Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

# Risk variants and polygenic architecture of disruptive behavior disorders in the context of attention-deficit/hyperactivity disorder

### Subjects

An Author Correction to this article was published on 15 February 2021

## Introduction

Several genome-wide association studies (GWASs) have focused on diagnosed DBDs31,32 or aggressive and anti-social behaviors33,34, with only limited success in identifying genome-wide significant loci and no conclusive, replicated findings31,33,34. Only two genome-wide studies have focused specifically on ADHD + DBDs. One small genome-wide linkage study examined DBDs in individuals with ADHD35 and another, while not assessing diagnosed DBDs, examined aggressive behaviors in individuals with ADHD36. Neither studies reported genome-wide significant loci.

In the current study we perform a large GWAS meta-analysis of ADHD + DBDs using a Danish nation-wide cohort from iPSYCH and samples from the Psychiatric Genomics Consortium (PGC). We identify three genome-wide significant loci for ADHD + DBDs, located on chromosomes 1, 7, and 11, and show evidence of transancestral association for the locus on chromosome 11 in a Chinese cohort and, we find high polygenic overlap of ADHD + DBDs with childhood aggression and antisocial behavior in the general population, higher than found for ADHDwoDBDs.

## Results

### GWAS meta-analysis of ADHD + DBDs

The meta-analysis included data from the Danish iPSYCH cohort (2155 cases, 22,664 controls) and six European ancestry PGC cohorts (1647 cases, 8641 controls). All cases were diagnosed with both ADHD and DBDs or had a diagnosis of hyperkinetic conduct disorder, which according to the ICD10 criteria implies that both disorders are present. Selection of controls was population-based and they were not diagnosed with either ADHD or DBDs. Results were in total based on 3802 cases and 31,305 controls and included 8,285,688 variants after filtering. Three loci passed the threshold for genome-wide significance (P = 5 × 10−8); these were located on chromosome 1 (index variant rs549845, P = 2.38 × 10−8, OR = 1.16), 7 (index variant rs11982272, P = 4.38 × 10−8, OR = 0.83), and 11 (index variant, rs7118422, P = 8.97 × 10−9, OR = 1.16) (Table 1, Fig. 1a and Supplementary Fig. 1A–C). The directions of association of the index variants in the three loci were consistent across all cohorts (Supplementary Fig. 2A–C).

### Homogeneity of effects in the PGC and iPSYCH cohorts and intercept evaluation

To evaluate the consistency of the genetic architecture underlying ADHD + DBDs in iPSYCH and PGC cohorts, we estimated the genetic correlation between the two using LD score regression37,38. The genetic correlation between the iPSYCH cohort and the meta-analyzed PGC cohorts was high (rg = 0.934, SE = 0.14, P = 3.26 × 10−11) supporting consistency of the ADHD + DBDs phenotypes analyzed in the cohorts. In addition, no variants demonstrated significant heterogeneity between studies (Supplementary Figs. 3 and 4).

LD score regression analysis indicated that the observed deviation of the genome-wide test statistics from the null distribution (lambda = 1.11, Fig. 1b) was mainly caused by polygenicity. The intercept ratio estimate suggests that the majority of the inflation of the mean χ2 statistic of the GWAS meta-analysis is attributable to polygenic effects (ratio = 0.12, SE = 0.0662) rather than confounding factors. The estimated remaining contribution of confounding factors was small and non-significant (intercept = 1.015; SE = 0.008; P = 0.064).

### Transancestry GWAS meta-analysis across European and Han Chinese ancestry

To replicate and generalize the findings to other ethnicities, a GWAS of ADHD + DBDs was performed in a Han Chinese cohort (referred to as the Chinese cohort (406 cases, 917 controls; Supplementary Fig. 5). Of the three loci identified in the main analysis, the locus on chromosome 11 was nominally significant in the Chinese cohort (P = 0.006, Supplementary Fig. 8C). A fixed effects meta-analysis including the Chinese, European iPSYCH and PGC cohorts was performed in total including 4208 cases and 32,222 controls (no variants demonstrated significant heterogeneity across European and Chinese ancestries, Supplementary Figs. 6 and 7). For the locus on chromosome 11 the association P-value became stronger in the trans-ancestry GWAS meta-analysis (P = 3.15 × 10−10, OR = 1.17) (Fig. 2 and Supplementary Data 1, Supplementary Fig. 9), suggesting the locus is a risk locus for ADHD + DBDs across ethnicities. The results incorporating the Chinese cohort did not support replication of the other two loci (Table 1).

### Secondary GWASs

The summary statistics from the two secondary GWASs were used to evaluate the direction of association of the top loci (281 loci, P < 1 × 10−4) from the GWAS meta-analysis of ADHD + DBDs. A consistent direction of association was observed for 221 loci (out of the 281 loci) in the ADHDwoDBDs GWAS (sign test P < 2.2 × 10−16), while all the 281 loci demonstrated consistent direction of association in the ADHD + DBDs vs. ADHDwoDBDs GWAS. The proportion of variants having a consistent direction of association in the ADHD + DBDs vs. ADHDwoDBDs GWAS was significantly larger than the proportion in the ADHDwoDBDs GWAS (P = 7.7 × 10−16), which suggests that the associations in the GWAS meta-analysis of ADHD + DBDs reflects association with the comorbid phenotype beyond association with risk for ADHD alone.

All three genome-wide significant loci demonstrated higher effect sizes in ADHD + DBDs compared to ADHDwoDBDs (Supplementary Data 2). In particular, the effect sizes for the loci located on chromosomes 7 and 11 (ORchr7 = ORs 1.199; ORchr11 = 1.164) remained strong in the ADHD + DBDs vs. ADHDwoDBDs GWAS (ORchr7 1.128; ORchr11 = 1.126) confirming a stronger effect of the risk allele for these two loci in ADHD + DBDs compared to ADHDwoDBDs (Supplementary Data 2). The difference was most striking for rs7118422 on chromosome 11 (ADHD + DBDs: OR = 1.164, P = 8.97 × 10−9), which showed no evidence for association with ADHDwoDBDs (OR = 1.022, P = 0.175) versus a suggestive evidence of association with ADHD + DBDs vs. ADHDwoDBDs (OR = 1.126, P = 7.07 × 10−04).

To help formalize the comparison of ADHD + DBDs with ADHDwoDBDs, we also used mtCOJO39 to estimate the joint effects of the significant loci from the GWAS of ADHD + DBDs conditional on effects mediated through the genetics of ADHDwoDBDs. Under this model (see “Methods” section) none of the three loci reached genome-wide significance for a direct effect on ADHD + DBDs, although the locus on chromosome 11 retained the most robust signal after correction for ADHDwoDBDs (ORadjusted = 1.14; Padjusted = 1.43 × 10−06, Supplementary Data 3).

### Gene-based association test

A gene-based association analysis was performed using MAGMA40. Six genes (RRM1, STIM1, MAML3, ST3GAL3, KDM4A, and PTPRF) were significantly associated with ADHD + DBDs (P < 2.7 × 10−6 correcting for 18,553 genes analyzed; Supplementary Fig. 10 and Supplementary Data 4). Three genes (ST3GAL3, KDM4A, and PTPRF) are located in the genome-wide significant locus on chromosome 1, and two genes (RRM1, STIM1) in the genome-wide significant locus on chromosome 11. One gene (MAML3) located on chromosome 4 had not been identified as a risk locus in the single variant analysis.

To evaluate if the gene-based association signals reflected the aggressive and disruptive component of the ADHD + DBDs phenotype rather than ADHD alone, we did a gene-set test of the most associated genes from the primary ADHD + DBDs GWAS meta-analysis (P < 10−3, 79 genes) using the results from the two secondary GWASs. The gene set was significantly associated with ADHDwoDBDs (beta = 0.312 (SE = 0.02), P = 9 × 10−4), but had a stronger association in the ADHD + DBD vs. ADHD only GWAS (beta = 1.1 (SE = 0.07), P = 9.28 × 10−32).

### Association of genetically regulated gene expression with ADHD + DBDs

Association of the genetically regulated gene expression with ADHD + DBDs was analyzed in 12 brain tissues from GTEx41 (version 6p) using MetaXcan42. Depending on the tissue, 2042–6094 genes were tested (Supplementary Data 5). Three genes were predicted to be differently expressed in ADHD + DBDs cases compared with controls after Bonferroni correction (correcting for the total number of tests performed (43,142); P < 1.16 × 10−6); RRM1 (chromosome 11) was less expressed in cases, while RAB3C (chromosome 5) and LEPRE1 (chromosome 1) showed a higher expression in cases when compared to controls (Supplementary Data 5). The genes on chromosome 1 and 11 were located in or near genome-wide significant loci, whereas the gene on chromosome 5 was novel.

### Genetic correlation with aggression-related phenotypes

We estimated the genetic correlations of ADHD + DBDs with aggression-related phenotypes using GWAS results from analyses of aggressive behaviors in 18,988 children33 (EAGLE aggression) and antisocial behavior in 16,400 individuals34 (Broad Antisocial Behavior Consortium (BroadABC)) using LD score regression37. We found a high genetic correlation of ADHD + DBDs with aggression in children (EAGLE aggression, rg = 0.81; SE = 0.24; P = 0.001) and antisocial behavior (BroadABC, rg = 0.82; SE = 0.30; P = 0.007) (Supplementary Data 7). In contrast, ADHDwoDBDs (analysed solely in iPSYCH) was only significantly correlated with aggression in children (rg = 0.74; SE = 0.18; P = 4.6 × 10−5). Analyzing only the iPSYCH cohort, ADHD + DBDs demonstrated a nominally higher positive genetic correlation than ADHDwoDBDs with aggression in children (rg = 0.85; SE = 0.24; P = 5 × 10−4; and rg = 0.74; SE = 0.18; P = 4.58 × 10−5, respectively) and antisocial behavior (rg = 0.92; SE = 0.35; P = 9 × 10−3; and rg = 0.56; SE = 0.22; P = 0.01, respectively). The differences in the genetic correlations, however, were not statistically significant when assessed using the jackknife method43. Finally, we estimated the genetic correlation of ADHD + DBDs vs ADHDwoDBDs (rg = 0.99, SE = 0.07, P = 2.64 × 10−45).

## Discussion

This study identifies genome-wide significant loci for ADHD + DBDs based on a meta-analysis of 3802 cases and 31,305 controls from the iPSYCH cohort and six cohorts from PGC. We identified three risk loci on chromosomes 1, 7, and 11 with odds ratios ranging from 1.16 to 1.20, in line with what was found in the recent GWAS meta-analysis of ADHD47. These risk loci demonstrated high consistency in the direction of association in the included cohorts, indicating that the associations likely have a biological cause rather than being spurious signals driven by one or few cohorts (Fig. 2 and Supplementary Fig. 2A–C). The high genetic correlation observed between the PGC cohorts and the iPSYCH cohort suggests that the genetic architecture underlying ADHD + DBDs were similar in the two samples. In the GWAS meta-analysis for trans-ancestry risk of the identified loci in a Chinese sample, only the locus on chromosome 11 replicated the findings in the European samples. This locus seems to be specifically associated with the aggressive and disruptive component of the ADHD + DBDs phenotype, since the effect disappeared in the ADHDwoDBDs GWAS. This was further supported in the GWAS comparing comorbid ADHD + DBD to ADHDwoDBDs where the locus remained strongly associated, although not genome-wide significant (Supplementary Data 2). Consistent with this, evidence for a direct effect of the locus on ADHD + DBDs remained after adjusting for the effect of ADHDwoDBDs in the mtCOJO analysis (Supplementary Data 3).

In contrast, the locus on chromosome 1, which was previously identified as a strong risk locus for ADHD47, seems to reflect an association with ADHD. This locus remained genome-wide significant in the GWAS of ADHDwoDBDs (Supplementary Data 2) and the association with ADHD + DBDs decreased considerably in the analyses adjusting for the effect of ADHDwoDBDs and in the ADHD + DBDs vs. ADHDwoDBDs GWAS (Supplementary Data 2 and 3).

The locus on chromosome 7 seems to be a shared risk locus between ADHD + DBDs and ADHDwoDBDs. The locus remained moderately associated in the GWASs adjusted for ADHD (Supplementary Data 2 and 3) as well as in the ADHDwoDBDs GWAS (Supplementary Data 2). The locus is located in MAD1L1, which encodes a protein involved in mitotic spindle-assembly checking before anaphase. The locus is novel with respect to ADHD and DBDs, but was found genome-wide significant in the recent large cross-disorder GWAS48 and has previously been associated with schizophrenia and bipolar disorder49,50,51, suggesting that MAD1L1 is a risk gene for several psychiatric disorders.

The locus most strongly associated with ADHD + DBDs on chromosome 11 is located in STIM1 (Supplementary Fig. 1C), a gene not previously implicated in ADHD, DBDs, aggression-related phenotypes, or psychiatric disorders. STIM1 encodes a transmembrane protein (STIM1) in the endoplasmatic reticulum (ER) that acts as a sensor of calcium. Upon calcium depletion from the ER, STIM1 is responsible for an influx of calcium ions from the extracellular space through store-operated calcium channels to refill ER stores52,53,54. Store-operated calcium entry may also be involved in neuronal calcium signaling55, and recent evidence indicates that STIM1 plays a role in synaptic plasticity affecting learning and memory55,56. These results are interesting in the light of the observed learning deficits associated with aggressive behaviors and accumulating evidence that suggests calcium signaling is involved in several psychiatric disorders57,58,59. Alternatively, analysis of genetically regulated gene expression suggested that the variants in the genome-wide significant locus might affect expression of RRM1, with a decreased RRM1 expression being associated with ADHD + DBDs. RRM1 is oriented in a tail-to-head configuration with STIM1, which lies 1.6 kb apart, and encodes a subunit of a reductase involved in the biosynthesis of deoxyribonucleotides from the corresponding ribonucleotides necessary for DNA replication. To our knowledge, this gene has not previously been associated with psychiatric disorders.

In the gene-based analysis six genes were exome-wide significantly associated with ADHD + DBDs, including two implicated by variants in or near the genome-wide significant locus on chromosome 11 (RRM1 and STIM1) and with three (ST3GAL3, KDM4A, and PTPRF) out of the remaining four located in or near the genome-wide significant locus on chromosome 1. The top-associated genes (79 genes) seem to mainly reflect association with the aggressive and disruptive component of the ADHD + DBDs phenotype. The geneset was significantly associated with ADHDwoDBDs but even more strongly associated in the ADHD + DBDs vs. ADHDwoDBDs GWAS (where the effect of ADHD is corrected out). Likewise, the most strongly associated single markers (with P < 1 × 10−4) in the GWAS meta-analysis of ADHD + DBDs showed high consistency in the direction of association in the GWAS of ADHDwoDBDs, but even higher consistency in the ADHD + DBDs vs. ADHDwoDBDs GWAS, reinforcing the notion that the associations mainly reflect the aggressive and disruptive component of the phenotype.

Going beyond ADHD risk burden, the common variant component of ADHD + DBDs could also include variants mainly associated with aggression. This idea is supported by our finding of increased PGS for aggression in ADHD + DBDs compared to ADHDwoDBDs (Supplementary Fig. 11B). This conclusion is reinforced by the genetic correlation results, where we found somewhat higher genetic correlation of ADHD + DBDs with both aggressive behavior in children33 and antisocial behavior34 (Supplementary Data 7) compared to those found for ADHDwoDBDs. Additionally, these results imply that the genetic architecture underlying the aggressive and disruptive behavioral component of the ADHD + DBDs phenotype overlaps strongly with that affecting aggressive and antisocial behavior in the general population. Thus, aggressive and antisocial behaviors seem to have a continuous distribution in the population, with individuals having ADHD + DBDs representing an extreme. This is in line with what has been observed for other complex phenotypes, such as diagnosed ADHD representing the upper tail of impulsive and inattention behaviors47, and diagnosed autism spectrum disorder representing the upper tail for social communication difficulties and rigidity60,61.

Aggressive behavior is stable across age intervals during childhood62, and twin studies have suggested genetics play an important role in this stability62,63. Moreover, early aggression might be predictive of later serious antisocial behavior64 resulting in increased risk of a diagnosis of antisocial personality disorder65. Our results suggest that common genetic variants play an important role in childhood aggression, which has also been reported previously33, and that the subsequent risk for antisocial behavior in individuals with ADHD + DBDs to some extent has an underlying genetic cause involving common variants. However, our results do not reveal whether the increased polygenic load of variants associated with aggression observed in ADHD + DBDs is caused by variants specific to DBDs or due to a general increased load of variants that are also shared with ADHD.

It should also be noted that the SNP heritabilities of childhood aggression33 and antisocial behavior34 are at the low end (0.05 and 0.06, respectively), and probably biased downwards by heterogeneity in the cohorts analyzed33. The studies do therefore not capture the full impact of common variants in aggressive behavior, and the observed high genetic correlation of the two phenotypes with ADHD + DBDs involves variants that only explain a small proportion of variance in aggression.

In summary, we identified three genome-wide significant loci for ADHD + DBDs. The locus on chromosome 11 was associated most strongly with the comorbid phenotype, and seems to be a cross-ancestry risk locus in Europeans and Chinese. Our results suggest that the aggressive and disruptive behavioral component of the ADHD + DBDs phenotype has a genetic risk component, which in part include common risk variants associated with ADHD, aggressive, and antisocial behavior. Individuals with ADHD + DBDs therefore represent a phenotype with an increased genetic risk load compared to ADHDwoDBDs, including at least one genome-wide significant locus specific to ADHD + DBDs. This study represents the first step towards a better understanding of the biological mechanism underlying ADHD + DBDs.

## Methods

### Samples—the iPSYCH cohort

The iPSYCH cohort is a population-based nation-wide cohort which includes 79,492 genotyped individuals (50,000 diagnosed with major psychiatric disorders and 30,000 controls). The cohort was selected, based on register information from a baseline birth cohort of all singletons born in Denmark between May 1st, 1981 and December 31, 2005 (N = 1,472,762) (see a detailed description in the ref. 70). A biological sample of the included individuals were obtained from the Newborn Screening Biobank at Statens Serum Institute, Denmark. DNA was extracted from dried blood spot samples and whole genome amplified in triplicates71,72. Genotyping and calling of genotypes were performed as described in our previous publications47,70.

For this study cases and controls were identified based on diagnoses given in 2016 or earlier in the Danish Psychiatric Central Research Register73. Cases with ADHD + DBDs had a diagnosis of hyperkinetic conduct disorder (F90.1) or an ADHD diagnosis (ICD-10 F90.0) occurring together with a diagnosis of ODD (ICD-10 F91.3) or conduct disorder (ICD-10 F91.0, F91.1, F91.2, F91.8, and F91.9). Distribution of cases with ADHD + DBDs over diagnosis codes is presented in Supplementary Data 9. ADHD cases without DBDs were defined as individuals having ADHD (ICD-10 F90.0) without any diagnosis of DBDs. Controls were randomly selected from the same nation-wide birth cohort and not diagnosed with ADHD or DBDs.

The study was approved by the Danish Data Protection Agency and the Scientific Ethics Committee in Denmark. All analyses of the iPSYCH cohort were performed at the secured national high performance-computing cluster, GenomeDK (https://genome.au.dk).

### Samples—cohorts from the Psychiatric Genomics Consortium

For the meta-analysis, seven ADHD cohorts (six cohorts of European ancestry and one of Chinese ancestry) provided by PGC with information about diagnoses of ADHD + DBDs were included. An overview of the cohorts including genotyping information and diagnosis criteria can be found in Supplementary Data 10. Detailed descriptions of the cohorts can be found elsewhere74. Details on approval authorities can be found in Supplementary Data 10.

### Quality control and imputation

Quality control, imputation, and primary GWASs of the iPSYCH and PGC cohorts (including the Chinese cohort) were done separately for each using the bioinformatics pipeline Ricopili75. Pre-imputation quality control allowed an inclusion of individuals with a call rate > 0.98 (>0.95 for iPSYCH) and genotypes with a call rate >0.98, difference in SNP missingness between cases and controls < 0.02, no strong deviation from Hardy–Weinberg equilibrium (P > 1 × 10−6 in controls or P > 1 × 10−10 in cases) and low individual heterozygosity rates (|Fhet | < 0.2). Genotypes were phased and imputed using SHAPEIT76 and IMPUTE277 and the 1000 Genomes Project phase 3 (1KGP3)78 as imputation reference panel (the East Asian reference genome was used for imputation of the Chinese sampels). Trio imputation was done with a case-pseudocontrol setup.

Relatedness and population stratification were evaluated using a set of high-quality genotyped markers (minor allele frequency (MAF) > 0.05, HWE P > 1 × 10−4 and SNP call rate >0.98) pruned for linkage disequilibrium (LD) resulting in ~30,000 pruned variants (variants located in long-range LD regions defined by Price et al.79 were excluded). Genetic relatedness was estimated using PLINK v1.980,81 to identify first and second-degree relatives ($$\hat \pi$$ > 0.2) and one individual was excluded from each related pair (cases preferentially retained over controls). Genetic outliers were excluded based on principal component analyses (PCA) using EIGENSOFT82,83. For iPSYCH a genetic homogenous sample was defined based on a subsample of individuals being Danes for three generations as described in Demontis and Walters et al.47. For the PGC samples genetic outliers were removed based on visual inspection of the first six PCs. For all cohorts PCA was redone after exclusion of genetic outliers.

### GWAS meta-analysis and transancestry risk in European and Chinese ethnicities

Association analysis was done in PLINK80 using additive logistic regression and the imputed marker dosages, covariates from principal component analyses (after removal of genetic outliers) and other relevant covariates (Supplementary Data 10). Meta-analysis of the iPSYCH cohort (2155 cases, 22,664 controls) and the six PGC cohorts (1647 cases, 8641 controls) was done using an inverse standard error weighted fixed effects model and the software METAL84 and included in total 3802 cases and 31,305 controls.

For transancestry genetic risk variants in European and Chinese cohorts, a GWAS meta-analysis was done as described above including the iPSYCH cohort, the six European PGC cohorts and the cohort of Chinese ancestry. In total, 4208 cases and 32,222 controls were included. No individual genotypes were used for the meta-analysis.

In the two meta-analyses only variants with MAF > 0.01 and imputation INFO score > 0.8 were included. All variants that were not supported by an effective sample size of 70% in the meta-analysis output were filtered out.

### Homogeneity of effects in the PGC and iPSYCH cohorts and intercept evaluation

LD score regression37,38 was used to estimate the genetic correlation using summary statistics from GWAS of ADHD + DBDs in the iPSYCH cohort and meta-analysis of the six European PGC cohorts. Only variants with an imputation info score > 0.9 were included. The intercept was restricted to one as there was no sample overlap and no indication of population stratification.

The ratio (ratio = (intercept−1)/(mean χ2 − 1)) from LD score regression was used to evaluate the relative contribution of polygenic effects and confounding factors to the observed deviation from the null in the genome-wide distribution of the χ2 statistics of the GWAS meta-analysis of ADHD + DBDs.

### Secondary GWASs

In order to adjust for the effect of ADHD we did a case-only GWAS comparing 1,959 individuals having ADHD + DBDs against 13,539 individuals having ADHDwoDBDs, referred to as the “ADHD + DBDs vs. ADHDwoDBDs GWAS”. Additionally, a GWAS of 13,583 cases having ADHDwoDBDs and 22,314 population-based controls referred to as the “ADHDwoDBDs GWAS” was performed. Both GWASs were based only on iPSYCH samples and performed using additive logistic regression and the imputed marker dosages, covariates from principal component analyses (after removal of genetic outliers) and covariates indicating genotyping waves.

The summary statistics from the two secondary GWASs were used to evaluate direction of association of the top loci associated with ADHD + DBDs using a sign test based on LD distinct variants (r2 < 0.2, 281 variants) with association P-values less than 1 × 10−4 in the GWAS meta-analysis of ADHD + DBDs.

We also did an mtCOJO39 analysis to estimate the effect of the top loci for ADHD + DBDs conditional on genetic effects on ADHD alone. This was done using summary statistics from the GWAS meta-analysis of ADHD + DBDs and from the GWAS of ADHDwoDBDs. The analysis was run using mtCOJO39 implemented in GCTA85 using standard procedures. Following default settings, estimation of the effect of ADHDwoDBDs on ADHD + DBDs (as part of the indirect path contributing to marginal ADHD + DBDs associations) was performed using variants that were genome-wide significant in the GWAS of ADHDwoDBDs (P < 5 × 10−8), and not in linkage disequilibrium (r2 < 0.05; 7 index variants). No variants were removed due to evidence of pleiotropy (HEIDI-outlier threshold of P = 0.01).

### Gene-based association test

Gene-based association analysis was done using MAGMA 1.0540 and summary statistics from the GWAS meta-analysis. Variants were annotated to genes using the NCBI37.3 gene definitions and no window around genes was used. MAGMA summarizes association signals observed for variants located in a gene into a single P-value while correcting for LD in a reference genome. For this the European samples from the 1000 Genomes phase 3 were used.

The most associated genes in the GWAS meta-analysis of ADHD + DBDs (79 genes, P < 10−3 Supplementary Data 4) were evaluated in a gene-set test for association with ADHD + DBDs compared to ADHDwoDBDs and for association with ADHDwoDBDs. Gene-based P-values were generated using summary statistics from the two secondary GWASs (ADHD + DBDs vs. ADHD-only GWAS and ADHDwoDBDs GWAS) and subsequently gene-set tests were done using MAGMA 1.0540. MAGMA performs a competitive test to analyze if the gene set is more strongly associated with the phenotype than other genes, while correcting for a series of confounding effects such as gene length and size of the gene set.

### Association of the genetically regulated gene expression with ADHD + DBDs

Association of the genetically regulated gene expression with ADHD + DBDs was analyzed in 12 brain tissues from GTEx86 (version 6p) using MetaXcan42 imlemented in the R-package metaxcanr (https://github.com/drveera/metaxcanr). MetaXcan is an extension of PrediXcan87 that can be used to test for differences in gene expression using summary statistics. We used high-performance prediction models for MetaXcan based on variants located within 1 Mb +/− of transcription start site and trained using elastic net regression and 10-fold cross-validation4 downloaded from http://predictdb.org. MetaXcan also requires covariance matrices of the variants within each gene model for each tissue. Covariance matrices calculated from 503 individuals with European ancestry from the 1000 genomes project88 available with the prediction models at http://predictdb.org were used.

### SNP heritability

The SNP heritability (h2SNP) was estimated using LD score regression37 and the summary statistics from the GWAS meta-analysis of ADHD + DBDs. The heritability was estimated on the liability scale assuming a population prevalence of ADHD + DBDs of 2 and 1%.

In order to evaluate the extent to which common genetic variants contributes to the risk of ADHD + DBDs compared to having ADHDwoDBDs, the SNP heritability of for the two phenotypes were estimated only in iPSYCH samples. This was done using LD score regression and univariate GREML analyses in GCTA85. h2SNP was estimated on the liability scale assuming a population prevalence of 2 and 1% for ADHD + DBDs and 3 and 4% for ADHDwoDBDs. The GCTA analyses were corrected for the same covariates as used in the GWASs.

In order to be able to test for difference in h2SNP between ADHD + DBDs and ADHDwoDBDs we re-estimated h2SNP using GCTA based on independent iPSYCH controls. For this analysis the iPSYCH controls were split randomly into two groups within each genotyping wave. One group was used as controls for estimating h2SNP of ADHD + DBDs (2155 cases and 11,659 controls) and the other group was used to estimate h2SNP of ADHDwoDBDs (13,583 cases and 11,250 controls). The analysis using independent controls was done using prevalances of 2 and 1% for ADHD + DBDs and 3 and 4% for ADHDwoDBDs. Test for difference in h2SNP between ADHD + DBDs and ADHDwoDBDs was done using Eq. 1 below:

$${{Z}}_{{\mathrm{diff}}} = \left( {{{h}}^2_{{\mathrm{SNP}}\left( {{\mathrm{ADHD}} + {\mathrm{DBDs}}} \right)} - {\mathrm{h}}^2_{{\mathrm{SNP}}\left( {{\mathrm{ADHDwoDBDs}}} \right)}/{\mathrm{sqrt}}\left( {{\mathrm{SE}}^2_{\left( {{\mathrm{ADHD}} + {\mathrm{DBDs}}} \right)} + {\mathrm{SE}}^2_{\left( {{\mathrm{ADHDwoDBDs}}} \right)}} \right.} \right),$$
(1)

where Zdiff is the Z-score for the difference in h2SNP and SE is the standard errors for the heritabilities. We calculated two-tailed P-values in R.

Additionally, we evaluated how much of the variance in the ADHD + DBDs phenotype could be explained by common genetic variation in the context of ADHD. For this we did a case-only approach including 1959 cases with ADHD + DBDs and 13,539 individuals with ADHDwoDBDs. This was only done using GCTA due to a low polygenic signal in the ADHD + DBDs vs. ADHDwoDBDs GWAS (mean χ2 = 1.06).

### Genetic correlation with aggression-related phenotypes

The genetic overlap of ADHD + DBDs with aggression-related phenotypes was evaluated by estimating genetic correlations using LD score regression37 and the summary statistics from the GWAS meta-analysis of ADHD + DBDs and results from two aggression related GWASs. One is a GWAS meta-analysis of scores of aggressive behaviors in 18,988 children33 (EAGLE aggression) obtained by questionnaires filled by their parents. Another is a GWAS meta-analysis of antisocial behavior conducted by the Broad Antisocial Behavior Consortium (BroadABC) including 16,400 individuals34. Both children and adults were accessed for a broad range of antisocial measures, including aggressive and non-aggressive domains. The BroadABC study has a minor overlap with the EAGLE aggression GWAS with respect to the included cohorts. We also estimated the genetic correlations of ADHDwoDBDs and ADHD+DBDs (only including iPSYCH individuals) with the two aggression-related phenotypes.

The genetic correlation between ADHD + DBDs and ADHDwoDBDs was calculated using LD score regression and summary statistics from the GWAS meta-analysis of ADHD + DBDs and the GWAS of ADHDwoDBDs, the latter based on iPSYCH data only. To supplement the LD score regression analysis we used iPSYCH genotypes and GCTA to estimate the genetic correlation between ADHD + DBDs (2155 cases and 11,659 controls) and ADHDwoDBDs (13,583 cases and 11,250 controls), showing results consistent with LD score regression (rg = 0.97; SE = 0.06), and not statistically different from one.

Statistical difference between two rg estimates was calculated using the block jackknife method43 implemented in the LD score regression software37,89. The variants across the genome were divided in 200 blocks and jackknife deleted values were calculated by excluding one block at a time. The computed jackknife deleted values were then used to calculate corresponding jackknife pseudo values. By using the mean and variance of the jackknife pseudovalues, Z-score and corresponding P-values were computed, testing the null hypothesis that the difference between the rgs is equal to zero.

### Polygenic score analysis

Subsequently individuals were divided into quintiles based on their PGS. OR for ADHD + DBDs compared to ADHDwoDBDs was estimated within each quintile with reference to the lowest risk quintile (using the training data P-value threshold resulting in the highest Nagelkerke’s R2 in the target data).

### Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## Data availability

All relevant iPSYCH data are available from the authors after approval by the iPSYCH Data Access Committee and can only be accessed on the secured Danish server as the data are protected by Danish legislation. Access to data provided by the Psychiatric Genomics Consortium can be granted through the Psychiatric Genomics Data Access Committee https://www.med.unc.edu/pgc/about-us/people/data-access-committee/”. For data access please contact: Ditte Demontis, email: ditte@biomed.au.dk, Anders D. Børglum, email: anders@biomed.au.dk. The summary statistics with the results from the GWAS meta-analysis of ADHD + DBDs are available on the iPSYCH website (https://ipsych.dk/en/research/downloads/).

## Code availability

MetaXcan analysis was performed using the R-package metaxcanr (https://github.com/drveera/metaxcanr), and GTEx v6p expression prediction models and covariance matrices downloaded from http://predictdb.org.

## References

1. Faraone, S. V. et al. Attention-deficit/hyperactivity disorder. Nat. Rev. Dis. Primers 1, 15020 (2015).

2. Franke, B. et al. Live fast, die young? A review on the developmental trajectories of ADHD across the lifespan. Eur. Neuropsychopharmacol. 28, 1059–1088 (2018).

3. Fairchild, G. et al. Conduct disorder. Nat. Rev. Dis. Primers 5, 43 (2019).

4. Canino, G., Polanczyk, G., Bauermeister, J. J., Rohde, L. A. & Frick, P. J. Does the prevalence of CD and ODD vary across cultures? Soc. Psychiatry Psychiatr. Epidemiol. 45, 695–704 (2010).

5. Nock, M. K., Kazdin, A. E., Hiripi, E. & Kessler, R. C. Lifetime prevalence, correlates, and persistence of oppositional defiant disorder: results from the National Comorbidity Survey Replication. J. Child Psychol. Psychiatry 48, 703–713 (2007).

6. Dalsgaard, S. et al. Incidence rates and cumulative incidences of the full spectrum of diagnosed mental disorders in childhood and adolescence. JAMA Psychiatry 77, 155–164 (2020).

7. Scott, J. G. et al. Mortality in individuals with disruptive behavior disorders diagnosed by specialist services—A nationwide cohort study. Psychiatry Res. 251, 255–260 (2017).

8. Dalsgaard, S., Ostergaard, S. D., Leckman, J. F., Mortensen, P. B. & Pedersen, M. G. Mortality in children, adolescents, and adults with attention deficit hyperactivity disorder: a nationwide cohort study. Lancet 385, 2190–2196 (2015).

9. Larson, K., Russ, S. A., Kahn, R. S. & Halfon, N. Patterns of comorbidity, functioning, and service use for US children with ADHD, 2007. Pediatrics 127, 462–470 (2011).

10. Maughan, B., Rowe, R., Messer, J., Goodman, R. & Meltzer, H. Conduct disorder and oppositional defiant disorder in a national sample: developmental epidemiology. J. Child Psychol. Psychiatry 45, 609–621 (2004).

11. Smalley, S. L. et al. Prevalence and psychiatric comorbidity of attention-deficit/hyperactivity disorder in an adolescent Finnish population. J. Am. Acad. Child Adolesc. Psychiatry 46, 1575–1583 (2007).

12. Biederman, J. et al. Is ADHD a risk factor for psychoactive substance use disorders? Findings from a four-year prospective follow-up study. J. Am. Acad. Child Adolesc. Psychiatry 36, 21–29 (1997).

13. Groenman, A. P. et al. Substance use disorders in adolescents with attention deficit hyperactivity disorder: a 4-year follow-up study. Addiction 108, 1503–1511 (2013).

14. Ottosen, C., Petersen, L., Larsen, J. T. & Dalsgaard, S. Gender differences in associations between attention-deficit/hyperactivity disorder and substance use disorder. J. Am. Acad. Child Adolesc. Psychiatry 55, 227–234 (2016).

15. Dalsgaard, S., Mortensen, P. B., Frydenberg, M. & Thomsen, P. H. Conduct problems, gender and adult psychiatric outcome of children with attention-deficit hyperactivity disorder. Br. J. Psychiatry 181, 416–421 (2002).

16. Pingault, J. B. et al. Childhood hyperactivity, physical aggression and criminality: a 19-year prospective population-based study. PLoS ONE 8, e62594 (2013).

17. Mannuzza, S., Klein, R. G., Konig, P. H. & Giampino, T. L. Hyperactive boys almost grown up. IV. Criminality and its relationship to psychiatric status. Arch. Gen. Psychiatry 46, 1073–1079 (1989).

18. Dekkers, T. J., Popma, A., Agelink van Rentergem, J. A., Bexkens, A. & Huizenga, H. M. Risky decision making in attention-deficit/hyperactivity disorder: a meta-regression analysis. Clin. Psychol. Rev. 45, 1–16 (2016).

19. Faraone, S. V. & Larsson, H. Genetics of attention deficit hyperactivity disorder. Mol. Psychiatry 24, 562–575 (2019).

20. Slutske, W. S. et al. Modeling genetic and environmental influences in the etiology of conduct disorder: a study of 2,682 adult twin pairs. J. Abnorm. Psychol. 106, 266–279 (1997).

21. Goldstein, R. B., Prescott, C. A. & Kendler, K. S. Genetic and environemental factors in conduct problems and adult antisocial behavior among adult female twins. J. Nerv. Ment. Dis. 189, 201–209 (2001).

22. Rose, R. J., Dick, D. M., Viken, R. J., Pulkkinen, L. & Kaprio, J. Genetic and environmental effects on conduct disorder and alcohol dependence symptoms and their covariation at age 14. Alcohol. Clin. Exp. Res. 28, 1541–1548 (2004).

23. Thapar, A., Harrington, R. & McGuffin, P. Examining the comorbidity of ADHD-related behaviours and conduct problems using a twin study design. Br. J. Psychiatry 179, 224–229 (2001).

24. Christiansen, H. et al. Co-transmission of conduct problems with attention-deficit/hyperactivity disorder: familial evidence for a distinct disorder. J Neural Transm. 115, 163–175 (2008).

25. Faraone, S. V., Biederman, J., Mennin, D., Russell, R. & Tsuang, M. T. Familial subtypes of attention deficit hyperactivity disorder: a 4-year follow-up study of children from antisocial-ADHD families. J. Child Psychol. Psychiatry 39, 1045–1053 (1998).

26. Hamshere, M. L. et al. High loading of polygenic risk for ADHD in children with comorbid aggression. Am. J. Psychiatry 170, 909–916 (2013).

27. Dick, D. M., Viken, R. J., Kaprio, J., Pulkkinen, L. & Rose, R. J. Understanding the covariation among childhood externalizing symptoms: genetic and environmental influences on conduct disorder, attention deficit hyperactivity disorder, and oppositional defiant disorder symptoms. J. Abnorm. Child Psychol. 33, 219–229 (2005).

28. Martin, N. C., Levy, F., Pieka, J. & Hay, D. A. A Genetic study of attention deficit hyperactivity disorder, conduct disorder, oppositional defiant disorder and reading disability: aetiological overlaps and implications. Int. J. Disabil. Dev. Educ. 53, 21–34 (2006).

29. Faraone, S. V., Biederman, J., Keenan, K. & Tsuang, M. T. Separation of DSM-III attention deficit disorder and conduct disorder: evidence from a family-genetic study of American child psychiatric patients. Psychol. Med. 21, 109–121 (1991).

30. Knopik, V. S. et al. DSM-IV defined conduct disorder and oppositional defiant disorder: an investigation of shared liability in female twins. Psychol. Med. 44, 1053–1064 (2014).

31. Dick, D. M. et al. Genome-wide association study of conduct disorder symptomatology. Mol. Psychiatry 16, 800–808 (2011).

32. Aebi, M. et al. Gene-set and multivariate genome-wide association analysis of oppositional defiant behavior subtypes in attention-deficit/hyperactivity disorder. Am. J. Med. Genet. Part B 171, 573–588 (2016).

33. Pappa, I. et al. A genome-wide approach to children’s aggressive behavior: The EAGLE consortium. Am. J. Med. Genet. Part B 171, 562–572 (2016).

34. Tielbeek, J. J. et al. Genome-wide association studies of a broad spectrum of antisocial behavior. JAMA Psychiatry 74, 1242–1250 (2017).

35. Anney, R. J. et al. Conduct disorder and ADHD: evaluation of conduct problems as a categorical and quantitative trait in the international multicentre ADHD genetics study. Am. J. Med. Genet. Part B 147B, 1369–1378 (2008).

36. Brevik, E. J. et al. Genome-wide analyses of aggressiveness in attention-deficit hyperactivity disorder. Am. J. Med. Genet. Part B 171, 733–747 (2016).

37. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

38. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

39. Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

40. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

41. GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

42. Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

43. Shao, J. & Wu, C. F. J. A general theory for Jackknife variance estimation. Ann. Stat. 17, 1176–1197 (1989).

44. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

45. Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151). Mol. Psychiatry 21, 758–767 (2016).

46. Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49, 1107–1112 (2017).

47. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

48. Lee, P. H. et al. Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482 (2019).

49. Schizophrenia Working Group of the Psychiatric Genomics. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

50. Ikeda, M. et al. A genome-wide association study identifies two novel susceptibility loci and trans population polygenicity associated with bipolar disorder. Mol. Psychiatry 23, 639–647 (2018).

51. Ruderfer, D. M. et al. Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol. Psychiatry 19, 1017–1024 (2014).

52. Zhang, S. L. et al. STIM1 is a Ca2+ sensor that activates CRAC channels and migrates from the Ca2+ store to the plasma membrane. Nature 437, 902–905 (2005).

53. Yen, M. & Lewis, R. S. Numbers count: How STIM and Orai stoichiometry affect store-operated calcium entry. Cell Calcium 79, 35–43 (2019).

54. Klejman, M. E. et al. Expression of STIM1 in brain and puncta-like co-localization of STIM1 and ORAI1 upon depletion of Ca(2+) store in neurons. Neurochem. Int. 54, 49–55 (2009).

55. Majewski, L. et al. Overexpression of STIM1 in neurons in mouse brain improves contextual learning and impairs long-term depression. Biochim. Biophys. Acta 1864, 1071–1087 (2017).

56. Garcia-Alvarez, G. et al. Impaired spatial memory and enhanced long-term potentiation in mice with forebrain-specific ablation of the Stim genes. Front. Behav. Neurosci. 9, 180 (2015).

57. Ferreira, M. A. et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat. Genet. 40, 1056–1058 (2008).

58. Cross-Disorder Group of the Psychiatric Genomics. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

59. Nanou, E. & Catterall, W. A. Calcium channels, synaptic plasticity, and neuropsychiatric disease. Neuron 98, 466–481 (2018).

60. Robinson, E. B. et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552–555 (2016).

61. Bralten, J. et al. Autism spectrum disorders and autistic traits share genetics and biology. Mol. Psychiatry 23, 1205–1212 (2018).

62. van Beijsterveldt, C. E., Bartels, M., Hudziak, J. J. & Boomsma, D. I. Causes of stability of aggression from early childhood to adolescence: a longitudinal genetic analysis in Dutch twins. Behav. Genet. 33, 591–605 (2003).

63. Porsch, R. M. et al. Longitudinal heritability of childhood aggression. Am. J. Med. Genet. Part B 171, 697–707 (2016).

64. Huesmann, L. R., Eron, L. D., Lefkowitz, M. M. & Walder, L. O. Stability of aggression over time and generations. Dev. Psychol. 20, 1120–1134 (1984).

65. Zoccolillo, M., Pickles, A., Quinton, D. & Rutter, M. The outcome of childhood conduct disorder: implications for defining adult personality disorder and conduct disorder. Psychol. Med. 22, 971–986 (1992).

66. Erskine, H. E. et al. Long-term outcomes of attention-deficit/hyperactivity disorder and conduct disorder: a systematic review and meta-analysis. J. Am. Acad. Child Adolesc. Psychiatry 55, 841–850 (2016).

67. Estevez, E., Jimenez, T. I. & Moreno, D. Aggressive behavior in adolescence as a predictor of personal, family, and school adjustment problems. Psicothema 30, 66–73 (2018).

68. Bierman, K. L. et al. School outcomes of aggressive-disruptive children: prediction from kindergarten risk factors and impact of the fast track prevention program. Aggress. Behav. 39, 114–130 (2013).

69. Ainsworth, S. E. & Maner, J. K. Sex begets violence: mating motives, social dominance, and physical aggression in men. J. Pers. Soc. Psychol. 103, 819–829 (2012).

70. Pedersen, C. B. et al. The iPSYCH2012 case-cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 23, 6–14 (2018).

71. Borglum, A. D. et al. Genome-wide study of association and interaction with maternal cytomegalovirus infection suggests new schizophrenia loci. Mol. Psychiatry 19, 325–333 (2014).

72. Hollegaard, M. V. et al. Robustness of genome-wide scanning using archived dried blood spot samples as a DNA source. BMC Genet. 12, 58 (2011).

73. Mors, O., Perto, G. P. & Mortensen, P. B. The Danish Psychiatric Central Research Register. Scand. J. Public Health 39, 54–57 (2011).

74. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2018).

75. Lam, M. et al. RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics https://doi.org/10.1093/bioinformatics/btz633 (2019).

76. Delaneau, O., Marchini, J. & Zagury, J. F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).

77. Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 1, 457–470 (2011).

78. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1092 human genomes. Nature 491, 56–65 (2012).

79. Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135 (2008).

80. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

81. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

82. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

83. Galinsky, K. J. et al. Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia. Am. J. Hum. Genet. 98, 456–472 (2016).

84. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

85. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–−82 (2011).

86. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

87. Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

88. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

89. Hubel, C. et al. Genomics of body fat percentage may contribute to sex bias in anorexia nervosa. Am. J. Med. Genet. Part B 180, 428–438 (2019).

90. Lee, S. H., Goddard, M. E., Wray, N. R. & Visscher, P. M. A better coefficient of determination for genetic profile analysis. Genet. Epidemiol. 36, 214–224 (2012).

## Author information

Authors

### Contributions

D.D., S.V.F., and A.D.B. conceived the idea, supervised and directed the study. I.D.W., M.R., J.B.-G., M.B.-H., T.W., O.M., P.B.M., M.N., PGC-ADHD Consortium, B.C, D.M.H., B.M.N, B.F., S.V.F, and A.D.B. provided samples and/or data. D.D., R.K.W., V.M.R., I.D.W., J.G., and T.D.A. performed the analyses. D.D. wrote the manuscript. D.D., R.K.W., B.C., S.V.F., and A.D.B. comprised the core revision group. All authors discussed the results and approved the final version of the manuscript.

### Corresponding authors

Correspondence to Ditte Demontis, Stephen V. Faraone or Anders D. Børglum.

## Ethics declarations

### Competing interests

Ditte Demontis has received speaking fee from Takeda. Barbara Franke has received educational speaking fees from Medice. Benjamin M. Neale is a member of the scientific advisory board at Deep Genomics and consultant for Camp4 Therapeutics, Takeda Pharmaceutical and Biogen.

Peer review information Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Rights and permissions

Reprints and Permissions

Demontis, D., Walters, R.K., Rajagopal, V.M. et al. Risk variants and polygenic architecture of disruptive behavior disorders in the context of attention-deficit/hyperactivity disorder. Nat Commun 12, 576 (2021). https://doi.org/10.1038/s41467-020-20443-2

• Accepted:

• Published:

• DOI: https://doi.org/10.1038/s41467-020-20443-2