note also that the Cal-0 gene at the MAM2 position is actually mostly MAM2, except for a converted stretch towards the 3' end of the gene; see: Figure 2 in Kroymann et al., 2003. Hence, the most parsimonious interpretation of available data is that the ancestral state of GS-Elong in Arabidopsis is indeed: MAM2, MAM1 and MAM3. Note also that the Arabidopsis genes in Abrahams et al., 2020, are from TAIR 10, i.e. from Col-0 . Because Col-0 does not have MAM2, this gene is not included in the gene trees presented by Abrahams et al., 2020.A. lyrata MAMb, and not MAMc as was written in the manuscript. This was a typo that was corrected. As for the gene tree presented in Abrahams et al., – we teamed up with them to recreate the tree, this time including MAM2 .The MAM3 tree was indeed rooted by A. lyrata and ensuing structural variation to create the haplotypes.For the MAM3 tree, we have worked in several ways to determine the proper support for this tree and the claims. The tree with all accessions has some sequences that are not maximal quality which influences the bootstrapping but not the clade assignment. To improve our precision, we created a tree using 637 accessions that have the highest quality accessions for MAM3 . The accessions in the new tree classified to the same clades as in the original MAM3 tree. The only difference between the trees  was the order of the clades. We then created multiple smaller trees, each contain a different combination of 200300 accessions. In each of these trees the accessions classified to the same clades as the original MAM3 tree, with bootstrap support. This agrees with the reviewer’s assessment that the evolutionary relationships between the clades are not resolvable but that the clades are decently defined. As we don’t have a strong support to the orders of the clades, we soften our tone in the manuscript in the part talking about the clades order and the ancestral state of the locus. We have discussed the most functionally parsimonious model of the ancestral state being MAM2/1/3 but also noted that this model requires a MAM1/2 duplication and diversification following the split from 4. Despite the use of an impressive number of accessions, sufficiently dense sampling appears to be only available for the Iberian peninsula, the southern coast of Sweden and the south-western coast of Italy. In this regard, the authors overstate their findings .We added this clarification to several locations along the text (results and discussion) and soften our claims.5. While the link between glucosinolates and biotic stress is well documented, evidence for a major role of abiotic factors in shaping glucosinolate profiles is less well substantiated. Now, the authors seem to find that certain chemotypes are favored by high precipitation in 'the North' but that the same chemotypes are favored by low precipitation in 'the South'. This is interesting, but what could explain this unexpected pattern? Any idea? Furthermore, with additional sampling on the eastern coast of Italy and the eastern coast of the Adriatic, would you expect to obtain similar patterns?We think what is happening is that the abiotic factors are capturing the biotic environment but there are no universal databases describing the biotic environment. We have added the following to try and describe this “The above analysis is extensively using Abiotic factors because of their availability while Aliphatic GSLs have mainly been linked biotic interactions. Our best hypothesis is that the abiotic factors used in this model are capturing variation in the biotic environments. However, an Aliphatic GSL was recently mechanistically linked to drought resistance in Arabidopsis . This suggests a need to capture the biotic as well as the abiotic components of any given environment to distinguish what may be driving these patterns.”6. Please, make sure that the citation style is uniform. Please also make sure that the tense is uniform throughout the manuscript. Please, simplify the introduction in a way that it is accessible by non-specialists. Please improve Figure 5D – this is a mess.We have worked to correct tense and citation style. We re-organized the whole figure to make it cleaner and clearer. We moved part D to the supplementary so part C (the map) will have more space. Hopefully it helped to make things less messy. We have worked on the introduction and have run it past five individuals who are not glucosinolate specialists and they have helped greatly.[Editors’ note: what follows is the authors’ response to the second round of review.]The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential Revisions:While we find the overall story to be insightful and advancing the field, we do think that some of the analyses still need to be redone, while the eco-evolutionary framework needs to be better defined.1. We have serious concerns regarding the environmental association analyses. Since there is a general correlation between genetic/genomic/phenotypic diversity and environmental factors due to Arabidopsis demographic history, the overall genetic structure needs to be included in the statistical models testing the associations between traits and environmental factors. You should produce tables similar to that of Figure 4—source data 1 for the chemotypes of Figure 4 and derive statistical significances from these tables, in order to provide convincing evidence of the differential climatic patterns in both European regions.We apologize as we had run all the models with and without genomic group in them and did not include this table. We have rerun the models to include the genomic group to account for population structure. As suggested, for Figure 4 (now Table 1) we created a table in the same manner as the one in supp figure 8 that now accounts for structure. All the p-values in the analyses are derived from models that include the genomic group. We rewrote this section of the results, discussion and the methods accordingly.2. The conceptual framework of repeated evolution that differentiates between parallel and convergent evolution was traditionally developed for interspecific evolution, not for intraspecific evolution where it refers to parallel evolution by multiple independent mutations in the same gene. The differentiation between parallel and convergent evolution for intraspecific evolution is not so straightforward. Therefore we want you to provide a more precise definition of intraspecific convergent and parallel chemotype evolution and outline the role of different or the same genes from a tandem repeat cluster, as well as the role of epistasis between duplicated genes, therein. To this end, the LD analysis between MAM genes and flowering genes should also be removed from the argumentation, because this analysis clearly shows that LD between these genes is not larger than average LD between random pairs of genes. Therefore, the conclusion drawn from this LD analysis is not supported.We rewrote the sections concerning parallel/convergent evolution throughout the manuscript (more details are below) and added an introductory figure that will hopefully facilitate a better conveyance of how we are attempting to incorporate these terms in this work. The figure and the section concerning the trans-LD analysis were removed from the manuscript according to the reviewers’ suggestion.We are optimistic that regardless of the outcome of the corrected environmental analysis (point 1), this study represents a valuable contribution, especially if the revised framework for intraspecific repeated evolution – helping to conceptually unite studies of population and functional genetics in an apt system – can be articulated and illustrated more clearly in the revised analysis (point 2).Reviewer #1 (Recommendations for the authors):In this new version of the Manuscript by Katz et al., authors address numerous important questions raised in the previous version, and rewrite many of the paragraphs that were confusing or incorrect. In this version authors provide better support for some of their conclusions in a more comprehensive manuscript. However, this Reviewer still has serious concerns regarding the environmental association analyses carried out in this study to support their conclusions. In addition, for clarity and comprehension I also suggest addressing an important conceptual issue, as well as the numerous editorial points described below.A. thaliana there is a general correlation between genetic/genomic/phenotypic diversity and environmental factors due to Arabidopsis demographic history. For this reason, the overall genetic structure needs to be included in the statistical models testing the associations between traits and environmental factors. Authors now provide more detailed description of the statistical tests applied for such associations. However, as stated in Materials and methods, the statistical models used to build Figure 4 and Supplemental Figure 8B do not include the genomic groups. Therefore, significant associations described in these Figures and throughout the manuscript are mainly reflecting the association of genomic groups (demographic history) with climate. These two figures are the main support for the differential environmental associations in Northern and Southern Europe, but they just show that different genomic groups are present in each geographic area. It seems that such differential association between climate and chemotypes might be true, as shown by the table of Supplementary Figure 8A, where authors include the genomic groups to test the differential association specifically for C3 and C4 status. However, in this table the variable "Precipitation for the driest moth" appears as non-significantly associated. In spite of this, authors choose this variable in Supplementary Figure 8B  to illustrate that this climatic variable is associated when applying a simple t-test to compare both groups. This test does not support such environmental association because it is not controlling for the genomic group (demographic history). By contrast, the table of Supplementary Figure 8 shows a stronger differential association in Northern and Southern Europe for minimum temperature, which is associated only in the south but not in the north. Tables similar to that of Supplementary Figure 8A should be provided for the chemotypes of Figure 4, and statistical significances should be derived from them, in order to provide convincing evidence of their differential climatic patterns in both European regions.1A. As explained in previous Reviewer 2 report to Authors, in We rerun the models, and now all the models include the genomic group. As suggested, for Figure 4 (now Table 1) we created a table in the same manner as the one in supp figure 8. All the p-values in the analyses are derived from models that include the genomic group. We rewrote the corresponding sections accordingly.1B. Related with the environmental associations, authors include random forest analyses. However, in the way these analyses are presented, they are hard to interpret because variables with the lowest mean decrease Gini (the probability of the variable of being wrong according to Authors) mostly have the lowest mean decrease accuracy (suitability of the variable as predictor).We had intended the random forest to convey the differential ranks of the different genetic and environmental parameters across geography. We agree that this is not well shown in the current representation and we removed this analysis from the manuscript.2. A major goal of this manuscript is the distinction between convergent and parallel evolutions in GSL chemotypes, which are described as two different patterns of repeated evolution. However, these concepts have been classically developed to characterize interspecific evolution, and their translation into the intraspecific variation is not straight forward, thus requiring a precise definition of both alternatives. Authors define parallel and convergent evolution as traits derived from either the same or different "genetic backgrounds". In this definition, the concept of genetic background is very vague and it is unclear what is meant. Authors apply these two concepts to the particular case of repeated evolution of GSL chemotypes evolved specifically from two tandem duplicated genes (MAM and AOP loci). Given the epistasis between these pairs of duplicated genes, the same trait can be derived from the same or different gene within. Then, it seems to this Reviewer that Authors mean by parallel and convergent evolution, the evolution occurring from the same or different gene of the cluster, respectively. In my opinion, this dissection of repeated evolution for the specific case of duplicated genes with epistasis is a major contribution of this study, but it is hard to understand. The manuscript and the audience would benefit by making these concepts clearer. I suggest that Authors add this in discussion, (e.g. paragraph of lines 550-554), explaining the role of different or the same gene from tandem repeat cluster, as well as the role of epistasis between duplicated genes for the distinction of their concepts of parallel and convergent evolution.We rewrote this section to emphasize the above points. For the discussion we split this into two paragraphs, one on epistasis and one on convergent/parallel to better convey these concepts. We have also reworked the initial paragraph within the introduction to try and translate the interspecific concept to the intraspecific analogy we are contemplating. Specifically that genetic background is a specific haplotype and that if parallel evolution would be a new chemotype arising by independent mutations in a single haplotype to give the new chemotype multiple times. Convergent evolution would be where a new chemotype arose by independent mutations in different haplotypes both giving rise to a single new chemotype. To make sure that our idea is clear we added an introductory figure  that presents how we are trying to incorporate these terms in this work.