Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA). We model this multivariate system of genetic relationships by applying Genomic Structural Equation Modeling (Genomic SEM) and parsimoniously define five genomic brain factors underlying both CT and SA along with a general factor capturing genetic overlap across all brain regions. We validate these factors by demonstrating the generalizability of the model to a semi-independent sample and show that the factors align with biologically and functionally relevant parcellations of the cortex. We apply Stratified Genomic SEM to identify specific categories of genes  that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. Finally, we examine genetic associations with psychiatric and cognitive correlates, finding that broad aspects of cognitive function are associated with a general factor for SA and that psychiatric associations are null. These analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex. The current study identifies five genomic subclusters of brain regions for cortical thickness and surface area characterized by high levels of shared genetic signal. These subclusters map onto biological and functional parcellations of the cortex. In the last decade, twin studies have shown that both metrics are highly heritable, while characterized by distinct genetic underpinnings8. Even more recently, genotyped samples with neuroimaging data have become large enough to employ genome-wide association studies (GWAS) as a means of identifying the specific genetic variants associated with these structural phenotypes. For example, the ENIGMA consortium examined bilateral averages of 34 cortical brain regions to identify 175 and 48 genetic loci associated with regional SA and CT, respectively9. Two additional findings include the observation, consistent with the family-based literature, that the different SA and CT brain regions were highly positively correlated within measures of CT and SA and negatively correlated across CT and SA9. These results point towards distinct, multivariate genetic architectures.The human cerebral cortex broadly refers to the brain’s outer sheet of gray matter and is typically indexed using two central metrics: cortical thickness (CT) and surface area (SA). In practice, CT is operationalized as the distance between pial surfaces and white matter, and SA as geodesics along the gray-white matter boundary. These two measures are both key predictors of important life outcomes; for example, CT has been associated with a range of psychiatric disorders10. We began by performing exploratory and confirmatory factor analyses of the genetic correlations estimated from the ENIGMA CT and SA summary statistics. We then replicate this factor structure in a semi-independent sample from UK Biobank (UKB), showing that the multivariate structure identified in ENIGMA fits the data well for both the left and right hemispheres in UKB. Having established the portability of this factor structure, we characterize the genomic factors at three levels of analysis. First, we examined the boundaries that define these genomic factors by examining relationships with molecular, cellular, and functional topographical maps. Second, we applied Stratified Genomic SEM11 to identify classes of genes that are enriched at the level of the structural imaging factors. Finally, we examined associations between the brain factors and both general and domain-specific facets of cognitive function and psychiatric disorders. Collectively, our multivariate analyses of structural imaging phenotypes provide key insights into the biological, functional, and clinical relevance of varying levels of structural brain organization.The current study utilizes large-scale, imaging genetics datasets to formally model the genetic overlap across brain regions within CT and SA using the Genomic Structural Equation Modeling (Genomic SEM) frameworkN9 defined using the Desikan–Killiany atlas segmentations12. We note that GWAS summary statistics were not corrected for a global structural metric . Instead, global (co)variation was accounted for psychometrically by modeling a latent, general factor in the context of the bifactor model described below. This analytic pipeline has the advantage of avoiding bias due to adjusting for a heritable trait 13. As expected, LD-score regression revealed high levels of genetic overlap across brain regions within each metric , previously described unique genetic underpinnings9, and extant hypotheses about divergent developmental pathways across CT and SA 14.Our primary analyses utilize the ENIGMA GWAS summary statistics for mentclass2pt{minim15, acceleration factor, and optimal coordinates16) to determine the optimal number of genomic factors that could be used to parsimoniously describe the data. We went on to fit exploratory factor analyses (EFAs) using the promax  rotation based on these three tests. These EFA results were used to inform fitting confirmatory factor analytic models (CFAs) within Genomic SEM. More specifically, individual brain regions were assigned to a factor when their standardized loading was >0.5, or if the brain region did not achieve a loading of 0.5 for any factor, assigning the region to the factor with the largest standardized loading ; Akaike Information Criteria [AIC]18). For all models, including expanded models that incorporate psychiatric and cognitive correlates, residual covariances were iteratively added to the model where indicated. This was done by obtaining the residual covariance matrix—calculated as the difference between the model-implied genetic covariance and observed genetic covariance matrix—and adding the residual covariances one at a time until they no longer reached a significance threshold of p < 0.01. This procedure resulted in adding eight and seven residual covariances for CT and SA, respectively.We applied three tests  and did not fit the data well for SA . A correlated factors model with five genomic factors defined by subsets of brain regions fit the data great and provided a better fit for both CT  and SA . Finally, a bifactor model fit the data best for CT  and SA . The bifactor model consisted of a general factor defined by all 34 brain regions along with five residual, uncorrelated factors capturing brain regions that covary above and beyond the global  structure (Supplementary Data CT), mesolimbic (F2CT), prefrontal (F3CT), occipital (F4CT), and temporal (F5CT) brain regions. For SA, the first two factors were more diffuse, representing a combination of frontal dorsotemporal (F1SA) and parietal ventrotemporal (F2SA), followed by a third factor defined by occipitoparietal regions (F3SA), and finally two highly specific factors, one reflecting the anterior cingulate regions (F4SA), and a fifth factor defined solely by the medial orbitofrontal region (F5SA). We note that these characterizations do not consistently describe every brain region that loads on the factor and refer the reader to Fig. As would be expected, physically proximal brain regions clustered within the five residual factors. For CT, these factors can be approximately described as representing somatomotor frontoparietal . The UKB sample also offered the opportunity to examine the portability of the identified factor structure for a more age-homogenous cohort (age range: 39–73). We found that the bifactor models fit the data well for both the left and right hemispheres in UKB , suggesting that the genomic factors reflect meaningful boundaries of cortical (co)variation that are partially consistent across these two morphological indices. Moreover, these genomic parcellations appear to capture biological differences of the cortical sheet, as comparisons to a digitized parcellation of von Economo and Koskinas’s20 cytoarchitectonic mapping of the cortex . First, we asked whether there was a statistically nonrandom overlap in the assignment of regions of interest to the five residual CT and SA genomic factors. We find that the spatial organization of both metrics did significantly overlap with one another , CT- and SA-specific effects were also observed, such as In1 inhibitory neuron signatures that were more associated with CT and somatostatin interneuron transcriptional signatures more associated with SA. This indicates that the factor structures identified within CT and SA partially index unique biological signatures.Consistent with the notion that these genomic factors differentiate broad areas of the cortex by their biological underpinnings, further topographical annotation identified myriad aspects of intracortical microstructure, laminar differentiation, cellular/neuronal density, neurotransmitter receptor density, and cell-type-specific transcriptional signatures that significantly varied as a function of the CT and SA factors Fig. . General21  genes (see “Methods” for further details). A functional annotation is deemed enriched if the proportion of genetic variance explained by the annotation is greater than the proportional size of the annotation. Multivariate functional analyses then allow for distilling both highly polygenic and pleiotropic signals into their biologically meaningful, constituent parts. To this end, we specifically examined the enrichment of the genomic factor variances.Stratified Genomic SEMp < 5.48E-5 , we identified two significant annotations for the general CT factor: H3K4me1 union and the cingulate gyrus H3K27ac histone mark. We highlight that the oligodendrocytes annotation was also just below the significant threshold for CT , we examined a total of 152 annotations. Employing a Bonferroni correction of g-factor estimated from UKB GWAS summary statistics for seven cognitive traits: trail-making tests-B, tower rearranging, verbal numerical reasoning (VNR), symbol digit substitution, memory pairs-matching test, matrix pattern recognition, and reaction time . As each family of tests for CT and SA with g consisted of 204 possible associations . There were no significant factor correlations for CT in the bifactor model after correcting for multiple comparisons, though we identified a nominally significant residual genetic correlation across VNR and the precentral region . Bifactor model results revealed a significant genetic correlation between the general SA and g-factor . None of the correlations between g and the five residual factors or individual brain regions were significant. These results indicate that the genetic overlap between SA and cognitive function can be conceptualized as operating through largely general pathways shared across brain regions and different classes of cognitive tasks. Supplementary Data We next turned our attention to both global and localized patterns of genetic overlap between the brain factors and mentclasspt{minima11. This model consists of a Compulsive disorders factor defined by anorexia nervosa24, obsessive-compulsive disorder (OCD)25, and Tourette’s syndrome26, a Psychotic disorders factor defined by schizophrenia27 and bipolar disorder28, a Neurodevelopmental disorders factor defined by autism spectrum disorder (ASD)29, attention-deficit hyperactivity disorder (ADHD)30, and post-traumatic stress disorder (PTSD)31, and an Internalizing disorders factor defined by major depressive disorder (MDD), anxiety disorders32, and PTSD31 , indicating that there is significant variation across the cortex in the degree of genetic overlap with the psychiatric factors.To examine the multivariate system of genetic relationship with psychiatric traits, we employed the same four-factor correlated factors model of 11 major disorders identified in ref. p < 1.34E-4 , though this was not significant at a Bonferroni-corrected threshold. Four nominally significant residual relationships were also identified between ASD and specific brain regions, the strongest of which was with the rostral anterior cingulate region . In summary, the associations with psychiatric traits were largely null at the level of both genomic factors and individual brain regions.We then estimated genetic correlations between all psychiatric and brain factors and added residual covariances between individual disorders and brain regions when indicated. We employed a Bonferroni-corrected significance threshold of 14. Indeed, previous studies have shown that CT and SA are characterized by differing developmental trajectories35, and the family-based literature corroborates the finding that genetic variation in these two metrics is distinct 8.The current study examined the multivariate genomic architecture of CT and SA at various levels of analysis using two of the largest available imaging genomic datasets. At the genome-wide level, we find that ENIGMA summary statistics for 34 physically proximal brain regions can be grouped across a general factor and five residual factors. In addition, we observe that this factor structure fits the data well for the left and right hemispheres in UK Biobank, indicating a bilaterally symmetrical structure. We also find that these five genomic factors explain significant genetic variation in the individual brain regions even when pulling out shared global variation via the general factor. It is of note that the multivariate architectures with respect to the make-up of the genomic factors were distinct across CT and SA. This is in line with prevailing developmental accounts of these structures, including the radial unit hypothesis that argues for distinct developmental trajectories categorized by neurogenetic division of neural progenitor cells for CT, as compared to the propagation of these cells for SA20 and the functional networks derived by Yeo and Krienen21. For both canonical parcellations, we found significant patterns of overlap between classifications of cortical regions, suggesting the organization of the CT and SA factors reflected a biologically and functionally relevant partitioning of the cortex. Further annotation revealed relationships across the identified genomic factor structures and myriad quantitative features of the cortex, such as neurotransmitter receptor densities, cell-type transcriptional signatures, and patterns of functional activation measured by fMRI.To understand these genomic parcellations of the cortex, we used recently developed surface-based methods to place our findings in the broader context of the neuroimaging literature. This topographical annotation allowed us to evaluate the spatial organization of our CT and SA factors relative to two canonical parcellations of the cortex: the cytoarchitectonic classes defined by von Economo and KoskinasThese results provide an anatomical context for our factor analytic findings and lend insight into what underlies the differences between CT and SA factors. For example, while the SA factors were more broadly related to biologically derived features of the cortex , the CT factors were more strongly related to functional activation underlying a variety of psychological processes. Interestingly, the spatial organization of both CT and SA factors captured functional boundaries of the cortex underlying emotion regulation, reward learning, decision-making, and visual processing. At the functional genomic level, we find that astrocytic transporters and  the PI genes and Super Enhancer annotations are broadly relevant for SA, with significant enrichment identified for the general factor. Notably, we do not observe enrichment for these annotations for CT, indicating that these classes of genes are specifically relevant to SA.6, we find that the genetic underpinnings for a general factor of SA are associated with a diverse set of cognitive functions, as indexed by a genetic g-factor. Conversely, we observe no significant genetic relationships across CT or SA and the four psychiatric factors. These null results are in line with recent findings indicating that the majority of associations across structural metrics and human complex traits are much smaller than initially thought and that the bulk of prior studies has been underpowered36. At the same time, large-scale phenotypic meta-analyses indicate widespread associations across structural metrics and various psychiatric disorders37. These findings may reflect associations that operate through largely environmental pathways or the current analyses may not be sufficiently powered to detect genetic effects for psychiatric disorders. It is also possible that psychiatric-structural associations are specific to different parcellations of the cortex or to clinically ascertained samples. In line with this latter account, associations between CT and various psychiatric disorders have been shown to reflect responses to treatment 39.The current analyses reflect a well-powered, psychometrically informed approach to examining genetic overlap across CT, SA, and a broad range of psychiatric and cognitive outcomes. Consistent with prior work9. As with the current analyses, those analyses indicated that physically adjacent regions tend to cluster together. We build on these findings by characterizing our genomic factors using topographical annotation, Stratified Genomic SEM, and patterns of relationships with external correlates within the broader SEM framework. GWAS has also been conducted on the first two principal components (PCs) for CT and SA and, in line with our cognitive results, the first principal component for SA was found to be significantly associated with cognitive function40. Our findings are conceptually overlapping but statistically distinct from the latter application of PCs in that these are derived based on patterns of phenotypic associations, whereas Genomic SEM explicitly models genetic overlap.Prior work using dimension reduction techniques applied hierarchical clustering of the genetic correlations to identify five spherical components for the CT and SA ENIGMA summary statisticsτ matrices that do control for annotation overlap (see “Methods”), but as GWAS sample sizes continue to grow, future work can examine the robustness of these results.The current analyses have a number of limitations. First, we highlight that our analyses were restricted to participants of European ancestry only due to the availability of sufficiently well-powered GWAS data for this ancestral group coupled with the requirement of LD-score regression to produce estimates within a single ancestral population due to differences in LD structure across groups. It will be of the utmost importance, both with respect to scientific value and representation, that future analyses build on the expanding, genetically informed datasets for different populations. Second, while we were able to evaluate the fit of the factor structure in both ENIGMA and UKB, these were not entirely independent samples. In addition, ENIGMA reflects an age-heterogeneous sample, and findings should be interpreted in this light. These models should continue to be evaluated in external, genetically informed imaging datasets for specific developmental windows. For Stratified Genomic SEM analyses, we utilized the zero-order stratified genetic covariance matrices that do not control for overlap with other annotations for the estimation of enrichment. This decision point reflects the power needed to utilize the 41. As our analyses utilized the Desikan–Killiany (DK) atlas parcellations12, future work could examine both more fine-grained parcellations  and extend analyses to examine phenotypes derived from other scanning modalities, such as DWI-derived outcomes. We note that F1CT mapped onto both physically proximal regions and regions that have previously shown the highest reliability for the DK atlas across manual and automated regional definitions12. Future work applying different parcellations may help additionally clarify whether the structure for this particular factor is merely a reflection of a more reliable signal.Polygenic risk for schizophrenia was recently found to have more robust associations with microstructural metrics derived from diffusion-weighted imaging (DWI) relative to macrostructural imaging metricsSubstantially overlapping genetic signal within CT and SA brain regions necessitates statistical tools that allow for examining the multivariate system of relationships across these phenotypes. To this end, we employed Genomic SEM to examine the factor structure and its correlates at the genome-wide level and went on to better characterize these factors by performing topographical annotation and estimating multivariate functional enrichment. In line with prior studies that utilize the global metrics of CT and SA, we find that a general factor explains significant genetic variation and captures patterns of enrichment shared across the 34 brain regions. At the same time, we observe that the five residual genomic factors reflect biologically and functionally relevant partitionings of the cortex. As all data used here is publicly available, we present an accessible framework for studying the multivariate genomic architecture of the cerebral cortex. We propose that future studies use this approach to examine other research questions relevant to this highly studied set of imaging outcomes. Collectively, these findings point towards the utility and need to simultaneously model the different levels of genetic risk sharing, from the most general level across all the cortex to the specific subclusters indexed by the genomic factors, and down to the variation unique to a single brain region.10 implementation of LDSC43. These filters included restricting to SNPs present in HapMap3 and, when this information was available, restricting to minor allele frequency (MAF) >1% and excluding SNPs with information scores (INFO) <0.9. We highlight that UKB and ENIGMA structural summary statistics, and the UKB cognitive summary statistics, included MAF but not INFO. However, ENIGMA filtered summary statistics on imputation quality at the level of the contributing cohorts prior to performing the meta-analysis9. Imputation quality was available for a subset of psychiatric disorders. The LD-scores used for LDSC were calculated using the European subsample of the 1000 Genomes phase 3 project; the scores excluded the MHC region due to the high degree of LD outliers which is known to unduly influence estimates. We note also that when calculating the liability scale heritability for the psychiatric traits we used the sum of effective sample sizes, and a sample prevalence of 0.5 to reflect the fact that the corrected sample size already accounts for sample ascertainment; we have shown that this produces a more accurate estimate of heritability for binary traits as it more appropriately accounts for ascertainment differences across cohorts contributing to GWAS meta-analysis44. For comparative purposes, population prevalences for the liability conversion were chosen to reflect those used from the corresponding univariate GWAS publication and are reported in Supplementary Data Quality control filters for estimating the genetic covariance and sampling covariance matrices followed the defaults in the Genomic SEM9 publication for details about how the univariate GWAS were performed. We note briefly here that ENIGMA9 used the Desikan–Killiany12 atlas segmentations to define the 34 brain regions and that SA and CT were measured using T1-weighted magnetic resonance imaging scans. We used the publicly available summary statistics that apply genomic control. We note also that all analyses presented here utilize the GWAS summary statistics that were not corrected for global volume as this allowed for explicitly modeling the shared genetic variation across the 34 regions in the context of a bifactor model. Factor analysis of ENIGMA GWAS summary statistics proceeded in five primary steps. First, standard quality control filters  were applied to the GWAS summary statistics using the munge function in Genomic SEM. Second, LD-score regression45 was applied to the ENIGMA GWAS summary statistics to produce a genetic heatmap across the 34 CT and SA brain regions. As certain SNPs were not present across all cohorts that comprise the ENIGMA consortium, the SNP-specific participant sample sizes were used for LDSC estimation. Third, the Kaiser15, acceleration factor, and optimal coordinates16 rules were applied to these genetic correlation matrices in order to collectively determine the number of genomic factors that could be used to parsimoniously represent the data. For both SA and CT, these results pointed towards five factors according to the Kaiser and optimal coordinates tests, and a single, common factor according to the acceleration factor test. Fourth, exploratory factor analyses (EFAs) were conducted using the promax  rotation in the factanal R package.We refer the reader to the original ENIGMA10. More specifically, comparative fit index (CFI) values above 0.917 and standardized root-mean-squared residual (SRMR) values less than 0.10 were considered indicative of acceptable model fit. We also report the Akaike Information Criteria (AIC)18 a fit index that balances overall model fit with the number of estimated parameters , with lower values indicating better fit. We fit three primary confirmatory models in Genomic SEM. The first was a common factor model that was used to determine whether, consistent with the acceleration factor test, a single factor was sufficient for describing the data. The second was a five-factor, correlated factors model specified based on the five-factor EFA results. More specifically, individual brain regions were assigned to a factor when their standardized loading was >0.5, or if the brain region did not achieve a loading of 0.5 for any factor assigning the region to the factor with the largest standardized loading. This was with the one exception that the medial orbitofrontal region was the one indicator that showed evidence of cross-loadings for both CT and SA, with standardized loadings >0.5 for both the second and third factor in the CT model and the first and fifth factor in the SA model. However, including this cross-loading in the confirmatory model produced a worse model fit for CT ; it also included the only negative factor loadings for CT and caused model convergence issues for SA. This cross-loading was consequently removed for both structural metrics with the medial orbitofrontal factor specified to load on the factor with the highest EFA loading.Finally, we fit confirmatory factor models in Genomic SEM and evaluated these models using standard metrics of model fitThe third type of model we fit, and the model presented in the main text, was a bifactor model. This consisted of a general factor that captures shared variation across the 34 brain regions and five, residual factors (defined by the same brain regions from the correlated factors model) that model covariation not accounted for by the general factor. As the general factor defined by all indicators within a bifactor model is conceptually posited to account for the covariation across the remaining factors, the five residual factors were all specified to be orthogonal . For all models, we used unit variance identification such that the factor variances were fixed to 1. For the SA models, the fourth factor was defined by two brain regions  and the fifth factor was defined only by the medial orbitofrontal brain region. To ensure that the SA models were locally identified, the factor loadings were then constrained to equality for the fourth factor, and the residual variance of the medial orbitofrontal region that solely defined the fifth factor was fixed to 0.p < 0.01. This procedure resulted in adding eight residual covariances for CT and seven residual covariances for SA. We confirmed that these residual covariances improved model fit for both the correlated factors and the bifactor model for CT and SA  is a large population-based cohort study that recruited approximately 500,000 volunteers between 2006 and 2010 across the UK. A subset of participants underwent brain MRI scans since 2014. UKB received ethical approval from the North West Centre Research Ethics Committee (REC number 11/NW/0382). The current analyses were conducted under the approved UKB application 32568.The UK Biobank  (https://www.fmrib.ox.ac.uk/ukbiobank/fbp). Details of the MRI protocol and processing are publicly available47. Here, we focused on CT and SA measures of the 34 brain regions defined by the Desikan–Killiany atlas12. The temporal pole was excluded from analysis, as part of the temporal lobe is difficult to segment, leading to a large amount of missing data for the region.Raw brain imaging data were processed through an automated image processing pipelinehttps://www.cog-genomics.org/plink/2.0/assoc) was first used to perform LD pruning on the QC’d genetic data using an r2 threshold of 0.2 within a 100 kb window that shifted by 50 kb each time. Principal components (PCs) of ancestry calculated in 1000 Genomes Phase 3 data were then projected onto the LD-pruned UKB genetic data. The top six PCs were subsequently used as input to a Random Forest classifier with 1000 Genomes as the training set to calculate predicted probabilities of belonging to a particular ancestral population. Of the initial pool of 40,733 UKB participants with genetically informed MRI data, we retained 31,522 individuals with predicted probabilities of belonging to the European population >90%.The genetic data for all UKB participants were subjected to a standard set of QC filters consisting of removing strand ambiguous SNPs, regions with long-range LD, SNPs with call rates <0.98, and SNPs with a minor allele frequency <0.05. Analyses were also restricted to autosomal SNPs. In order to calculate predicted ancestry, PLINK  mismatch between self-reported and genetically inferred sex; (2) missingness or heterozygosity outliers; (3) sex chromosome aneuploidy; or (4) related participants in the sample. Related participants were specifically removed by restricting to participants classified in UKB Data-Field 22020 as having been used for genetic principal component estimation, which was performed on an unrelated sample that removed inferred, third-degree relatives. We note that kinship  was carefully estimated within the full UKB sample using a restricted set of 93,511 SNPs that weakly loaded on principal components of ancestry. This selected set of SNPs is consequently less likely to upwardly bias kinship estimates due to recent admixtureGWAS was performed using the non-LD pruned, QC’d genetic and imaging data as input to PLINK. We specifically used the linear regression model, adjusting for age, sex, X/Y/Z/T position of the head and the radio-frequency receive coil in the scanner, UKB imaging acquisition center, mean resting-state and task-based functional MRI head motion, volumetric scaling factor, T1 density, genotyping chip, and the top 40 principal components of the genetic data (estimated within the UKB sample) as covariates.N = 10,083 participants9. While it would be possible to restrict to a strictly independent holdout sample based on the date of the imaging visit in UKB, the ENIGMA paper also included 5095 unrelated European individuals with imaging data from UKB as a holdout sample for polygenic risk score analyses. Consequently, a strict UKB holdout sample based on imaging date would reflect only ~11,500 participants and consequently be underpowered relative to the full UKB imaging sample. As we note in the “Results” section, we view the semi-independent UKB sample as informative both as a replication sample and, more specifically, as a sample that is far more age homogenous relative to ENIGMA. We also quantify the level of shared information across our UKB summary statistics and the ENIGMA summary statistics using the bivariate LDSC intercept. The bivariate  LDSC intercept is estimated directly from the GWAS summary data, and for two traits (1 and 2) is expressed as:N1 and N2 reflect the total sample size for traits 1 and 2, respectively. The bivariate LDSC intercept then reflects the phenotypic correlation weighed by proportional sample overlap, thereby providing a quantitative index of the sampling dependence across the ENIGMA summary statistics and our UKB summary statistics. We specifically estimated the bivariate intercept for global CT and SA metrics for ENIGMA with the global metrics for the left and right hemispheres in UKB. For CT, the bivariate intercept was 0.178 across ENIGMA and both global metrics of the left and right hemispheres in UKB. For SA, the bivariate intercept was 0.262 across ENIGMA and the left and right global metrics in UKB. As expected, this indicates that our updated UKB summary statistics reflect a largely independent replication cohort relative to the primary ENIGMA analyses.The ENIGMA imaging sample utilized in the primary analyses also includes an earlier release of the UKB imaging data for In order to better understand the spatial organization of our genomic brain factors, we sought to characterize their relationships with established canonical and meta-analytic maps from the neuroimaging literature. The specific test employed was dependent on the nature of the comparison , as described in the following sections. However, regardless of the specific test used in a given comparison, a spin test approach was used to assess the statistical significance of the observed spatial correspondence. We refer the reader to previous publications for a detailed description of this method, but it critically allows for two neuroimaging maps to be compared while accounting for spatial contiguity and hemispheric symmetry of the cortex. Here, we used the spin test approach to generate an empirical null distribution of 10,000 spatially permuted test statistics.49, the functional intrinsic connectivity networks derived from resting-state functional magnetic resonance imaging [fMRI] by Yeo and Krienen50), we first assigned labels from each parcellation to corresponding Desikan–Killiany regions on the basis of maximal overlap. We then performed a Fisher’s exact test to evaluate the degree of dependence between the two categorical maps of interest. The observed p-value from this test was subsequently compared against an empirical null distribution generated via the spin test approach described above, yielding a final Pspin value for interpretation.To examine the spatial correspondence between our genomic factors of brain morphology and other categorical neuroimaging maps 55, while the latter is a collection of cortical association maps obtained from Neurosynth56, an online platform for automated meta-analysis of more than 15,000 published fMRI studies. Here, we used a subset of 123 probabilistic association maps that correspond to cognitive and psychological processes described in the Cognitive Atlas57 as described in a previous study using similar methods54. For these continuous maps, we fit an analysis of variance model and computed an omnibus F statistic for each comparison. As done for the categorical comparisons, Pspin values were subsequently calculated and adjusted for false discovery rate.We also compared the spatial relationships between our genomic factors and numerous features that varied continuously across the cortex, which can be generally described as being derived from either biological/physiological or cognitive/psychological data sources. The former is a collection of cortical maps derived from the BigBrain project 11 reflects a multistep process that ends in the ability to estimate enrichment within functional annotations for any parameter of interest  within the genomic model. Functional annotations reflect a subset of genetic variants that are categorized using collateral gene expression data, such as single-cell RNA sequencing, with respect to some shared characteristic, including upward or downward expression within specific tissue types, histone marks, or brain regions. A functional annotation is enriched when the proportion of genetic variation explained by the variants within that annotation is greater than the proportional size of the annotation. The first step in Stratified Genomic SEM is to estimate genetic covariance matrices stratified across a chosen set of annotations. This is achieved by estimating the multivariable version of Stratified LDSC22 More specifically, stratified genetic covariance within a functional annotation that controls for overlap with other annotations is estimated as:Ni is the sample size for study i, c is a specific genomic annotation, Mc is the number of SNPs in the annotation, τc is the coheritability within annotation c controlling for overlap with other annotations, Ns is the sample overlap across the two GWAS studies, a is a constant term across annotations that captures unmeasured confounding , and ρ is the phenotypic correlation within overlapping participants. Stratified heritability estimates are produced using the same general formula reduced to the univariate S-LDSC model15, where the expectation across two zj statistics for the same trait is given as Stratified Genomic SEMτc values are converted to their zero-order form that does not control for overlap with other annotations. The zero-order stratified values are useful as the estimates are not directly contingent on the other annotations included in the model and produce more stable estimates at moderate GWAS sample sizes, such as those observed here. This comes with the caveat that enriched signal can, in part, reflect signal shared with other annotations and this limitation should be kept in mind when interpreting results. The τc estimates are converted to zero-order values t by taking the sum of weighted τc values given as:c, and c and t. Putting these pieces together, c weighted by the proportion of annotation c SNPs that are shared with target annotation t. The summation of these estimates then produces the zero-order estimates used to populate the zero-order, stratified genetic covariance matrix across the pairwise combinations of included phenotypes.For the current application, the s_ldsc function within the GenomicSEM R package.Each stratified, zero-order genetic covariance matrix is also paired with a stratified, zero-order sampling covariance matrix. The diagonal of the sampling covariance matrix contains squared standard errors of stratified heritability and covariance estimates. The off-diagonals reflect the sampling covariances that capture dependencies among estimation errors that can arise in cases such as participant sample overlap. Both the diagonals and off-diagonals are estimated using a multivariate block jackknife and taken together allow for producing unbiased standard errors in the context of Stratified Genomic SEM. The stratified, zero-order genetic covariance and sampling covariance matrices were specifically estimated using the When the parameter being examined for enrichment reflects pleiotropic effects captured by the factor variances, these stratified matrices are used as input to Genomic SEM wherein the factor loadings are fixed from the model estimated using the genome-wide annotation that includes all SNPs, and the factor and residual indicator variances are freely estimated. The factor variances estimated within an annotation then reflect the proportion of pleiotropic genetic variation captured by a given annotation. This estimate and its standard error are subsequently divided by the proportional size of the annotation to produce estimates of multivariate enrichment.58. These annotations include minor allele frequency bins, histone marks, classes of enhancers and evolutionarily conserved variants, and flanking window annotations. We also include brain and endocrine relevant annotations for tissue-specific gene expression from GTEx59 and DEPICT60 and histone marks from the Roadmap Epigenetics project61. This was in addition to five randomly selected control regions for gene expression and histone marks . Finally, we utilized 29 functional annotations that reflected the main effects of protein-truncating-variant (PTV)-intolerant (PI) genes, human hippocampal and prefrontal brain cells, and their interactions. These annotations were created using collateral data from the Genome Aggregation Database (gnomAD)62 and GTEx59 data, with the parameters used to construct these annotations outlined in ref. 11.The current project utilized functional annotations from various sources. This included functional annotations from the most recent 1000 Genomes Phase 3 baseline set of annotations (BaselineLD Version 2.2) recommended by the original developers of univariate S-LDSCp < 5.48E-5 . CT required removing 22 annotations due to high degrees of matrix smoothing, but we employed the same Bonferroni correction of p < 5.48E-5 for comparative purposes.Flanking window, control, and continuous annotations were used to produce unbiased estimates of stratified genetic covariance but were excluded from enrichment analyses as these results would not be directly interpretable. This resulted in a total of 168 binary annotations. For SA, we removed 16 annotations that were nonpositive definite and required smoothing the stratified covariance matrix such that any point estimate in the matrix produced a Z-statistic discrepancy >1.96 pre- and post-smoothing. As we examined the enrichment of the genetic variance for six factors  we employed a Bonferroni correction of g was examined in the context of the finalized bifactor and correlated factors models  for both CT and SA. This was achieved by simultaneously estimating all brain factors with g-factor correlations in the context of the models. As a first step in these analyses, we estimated an omnibus model g shows a uniform or factor-specific pattern of relationships for CT or SA. In line with the model fitting procedure used for the brain-regions-only models, residual covariances between individual brain regions and cognitive tests were iteratively added until they no longer reached a significance threshold of p < 0.01 within the context of the correlated factors model. For CT, this resulted in adding two residual covariances between verbal numerical reasoning and both the insula and precentral region. No residual covariances were added for SA. As each family of tests for CT and SA with g consisted of 204 possible associations .Genetic overlap with genetic 11 was used to model the multivariate architecture across the same 11 disorders. We note a few differences in the summary statistics used for the 11 disorders paper relative to the current analyses. This includes utilizing only publicly available summary statistics such that 23andMe data was not included for ADHD or MDD, using the most recent Freeze 3 summary statistics for bipolar disorder28, and using the recently released anxiety summary statistics that reflect meta-analysis across the UK Biobank, ANGST, and iPSYCH consortium32. We began by confirming that the model for psychiatric disorders only still provided a good fit to the data even with the noted differences for some summary statistics , and that the factor loadings were largely concordant with those reported in prior work . We also estimated the same omnibus model As with the Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-14Reporting Summary


Zymoseptoria tritici, a major fungal pathogen of wheat reported in all production areas worldwide. We identify the global invasion routes and ongoing genetic exchange of the pathogen among wheat-growing regions. We find that the global expansion was accompanied by increased activity of transposable elements and weakened genomic defenses. Finally, we find significant standing variation for adaptation to new climates encountered during the global spread. Our work shows how large population genomic panels enable deep insights into the evolutionary trajectory of a major crop pathogen.Human activity impacts the evolutionary trajectories of many species worldwide. Global trade of agricultural goods contributes to the dispersal of pathogens reshaping their genetic makeup and providing opportunities for virulence gains. Understanding how pathogens surmount control strategies and cope with new climates is crucial to predicting the future impact of crop pathogens. Here, we address this by assembling a global thousand-genome panel of  Zymoseptoria tritici is an important fungal pathogen of wheat which has spread globally. Here, the authors perform genomic analyses on a collection of ~1100 Z. tritici samples from 42 countries to describe its global spread and elucidate mechanisms of adaptation to different environmental conditions. A major contributor to the range expansion of pathogens is the distribution of suitable host species. Pathogens and their hosts often share a common evolutionary history either through co-evolution or through shared constraints of their common environment3. Discrepancies in the evolutionary history of hosts and their pathogens can be caused by host jumps, significant differences in gene flow, or local adaptation. Agricultural pathogens have often emerged during the domestication of their host species4 causing significant threats to food production. Increased global trade of agricultural products has precipitated serious disease outbreaks over the past decades5. Crop pathogens are exposed to globally homogeneous host conditions created by planting genetically similar crop cultivars and application of similar pesticidal compounds to control diseases7. Furthermore, climate change reshapes the geographic distribution of pathogen species, with poleward range expansions being suspected since the 1960s8. Range expansions may lead to significant changes in the genetic make-up of pathogen species by founder effects and shifting barriers to gene flow9. Understanding how emerging pathogens surmount control strategies and cope with climate adaptation is crucial to predict the future impact of crop pathogens in a changing world.Human activity has broken down natural barriers to gene flow for many species through trade and travel. Reshaped species distributions helped spread invasive plants and pathogens5. In addition to episodic damage, most crop pathogens are endemic and continuously reduce yields. The ascomycete Zymoseptoria tritici is a major pathogen of bread and durum wheat, causing the disease Septoria tritici blotch, which is now reported in most wheat-growing regions and causes significant damage10. The center of origin of Z. tritici is located in the Middle East, where sister species are found to infect wild grasses11. The emergence of Z. tritici was concomitant with the domestication of wheat11. The timing and shared geographic origin of the pathogen and domesticated wheat strongly suggests coevolution between the two species. The pathogen harbors extensive standing variation from individual infected leaves to large agricultural regions13. As a consequence, the pathogen showed rapid responses across all major wheat-producing areas to overcome host resistance and gain tolerance to fungicides in less than a decade10. Population genomic analyses showed that rapid adaptation of the pathogen was facilitated by parallel evolution across geographic regions15. However, a comprehensive picture of pathogen dispersal and adaptation across the global distribution range is lacking.Outbreaks of fungal diseases on crops are reported regularly across continentsZ. tritici to retrace worldwide invasion routes out of its Middle Eastern origin and identify ongoing genetic exchange among major wheat-producing regions. We show that the global expansion was accompanied by increased activity of transposable elements and weakened genomic defenses. Finally, we identify standing genetic variation for adaptation to new climates encountered during the global spread.Here, we assembled over one thousand genomes of the fungal crop pathogen Z. tritici isolates from naturally infected fields  as well as Southern and Eastern Europe  and non-reference TE (i.e. absent). Detected TEs among isolates were binned into loci (width 100 bp) to account for uncertainties about the precise mapping of the insertion point. We found that the frequency spectrum of TE insertions is heavily skewed towards low frequencies with 77% of TE insertions being found in single isolates (~0.1% frequency) and 96% of insertions were found in ten or fewer isolates  polymorphism was positively correlated with the heterochromatic accessory regions, consistent with purifying selection acting against indels mostly in gene-dense regions.Transposable elements (TEs) are drivers of genome evolution. In ets Fig. . The pri29. Hence, we tested whether recently established populations were differentiated from center-of-origin populations. We found that TE insertion polymorphisms (TIPs) are most often specific to a genetic cluster with only 31% of insertions shared by two or more populations  and other LTR retrotransposons (RLX)  Juno element mostly present in North American and Australian populations, the Helitron (DHH) Fatima expanded primarily in the Australian population, and the DTX miniature inverted-repeat TE (MITE) Unicorn most widespread in North American and New Zealand populations  Fig. . The RLCgin Fig. , 10. Thegin Fig. 10. The Sns Fig. S10. The b32. RIP machinery is active in genomes of Z. tritici, with high levels of RIP-like mutations identified in genomes from the center of origin and wild-grass-infecting sister species33. We analyzed the global panel for evidence of RIP-like mutations by reporting the RIP composite index. The median index is above 1 in TE sequences across all genetic clusters. However, we found that RIP strength varies considerably among genetic clusters with the strongest signatures found in genomes from Middle Eastern and African isolates tes Fig. . The Midtes Fig. , S8a, b.Neurospora34, thus suggesting that reduced sexual reproduction could lower the efficacy of RIP. In Z. tritici, sexual reproduction can occur during the wheat growing season but most reproduction is thought to occur at the end of the season35. The rate of sexual reproduction is high in all populations as we found that the ratio of mating types was consistently close to 50:50 . This time span covers the introduction of several major fungicides to agricultural fields. Resistance to the ubiquitously used azole fungicides is often mediated by mutations in the CYP51 gene38. Recent North American populations gained the Y137F mutation but not the I381V or V136A mutations rising in frequency in Europe over the same period  mapping based on the climatic conditions of the sampling locations. We analyzed a total of 19 bioclimatic variables covering annual trends, seasonality, and extreme environmental factors, such as the maximum temperature of the warmest month or the precipitation of the driest quarter year  than the Middle East, thus leading to an indirect association between fungicide resistance genes and climatic variables. However, at the same location on chromosome 7 is a quantitative trait locus (QTL) for growth at suboptimal temperature42, so that locus could well underpin climatic adaptation independent of Cyp51. Further analyses of temperature sensitivity QTLs showed that three out of the four previously described loci are overlapping with loci associated with climatic variables  higher than 5%. The number of associated variants per climatic variable ranged between 36 and 541 (for BIO9 and BIO6 respectively), including 1–190 significant SNPs with a MAF > = 5% per climatic variable Fig. . We inveNPs Fig. . HoweverNPs Fig. . We idenTo identify whether adaptive mutations tend to arise locally or occur across large geographic areas, we clustered significant loci based on the presence or absence of adaptive variants per country. We identified eight clusters characterized by shared adaptive variants with a similar geographic distribution Fig. . Some cl43. Remarkably, the TE activity is underpinned by a marked relaxation or even loss of genomic defenses following population bottlenecks during global colonization. The higher activity of TEs is likely a direct consequence of reduced control and may have long-term consequences for the pathogen. The relaxation of genomic defenses in populations from the American, Oceanian, and European continents could have been selected as an evolvability trait, thus increasing variance in fitness in populations. The relaxation of genomic defenses likely underpins incipient genome expansion within the species while increasing the risk of mutational meltdown. The resilience of crops and agricultural ecosystems is threatened by the changing climate. The ability of pathogens to adapt and expand their range under altered humidity and temperature regimes as well as changes in seasonal patterns is a major concern. The identified genomic regions associated with adaptation to environmental conditions highlight how a global pathogen carries extensive variation to cope with climate change. Integrating population genetic information of pathogen adaptation to climatic gradients is a powerful asset for risk models of future pathogen spread. As climate change impacts crop production and most likely resistance to pathogens, future research explicitly addressing joint host and pathogen responses to warmer and drier climates is necessary to mitigate future crop losses.Analysis of a thousand-genome panel recapitulated the spread of a major fungal crop pathogen revealing tight links to the history of global wheat cultivation. The early divergence between Middle Eastern and African genotypes from those collected in the rest of the world is consistent with a single expansion event from the center of origin dating back millennia. The extant genetic variation was strongly shaped by successive colonization bottlenecks during the introduction of the pathogen to the Americas and Oceania. The distinct loss of genetic diversity and increased linkage disequilibrium likely caused the loss of adaptive genetic variation and reduced evolutionary potential in the most recently colonized regions. TE activity has underpinned the rise of major adaptive mutations in the speciesFungal isolates were grown on V8, yeast sucrose broth (YSB) or yeast malt agar (YMA) plates, or liquid culture prior to DNA extraction. Complete information about the geographic origin, date of collection, available sequencing datasets, and references are given in Supplementary Data 44 with the “careful” method  and extracted the subgraphs designating groups of clones . In each group of clones, we filtered out all isolates except for the isolate with the lowest amount of missing data. These per-sample filters resulted in a final isolate count of 1109.We further assessed the quality of our SNP calling using 8 isolates that were sequenced two times (including in some cases in different sequencing datasets). We assumed that the real variation between these pairs should be close to 0, although we cannot completely exclude the possibility of a small number of mutations happening during culturing or maintaining of these isolates in collection. We used the differences, i.e., erroneously called variants, between the resequencing pairs as an estimation of genotyping errors and to identify the causes of genotyping errors. Most of the erroneously called variants remaining after the hard filtering were related to genotypes with near-equal numbers of reads supporting the reference allele and an alternative allele, i.e., “heterozygous” alleles. Such genotypes were called with high confidence even though such a heterozygous-like pattern should be recognized as errors in a haploid organism and could be due to misalignment or repeated sequences in the genomes. We consequently implemented an allelic balance custom script to recognize such positions and to filter out any genotype that had fewer than 90% of reads supporting the called allele. This filter removed 75% of the erroneous variants left between the resequenced pairs after the hard filtering. As the rest of the erroneous variants were related to low sequencing depth, we further implemented a per-sample missing data and low-depth filtering, removing any sample with more than 20% of missing data and a mean depth of coverage lower than 6 on the core chromosomes (based on vcftools –missing-indv and–depth options)Z. tritici contains accessory chromosomes that are expected to be present in some isolates and absent in others, the relevant threshold of missing data had to be adapted per chromosome. To identify the presence-absence of accessory chromosomes, we assessed the depth of coverage in windows across all chromosomes with bedtools v.2.29.2 50. We normalized the depth estimates using the median depth over all windows of the core chromosomes for the per-window depth. The normalized depth was then used to infer presence-absence variants or copy-number variation of chromosomes, in which we considered that any chromosome with a normalized depth lower than 0.2 was absent. Based on the estimated number of chromosomes present in the dataset, we calculated missing data thresholds at 80% of genotyped isolates with the–max-missing-count option of vcftools (NAmax = 222 for the core chromosomes and between 328 and 1048 for the accessory chromosomes)48.The final filtering step was a per-site filter based on the number of missing genotypes. Considering that Z. tritici collection. This was done separately with a principal component analysis (R package SNPRelate51) and with a snmf clustering method from the LEA package52. The clustering analysis ran for a value of K  ranging from 1 to 15 and with 10 repetitions per K. To identify the best K, we used the entropy method implemented in snmf which evaluates the quality of fit of the model to the data, as well as the smallest cluster size and the number of isolates assigned to any cluster with a coefficient higher than 0.75 (considered as non-admixed). We created a phylogenetic network with a subset of the isolates , with the software SplitsTree553. As an outgroup, we also included resequenced Z. ardabiliae isolates (“SRR6671804”-“SRR6671820”)54, for which the variant calling was performed using the same parameters and filters as above but only hard filters were applied (retained 11 isolates after filtering). For the network construction, SNPs were filtered to include only variants with no missing data in either Z. ardabiliae or Z. tritici, a MAF of 20%, and no variants closer than 1000 bp to reduce biases. We retained 9556 SNPs to compute the SplitsTree network.We used a subset of the filtered biallelic SNPs  to estimate the population structure of our worldwide K = 11, the inferred best number of clusters. These genotypes were then used as input for treemix which infers splits between populations and creates a population tree55. We ensured that the tree shape was consistent regardless of possible migration events by running treemix with several possible migration events ranging from 0-6. We used the assembled genomes of Z. ardabiliae and Z. passerinii as outgroups to root the tree56. We used the scikit-allel python package to measure genetic diversity (pi), taking into account only the non-admixed isolates from each cluster, in non-overlapping windows of 1 kb57. To remove windows with too much missing data , we selected only windows in which less than 20% of the variants were filtered out. We controlled for the variation in isolate numbers between clusters by subsampling each cluster 10 times to the smallest cluster size (N = 16) and averaging the obtained diversity estimates per window over the 10 subsamples.For the analyses relying on the comparison of distinct groups, we discretized the populations by using only isolates belonging to one of the populations with a proportion higher than 0.75 at 58. In this process, the sequencing reads are first queried against a library of TE sequences, for which we used the TE consensus sequences obtained from 20 fully assembled genomes of 19 global isolates59. The “junction reads” that align both on a TE consensus sequence and on the flanking genome are used to determine the site of insertion of reference and non-reference TEs. To take any inaccuracies in the detection of the insertion sites into account, the positions of the insertions were rounded to 100 bp, so that insertions of the same element in a short window were considered to be the same insertion for further analyses. The insertions found in more than 10 samples were used to create a PCA (prcomp function from the stats R package), clustering the isolates based on their shared TE insertions.We called the TE insertions with the software ngs-te-mapper260 and analyzed to calculate TPM56, representing the gene expression during the necrotrophic and the asymptomatic phases of infection for the reference isolate. To include epigenomics data, we used previously published histone mark ChIP-seq data26 and identified the peaks for several histone marks: H3K4me2, H3K27me3, and H3K9me2 (NCBI BioProject “PRJNA286790”) assessed in two biological replicates. We trimmed and mapped the reads using Trimmomatic as above and bowtie, and filtered the mapped reads to keep only reads aligning with a quality higher than 30 using samtools. The histone mark peaks were called using macs2 (option —no-model)61. Only peak regions consistently identified in both replicates were kept (bedtools intersect). The coverage of histone mark peaks along chromosomes was computed by assessing the number of bases belonging to a histone mark peak per window. To remove windows with low variant counts due to a low mappability, we used genmap to estimate mappability and removed windows with a value lower than 0.85 62.We investigated the genomic distribution of variants, in relation to genomics, transcriptomics and epigenomics estimates. We gathered information from several sources and aggregated the data in 10-kb windows. We used transcriptomic data produced previously59 as a reference genome for mapping of reads (as single reads) with bowtie2. Using a custom python script based on the biopython library63, we also estimated the GC content and the RIP composite index64, an estimate created to detect ratios of dimers indicative of mutations typical of the repeat-induced point mutation process (RIP). This was done on the reads aligning to the TE consensus sequences, thus providing an estimation of RIP in all transposable elements as well as per TE consensus. We also used previously computed values of the RIP composite index for 20 chromosome-level assemblies29.To analyze the repeat-induced point mutations in the collection of genomes, we used the same consensus TE sequences33, we extracted the sequence of the gene (Zt10_unitig_006_0417) as well as its two flanking genes . These were then used as query sequences for the software blast to detect the presence and location of the 3 genes in de novo draft assemblies based on the Illumina resequencing. In many isolates dim2 is found in multiple copies. We thus identified the native copy as the copy found between the two flanking genes or, when the assemblies did not include all three genes on one contig, the copy found within less than 10 kb of one of the flanking genes. We then considered the percentage of identity between the native copies and the functional copy of Zt10.One of the most important enzymes for RIP is dim2. Based on previous knowledge that the strain Zt10 contains a functional copy of dim265 probably due to a strong GC bias in the sequencing. As a post-hoc analysis, we used mean separation tests (least-square means) and displayed the results as letters on the corresponding plots.Statistical differences between geographical groups were assessed using a one-way ANOVA with blocks, with the sequencing batch considered as the confounding block. The sequencing batch effect was especially strong for the genomes from Hartmann et al.https://worldclim.org). The geographical coordinates were used to approximate the environmental conditions of origin for each isolate from all bioclimatic variables, which include for example the mean diurnal range and the annual precipitation as an average between 1970 and 2000. Based on these environmental estimates and the genomic variants, we identified genotype-to-environment associations with the software GEMMA 0.98.366. We used a LOCO (Leave-One-Chromosome-Out) approach to estimate the kinship matrix on the genome excluding the chromosome on which we were estimating the associations. Significance threshold was set using the Bonferroni correction method, i.e., by dividing the traditional threshold of significance of 0.05 by the numbers of variants that were tested. Nearby significant SNPs were grouped together in “significant loci” if they were closer than 10 kb. To identify the genes which are potentially causal for adaptation to climatic conditions, we predicted the effect of the significant variants on the genes and proteins using SnpEff67. We created a custom SnpEff database so that the predictions would match the gene annotation we are using68 and setting the upstream/downstream interval length to 1 kb. We also used the SnpEff predictions to compare the distribution of effects  in the significantly associated variants and in all the variants using 200 random draws.We obtained geographical coordinates from the metadata attached to the isolates or inferred them based on the most precise sampling location available. Coordinates inferred can be found in Supplementary Data p-value , and identified whether the minor allele was present or absent per country/state including more than 5 isolates. The matrix of presence/absence was then used for the clustering. This analysis did not reveal a clear-cut pattern of adaptation sharing a set number of geographic distributions. Although there was no clear-cut best number of clusters even when testing up to 30 clusters, we chose eight clusters for graphical representation, using the elbow method. To investigate fungicide resistance, we identified the presence/absence of known resistance alleles in the isolates of the dataset, following the method in ref. 15. We then compared the frequency of these alleles in the different geographic locations, and through time.We investigated the geographic distribution of the potentially adaptive alleles we found through k-means clustering of each allele’s presence-absence per country. Per significant locus and per bioclimatic variable, we selected the variant with the lowest Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-7Reporting Summary