Copy number variants reveal divergent genetic and diagnostic cortical signatures across psychiatric disorders

Copy number variants reveal divergent genetic and diagnostic cortical signatures across psychiatric disorders | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Copy number variants reveal divergent genetic and diagnostic cortical signatures across psychiatric disorders Kuldeep Kumar, Zhijie Liao, Jakub Kopal, Clara Moreau, Christopher Ching, and 29 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9246968/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Structural variants, including copy number variants (CNVs), confer substantial risk for neurodevelopmental and psychiatric disorders (NPDs), yet whether their cortical effects relate to those observed in the psychiatric conditions they predispose to remains unclear. Here, we present the first systematic comparison of cortical phenotypes across 18 NPD-associated CNVs and aneuploidies, disorder-associated common variants, and 8 psychiatric disorders. Rare CNVs preferentially affected total surface area (SA), with 11-fold larger effects than psychiatric diagnoses, while NPDs preferentially affected mean cortical thickness (CT), with most CT effects observed in medicated subgroups, suggesting non-genetic contributions. NPD-associated common variants showed enrichment in SA but not CT associations. Regionally, both rare and common genetic variants showed larger effects in sensorimotor regions, aligning with the sensorimotor-to-association cortical gradient as well as regional heritability estimates. In contrast, psychiatric diagnoses showed larger effects in association regions. Individual NPD-associated variants were evenly split between those increasing and decreasing surface area. This heterogeneity likely explains why aggregating variants using polygenic scores shows only weak associations with SA. Overall, cortical signatures of psychiatric diagnoses diverge from those associated with genetic risk. Genetic variants preferentially impact SA and sensorimotor regions through early developmental mechanisms, while psychiatric diagnoses are associated with CT and association regions likely reflecting medication, illness chronicity, and environmental factors. Biological sciences/Genetics/Mutation Biological sciences/Genetics/Neurodevelopmental disorders Biological sciences/Neuroscience/Diseases of the nervous system/Schizophrenia Biological sciences/Neuroscience/Diseases of the nervous system/Bipolar disorder Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Structural variants, including rare copy number variants (CNVs) and sex chromosome aneuploidies (SCA), alter gene dosage and confer particularly elevated risk for neurodevelopmental and psychiatric disorders (NPDs), with hazard ratios ranging from 2 to over 10 1–4 for NPDs. On the other end of the allele frequency distribution, genome-wide association studies (GWAS) have identified hundreds of common variants associated with conditions including schizophrenia 5 , bipolar disorder 6 , attention deficit hyperactivity disorder 7 , and depression 8 . Together, both rare and common genetic variants contribute substantially to the heritable architecture of NPDs 2–4,9–11 . In parallel, large-scale neuroimaging studies, particularly through the ENIGMA consortium 12 , have characterized cortical phenotypes across multiple psychiatric conditions, revealing both disorder-specific and shared patterns of cortical thickness reductions and comparatively modest surface area alterations 13–16 . A central assumption in psychiatric neuroimaging studies is that the cortical phenotypes observed in psychiatric conditions reflect, in part, the effect of genetic variants that increase psychiatric risk. However, case-control neuroimaging studies conflate genetic liability, environmental exposures, medication effects, and consequences of chronic illness, making it difficult to distinguish the cortical signature of genetic risk from non-genetic factors. A fundamental question therefore remains: are the cortical differences associated with genetic risk variants related to the psychiatric risk they confer? Conversely, do the cortical differences observed in psychiatric disorders reflect their genetic contributions? Convergence would support a genetic interpretation, while divergence would implicate non-genetic factors. Addressing this question requires comparing cortical phenotypes across genetic variants and psychiatric disorders. Previous cross-disorder studies compared cortical thickness across psychiatric conditions 17–20 , but did not incorporate genetic variant data. Neuroimaging studies of individual CNVs have demonstrated robust cortical differences with larger effect sizes than psychiatric conditions 21–23 . However, no study has systematically compared cortical phenotypes across multiple NPD-associated CNVs, common variants, and psychiatric diagnoses. Cortical thickness (CT) and surface area (SA) are linked to distinct neurobiological processes with largely non-overlapping genetic architectures 24,25 . CT reflects processes including dendritic arborization and myelination continuing into adulthood 26,27 . SA reflects tangential expansion of cortical progenitors during early development and remains stable thereafter 26,28 . These distinct developmental trajectories make CT and SA complementary probes for dissecting genetic versus non-genetic contributions. At the common variant level, prior studies investigating genetic overlap 24,29–31 between NPDs and cortical phenotypes have reported weak genetic correlations 24 , but these analyses did not distinguish between CT and SA in the context of cross-variant comparisons. Here, we systematically characterize cortical phenotypes across 18 NPD-associated CNVs and aneuploidies, constituting the largest cross-variant neuroimaging analysis of structural variants to date, and directly compare them with cortical phenotypes across psychiatric diagnoses. Specifically, we assessed global and regional measures of CT and SA for the following three categories: i) 8 psychiatric disorders, including medication subgroups; ii) 18 NPD-associated CNVs and aneuploidies; and iii) genome-wide significant common variants (SNPs) associated with NPDs. Variants altering gene dosage of 1 or more genes (CNVs and aneuploidies) were selected based on previously established association with one or more psychiatric disorders 1–4,21,23,32–35 . By characterizing the cortical consequences of structural variants alongside diagnostic and common-variant phenotypes, we reveal a striking dissociation between cortical phenotypes associated with genetic risk, both rare and common, and those associated with psychiatric diagnoses, with implications for the interpretation of case-control psychiatric neuroimaging studies. Results Structural variants preferentially affect surface area with larger effects than psychiatric diagnoses We computed effect sizes (Cohen's d) for cortical phenotypes across 11 CNVs and aneuploidies from individual-level data, supplemented by published meta-analytic estimates for 7 additional variants (Table 1, Supplementary Table 1). Across these 18 structural variants, effects on total SA were consistently larger than on mean CT (paired comparison, FDR q=2E-2, Supplementary Figures 1A,2A ). Effect sizes (Cohen’s d ) on total SA were 11-fold larger for rare genetic variants compared with NPDs (Wilcoxon rank-sum test, FDR q=8E-4, Figures 2A-B ). This difference in effect sizes was less pronounced for mean CT (4-fold, Wilcoxon rank-sum test, FDR q=7E-3). To assess preferential impact, we computed the ratio of absolute effect sizes for CT and SA in each study. Rare genetic variants showed preferential total SA effects compared to mean CT (median ratio=0.6, one-sample Wilcoxon against 1, FDR q=3E-2, Figure 2C, Supplementary Figures 1A-B,2A-B ). NPDs showed the opposite pattern with preferential mean CT effects (median ratio=2.1, FDR q=3E-2). This was consistent across deletions, duplications, and aneuploidies ( Supplementary Figure 3 ). Among clinical high-risk individuals, those later developing psychosis (CHR-PS+) showed a CT/SA ratio of 2.9, contrasting with 1.2 for non-converters. The only NPDs showing preferential SA effects were ADHD and conduct disorder, both with childhood onset, but this did not generalize to ASD. We next tested whether the preferential SA effects observed for rare variants extended to common genetic variants associated with NPDs. We first examined genetic correlations between NPDs and global cortical metrics using summary statistics from Grasby et al. (2020). We observed weak genetic overlap with SA only, where ADHD, BD, and MDD showed significant correlations (FDR q<0.1; Supplementary Figure 4 ). To capture overlap beyond global correlations, we tested whether NPD-associated single-nucleotide polymorphisms (SNPs) were enriched for associations with cortical metrics. We ranked NPD-associated SNPs by their cortical GWAS associations. NPD SNPs showed significant enrichment (above-median ranking) in SA associations for ADHD, BD, and MDD (permutation FDR q<0.0001; Supplementary Figure 5, Supplementary Table 5 ). None showed CT enrichment. Together, these findings indicate that both rare and common NPD-associated variants preferentially associate with total SA rather than mean CT. Cortical thickness effects in psychiatric diagnoses reflect non-genetic factors rather than structural variant contributions We asked if the preferential association of NPDs with mean CT compared to total SA could be in part influenced by non-genetic factors rather than structural variant contributions. To do so, we first assessed NPD medication subgroups (also a proxy for disorder severity 36 , Supplementary Table 3 ). Effect sizes on mean CT were 8-fold larger in medicated compared to unmedicated subgroups (FDR q=4E-2, Figure 2D-E ). Total SA did not differ by medication status. Medicated subgroups showed preferential CT effects (ratio>1), while unmedicated subgroups showed balanced effects ( Figure 2F, Supplementary Figure 1C-D,2C-D ). We acknowledge that medication status may proxy for disorder severity, illness duration, or other clinical factors. Nevertheless, the selective sensitivity of CT, but not SA, to the medication subgroup is consistent with CT reflecting factors that vary with clinical state rather than genetic liability. Second, using summary statistics from the ENIGMA relatives study 37 , we examined cortical differences in first-degree relatives of individuals with BD or SCZ. Individuals with BD or SCZ diagnoses showed significantly reduced mean CT compared to controls, while their unaffected first-degree relatives did not show any significant associations with CT ( Supplementary Figure 6 ). Neither group showed SA differences. This pattern suggests that CT differences may emerge with, or following, disorder onset, rather than reflecting genetic liability shared by first-degree relatives. Third, because SA expansion occurs early in life, we asked if SA phenotypes were easier to detect in pediatric NPD groups. This was not the case, and age-stratified analyses in NPDs were unable to detect a clear shift toward associations with SA ( Supplementary Figure 7 ), though power limitations and heterogeneity across disorders constrain interpretation. Structural variants preferentially affect sensorimotor cortex, diverging from psychiatric diagnostic patterns We asked if the preferential effects on total SA for genetic variants and mean CT for psychiatric diagnoses were uniformly distributed across the cerebral cortex or localized to specific cortical regions. To do so, we computed the regional cortical effect size maps for 11 CNVs and compared them to regional maps of the 8 NPDs examined above, stratified by age groups and medication Supplementary Tables 1-3) . Significant associations with regional SA or CT (at least one ROI) were observed for 11 and 7 out of 18 NPD groups, respectively, and for all 11 CNVs ( Supplementary Figure 8 ). We did not detect significant associations for un-medicated sub-groups; as such, the medication sub-groups were excluded from the regional analyses below. Following previous cross-disorder analyses 17,18,38 , we computed latent dimensions of cortical regional phenotypes using principal component analysis across effect size profiles ( Figures 3-4 and Supplementary Table 6 ). The first principal component explained more than a quarter of the variance of regional beta-estimates across CNVs and NPDs, for both CT and SA. To contextualize these latent regional cortical profiles, we tested their similarities with the well-established cortical gradient ( Methods ), which ranks regions from primary sensory-motor cortices to higher-order association cortices 39,40 . NPD-associated rare genetic variants showed higher loadings in sensorimotor regions compared to association regions (negative correlations with the cortical gradient, Figure 5, and Supplementary Tables 6,7 ). NPDs maps showed the opposite correlations with the cortical gradient, highlighting higher loadings in association regions. The CNVs and NPDs latent dimensions showed negative pairwise correlations (SA: r=-0.38, p-spin < 0.05; CT: r=-0.32, not significant; Supplementary Figure 9 ). We then asked if our rare variant findings were generalizable to the broader genetic contribution to cortical structure. Towards this, we examined both twin and SNP heritability estimates for regional CT and SA 24 . Heritability was up to 2-fold higher in sensorimotor regions compared to association regions ( Supplementary Figure 10, negative correlations with the cortical gradient, r=-0.47 to -0.75, p-spin<0.05, Figure 5 ). Finally, we assessed the regional profiles of beta estimates from SA GWAS for the NPD-associated common variant (SNPs), to estimate the latent dimension of NPD-associated SNP profiles ( Supplementary Figure 11 ). The PC1 for SA explained 24% of variance, and SA-SNPs showed higher effects in sensorimotor regions (r=-0.49, p-spin<0.05) and correlated positively with PC1 for CNVs (SA, r=0.78, p-spin<0.05). The alignment between rare variant effects, common variant effects, and heritability estimates suggests that the sensorimotor preference reflects elemental aspects of how genetic variation influences cortical development, rather than a peculiarity of selected CNVs. These findings were robust to sensitivity analyses using three alternative consensus cortical maps: mean absolute effect size, percentage of significance, and variance; as well as leave-one-out analyses across CNVs and NPDs ( Supplementary Figures 9,12,13 , and Supplementary Tables 6,7 ). All three showed sensorimotor cortices preference for genetic variants and association cortices preference for NPDs. Opposing genetic effects on surface area resolve the polygenic paradox The preceding analyses reveal a paradox: individual genetic risk variants, both rare CNVs and common SNPs, produce preferential surface area associations with global metric and sensorimotor regions, yet psychiatric disorders show negligible surface area associations. We provide three converging lines of evidence to explain this discrepancy. First, polygenic risk scores (PRS) for BD and SCZ showed no association with total SA in 31,000 UK Biobank participants of European ancestry ( Supplementary Figure 14, Supplementary Tables 8,9 ), despite individual BD-associated SNPs showing significant SA enrichment ( Supplementary Figure 5 ). To investigate this null PRS finding, we examined effect sizes on SA for each independent genome-wide significant SNP using cortical GWAS summary statistics 24 , after harmonizing effect alleles between disorder and cortical GWAS. Approximately half of BD- and SCZ-associated SNPs showed negative beta estimates for total SA (BD: 52%; SCZ: 54%; allele harmonized, Supplementary Figure 14 ), with the remainder showing positive effects. A permutation-based variance inflation test confirmed that these disorder-associated SNPs carry individually meaningful SA effects: their cortical effect sizes were significantly more variable than those of minor allele frequency (MAF) matched random SNPs (SCZ: variance ratio = 1.32, p=0.037; BD: variance ratio = 1.23, p=0.089; 10,000 permutations, Supplementary Table 10 ), despite the near-zero mean. Simulations using empirical SNP effect sizes demonstrated that under the observed distribution of positive and negative cortical effects, the aggregate PRS–SA association (R² ≈ 10⁻⁵) is over two orders of magnitude weaker than when all effects are forced to align in the same direction (R² ≈ 6 × 10⁻³), without changing their magnitudes (10,000 simulations; Supplementary Table 11 ). Thus, cancellation of opposing effects, not the absence of individual SNP effects, accounts for the null PRS–SA finding. Second, this balanced proportion of negative and positive effects on regional SA extended to rare variants (CNVs). Nearly half of the CNVs loaded positively while the other half loaded negatively on the first regional SA latent dimension ( Figure 3D ). NPD-associated SNPs showed a similar balanced proportion of negative and positive loadings ( Supplementary Figure 11B ). Third, consistent with polygenic cancellation, unaffected first-degree relatives of a proband with a psychiatric diagnosis, a proxy for multifactorial (including polygenic) liability, also showed weak associations with SA (similar to probands, Supplementary Figure 6 ). Together, these findings resolve the apparent contradiction between i) high SA heritability, ii) clear individual variant effects on SA, and iii) null associations between NPD-PRS and SA. Discussion This study provides the first systematic characterization of cortical phenotypic consequences across multiple NPD-associated structural variants (CNVs and aneuploidies), revealing a striking dissociation with cortical signatures of the psychiatric conditions these variants predispose to. Genetic variants associated with NPDs, both rare CNVs and common SNPs, preferentially affected surface area, while psychiatric conditions preferentially affected cortical thickness. This dissociation extended to regional patterns: genetic variants showed larger effects in sensorimotor cortical regions, while psychiatric diagnoses showed larger effects in association regions. We further demonstrate that individual genetic variants produce clear SA effects that are heterogeneous and cancel out when genetic variants are aggregated into polygenic scores. These heterogeneous effects may also explain why the association between NPDs (polygenic conditions) and cortical SA remains difficult to detect at the group level. For the structural variant field, our findings demonstrate that systematic cross-variant neuroimaging analyses reveal shared properties of CNV effects on brain structure, including preferential SA and sensorimotor impact, that would be invisible when studying individual variants in isolation 21,23,33,34,41 . Notably, these properties extend to NPD-associated common variants, indicating a consistent genetic architecture of cortical effects across the allele frequency spectrum. A distinct but complementary finding is that individual structural and common variants show both positive and negative effects on SA, resolving why these clear individual-variant effects vanish in aggregate genetic analyses. Cortical surface area in sensorimotor regions may therefore represent a more genetically interpretable and sensitive endophenotype for structural variant effects than cortical thickness, which appears more sensitive to non-genetic factors. These results underscore the value of phenotyping structural variants through neuroimaging to understand how altered gene dosage shapes cortical development. The preferential association between genetic variants and total SA aligns with the higher heritability estimates for total SA compared to mean CT 24–27,42 . NPD-associated variants impacting SA suggest that they operate through early mechanisms of cortical expansion 24,27,43 . This SA preference is consistent across diverse structural variants, including deletions, duplications, and aneuploidies, and aligns with recent evidence that CNV deletions and duplications across the genome reduce surface area by disrupting the proliferation of neural progenitor cells during fetal cortical development 43 . The preferential association between mean CT and psychiatric diagnoses may reflect non-genetic factors. Medicated subgroups showed larger CT but not SA effects, consistent with randomized trial evidence of medication-induced CT changes 44 . We acknowledge that medication status may co-vary with disorder severity, illness duration, or other clinical variables. Disentangling medication effects from illness severity requires longitudinal and randomized designs beyond the scope of this cross-sectional analysis. At the regional level, structural and common genetic variants preferentially affected sensorimotor regions, which develop earlier 39 and show higher heritability 24–27,42 compared to association regions. Psychiatric diagnoses were preferentially associated with association cortices, consistent with prior cross-disorder studies reporting frontotemporal thickness reductions 17,18 . Notably, even within a single structural variant, the 22q11.2 deletion, carriers who develop psychosis show focal CT reductions in association regions compared to non-psychotic carriers, consistent with diagnosis-related CT effects being layered on top of the genetic signature 45 . Association regions with protracted development may be more vulnerable to environmental perturbations and illness-related plasticity, while sensorimotor regions may reflect earlier effects of genetic liability. A central puzzle in psychiatric and neuroimaging genetics has been the weak genetic correlations between NPDs and cortical structure, despite substantial heritability of both and the assumption that NPD risk conferred by genetic variants is mechanistically related to the effects of the same variants on brain structure 30 . Previous studies have demonstrated that genetic overlap (of common variants) between two traits may be under-estimated by genetic correlation values due to the heterogeneous effects of genetic variants 29,46 . In line with these studies, we show that rare and common genetic variants increasing risk for NPDs do associate with cortical structure, particularly SA, suggesting a higher genetic overlap despite weak genetic correlations 24,30 . However, we show that their effects are heterogeneous, with roughly equal numbers of NPD-risk variants increasing versus decreasing SA. When genetic-risk variants are aggregated into additive polygenic risk scores for NPDs or in psychiatric neuroimaging case-control group analysis, these heterogeneous/opposing effects on SA are canceled, producing null or weak associations. Our findings have several implications for psychiatric neuroimaging research. The CT and association-region effects predominant in psychiatric diagnoses may largely reflect consequences of illness, including medication, chronicity, comorbidity, and lifestyle factors, rather than the genetic variants that increase disorder risk. We note that this does not diminish the importance of such findings for understanding disease burden or treatment effects, but it reframes their interpretation. Second, additive polygenic models have limitations for imaging outcomes; methods separating positive and negative effects may improve prediction. Third, cross-sectional case-control designs conflate genetic and non-genetic contributions; longitudinal designs beginning before illness onset are needed to disentangle genetic and non-genetic contributions. Several limitations of our study warrant consideration. ENIGMA summary statistics were derived from heterogeneous studies with varying protocols, age ranges, and clinical characterization. While ENIGMA's harmonization procedures mitigate protocol differences, residual heterogeneity may affect comparisons across disorders. Medication subgroup analyses are constrained by the information available in ENIGMA publications; we could not examine dose-response or duration. Third, while genetic risk variants have strong effects on SA, with substantial heterogeneity, our study design does not allow us to distinguish whether SA phenotypes are related to mechanisms associated with increasing risk for NPDs or merely reflect the highly pleiotropic nature of these variants 47,48 . Overall, genetic variants preferentially affect surface area and sensorimotor regions, consistent with higher heritability and early developmental processes involved in these regions and phenotypes. Psychiatric diagnoses are preferentially associated with cortical thickness and association regions, with sensitivity to medication status suggesting non-genetic contributions. NPD-associated risk variants were evenly split between those increasing and decreasing cortical surface area, likely explaining why aggregating risk variants for NPDs using polygenic scores only yields weak correlations with this cortical phenotype. These findings suggest that case-control differences in psychiatric neuroimaging may reflect other factors, including consequences of illness rather than its genetic contributions, with implications for interpretation of case-control findings. By systematically characterizing cortical consequences across multiple structural variants, this study provides a framework for understanding how gene dosage alterations shape brain phenotypes and why these effects remain largely undetectable in aggregate genetic or diagnostic analyses. Methods Participants We analyzed CT and SA integrating individual-level neuroimaging data from approximately 33,000 participants (730 CNV/aneuploidy carriers, 870 matched controls, and 31,413 UK Biobank participants for polygenic risk score analyses), together with published ENIGMA summary statistics for 8 psychiatric disorders ( Figure 1 , Table 1, Supplementary Tables 1-4 ). ENIGMA summary statistics encompassed approximately 12,600 cases and 19,200 controls across 8 psychiatric disorders ( Supplementary Table 2 ), along with medication, age-stratified, and diagnostic subgroups ( Supplementary Table 3 ). Rare genetic variant participants We used individual-level neuroimaging data for carriers of recurrent CNVs or sex chromosome aneuploidies from clinical cohorts and the UK Biobank general population sample. The final sample included 730 carriers of 18 distinct variants and 870 matched controls. Demographic details, coordinates of each of the CNVs, as well as NPD risk, the Hazard ratios (HR) or Odds Ratios (OR) for each CNV, derived from the Danish iPSYCH2015 dataset 1,2 and Modenato et al. 21 are provided in Table 1 and Supplement Table 1 . Ethics : Signed consents were obtained by investigators from each cohort for all participants and/or their legal representatives prior to the investigation. This study was approved by the Ethics committee from the CHU Sainte-Justine Hospital. Clinical cohorts : CNV carriers were recruited following genetic testing referral for neurodevelopmental concerns or as relatives of identified carriers. Contributing cohorts included: 16p11.2 European Consortium, Brain Canada multi-site cohort, UCLA 22q11.2 deletion syndrome cohort, Cardiff University CNV cohort, and NIMH sex chromosome aneuploidy cohort (Detailed in prior publications 34 ). Controls were defined as individuals from the same cohorts not carrying any NPD-associated CNVs at the examined loci. UK Biobank : Additional CNV carriers were identified in the UK Biobank 49 general population sample (application 40980) using a validated CNV calling pipeline 22,41 . UK Biobank controls were participants not carrying any recurrent CNVs selected for this study. Psychiatric disorder and cortical phenotype associated common variants Genome-wide significant SNPs were extracted from published Psychiatric Genomics Consortium GWAS for ADHD 7 ; BD 6 ; MDD 8 ; and SCZ 5 ( Table 1 and Supplementary Tables 4,5 ). To assess cortical associations of these NPD-associated SNPs, we obtained estimates from the ENIGMA cortical structure GWAS 24 , which provides effect estimates and association statistics for regional and global CT and SA measures. We also obtained genetic correlation estimates (rg) between NPDs and global cortical metrics from the same source 24 . This allowed two complementary approaches: (i) genome-wide genetic correlations to assess genetic overlap between NPDs and cortical structure, and (ii) a SNP-level cross-referencing approach testing whether variants identified through psychiatric GWAS show enrichment for cortical structure associations beyond what genome-wide correlations capture. Neurodevelopmental and psychiatric disorder (NPD) participants In this study, we analyzed published summary statistics for 8 psychiatric disorders from the following published ENIGMA 12 studies: attention-deficit hyperactivity disorder (ADHD) 13,50 ; autism spectrum disorder (ASD) 13,51 ; bipolar disorder (BD) 14 ; clinical high-risk for psychosis (CHR-PS) 52 ; conduct disorder (CD) 53 ; major depressive disorder (MDD) 15 ; obsessive-compulsive disorder (OCD) 13,54 ; and schizophrenia (SCZ) 16 ) ( Supplementary Tables 2,3). Where available, we extracted summary statistics for: (i) medication subgroups (BD: lithium, antiepileptics, atypical antipsychotics; SCZ: first-generation antipsychotics, second-generation antipsychotics, combined); (ii) age-stratified samples (pediatric, adolescent/young adult, adult); and (iii) diagnostic subtypes (CHR-PS converters versus non-converters). MRI acquisition and preprocessing Rare genetic variants T1-weighted volumetric images at 0.8-1mm isotropic resolution were acquired across contributing sites using 1.5T and 3T scanners from multiple vendors (Siemens, GE, Philips). Detailed acquisition parameters for each cohort are provided in Supplementary Information. Visual quality control was performed by two trained raters (C.M., K.K.) following ENIGMA standardized protocols (https://github.com/ENIGMA-git). Sex chromosome aneuploidy samples underwent separate quality control (W.S., A.R.) using equivalent criteria. Neurodevelopmental and psychiatric disorder samples All contributing ENIGMA working groups followed standardized quality control and FreeSurfer 55 processing protocols as described in respective source publications. Cortical metric extraction FreeSurfer version 5.3.0 was used to extract cortical thickness (CT) and surface area (SA) for 68 regions of the Desikan-Killiany atlas, plus global measures (total surface area, mean cortical thickness). Left and right hemisphere values were averaged for primary analyses to reduce multiple comparisons and because i) some of the ENIGMA summary statistics are only available for averaged metrics; and ii) prior work has shown high bilateral correlations for these metrics in CNV carriers 21,22,41 . UK Biobank samples used for PRS analyses were processed using FreeSurfer 6.0 via the UK Biobank imaging pipeline 56 . Statistical analysis Effect size computation For rare genetic variants, Cohen's d effect sizes for carrier-control differences were computed using linear regression models adjusting for age, sex, and site. For regional surface area analyses, total surface area was included as an additional covariate to isolate regional effects from global scaling, consistent with ENIGMA protocols. For NPDs, Cohen's d values were extracted directly from published ENIGMA studies. All NPD effect sizes were computed with adjustment for age, sex, and site. Regional surface area analyses in ENIGMA studies included adjustment for total SA or intracranial volume, except for MDD, where such adjusted results were unavailable. Summary statistics from ENIGMA studies used site-covariate approaches. As such, site effects were modeled as covariates in all analyses. Maintaining the same statistical framework across NPD and CNV analyses ensured consistency. For additional CNV and aneuploidy variants with smaller sample sizes in our cohorts (Turner syndrome, Down syndrome, XXX, XXYY, Williams-Beuren syndrome, 16p11.2 distal deletion/duplication), we utilized previously published meta-analytic effect sizes from the literature 21 ( Supplementary Table 1 ). Multiple comparison correction False discovery rate (FDR) correction using the Benjamini-Hochberg procedure was applied within each analysis. Statistical significance threshold was set at FDR-corrected q<0.05. Rationale for cross-variant and cross-disorder analyses Because many individuals meet criteria for several psychiatric disorders during their lifetime, there have been significant cross-disorder efforts to study the genetic architecture across psychiatric disorders 9,11 . The same rationale has led neuroimaging consortia to investigate neurobiological processes across psychiatric conditions. Neuroimaging studies of individual CNVs have demonstrated distinct clinical features and brain signatures 21,22,33,34,41 . However, a cross-disorder, cross-CNV neuroimaging investigation has not yet been conducted. Assessing non-structural variant contributions using medication subgroup analysis Effect sizes for medicated versus unmedicated NPD subgroups were extracted from published ENIGMA studies where available. We compared effect sizes between medication subgroups using Wilcoxon rank-sum tests and computed CT/SA ratios for each subgroup to assess whether medication status differentially influenced the two metrics. We acknowledge that medication status in observational studies may proxy for disorder severity, illness duration, treatment resistance, or other clinical factors that co-vary with pharmacological treatment. Causal interpretation of medication effects requires randomized designs beyond the scope of this cross-sectional analysis. Global metric comparisons We compared absolute Cohen's d values between CNVs/aneuploidies and NPDs using Wilcoxon rank-sum tests. To assess preferential effects on CT versus SA within each category, we computed the ratio of absolute effect sizes ( Cohen_d_CT / Cohen_d_SA ) for each condition and tested whether median ratios differed from 1 (indicating balanced effects) using one-sample Wilcoxon signed-rank tests. Paired comparisons of CT versus SA effect size distributions within CNV and NPD groups used paired t-tests. Common variant enrichment analysis To test whether NPD-associated SNPs showed enrichment for cortical structure associations beyond chance, we employed a ranking-based approach. All SNPs with available data in the ENIGMA cortical GWAS were ranked by their p-value association with CT or SA. For each NPD, we identified genome-wide significant SNPs (p<5E-8) and computed the median rank of these SNPs within the cortical GWAS ranking. Statistical significance was assessed using permutation testing (10,000 permutations), randomly sampling equal numbers of SNPs from the full GWAS and recomputing median ranks to generate a null distribution. Enrichment was defined as median rank significantly above the 50th percentile (i.e., NPD SNPs ranked higher than expected by chance in cortical associations). FDR correction was applied across the four NPDs tested. Reciprocal analyses ranked cortical GWAS significant SNPs within NPD GWAS to assess bidirectional enrichment. Common variant variance inflation test To test whether disorder-associated SNPs carry individually meaningful effects on cortical surface area, we performed a permutation-based variance inflation test. For each disorder (BD, SCZ), we identified independent genome-wide significant lead SNPs from published GWAS (BD: 261 loci 6 ; SCZ: 287 loci 5 ) and extracted their total surface area effect sizes from the ENIGMA cortical GWAS summary statistics 24 . Effect alleles were harmonized between the disorder and cortical GWAS; SNPs with unresolvable allele mismatches were excluded, yielding 234 BD and 259 SCZ SNPs. We computed the observed variance of SA betas across disorder-associated SNPs and compared it to a null distribution generated by randomly sampling the same number of SNPs from the full cortical GWAS, matched on minor allele frequency using decile bins (10,000 permutations). The one-sided permutation p-value was computed as the proportion of null variance values equal to or exceeding the observed variance. Regional consensus maps To characterize consistent regional patterns for the cross-CNVs and cross-NPDs comparison, we followed the approach used in prior cross-disorder studies 17,18,34,38,41 and ran Principal Component Analysis (PCA). PCA was performed on the matrix of Cohen's d values (regions × conditions) using the FactoMineR 57 package. The first principal component (PC1) captures the dominant pattern of covariation across conditions. PC1 loadings were aligned so that regions with higher variance corresponded to positive loadings for interpretability. Only conditions with at least one FDR-significant regional association were included in regional consensus analyses, ensuring patterns were driven by detectable effects. This criterion excluded medication subgroups, which showed no significant regional effects individually. As a sensitivity analysis, we computed regional consensus maps using three complementary approaches. i) Mean absolute effect size: Mean of absolute Cohen's d values per region across all conditions within each category, identifying regions with consistently large effects. ii) Percentage significance: Proportion of conditions showing FDR-significant effects per region, identifying regions frequently affected regardless of effect magnitude. and iii) Variance: Variance in Cohen's d values per region across conditions, identifying regions of heterogeneous versus homogeneous effects. Cortical gradient analysis Regional profiles were correlated with the sensorimotor-to-association cortical gradient derived from the first principal component of gene expression across cortical regions 39,40 . This gradient ranks regions from primary sensory-motor cortices (low values) to higher-order association cortices (high values), capturing the hierarchy of cortical organization related to development, connectivity, and function. Gradient values for 34 left hemisphere Desikan-Killiany regions were obtained using the neuromaps package 40 . Statistical significance of spatial correlations Statistical significance of spatial correlations was assessed using spin permutation 58 testing (1,000 permutations) to account for spatial autocorrelation in cortical data. Spin permutation preserves the spatial structure of the cortex while generating null distributions, providing more conservative and appropriate inference than parametric tests for spatially embedded data. Brain map visualizations were generated using ggseg 59 . Twin and SNP Heritability Estimates Twin heritability and SNP heritability estimates for regional CT and SA were obtained from published ENIGMA genetic architecture studies 24 . Regional heritability profiles were correlated with the cortical gradient using spin permutation 58 testing to assess whether genetic determination of cortical structure varies systematically across the cortical hierarchy. Polygenic risk score analysis Standard polygenic risk scores (PRS) for BD and SCZ were obtained from UK Biobank (data fields 26214, 26275), computed using established methods with optimized p-value thresholds. We restricted analyses to participants of European ancestry (to match GWAS discovery samples) who did not carry any of the recurrent CNVs examined in this study (final n=31,000, Supplementary Table 8 ). Linear regression models tested associations between PRS and cortical metrics, adjusting for age, sex, imaging assessment center, and the first 10 genetic ancestry principal components. For regional SA analyses, total SA was included as an additional covariate. FDR correction was applied across all PRS-cortical associations ( Supplementary Table 9 ). To examine the directionality of individual SNP effects, we extracted beta estimates for NPD-associated SNPs from the ENIGMA cortical structure GWAS 24 and computed the proportion with negative versus positive effects on CT and SA. This analysis tests whether the null PRS associations reflect the true absence of genetic effects or cancellation of opposing individual SNP effects. Polygenic risk score (PRS)–surface area synthetic simulation To quantify the impact of opposing SNP effects on PRS–surface area associations, we simulated PRS–SA associations under empirical and counterfactual sign distributions. For each disorder, we used the harmonized set of independent lead SNPs, their disorder effect sizes (log-odds ratios), and their SA effect sizes standardized to a per-SD scale by dividing by the phenotype standard deviation estimated from the GWAS summary statistics. For each of 10,000 replicates, genotypes for 31,000 synthetic individuals were drawn from a binomial distribution (n = 2, p = MAF) at each SNP. PRS was computed as the weighted sum of genotypes using disorder effect sizes. The cortical phenotype was constructed as the sum of a genetic component (weighted by standardized cortical betas) and Gaussian noise, calibrated so that the genetic component contributed approximately 1% of total phenotypic variance, consistent with the expected contribution of ~240 genome-wide significant loci. Two scenarios were compared: the empirical scenario, using cortical betas with their original signs, and the concordant scenario, in which all cortical betas were forced to share the sign of the corresponding disorder beta while preserving their magnitudes. R² was computed for each replicate as the squared correlation between PRS and the simulated cortical phenotype. First Degree Relatives analysis Effect sizes for first-degree relatives (parents and siblings) of BD and SCZ probands were extracted from published ENIGMA relatives study summary statistics 37 . We compared effect sizes between diagnosed probands and their unaffected relatives using descriptive approaches and assessed whether relative effects were significantly different from zero using one-sample t-tests, testing the hypothesis that cortical differences emerge with disorder diagnosis rather than being present in at-risk individuals. Declarations Resource Availability Materials & Correspondence Requests for further information and resources should be directed to and will be fulfilled by the lead PI, Sebastien Jacquemont ( [email protected] ). Data availability UK Biobank data was downloaded under the application 40980 and may be accessed via their standard data access procedure (see http://www.ukbiobank.ac.uk/register-apply). UK Biobank CNVs were called using the pipeline developed in the Jacquemont Lab, as described at https://github.com/MartineauJeanLouis/MIND-GENESPARALLELCNV. The final CNV calls are available for download from the UK Biobank returned datasets (Return ID: 3104, https://biobank.ndph.ox.ac.uk/ukb/dset.cgi?id=3104). The 22q11.2 UCLA raw data are currently available by request from the project PI. Raw neuroimaging data for rare variants are available through request and data access agreement from the PIs of the projects (Brain Canada: S.J. CHUSJ Montreal; 22q11.2: C.E.B. UCLA, Cardiff: D.E.J.L., M.J.O., M.V.B., J.H, Cardiff University; SCA: A.R. NIMH). References to the processing pipeline and R package versions used for analysis are listed in the methods. The GWAS summary statistics are publicly available and can be accessed following the reference papers. Code availability The code for generating all the figures, along with processed summary measures, is available in the following GitHub repository: https://github.com/kkumar-iitkgp/ct_sa_across_disorders_and_variants.git Source data The source data for generating all the figures, and statistics, is included in the supplement tables. Acknowledgements This research was supported by Calcul Quebec (http://www.calculquebec.ca) and Compute Canada (http://www.computecanada.ca), the Brain Canada Multi-Investigator initiative, NIH U01 grant for CAMP (1U01MH119690-01), the Canadian Institutes of Health Research, CIHR_400528, The Institute of Data Valorization (IVADO) through the Canada First Research Excellence Fund, Healthy Brains for Healthy Lives through the Canada First Research Excellence Fund. The Cardiff CNV cohort was supported by the Wellcome Trust Strategic Award “DEFINE” and the National Centre for Mental Health with funds from Health and Care Research Wales (code 100202/Z/12/Z). Data from the UCLA cohort provided by Dr. Bearden (participants with 22q11.2 deletions or duplications and controls) was supported through grants from the NIH (U54EB020403), NIMH (R01MH085953, R01MH100900, R03MH105808), and the Simons Foundation (SFARI Explorer Award). Claudia Modenato was supported by the doc.mobility grant provided by the Swiss National Science Foundation (SNSF). Kuldeep Kumar was supported by the Institute of Data Valorization (IVADO) Postdoctoral Fellowship program, through the Canada First Research Excellence Fund. CRKC and PMT are supported in part by NIMH grants R01MH116147, R01MH123163, and R01MH121246, and by the Milken Institute and the Baszucki Brain Research Fund. Dr. Sønderby is supported by the Research Council of Norway (#223273), South-Eastern Norway Regional Health Authority (#2020060), European Union’s Horizon2020 Research and Innovation Programme (CoMorMent project; Grant #847776), and Kristian Gerhard Jebsen Stiftelsen (SKGJ-MED-021). BD is supported by the Swiss National Science Foundation (NCCR Synapsy, project grant numbers 32003B_135679, 32003B_159780, 324730_192755, and CRSK-3_190185), the Roger De Spoelberch and the Leenaards Foundations. G.D. is supported by the Institute for Data Valorization, Montreal (IVADO; CF00137433), the Fonds de recherche du Québec (FRQ; 285289), the Natural Sciences and Engineering Research Council of Canada (NSERC; DGECR-2023-00089), and the Azrieli Global Scholars Fellowship from the Canadian Institute for Advanced Research (CIFAR) in the Brain, Mind, & Consciousness program. We thank all of the families participating at the Simons Searchlight sites, as well as the Simons Searchlight Consortium. We appreciate obtaining access to imaging and phenotypic data on SFARI Base. Approved researchers can obtain the Simons Searchlight population dataset described in this study by applying at https://base.sfari.org. We are grateful to all families who participated in the 16p11.2 European Consortium. Author contributions K.K. and S.J. designed the study, analyzed imaging data, and drafted the manuscript. Analyses: K.K. performed all the analyses of CNV neuroimaging data and summary statistics. W.S. and A.R. performed analyses of neuroimaging data from sex chromosome aneuploidies. Data collection: C.Mod., A.M., B.R-H., A.P., S.R., and S.M-B. recruited and scanned participants in the 16p11.2 European Consortium. S.L., C.O.M., E.D., F. T-D., V.C., A.R.C., F.D. recruited and scanned participants in the Brain Canada cohort. L.K., C.E.B. collected and provided the data for the UCLA cohort. D.E.J.L., M.J.O., M.B.M. V.d.B., J.H., and A.I.S., provided the data for the Cardiff cohort. W.S. and A.R. provided the data for sex chromosome aneuploidies. All authors provided feedback on the manuscript. Competing interests MvdB reports grants from Takeda Pharmaceuticals, outside the submitted work. P.M.T. and CRKC received a research grant from Biogen, Inc., for work unrelated to this manuscript. All other authors reported no biomedical financial interests or potential conflicts of interest. References Sánchez, X. C. et al. Associations of psychiatric disorders with sex chromosome aneuploidies in the Danish iPSYCH2015 dataset: a case-cohort study. Lancet Psychiatry 10 , 129–138 (2023). Vaez, M. et al. Population-Based Risk of Psychiatric Disorders Associated With Recurrent Copy Number Variants. JAMA Psychiatry (2024) doi:10.1001/jamapsychiatry.2024.1453. Jacquemont, S. et al. Genes To Mental Health (G2MH): A framework to map the combined effects of rare and common variants on dimensions of cognition and psychopathology. Am. J. Psychiatry (2021). Owen, M. J., Bray, N. J., Walters, J. T. R. & O’Donovan, M. C. Genomics of schizophrenia, bipolar disorder and major depressive disorder. Nat. Rev. Genet. 1–16 (2025). Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 1–13 (2022). O’Connell, K. S. et al. Genomics yields biological and phenotypic insights into bipolar disorder. Nature 1–12 (2025). Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nature https://www.nature.com/articles/s41588-022-01285-8. Meng, X. et al. Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference. Nat. Genet. 56 , 222–233 (2024). Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360 , (2018). Grotzinger, A. D. et al. The landscape of shared and divergent genetic influences across 14 psychiatric disorders. medRxiv 2025.01.14.25320574 (2025) doi:10.1101/2025.01.14.25320574. Grotzinger, A. D. et al. Mapping the genetic landscape across 14 psychiatric disorders. Nature 1–15 (2025). Thompson, P. M. et al. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl. Psychiatry 10 , 100 (2020). Boedhoe, P. S. W. et al. Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups. Am. J. Psychiatry 177 , 834–843 (2020). Hibar, D. P. et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol. Psychiatry 23 , 932–942 (2018). Schmaal, L. et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 22 , 900–909 (2017). van Erp, T. G. M. et al. Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biol. Psychiatry 84 , 644–654 (2018). Patel, Y. et al. Virtual histology of cortical thickness and shared neurobiology in 6 psychiatric disorders. JAMA Psychiatry 78 , 47–63 (2021). Hettwer, M. D. et al. Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders. Nat. Commun. 13 , 6851 (2022). Cao, Z. et al. Cortical profiles of numerous psychiatric disorders and normal development share a common pattern. Mol. Psychiatry (2022) doi:10.1038/s41380-022-01855-6. Patel, Y. et al. Virtual Ontogeny of Cortical Growth Preceding Mental Illness. Biol. Psychiatry (2022) doi:10.1016/j.biopsych.2022.02.959. Modenato, C. et al. Lessons learnt from neuroimaging studies of Copy Number Variants, a systematic review. Biol. Psychiatry (2021) doi:10.1016/j.biopsych.2021.05.028. Modenato, C. et al. Effects of eight neuropsychiatric copy number variants on human brain structure. Transl. Psychiatry 11 , 399 (2021). Sønderby, I. E. et al. Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs. Hum. Brain Mapp. 43 , 300–328 (2021). Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science 367 , (2020). Makowski, C. et al. Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases. Science 375 , 522–528 (2022). Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604 , 525–533 (2022). Paus, T. Population Neuroscience: Principles and advances. Curr. Top. Behav. Neurosci. 68 , 3–34 (2024). Rakic, P. Specification of cerebral cortical areas. Science 241 , 170–176 (1988). Sha, Z. et al. The overlapping genetic architecture of psychiatric disorders and cortical brain structure. Nat. Ment. Health 1–17 (2025). Stauffer, E.-M. et al. The genetic relationships between brain structure and schizophrenia. Nat. Commun. 14 , 7820 (2023). Cheng, W. et al. Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness. JAMA Psychiatry (2021) doi:10.1001/jamapsychiatry.2021.1435. Berry, A. S. F. et al. A genome-first study of sex chromosome aneuploidies provides evidence of Y chromosome dosage effects on autism risk. Nat. Commun. 15 , 8897 (2024). Raznahan, A., Won, H., Glahn, D. C. & Jacquemont, S. Convergence and Divergence of Rare Genetic Disorders on Brain Phenotypes: A Review. JAMA Psychiatry (2022) doi:10.1001/jamapsychiatry.2022.1450. Levitis, E. et al. The variegation of human brain vulnerability to rare genetic disorders and convergence with behaviorally defined disorders. Biol. Psychiatry (2023) doi:10.1016/j.biopsych.2023.07.008. Mollon, J., Almasy, L., Jacquemont, S. & Glahn, D. C. The contribution of copy number variants to psychiatric symptoms and cognitive ability. Mol. Psychiatry 28 , 1480–1493 (2023). Zimmerman, M., Morgan, T. A. & Stanton, K. The severity of psychiatric disorders: World Psychiatry. World Psychiatry 17 , 258–275 (2018). de Zwarte, S. M. C. et al. The association between familial risk and brain abnormalities is disease specific: An ENIGMA-relatives study of schizophrenia and bipolar disorder. Biol. Psychiatry 86 , 545–556 (2019). Opel, N. et al. Cross-Disorder Analysis of Brain Structural Abnormalities in Six Major Psychiatric Disorders: A Secondary Analysis of Mega- and Meta-analytical Findings From the ENIGMA Consortium. Biol. Psychiatry 88 , 678–686 (2020). Sydnor, V. J. et al. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron Preprint at https://doi.org/10.1016/j.neuron.2021.06.016 (2021). Markello, R. D. et al. neuromaps: structural and functional interpretation of brain maps. Nat. Methods 19 , 1472–1479 (2022). Kumar, K. et al. Subcortical Brain Alterations in Carriers of Genomic Copy Number Variants. Am. J. Psychiatry 180 , 685–698 (2023). Norbom, L. B. et al. New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings. Prog. Neurobiol. 204 , 102109 (2021). Liao, Z. et al. Copy number variants and the tangential expansion of the cerebral cortex. Nat. Commun. 16 , 1–12 (2025). Voineskos, A. N. et al. Effects of antipsychotic medication on brain structure in patients with major depressive disorder and psychotic features: Neuroimaging findings in the context of a randomized placebo-controlled clinical trial: Neuroimaging findings in the context of a randomized placebo-controlled clinical trial. JAMA Psychiatry 77 , 674–683 (2020). Sun, D. et al. Large-scale mapping of cortical alterations in 22q11.2 deletion syndrome: Convergence with idiopathic psychosis and effects of deletion size. Mol. Psychiatry 25 , 1822–1834 (2020). Hindley, G. et al. Charting the landscape of genetic overlap between mental disorders and related traits beyond genetic correlation. Am. J. Psychiatry 179 , 833–843 (2022). Kazem, S. et al. Determinants of pleiotropy and monotonic gene dosage responses across human traits. Genetic and Genomic Medicine (2025). Auwerx, C., Moix, S., Kutalik, Z. & Reymond, A. Disentangling mechanisms behind the pleiotropic effects of proximal 16p11.2 BP4-5 CNVs. Am. J. Hum. Genet. 111 , 2347–2361 (2024). Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12 , e1001779 (2015). Hoogman, M. et al. Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples. Am. J. Psychiatry 176 , 531–542 (2019). van Rooij, D. et al. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group. Am. J. Psychiatry 175 , 359–369 (2018). ENIGMA Clinical High Risk for Psychosis Working Group et al. Association of structural magnetic resonance imaging measures with psychosis onset in individuals at Clinical High Risk for developing psychosis: An ENIGMA working group mega-analysis: An ENIGMA working group mega-analysis. JAMA Psychiatry 78 , 753–766 (2021). Gao, Y., Staginnus, M. & ENIGMA-Antisocial Behavior Working Group. Cortical structure and subcortical volumes in conduct disorder: a coordinated analysis of 15 international cohorts from the ENIGMA-Antisocial Behavior Working Group. Lancet Psychiatry 11 , 620–632 (2024). Boedhoe, P. S. W. et al. Cortical abnormalities associated with pediatric and adult obsessive-compulsive disorder: Findings from the ENIGMA obsessive-compulsive disorder working group. Am. J. Psychiatry 175 , 453–462 (2018). Fischl, B. et al. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14 , 11–22 (2004). Alfaro-Almagro, F. et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166 , 400–424 (2018). Lê, S., Josse, J. & Husson, F. FactoMineR: AnRPackage for Multivariate Analysis. J. Stat. Softw. 25 , 1–18 (2008). Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 178 , 540–551 (2018). Mowinckel, A. M. & Vidal-Piñeiro, D. Visualization of brain statistics with R packages ggseg and ggseg3d. Adv. Methods Pract. Psychol. Sci. 3 , 466–483 (2020). Table Table 1. Description of structural variants, psychiatric disorders, and disorder summary statistics. A. Rare Genetic Variants (CNVs and Aneuploidies) Type N (Cases / Ctrls) HR ADHD HR BD HR MDD OR ASD OR SCZ 1q21.1 Deletion CNV 40 / 782 1.93 3.52 2.08 1.56 6 1q21.1 Duplication CNV 30 / 782 2.65 1.63 1.43 8.03 3 7q11.23 Deletion (WBS) CNV 44 (meta) — — — 32 — 15q11.2 Deletion CNV 108 / 782 1.43 0.63 1.17 1.3 1 15q11.2 Duplication CNV 144 / 782 1.42 1.26 1.07 1.8 1 16p11.2 Deletion (distal) CNV 15 (meta) 1.26 — 1.04 1.73 4 16p11.2 Duplication (distal) CNV 18 (meta) 1.57 1.89 1.17 1.15 1 16p11.2 Deletion (proximal) CNV 82 / 782 0.77 — 0.42 9.5 1 16p11.2 Duplication (proximal) CNV 75 / 782 2.63 0.53 1.04 11.81 12 16p13.11 Duplication CNV 50 / 782 2.01 0.8 1.38 1.5 2 Trisomy 21 (Down Syndrome) Aneuploidy 84 (meta) — — — 6.83 3.67 22q11.2 Deletion (VCFS) CNV 68 / 782 1.17 — 1.03 32.37 92 22q11.2 Duplication CNV 26 / 782 2.61 — 1.02 3.28 0.15 XXX Syndrome Aneuploidy 35 (meta) 2.67 4.32 2.2 5.6 17.86 XXY (Klinefelter) Aneuploidy 77 / 53 1.99 1.62 1.88 4 17.86 XYY Syndrome Aneuploidy 30 / 35 4.45 3.22 2.65 4.6 — XXYY Syndrome Aneuploidy 25 (meta) — — — 1.92 — X Monosomy (Turner) Aneuploidy 55 (meta) 6.15 — 1.62 — — B. Neurodevelopmental & Psychiatric Disorders PubMed ID N (Cases / Ctrls) Source Medication subgroups Subgroups ADHD (Attention-Deficit /Hyperactivity Disorder) 32539527 2271 / 5827 ENIGMA Yes Ped, Young, Adult ASD (Autism Spectrum Disorder) 32539527 1777 / 5827 ENIGMA Yes Ped, Young, Adult BD (Bipolar Disorder) 28461699 2447 / 4056 ENIGMA Yes Young, Adult CD (Conduct Disorder) 39025633 1185 / 1253 ENIGMA No Young CHR-PS (Clinical High Risk for Psychosis) 33950164 1792 / 1377 ENIGMA No Converters / Non MDD (Major Depressive Disorder) 27137745 2148 / 7957 ENIGMA Yes Young, Adult OCD (Obsessive Compulsive Disorder) 32539527 2323 / 5827 ENIGMA Yes Ped, Young, Adult SCZ (Schizophrenia) 29960671 4474 / 5098 ENIGMA Yes Adult C. Common Variants (GWAS Statistics) Reference N (Cases / Ctrls) Source N Signif SNPs SNP heritability Attention-deficit/hyperactivity disorder (ADHD) Demontis et al., 2023 38691 / 186843 PGC 27 14% (1%) Bipolar Disorder (BD) O’Connell et al., 2025 131969 / 2322416 PGC 239 22% (1%) Major Depression (MDD) Meng et al., 2024 258364 / 571252 PGC 180 8.4% (0.07%) Schizophrenia (SCZ) Trubetskoy et al., 2022 76755 / 243649 PGC 259 24% (0.07%) Cortical MRI (Mean CT) Grasby et al., 2020 51,665 (Total) ENIGMA 6 26% (2%) Cortical MRI (Total SA) Grasby et al., 2020 51,665 (Total) ENIGMA 20 34% (3%) This study integrates data across three domains: A ) Rare genetic variants, including copy number variants (CNVs) and sex chromosome aneuploidies (SCAs); B ) Neurodevelopmental and psychiatric disorders (NPDs); and C ) Common genetic variants from genome-wide association studies (GWAS). For rare variants (A), N Carriers denotes unique individuals analyzed from individual-level data (clinical cohorts and UK Biobank) or aggregated via meta-analysis (meta). HR denotes the Hazard Ratio for developing specific psychiatric disorders (ADHD, BD, MDD) derived from the iPSYCH2015 case-cohort study (Vaez et al., 2024 2 , or Sánchez et al., 2023 1 ). OR denotes the Odds Ratio for ASD or SCZ from Modenato et al., 2021 21 or Kumar et al., 2023 41 . N Controls refers to the shared control sets used for CNV (n=782) and SCA (n=870) comparisons. For NPDs (B), values represent aggregated demographics from ENIGMA working groups for individuals with quality-controlled cortical thickness and surface area data. Medication denotes the availability of medication status for subgroup analyses; Subgroups indicate the age cohorts available (Ped: Pediatric, Young: Young Adult, Adult). For common variants (C), sample sizes refer to the discovery GWAS for the psychiatric disorder or cortical metrics. In addition, for polygenic risk score (PRS) analysis, we used CT and SA metrics for 31,413 participants of European (White-British) ancestry from the UK Biobank. Abbreviations: Abbreviations: ADHD=attention deficit hyperactivity disorder; ASD=autism spectrum disorder; BD=bipolar disorder; CD=conduct disorder; CHR-PS=clinical high risk for psychosis; CNV=copy number variant; Del=deletion; Dup=duplication; ENIGMA=Enhancing Neuro Imaging Genetics through Meta Analysis; MDD=major depressive disorder; NPD=neurodevelopmental and psychiatric disorders; OCD=obsessive-compulsive disorder; PGC=Psychiatric Genomics Consortium; prox.=proximal; SCA=sex chromosome aneuploidy; SCZ=schizophrenia; TS=Turner syndrome; VCFS=Velo-Cardio-Facial syndrome; WBS=Williams-Beuren syndrome. Additional Declarations Yes there is potential Competing Interest. MvdB reports grants from Takeda Pharmaceuticals, outside the submitted work. P.M.T. and CRKC received a research grant from Biogen, Inc., for work unrelated to this manuscript. All other authors reported no biomedical financial interests or potential conflicts of interest. Supplementary Files SupplementaryTables.xlsx Supplementary Tables SupplementaryMaterial.docx Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Montreal","correspondingAuthor":false,"prefix":"","firstName":"Charles-Olivier","middleName":"","lastName":"Martin","suffix":""},{"id":624430015,"identity":"2eb8302c-5030-47ba-aea6-ae6676b455ac","order_by":9,"name":"Anne-Marie Belanger","email":"","orcid":"","institution":"Centre de recherche CHU Sainte-Justine and University of Montreal","correspondingAuthor":false,"prefix":"","firstName":"Anne-Marie","middleName":"","lastName":"Belanger","suffix":""},{"id":624430016,"identity":"b31631d8-4e58-4d33-a05e-c9948f524dc6","order_by":10,"name":"Valerie Fontaine","email":"","orcid":"https://orcid.org/0009-0004-9138-9126","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Valerie","middleName":"","lastName":"Fontaine","suffix":""},{"id":624430017,"identity":"ad51a110-4efe-49e5-acf1-5ea2da1340c0","order_by":11,"name":"Khadije Jizi","email":"","orcid":"","institution":"University of 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University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Linden","suffix":""},{"id":624430024,"identity":"bc999301-79f3-443a-80e9-c3d0fee47477","order_by":18,"name":"Michael Owen","email":"","orcid":"https://orcid.org/0000-0003-4798-0862","institution":"Cardiff University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Owen","suffix":""},{"id":624430025,"identity":"c5cf62c2-2660-41ce-8644-22da7a6d0688","order_by":19,"name":"Jeremy Hall","email":"","orcid":"https://orcid.org/0000-0002-0403-3278","institution":"Cardiff University","correspondingAuthor":false,"prefix":"","firstName":"Jeremy","middleName":"","lastName":"Hall","suffix":""},{"id":624430026,"identity":"3b12dff4-65d9-4cea-b459-3898b59da8e6","order_by":20,"name":"Sarah Lippé","email":"","orcid":"","institution":"University of Montreal","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Lippé","suffix":""},{"id":624430027,"identity":"5f203658-1b64-412a-82a0-9438a7d2f1f3","order_by":21,"name":"Guillaume Dumas","email":"","orcid":"https://orcid.org/0000-0002-2253-1844","institution":"Centre de recherche CHU Sainte-Justine and University of Montreal, and Mila, Quebec Artificial Intelligence Institute","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Dumas","suffix":""},{"id":624430028,"identity":"5497a2cf-4fd4-44af-9b3c-3e407aeac8ab","order_by":22,"name":"Bodgan Draganski","email":"","orcid":"https://orcid.org/0000-0002-5159-5919","institution":"Lausanne University Hospital (CHUV) and University of Lausanne (UNIL),","correspondingAuthor":false,"prefix":"","firstName":"Bodgan","middleName":"","lastName":"Draganski","suffix":""},{"id":624430029,"identity":"2a1e4311-9cf4-4c9c-b040-5c5cdc008a4e","order_by":23,"name":"Laura Almasy","email":"","orcid":"","institution":"Children's Hospital of Philadelphia","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Almasy","suffix":""},{"id":624430030,"identity":"21940b09-56c3-4926-b8d9-4c16911b0341","order_by":24,"name":"Sophia Thomopoulos","email":"","orcid":"https://orcid.org/0000-0002-0046-4070","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Sophia","middleName":"","lastName":"Thomopoulos","suffix":""},{"id":624430031,"identity":"733feda0-1212-4b36-a66c-fec1e22b2381","order_by":25,"name":"Neda Jahanshad","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Neda","middleName":"","lastName":"Jahanshad","suffix":""},{"id":624430032,"identity":"67e287c5-9bb9-4d61-9a08-d0987d213b9b","order_by":26,"name":"Ida Sønderby","email":"","orcid":"https://orcid.org/0000-0001-7297-7855","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Ida","middleName":"","lastName":"Sønderby","suffix":""},{"id":624430033,"identity":"40c1d170-da67-481d-8406-d7472157b1e3","order_by":27,"name":"Ole Andreassen","email":"","orcid":"https://orcid.org/0000-0002-4461-3568","institution":"Oslo University Hospital \u0026 Institute of Clinical Medicine, University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Ole","middleName":"","lastName":"Andreassen","suffix":""},{"id":624430034,"identity":"669dcdd3-a2e2-466c-9b11-53b2d3f9c59d","order_by":28,"name":"David Glahn","email":"","orcid":"https://orcid.org/0000-0002-4749-6977","institution":"Department of Psychiatry, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115;Harvard Medical School, Boston, MA 02115","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Glahn","suffix":""},{"id":624430035,"identity":"0cb6a058-f7a0-441d-9e19-27061bc9c738","order_by":29,"name":"Armin Raznahan","email":"","orcid":"https://orcid.org/0000-0002-5622-1190","institution":"National Institute of Mental Health","correspondingAuthor":false,"prefix":"","firstName":"Armin","middleName":"","lastName":"Raznahan","suffix":""},{"id":624430036,"identity":"23578220-1222-404f-a751-31a6fefdbbc5","order_by":30,"name":"Carrie Bearden","email":"","orcid":"https://orcid.org/0000-0002-8516-923X","institution":"UCLA","correspondingAuthor":false,"prefix":"","firstName":"Carrie","middleName":"","lastName":"Bearden","suffix":""},{"id":624430037,"identity":"ba52ff1b-a6e1-4089-aba0-82f90a32581e","order_by":31,"name":"Tomas Paus","email":"","orcid":"https://orcid.org/0000-0003-1495-9338","institution":"University of Montreal","correspondingAuthor":false,"prefix":"","firstName":"Tomas","middleName":"","lastName":"Paus","suffix":""},{"id":624430038,"identity":"2b054cd5-fd80-4903-a767-176f140e2406","order_by":32,"name":"Paul Thompson","email":"","orcid":"","institution":"University of Southern California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Thompson","suffix":""},{"id":624430039,"identity":"cf8714b3-a864-495f-85c1-b60b24a9f4bb","order_by":33,"name":"Sebastien Jacquemont","email":"","orcid":"https://orcid.org/0000-0001-6838-8767","institution":"Université de Montréal","correspondingAuthor":false,"prefix":"","firstName":"Sebastien","middleName":"","lastName":"Jacquemont","suffix":""}],"badges":[],"createdAt":"2026-03-27 16:40:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9246968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9246968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108640503,"identity":"e06e8074-0616-4550-839b-7d1f8b6f1462","added_by":"auto","created_at":"2026-05-06 19:29:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":833039,"visible":true,"origin":"","legend":"\u003cp\u003eStudy overview.\u003c/p\u003e\n\u003cp\u003eLegend: We compared cortical thickness (CT) and surface area (SA) effect sizes across three categories of NPD-related data in ~33,000 individuals: i) 18 NPD-associated rare structural variants (CNVs and aneuploidies; 730 carriers and 870 matched controls); ii) genome-wide significant common variants (SNPs) from PGC GWAS for 4 NPDs, cross-referenced with ENIGMA cortical GWAS; and iii) 8 psychiatric disorders from ENIGMA consortium summary statistics, including medication and age-stratified subgroups. CT and SA were extracted using FreeSurfer. \u003cstrong\u003eA\u003c/strong\u003e) Global analysis: CT and SA effect sizes were compared across categories, with sensitivity analyses for medication subgroups and age-stratified samples. \u003cstrong\u003eB\u003c/strong\u003e) Regional analysis: latent dimensions of regional cortical differences (PCA) were correlated with the sensorimotor-to-association cortical gradient and with twin and SNP heritability estimates; alternative consensus maps (mean absolute effect size, percentage of significance, and variance) served as sensitivity analyses. \u003cstrong\u003eC\u003c/strong\u003e) Polygenic cortical cancellation: polygenic risk scores (PRS) for NPDs were tested against cortical metrics in UK Biobank (N~31,000), and directionality of individual SNP and CNV effects was assessed; first-degree relatives of probands served as a sensitivity analysis. Summary: Genetic variants (both rare and common) preferentially affect total SA and sensorimotor regions, aligning with heritability estimates, while NPD diagnoses preferentially affect mean CT and association cortices. Insights: Individual genetic variants produce bidirectional cortical effects (~50% positive, ~50% negative for SA), which cancel in additive polygenic models, explaining weak NPD effect sizes for SA. CT effects in NPDs appear driven by non-genetic factors (medication, chronicity), while SA effects are masked by bidirectional cancellation.\u003cbr\u003e\nBrain and cortex maps were generated using the ggseg package in R(50). Common and rare variant illustrations are from the NIAID NIH BIOART Source (https://bioart.niaid.nih.gov/bioart/170 and https://bioart.niaid.nih.gov/bioart/204)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9246968/v1/26e78bae4524e1c9c980e47c.png"},{"id":108640505,"identity":"f0c6873b-adbe-4422-a79b-870f03eada04","added_by":"auto","created_at":"2026-05-06 19:29:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":638138,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal cortical differences across genetic variants and psychiatric diagnoses.\u003c/p\u003e\n\u003cp\u003eLegend: \u003cstrong\u003eA-C\u003c/strong\u003e) Comparing cortical phenotypes between neurodevelopmental and psychiatric disorders (NPDs), and rare genetic variants (CNVs) associated with NPDs, for \u003cstrong\u003eA\u003c/strong\u003e) mean cortical thickness (CT) effect sizes; \u003cstrong\u003eB\u003c/strong\u003e) total surface area (SA) effect sizes; \u003cstrong\u003eC\u003c/strong\u003e) Ratio of Cohen’s d CT and Cohen’s d SA (effect sizes). \u003cstrong\u003eD-F\u003c/strong\u003e) Comparing sub-groups with and without medications across NPDs for \u003cstrong\u003eD\u003c/strong\u003e) mean cortical thickness (CT) effect sizes; \u003cstrong\u003eE\u003c/strong\u003e) total surface area (SA) effect sizes; \u003cstrong\u003eF\u003c/strong\u003e) Ratio of Cohen’s d CT and Cohen’s d SA. Case-control differences were adjusted for age, sex, and site. Effect sizes correspond to absolute Cohen’s d values. *: FDR significant (q\u0026lt;0.05), across all pairs of comparisons. For panel C and F, * and arrows denote the direction of the median ratio shift from 1 (FDR q\u0026lt;0.05). Y-axis: Absolute effect sizes, and effect size ratios, plotted on a log10 scale.\u003c/p\u003e\n\u003cp\u003eAbbreviations: \u003cbr\u003e\nAbs=absolute; ADHD=attention deficit hyperactivity disorder; ASD=autism spectrum disorder; BD=bipolar disorder; CD: conduct disorder; CHR-PS: clinical high risk for psychosis; CHR-PS-: CHR who did not develop a psychotic disorder; CHR-PS+: CHR who later developed a psychotic disorder; CNV=copy number variant; CT=cortical thickness; Del=deletion; Dup=duplication; MDD=major depressive disorder; NPD=neurodevelopmental and psychiatric disorders; OCD=obsessive-compulsive disorder; prox.=proximal; SA=surface area; SCZ=schizophrenia; TS=Turner syndrome; WBS=Williams-Beuren syndrome;\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9246968/v1/ac6047621a8dff5eeda65610.png"},{"id":108640507,"identity":"cc206f60-f09b-44b8-aaa0-c090d2cd7dd9","added_by":"auto","created_at":"2026-05-06 19:29:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":880284,"visible":true,"origin":"","legend":"\u003cp\u003eRegional cortical alterations across rare genetic variants.\u003c/p\u003e\n\u003cp\u003eLegend: Cohen’s \u003cem\u003ed\u003c/em\u003e maps for regional cortical alterations for 11 rare genetic variants (CNVs, including aneuploidies): \u003cstrong\u003eA\u003c/strong\u003e) cortical thickness; \u003cstrong\u003eB\u003c/strong\u003e) surface area. Case-control differences were calculated after adjusting for age, sex, and site (and total SA for SA). FDR (q\u0026lt;0.05) significant regions are shown in black boundaries. \u003cstrong\u003eC-H\u003c/strong\u003e) Principal component analysis across regional cortical differences. PC1 variable loadings for \u003cstrong\u003eC\u003c/strong\u003e) CT and \u003cstrong\u003eD\u003c/strong\u003e) SA. Latent dimension map: the first principal component from the principal component analysis (PCA) across \u003cstrong\u003eE\u003c/strong\u003e) CT and \u003cstrong\u003eF\u003c/strong\u003e) SA alterations for CNVs. The variance explained by the first two principal components (PC1 and PC2) for \u003cstrong\u003eG\u003c/strong\u003e) CT, and \u003cstrong\u003eH\u003c/strong\u003e) SA.\u003c/p\u003e\n\u003cp\u003eAbbreviations. CNV=copy number variants; Corr.=correlation; CT: cortical thickness; Del=deletions; Dup=duplications; PC: principal component; SA=surface area; % var. explained = percentage of variance explained.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9246968/v1/77718fba200f678cb0b803d4.png"},{"id":108805989,"identity":"1f7ba3a8-61d0-4e21-878f-15049fd2667a","added_by":"auto","created_at":"2026-05-08 15:27:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":717365,"visible":true,"origin":"","legend":"\u003cp\u003eRegional cortical alterations across neurodevelopmental and psychiatric disorders.\u003c/p\u003e\n\u003cp\u003eLegend: Cohen’s \u003cem\u003ed\u003c/em\u003e maps for regional cortical alterations across neurodevelopmental and psychiatric disorders (NPDs) for \u003cstrong\u003eA\u003c/strong\u003e) cortical thickness (CT); \u003cstrong\u003eB\u003c/strong\u003e) surface area (SA). Case-control differences were calculated after adjusting for age, sex, and site (and total SA for SA). FDR (q\u0026lt;0.05) significant regions are shown by black boundaries. Only SA and CT effect sizes maps with FDR significant associations for at least one ROI are shown. \u003cstrong\u003eC-H\u003c/strong\u003e) Principal component analysis across regional cortical differences. PC1 variable loadings for \u003cstrong\u003eC\u003c/strong\u003e) CT and \u003cstrong\u003eD\u003c/strong\u003e) SA. Latent dimension map: the first principal component from the principal component analysis (PCA) across \u003cstrong\u003eE\u003c/strong\u003e) CT and \u003cstrong\u003eF\u003c/strong\u003e) SA alterations for NPDs. The variance explained by the first two principal components (PC1 and PC2) for \u003cstrong\u003eG\u003c/strong\u003e) CT, and \u003cstrong\u003eH\u003c/strong\u003e) SA.\u003c/p\u003e\n\u003cp\u003eAbbreviations. ADHD=attention deficit hyperactivity disorder; ASD=autism spectrum disorder; BD=bipolar disorder; CD: conduct disorder; CHR-PS: clinical high risk for psychosis; CHR-PS-: CHR who did not develop a psychotic disorder; CHR-PS+: CHR who later developed a psychotic disorder; CT=cortical thickness; MDD=major depressive disorder; NPD=neurodevelopmental and psychiatric disorders; OCD=obsessive-compulsive disorder; PC: principal component; SA=surface area; SCZ=schizophrenia; % var. explained = percentage of variance explained.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9246968/v1/e253e1ad2ee578288deb61fa.png"},{"id":108640508,"identity":"5cb63afb-87e2-411f-85e1-d4ba2851ac42","added_by":"auto","created_at":"2026-05-06 19:29:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":831614,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the cortical gradient and latent dimensions of regional cortical differences.\u003c/p\u003e\n\u003cp\u003eLegend: Correlation between the cortical gradient (Pearson’s r) and latent dimension (PC1) of regional differences / regional heritability estimates for \u003cstrong\u003eA\u003c/strong\u003e) cortical thickness (CT) and \u003cstrong\u003eB\u003c/strong\u003e) surface area (SA). Latent dimension (PC1) profiles were computed for effect sizes across rare genetic variants (CNVs), common genetic variants (SNPs), and NPDs for cortical thickness and surface area across 34 Desikan cortical regions. Regional profiles of twin and SNP heritability estimates for cortical thickness and surface area across 34 Desikan cortical regions from Grasby et al. \u003ca href=\"https://paperpile.com/c/6surh4/DSqjK\"\u003e\u003csup\u003e24\u003c/sup\u003e\u003c/a\u003e. The regional surface area estimates are adjusted for Total SA.\u003cbr\u003e\nAbbreviations, CT=cortical thickness; h2: heritability; NPD=neurodevelopmental and psychiatric disorders; PC: principal component; SA=surface area; SNP: single nucleotide polymorphism; r= Pearson correlation; *:spin-permutation significant, p-spin \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9246968/v1/255e19ea26108209f75b176a.png"},{"id":108809781,"identity":"e3758a9a-0378-4b87-a2c4-5457b30cbedf","added_by":"auto","created_at":"2026-05-08 15:55:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3743225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9246968/v1/90424a13-8939-4751-846b-27f1837e9a01.pdf"},{"id":108805883,"identity":"574e5655-f8f8-4af6-a16f-1ba7394456df","added_by":"auto","created_at":"2026-05-08 15:27:05","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":141253,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9246968/v1/f765949393656e3a49bebc32.xlsx"},{"id":108805470,"identity":"5511d698-1048-44ec-ab36-267096bce244","added_by":"auto","created_at":"2026-05-08 15:26:03","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7187612,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9246968/v1/7f520d914a41da371a990179.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nMvdB reports grants from Takeda Pharmaceuticals, outside the submitted work. P.M.T. and CRKC received a research grant from Biogen, Inc., for work unrelated to this manuscript. All other authors reported no biomedical financial interests or potential conflicts of interest.","formattedTitle":"Copy number variants reveal divergent genetic and diagnostic cortical signatures across psychiatric disorders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStructural variants, including rare copy number variants (CNVs) and sex chromosome aneuploidies (SCA), alter gene dosage and confer particularly elevated risk for neurodevelopmental and psychiatric disorders (NPDs), with hazard ratios ranging from 2 to over 10 \u003csup\u003e1\u0026ndash;4\u003c/sup\u003e for NPDs. On the other end of the allele frequency distribution, genome-wide association studies (GWAS) have identified hundreds of common variants associated with conditions including schizophrenia \u003csup\u003e5\u003c/sup\u003e, bipolar disorder\u003csup\u003e6\u003c/sup\u003e, attention deficit hyperactivity disorder\u003csup\u003e7\u003c/sup\u003e, and depression\u003csup\u003e8\u003c/sup\u003e. Together, both rare and common genetic variants contribute substantially to the heritable architecture of NPDs \u003csup\u003e2\u0026ndash;4,9\u0026ndash;11\u003c/sup\u003e. In parallel, large-scale neuroimaging studies, particularly through the ENIGMA consortium\u003csup\u003e12\u003c/sup\u003e, have characterized cortical phenotypes across multiple psychiatric conditions, revealing both disorder-specific and shared patterns of cortical thickness reductions and comparatively modest surface area alterations\u003csup\u003e13\u0026ndash;16\u003c/sup\u003e. A central assumption in psychiatric neuroimaging studies is that the cortical phenotypes observed in psychiatric conditions reflect, in part, the effect of genetic variants that increase psychiatric risk. However, case-control neuroimaging studies conflate genetic liability, environmental exposures, medication effects, and consequences of chronic illness, making it difficult to distinguish the cortical signature of genetic risk from non-genetic factors.\u003c/p\u003e\n\u003cp\u003eA fundamental question therefore remains: are the cortical differences associated with genetic risk variants related to the psychiatric risk they confer? Conversely, do the cortical differences observed in psychiatric disorders reflect their genetic contributions? Convergence would support a genetic interpretation, while divergence would implicate non-genetic factors. Addressing this question requires comparing cortical phenotypes across genetic variants and psychiatric disorders. Previous cross-disorder studies compared cortical thickness across psychiatric conditions\u003csup\u003e17\u0026ndash;20\u003c/sup\u003e, but did not incorporate genetic variant data. Neuroimaging studies of individual CNVs have demonstrated robust cortical differences with larger effect sizes than psychiatric conditions\u003csup\u003e21\u0026ndash;23\u003c/sup\u003e. However, no study has systematically compared cortical phenotypes across multiple NPD-associated CNVs, common variants, and psychiatric diagnoses.\u003c/p\u003e\n\u003cp\u003eCortical thickness (CT) and surface area (SA) are linked to distinct neurobiological processes with largely non-overlapping genetic architectures\u003csup\u003e24,25\u003c/sup\u003e. CT reflects processes including dendritic arborization and myelination continuing into adulthood\u003csup\u003e26,27\u003c/sup\u003e. SA reflects tangential expansion of cortical progenitors during early development and remains stable thereafter\u003csup\u003e26,28\u003c/sup\u003e. These distinct developmental trajectories make CT and SA complementary probes for dissecting genetic versus non-genetic contributions. At the common variant level, prior studies investigating genetic overlap \u003csup\u003e24,29\u0026ndash;31\u003c/sup\u003e between NPDs and cortical phenotypes have reported weak genetic correlations\u003csup\u003e24\u003c/sup\u003e, but these analyses did not distinguish between CT and SA in the context of cross-variant comparisons. \u003c/p\u003e\n\u003cp\u003eHere, we systematically characterize cortical phenotypes across 18 NPD-associated CNVs and aneuploidies, constituting the largest cross-variant neuroimaging analysis of structural variants to date, and directly compare them with cortical phenotypes across psychiatric diagnoses. Specifically, we assessed global and regional measures of CT and SA for the following three categories: i) 8 psychiatric disorders, including medication subgroups; ii) 18 NPD-associated CNVs and aneuploidies; and iii) genome-wide significant common variants (SNPs) associated with NPDs. Variants altering gene dosage of 1 or more genes (CNVs and aneuploidies) were selected based on previously established association with one or more psychiatric disorders \u003csup\u003e1\u0026ndash;4,21,23,32\u0026ndash;35\u003c/sup\u003e. By characterizing the cortical consequences of structural variants alongside diagnostic and common-variant phenotypes, we reveal a striking dissociation between cortical phenotypes associated with genetic risk, both rare and common, and those associated with psychiatric diagnoses, with implications for the interpretation of case-control psychiatric neuroimaging studies. \u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eStructural variants preferentially affect surface area with larger effects than psychiatric diagnoses\u003c/h2\u003e\n\u003cp\u003eWe computed effect sizes (Cohen\u0026apos;s d) for cortical phenotypes across 11 CNVs and aneuploidies from individual-level data, supplemented by published meta-analytic estimates for 7 additional variants (Table 1, Supplementary Table 1). Across these 18 structural variants, effects on total SA were consistently larger than on mean CT (paired comparison, FDR q=2E-2, \u003cstrong\u003eSupplementary Figures 1A,2A\u003c/strong\u003e). Effect sizes (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e) on total SA were 11-fold larger for rare genetic variants compared with NPDs (Wilcoxon rank-sum test, FDR q=8E-4, \u003cstrong\u003eFigures 2A-B\u003c/strong\u003e). This difference in effect sizes was less pronounced for mean CT (4-fold, Wilcoxon rank-sum test, FDR q=7E-3). To assess preferential impact, we computed the ratio of absolute effect sizes for CT and SA in each study. Rare genetic variants showed preferential total SA effects compared to mean CT (median ratio=0.6, one-sample Wilcoxon against 1, FDR q=3E-2, \u003cstrong\u003eFigure 2C, Supplementary Figures 1A-B,2A-B\u003c/strong\u003e). NPDs showed the opposite pattern with preferential mean CT effects (median ratio=2.1, FDR q=3E-2). This was consistent across deletions, duplications, and aneuploidies (\u003cstrong\u003eSupplementary Figure 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAmong clinical high-risk individuals, those later developing psychosis (CHR-PS+) showed a CT/SA ratio of 2.9, contrasting with 1.2 for non-converters. The only NPDs showing preferential SA effects were ADHD and conduct disorder, both with childhood onset, but this did not generalize to ASD. \u003c/p\u003e\n\u003cp\u003eWe next tested whether the preferential SA effects observed for rare variants extended to common genetic variants associated with NPDs. We first examined genetic correlations between NPDs and global cortical metrics using summary statistics from Grasby et al. (2020). We observed weak genetic overlap with SA only, where ADHD, BD, and MDD showed significant correlations (FDR q\u0026lt;0.1; \u003cstrong\u003eSupplementary Figure 4\u003c/strong\u003e). To capture overlap beyond global correlations, we tested whether NPD-associated single-nucleotide polymorphisms (SNPs) were enriched for associations with cortical metrics. We ranked NPD-associated SNPs by their cortical GWAS associations. NPD SNPs showed significant enrichment (above-median ranking) in SA associations for ADHD, BD, and MDD (permutation FDR q\u0026lt;0.0001; \u003cstrong\u003eSupplementary Figure 5, Supplementary Table 5\u003c/strong\u003e). None showed CT enrichment. Together, these findings indicate that both rare and common NPD-associated variants preferentially associate with total SA rather than mean CT. \u003c/p\u003e\n\u003ch2\u003eCortical thickness effects in psychiatric diagnoses reflect non-genetic factors rather than structural variant contributions\u003c/h2\u003e\n\u003cp\u003eWe asked if the preferential association of NPDs with mean CT compared to total SA could be in part influenced by non-genetic factors rather than structural variant contributions. To do so, we first assessed NPD medication subgroups (also a proxy for disorder severity \u003csup\u003e36\u003c/sup\u003e, \u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTable 3\u003c/strong\u003e). Effect sizes on mean CT were 8-fold larger in medicated compared to unmedicated subgroups (FDR q=4E-2, \u003cstrong\u003eFigure 2D-E\u003c/strong\u003e). Total SA did not differ by medication status. Medicated subgroups showed preferential CT effects (ratio\u0026gt;1), while unmedicated subgroups showed balanced effects (\u003cstrong\u003eFigure 2F, Supplementary Figure 1C-D,2C-D\u003c/strong\u003e). We acknowledge that medication status may proxy for disorder severity, illness duration, or other clinical factors. Nevertheless, the selective sensitivity of CT, but not SA, to the medication subgroup is consistent with CT reflecting factors that vary with clinical state rather than genetic liability.\u003c/p\u003e\n\u003cp\u003eSecond, using summary statistics from the ENIGMA relatives study \u003csup\u003e37\u003c/sup\u003e, we examined cortical differences in first-degree relatives of individuals with BD or SCZ. Individuals with BD or SCZ diagnoses showed significantly reduced mean CT compared to controls, while their unaffected first-degree relatives did not show any significant associations with CT (\u003cstrong\u003eSupplementary Figure 6\u003c/strong\u003e). Neither group showed SA differences. This pattern suggests that CT differences may emerge with, or following, disorder onset, rather than reflecting genetic liability shared by first-degree relatives.\u003c/p\u003e\n\u003cp\u003eThird, because SA expansion occurs early in life, we asked if SA phenotypes were easier to detect in pediatric NPD groups. This was not the case, and age-stratified analyses in NPDs were unable to detect a clear shift toward associations with SA (\u003cstrong\u003eSupplementary Figure 7\u003c/strong\u003e), though power limitations and heterogeneity across disorders constrain interpretation.\u003c/p\u003e\n\u003ch2\u003eStructural variants preferentially affect sensorimotor cortex, diverging from psychiatric diagnostic patterns\u003c/h2\u003e\n\u003cp\u003eWe asked if the preferential effects on total SA for genetic variants and mean CT for psychiatric diagnoses were uniformly distributed across the cerebral cortex or localized to specific cortical regions. To do so, we computed the regional cortical effect size maps for 11 CNVs and compared them to regional maps of the 8 NPDs examined above, stratified by age groups and medication \u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTables 1-3)\u003c/strong\u003e. \u003c/p\u003e\n\u003cp\u003eSignificant associations with regional SA or CT (at least one ROI) were observed for 11 and 7 out of 18 NPD groups, respectively, and for all 11 CNVs (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 8\u003c/strong\u003e). We did not detect significant associations for un-medicated sub-groups; as such, the medication sub-groups were excluded from the regional analyses below. Following previous cross-disorder analyses \u003csup\u003e17,18,38\u003c/sup\u003e, we computed latent dimensions of cortical regional phenotypes using principal component analysis across effect size profiles (\u003cstrong\u003eFigures 3-4 and Supplementary\u003c/strong\u003e \u003cstrong\u003eTable 6\u003c/strong\u003e). The first principal component explained more than a quarter of the variance of regional beta-estimates across CNVs and NPDs, for both CT and SA. \u003c/p\u003e\n\u003cp\u003eTo contextualize these latent regional cortical profiles, we tested their similarities with the well-established cortical gradient (\u003cstrong\u003eMethods\u003c/strong\u003e), which ranks regions from primary sensory-motor cortices to higher-order association cortices\u003csup\u003e39,40\u003c/sup\u003e. NPD-associated rare genetic variants showed higher loadings in sensorimotor regions compared to association regions (negative correlations with the cortical gradient, \u003cstrong\u003eFigure 5, and Supplementary\u003c/strong\u003e \u003cstrong\u003eTables 6,7\u003c/strong\u003e). NPDs maps showed the opposite correlations with the cortical gradient, highlighting higher loadings in association regions. The CNVs and NPDs latent dimensions showed negative pairwise correlations (SA: r=-0.38, p-spin \u0026lt; 0.05; CT: r=-0.32, not significant; \u003cstrong\u003eSupplementary Figure 9\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eWe then asked if our rare variant findings were generalizable to the broader genetic contribution to cortical structure. Towards this, we examined both twin and SNP heritability estimates for regional CT and SA \u003csup\u003e24\u003c/sup\u003e. Heritability was up to 2-fold higher in sensorimotor regions compared to association regions (\u003cstrong\u003eSupplementary Figure 10, \u003c/strong\u003enegative correlations with the cortical gradient, r=-0.47 to -0.75, p-spin\u0026lt;0.05, \u003cstrong\u003eFigure 5\u003c/strong\u003e). Finally, we assessed the regional profiles of beta estimates from SA GWAS for the NPD-associated common variant (SNPs), to estimate the latent dimension of NPD-associated SNP profiles (\u003cstrong\u003eSupplementary Figure 11\u003c/strong\u003e). The PC1 for SA explained 24% of variance, and SA-SNPs showed higher effects in sensorimotor regions (r=-0.49, p-spin\u0026lt;0.05) and correlated positively with PC1 for CNVs (SA, r=0.78, p-spin\u0026lt;0.05). The alignment between rare variant effects, common variant effects, and heritability estimates suggests that the sensorimotor preference reflects elemental aspects of how genetic variation influences cortical development, rather than a peculiarity of selected CNVs.\u003c/p\u003e\n\u003cp\u003eThese findings were robust to sensitivity analyses using three alternative consensus cortical maps: mean absolute effect size, percentage of significance, and variance; as well as leave-one-out analyses across CNVs and NPDs (\u003cstrong\u003eSupplementary Figures 9,12,13\u003c/strong\u003e, and \u003cstrong\u003eSupplementary Tables 6,7\u003c/strong\u003e). All three showed sensorimotor cortices preference for genetic variants and association cortices preference for NPDs.\u003c/p\u003e\n\n\u003ch2\u003eOpposing genetic effects on surface area resolve the polygenic paradox \u003c/h2\u003e\n\u003cp\u003eThe preceding analyses reveal a paradox: individual genetic risk variants, both rare CNVs and common SNPs, produce preferential surface area associations with global metric and sensorimotor regions, yet psychiatric disorders show negligible surface area associations. We provide three converging lines of evidence to explain this discrepancy. First, polygenic risk scores (PRS) for BD and SCZ showed no association with total SA in 31,000 UK Biobank participants of European ancestry (\u003cstrong\u003eSupplementary Figure 14, Supplementary Tables 8,9\u003c/strong\u003e), despite individual BD-associated SNPs showing significant SA enrichment (\u003cstrong\u003eSupplementary Figure 5\u003c/strong\u003e). To investigate this null PRS finding, we examined effect sizes on SA for each independent genome-wide significant SNP using cortical GWAS summary statistics \u003csup\u003e24\u003c/sup\u003e, after harmonizing effect alleles between disorder and cortical GWAS. Approximately half of BD- and SCZ-associated SNPs showed negative beta estimates for total SA (BD: 52%; SCZ: 54%; allele harmonized, \u003cstrong\u003eSupplementary Figure 14\u003c/strong\u003e), with the remainder showing positive effects. A permutation-based variance inflation test confirmed that these disorder-associated SNPs carry individually meaningful SA effects: their cortical effect sizes were significantly more variable than those of minor allele frequency (MAF) matched random SNPs (SCZ: variance ratio = 1.32, p=0.037; BD: variance ratio = 1.23, p=0.089; 10,000 permutations, \u003cstrong\u003eSupplementary Table 10\u003c/strong\u003e), despite the near-zero mean. Simulations using empirical SNP effect sizes demonstrated that under the observed distribution of positive and negative cortical effects, the aggregate PRS\u0026ndash;SA association (R\u0026sup2; \u0026asymp; 10⁻⁵) is over two orders of magnitude weaker than when all effects are forced to align in the same direction (R\u0026sup2; \u0026asymp; 6 \u0026times; 10⁻\u0026sup3;), without changing their magnitudes (10,000 simulations; \u003cstrong\u003eSupplementary Table 11\u003c/strong\u003e). Thus, cancellation of opposing effects, not the absence of individual SNP effects, accounts for the null PRS\u0026ndash;SA finding.\u003c/p\u003e\n\u003cp\u003eSecond, this balanced proportion of negative and positive effects on regional SA extended to rare variants (CNVs). Nearly half of the CNVs loaded positively while the other half loaded negatively on the first regional SA latent dimension (\u003cstrong\u003eFigure 3D\u003c/strong\u003e). NPD-associated SNPs showed a similar balanced proportion of negative and positive loadings (\u003cstrong\u003eSupplementary Figure 11B\u003c/strong\u003e). Third, consistent with polygenic cancellation, unaffected first-degree relatives of a proband with a psychiatric diagnosis, a proxy for multifactorial (including polygenic) liability, also showed weak associations with SA (similar to probands, \u003cstrong\u003eSupplementary Figure 6\u003c/strong\u003e). Together, these findings resolve the apparent contradiction between i) high SA heritability, ii) clear individual variant effects on SA, and iii) null associations between NPD-PRS and SA. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the first systematic characterization of cortical phenotypic consequences across multiple NPD-associated structural variants (CNVs and aneuploidies), revealing a striking dissociation with cortical signatures of the psychiatric conditions these variants predispose to. Genetic variants associated with NPDs, both rare CNVs and common SNPs, preferentially affected surface area, while psychiatric conditions preferentially affected cortical thickness. This dissociation extended to regional patterns: genetic variants showed larger effects in sensorimotor cortical regions, while psychiatric diagnoses showed larger effects in association regions. We further demonstrate that individual genetic variants produce clear SA effects that are heterogeneous and cancel out when genetic variants are aggregated into polygenic scores. These heterogeneous effects may also explain why the association between NPDs (polygenic conditions) and cortical SA remains difficult to detect at the group level. \u003c/p\u003e\n\u003cp\u003eFor the structural variant field, our findings demonstrate that systematic cross-variant neuroimaging analyses reveal shared properties of CNV effects on brain structure, including preferential SA and sensorimotor impact, that would be invisible when studying individual variants in isolation \u003csup\u003e21,23,33,34,41\u003c/sup\u003e. Notably, these properties extend to NPD-associated common variants, indicating a consistent genetic architecture of cortical effects across the allele frequency spectrum. A distinct but complementary finding is that individual structural and common variants show both positive and negative effects on SA, resolving why these clear individual-variant effects vanish in aggregate genetic analyses. Cortical surface area in sensorimotor regions may therefore represent a more genetically interpretable and sensitive endophenotype for structural variant effects than cortical thickness, which appears more sensitive to non-genetic factors. These results underscore the value of phenotyping structural variants through neuroimaging to understand how altered gene dosage shapes cortical development.\u003c/p\u003e\n\u003cp\u003eThe preferential association between genetic variants and total SA aligns with the higher heritability estimates for total SA compared to mean CT\u003csup\u003e24\u0026ndash;27,42\u003c/sup\u003e. NPD-associated variants impacting SA suggest that they operate through early mechanisms of cortical expansion\u003csup\u003e24,27,43\u003c/sup\u003e. This SA preference is consistent across diverse structural variants, including deletions, duplications, and aneuploidies, and aligns with recent evidence that CNV deletions and duplications across the genome reduce surface area by disrupting the proliferation of neural progenitor cells during fetal cortical development\u003csup\u003e43\u003c/sup\u003e. The preferential association between mean CT and psychiatric diagnoses may reflect non-genetic factors. Medicated subgroups showed larger CT but not SA effects, consistent with randomized trial evidence of medication-induced CT changes \u003csup\u003e44\u003c/sup\u003e. We acknowledge that medication status may co-vary with disorder severity, illness duration, or other clinical variables. Disentangling medication effects from illness severity requires longitudinal and randomized designs beyond the scope of this cross-sectional analysis. \u003c/p\u003e\n\u003cp\u003eAt the regional level, structural and common genetic variants preferentially affected sensorimotor regions, which develop earlier \u003csup\u003e39\u003c/sup\u003e and show higher heritability \u003csup\u003e24\u0026ndash;27,42\u003c/sup\u003e compared to association regions. Psychiatric diagnoses were preferentially associated with association cortices, consistent with prior cross-disorder studies reporting frontotemporal thickness reductions \u003csup\u003e17,18\u003c/sup\u003e. Notably, even within a single structural variant, the 22q11.2 deletion, carriers who develop psychosis show focal CT reductions in association regions compared to non-psychotic carriers, consistent with diagnosis-related CT effects being layered on top of the genetic signature\u003csup\u003e45\u003c/sup\u003e. Association regions with protracted development may be more vulnerable to environmental perturbations and illness-related plasticity, while sensorimotor regions may reflect earlier effects of genetic liability.\u003c/p\u003e\n\u003cp\u003eA central puzzle in psychiatric and neuroimaging genetics has been the weak genetic correlations between NPDs and cortical structure, despite substantial heritability of both and the assumption that NPD risk conferred by genetic variants is mechanistically related to the effects of the same variants on brain structure \u003csup\u003e30\u003c/sup\u003e. Previous studies have demonstrated that genetic overlap (of common variants) between two traits may be under-estimated by genetic correlation values due to the heterogeneous effects of genetic variants \u003csup\u003e29,46\u003c/sup\u003e. In line with these studies, we show that rare and common genetic variants increasing risk for NPDs do associate with cortical structure, particularly SA, suggesting a higher genetic overlap despite weak genetic correlations\u003csup\u003e24,30\u003c/sup\u003e. However, we show that their effects are heterogeneous, with roughly equal numbers of NPD-risk variants increasing versus decreasing SA. When genetic-risk variants are aggregated into additive polygenic risk scores for NPDs or in psychiatric neuroimaging case-control group analysis, these heterogeneous/opposing effects on SA are canceled, producing null or weak associations. \u003c/p\u003e\n\u003cp\u003eOur findings have several implications for psychiatric neuroimaging research. The CT and association-region effects predominant in psychiatric diagnoses may largely reflect consequences of illness, including medication, chronicity, comorbidity, and lifestyle factors, rather than the genetic variants that increase disorder risk. We note that this does not diminish the importance of such findings for understanding disease burden or treatment effects, but it reframes their interpretation. Second, additive polygenic models have limitations for imaging outcomes; methods separating positive and negative effects may improve prediction. Third, cross-sectional case-control designs conflate genetic and non-genetic contributions; longitudinal designs beginning before illness onset are needed to disentangle genetic and non-genetic contributions.\u003c/p\u003e\n\u003cp\u003eSeveral limitations of our study warrant consideration. ENIGMA summary statistics were derived from heterogeneous studies with varying protocols, age ranges, and clinical characterization. While ENIGMA\u0026apos;s harmonization procedures mitigate protocol differences, residual heterogeneity may affect comparisons across disorders. Medication subgroup analyses are constrained by the information available in ENIGMA publications; we could not examine dose-response or duration. Third, while genetic risk variants have strong effects on SA, with substantial heterogeneity, our study design does not allow us to distinguish whether SA phenotypes are related to mechanisms associated with increasing risk for NPDs or merely reflect the highly pleiotropic nature of these variants \u003csup\u003e47,48\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOverall, genetic variants preferentially affect surface area and sensorimotor regions, consistent with higher heritability and early developmental processes involved in these regions and phenotypes. Psychiatric diagnoses are preferentially associated with cortical thickness and association regions, with sensitivity to medication status suggesting non-genetic contributions. NPD-associated risk variants were evenly split between those increasing and decreasing cortical surface area, likely explaining why aggregating risk variants for NPDs using polygenic scores only yields weak correlations with this cortical phenotype. These findings suggest that case-control differences in psychiatric neuroimaging may reflect other factors, including consequences of illness rather than its genetic contributions, with implications for interpretation of case-control findings. By systematically characterizing cortical consequences across multiple structural variants, this study provides a framework for understanding how gene dosage alterations shape brain phenotypes and why these effects remain largely undetectable in aggregate genetic or diagnostic analyses. \u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eParticipants\u003c/h2\u003e\n\u003cp\u003eWe analyzed CT and SA integrating individual-level neuroimaging data from approximately 33,000 participants (730 CNV/aneuploidy carriers, 870 matched controls, and 31,413 UK Biobank participants for polygenic risk score analyses), together with published ENIGMA summary statistics for 8 psychiatric disorders (\u003cstrong\u003eFigure 1\u003c/strong\u003e, \u003cstrong\u003eTable 1, Supplementary Tables 1-4\u003c/strong\u003e). ENIGMA summary statistics encompassed approximately 12,600 cases and 19,200 controls across 8 psychiatric disorders (\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e), along with medication, age-stratified, and diagnostic subgroups (\u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e).\u003c/p\u003e\n\n\u003ch3\u003eRare genetic variant participants\u003c/h3\u003e\n\u003cp\u003eWe used individual-level neuroimaging data for carriers of recurrent CNVs or sex chromosome aneuploidies from clinical cohorts and the UK Biobank general population sample. The final sample included 730 carriers of 18 distinct variants and 870 matched controls. Demographic details, coordinates of each of the CNVs, as well as NPD risk, the Hazard ratios (HR) or Odds Ratios (OR) for each CNV, derived from the Danish iPSYCH2015 dataset\u003csup\u003e1,2\u003c/sup\u003e and Modenato et al. \u003csup\u003e21\u003c/sup\u003e are provided in \u003cstrong\u003eTable 1 and\u003c/strong\u003e \u003cstrong\u003eSupplement Table 1\u003c/strong\u003e. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e: Signed consents were obtained by investigators from each cohort for all participants and/or their legal representatives prior to the investigation. This study was approved by the Ethics committee from the CHU Sainte-Justine Hospital. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical cohorts\u003c/strong\u003e: CNV carriers were recruited following genetic testing referral for neurodevelopmental concerns or as relatives of identified carriers. Contributing cohorts included: 16p11.2 European Consortium, Brain Canada multi-site cohort, UCLA 22q11.2 deletion syndrome cohort, Cardiff University CNV cohort, and NIMH sex chromosome aneuploidy cohort (Detailed in prior publications \u003csup\u003e34\u003c/sup\u003e). Controls were defined as individuals from the same cohorts not carrying any NPD-associated CNVs at the examined loci.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUK Biobank\u003c/strong\u003e: Additional CNV carriers were identified in the UK Biobank\u003csup\u003e49\u003c/sup\u003e general population sample (application 40980) using a validated CNV calling pipeline \u003csup\u003e22,41\u003c/sup\u003e. UK Biobank controls were participants not carrying any recurrent CNVs selected for this study.\u003c/p\u003e\n\n\u003ch3\u003ePsychiatric disorder and cortical phenotype associated common variants\u003c/h3\u003e\n\u003cp\u003eGenome-wide significant SNPs were extracted from published Psychiatric Genomics Consortium GWAS for ADHD \u003csup\u003e7\u003c/sup\u003e; BD\u003csup\u003e6\u003c/sup\u003e; MDD \u003csup\u003e8\u003c/sup\u003e; and SCZ \u003csup\u003e5\u003c/sup\u003e (\u003cstrong\u003eTable 1\u003c/strong\u003e and \u003cstrong\u003eSupplementary Tables 4,5\u003c/strong\u003e). To assess cortical associations of these NPD-associated SNPs, we obtained estimates from the ENIGMA cortical structure GWAS \u003csup\u003e24\u003c/sup\u003e, which provides effect estimates and association statistics for regional and global CT and SA measures. We also obtained genetic correlation estimates (rg) between NPDs and global cortical metrics from the same source \u003csup\u003e24\u003c/sup\u003e. This allowed two complementary approaches: (i) genome-wide genetic correlations to assess genetic overlap between NPDs and cortical structure, and (ii) a SNP-level cross-referencing approach testing whether variants identified through psychiatric GWAS show enrichment for cortical structure associations beyond what genome-wide correlations capture.\u003c/p\u003e\n\n\u003ch3\u003eNeurodevelopmental and psychiatric disorder (NPD) participants\u003c/h3\u003e\n\u003cp\u003eIn this study, we analyzed published summary statistics for 8 psychiatric disorders from the following published ENIGMA\u003csup\u003e12\u003c/sup\u003e studies: attention-deficit hyperactivity disorder (ADHD)\u003csup\u003e13,50\u003c/sup\u003e; autism spectrum disorder (ASD)\u003csup\u003e13,51\u003c/sup\u003e; bipolar disorder (BD)\u003csup\u003e14\u003c/sup\u003e; clinical high-risk for psychosis (CHR-PS)\u003csup\u003e52\u003c/sup\u003e; conduct disorder (CD)\u003csup\u003e53\u003c/sup\u003e; major depressive disorder (MDD)\u003csup\u003e15\u003c/sup\u003e; obsessive-compulsive disorder (OCD)\u003csup\u003e13,54\u003c/sup\u003e; and schizophrenia (SCZ)\u003csup\u003e16\u003c/sup\u003e) (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTables 2,3).\u003c/strong\u003e Where available, we extracted summary statistics for: (i) medication subgroups (BD: lithium, antiepileptics, atypical antipsychotics; SCZ: first-generation antipsychotics, second-generation antipsychotics, combined); (ii) age-stratified samples (pediatric, adolescent/young adult, adult); and (iii) diagnostic subtypes (CHR-PS converters versus non-converters).\u003c/p\u003e\n\n\n\u003ch2\u003eMRI acquisition and preprocessing\u003c/h2\u003e\n\u003ch3\u003eRare genetic variants\u003c/h3\u003e\n\u003cp\u003eT1-weighted volumetric images at 0.8-1mm isotropic resolution were acquired across contributing sites using 1.5T and 3T scanners from multiple vendors (Siemens, GE, Philips). Detailed acquisition parameters for each cohort are provided in Supplementary Information. Visual quality control was performed by two trained raters (C.M., K.K.) following ENIGMA standardized protocols (https://github.com/ENIGMA-git). Sex chromosome aneuploidy samples underwent separate quality control (W.S., A.R.) using equivalent criteria.\u003c/p\u003e\n\n\u003ch3\u003eNeurodevelopmental and psychiatric disorder samples\u003c/h3\u003e\n\u003cp\u003eAll contributing ENIGMA working groups followed standardized quality control and FreeSurfer \u003csup\u003e55\u003c/sup\u003e processing protocols as described in respective source publications.\u003c/p\u003e\n\n\u003ch3\u003eCortical metric extraction\u003c/h3\u003e\n\u003cp\u003eFreeSurfer version 5.3.0 was used to extract cortical thickness (CT) and surface area (SA) for 68 regions of the Desikan-Killiany atlas, plus global measures (total surface area, mean cortical thickness). Left and right hemisphere values were averaged for primary analyses to reduce multiple comparisons and because i) some of the ENIGMA summary statistics are only available for averaged metrics; and ii) prior work has shown high bilateral correlations for these metrics in CNV carriers\u003csup\u003e21,22,41\u003c/sup\u003e. UK Biobank samples used for PRS analyses were processed using FreeSurfer 6.0 via the UK Biobank imaging pipeline \u003csup\u003e56\u003c/sup\u003e.\u003c/p\u003e\n\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\n\u003ch3\u003eEffect size computation\u003c/h3\u003e\n\u003cp\u003eFor rare genetic variants, Cohen\u0026apos;s d effect sizes for carrier-control differences were computed using linear regression models adjusting for age, sex, and site. For regional surface area analyses, total surface area was included as an additional covariate to isolate regional effects from global scaling, consistent with ENIGMA protocols.\u003c/p\u003e\n\u003cp\u003eFor NPDs, Cohen\u0026apos;s d values were extracted directly from published ENIGMA studies. All NPD effect sizes were computed with adjustment for age, sex, and site. Regional surface area analyses in ENIGMA studies included adjustment for total SA or intracranial volume, except for MDD, where such adjusted results were unavailable. Summary statistics from ENIGMA studies used site-covariate approaches. As such, site effects were modeled as covariates in all analyses. Maintaining the same statistical framework across NPD and CNV analyses ensured consistency. \u003c/p\u003e\n\u003cp\u003eFor additional CNV and aneuploidy variants with smaller sample sizes in our cohorts (Turner syndrome, Down syndrome, XXX, XXYY, Williams-Beuren syndrome, 16p11.2 distal deletion/duplication), we utilized previously published meta-analytic effect sizes from the literature \u003csup\u003e21\u003c/sup\u003e (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e).\u003c/p\u003e\n\n\u003ch3\u003eMultiple comparison correction\u003c/h3\u003e\n\u003cp\u003eFalse discovery rate (FDR) correction using the Benjamini-Hochberg procedure was applied within each analysis. Statistical significance threshold was set at FDR-corrected q\u0026lt;0.05.\u003c/p\u003e\n\n\u003ch3\u003eRationale for cross-variant and cross-disorder analyses\u003c/h3\u003e\n\u003cp\u003eBecause many individuals meet criteria for several psychiatric disorders during their lifetime, there have been significant cross-disorder efforts to study the genetic architecture across psychiatric disorders \u003csup\u003e9,11\u003c/sup\u003e. The same rationale has led neuroimaging consortia to investigate neurobiological processes across psychiatric conditions. Neuroimaging studies of individual CNVs have demonstrated distinct clinical features and brain signatures \u003csup\u003e21,22,33,34,41\u003c/sup\u003e. However, a cross-disorder, cross-CNV neuroimaging investigation has not yet been conducted.\u003c/p\u003e\n\n\u003ch3\u003eAssessing non-structural variant contributions using medication subgroup analysis\u003c/h3\u003e\n\u003cp\u003eEffect sizes for medicated versus unmedicated NPD subgroups were extracted from published ENIGMA studies where available. We compared effect sizes between medication subgroups using Wilcoxon rank-sum tests and computed CT/SA ratios for each subgroup to assess whether medication status differentially influenced the two metrics.\u003c/p\u003e\n\u003cp\u003eWe acknowledge that medication status in observational studies may proxy for disorder severity, illness duration, treatment resistance, or other clinical factors that co-vary with pharmacological treatment. Causal interpretation of medication effects requires randomized designs beyond the scope of this cross-sectional analysis.\u003c/p\u003e\n\n\u003ch3\u003eGlobal metric comparisons\u003c/h3\u003e\n\u003cp\u003eWe compared absolute Cohen\u0026apos;s d values between CNVs/aneuploidies and NPDs using Wilcoxon rank-sum tests. To assess preferential effects on CT versus SA within each category, we computed the ratio of absolute effect sizes (\u003cem\u003eCohen_d_CT / Cohen_d_SA\u003c/em\u003e) for each condition and tested whether median ratios differed from 1 (indicating balanced effects) using one-sample Wilcoxon signed-rank tests. Paired comparisons of CT versus SA effect size distributions within CNV and NPD groups used paired t-tests.\u003c/p\u003e\n\n\u003ch3\u003eCommon variant enrichment analysis\u003c/h3\u003e\n\u003cp\u003eTo test whether NPD-associated SNPs showed enrichment for cortical structure associations beyond chance, we employed a ranking-based approach. All SNPs with available data in the ENIGMA cortical GWAS were ranked by their p-value association with CT or SA. For each NPD, we identified genome-wide significant SNPs (p\u0026lt;5E-8) and computed the median rank of these SNPs within the cortical GWAS ranking.\u003c/p\u003e\n\u003cp\u003eStatistical significance was assessed using permutation testing (10,000 permutations), randomly sampling equal numbers of SNPs from the full GWAS and recomputing median ranks to generate a null distribution. Enrichment was defined as median rank significantly above the 50th percentile (i.e., NPD SNPs ranked higher than expected by chance in cortical associations). FDR correction was applied across the four NPDs tested. Reciprocal analyses ranked cortical GWAS significant SNPs within NPD GWAS to assess bidirectional enrichment.\u003c/p\u003e\n\n\u003ch3\u003eCommon variant variance inflation test\u003c/h3\u003e\n\u003cp\u003eTo test whether disorder-associated SNPs carry individually meaningful effects on cortical surface area, we performed a permutation-based variance inflation test. For each disorder (BD, SCZ), we identified independent genome-wide significant lead SNPs from published GWAS (BD: 261 loci\u003csup\u003e6\u003c/sup\u003e; SCZ: 287 loci\u003csup\u003e5\u003c/sup\u003e) and extracted their total surface area effect sizes from the ENIGMA cortical GWAS summary statistics\u003csup\u003e24\u003c/sup\u003e. Effect alleles were harmonized between the disorder and cortical GWAS; SNPs with unresolvable allele mismatches were excluded, yielding 234 BD and 259 SCZ SNPs. We computed the observed variance of SA betas across disorder-associated SNPs and compared it to a null distribution generated by randomly sampling the same number of SNPs from the full cortical GWAS, matched on minor allele frequency using decile bins (10,000 permutations). The one-sided permutation p-value was computed as the proportion of null variance values equal to or exceeding the observed variance.\u003c/p\u003e\n\n\u003ch3\u003eRegional consensus maps\u003c/h3\u003e\n\u003cp\u003eTo characterize consistent regional patterns for the cross-CNVs and cross-NPDs comparison, we followed the approach used in prior cross-disorder studies\u003csup\u003e17,18,34,38,41\u003c/sup\u003e and ran Principal Component Analysis (PCA). PCA was performed on the matrix of Cohen\u0026apos;s d values (regions \u0026times; conditions) using the FactoMineR\u003csup\u003e57\u003c/sup\u003e package. The first principal component (PC1) captures the dominant pattern of covariation across conditions. PC1 loadings were aligned so that regions with higher variance corresponded to positive loadings for interpretability. Only conditions with at least one FDR-significant regional association were included in regional consensus analyses, ensuring patterns were driven by detectable effects. This criterion excluded medication subgroups, which showed no significant regional effects individually.\u003c/p\u003e\n\u003cp\u003eAs a sensitivity analysis, we computed regional consensus maps using three complementary approaches. i) Mean absolute effect size: Mean of absolute \u003cem\u003eCohen\u0026apos;s d\u003c/em\u003e values per region across all conditions within each category, identifying regions with consistently large effects. ii) Percentage significance: Proportion of conditions showing FDR-significant effects per region, identifying regions frequently affected regardless of effect magnitude. and iii) Variance: Variance in \u003cem\u003eCohen\u0026apos;s d\u003c/em\u003e values per region across conditions, identifying regions of heterogeneous versus homogeneous effects. \u003c/p\u003e\n\n\u003ch3\u003eCortical gradient analysis\u003c/h3\u003e\n\u003cp\u003eRegional profiles were correlated with the sensorimotor-to-association cortical gradient derived from the first principal component of gene expression across cortical regions \u003csup\u003e39,40\u003c/sup\u003e. This gradient ranks regions from primary sensory-motor cortices (low values) to higher-order association cortices (high values), capturing the hierarchy of cortical organization related to development, connectivity, and function. Gradient values for 34 left hemisphere Desikan-Killiany regions were obtained using the neuromaps package \u003csup\u003e40\u003c/sup\u003e. \u003c/p\u003e\n\n\u003ch3\u003eStatistical significance of spatial correlations\u003c/h3\u003e\n\u003cp\u003eStatistical significance of spatial correlations was assessed using spin permutation\u003csup\u003e58\u003c/sup\u003e testing (1,000 permutations) to account for spatial autocorrelation in cortical data. Spin permutation preserves the spatial structure of the cortex while generating null distributions, providing more conservative and appropriate inference than parametric tests for spatially embedded data.\u003c/p\u003e\n\n\u003ch3\u003eBrain map visualizations were generated using \u003cem\u003eggseg\u003c/em\u003e \u003csup\u003e59\u003c/sup\u003e. \u003c/h3\u003e\n\n\u003ch3\u003eTwin and SNP Heritability Estimates\u003c/h3\u003e\n\u003cp\u003eTwin heritability and SNP heritability estimates for regional CT and SA were obtained from published ENIGMA genetic architecture studies \u003csup\u003e24\u003c/sup\u003e. Regional heritability profiles were correlated with the cortical gradient using spin permutation\u003csup\u003e58\u003c/sup\u003e testing to assess whether genetic determination of cortical structure varies systematically across the cortical hierarchy.\u003c/p\u003e\n\n\u003ch3\u003ePolygenic risk score analysis\u003c/h3\u003e\n\u003cp\u003eStandard polygenic risk scores (PRS) for BD and SCZ were obtained from UK Biobank (data fields 26214, 26275), computed using established methods with optimized p-value thresholds. We restricted analyses to participants of European ancestry (to match GWAS discovery samples) who did not carry any of the recurrent CNVs examined in this study (final n=31,000, \u003cstrong\u003eSupplementary Table 8\u003c/strong\u003e). Linear regression models tested associations between PRS and cortical metrics, adjusting for age, sex, imaging assessment center, and the first 10 genetic ancestry principal components. For regional SA analyses, total SA was included as an additional covariate. FDR correction was applied across all PRS-cortical associations (\u003cstrong\u003eSupplementary Table 9\u003c/strong\u003e). To examine the directionality of individual SNP effects, we extracted beta estimates for NPD-associated SNPs from the ENIGMA cortical structure GWAS\u003csup\u003e24\u003c/sup\u003e and computed the proportion with negative versus positive effects on CT and SA. This analysis tests whether the null PRS associations reflect the true absence of genetic effects or cancellation of opposing individual SNP effects.\u003c/p\u003e\n\n\u003ch3\u003ePolygenic risk score (PRS)\u0026ndash;surface area synthetic simulation\u003c/h3\u003e\n\u003cp\u003eTo quantify the impact of opposing SNP effects on PRS\u0026ndash;surface area associations, we simulated PRS\u0026ndash;SA associations under empirical and counterfactual sign distributions. For each disorder, we used the harmonized set of independent lead SNPs, their disorder effect sizes (log-odds ratios), and their SA effect sizes standardized to a per-SD scale by dividing by the phenotype standard deviation estimated from the GWAS summary statistics. For each of 10,000 replicates, genotypes for 31,000 synthetic individuals were drawn from a binomial distribution (n = 2, p = MAF) at each SNP. PRS was computed as the weighted sum of genotypes using disorder effect sizes. The cortical phenotype was constructed as the sum of a genetic component (weighted by standardized cortical betas) and Gaussian noise, calibrated so that the genetic component contributed approximately 1% of total phenotypic variance, consistent with the expected contribution of ~240 genome-wide significant loci. Two scenarios were compared: the empirical scenario, using cortical betas with their original signs, and the concordant scenario, in which all cortical betas were forced to share the sign of the corresponding disorder beta while preserving their magnitudes. R\u0026sup2; was computed for each replicate as the squared correlation between PRS and the simulated cortical phenotype.\u003c/p\u003e\n\n\u003ch3\u003eFirst Degree Relatives analysis\u003c/h3\u003e\n\u003cp\u003eEffect sizes for first-degree relatives (parents and siblings) of BD and SCZ probands were extracted from published ENIGMA relatives study summary statistics\u003csup\u003e37\u003c/sup\u003e. We compared effect sizes between diagnosed probands and their unaffected relatives using descriptive approaches and assessed whether relative effects were significantly different from zero using one-sample t-tests, testing the hypothesis that cortical differences emerge with disorder diagnosis rather than being present in at-risk individuals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eResource Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaterials \u0026amp; Correspondence\u003c/p\u003e\n\u003cp\u003eRequests for further information and resources should be directed to and will be fulfilled by the lead PI, Sebastien Jacquemont ([email protected]).\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eUK Biobank data was downloaded under the application 40980 and may be accessed via their standard data access procedure (see http://www.ukbiobank.ac.uk/register-apply). UK Biobank CNVs were called using the pipeline developed in the Jacquemont Lab, as described at https://github.com/MartineauJeanLouis/MIND-GENESPARALLELCNV. The final CNV calls are available for download from the UK Biobank returned datasets (Return ID: 3104, https://biobank.ndph.ox.ac.uk/ukb/dset.cgi?id=3104). The \u0026nbsp;22q11.2 \u0026nbsp;UCLA \u0026nbsp; raw data are currently available by request from the project PI. \u0026nbsp;Raw neuroimaging data for rare variants are available through request and data access agreement from the PIs of the projects (Brain Canada: S.J. CHUSJ Montreal; 22q11.2: C.E.B. UCLA, Cardiff: D.E.J.L., M.J.O., M.V.B., J.H, Cardiff University; SCA: A.R. NIMH). References to the processing pipeline and R package versions used for analysis are listed in the methods. The GWAS summary statistics are publicly available and can be accessed following the reference papers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eThe code for generating all the figures, along with processed summary measures, is available in the following GitHub repository:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://github.com/kkumar-iitkgp/ct_sa_across_disorders_and_variants.git\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSource data\u003c/p\u003e\n\u003cp\u003eThe source data for generating all the figures, and statistics, is included in the supplement tables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Calcul Quebec (http://www.calculquebec.ca) and Compute Canada (http://www.computecanada.ca), the Brain Canada Multi-Investigator initiative, NIH U01 grant for CAMP (1U01MH119690-01), the Canadian Institutes of Health Research, CIHR_400528, The Institute of Data Valorization (IVADO) through the Canada First Research Excellence Fund, Healthy Brains for Healthy Lives through the Canada First Research Excellence Fund. The Cardiff CNV cohort was supported by the Wellcome Trust Strategic Award \u0026ldquo;DEFINE\u0026rdquo; and the National Centre for Mental Health with funds from Health and Care Research Wales (code 100202/Z/12/Z). Data from the UCLA cohort provided by Dr. Bearden (participants with 22q11.2 deletions or duplications and controls) was supported through grants from the NIH (U54EB020403), NIMH (R01MH085953, R01MH100900, R03MH105808), and the Simons Foundation (SFARI Explorer Award). Claudia Modenato was supported by the doc.mobility grant provided by the Swiss National Science Foundation (SNSF). Kuldeep Kumar was supported by the Institute of Data Valorization (IVADO) Postdoctoral Fellowship program, through the Canada First Research Excellence Fund. CRKC and PMT are supported in part by NIMH grants R01MH116147, R01MH123163, and R01MH121246, and by the Milken Institute and the Baszucki Brain Research Fund. Dr. S\u0026oslash;nderby is supported by the Research Council of Norway (#223273), South-Eastern Norway Regional Health Authority (#2020060), European Union\u0026rsquo;s Horizon2020 Research and Innovation Programme (CoMorMent project; Grant #847776), and Kristian Gerhard Jebsen Stiftelsen (SKGJ-MED-021). BD is supported by the Swiss National Science Foundation (NCCR Synapsy, project grant numbers 32003B_135679, 32003B_159780, 324730_192755, and CRSK-3_190185), the Roger De Spoelberch and the Leenaards Foundations. G.D. is supported by the Institute for Data Valorization, Montreal (IVADO; CF00137433), the Fonds de recherche du Qu\u0026eacute;bec (FRQ; 285289), the Natural Sciences and Engineering Research Council of Canada (NSERC; DGECR-2023-00089), and the Azrieli Global Scholars Fellowship from the Canadian Institute for Advanced Research (CIFAR) in the Brain, Mind, \u0026amp; Consciousness program. We thank all of the families participating at the Simons Searchlight sites, as well as the Simons Searchlight Consortium. We appreciate obtaining access to imaging and phenotypic data on SFARI Base. Approved researchers can obtain the Simons Searchlight population dataset described in this study by applying at https://base.sfari.org. We are grateful to all families who participated in the 16p11.2 European Consortium.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.K. and S.J. designed the study, analyzed imaging data, and drafted the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAnalyses:\u003c/u\u003e\u003c/em\u003e K.K. performed all the analyses of CNV neuroimaging data and summary statistics. W.S. and A.R. performed analyses of neuroimaging data from sex chromosome aneuploidies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eData collection:\u003c/u\u003e\u003c/em\u003e C.Mod., A.M., B.R-H., A.P., S.R., and S.M-B. recruited and scanned participants in the 16p11.2 European Consortium. S.L., C.O.M., E.D., F. T-D., V.C., A.R.C., F.D. recruited and scanned participants in the Brain Canada cohort. L.K., C.E.B. collected and provided the data for the UCLA cohort. D.E.J.L., M.J.O., M.B.M. V.d.B., J.H., and A.I.S., provided the data for the Cardiff cohort. W.S. and A.R. provided the data for sex chromosome aneuploidies. All authors provided feedback on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMvdB reports grants from Takeda Pharmaceuticals, outside the submitted work. P.M.T. and CRKC received a research grant from Biogen, Inc., for work unrelated to this manuscript. All other authors reported no biomedical financial interests or potential conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eS\u0026aacute;nchez, X. C. \u003cem\u003eet al.\u003c/em\u003e Associations of psychiatric disorders with sex chromosome aneuploidies in the Danish iPSYCH2015 dataset: a case-cohort study. \u003cem\u003eLancet Psychiatry\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 129\u0026ndash;138 (2023).\u003c/li\u003e\n\u003cli\u003eVaez, M. \u003cem\u003eet al.\u003c/em\u003e Population-Based Risk of Psychiatric Disorders Associated With Recurrent Copy Number Variants. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e (2024) doi:10.1001/jamapsychiatry.2024.1453.\u003c/li\u003e\n\u003cli\u003eJacquemont, S. \u003cem\u003eet al.\u003c/em\u003e Genes To Mental Health (G2MH): A framework to map the combined effects of rare and common variants on dimensions of cognition and psychopathology. \u003cem\u003eAm. J. Psychiatry\u003c/em\u003e (2021).\u003c/li\u003e\n\u003cli\u003eOwen, M. J., Bray, N. J., Walters, J. T. R. \u0026amp; O\u0026rsquo;Donovan, M. C. Genomics of schizophrenia, bipolar disorder and major depressive disorder. \u003cem\u003eNat. Rev. Genet.\u003c/em\u003e 1\u0026ndash;16 (2025).\u003c/li\u003e\n\u003cli\u003eTrubetskoy, V. \u003cem\u003eet al.\u003c/em\u003e Mapping genomic loci implicates genes and synaptic biology in schizophrenia. \u003cem\u003eNature\u003c/em\u003e 1\u0026ndash;13 (2022).\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Connell, K. S. \u003cem\u003eet al.\u003c/em\u003e Genomics yields biological and phenotypic insights into bipolar disorder. \u003cem\u003eNature\u003c/em\u003e 1\u0026ndash;12 (2025).\u003c/li\u003e\n\u003cli\u003eGenome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. \u003cem\u003eNature\u003c/em\u003e https://www.nature.com/articles/s41588-022-01285-8.\u003c/li\u003e\n\u003cli\u003eMeng, X. \u003cem\u003eet al.\u003c/em\u003e Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 222\u0026ndash;233 (2024).\u003c/li\u003e\n\u003cli\u003eBrainstorm Consortium \u003cem\u003eet al.\u003c/em\u003e Analysis of shared heritability in common disorders of the brain. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e360\u003c/strong\u003e, (2018).\u003c/li\u003e\n\u003cli\u003eGrotzinger, A. D. \u003cem\u003eet al.\u003c/em\u003e The landscape of shared and divergent genetic influences across 14 psychiatric disorders. \u003cem\u003emedRxiv\u003c/em\u003e 2025.01.14.25320574 (2025) doi:10.1101/2025.01.14.25320574.\u003c/li\u003e\n\u003cli\u003eGrotzinger, A. D. \u003cem\u003eet al.\u003c/em\u003e Mapping the genetic landscape across 14 psychiatric disorders. \u003cem\u003eNature\u003c/em\u003e 1\u0026ndash;15 (2025).\u003c/li\u003e\n\u003cli\u003eThompson, P. M. \u003cem\u003eet al.\u003c/em\u003e ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. \u003cem\u003eTransl. Psychiatry\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 100 (2020).\u003c/li\u003e\n\u003cli\u003eBoedhoe, P. S. W. \u003cem\u003eet al.\u003c/em\u003e Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups. \u003cem\u003eAm. J. Psychiatry\u003c/em\u003e \u003cstrong\u003e177\u003c/strong\u003e, 834\u0026ndash;843 (2020).\u003c/li\u003e\n\u003cli\u003eHibar, D. P. \u003cem\u003eet al.\u003c/em\u003e Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. \u003cem\u003eMol. Psychiatry\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 932\u0026ndash;942 (2018).\u003c/li\u003e\n\u003cli\u003eSchmaal, L. \u003cem\u003eet al.\u003c/em\u003e Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. \u003cem\u003eMol. Psychiatry\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 900\u0026ndash;909 (2017).\u003c/li\u003e\n\u003cli\u003evan Erp, T. G. M. \u003cem\u003eet al.\u003c/em\u003e Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 644\u0026ndash;654 (2018).\u003c/li\u003e\n\u003cli\u003ePatel, Y. \u003cem\u003eet al.\u003c/em\u003e Virtual histology of cortical thickness and shared neurobiology in 6 psychiatric disorders. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 47\u0026ndash;63 (2021).\u003c/li\u003e\n\u003cli\u003eHettwer, M. D. \u003cem\u003eet al.\u003c/em\u003e Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 6851 (2022).\u003c/li\u003e\n\u003cli\u003eCao, Z. \u003cem\u003eet al.\u003c/em\u003e Cortical profiles of numerous psychiatric disorders and normal development share a common pattern. \u003cem\u003eMol. Psychiatry\u003c/em\u003e (2022) doi:10.1038/s41380-022-01855-6.\u003c/li\u003e\n\u003cli\u003ePatel, Y. \u003cem\u003eet al.\u003c/em\u003e Virtual Ontogeny of Cortical Growth Preceding Mental Illness. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e (2022) doi:10.1016/j.biopsych.2022.02.959.\u003c/li\u003e\n\u003cli\u003eModenato, C. \u003cem\u003eet al.\u003c/em\u003e Lessons learnt from neuroimaging studies of Copy Number Variants, a systematic review. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e (2021) doi:10.1016/j.biopsych.2021.05.028.\u003c/li\u003e\n\u003cli\u003eModenato, C. \u003cem\u003eet al.\u003c/em\u003e Effects of eight neuropsychiatric copy number variants on human brain structure. \u003cem\u003eTransl. Psychiatry\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 399 (2021).\u003c/li\u003e\n\u003cli\u003eS\u0026oslash;nderby, I. E. \u003cem\u003eet al.\u003c/em\u003e Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs. \u003cem\u003eHum. Brain Mapp.\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 300\u0026ndash;328 (2021).\u003c/li\u003e\n\u003cli\u003eGrasby, K. L. \u003cem\u003eet al.\u003c/em\u003e The genetic architecture of the human cerebral cortex. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e367\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eMakowski, C. \u003cem\u003eet al.\u003c/em\u003e Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e375\u003c/strong\u003e, 522\u0026ndash;528 (2022).\u003c/li\u003e\n\u003cli\u003eBethlehem, R. A. I. \u003cem\u003eet al.\u003c/em\u003e Brain charts for the human lifespan. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e604\u003c/strong\u003e, 525\u0026ndash;533 (2022).\u003c/li\u003e\n\u003cli\u003ePaus, T. Population Neuroscience: Principles and advances. \u003cem\u003eCurr. Top. Behav. Neurosci.\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 3\u0026ndash;34 (2024).\u003c/li\u003e\n\u003cli\u003eRakic, P. Specification of cerebral cortical areas. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e241\u003c/strong\u003e, 170\u0026ndash;176 (1988).\u003c/li\u003e\n\u003cli\u003eSha, Z. \u003cem\u003eet al.\u003c/em\u003e The overlapping genetic architecture of psychiatric disorders and cortical brain structure. \u003cem\u003eNat. Ment. Health\u003c/em\u003e 1\u0026ndash;17 (2025).\u003c/li\u003e\n\u003cli\u003eStauffer, E.-M. \u003cem\u003eet al.\u003c/em\u003e The genetic relationships between brain structure and schizophrenia. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 7820 (2023).\u003c/li\u003e\n\u003cli\u003eCheng, W. \u003cem\u003eet al.\u003c/em\u003e Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e (2021) doi:10.1001/jamapsychiatry.2021.1435.\u003c/li\u003e\n\u003cli\u003eBerry, A. S. F. \u003cem\u003eet al.\u003c/em\u003e A genome-first study of sex chromosome aneuploidies provides evidence of Y chromosome dosage effects on autism risk. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 8897 (2024).\u003c/li\u003e\n\u003cli\u003eRaznahan, A., Won, H., Glahn, D. C. \u0026amp; Jacquemont, S. Convergence and Divergence of Rare Genetic Disorders on Brain Phenotypes: A Review. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e (2022) doi:10.1001/jamapsychiatry.2022.1450.\u003c/li\u003e\n\u003cli\u003eLevitis, E. \u003cem\u003eet al.\u003c/em\u003e The variegation of human brain vulnerability to rare genetic disorders and convergence with behaviorally defined disorders. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e (2023) doi:10.1016/j.biopsych.2023.07.008.\u003c/li\u003e\n\u003cli\u003eMollon, J., Almasy, L., Jacquemont, S. \u0026amp; Glahn, D. C. The contribution of copy number variants to psychiatric symptoms and cognitive ability. \u003cem\u003eMol. Psychiatry\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 1480\u0026ndash;1493 (2023).\u003c/li\u003e\n\u003cli\u003eZimmerman, M., Morgan, T. A. \u0026amp; Stanton, K. The severity of psychiatric disorders: World Psychiatry. \u003cem\u003eWorld Psychiatry\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 258\u0026ndash;275 (2018).\u003c/li\u003e\n\u003cli\u003ede Zwarte, S. M. C. \u003cem\u003eet al.\u003c/em\u003e The association between familial risk and brain abnormalities is disease specific: An ENIGMA-relatives study of schizophrenia and bipolar disorder. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e \u003cstrong\u003e86\u003c/strong\u003e, 545\u0026ndash;556 (2019).\u003c/li\u003e\n\u003cli\u003eOpel, N. \u003cem\u003eet al.\u003c/em\u003e Cross-Disorder Analysis of Brain Structural Abnormalities in Six Major Psychiatric Disorders: A Secondary Analysis of Mega- and Meta-analytical Findings From the ENIGMA Consortium. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e \u003cstrong\u003e88\u003c/strong\u003e, 678\u0026ndash;686 (2020).\u003c/li\u003e\n\u003cli\u003eSydnor, V. J. \u003cem\u003eet al.\u003c/em\u003e Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. \u003cem\u003eNeuron\u003c/em\u003e Preprint at https://doi.org/10.1016/j.neuron.2021.06.016 (2021).\u003c/li\u003e\n\u003cli\u003eMarkello, R. D. \u003cem\u003eet al.\u003c/em\u003e neuromaps: structural and functional interpretation of brain maps. \u003cem\u003eNat. Methods\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 1472\u0026ndash;1479 (2022).\u003c/li\u003e\n\u003cli\u003eKumar, K. \u003cem\u003eet al.\u003c/em\u003e Subcortical Brain Alterations in Carriers of Genomic Copy Number Variants. \u003cem\u003eAm. J. Psychiatry\u003c/em\u003e \u003cstrong\u003e180\u003c/strong\u003e, 685\u0026ndash;698 (2023).\u003c/li\u003e\n\u003cli\u003eNorbom, L. B. \u003cem\u003eet al.\u003c/em\u003e New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings. \u003cem\u003eProg. Neurobiol.\u003c/em\u003e \u003cstrong\u003e204\u003c/strong\u003e, 102109 (2021).\u003c/li\u003e\n\u003cli\u003eLiao, Z. \u003cem\u003eet al.\u003c/em\u003e Copy number variants and the tangential expansion of the cerebral cortex. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1\u0026ndash;12 (2025).\u003c/li\u003e\n\u003cli\u003eVoineskos, A. N. \u003cem\u003eet al.\u003c/em\u003e Effects of antipsychotic medication on brain structure in patients with major depressive disorder and psychotic features: Neuroimaging findings in the context of a randomized placebo-controlled clinical trial: Neuroimaging findings in the context of a randomized placebo-controlled clinical trial. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, 674\u0026ndash;683 (2020).\u003c/li\u003e\n\u003cli\u003eSun, D. \u003cem\u003eet al.\u003c/em\u003e Large-scale mapping of cortical alterations in 22q11.2 deletion syndrome: Convergence with idiopathic psychosis and effects of deletion size. \u003cem\u003eMol. Psychiatry\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1822\u0026ndash;1834 (2020).\u003c/li\u003e\n\u003cli\u003eHindley, G. \u003cem\u003eet al.\u003c/em\u003e Charting the landscape of genetic overlap between mental disorders and related traits beyond genetic correlation. \u003cem\u003eAm. J. Psychiatry\u003c/em\u003e \u003cstrong\u003e179\u003c/strong\u003e, 833\u0026ndash;843 (2022).\u003c/li\u003e\n\u003cli\u003eKazem, S. \u003cem\u003eet al.\u003c/em\u003e Determinants of pleiotropy and monotonic gene dosage responses across human traits. \u003cem\u003eGenetic and Genomic Medicine\u003c/em\u003e (2025).\u003c/li\u003e\n\u003cli\u003eAuwerx, C., Moix, S., Kutalik, Z. \u0026amp; Reymond, A. Disentangling mechanisms behind the pleiotropic effects of proximal 16p11.2 BP4-5 CNVs. \u003cem\u003eAm. J. Hum. Genet.\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 2347\u0026ndash;2361 (2024).\u003c/li\u003e\n\u003cli\u003eSudlow, C. \u003cem\u003eet al.\u003c/em\u003e UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. \u003cem\u003ePLoS Med.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, e1001779 (2015).\u003c/li\u003e\n\u003cli\u003eHoogman, M. \u003cem\u003eet al.\u003c/em\u003e Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples. \u003cem\u003eAm. J. Psychiatry\u003c/em\u003e \u003cstrong\u003e176\u003c/strong\u003e, 531\u0026ndash;542 (2019).\u003c/li\u003e\n\u003cli\u003evan Rooij, D. \u003cem\u003eet al.\u003c/em\u003e Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group. \u003cem\u003eAm. J. Psychiatry\u003c/em\u003e \u003cstrong\u003e175\u003c/strong\u003e, 359\u0026ndash;369 (2018).\u003c/li\u003e\n\u003cli\u003eENIGMA Clinical High Risk for Psychosis Working Group \u003cem\u003eet al.\u003c/em\u003e Association of structural magnetic resonance imaging measures with psychosis onset in individuals at Clinical High Risk for developing psychosis: An ENIGMA working group mega-analysis: An ENIGMA working group mega-analysis. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 753\u0026ndash;766 (2021).\u003c/li\u003e\n\u003cli\u003eGao, Y., Staginnus, M. \u0026amp; ENIGMA-Antisocial Behavior Working Group. Cortical structure and subcortical volumes in conduct disorder: a coordinated analysis of 15 international cohorts from the ENIGMA-Antisocial Behavior Working Group. \u003cem\u003eLancet Psychiatry\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 620\u0026ndash;632 (2024).\u003c/li\u003e\n\u003cli\u003eBoedhoe, P. S. W. \u003cem\u003eet al.\u003c/em\u003e Cortical abnormalities associated with pediatric and adult obsessive-compulsive disorder: Findings from the ENIGMA obsessive-compulsive disorder working group. \u003cem\u003eAm. J. Psychiatry\u003c/em\u003e \u003cstrong\u003e175\u003c/strong\u003e, 453\u0026ndash;462 (2018).\u003c/li\u003e\n\u003cli\u003eFischl, B. \u003cem\u003eet al.\u003c/em\u003e Automatically parcellating the human cerebral cortex. \u003cem\u003eCereb. Cortex\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 11\u0026ndash;22 (2004).\u003c/li\u003e\n\u003cli\u003eAlfaro-Almagro, F. \u003cem\u003eet al.\u003c/em\u003e Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e166\u003c/strong\u003e, 400\u0026ndash;424 (2018).\u003c/li\u003e\n\u003cli\u003eL\u0026ecirc;, S., Josse, J. \u0026amp; Husson, F. FactoMineR: AnRPackage for Multivariate Analysis. \u003cem\u003eJ. Stat. Softw.\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1\u0026ndash;18 (2008).\u003c/li\u003e\n\u003cli\u003eAlexander-Bloch, A. F. \u003cem\u003eet al.\u003c/em\u003e On testing for spatial correspondence between maps of human brain structure and function. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e178\u003c/strong\u003e, 540\u0026ndash;551 (2018).\u003c/li\u003e\n\u003cli\u003eMowinckel, A. M. \u0026amp; Vidal-Pi\u0026ntilde;eiro, D. Visualization of brain statistics with R packages ggseg and ggseg3d. \u003cem\u003eAdv. Methods Pract. Psychol. Sci.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 466\u0026ndash;483 (2020).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1. Description of structural variants, psychiatric disorders, and disorder summary statistics.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA. Rare Genetic Variants (CNVs and Aneuploidies)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (Cases / Ctrls)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR ADHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR BD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR MDD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR ASD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR SCZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1q21.1 Deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e40 / 782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1q21.1 Duplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e30 / 782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e7q11.23 Deletion (WBS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e44 (meta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e15q11.2 Deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e108 / 782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e15q11.2 Duplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e144 / 782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e16p11.2 Deletion (distal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e15 (meta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e16p11.2 Duplication (distal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e18 (meta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e16p11.2 Deletion (proximal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e82 / 782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e16p11.2 Duplication (proximal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e75 / 782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e11.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e16p13.11 Duplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e50 / 782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eTrisomy 21 (Down Syndrome)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAneuploidy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e84 (meta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e22q11.2 Deletion (VCFS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e68 / 782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e32.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e22q11.2 Duplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e26 / 782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eXXX Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAneuploidy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e35 (meta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e17.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eXXY (Klinefelter)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAneuploidy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e77 / 53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e17.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eXYY Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAneuploidy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e30 / 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eXXYY Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAneuploidy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e25 (meta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eX Monosomy (Turner)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAneuploidy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e55 (meta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB. Neurodevelopmental \u0026amp; Psychiatric Disorders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePubMed ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (Cases / Ctrls)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication subgroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eADHD (Attention-Deficit /Hyperactivity Disorder)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e32539527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2271 / 5827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePed, Young, Adult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eASD (Autism Spectrum Disorder)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e32539527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1777 / 5827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePed, Young, Adult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eBD (Bipolar Disorder)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e28461699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2447 / 4056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eYoung, Adult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eCD (Conduct Disorder)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e39025633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1185 / 1253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eYoung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eCHR-PS (Clinical High Risk for Psychosis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e33950164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1792 / 1377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eConverters / Non\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eMDD (Major Depressive Disorder)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e27137745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2148 / 7957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eYoung, Adult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eOCD (Obsessive Compulsive Disorder)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e32539527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2323 / 5827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePed, Young, Adult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eSCZ (Schizophrenia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e29960671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4474 / 5098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC. Common Variants (GWAS Statistics)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (Cases / Ctrls)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN Signif SNPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP heritability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDemontis et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e38691 / 186843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e14% (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eBipolar Disorder (BD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eO\u0026rsquo;Connell et al., 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e131969 / 2322416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e22% (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eMajor Depression (MDD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eMeng et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e258364 / 571252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e8.4% (0.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eSchizophrenia (SCZ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTrubetskoy et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e76755 / 243649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e24% (0.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eCortical MRI \u003cbr\u003e (Mean CT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGrasby et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e51,665 (Total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e26% (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eCortical MRI \u003cbr\u003e (Total SA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGrasby et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e51,665 (Total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eENIGMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e34% (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis study integrates data across three domains: \u003cstrong\u003eA\u003c/strong\u003e) Rare genetic variants, including copy number variants (CNVs) and sex chromosome aneuploidies (SCAs); \u003cstrong\u003eB\u003c/strong\u003e) Neurodevelopmental and psychiatric disorders (NPDs); and \u003cstrong\u003eC\u003c/strong\u003e) Common genetic variants from genome-wide association studies (GWAS). For rare variants (A), N Carriers denotes unique individuals analyzed from individual-level data (clinical cohorts and UK Biobank) or aggregated via meta-analysis (meta). HR denotes the Hazard Ratio for developing specific psychiatric disorders (ADHD, BD, MDD) derived from the iPSYCH2015 case-cohort study (Vaez et al., 2024 \u003csup\u003e2\u003c/sup\u003e, or S\u0026aacute;nchez et al., 2023 \u003csup\u003e1\u003c/sup\u003e). OR denotes the Odds Ratio for ASD or SCZ from Modenato et al., 2021 \u003csup\u003e21\u003c/sup\u003e or Kumar et al., 2023 \u003csup\u003e41\u003c/sup\u003e. N Controls refers to the shared control sets used for CNV (n=782) and SCA (n=870) comparisons. For NPDs (B), values represent aggregated demographics from ENIGMA working groups for individuals with quality-controlled cortical thickness and surface area data. Medication denotes the availability of medication status for subgroup analyses; Subgroups indicate the age cohorts available (Ped: Pediatric, Young: Young Adult, Adult). For common variants (C), sample sizes refer to the discovery GWAS for the psychiatric disorder or cortical metrics. In addition, for polygenic risk score (PRS) analysis, we used CT and SA metrics for 31,413 participants of European (White-British) ancestry from the UK Biobank. \u003c/p\u003e\n\u003cp\u003eAbbreviations: Abbreviations: ADHD=attention deficit hyperactivity disorder; ASD=autism spectrum disorder; BD=bipolar disorder; CD=conduct disorder; CHR-PS=clinical high risk for psychosis; CNV=copy number variant; Del=deletion; Dup=duplication; ENIGMA=Enhancing Neuro Imaging Genetics through Meta Analysis; MDD=major depressive disorder; NPD=neurodevelopmental and psychiatric disorders; OCD=obsessive-compulsive disorder; PGC=Psychiatric Genomics Consortium; prox.=proximal; SCA=sex chromosome aneuploidy; SCZ=schizophrenia; TS=Turner syndrome; VCFS=Velo-Cardio-Facial syndrome; WBS=Williams-Beuren syndrome.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9246968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9246968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Structural variants, including copy number variants (CNVs), confer substantial risk for neurodevelopmental and psychiatric disorders (NPDs), yet whether their cortical effects relate to those observed in the psychiatric conditions they predispose to remains unclear. Here, we present the first systematic comparison of cortical phenotypes across 18 NPD-associated CNVs and aneuploidies, disorder-associated common variants, and 8 psychiatric disorders. Rare CNVs preferentially affected total surface area (SA), with 11-fold larger effects than psychiatric diagnoses, while NPDs preferentially affected mean cortical thickness (CT), with most CT effects observed in medicated subgroups, suggesting non-genetic contributions. NPD-associated common variants showed enrichment in SA but not CT associations. Regionally, both rare and common genetic variants showed larger effects in sensorimotor regions, aligning with the sensorimotor-to-association cortical gradient as well as regional heritability estimates. In contrast, psychiatric diagnoses showed larger effects in association regions. Individual NPD-associated variants were evenly split between those increasing and decreasing surface area. This heterogeneity likely explains why aggregating variants using polygenic scores shows only weak associations with SA. Overall, cortical signatures of psychiatric diagnoses diverge from those associated with genetic risk. Genetic variants preferentially impact SA and sensorimotor regions through early developmental mechanisms, while psychiatric diagnoses are associated with CT and association regions likely reflecting medication, illness chronicity, and environmental factors.","manuscriptTitle":"Copy number variants reveal divergent genetic and diagnostic cortical signatures across psychiatric disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 19:29:20","doi":"10.21203/rs.3.rs-9246968/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce19bdc6-c093-4e0a-8bef-205b526b40a5","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66462279,"name":"Biological sciences/Genetics/Mutation"},{"id":66462280,"name":"Biological sciences/Genetics/Neurodevelopmental disorders"},{"id":66462281,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Schizophrenia"},{"id":66462282,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Bipolar disorder"}],"tags":[],"updatedAt":"2026-05-07T19:15:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 19:29:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9246968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9246968","identity":"rs-9246968","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}