OCD is a chronic psychiatric disorder that affects 1–3% of the population¹ and is characterized by obsessions and compulsions that vary in type and severity and over time. OCD is responsible for profound personal and societal costs², including increased risk of suicide³ and overall mortality⁴. OCD is moderately heritable; twin-based heritability estimates range between 27% and 47% in adults and between 45% and 65% in children^5,6,7,8, with SNP-based heritability estimates between 28% and 37%^9,10,11.

Two earlier OCD GWAS meta-analyses, both containing a subset of the data included in this analysis^12,13, showed SNP-based heritabilities of 8.5% (assuming a 3% population prevalence) and 16% (assuming a 2% population prevalence). The first GWAS (n_cases = 14,140,n_controls = 562,117)¹² found one genome-wide significant locus associated with OCD, while the second (n_cases = 37,015,n_controls = 948,616)¹³ identified 15 independent genome-wide significant loci. As with other complex traits, increased sample sizes are needed for a more comprehensive understanding of the underlying genetic etiology of OCD and its genetic relationships with related disorders.

The current study combines data from the two unpublished OCD GWASs described above and includes additional cohorts (~9,000 cases). This results in one of the largest and most well-powered GWAS of OCD so far, with a ~20-fold increase of OCD cases compared to the previously published OCD GWASs¹⁰. Based on the results from the meta-analysis, we conducted secondary analyses, including positional and functional fine-mapping of SNPs and genes, structural equation modeling to examine possible genetic differences in sample ascertainment across cohorts, protein and transcriptome-wide association analyses, single-cell enrichment and genetic correlations with other traits (Supplementary Fig.1). Our results provide more detailed insight into the genetic underpinnings and biology of OCD.

GWAS meta-analysis identifies 30 genome-wide significant loci

We conducted a GWAS meta-analysis of 28 OCD case–control cohorts of European ancestry, comprising 53,660 cases and 2,044,417 controls (effective sample size, ~210,000 individuals). Ascertainment of cases varied across cohorts: OCD diagnosis was determined (1) by a healthcare professional in a clinical setting (18 cohorts,n = 9,089 cases), (2) from health records or biobanks (seven cohorts,n = 9,138 cases), (3) in a clinical setting or from health records with the additional characteristic that all OCD cases were primarily collected for another psychiatric disorder (three cohorts,n = 5,266 cases) or (4) by self-reported diagnosis in a consumer-based setting (23andMe, Inc.,n = 30,167 cases). Cohort details, including phenotypic assessment, quality control and individual cohort GWAS analyses, are described in Supplementary Note2 and Supplementary Table1. We identified 30 independent (defined in Supplementary Note3) loci among the 1,672 SNPs that exceeded the genome-wide threshold for significance (\(P < 5{\times 10}^{-8}\); Manhattan plot in Fig.1, regional association plots and forest plots in Supplementary Figs.2–31 and a list of all independent genome-wide significant SNPs in Table1 with additional details in Supplementary Tables2 and3). The independence of the 30 lead SNPs was subsequently validated using conditional and joint analysis (GCTA-COJO)¹⁴ (Supplementary Table4). Analysis of the X chromosome, conducted in a subset of the data for which this information was available (23andMe), yielded no significant associations (Supplementary Note4 and Supplementary Fig.37e). Of the 15 genome-wide significant loci previously reported in preprints^12,13, 13 were genome-wide significant in the current GWAS, with the remaining two showing suggestive significance (\(P=5.23{\times 10}^{-8}\,{\rm{and}}\,{P}=2.2{\times 10}^{-7}\); Supplementary Table5). Using MiXeR¹⁵, we estimated that approximately 11,500 (standard error of the effect estimate (s.e.) = 607) causal variants account for 90% of the OCD SNP-based heritability.

They axis represents −log₁₀ (P values) (two sided, not adjusted for multiple testing) for the association of variants with OCD using an inverse-variance-weighted fixed-effects model (n_cases = 53,660 andn_controls = 2,044,417). Thex axis shows chromosomes 1–22. The horizontal red line represents the threshold for genome-wide significance (\(P=5\times {10}^{-8})\). Index variants of genome-wide significant loci are highlighted as green diamonds.

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No statistically significant heterogeneity was observed across individual cohorts for the 30 genome-wide significant loci, as assessed with Cochran’sQ-test (Supplementary Fig.32), theI² statistic and the genomic structural equation modeling (GenomicSEM)\({Q}_{{\rm{SNP}}}\) statistic¹⁶ (Supplementary Table2). Genome-wide analyses of samples grouped by clinical, comorbid, biobank and 23andMe information (Supplementary Table3 and Supplementary Figs.33–37) showed evidence that sample ascertainment impacted results at a genome-wide scale, although not beyond what is observed with closely related psychiatric disorders^17,18. We observed moderate to high genetic correlations across the subgroups (between 0.63, s.e. = 0.11 for biobanks and comorbid information and 0.92, s.e. = 0.07 for 23andMe and comorbid information; Supplementary Table7) and a satisfactory fit for a one-factor GenomicSEM model (Supplementary Table8 and Supplementary Fig.39). A common factor GWAS based on the one-factor GenomicSEM model resulted in 20 significant loci, all of which were also significant in the primary GWAS (Supplementary Table8 and Supplementary Fig.40; analysis details in Supplementary Note5). SNP heritability (assuming a 1% population prevalence) was 6.7% (s.e. = 0.3%), with slightly higher estimates for the clinical (\({h}_{{\rm{SNP}}}^{2}\) = 16.4%, s.e. = 1.5%) and comorbid (\({h}_{{\rm{SNP}}}^{2}\) = 13.3%, s.e. = 1.7%) subgroups (Supplementary Table1).

Gene-based findings

We prioritized putative risk genes for OCD using six positional and functional QTL gene-based mapping approaches. Positional mapping was performed with mBAT-combo¹⁹. Functional expression quantitative trait locus (eQTL) mapping was performed with transcriptome-wide association study (TWAS)²⁰, using PsychENCODE gene expression weights²¹, and summary-based Mendelian randomization (SMR)²² using the whole-blood eQTLGen²³ and MetaBrain²⁴ datasets. Functional protein QTL mapping was done using a protein-wide association study (PWAS) of human brain protein expression panels²⁵. Finally, we used the psychiatric omnilocus prioritization score (PsyOPS)²⁶, which combines positional mapping with biological annotations, to further prioritize risk genes within genome-wide significant loci. We identified 207 significant genes (Bonferroni correction,P < 2.67 × 10⁻⁶) with mBAT-combo and 24 genes using TWAS (P < 4.76 × 10⁻⁶), 14 of which were conditionally independent. The SMR–eQTLGen analysis identified 39 significant risk genes (P < 4.28 × 10⁻⁶), and the SMR–MetaBrain analysis identified 14 risk genes (P < 9.23 × 10⁻⁶). The PWAS identified three significant genes (P < 3.39 × 10⁻⁵), while PsyOPS prioritized 29 genes. In total, 251 genes were significantly associated with OCD through at least one gene-based approach, and 48 were implicated by at least two methods (Methods, Supplementary Note7 and Supplementary Tables9–14).

From the 48 genes implicated by at least two approaches, we prioritized likely causal genes for OCD using colocalization (TWAS-COLOC)^27,28 and SMR–heterogeneity in dependent instruments (SMR-HEIDI)²² tests. Colocalization was used to identify significant TWAS associations for which the underlying GWAS and eQTL summary statistics are likely to share a single causal variant. Similarly, HEIDI was used to select SMR associations for which the same causal variant affects gene expression and trait variation. Of the 48 genes implicated by at least two gene-based tests, 25 were also significant in either the TWAS-COLOC or the SMR-HEIDI tests, suggesting causality (Fig.2a). Only 2 of these 25 genes were prioritized by both TWAS-COLOC and SMR-HEIDI:WDR6 (WD repeat domain 6) andDALRD3 (DALR anticodon binding domain-containing 3). Another gene of interest,CTNND1 (catenin δ1), was implicated by three of our five approaches (multivariate set-based association test (mBAT-combo), TWAS, PWAS) and showed evidence for colocalization. Only three genes were implicated in the PWAS; of these,CTNND1 was the only gene also implicated in the TWAS. In the PWAS, downregulation of CTNND1 protein expression in the human dorsolateral prefrontal cortex (dlPFC) was significantly associated with OCD risk (\(Z=-4.49,P=7.11\times {10}^{-6}\); Supplementary Table13), consistent with the downregulation ofCTNND1 gene expression in the prefrontal cortex seen in the TWAS (\(Z=-6.86,P=6.90\times {10}^{-12};\) Supplementary Table10). For a discussion of the overlap between the gene findings with rare coding variants in OCD, see Supplementary Table6 and Supplementary Note7.

a, List of 25 genes that were implicated in at least two of the five different gene-based tests (significance indicated by gray dots) and passed the TWAS colocalization and/or SMR-HEIDI filters (significance indicated by orange dots). Conditionally independent (cond. ind.) genes within each locus are indicated by blue dots.b, Enrichment of OCD GWAS signal in human brain-related tissues from GTEx (version 8). No significant enrichment was observed in the peripheral tissues (not included in the figure). The horizontal bar size represents the significance of the enrichment measured using the MAGMA gene set enrichment test or partitioned LDSC.c, Top 20 groups of brain cell types (n = 35 total tested) enriched with OCD GWAS signal using MAGMA. Dots represent −log₁₀(P values) from MAGMA gene set enrichment tests of individual neuronal cell types from Zeisel et al.³⁰. Vertical crosses represent the mean −log₁₀(P value) observed for each brain cell type group. Blue crosses represent a significant enrichment of OCD GWAS signals (FDR across 35 groups, FDR < 0.05), while pink crosses indicate nonsignificant enrichment. Gray points represent the association (−log₁₀(P value)) for each single cell cluster (‘level 5’ analysis defined by Zeisel et al.³⁰) in a given cell type (for example, excitatory neurons, cerebral cortex). CCK, cholecystokinin-expressing; R-LM, stratum radiatum-stratum lacunosum-moleculare.

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Tissue and cell type enrichment analysis

After mapping significantly associated SNPs from the GWAS meta-analysis to likely causal genes, we explored which tissues or cell types showed enriched gene expression of OCD-associated genetic signals using a previously described approach²⁹ on published human gene expression datasets from bulk tissue RNA-seq data from the Genotype–Tissue Expression (GTEx) project and single-cell RNA-sequencing data from the adult mouse central and peripheral nervous systems³⁰. We found enrichment of OCD GWAS signals in six of 13 human brain tissue types in GTEx but no enrichment in human peripheral tissues (Fig.2b and Supplementary Table15). In the adult mouse central and peripheral nervous systems, we found enrichment of OCD GWAS signals in 41 of 166 tested specific single cell types using the MAGMA gene set enrichment test (Supplementary Table16). When summarizing results of individual single cell types into groups of cell types defined by the same region or tissue and cell type, nine of 35 were enriched for OCD GWAS signals (top 20 shown in Fig.2c). Strong enrichment of OCD GWAS signal was especially observed in excitatory neurons of the hippocampus and the cerebral cortex as well as in D₁ and D₂ medium spiny neurons (MSNs).

Genetic relationship of OCD with other phenotypes

Using phenome-wide association analysis, we examined whether the 30 independent OCD-associated loci identified by our GWAS meta-analysis have previously been associated with other phenotypes (see Supplementary Tables17a–d for lookups in four, partially overlapping GWAS databanks and Table1 for highlighted associations). We found that 22 of the 30 loci were associated with other phenotypes, including schizophrenia (seven loci), depression and major depressive disorder (two loci), bipolar disorder (one locus), neuroticism (seven loci), educational attainment (seven loci) and body fat mass or body mass index (eight loci).

We further used bivariate linkage disequilibrium score regression (LDSC)³¹ to investigate the extent of genetic correlations between OCD and 112 previously published GWASs encompassing psychiatric, substance use and neurological phenotypes, among others (Fig.3). We found that 65 phenotypes were significantly correlated with OCD after correcting for multiple testing using the Benjamini–Hochberg³² procedure to control the false discovery rate (FDR) at a threshold of 0.05. OCD was significantly positively correlated with all tested psychiatric phenotypes; the highest correlations were with anxiety (\({r}_{\rm{G}}=0.70\)), depression (\({r}_{\rm{G}}=0.60\)), anorexia nervosa (\({r}_{\rm{G}}=0.52\)), Tourette syndrome (\({r}_{\rm{G}}=0.47\)) and post-traumatic stress disorder (PTSD;\({r}_{\rm{G}}=0.48\)). Significant positive genetic correlations were also obtained for neuroticism (\({r}_{\rm{G}}=0.53\)), in particular for the worry subcluster (\({r}_{\rm{G}}=0.64\)), and all individual items in the worry subcluster, with slightly lower estimates for the depressive subcluster (\({r}_{\rm{G}}=0.35\)). Suicide attempt (\({r}_{\rm{G}}=0.40\)), history of childhood maltreatment (\({r}_{\rm{G}}=0.37\)) and tiredness (\({r}_{\rm{G}}=0.36\)) were also notable for strong positive associations with OCD. Of the assessed neurological disorders, OCD was only significantly correlated with migraine (\({r}_{\rm{G}}=0.15\)). Some autoimmune disorders, such as Crohn’s disease (\({r}_{\rm{G}}=-0.13\)), ulcerative colitis (\({r}_{\rm{G}}=-0.14\)) and inflammatory bowel disease (\({r}_{\rm{G}}=-0.14\)), showed negative correlations with OCD (see Fig.3 and Supplementary Table18 for all genetic correlation estimates, 95% confidence intervals andP values, Supplementary Note6 for a more in-depth discussion of all significant genetic correlations and Supplementary Table19 and Supplementary Figs.41 and42 for subgroup-specific genetic correlation estimates).

This includes psychiatric, substance use, cognition–socioeconomic status (SES), personality, psychological, neurological, autoimmune, cardiovascular (cardiovasc.), anthropomorphic–diet, fertility and other phenotypes. References and sample sizes of the corresponding summary statistics of the GWAS studies can be found in Supplementary Table18. The OCD summary statistics are of the main meta-analysis (n_cases = 53,660 andn_controls = 2,044,417). Error bars represent the 95% confidence intervals for the genetic correlation estimates (r_G). Red circles indicate significant associations witha P value adjusted for multiple testing with the Benjamini–Hochberg procedure to control the FDR (<0.05). Black circles indicate associations that are not significant. a., after; ADHD, attention-deficit hyperactivity disorder; ALS, amyotrophic lateral sclerosis; BMI, body mass index; embarras., embarrassment; freq, frequency; fr., from; HDL, high-density lipoprotein; IQ, intelligence quotient; LDL, low-density lipoprotein; neurot., neuroticism; nr., number; PTSD, post-traumatic stress disorder; sat., satisfaction; VN, verbal-numerical.

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The OCD GWAS reported here, comprising over 53,000 cases, identified 30 independent genome-wide significant loci. Common SNPs explained 6.7% of the variation in OCD risk in our meta-analysis (LDSC with an assumed population prevalence of 1%), a significant reduction from the 28% reported previously¹⁰. However, differences in the assumed population prevalence (where a lower assumed prevalence for LDSC heritability calculation results in a lower heritability estimate) and an increase in sample heterogeneity likely contributed to this discrepancy. The reduction in SNP heritability is in line with previous observations for closely related psychiatric disorders such as attention deficit hyperactivity disorder (ADHD)^33,34 or depression^17,35,36,37, where expanding the phenotype definition increased genetic heterogeneity, potentially accounting for the observed decrease in SNP heritability. This aligns with the fact that heritability estimates for more homogeneous OCD subgroups were higher: 16.4% for the clinically ascertained subgroup and 13.3% for the comorbid subgroup (Supplementary Note10). The current estimates are comparable to those of other psychiatric and substance use disorders, with SNP heritability estimates ranging between 9% and 28%³⁸.

The most significant SNP (rs78587207 (\(P=5.28\times 1{0}^{-12}\))) identified in the GWAS is located on chr11q12.1 and has been previously associated with several traits, including neuropsychiatric phenotypes³⁹ such as depressive symptoms⁴⁰ and neuroticism⁴⁰. Gene-based analyses identified four putative causal genes within this locus. The closest gene tors78587207 isCTNND1, which encodes the cell adhesion molecule p120 catenin. This gene was associated with OCD using three gene-based tests (mBAT-combo, TWAS and PWAS), and we found strong evidence for colocalization of the TWAS signal forCTNND1 in the dlPFC. The dlPFC has been consistently implicated in the neural circuitry of OCD as well as in compulsivity more broadly as part of the cortico–striatal–thalamo–cortical circuitry^41,42. The protein product ofCTNND1 is a regulator of cell–cell adhesion⁴³ and has a crucial role in gene transcription, Rho GTPase activity and cytoskeletal organization^44,45,46. Other credible causal genes in the locus includeCLP1 (cleavage factor polyribonucleotide kinase subunit 1),TMX2 (thioredoxin-related transmembrane protein 2) andZDHHC5 (zinc finger DHHC type palmitoyltransferase 5). Rare genetic mutations inCLP1 are associated with pontocerebellar hypoplasia type 10, a very rare autosomal recessive neurodegenerative disease characterized by brain atrophy and delayed myelination resulting in intellectual disability⁴⁷.TMX2 is associated with increased risk of neurodevelopmental disorders with microcephaly, cortical malformations, spasticity and congenital nervous system abnormalities⁴⁸.ZDHHC5 is broadly expressed in the brain, including the frontal cortex.ZDHHC5 has not been implicated in brain development but has been linked to lung acinar adenocarcinoma and lung papillary adenocarcinoma in prior studies⁴⁹.

Our finding that approximately 11,500 (s.e. = 607) causal variants account for 90% of the SNP-based heritability of OCD suggests that OCD is more polygenic than other complex traits such as height (n_causal = 4,000), schizophrenia (n_causal = 9,600) and ADHD (n_causal = 5,600) but less polygenic than major depression (n_causal = 14,500) and educational attainment (n_causal = 13,200)⁵⁰.

We identified a total of 25 credible causal genes based on robust evidence using multiple positional and functionally informed gene-based approaches. Notably,DLGAP1, which has been previously implicated in OCD pathogenesis^10,51, was not identified in either the GWAS or in the gene-based analyses. Of the 25 genes that were implicated, 15 were within 6.5 kb of a SNP that surpassed genome-wide significance in the meta-analysis. In addition to the four genes discussed above, several others are of particular interest, includingWDR6 andDALRD3, which had the strongest evidence from the gene-based analyses. These genes lie in a gene-rich region on chr3p21.31, which, in addition to harboring multiple genome-wide significant SNPs, has been previously associated with a broad range of psychiatric disorders and related traits, including schizophrenia³⁹, well-being⁵² and the worry subcluster of neuroticism⁵³.

WDR6 is broadly expressed in the brain, particularly the hypothalamus. Its protein product is involved in cell growth arrest⁵⁴, and recent studies have implicated it in anorexia nervosa⁵⁵ and Parkinson’s disease⁵⁶.DALRD3 is located on chromosome 3 in the same region asWDR6. DALRD3, when fully disrupted, is implicated in a form of epileptic encephalopathy with associated developmental delay⁵⁷. Finally, a third gene in the 3p21 locus,CELSR3 (cadherin EGF LAG seven-pass G type receptor 3), encodes a protocadherin that is highly expressed in the developing basal ganglia⁵⁸. Multiple loss-of-function mutations inCELSR3 have been associated with Tourette syndrome^59,60, which co-occurs with OCD in 10–20% of patients.

Four other genes identified through these analyses are located in the MHC locus, a region on chromosome 6 that has a major role in the adaptive immune system and has been repeatedly linked to major psychiatric disorders⁶¹. The newly identified MHC association for OCD is noteworthy given evidence linking OCD with autoimmune disorders^62,63,64. Genetic pleiotropy may underlie this connection, with variants predisposing individuals to both autoimmune conditions and OCD⁶⁵. Furthermore, some OCD subtypes, such as pediatric acute-onset neuropsychiatric disorders associated withStreptococcus and pediatric acute-onset neuropsychiatric syndrome, may have autoimmune origins^66,67. Nevertheless, we were surprised to discover several negative genetic correlations between OCD and autoimmune disorders such as Crohn’s disease, ulcerative colitis and inflammatory bowel disease in our analyses, suggesting that there is heterogeneity (and perhaps pleiotropy) in the genetic relationships between autoimmune disorders and OCD.

Tissue and cell type enrichment analysis revealed significant enrichment of OCD SNP heritability in several tissues and cell types, with the strongest enrichment in excitatory neurons of the hippocampus and the cerebral cortex and in dopamine D₁ receptor (D₁R)-positive and dopamine D₂ receptor (D₂R)-positive MSNs in the striatum. These findings are in line with traditional neural circuitry models of OCD, which focus on frontal cortical–striatal pathways^68,69. These findings are consistent with and build on previous work linking various neuronal cell types to psychiatric and cognitive phenotypes⁷⁰.

Interestingly, the frontal and anterior cingulate cortices, which were enriched in our tissue-based analyses, as well as the hippocampus and the striatum, which were implicated in our cell type-based analyses, are among the regions that are consistently implicated in neuroimaging studies of OCD^41,71,72,73. Enrichment in MSNs in the striatum is consistent with their role in the observed aberrant circuitry in OCD, where the D₁ MSNs project to the globus pallidus interna and the substantia nigra in the direct pathway and the D₂ type MSNs project to the globus pallidus externa in the indirect pathway⁷⁴. However, MSNs are also enriched in major depressive disorder⁷⁵, schizophrenia⁷⁶ and intelligence⁷⁷, suggesting that the observed enrichment is not specific for OCD.

Our analyses of the shared genetic risk between OCD and other psychiatric disorders provides further insights into the etiology of OCD. In line with previous observations^38,78, OCD was significantly genetically correlated with multiple psychiatric disorders and traits. The strongest genetic correlations were observed for anxiety disorders, depression and anorexia nervosa, all of which are highly comorbid with OCD⁷⁹. This aligns with previous findings from cross-disorder analyses suggesting a shared genetic susceptibility among most psychiatric disorders^38,80,81. A notable exception is our finding that risk variants for OCD are protective for alcohol dependence⁸², which is at odds with epidemiological evidence strongly linking OCD and alcohol-related disorders⁸³ but in line with a recent paper⁷⁹ reporting a lower-than-expected lifetime comorbidity of substance use disorders in OCD. The observed pattern of correlations with other phenotypes can be thought of as falling into two categories: compulsivity–impulsivity and rumination–worry–neuroticism. In both categories, the patterns of genetic correlations appear to follow a gradient across disorders and traits. For example, in the compulsivity–impulsivity category, strong positive correlations are seen with anorexia nervosa and Tourette syndrome, which are disorders with strong compulsive features, with less positive associations seen with ADHD and negative correlations with alcohol dependence and risk-taking behaviors, which are all phenotypes characterized by impulsivity. A similar gradient is observed for the rumination–worry–neuroticism-related phenotypes, with strong positive correlations with anxiety and other ruminative phenotypes such as worry, transitioning to less strong correlations with individual depression-related items.

This study marks the transition from the flat (sample-building) phase of SNP discovery described for GWAS⁸⁴ (Supplementary Fig.20), where few to no genome-wide significant loci are identified^10,12,51,85, to the linear phase of SNP discovery, where even relatively small increases in sample size identify additional genome-wide significant loci¹⁸. The strengths of the current study therefore include the marked increase in the number of OCD cases and the rigorous analytic methods, including two multivariate approaches (multi-trait analysis of GWAS (MTAG) and GenomicSEM) to control for potential overlapping study participants and to examine potential heterogeneity between the multiple ascertainment approaches. Potential weaknesses include the inability to document comorbid psychiatric disorders in the majority of cases that were not ascertained from clinical collections or electronic registries, the lack of inclusion of non-European ancestries and the limited availability of sex chromosome data. Owing to the nature of our study, imputation references used in the different cohorts were heterogeneous and did not allow for confident analysis of rare variant associations. Future larger-scale sequencing studies that are currently underway will be needed to identify associations in this allele frequency spectrum. We also note that the genetic correlation analyses are impacted by residual heterogeneity in genetic signals owing to the employment of heterogeneous ascertainment strategies.

In summary, this work substantially advances the field of OCD genetics by identifying new OCD genetic risk loci and multiple credible candidate causal genes, including those expressed in brain regions and cell types previously implicated in OCD⁸⁶. We have also shown that OCD is highly polygenic in nature, with many variants implicated not only in OCD but also in commonly comorbid disorders or traits, in particular, anxiety, neuroticism, anorexia nervosa and depression. The observation that common variants explain only a modest amount of the phenotypic variation in OCD suggests that other types of genetic variation may also contribute to the etiology of OCD. Notably, whole-exome-sequencing studies have suggested that a substantial proportion of OCD cases (22%) may be influenced by rare de novo coding variants⁸⁷, especially in genes that are intolerant to loss of function⁸⁸. Similarly, rare potentially damaging copy number variations represent part of the risk architecture for OCD⁹. These findings emphasize the need for a comprehensive exploration of the contribution of both common and rare genetic factors as well as their interplay to OCD risk. Finally, with the implication of the MHC complex, we provide additional evidence for potential shared genetic influences underlying both OCD and increased liability to autoimmune processes, although the directionality of those relationships remains to be definitively elucidated. In addition to continuing to increase sample sizes, future studies will require ancestrally diverse samples to further facilitate the discovery of additional OCD risk variants. Similarly, sex-specific analyses and additional clinical phenotyping will allow for the further elucidation of genetic and clinical relationships between OCD and co-occurring disorders. Finally, with the emergence of drug databases describing the relations between drugs and molecular phenotypes⁸⁹, our results may be useful for drug repurposing (that is, identifying existing drugs targeting OCD risk genes), leading to new opportunities to find more effective treatments.

Ethics

All relevant ethics approvals have been obtained by the respective cohort’s institutions, and a list of all respective approvals can be found in Supplementary Note2.

Study participants

We analyzed genomic data from 28 OCD case–control cohorts including 53,660 OCD cases and 2,044,417 controls of European ancestry. Supplementary Table1 provides an overview of the individual cohorts. A subset of the cases and controls have been included in previous studies^10,51,85 and preprints^12,13, as described in Supplementary Note2. Among all included individuals, 323 cases were part of a parent–proband trio; in these cases, parents were used as pseudocontrols. A total of 20,427 cases met DSM-5 (ref.⁹³) or ICD-10 (https://icd.who.int/) criteria for OCD as assessed by a healthcare professional or derived from (electronic) health records, while the remaining 32,233 cases were based on self-reported OCD diagnosis (23andMe, AGDS and parts of UKBB). Cohort-specific sample and analytic details can be found in Supplementary Note2. Data collections were approved by the relevant institutional review boards at all participating sites, and all participants provided written informed consent.

Individual GWAS analyses and harmonizing of results

First, the data of each participating cohort were analyzed individually (see Supplementary Note2 for details). Genetic data were imputed using either the Haplotype Reference Consortium (HRC)⁹⁴ or 1000 Genomes Project Phase 3 reference panels⁹⁵. The resulting GWAS summary statistics were then harmonized before a conjoint meta-analysis of all autosomes was conducted. Each summary statistic dataset was transformed to the ‘daner’ file format following RICOPILI⁹⁶ specifications. All variants had to meet the following criteria for inclusion: minor allele frequency (MAF) > 1% in cases and controls, INFO score > 0.8 and <1.2. If the effect measure,P value or s.e. was missing or was out of bounds (infinite), the SNP was removed. Once cleaned summary statistics were produced, all datasets were aligned to the HRC reference panel. If variants were reported on different strands, they were flipped to the orientation in the HRC reference. Furthermore, strand-ambiguous A/T and C/G SNPs were removed if their MAF was >0.4. In the case that A/T and C/G SNPs showed a MAF < 0.4, allele frequencies were compared to frequencies in the HRC reference. If an allele frequency match was found, that is, minor alleles were the same in the summary statistics and the HRC reference, the same strand orientation was assumed. If an allele mismatch was found, that is, the allele had a frequency > 0.5 in the HRC reference, it was assumed that alleles were reported on different strands, and alleles were flipped subsequently. Marker names were uniformly switched to those present in the HRC reference. If a variant did not overlap with the variants in the HRC reference, it was removed.

GWAS meta-analysis

Inverse-variance-weighted meta-analysis was conducted on 28 European cohorts using METAL⁹⁷. Weighting was based on standard error primarily to account for the large case–control imbalances in cohorts that used linear mixed model approaches in their primary GWAS. Heterogeneity was assessed with Cochran’sQ statistic and theI² statistic^98,99 (see Supplementary Note5 for details). The genomic control factor lambda (λ) was calculated for each individual GWAS and for the overall meta-analysis to identify residual population stratification or systematic technical artifacts. GWAS summary statistics were subjected to LDSC analyses on high-quality common SNPs (INFO score > 0.9) to examine the LDSC intercept to distinguish polygenicity from other types of inflation and to estimate the genetic heritability from the meta-analysis and genetic correlations between cohorts. The genomic inflation factorλ was estimated at 1.330 with aλ₁₀₀₀ of 1.033, while the LDSC intercept was 1.0155 (s.e. = 0.0085), indicating that the inflation was mostly due to polygenic signal and unlikely to be substantially confounded by population structure. The genome-wide significance threshold for the GWAS was set ata P value of\(5.0\times 1{0}^{-8}\). The 23andMe data included information on the X chromosome; as this information was not present for all other cohorts, analysis of the X chromosome was only conducted in this subcohort (see Supplementary Note4 for details).

We further conducted GWAS meta-analyses on the following four subgroups, defined by differences in their sample ascertainment: (1) clinical OCD cases diagnosed by a healthcare professional in a clinical setting (n_cases = 9,089,n_controls = 21,077; including IOCDF, IOCDF_trio, EPOC, NORDiC-nor, NORDiC-swe, EGOS, OCGAS, OCGAS-ab, OCGAS-gh, OCGAS-nes, Psych_Broad, WWF, MVP, Michigan/Toronto IGS, YalePenn, Chop, CoGa), (2) comorbid individuals who were primarily ascertained for another comorbid psychiatric disorder (n_cases = 5,266,n_controls = 43,760; AGDS, iPSYCH), (3) biobank data from large-scale biobanks or registries with ICD or DSM codes (n_cases = 9,138,n_controls = 1,049,776; BioVU, EstBB, FinnGen, HUNT, MoBa, UKBB) or (4) 23andMe data (n_cases = 30,167,n_controls = 929,804). While these groups are not exclusive (for example, diagnoses in health records were originally given in a clinical setting or comorbid cases were also assessed in a clinical setting or derived from health records), we defined these groups by the cohort’s primary characteristic. We also conducted one meta-analysis including all clinical, comorbid and biobank subgroups, while excluding the 23andMe data, resulting in 23,493 cases and 1,114,613 controls. As 23andMe is the only consumer-based dataset, we intended to compare this dataset to all others.

Number of trait-specific causal variants (MiXeR analysis)

We applied MiXeR version 1.3 (ref.¹⁵) to quantify the polygenicity of OCD (that is, estimate the total number of trait-influencing genetic variants). MiXeR fits a Gaussian mixture model assuming that common genetic effects on a trait are a mixture of causal variants and noncausal variants. Polygenicity is reported as the number of causal variants that explain 90% of SNP heritability of OCD (to avoid extrapolating model parameters into the area of infinitesimally small effects).

SNP-based fine-mapping (GCTA-COJO)

We performed a conditional and joint analysis (GCTA-COJO)¹⁴ to identify independent signals within significant OCD loci. This approach performs a conditional and joint analysis on the basis of conditionalP values before calculating the joint effects of all selected SNPs. We used the stepwise model selection procedure to select independently associated SNPs. The linkage disequilibrium reference sample was created from 73,005 individuals from the QIMR Berghofer Medical Research Institute genetic epidemiology cohort. The distance assumed for complete linkage disequilibrium was 10 Mb, and we used the defaultP-value threshold of\(5\times {10}^{-8}\) to define a genome-wide significant hit.

Multi-trait analysis of ascertainment subgroups

We used MTAG¹⁰⁰ to conduct multivariable GWAS analyses, reporting GWAS results for each of the ascertainment-specific subgroups. Through this approach, we aimed to address potential concerns about heterogeneity in genetic liability for individual subgroups following different ascertainment strategies. MTAG is a multi-trait analysis that is usually used to combine different but related traits into one meta-analysis by leveraging the shared heritability among the different traits and thereby gaining power. In this case, our aim was to generate ascertainment-specific estimates, while boosting power by leveraging the high shared heritability between the subgroups. The MTAG analysis resulted in four different GWAS summary statistics, one for each subgroup (clinical, comorbid, biobanks, 23andMe). We performed maxFDR analyses to approximate the upper bound on the FDR of MTAG results.

GenomicSEM

Similarly, we used GenomicSEM¹⁶ to model the joint genetic architecture of the four subgroups. First, we ran a common factor model without individual SNP effects, following the tutorial ‘Models without individual SNP effects’ on the GenomicSEM GitHub website (Code availability). Second, we ran a multivariate GWAS of the common factor (see Supplementary Note5 for details). We specified the model using unit variance identification, for which the latent factor variance is fixed to 1 and the loadings of the traits are estimated freely. This ensures that we capture how much of each subgroup contributes to the latent factor. GenomicSEM also generates\({Q}_{{\rm{SNP}}}\) values, which indicate possible heterogeneous effects across the subgroups. The\({Q}_{{\rm{SNP}}}\) statistic is mathematically similar to theQ statistic from standard meta-analysis and is a\({ X}^{2}\)-distributed test statistic, with larger values indexing a violation of the null hypothesis that the SNP acts entirely through the common factor.

SNP heritability estimation

The proportion of the phenotypic variance that could be explained by the aggregated effect of all included SNPs (SNP-based heritability,\({h}_{{\rm{SNP}}}^{2}\)) was estimated using LDSC³¹. The analysis was performed using precomputed linkage disequilibrium scores from samples restricted to European ancestry in the 1000 Genomes Project⁹⁵, filtered for SNPs included in the HapMap 3 reference panel¹⁰¹. SNP heritability was estimated based on the slope of the LDSC, with heritability on the liability scale calculated assuming a 1% population prevalence of OCD¹. To omit a downward bias in our estimates of liability-scale heritability, following Grotzinger et al.¹⁰², we accounted for varying levels of ascertainment across cohorts in our meta-analysis by summing the effective sample sizes across the contributing cohorts and using that as the input sample size for LDSC. For conversion to the liability scale (1%), the sample prevalence was then specified as 0.5. The SNP heritability was calculated for the whole OCD sample as well as for ascertainment-specific subgroups.

Genetic correlations

We used cross-trait LDSC³¹, a method that computes genetic correlations between GWASs without bias from ancestry differences or sample overlap to calculate genetic correlations between the primary OCD meta-analysis and other phenotypes of interest. The selection of traits was based on phenotypic relevance and/or prior report of a genetic relationship with OCD. The genetic correlation between traits was based on the estimated slope from the regression of the product ofZ scores from two GWASs on the linkage disequilibrium score and represents the genetic covariation between two traits based on all polygenic effects captured by the included SNPs. The genome-wide linkage disequilibrium information used by these methods was based on European populations from the HapMap 3 reference panel¹⁰¹, and GWAS summary statistics were filtered to only include SNPs that were part of the 1,290,028 HapMap 3 SNPs.

To ensure the internal consistency of the datasets included in our meta-analysis, we calculated genetic correlations between all cohorts we considered to have a sample size large enough for LDSC (effective sample size of ≥1,000) and between the four ascertainment-specific subgroups.

We further calculated genetic correlations between OCD and 112 other disorders and traits. The source studies of the GWAS summary statistics can be found in Supplementary Table18. As a follow-up, we also calculated genetic correlations between the 112 phenotypes and each ascertainment-specific subcohort and compared the genetic correlation patterns between the four groups. For all cross-phenotype genetic correlation analyses, we adjustedP values for multiple testing using the Benjamini–Hochberg procedure to control for the FDR (<0.05).

Gene-based analyses

To match the significant SNPs to the genes for which they likely influence function, we conducted a series of positional and functional gene-mapping analyses. The positional mapping employed MBAT-combo¹⁹, while the functional mapping tested whether genetic variants associated with OCD were also associated with differential expression of nearby genes (within a 1-Mb window) using (1) TWAS²⁰ using PsychENCODE data and included colocalization with COLOC^27,28, and (2) SMR²² using whole-blood eQTL information and brain tissues from MetaBrain, alongside the HEIDI test, which tests for heterogeneity in GWAS signal and eQTL association. Furthermore, a PWAS was conducted. As a final step, genes within each locus were prioritized using PsyOPS²⁶, which integrates both positional and functional information. The details of each method are described below.

Positional gene mapping (MBAT-combo)

A gene-based analysis was conducted using mBAT-combo¹⁹ within GCTA version 1.94.1 (ref.¹⁴). The European subsample (n = 503 individuals) from phase 3 of the 1000 Genomes Project⁹⁵ was used as the linkage disequilibrium reference panel with the fastBAT default linkage disequilibrium cutoff of 0.9 applied. After filtering SNPs with MAF > 0.01, there were 6,629,124 SNPs for analysis in our sample. A gene list consisting of 19,899 protein-coding genes was used to map the base pair position of genes using genome build hg19 (see Supplementary Note7 for details).

Functional gene mapping

Transcriptome-wide association study

We used TWAS FUSION²⁰ to perform a TWAS of OCD. We used brain gene expression weights from the PsychENCODE¹⁰³ and linkage disequilibrium information from the 1000 Genomes Project Phase 3 (ref.⁹⁵). TWAS FUSION uses reference linkage disequilibrium and reference gene expression panels with GWAS summary statistics to estimate the association between gene expression and OCD risk. These data were processed with the test statistics from the OCD GWAS to estimate the expression–GWAS association statistic. We corrected for multiple testing using Bonferroni correction.

We performed colocalization analyses using the COLOC R function^27,28 implemented in TWAS FUSION. Colocalization is a Bayesian method used to calculate the posterior probabilities (PP) that individual lead SNPs within a significant TWAS locus are (1) independent (for example, two causal SNPs in linkage disequilibrium, one affecting transcription and one affecting OCD; PP₃) or (2) share the same associated variant (for example, a single causal SNP affects both transcription and OCD (PP₄)). We also performed a conditional analysis to determine whether identified associations represented independent associations. This was performed using the FUSION software, which jointly estimates the effect of all significant features within each locus by using residual SNP associations with OCD after accounting for the predicted expression of other features.

Summary-based Mendelian randomization

SMR²² was performed using default settings and eQTL meta-analysis summary statistics from European populations for whole blood from eQTLGen²³ and all five nervous system tissues from MetaBrain (basal ganglia, cerebellum, cortex, hippocampus and spinal cord)¹⁰⁴. The HEIDI test was performed alongside SMR to test for effect size heterogeneity between the GWAS and eQTL summary statistics. Both SMR and TWAS have a number of important assumptions and limitations, which we discuss in Supplementary Note9.

Psychiatric omnilocus prioritization score

We used the gene prioritization method PsyOPS²⁶ to rank genes within genome-wide significant loci. This supervised approach integrates biological annotations on mutational intolerance, brain-specific expression and involvement in neurodevelopmental disorder for genes within significant loci. Genes with the top PsyOPS score within each locus were used for further gene prioritization (Gene prioritization). In the instance where two genes in the same locus had the same PsyOPS score, the gene nearest the index SNP was prioritized.

Protein-wide association study

We performed a PWAS using protein expression data from human brain samples. Human brain proteome reference weight data were obtained using the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) and the Banner Sun Health Research Institute (Banner) study. The ROS/MAP proteomes were generated from the dlPFC of 376 participants of European ancestry and included 1,476 proteins with significant SNP-based heritability (P < 0.01). The Banner PWAS weights were generated from 152 individuals of European ancestry and included 1,147 proteins with significant SNP-based heritability. The PWAS was performed using the TWAS FUSION software²⁰ with linkage disequilibrium reference information from the 1000 Genomes Project Phase 3 (ref.⁹⁵). We corrected for multiple testing using Bonferroni correction.

Gene prioritization

We created a list of prioritized genes using both gene-based tests and colocalization–HEIDI filters. Results from each gene-based test were first restricted to protein-coding genes with unique gene identifiers based on the release from GENCODE (version 40) for hg19. The following criteria were then used to prioritize genes: (1) a significant (Bonferroni-corrected) association from at least two gene-based tests (mBAT-combo, TWAS FUSION, SMR or PsyOPS) and (2) evidence of colocalization (COLOC PP₄ > 0.8) and/or significant SMR association with HEIDIP > 0.05. Joint–conditional tests of association and significant PWAS associations were used as ancillary approaches to further annotate the prioritized gene list.

Tissue and cell type enrichment analysis

An analysis of tissue and cell type enrichment of OCD GWAS association signals was conducted using MAGMA (version 1.08)¹⁰⁵ and partitioned LDSC¹⁰⁶. We used the previously described approach²⁹ to determine gene expression specificity in bulk tissue RNA-seq data from 37 tissues in GTEx (version 8) and single-cell RNA-sequencing data from 19 regions in the mouse central and peripheral nervous systems³⁰. The analysis was limited to protein-coding genes with 1:1 orthologs between mice and humans. Gene expression in each tissue or cell type was calculated relative to total expression across all tissues or cell types. Enrichment analysis was performed on genes with the top 10% specificity values in each tissue or cell type, as previously defined²⁹.

To evaluate the enrichment of tissue- and cell type-specific genes in OCD genetic association signals, we applied MAGMA and partitioned LDSC. We restricted the analysis to summary statistics for SNPs with a high INFO score (>0.6) and frequency in the entire cohort (MAF > 0.01). Using MAGMA (version 1.08), we tested whether genes with the top 10% specificity in a tissue or cell type showed enrichment in gene-level genetic associations for OCD, with the 1000 Genomes Phase 3 European sample genotypes serving as the linkage disequilibrium reference panel. We used standard gene boundaries (35 kb upstream of the transcription start site to 10 kb downstream of the transcription stop site). Partitioned LDSC was used to examine whether SNPs within 100-kb regions of the top 10% specifically expressed genes were enriched for SNP-based heritability for OCD. All results were corrected for multiple testing with an FDR threshold of 0.05.

SNP and gene findings in the context of previous analyses

Previously reported associations for significant SNPs (PheWAS)

Multiple resources were used to identify previously reported associations of our 30 significant SNPs with other phenotypes. We used the IEU Open GWAS project⁹², PheWAS analysis of GWAS ATLAS⁵² and the NHGRI-EBI GWAS Catalog⁹¹ and identified credible SNPs through CAUSALdb⁹⁰. CAUSALdb estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools including PAINTOR, CAVIARBF and FINEMAP^{107,108,109,110}. We used default settings for our CAUSALdb queries.

Lookup of previous OCD GWAS findings

We performed a lookup of SNPs identified to be significantly associated with OCD-related phenotypes in previous GWASs. Note that this is not an independent replication, as previous studies partially overlap with the cohorts included in this GWAS.

Overlap of previous rare coding variants in OCD and GWAS gene findings

We performed a bidirectional lookup, assessing (1) whether gene findings from our GWAS showed evidence for rare variant involvement and (2) vice versa, whether findings from rare variant testing showed evidence of common variant association in our GWAS.

First, we comprehensively assessed the overlap between 251 genes that we highlighted in our study as carrying common risk variation for OCD (Supplementary Table14) and current gene-based summary statistics from OCD exome-sequencing data. We used data from Halvorsen et al.⁸⁸ because it is the largest published exome-sequencing study of OCD presently. The supplementary materials from that paper include de novo variant calls from 771 case trios and 1,911 controls (supplementary table 14 in ref.⁸⁸). We compared the burden of de novo variants, partitioned by variant annotation (synonymous, missense, loss of function) in trio cases versus trio controls within these 251 GWAS-prioritized genes. As described previously⁸⁸, we only included de novo variants that were in loci well covered in both case and control data (In_Jointly_Covered_Loci==TRUE). We also excluded all calls from quartet samples in ref.⁸⁸ (Cohort!=“OCD_JHU_quartets”). For each of the four variant annotation classes, we compared the proportion of cases that had at least one qualifying de novo variant to the proportion of controls using a two-sided Fisher’s exact test.

Second, as Halvorsen et al.⁸⁸ describe an overall excess of loss-of-function variants in OCD cases relative to controls specifically within loss-of-function intolerant genes (supplementary table 13 in ref.⁸⁸), we analyzed the overlap between those genes and our GWAS-derived genes. We looked up 200 genes with a probability of loss-of-function intolerance > 0.995 (derived from ref.¹¹¹) and effect size estimate > 1. We further tested for a difference in the proportion of these pLI > 0.995 genes with effect size estimate > 1 versus ≤1 within the set of genes highlighted in the OCD GWAS (n = 251) versus outside this set using a two-sided Fisher’s exact test.

Reporting summary

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

The meta-analyzed summary statistics (not including 23andMe data) are available from the Psychiatric Genomics Consortium Download page (https://www.med.unc.edu/pgc/download-results/). In line with 23andMe regulations, 10,000 SNPs from the full GWAS including 23andMe are also being made available athttps://www.med.unc.edu/pgc/download-results/. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Datasets will be made available at no cost for academic use. Please visithttps://research.23andme.com/collaborate/#dataset-access/ for more information and to apply to access the data. MVP summary statistics are made available through dbGAP request under accessionhttps://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001672.v12.p1 phs001672.v12.p1.

Core analysis code for RICOPILI can be found athttps://sites.google.com/a/broadinstitute.org/ricopili/. This includes PLINK (https://www.cog-genomics.org/plink2/), EIGENSOFT (https://www.hsph.harvard.edu/alkes-price/software/), Eagle2 (https://alkesgroup.broadinstitute.org/Eagle/), Minimac3 (https://genome.sph.umich.edu/wiki/Minimac3), SHAPEIT3 (https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html), METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation) and LDSR (https://github.com/bulik/ldsc). MAGMA can be found athttps://ctg.cncr.nl/software/magma. GenomicSEM, specifically the tutorial ‘Models without Individual SNP effects’ can be found here:https://github.com/GenomicSEM/GenomicSEM/wiki/3.-Models-without-Individual-SNP-effects. TWAS FUSION can be found athttp://gusevlab.org/projects/fusion/. PWAS: for access to the protein weights, seehttps://www.synapse.org/#!Synapse:syn24872746. GCTA (mBAT-combo and COJO) can be found athttps://yanglab.westlake.edu.cn/software/gcta/#Overview. LDSC and partitioned heritability can be found athttps://github.com/bulik/ldsc. Additional code for data processing (for example, harmonization of summary statistics) can be found athttps://doi.org/10.6084/m9.figshare.28451894 (ref.¹¹²).

We thank the research participants and employees of all cohorts included in this study for making this work possible. A list of members of the 23andMe Research Team, HUNT, CoGa and the MVP who contributed to this study can be found in Supplementary Note1. We thank the research participants and employees of 23andMe for making this work possible. The Trøndelag Health Study (HUNT) is a collaboration between the HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU), Trøndelag County Council, Central Norway Regional Health Authority and the Norwegian Institute of Public Health. Genotype quality control and imputation were conducted by the K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU. HUNT analyses were performed in digital laboratories at HUNT Cloud, HUNT Research Centre Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Trondheim, Norway. NORDiC is funded by National Institute of Mental Health (NIMH) R01 MH110427 (PI J.J.C.), NIMH R01 MH105500 (PI J.J.C.) and the Swedish Research Council grant 2015–02271 (PI D.M.-C.). NORDiC was further supported by the Swedish Research Council (grants 2012-07111 and 2018-02487), Swedish Research Council for Health, Working Life and Welfare 2018-00221 and the Center for Innovative Medicine (CIMED). We are deeply grateful for the study participants contributing to the NORDiC research. We thank the collection team that worked to recruit them: A. Juréus, J. Pege, M. Rådström, R. Satgunanthan-Dawoud, M. Krestelica and B. Ohlander, as well as the data manager B. Iliadou. We also thank the National Quality Registry for Eating Disorders (RIKSÄT) for help with recruiting patients. We finally thank the BBMRI.se and KI Biobank at Karolinska Institutet for professional biobank services. Grant support for the MoBa team was also provided from RCN (273291, 262656, 248778, 223273) and the KG Jebsen Stiftelsen. MoBa is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We are grateful to all the participating families in Norway who take part in this ongoing cohort study. The AGDS was primarily funded by National Health and Medical Research Council (NHMRC) of Australia grant 1086683. This work was further supported by NHMRC grants 1145645, 1078901 and 1087889. We thank all the people who helped in the conception, implementation, beta testing, media campaign and data cleaning of the AGDS data. We specifically acknowledge D. Nyholt for advice on using the PBS for research; K. Kendler, P. Sullivan, A. McIntosh and C. Lewis for input on the questionnaire; L. Nunn, M. Ferguson, L. Winkler and N. Garden for data and sample collection; N. Zmicerevska, A. Nichles and C. Brennan for participant recruitment support; J. Davies, L. Lowrey and V. Antonini for support with IT aspects; and V. Morgan and K. Kirkby for help with the media campaign. We thank VIVA! Communications for their effort in promoting the study. We also acknowledge D. Whiteman and C. Olsen from QSkin. The work done by the EstBB team has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement 847776 (CoMorMent). Data analysis for EstBB was carried out in part at the High-Performance Computing Center of the University of Tartu. We thank participants, families and staff of primary and secondary schools who kindly contribute to this research (M.R., Metal-Cat and INSchool). EGOS was supported by a grant from the Beatrice and Samuel A. Seaver Foundation to D.E.G. The genotyping of HUNT was financed by the National Institutes of Health (NIH), the University of Michigan, the Norwegian Research Council and Central Norway Regional Health Authority and the Faculty of Medicine and Health Sciences, NTNU. This research is based in part on data from the MVP, Office of Research and Development, Veterans Health Administration and was supported by awards CSP575b, I01CX001849-01 and 1P1HX002375 and the National Center for PTSD Research. The MVP was supported by funding from the Department of Veterans Affairs Office of Research and Development, USVA, grants CSP575B and I01CX001849, MVP-025 and the VA Cooperative Studies Program study no. 575B, the VA National Center for PTSD Research and the West Haven VA Mental Illness Research, Education and Clinical Center and by NIH grant R01 AA026364 (J. Gelernter). This publication does not represent the views of the Department of Veterans Affairs or the United States government. The EPOC study was funded by the Deutsche Forschungsgemeinschaft (DFG; KA815/6-1 and WA731/10-1). LifeGene was supported by the Swedish Research Council, Karolinska Institutet–Stockholm County Council research grants, AFA Insurance and the Torsten and Ragnar Söderbergs Foundation. GENOS was supported by the German Research Foundation (GR 1912/1-1). The OCD Collaborative Genetics Association Study (OCGAS) is a collaborative research study and was funded by the following NIMH grant numbers: MH071507, MH079489, MH079487, MH079488 and MH079494. This work (OCGAS and IOCDF) is supported by the Netherlands Organization for Scientific Research—Gravitation project ‘BRAINSCAPES: a Roadmap from Neurogenetics to Neurobiology’ (024.004.012) and the European Research Council advanced grant ‘From GWAS to Function’ (2018-ADG 834057). The OCGAS and IOCDF samples are supported through NIMH grant numbers MH071507 (G.N.), MH079489 (D.A.G.), MH079487 (J.T.M.), MH079488 (A.J.F.) and MH079494 (J.A.K.). The iPSYCH team was supported by grants from the Lundbeck Foundation (R102-A9118, R155-2014-1724 and R248-2017-2003), the NIH/NIMH (1R01MH124851-01 to A.D.B.) and the universities and university hospitals of Aarhus and Copenhagen. The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity for handling and statistical analysis of iPSYCH data in the GenomeDK HPC facility was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to A.D.B.). A.D.B. was also supported by the EU’s HORIZON-HLTH-2021-STAYHLTH-01 program, project number 101057385: Risk and Resilience in Developmental Diversity and Mental Health (R2D2-MH). Mental-Cat and INSchool were supported by the Agència de Gestió d’Ajuts Universitaris i de Recerca (2017SGR-1461, 2021SGR-00840), the Instituto de Salud Carlos III (PI20/00041, PI23/00404 and PI23/00026), the European Regional Development Fund and the ECNP Network (‘ADHD across the Lifespan’ and ‘la Marató de TV3’, 202228-30 and 202228-31). BioVU: CTSA (S.D., Vanderbilt Resources) was supported by the National Center for Research Resources, grant UL1 RR024975-01 and is now at the National Center for Advancing Translational Sciences (grant 2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The dataset(s) used for the analyses (BioVU) described were obtained from Vanderbilt University Medical Center’s BioVU, which is supported by numerous sources: institutional funding, private agencies and federal grants. These include the NIH-funded Shared Instrumentation Grant S10RR025141 and CTSA grants UL1 TR002243, UL1 TR000445 and UL1 RR024975. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962 and R01HD074711 and additional funding sources listed athttps://victr.vumc.org/biovu-funding/. S.A. acknowledges a Miguel Servet contract (CP22/00026) awarded by the Instituto de Salud Carlos III and cofunded by the European Union Fund: Fondo Social Europeo Plus, FSE+. HYPERGENES and InterOmics cohorts provided controls of Italian origin for the present study. J.C.-D. acknowledges her contract from the Network Center for Biomedical Research (CIBER). R.D. acknowledges the Clinical Investigation Centre, Robert Debré Hospital. INSERM at APHP granted the study. B.T.F. acknowledges the Anorexia Nervosa Genetics Initiative, an initiative of the Klarman Family Foundation. J.H. acknowledges the Trond Mohn Foundation, Bergen, Norway. C. Lochner acknowledges the South African Medical Research Council and the National Research Foundation for their support. T.V.F: research reported in this publication was supported by the NIMH of the NIH under award number R01MH114927. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. N.G.M. has received funding from a project grant from the Australian NHMRC. The Research Council of Norway supported H.A., A.H. and T.R.-K. (274611). A. Havdahl was also supported by South East Norway Health Authority (2020022). Z.F.G. is supported by an Australian NHMRC EL1 Investigator Grant (2034743) and NIH/NIA grant AG068026. M.G. received support from the following grants (J. Gelernter): CSP575b, I01CX001849-01, 1P1HX002375, the National Center for PTSD Research, 5R01DA054869-01. A. Abdellaoui was supported by the Foundation Volksbond Rotterdam. T.B. is supported by NIMH grant 7R01MH103657 (GPC-OCD). J.R.C.: this study represents independent research partly funded by the National Institute for Health Research Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. C.B. was supported by grant EU FP7-HEALTH-2007-A-201550 and grant MIUR-CNR PB05. E.M.B. was supported by NHMRC project grant 1145645 and the University of Queensland Health Research Accelerator Program. C.C. was supported by grant K99MH128540-01A1. V.C. was supported by the Italian Ministry of Health grant RC18-19-20-21/A. M.A.G. was supported by NIMH K23 MH066284. J.H. was supported by Stiftelsen KG Jebsen (SKGJ MED-02). K. Hagen was supported by the Trond Mohn Foundation. E.K.K. was supported by NIH R21 MH109938. P.S.N. was supported by R01MH071507. Fabrizio and Federica Piras are supported by the Italian Ministry of Health RC18-19-20-21/A grant. This work was in part supported by the German Research Foundation (DFG) grants RA1971/8-1 and RA1971/7-1 and by the Bundesministerium für Bildung und Forschung grant 01ED2007A to A. Ramirez. S. Ripke was supported by research grant 1R01MH124873-01. M.S.A. was supported by the Instituto de Salud Carlos III (P19/01224, PI22/00464 and CP22/00128) and the European Regional Development Fund. A. Agrawal was supported by grant U10AA008401. P.A. was supported by the Spanish Ministry of Science, Innovation and Universities (ISCIII PI22/00752) and Fundació La Marató 202201-30. C.M.B. was supported by R01 MH124871 (Sullivan and Bulik) PGC4. H.E. was supported by grant U10AA008401. D.A.G. was supported by the NIMH (OCGAS and OCGS). G.L.H. was supported by the NIMH (R01 MH58376, K20 MH01065, R01 MH101493, R01 MH085321). N.K. has received funding from DFG KA815/6-1. S.E.M. is supported by an Australian NHMRC Investigator Grant (APP1172917). B.M.N. is funded by grant R01MH124851. M.P. and C. Pato have received support from R01MH103657 and R01MH079494 from the NIMH and the Della Martin Foundation, Los Angeles, CA. J.P. has received support through the NIMH (R01MH50214: Collaborative OCD Genetics Study (G.N., PI; J.T.M., UCLA PI)). M.A. Richter was supported by funding from the Canadian Institutes for Health Research and the Ontario Mental Health Foundation. D.R.R. was supported by NIMH R01MH059299. J.F.S. was supported by NIMH grant number MH071507. G.S. is supported by the Italian Ministry of Health RC18-19-20-21/A grant. E.A.S. collected data as part of the NIH grant 1R01MH093381. O.A.A. (MoBa) has received grant support from RCN (324499, 273291, 262656, 248778, 223273), KG Jebsen Stiftelsen and NordForsk 164218. J. Kaprio has been supported by the Academy of Finland (grant 336823). P.D.A. is supported by the Alberta Innovates Translational Health Chair in Child and Youth Mental Health. D.E.G. is supported by grant MH124679-01. J.A.K. is supported by the grants R01MH103657 and R01MH079494 from the NIMH and the Della Martin Foundation, Los Angeles, CA. K.J.V. is supported by the Foundation Volksbond Rotterdam. L.K.D. was supported by grants from the NIH including R01NS102371, R01MH113362, R01MH118223, R01NS105746 and R56MH120736. J.S. was supported by an NIH Training Grant in Human Genetics (2T32GM080178). J.J.C. was supported by NIH grants R01 MH105500 and R01 MH110427. M.B.S. has been funded by the US Veterans Affairs Administration.

Open access funding provided by Humboldt-Universität zu Berlin.

1. These authors contributed equally: Nora I. Strom, Zachary F. Gerring, Marco Galimberti, Dongmei Yu.

2. These authors jointly supervised this work: Jeremiah M. Scharf, Murray B. Stein, Joel Gelernter, Carol A. Mathews, Eske M. Derks, Manuel Mattheisen.

Authors and Affiliations

1. Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany

   Nora I. Strom, Julia Klawohn & Norbert Kathmann

2. Department of Psychiatric Phenomics and Genomics (IPPG), Ludwig-Maximilians University Munich, Munich, Germany

   Nora I. Strom & Manuel Mattheisen

3. Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Services, Region Stockholm, Stockholm, Sweden

   Nora I. Strom, Julia Bäckman, Elles J. De Schipper, Christian Rück, David Mataix-Cols & James J. Crowley

4. Department of Biomedicine, Aarhus University, Aarhus, Denmark

   Nora I. Strom, Jakob Grove, Anders D. Børglum & Manuel Mattheisen

5. University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland

   Nora I. Strom

6. Department of Mental Health and Neuroscience, Translational Neurogenomics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

   Zachary F. Gerring

7. Department of Population Health and Immunity, Healthy Development and Ageing, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia

   Zachary F. Gerring

8. Department of Psychiatry, Human Genetics, Yale University, New Haven, CT, USA

   Marco Galimberti

9. VA Connecticut Healthcare System, West Haven, CT, USA

   Marco Galimberti

10. Department of Center for Genomic Medicine, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Dongmei Yu

11. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Dongmei Yu

12. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Matthew W. Halvorsen & James J. Crowley

13. Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands

    Abdel Abdellaoui

14. Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Genomics and Bioinformatics, University of Santiago de Compostela, Santiago de Compostela, Spain

    Cristina Rodriguez-Fontenla

15. Grupo de Medicina Xenómica, Genetics, Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS), Santiago de Compostela, Spain

    Cristina Rodriguez-Fontenla

16. Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA

    Julia M. Sealock

17. Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA

    Tim Bigdeli

18. VA NY Harbor Healthcare System, Brooklyn, NY, USA

    Tim Bigdeli

19. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Jonathan R. Coleman

20. National Institute for Health and Care Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK

    Jonathan R. Coleman

21. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Behrang Mahjani, Michael Breen, Joseph D. Buxbaum, Magdalena Janecka, Laura G. Sloofman, Sven Sandin & Dorothy E. Grice

22. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Behrang Mahjani, Christina Hultman, Nancy L. Pedersen, Cynthia M. Bulik, Mikael Landén & Sven Sandin

23. Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Jackson G. Thorp

24. Faculty of Medicine, School of Biomedical Sciences, University of Queensland, Brisbane, Queensland, Australia

    Jackson G. Thorp

25. Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany

    Katharina Bey, Maureen Mulhern & Michael Wagner

26. Department of Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada

    Christie L. Burton

27. Department of Psychiatry, Brain University Medical Center Utrecht, Utrecht, the Netherlands

    Jurjen J. Luykx

28. Second Opinion Outpatient Clinic, GGNet, Warnsveld, the Netherlands

    Jurjen J. Luykx

29. Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada

    Gwyneth Zai

30. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada

    Gwyneth Zai, James Kennedy, Margaret A. Richter & Paul Sandor

31. Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d’Hebron Research Institute, Universitat Autònoma de Barcelona, Barcelona, Spain

    Silvia Alemany, Judit Cabana-Dominguez, María Soler Artigas & Marta Ribasés

32. Department of Mental Health, Hospital Universitari Vall d’Hebron, Barcelona, Spain

    Silvia Alemany, Judit Cabana-Dominguez, María Soler Artigas & Marta Ribasés

33. Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain

    Silvia Alemany, Judit Cabana-Dominguez, María Soler Artigas & Marta Ribasés

34. Obsessive–Compulsive Disorder Institute, McLean Hospital, Belmont, MA, USA

    Christine Andre, Brian P. Brennan, Jesse Crosby, Martha J. Falkenstein, Lauryn Garner, Christina Gironda, Eric Jenike, Kara Kelley, Brittany Mathes, Sriramya Potluri & Eric Tifft

35. Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Hamilton, Ontario, Canada

    Kathleen D. Askland

36. Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy

    Nerisa Banaj, Valentina Ciullo, Fabrizio Piras & Gianfranco Spalletta

37. Department of Health Sciences, University of Milano, Milano, Italy

    Cristina Barlassina

38. Department of Child and Adolescent Psychiatry, Aarhus University Hospital, Psychiatry, Aarhus, Denmark

    Judith Becker Nissen

39. Institute of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark

    Judith Becker Nissen

40. Department of Psychiatry and Behavioral Sciences, General Hospital Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    O. Joseph Bienvenu

41. Departments of Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, USA

    Donald Black

42. Department of Child Study Center and Psychiatry, Yale University, New Haven, CT, USA

    Michael H. Bloch

43. Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway

    Sigrid Børte & Bendik S. Winsvold

44. Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, HUNT Center for Molecular and Clinical Epidemiology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Sigrid Børte

45. Department of Medical Genetics, Oslo University Hospital, Oslo, Norway

    Sigrid Børte & Srdjan Djurovic

46. Department of Child and Adolescent Mental Health, Hospital Sant Joan de Déu, Esplugues de Llobregat, Spain

    Rosa Bosch

47. Instituto de Salut Carlos III, Centro de Investigación Biomédica en Red de Salut Mental (CIBERSAM), Madrid, Spain

    Rosa Bosch

48. Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Michael Breen

49. The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Michael Breen

50. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Brian P. Brennan, Jesse Crosby, Martha J. Falkenstein, Dan A. Geller & David L. Pauls

51. Department of Psychiatry, Universidade de São Paulo, São Paulo, Brazil

    Helena Brentani & Homero Vallada

52. Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark

    Jonas Bybjerg-Grauholm & David M. Hougaard

53. Child Health Research Centre, University of Queensland, Brisbane, Queensland, Australia

    Enda M. Byrne

54. Pharmacogenetics Department, Investigaciones Clínicas, Instituto Nacional de Psiquiatría Ramon de la Fuente Muñiz, Mexico City, México

    Beatriz Camarena

55. Department of Surgery, Duke University, Durham, NC, USA

    Adrian Camarena

56. Department of Psychiatry, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA

    Carolina Cappi

57. Department of Psychiatry, University of São Paulo, São Paulo, Brazil

    Carolina Cappi & Ana G. Hounie

58. CiMUS, Genomics and Bioinformatics Group, University of Santiago de Compostela, Santiago de Compostela, Spain

    Angel Carracedo

59. Galician Foundation of Genomic Medicine, Grupo de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago (IDIS), Santiago de Compostela, Spain

    Angel Carracedo

60. Medicina Genómica, Centro de Investigación Biomédica en Red, Enfermedades Raras (CIBERER), Santiago de Compostela, Spain

    Angel Carracedo

61. Programa MIND Escoles, Hospital Sant Joan de Déu, Esplugues de Llobregat, Spain

    Miguel Casas

62. Departamento de Psiquiatría y Medicina Legal, Universitat Autònoma de Barcelona, Bellaterra, Spain

    Miguel Casas

63. Department of Psychiatry, Ospedale San Raffaele, Milano, Italy

    Maria Cristina Cavallini

64. Department of Psychiatry, University of Illinois Chicago, Chicago, IL, USA

    Edwin H. Cook

65. Department of Psychiatry and Behavioral Sciences, Johns Hopkins Medical Institutions, Baltimore, MD, USA

    Bernadette A. Cullen

66. Department of Mental Health, Bloomberg School of Public Health, Baltimore, MD, USA

    Bernadette A. Cullen

67. Child and Adolesccent Psychiatry Department, APHP, Paris, France

    Richard Delorme

68. Department of Clinical Science, University of Bergen, Bergen, Norway

    Srdjan Djurovic, Kira D. Höffler & Stéphanie Le Hellard

69. Psychiatry, McLean Hospital OCDI, Harvard Medical School, Belmont, MA, USA

    Jason A. Elias

70. Adult Psychological Services, CBTeam LLC, Lexington, MA, USA

    Jason A. Elias

71. qGenomics (Quantitative Genomics Laboratories), Esplugues de Llobregat, Spain

    Xavier Estivill

72. Department of Medical Epidemiology and Biostatistics, Center for Eating Disorders Innovation, Karolinska Institutet, Stockholm, Sweden

    Bengt T. Fundin

73. Department of Psychiatry, Johns Hopkins University, Baltimore, MD, USA

    Fernando S. Goes

74. Department of Psychiatry and Behavioral Sciences, Child and Adolescent Psychiatry, Johns Hopkins University, Baltimore, MD, USA

    Marco A. Grados

75. The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark

    Jakob Grove

76. Center for Genomics and Personalized Medicine, Aarhus, Denmark

    Jakob Grove, David M. Hougaard & Preben B. Mortensen

77. Bioinformatics Research Centre, Aarhus, Denmark

    Jakob Grove

78. Genetic Epidemiology Research Branch, National Institute of Mental Health, Bethesda, MD, USA

    Wei Guo

79. Department of Biomedicine, University of Bergen, Bergen, Norway

    Jan Haavik & Olga Therese Ousdal

80. Bergen Center for Brain Plasticity, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway

    Jan Haavik & Stéphanie Le Hellard

81. Department of Psychiatry, Møre og Romsdal Hospital Trust, Molde, Norway

    Kristen Hagen

82. Bergen Center for Brain Plasticity, Psychiatry, Haukeland University Hospital, Bergen, Norway

    Kristen Hagen & Bjarne K. Hansen

83. Department of Mental Health, Norwegian University for Science and Technology, Trondheim, Norway

    Kristen Hagen & Gerd Kvale

84. Million Veteran Program (MVP) Coordinating Center, VA Boston Healthcare System, Boston, MA, USA

    Kelly Harrington

85. Department of Psychiatry, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA

    Kelly Harrington

86. PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway

    Alexandra Havdahl

87. Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway

    Alexandra Havdahl

88. Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway

    Kira D. Höffler

89. Department of Medical Genetics, Dr. Einar Martens Research Group for Biological Psychiatry, Haukeland University Hospital, Bergen, Norway

    Kira D. Höffler

90. Department of Medicine, Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Donald Hucks

91. Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY, USA

    Magdalena Janecka

92. Department of Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA

    Elinor K. Karlsson

93. Department of Vertebrate Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Elinor K. Karlsson & Kerstin Lindblad-Toh

94. Department of Medicine, MSB Medical School Berlin, Berlin, Germany

    Julia Klawohn

95. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA

    Janice E. Krasnow

96. Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia

    Kristi Krebs, Andres Metspalu, Tõnu Esko, Reedik Mägi, Mari Nelis, Georgi Hudjashov & Lili Milani

97. Department of Biostatistics, T.H. Chan School of Public Health, Boston, MA, USA

    Christoph Lange

98. Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Christoph Lange

99. Research Department, Grupo Medico Carracci, Mexico City, Mexico

    Nuria Lanzagorta

100. Department of Psychiatry, Yale University, West Haven, CT, USA

     Daniel Levey

101. Office of Research and Development, United States Department of Veterans Affairs, West Haven, CT, USA

     Daniel Levey

102. Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden

     Kerstin Lindblad-Toh

103. SciLifeLab, Uppsala University, Uppsala, Sweden

     Kerstin Lindblad-Toh

104. Department of Psychiatry, University of California, Irvine, Irvine, CA, USA

     Fabio Macciardi

105. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

     Brion Maher

106. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

     Evonne McArthur

107. COBRE Center for Neuromodulation, Butler Hospital, Providence, RI, USA

     Nathaniel McGregor

108. Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI, USA

     Nicole C. McLaughlin & Steven A. Rasmussen

109. Butler Hospital, Providence, RI, USA

     Nicole C. McLaughlin

110. Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada

     Sandra Meier

111. Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil

     Euripedes C. Miguel

112. Department of Psychiatry and Behavioral Science, Johns Hopkins University, Baltimore, MD, USA

     Paul S. Nestadt & Gerald Nestadt

113. Department of Psychiatry and Biobehavioral Sciences, Division of Child and Adolescent Psychiatry, University of California, Los Angeles, Los Angeles, CA, USA

     Erika L. Nurmi & James T. McCracken

114. Department of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

     Kevin S. O’Connell

115. NORMENT, University of Oslo, Oslo, Norway

     Kevin S. O’Connell

116. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

     Lisa Osiecki & Jeremiah M. Scharf

117. Psychiatric and Neurodevelopmental Genetics Unit, Harvard Medical School, Boston, MA, USA

     Lisa Osiecki

118. Department of Biomedicine, Haukeland University Hospital, Bergen, Norway

     Olga Therese Ousdal

119. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland

     Teemu Palviainen

120. Department of Clinical Neuroscience and Neurorehabilitation, Neuropsychiatry Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy

     Federica Piras

121. Department of Genetics, Microbiology and Statistics, IBUB, Universitat de Barcelona, Barcelona, Spain

     Raquel Rabionet

122. CIBERER, Centro de Investigación Biomédica en Red, Madrid, Spain

     Raquel Rabionet

123. Department of Human Molecular Genetics, Institut de Recerca Sant Joan de Déu, Barcelona, Spain

     Raquel Rabionet

124. Department of Psychiatry and Psychotherapy, Division of Neurogenetics and Molecular Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

     Alfredo Ramirez

125. Department of Neurodegenerative Diseases and Geriatric Psychiatry, Medical Faculty, University Hospital Bonn, Bonn, Germany

     Alfredo Ramirez

126. DZNE Bonn, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

     Alfredo Ramirez

127. Department of Psychiatry and Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA

     Alfredo Ramirez

128. Cologne Excellence Cluster for Stress Responses in Ageing-associated Diseases (CECAD), University of Cologne, Cologne, Germany

     Alfredo Ramirez

129. Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA

     Scott Rauch

130. Department of Mental Disorders, Norwegian Institute of Public Health, New York, NY, USA

     Abraham Reichenberg

131. Department of Psychiatry and Behavioral Sciences, Child and Adolescent, Johns Hopkins University School of Medicine, Baltimore, MD, USA

     Mark A. Riddle

132. Department of Psychiatry and Psychotherapy, Charité—Universitätsmedizin, Berlin, Germany

     Stephan Ripke

133. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA

     Stephan Ripke, Benjamin M. Neale & Jeremiah M. Scharf

134. Site Berlin–Potsdam, German Center for Mental Health (DZPG), Berlin, Germany

     Stephan Ripke

135. Department of Psychiatry, Child and Adolescent Psychiatry Unit (UPIA), Federal University of São Paulo (UNIFESP), São Paulo, Brazil

     Maria C. Rosário

136. Department of Neurosciences and Mental Health, Medical School, Federal University of Bahia, Salvador, Brazil

     Aline S. Sampaio

137. Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Medical Center—University of Freiburg, Freiburg, Germany

     Miriam A. Schiele & Katharina Domschke

138. Department of Public Health and Nursing, HUNT Center for Molecular and Clinical Epidemiology, Trondheim, Norway

     Anne Heidi Skogholt

139. Department of Psychiatry, Faculty of Medicine, Locaion VUmc, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

     Jan Smit

140. Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain

     María Soler Artigas & Marta Ribasés

141. Department of Clinical and Molecular Medicine, NTNU, Trondheim, Norway

     Laurent F. Thomas

142. Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Trondheim, Norway

     Laurent F. Thomas

143. BioCore, Bioinformatics Core Facility, NTNU, Trondheim, Norway

     Laurent F. Thomas

144. Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

     Laurent F. Thomas

145. Department of Molecular Medicine and Surgery, CMM, Karolinska Institutet, Stockholm, Sweden

     Homero Vallada

146. OCD Institute, Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA

     Nathanial van Kirk

147. Department of Psychiatry, Harvard Medical School, Belmont, MA, USA

     Nathanial van Kirk

148. Department of Psychiatry, Division of Child and Adolescent Psychiatry, Columbia University, New York, NY, USA

     Jeremy Veenstra-VanderWeele

149. Department of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA

     Jeremy Veenstra-VanderWeele

150. Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

     Nienke N. Vulink & Karin J. Verweij

151. Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ, USA

     Christopher P. Walker

152. Department of Neurology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA

     Ying Wang

153. Laboratory of Clinical Science, NIMH Intramural Research Program, Bethesda, MD, USA

     Jens R. Wendland

154. Department of Neurology, Oslo University Hospital, Oslo, Norway

     Bendik S. Winsvold

155. HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Trondheim, Norway

     Bendik S. Winsvold, Kristian Hveem & John-Anker Zwart

156. Department of Computional Biology, Institute of Life Science, Fudan University, Fudan, China

     Yin Yao

157. Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

     Hang Zhou

158. Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA

     Hang Zhou

159. Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA

     Hang Zhou

160. Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA

     Arpana Agrawal

161. Department of Psychiatry, OCD Clinical and Research Unit, Bellvitge Hospital, Barcelona, Spain

     Pino Alonso

162. Department of Clinical Sciences, University of Barcelona, Barcelona, Spain

     Pino Alonso

163. Department of Psychiatry and Mental Health, Bellvitge Biomedical Research Institute IDIBELLL, Barcelona, Spain

     Pino Alonso

164. CIBERSAM, Mental Health Network Biomedical Research Center, Madrid, Spain

     Pino Alonso

165. Psychosomatic Department, Windach Hospital of Neurobehavioural Research and Therapy, Windach, Germany

     Götz Berberich

166. Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA

     Kathleen K. Bucholz

167. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

     Cynthia M. Bulik & James J. Crowley

168. Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

     Cynthia M. Bulik

169. Departments of Rijksuniversiteit Groningen and Psychiatry, University Medical Center Groningen, Groningen, the Netherlands

     Danielle Cath

170. Department of Specialized Training, Drenthe Mental Health Care Institute, Groningen, the Netherlands

     Danielle Cath

171. Department of Psychiatry, Institute of the Royal Netherlands Academy of Arts and Sciences (NIN-KNAW), Amsterdam, the Netherlands

     Damiaan Denys

172. Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia

     Valsamma Eapen

173. Academic Unit of Child Psychiatry South-West Sydney, South-West Sydney Clinical School, SWSLHD and Ingham Institute, Sydney, New South Wales, Australia

     Valsamma Eapen

174. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA

     Howard Edenberg

175. Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany

     Peter Falkai

176. Department of Psychiatry, Max Planck Institute, Munich, Germany

     Peter Falkai

177. Child Study Center and Psychiatry, Yale School of Medicine, New Haven, CT, USA

     Thomas V. Fernandez

178. Department of Psychiatry, New York State Psychiatric Institute, New York, NY, USA

     Abby J. Fyer

179. Department of Psychiatry, Columbia University Medical Center, New York, NY, USA

     Abby J. Fyer

180. Department of Medicine, VA Boston Healthcare System, Boston, MA, USA

     J. M. Gaziano

181. Department of Medicine, Mass General Brigham, Boston, MA, USA

     J. M. Gaziano

182. Department of Psychiatry, Child Psychiatry, Massachusetts General Hospital, Boston, MA, USA

     Dan A. Geller

183. Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany

     Hans J. Grabe

184. COBRE Center on Neuromodulation, Butler Hospital, Providence, RI, USA

     Benjamin D. Greenberg

185. Center for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI, USA

     Benjamin D. Greenberg

186. Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI, USA

     Benjamin D. Greenberg

187. Department of Psychiatry, Child and Adolescent Psychiatry, University of Michigan, Ann Arbor, MI, USA

     Gregory L. Hanna

188. Brain and Mind Centre, the University of Sydney, Sydney, New South Wales, Australia

     Ian B. Hickie

189. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

     Dongbing Lai

190. Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden

     Mikael Landén

191. Department of Addictology and Psychiatry, Université Paris-Est Créteil, AP-HP, Inserm, Paris, France

     Marion Leboyer

192. Department of Psychiatry, SA MRC Unit on Risk and Resilience in Mental Disorders, Stellenbosch University, Stellenbosch, South Africa

     Christine Lochner

193. Department of Mental Health, Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

     Sarah E. Medland

194. National Centre for Register-based Research, Aarhus University, Aarhus, Denmark

     Preben B. Mortensen

195. Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark

     Preben B. Mortensen

196. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

     Benjamin M. Neale

197. Department of Psychiatry, Psychiatry, Carracci Medical Group, Mexico City, México

     Humberto Nicolini

198. Psiquiatría, Instituto Nacional de Medicina Genómica, Mexico City, México

     Humberto Nicolini

199. Mental Health Center Copenhagen, Copenhagen Research Center for Mental Health, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark

     Merete Nordentoft

200. Faculty of Health and Medical Sciences, Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

     Merete Nordentoft

201. Department of Psychiatry, Rutgers University, Piscataway, NJ, USA

     Michele Pato & Carlos Pato

202. Department of Psychiatry and Biobehavioral Sciences, Child and Adolescent Psychiatry, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA

     John Piacentini

203. Department of Psychiatry, Yale University, New Haven, CT, USA

     Christopher Pittenger

204. Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Amsterdam, the Netherlands

     Danielle Posthuma

205. Department of Child and Adolescent Psychiatric, Section Complex Trait Genetics, VU Medical Center Amsterdam, Amsterdam, the Netherlands

     Danielle Posthuma

206. Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Spain

     Josep Antoni Ramos-Quiroga

207. Group of Psychiatry, Mental Health and Addictions, Psychiatric Genetics Unit, Vall d’Hebron Research Institute, Barcelona, Spain

     Josep Antoni Ramos-Quiroga

208. CIBERSAM, Barcelona, Spain

     Josep Antoni Ramos-Quiroga

209. Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain

     Josep Antoni Ramos-Quiroga

210. Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

     Margaret A. Richter

211. Department of Psychiatry and Behavioral Neurosciences, Child and Adolescent Psychiatry, Wayne State University School of Medicine, Detroit, MI, USA

     David R. Rosenberg

212. Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany

     Stephan Ruhrmann

213. Department of Psychiatry and Behavioral Sciences, the Johns Hopkins University School of Medicine, Baltimore, MD, USA

     Jack F. Samuels

214. Department of Psychiatry and Behavioral Sciences, Division of Neuropsychiatry, Baylor College of Medicine, Houston, TX, USA

     Gianfranco Spalletta

215. Department of Psychiatry and Neuroscience Institute, SAMRC Unit on Risk and Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa

     Dan J. Stein

216. Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada

     S. Evelyn Stewart

217. British Columbia Children’s Hospital Research Institute, Vancouver, British Columbia, Canada

     S. Evelyn Stewart

218. British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, British Columbia, Canada

     S. Evelyn Stewart

219. Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA

     Eric A. Storch

220. Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

     Barbara E. Stranger

221. Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

     Barbara E. Stranger

222. Department of Cardiology, University of Milan, Milan, Italy

     Maurizio Turiel

223. Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Copenhagen University Hospital, Mental Health Services (RHP), Copenhagen, Denmark

     Thomas Werge

224. Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

     Thomas Werge

225. Institute of Clinical Medicine, NORMENT Centre, University of Oslo, Oslo, Norway

     Ole A. Andreassen

226. Division of Mental Health and Addiction, Center for Precision Psychiatry, Oslo University Hospital, Oslo, Norway

     Ole A. Andreassen

227. The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus University, Aarhus, Denmark

     Anders D. Børglum

228. Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark

     Anders D. Børglum

229. Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich (PUK), University of Zurich, Zürich, Switzerland

     Susanne Walitza & Edna Grünblatt

230. Neuroscience Center Zurich, University of Zurich and the ETH Zurich, Zürich, Switzerland

     Susanne Walitza & Edna Grünblatt

231. Zurich Center for Integrative Human Physiology, University of Zurich, Zürich, Switzerland

     Susanne Walitza & Edna Grünblatt

232. HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Trondheim, Norway

     Kristian Hveem

233. Department of Research, Innovation and Education, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

     Kristian Hveem

234. Centre for Crisis Psychology, Psychology, University of Bergen, Bergen, Norway

     Bjarne K. Hansen

235. Department of Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

     Nicholas G. Martin

236. Psychosis Research Unit, Psychiatry, Aarhus University Hospital, Aarhus, Denmark

     Ole Mors

237. Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway

     Ted Reichborn-Kjennerud

238. Institute of Clinical Medicine, University of Oslo, Oslo, Norway

     Ted Reichborn-Kjennerud

239. Department of Clinical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway

     Gerd Kvale

240. Partner Site Berlin, DZPG, Berlin, Germany

     Katharina Domschke

241. Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany

     Michael Wagner

242. DZNE, Bonn, Germany

     Michael Wagner

243. Department of Research and Innovation, Clinical Neuroscience, Oslo University Hospital and University of Oslo, Oslo, Norway

     John-Anker Zwart

244. Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

     Gerome Breen

245. FIMM, University of Helsinki, Helsinki, Finland

     Jaakko Kaprio

246. Department of Psychiatry, the Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

     Paul D. Arnold

247. Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario, Canada

     Paul D. Arnold

248. Department of Genetics, Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ, USA

     James A. Knowles

249. PsychGen Center for Genetic Epidemiology, Norwegian Institute of Public Health, Oslo, Norway

     Helga Ask

250. PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway

     Helga Ask

251. Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

     Lea K. Davis

252. Department of Psychiatry, Amsterdam UMC location AMC, Amsterdam, the Netherlands

     Dirk J. Smit

253. Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA

     Murray B. Stein

254. Department of Psychiatry and School of Public Health, University of California San Diego, La Jolla, CA, USA

     Murray B. Stein

255. Department of Psychiatry, Human Genetics (Psychiatry), Yale University School of Medicine, West Haven, CT, USA

     Joel Gelernter

256. Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA

     Joel Gelernter

257. Psychiatry and Genetics Institute, Evelyn F. and William L. Mc Knight Brain Institute, Center for OCD, Anxiety and Related Disorders, University of Florida, Gainesville, FL, USA

     Carol A. Mathews

258. Department of Mental Health and Neuroscience, QIMR Berghofer, Brisbane, Queensland, Australia

     Eske M. Derks

259. Department of Community Health and Epidemiology and Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

     Manuel Mattheisen

260. 23andMe, Inc., Sunnyvale, CA, USA

     Chris German

Consortia

Contributions

J.M. Scharf, M.B.S., J. Gelernter, C.A.M., E.M.D. and M. Mattheisen designed the study. N.I.S., Z.F.G., M.G., D.Y. and M.W.H. conducted data analysis. N.I.S., Z.F.G., M.G., D.Y., M.W.H., A. Abdellaoui, C.R.-F., J.M. Sealock, T.B., J.R.C., B. Mahjani, J.G.T., K.B., C.L.B., J.J.L., G.Z., S.A., C.A., K.D.A., J.B., N.B., C.B., J.B.N., O.J.B., D.B., M.H.B., S.B., R.B., M.B., B.P.B., H.B., J.D.B., J.B.-G., E.M.B., J.C.-D., B.C., A. Camarena, C.C., A. Carracedo, M.C., M.C.C., V.C., E.H.C., J.C., B.A.C., E.J.D.S., R.D., S.D., J.A.E., X.E., M.J.F., B.T.F., L.G., C. Gironda, F.S.G., M.A.G., J. Grove, W.G., J.H., K. Hagen, K. Harrington, A.H., K.D.H., A.G.H., D.H., C.H., M.J., E.J., E.K.K., K. Kelley, J. Klawohn, J.E.K., K. Krebs, C. Lange, N.L., D. Levey, K.L.-T., F.M., B. Maher, B. Mathes, E.M., N.M., N.C.M., S.M., E.C.M., M. Mulhem, P.S.N., E.L.N., K.S.O’C., L.O., O.T.O., T.P., N.L.P., Fabrizio Piras, Federica Piras, S.P., R.R., A. Ramirez, S. Rauch, A. Reichenberg, M.A. Riddle, S. Ripke, M.C.R., A.S.S., M.A.S., A.H.S., L.G.S., J.S., M.S.A., L.F.T., E.T., H.V., N.v.K., J.V.-V., N.N.V., C.P.W., Y.W., J.R.W., B.S.W., Y.Y., H.Z., A. Agrawal, P.A., G. Berberich, K.K.B., C.M.B., D.C., D.D., V.E., H.E., P.F., T.V.F., A.J.F., J.M.G., D.A.G., H.J.G., B.D.G., G.L.H., I.B.H., D.M.H., N.K., J. Kennedy, D. Lai, M. Landén, S.L.H., M. Leboyer, C. Lochner, J.T.M., S.E.M., P.B.M., B.M.N., H.N., M. Nordentoft, M.P., C. Pato, D.L.P., J.P., C. Pittenger, D.P., J.A.R.-Q., S.A.R., M.A. Richter, D.R.R., S. Ruhrmann, J.F.S., S.S., P.S., G.S., D.J. Stein, S.E.S., E.A.S., B.E.S., M.T., T.W., O.A.A., A.D.B., S.W., K. Hveem, B.K.H., C.R., N.G.M., L.M., O.M., T.R.-K., M.R., G.K., D.M.-C., K.D., E.G., M.W., J.-A.Z., G. Breen, G.N., J. Kaprio, P.D.A., D.E.G., J.A.K., H.A., K.J.V., L.K.D., D.J. Smit, J.J.C., J.M. Scharf, M.B.S., J. Gelernter, C.A.M., E.M.D. and M. Mattheisen provided samples and/or processed individual cohort data. N.I.S., Z.F.G., M.G., D.Y., M.W.H., J.M. Scharf, M.B.S., J. Gelernter, C.A.M., E.M.D. and M. Mattheisen wrote the paper and formed the core revision group. J.M. Scharf, M.B.S., J. Gelernter, C.A.M., E.M.D. and M. Mattheisen supervised and directed the study. All authors discussed the results and approved the final version of the manuscript.

Corresponding authors

Correspondence toNora I. Strom orManuel Mattheisen.

Competing interests

C. German is employed by and holds stock or stock options in 23andMe. E.L.N. is on the scientific advisory board for Myriad Genetics and the medical advisory board for the Tourette Association of America and received clinical trial funding from Emalex and Octapharma Pharmaceuticals. J.V.-V. has served on advisory boards or consulted with Roche, Novartis and SynapDx; received research funding from Roche, Novartis, SynapDx, Seaside Therapeutics, Forest, Janssen, Acadia, Yamo and MapLight; and received stipends for editorial work from Wiley and Springer. J.R.W. is a current employee and shareholder of Takeda Pharmaceuticals and a past employee and shareholder of F. Hoffmann-La Roche, Pfizer and Nestle Health Science. C.M.B. reports Pearson (author, royalty recipient). P.F. reports no conflict of interest regarding this study and reports having received financial support and served on the advisory board for Richter, Recordati, Boehringer Ingelheim, Otsuka, Janssen and Lundbeck. H.J.G. has received travel grants and speaker’s honoraria from Fresenius Medical Care, Neuraxpharm, Servier and Janssen-Cilag as well as research funding from Fresenius Medical Care. I.B.H. is the co-director of health and policy at the Brain and Mind Centre of the University of Sydney, Australia. The Brain and Mind Centre operates early-intervention youth services at Camperdown under contract to headspace. I.B.H. has previously led community-based and pharmaceutical industry-supported (Wyeth, Eli Lily, Servier, Pfizer, AstraZeneca, Janssen-Cilag) projects focused on the identification and better management of anxiety and depression. He is the chief scientific advisor to and a 3.2% equity shareholder in InnoWell, which aims to transform mental health services through the use of innovative technologies. B.M.N. is a member of the scientific advisory board at Deep Genomics and Neumora. C. Pittenger consults and/or receives research support from Biohaven Pharmaceuticals, Freedom Biosciences, Ceruvia Lifesciences, Transcend Therapeutics, UCB BioPharma and F-Prime Capital Partners. He owns equity in Alco Therapeutics. These relationships are not related to the current work. D.J. Stein has received consultancy honoraria from Discovery Vitality, Johnson & Johnson, Kanna, L’Oreal, Lundbeck, Orion, Sanofi, Servier, Takeda and Vistagen. E.A.S. reports receiving research funding to his institution from the Ream Foundation, the International OCD Foundation and the NIH. He was formerly a consultant for Brainsway and Biohaven Pharmaceuticals in the past 12 months. He owns stock less than $5,000 in nView–Proem for distribution related to the YBOCS scales. He receives book royalties from Elsevier, Wiley, Oxford, the American Psychological Association, Guildford, Springer, Routledge and Jessica Kingsley. O.A.A. reports being a consultant to Cortechs.ai and Precision Health and speaker honoraria from Otsuka, Lundbeck, Sunovion and Janssen. A.D.B. has received a speaker fee from Lundbeck. D.M.-C. receives royalties for contributing articles to UpToDate and Wolters Kluwer Health and personal fees for editorial work from Elsevier, all unrelated to the current work. M.B.S. has in the past 3 years received consulting income from Acadia Pharmaceuticals, Big Health, Biogen, Bionomics, Boehringer Ingelheim, Clexio, Eisai, EmpowerPharm, Engrail Therapeutics, Janssen, Jazz Pharmaceuticals, NeuroTrauma Sciences, Otsuka, PureTech Health, Sage Therapeutics, Sumitomo Pharma and Roche–Genentech. M.B.S. has stock options in Oxeia Biopharmaceuticals and EpiVario. He has been paid for his editorial work onDepression and Anxiety (editor in chief),Biological Psychiatry (deputy editor) and UpToDate (co-editor in chief for psychiatry). J. Gelernter is paid for editorial work by the journalComplex Psychiatry. P.A. has received funding from Biohaven, Boston Scientific and Medtronic. All other authors report no conflicts of interest.

Peer review information

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