www.nature.com/npp ARTICLE OPEN Shared genetic background between children and adults with attention deficit/hyperactivity disorder Paula Rovira1,2, Ditte Demontis 3,4,5, Cristina Sánchez-Mora 1,2,6,7, Tetyana Zayats8,9,10, Marieke Klein 11,12, Nina Roth Mota11,13,14, Heike Weber 15,16, Iris Garcia-Martínez1,2,17,18, Mireia Pagerols1,2, Laura Vilar-Ribó1,2, Lorena Arribas1,2, Vanesa Richarte2,6,19, Montserrat Corrales2,6,19, Christian Fadeuilhe2,6,19, Rosa Bosch2,6,19, Gemma Español Martin2,19,20, Peter Almos21, Alysa E. Doyle22,23, Eugenio Horacio Grevet13,24, Oliver Grimm16, Anne Halmøy8,25, Martine Hoogman11, Mara Hutz26, Christian P. Jacob21, Sarah Kittel-Schneider16, Per M. Knappskog27,28, Astri J. Lundervold 29, Olga Rivero 21, Diego Luiz Rovaris 13,26,30, Angelica Salatino-Oliveira26, Bruna Santos da Silva13,26, Evgeniy Svirin 21,31, Emma Sprooten32, Tatyana Strekalova21,31,33, ADHD Working Group of the Psychiatric Genomics Consortium, 23andMe Research team, Alejandro Arias-Vasquez11,14, Edmund J. S. Sonuga-Barke34,35, Philip Asherson36, Claiton Henrique Dotto Bau13,26, Jan K. Buitelaar 32,37, Bru Cormand7,38,39,40, Stephen V. Faraone41, Jan Haavik 8,25, Stefan E. Johansson 27,28, Jonna Kuntsi 36, Henrik Larsson42,43, Klaus-Peter Lesch 21,31,33, Andreas Reif 16, Luis Augusto Rohde44, Miquel Casas1,2,6,19, Anders D. Børglum 3,4,5, Barbara Franke 11,14, Josep Antoni Ramos-Quiroga 1,2,6,19, María Soler Artigas1,2,6,7 and Marta Ribasés 1,2,6,7 Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by age-inappropriate symptoms of inattention, impulsivity, and hyperactivity that persist into adulthood in the majority of the diagnosed children. Despite several risk 1234567890();,: factors during childhood predicting the persistence of ADHD symptoms into adulthood, the genetic architecture underlying the trajectory of ADHD over time is still unclear. We set out to study the contribution of common genetic variants to the risk for ADHD across the lifespan by conducting meta-analyses of genome-wide association studies on persistent ADHD in adults and ADHD in childhood separately and jointly, and by comparing the genetic background between them in a total sample of 17,149 cases and 32,411 controls. Our results show nine new independent loci and support a shared contribution of common genetic variants to ADHD in children and adults. No subgroup heterogeneity was observed among children, while this group consists of future remitting and persistent individuals. We report similar patterns of genetic correlation of ADHD with other ADHD-related datasets and different traits and disorders among adults, children, and when combining both groups. These findings confirm that persistent ADHD in adults is a neurodevelopmental disorder and extend the existing hypothesis of a shared genetic architecture underlying ADHD and different traits to a lifespan perspective. Neuropsychopharmacology (2020) 45:1617–1626; https://doi.org/10.1038/s41386-020-0664-5 1 Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain; 2 Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia, Spain; 3Department of Biomedicine (Human Genetics), and Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark; 4The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; 5Center for Genomics and Personalized Medicine, Aarhus, Denmark; 6Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; 7Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain; 8Department of Biomedicine, University of Bergen, Bergen, Norway; 9Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, MA, USA; 10Stanley Center for Psychiatric Research, Broad Institute of MIT, and Harvard, Cambridge, MA, USA; 11Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; 12University Medical Center Utrecht, UMC Utrecht Brain Center, Department of Psychiatry, Utrecht, The Netherlands; 13ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; 14Department of Psychiatry, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; 15Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Würzburg, Germany; 16Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany; 17Banc de Sang i Teixits (BST), Barcelona, Spain; 18Grup de Medicina Transfusional, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona (VHIR-UAB), Barcelona, Spain; 19Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain; 20Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Barcelona, Catalonia, Spain; 21 Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany; 22Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; 23Department of Psychiatry, Harvard Medical School, Boston, MA, USA; 24Department of Psychiatry, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; 25Division of Psychiatry, Haukeland University Hospital, Bergen, Norway; 26Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; 27Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway; 28Department of Clinical Science, University of Bergen, Bergen, Norway; 29Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; 30Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil; 31Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, IM Sechenov First Moscow State Medical University, Moscow, Russia; 32Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands; 33 Department of Neuroscience, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands; 34Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK; 35Department of Child and Adolescent Psychiatry, Aarhus University, Aarhus, Denmark; 36Social Genetic and Developmental Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King’s College London, London, UK; 37Karakter Child and Adolescent Psychiatry, Nijmegen, The Netherlands; 38 Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain; 39Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain; 40Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat, Catalonia, Spain; 41Departments of Psychiatry, of Neuroscience, and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA; 42School of medical Sciences, Örebro University, Örebro, Sweden; 43Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden and 44Division of Child Psychiatry, Hospital de Clinicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil Correspondence: María. Soler Artigas (maria.soler@vhir.org) or Marta Ribasés (marta.ribases@vhir.org) These authors contributed equally: Paula Rovira, Ditte Demontis, Anders D. Børglum, Barbara Franke, Josep Antoni Ramos-Quiroga, María Soler Artigas, Marta Ribasés. A list of members of ADHD Working Group of the Psychiatric Genomics Consortium and 23andMe Research team and their affiliations are listed in Supplementary information. Received: 23 December 2019 Revised: 25 February 2020 Accepted: 3 March 2020 Published online: 12 April 2020 © The Author(s) 2020 Shared genetic background between children and adults with attention. . . Paula Rovira et al. 1618 INTRODUCTION Integrative Psychiatric Research (iPSYCH), and the International Attention deficit/hyperactivity disorder (ADHD) is a common Multi-centre persistent ADHD CollaboraTion (IMpACT), were neurodevelopmental disorder that severely impairs the daily analyzed. All participants were of European ancestry, had provided functioning of patients due to age-inappropriate levels of informed consent, and all sites had documented permission from impulsivity and hyperactivity, and/or difficulties in focusing local ethics committees. attention [1]. ADHD has a prevalence of 5–6% in childhood, and The meta-analysis on persistent ADHD was conducted in 22,406 impairing symptoms persist into adulthood in around two-thirds individuals (6,532 ADHD adult cases and 15,874 controls) using six of children with ADHD diagnosis, with an estimated adult datasets from the IMpACT consortium, two datasets from the PGC, prevalence around 3.4% [1, 2]. and the adult subset from the iPSYCH cohort included in ADHD is a multifactorial disorder with heritability averaging Demontis and Walters et al. [6]. The meta-analysis on ADHD in 76% throughout the lifespan [3–5]. There is consistent evidence childhood included 27,154 individuals (10,617 cases and 16,537 that both common and rare variants make an important controls), comprising two Brazilian and Spanish cohorts, seven contribution to the risk for the disorder [6–11]. Several genome- datasets from the PGC, and the children subset from the iPSYCH wide association studies (GWAS) and meta-analyses across those cohort included in Demontis and Walters et al. [6]. All patients met have been conducted [7], but only the largest GWAS meta-analysis DSM-IV/ICD-10 diagnostic criteria. In total, 7,086 new samples not (GWAS-MA) performed to date reported genome-wide significant included in Demontis and Walters et al. [6] were considered in the loci [6]. This study concluded that common genetic variants present study. Detailed information on each dataset is provided in (minor allele frequency, MAF > 0.01) account for 22% of the Table S1 and in Supplementary Methods. heritability of the disorder [6] and supported substantial genetic overlap between ADHD and other brain disorders and behavioral/ GWAS and meta-analyses cognitive traits [6, 12]. Genotyping platforms and quality control (QC) filters for each of The presentation of ADHD symptoms changes from childhood the datasets are shown in Table S1. Pre-imputation QC at to adulthood, with lower levels of hyperactivity in adulthood but a individual and SNP level were performed using the Rapid high risk for ongoing attention problems, disorganization, and Imputation and COmputational PIpeLIne with the default settings emotional dysregulation [13, 14]. As in the general population, the (https://sites.google.com/a/broadinstitute.org/ricopili/). Non- pattern of psychiatric and somatic comorbid conditions in ADHD European ancestry samples, related and duplicated individuals, also changes substantially over time, with learning disabilities, and subjects with sex discrepancies were excluded. Phasing of oppositional defiant disorder, and conduct disorder being more genotype data was performed using the SHAPEIT2 algorithm, and prevalent in children, and substance use disorders, social phobia, imputation for unrelated samples and trios was performed with insomnia, obesity, and mood disorders becoming more pro- MaCH, IMPUTE2, or MINIMAC3 (http://genome.sph.umich.edu/ nounced in adulthood [1, 15–18]. In addition, persistent ADHD in wiki/Minimac3) depending on software availability at the time of adults is, compared with the general population (and to cases with imputation (Table S1) [41–43]. The European ancestry panel of the remitting ADHD), associated with higher risk for a wide range of 1000 Genomes Project using genome build hg19 was considered functional and social impairments, including unemployment, as reference for genotype imputation (ftp://ftp.1000genomes.ebi. accidents, and criminal behavior [7, 19–23]. ac.uk/vol1/ftp/). After imputation, the association with ADHD of Several risk factors measured in childhood predict the genotype dosages was tested using logistic regression in PLINK persistence of ADHD symptoms into adulthood, such as the 1.9 [44], assuming an additive genetic model and including sex, presence of comorbid disorders, the severity of ADHD symptoms, the first ten principal components, and other relevant covariates being exposed to psychosocial adversity, as well as having a high for each case-control study (Table S1). GWAS summary statistics polygenic risk score (PRS) for childhood ADHD [24–28]. Twin were filtered prior to meta-analysis, excluding variants with MAF < studies suggest that both stable and dynamic genetic influences 0.01, and imputation quality scores (INFO) ≤ 0.8. Inverse-variance affect the persistence of ADHD symptoms [4, 5, 29, 30]. However, weighted fixed-effects meta-analyses were conducted using specific genetic factors differentiating childhood and persistent METAL [45] and results were filtered by effective sample size > ADHD into adulthood are not well understood due to the lack of 70% of the total, defined as Neff ¼ 1 þ2 1 [46]. The genome- longitudinal studies. Molecular studies, including the most recent ðNcaÞ ðNcoÞ GWAS-MA of ADHD [6], have been performed in children and wide significance threshold was set at P < 5.00E−08 to correct for adults either separately or jointly [6, 31–40], but large-scale multiple testing. Independent loci for variants exceeding this analyses comparing their genetic basis are yet to be conducted. threshold were defined based on clumping using PLINK 1.9. Given this background, we set out to study the contribution of Variants that were ±250 kb away from the index variant (variant common genetic variants to the risk for ADHD from a lifespan with smallest P value in the region), with P value < 0.001, and with perspective by conducting the largest GWAS-MAs performed so an estimated linkage disequilibrium (LD) of r2 > 0.2 with the index far on persistent ADHD in adults (diagnosed according to DSM-IV/ variant were assigned to a clump (p1 = 5.00E−08, p2 = 0.001, r2 = ICD-10 criteria) and on ADHD in childhood (that may include 0.2, kb = 250). Manhattan and Forest plots were generated using remittent and persistent forms of the disorder) separately and the “qqman” and “forestplot” R packages (3.4.4R version), jointly. For the first time, we estimated the genetic correlation respectively. The LocusZoom software [47] was used to generate between childhood and persistent ADHD, compared their patterns regional association plots. of genetic correlation with other traits and disorders, assessed the Details of downstream analyses for top signals identified are effect of childhood ADHD PRSs on persistent ADHD, and explored provided in the online supplement and include conditional whether individuals in which ADHD symptoms may persist into analysis, Bayesian credible set analysis, and functional character- adulthood could be distinguished already in childhood using ization of the significant variants. genetic data. SNP-based heritability (SNP-h2) The SNP-h2 was estimated by single-trait LD score regression using MATERIAL AND METHODS summary statistics, HapMap 3 LD-scores, considering default SNP Sample description QC filters (INFO > 0.9 and MAF > 0.01) and assuming population A total of 19 GWAS of ADHD comprising 49,560 individuals (17,149 prevalence of 3.4, 5.5, and 5% for persistent ADHD, ADHD on cases and 32,411 controls), provided by the Psychiatric Genomics childhood, and ADHD across the lifespan, respectively, [48]. Data Consortium (PGC), the Lundbeck Foundation Initiative for of 1,113,287, 1,072,558, and 1,092,418 SNPs from the GWAS-MA of Neuropsychopharmacology (2020) 45:1617 – 1626 Shared genetic background between children and adults with attention. . . Paula Rovira et al. 1619 persistent ADHD, ADHD on childhood, and ADHD across the Genetic correlation lifespan, respectively, were considered to estimate the liability- Cross-trait LD score regression with unconstrained intercept was scale SNP-h2. Partitioning and enrichment of the heritability by used to calculate genetic correlations (rg) between pairs of traits, functional categories was analyzed using the 24 main annotations considering HapMap3 LD-scores, markers with INFO ≥ 0.90, and (no window around the functional categories) described by excluding the MHC region (hg19:chr6:25-35M) [48]. Other ADHD Finucane et al. [49]. Statistical significance was set using datasets [6, 52] and phenotypes from the LD-hub centralized Bonferroni correction (P < 2.08E−03). database [53] with heritability z-scores (observed heritability/ observed standard error) >4 and with an observed heritability > Gene-based and gene-set analyses 0.1 were considered (N = 139 out of 689 available traits). Statistical MAGMA software was undertaken for gene-based and gene-set significance was set using Bonferroni correction (P < 3.60E−04). association testing using summary data from our GWAS-MAs [50]. Pearson’s correlation coefficient (Pearson’s r) was calculated Variants were mapped to a gene if they were within 20 kb between the genetic correlations of persistent ADHD with the upstream or downstream from the gene according to dbSNP build phenotypes from the LD-hub and the genetic correlations of 135 and NCBI 37.3 gene definitions. Genes in the MHC region ADHD in childhood with the phenotypes from the LD-hub. (hg19:chr6:25-35M) were excluded from the analyses. LD patterns were estimated using the European ancestry reference panel of the 1000 Genomes Project. Gene sets denoting canonical path- RESULTS ways were downloaded from MSigDB (http://www.broadinstitute. GWAS-MA of persistent ADHD in adults org/gsea/msigdb), which integrates Kyoto Encyclopedia of Genes The GWAS-MA of persistent ADHD in adults included 6,532 adult and Genomes (http://www.genome.jp/kegg/), BioCarta (http:// ADHD cases and 15,874 controls. Minimal population stratification www.biocarta.com/), Reactome (https://reactome.org/), and Gene or other systematic biases were detected (LD score regression Ontology (GO) (http://www.geneontology.org/) resources. Bonfer- intercept = 1.01, Fig. S1a). The proportion of heritability of roni correction (P < 2.77E−06 for 18,038 genes in persistent ADHD; persistent ADHD attributable to common single-nucleotide P < 2.75E−06 for 18,218 genes in childhood ADHD; P < 2.79E−06 polymorphisms on the liability scale (SNP-h2) was 0.19 (SE = for 17,948 genes in ADHD across the lifespan) and 10,000 0.024), with a nominally significant enrichment in the heritability permutations were used for multiple testing correction in the of variants located in conserved genomic regions (P = 5.18E−03) gene-based and gene-set analyses, respectively. and in the cell-specific histone mark H3K4me1 (P = 3.17E−02) (Fig. S2a). The gene-based analysis revealed six genes in four loci BUHMBOX analysis (ST3GAL3, FRAT1/FRAT2, CGB1, and RNF225/ZNF584) significantly The Breaking Up Heterogeneous Mixture Based On cross(X)-locus associated with persistent ADHD, with ST3GAL3 being the most correlations (BUHMBOX) analysis [51] was used to test whether the significant one (P = 8.72E−07) (Table S2a). The single-marker genetic correlation between persistent ADHD and ADHD in analysis showed no variants exceeding genome-wide significance, childhood was driven by subgroup heterogeneity, found when with the most significant signal being rs3923931 (P = 1.69E−07) there is a subset of children enriched for persistent ADHD- (Fig. 1a and Table S3a). Similarly, no significant gene sets were associated alleles. Subgroup heterogeneity was tested in each identified in the pathway analysis after correction for multiple childhood dataset considering independent SNPs (r2 = 0.1, kb = comparisons (Table S4a [excel file]). 10,000) with MAF > 0.05 from the GWAS-MA of persistent ADHD using two different P value thresholds of P < 5.00E−05 (62 SNPs) GWAS-MA of ADHD in childhood and P < 1.00E−03 (710 SNPs). Results were meta-analyzed using To compare the genetic background between persistent ADHD in the standard weighted sum of z-score approach, where z-scores adults and ADHD in childhood (that may include future remittent are weighted by the square root of the effective sample size. The and persistent forms of the disorder), we conducted a GWAS-MA statistical power was calculated using 1,000 simulations, consider- on children with ADHD in a total of 10,617 ADHD cases and 16,537 ing the ADHD children meta-analysis sample size, the odds ratios controls. We found no evidence of genomic inflation or and risk allele frequencies from the GWAS-MA of persistent ADHD, population stratification (LD score regression intercept = 1.02, and assuming 65% of heterogeneity proportion (π). Fig. S1b). The liability-scale SNP-h2 for ADHD in childhood was 0.19 (SE = 0.021), with a significant enrichment in the heritability of Sign test variants located in conserved genomic regions after Bonferroni The direction of the effect of variants associated with ADHD in correction (P = 1.21E−06) (Fig. S2b). The gene-based analysis childhood was tested in persistent ADHD and vice versa, using highlighted a significant association between FEZF1 and ADHD in strict clumping (r2 = 0.05, kb = 500, p2 = 0.5) and different P value childhood (P = 5.42E−07) (Table S2b). No single genetic variant thresholds (1.00E−07, 5.00E−07, 1.00E−06, 5.00E−06, 1.00E−05, exceeded genome-wide significance, with the top signal being in 5.00E−05, 1.00E−04, and 5.00E−04). The concordant direction of rs55686778 (P = 1.67E−07) (Fig. 1b and Table S3b), and no effect was evaluated using a one sample test of the proportion significant gene sets were identified in the pathway analysis after with Yates’ continuity correction against a null hypothesis of P = correction for multiple comparisons (Table S4b [excel file]). 0.50 with the “stats” R package. Comparison of the genetic background of persistent ADHD in Polygenic risk scoring adults and ADHD in childhood PRSs were constructed using different P value thresholds (P < 0.001, We found a strong genetic correlation between persistent ADHD 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, and 1) to select independent variants (p1 = in adults and ADHD in childhood (rg = 0.81, 95% CI: 0.64–0.97), 1, p2 = 1, r2 = 0.1, kb = 250) from the childhood GWAS-MA of ADHD significantly different from 0 (P = 2.13E−21) and from 1 (P = 0.02). and were then tested for association with persistent ADHD in each of Sign test results provided evidence of a consistent direction of the nine datasets, adjusting for the covariates included in the GWAS effect of genetic variants associated with ADHD in childhood in and using PRSice-2 (https://choishingwan.github.io/PRSice/). Best persistent ADHD and vice versa (P = 6.60E−04 and P = 4.47E−03, guess genotypes for nonambiguous strand variants present in all respectively, for variants with P < 5.00E−05 in each dataset) the persistent ADHD studies (missing rate < = 0.02) were included (Table S5). In addition, PRS analyses showed that childhood ADHD (NSNPs = 32,584 for P =1). Results from the nine PRS analyses at each PRSs were associated with persistent ADHD at different pre- P value threshold were combined using inverse-variance weighted defined P value thresholds, with the P = 0.40 threshold (NSNPs = meta-analysis. 20,398) explaining the most variance (r2 = 0.0041 and P = 1.20E Neuropsychopharmacology (2020) 45:1617 – 1626 Shared genetic background between children and adults with attention. . . Paula Rovira et al. 1620 −27) (Fig. 2a). The quintiles of the PRS built using this threshold showed the expected trend of higher ADHD risk for individuals in higher quintiles (Fig. 2b, Table S6). We then tested whether the genetic correlation between persistent ADHD and ADHD in childhood was driven by a subset of children enriched for persistent ADHD-associated alleles using the Breaking Up Heterogeneous Mixture Based On Cross-locus correlations (BUHMBOX) analysis. We found no evidence of subgroup genetic heterogeneity in children, supporting that the sharing of persistent ADHD-associated alleles between children and adults was driven by the whole group of children, with a statistical power of 98.4 and 100% for thresholds of P < 5.00E−05 and P < 1.00E−03, respectively (Table S7). GWAS-MA of ADHD across the lifespan Given the strong genetic correlation between persistent ADHD in adults and in childhood, we performed a GWAS-MA of ADHD across the lifespan considering all datasets included in the GWAS- MAs. In total, 17,149 ADHD cases and 32,411 controls were included, and no evidence of genomic inflation or population stratification was found (LD score regression intercept = 1.03, Fig. S1c). The liability-scale SNP-h2 for ADHD across the lifespan was 0.17 (SE = 0.013), and a significant enrichment in the heritability of variants located in conserved genomic regions was observed after Bonferroni correction (P = 1.53E−06) (Fig. S2c). We identified four genome-wide significant variants (Figs. 1c and 3, Table 1a, and Fig. S3) and nine genes in seven loci (FEZF1, DUSP6, ST3GAL3/KDM4A, SEMA6D, C2orf82/GIGYF2, AMN, and FBXL17) significantly associated with ADHD across the lifespan (Table 1b). The most significantly associated locus was on chromosome 6 (index variant rs183882582-T, OR = 1.43 (95% CI: 1.26–1.60), P = 1.57E−08), followed by loci on chromosome 7 (index variant rs3958046), chromosome 4 (index variant rs200721207), and chromosome 3 (index variant rs1920644) (Table 1a, Fig. 3). The gene-set analysis showed a significant association of the “ribonucleoprotein complex” GO term with ADHD across the lifespan (P.adj = 0.021) (Table S4c [excel file]). One of the four loci identified in the single-variant analysis also reached genome-wide significance in the previous GWAS-MA on ADHD [6], and all of them showed consistent direction of the effect in that study (Table S8a). Significant loci reported by Demontis et al. [6] showed nominal association with ADHD across the lifespan in our study (Table S8b, c), with single variant hits showing the same direction of the effect (Table S8b). Analyses conditioning on the index variant for the four ADHD- associated loci did not reveal new independent markers. These four significant loci were functionally characterized by obtaining Bayesian credible sets and searching for expression quantitative trait loci (eQTL) using available data in blood or brain [54, 55]. We found that credible sets for three of the four loci contained at least one eQTL within 1 Mb of the index variant. The credible set on chromosome 6 included the index variant (rs183882582) and rs12197454. This variant, in LD with the index variant (r2 = 0.56), was associated with the expression of RSPH3 in blood and brain (P.adj < 1.65E−05 and P.adj = 2.36E−07, respectively), and with the expression of VIL2 in blood (P.adj = 3.21E−03). The credible set for the second most associated locus on chromosome 7 included 24 variants. The index variant, rs3958046, and other variants in this set, were eQTLs for CADPS2 in brain (maximum P.adj = 2.91E−03). The credible set for the locus on chromosome 4 contained 50 variants, most of them located in or near PCDH7, but Fig. 1 Manhattan plots of the three GWAS meta-analyses no eQTLs were identified. In the credible set for the locus on conducted. (a) GWAS-MA of nine cohorts of persistent ADHD in chromosome 3, which included 98 variants, the index variant, adults, (b) GWAS-MA of ten cohorts of ADHD in childhood, and rs1920644, was associated with the expression of KPNA4, IFT80, (c) GWAS-MA of all datasets of ADHD across the lifespan (ADHD in and KRT8P12 in brain (P.adj = 1.16E−04, P.adj = 1.40E−03, and childhood + persistent ADHD). Horizontal lines indicate suggestive (P value = 5.00E−06) and genome-wide significant (P = 5.00E−08) P.adj = 1.77E−03, respectively). Many other variants in this set thresholds in a-b, and c, respectively. were eQTLs for these genes and also for TRIM59, OTOL1, and/or C3orf80 in brain (P.adj < 0.05) (Table S9 [excel file]). Neuropsychopharmacology (2020) 45:1617 – 1626 Shared genetic background between children and adults with attention. . . Paula Rovira et al. 1621 GWAS-MA on ADHD across the lifespan (N = 7086), a significant genetic correlation was also obtained between their sample and ours (rg = 0.91, SE = 0.35, P = 8.70E−03). We also observed significant genetic correlations between childhood ADHD symptom scores from a GWAS-MA in a population of children reported by the EAGLE consortium [52] (N = 17,666) and persistent ADHD (rg = 0.65, SE = 0.20, P = 1.10E −03), ADHD in childhood (rg = 0.98, SE = 0.21, P = 2.76E−06), and ADHD across the lifespan (rg = 0.87, SE = 0.19, P = 4.80E−06). Similarly, significant genetic correlations between GWAS of self- reported ADHD status from 23andMe (N = 952,652) and persistent ADHD (rg = 0.75, SE = 0.05, P = 2.49E−45), ADHD in childhood (rg = 0.63, SE = 0.05, P = 1.39E−42), and ADHD across the lifespan (rg = 0.72, SE = 0.04, P = 4.86E−88) were observed (Table S11). We also estimated the genetic correlation of persistent ADHD in adults, ADHD in childhood, and ADHD across the lifespan with all available phenotypes in LD-hub. Results for 139 phenotypes passed the QC parameters and 41 genetic correlations were significant after Bonferroni correction in both children and adults with persistent ADHD (Table S12 [excel file]). Again, the genetic correlations with ADHD were consistent across the lifespan, with similar patterns found in adulthood and childhood (Pearson’s r = 0.89) (Fig. 4a, Table S12 [excel file]). The strongest genetic correlations with ADHD were found for traits related to academic performance, intelligence, and risk-taking behaviors, including smoking and early pregnancy (Fig. 4b). DISCUSSION In the current study, we set out to explore the contribution of common genetic variants to the risk of ADHD across the lifespan by conducting GWAS-MAs separately for children and adults with persistent ADHD that meet DSM-IV/ICD-10 criteria. Using the largest GWAS datasets available from the PGC, the iPSYCH, and IMpACT consortia we found evidence for a common genetic basis for ADHD in childhood and persistent ADHD in adults and identified nine new loci associated with the disorder. We found a highly similar proportion of the heritability of ADHD explained by common variants in children and in adults (SNP-h2 = 0.19), which is consistent with the SNP-h2 estimate reported in the recent GWAS-MA on ADHD [6] (SNP-h2 = 0.22), that included children and adults, and is in line with multiple studies supporting the stability of ADHD’s heritability from childhood to adulthood [3–5]. These results together with the 0.81 genetic correlation found between children and adults with Fig. 2 Polygenic risk scores for ADHD in childhood tested on persistent ADHD reinforce the hypothesis of the neurodevelop- persistent ADHD as target sample. a Bar plot and b quintile plot of mental nature of persistent ADHD in adults. Consistently, the meta-analysis odds ratios (OR meta) with 95% confidence intervals sign test and the PRS analysis confirmed the extensive overlap for P value threshold = 0.4 using the third quintile as baseline. of common genetic risk variants for ADHD in childhood and adulthood. In a summary-data-based Mendelian randomization (SMR) In the view of the fact that children with ADHD may be an analysis, we used summary data from the GWAS-MA of ADHD admixed group of individuals whose ADHD symptoms will persist across the lifespan and the eQTL data in blood and brain from or remit in adulthood, we ran a BUHMBOX analysis to elucidate if Westra et al. [54] and Qi et al. [55] to identify gene expression the potential “persistent” individuals could be distinguishable levels associated with ADHD. We found a significant association already in childhood. Our data supported genetic similarities in between ADHD across the lifespan and RMI1 expression in blood ADHD across the lifespan with no evidence of a subset of patients (PSMR = 5.36E−06) (Table S10 [in excel]), finding not likely to be an enriched for persistent ADHD-associated alleles within the group artifact due to LD between eQTL and other ADHD-associated of children. variants given that the PHEIDI was 0.47. Despite not having identified specific genetic contributions for ADHD in children or persistent ADHD, our results are not Genetic correlation with other ADHD datasets and phenotypes inconsistent with evidence suggesting changes in the genetic We found significant genetic correlations of ADHD in children and contribution to ADHD symptoms from childhood into adulthood, adults from the previous GWAS-MA [6] (N = 53,296) and persistent as described in previous twin studies in the general population ADHD (rg = 0.85, SE = 0.04, P = 5.49E−99), ADHD in childhood (rg [4, 5, 29, 30]. Our study design and the still limited statistical power = 0.99, SE = 0.03, P = 5.02E−273), and ADHD across the lifespan of the GWAS-MAs may have facilitated the identification of the (rg = 0.98, SE = 0.01, P < 2.23E−308) (Table S11). When removing shared genetic basis rather than specific genetic factors for sample overlap (LD score genetic covariance intercept = 0.75) and persistence. Also, differences between the origin of the samples considering only the subset of new samples included in our (population-based versus clinical) and/or discrepancies between Neuropsychopharmacology (2020) 45:1617 – 1626 Shared genetic background between children and adults with attention. . . Paula Rovira et al. 1622 Fig. 3 Regional association plots for genome-wide significant loci identified in the GWAS meta-analysis of ADHD across the lifespan. Each plot includes information about the locus, the location and orientation of the genes in the region, the local estimates of recombination rate (in the right corner), and the LD estimates of surrounding SNPs with the index SNP (r2 values are estimated based on 1000 Genomes European reference panel), which is indicated by color (in the upper left corner). self- and medical reports could explain why we found no group- We report 13 loci in gene- and SNP-based analyses for specific genetic variants. In addition, given that Chen et al. [56] childhood ADHD, adult ADHD, and/or ADHD across the lifespan. and Biederman et al. [57] reported that persistence of ADHD into Four ADHD-associated loci were previously identified by Demontis adulthood indexed stronger familial aggregation of ADHD, we et al. [6], which was expected due to the sample overlap between cannot yet discard influences of non-additive genetic effects, or the two datasets. The new loci identified in the present study other types of genetic variation, such as rare mutations or copy mainly included genes involved in brain formation and function, number variation, playing a role in the different ADHD trajectories such as FEZF1, a candidate for autism spectrum disorder across the lifespan. implicated in the formation of the diencephalon [58, 59], RSPH3, We also found strong and significant positive genetic correla- which participates in neuronal migration in embryonic brain [60], tions of ADHD ascertained in clinical populations of adults, CADPS2, which has been associated with psychiatric conditions children, or both with other ADHD-related measures from general due to its role in monoamine and neurotrophin neurotransmission population samples, including the largest GWAS of self-reported [61–64], AMN, which is involved in the uptake of vitamin B12 ADHD status from 23andMe participants (N = 952,652) and the [65, 66], essential for brain development, neural myelination, and GWAS-MA of childhood rating scales of ADHD symptoms in the cognitive function [67], and FBXL17, which has previously been general population [52]. In agreement with previous reports, these related to intelligence [68]. data suggest that a clinical diagnosis of ADHD in adults is an The main limitation of this study is the sample overlap (85.7%) extreme expression of continuous heritable traits [6] and that a between the present GWAS-MAs and the previous one by single question about ever having received an ADHD diagnosis, as Demontis et al. [6], which highlighted loci previously associated in the 23andMe sample, may be informative for molecular with ADHD. Although sample overlap may have inflated the genetics studies. genetic correlation found between these studies, the estimate Similar patterns of genetic correlation of ADHD with different remained strong and significant when excluding nonoverlapping somatic disorders and anthropometric, cognitive, and educational datasets. traits were identified for children and adults. These findings were In summary, the present cross-sectional analyses identify new highly similar to those observed in the recent GWAS-MA [6] and genetic loci associated with ADHD and, more importantly, support further extend the existing hypothesis of a shared genetic the hypothesis that persistent ADHD in adults is a neurodevelop- architecture underlying ADHD and these traits to a lifespan mental disorder that shows a high and significant genetic overlap perspective. with ADHD in children. Future longitudinal studies will be required Neuropsychopharmacology (2020) 45:1617 – 1626 Shared genetic background between children and adults with attention. . . Paula Rovira et al. 1623 Table 1.Genome-wide significant loci in the GWAS meta-analysis of ADHD across the lifespan identified through (A) single-variant analysis and (B) gene-based analysis. Chr BP SNP Effect allele Freq effect allele OR CI 95% P value Gene A 6 159384224 rs183882582 T 0.98 1.43 1.26–1.60 1.57E−08 RSPH3 (+14 kb) 7 121955328 rs3958046 T 0.40 1.09 1.06–1.10 2.28E−08 CADPS2 (+3.2 kb)/FEZF1 (−13.9 kb)/FEZF1-AS1 (+5.2 kb) 4 31151465 rs200721207 T 0.66 1.10 1.06–1.13 3.56E−08 PCDH7 (−3.0 kb) 3 160313354 rs1920644 T 0.52 1.09 1.05–1.12 4.74E−08 BC125159 (+27.9 kb)/KPNA4 (−30 kb)/ARL14 (−81.6 kb) Gene Chr Start Stop N SNPs* N PARAM** Z-STAT P value B FEZF1 7 121921373 121971173 108 18 5.6 9.57E−09 DUSP6 12 89721837 89766296 103 12 5.4 3.51E−08 ST3GAL3 1 44153204 44416837 521 19 5.4 3.58E−08 SEMA6D 15 47456403 48086420 1565 55 5.3 7.24E−08 KDM4A 1 44095797 44191189 169 13 4.9 4.34E−07 C2orf82 2 233713724 233761111 138 17 4.8 7.74E−07 GIGYF2 2 233542015 233745287 511 19 4.8 8.36E−07 AMN 14 103368993 103417179 101 21 4.6 2.56E−06 FBXL17 5 107174734 107738080 1273 35 4.6 2.59E−06 The location (chromosome (Chr) and base position (BP)), effect allele and its frequency, odds ratio (OR) of the effect allele with 95% confidence interval (CI 95%) and association P values, along with genes in the locus are shown for each index variant ID (SNP). For the gene-based results, the number of single- nucleotide polymorphisms in the genes (*) and the number of relevant parameters used in the model by MAGMA software (**) are given. Fig. 4 Genetic correlation of ADHD and several traits. a Dots represent genetic correlations (rg) for all traits considered (with h2 > 0.1 and z-score > 4) and those traits that met Bonferroni correction in both children and adult ADHD groups are presented in grey. r indicates Pearson’s correlation coefficient. b The ten strongest genetic correlations (with 95% confidence intervals) surpassing Bonferroni corrections in the children and persistent ADHD analysis are shown for each trait and ADHD. to disentangle the role of common genetic variants on ADHD awards for taking part in psychiatric meetings from Shire. CF remittance and/or persistence. received travel awards for taking part in psychiatric meetings from Shire and Lundbeck. GEM received travel awards for taking part in psychiatric meetings from Shire. EJSS-B received speaker fees, FUNDING AND DISCLOSURE consultancy, research funding, and conference support from Shire VR has served on the speakers for Eli Lilly, Rubio, and Shire in the Pharma. Consultancy from Neurotech Solutions, Copenhagen last 5 years. She has received travel awards from Eli Lilly and Co. University and Berhanderling, Skolerne & KU Leuven. Book and Shire for participating in psychiatric meetings. The ADHD royalties from OUP and Jessica Kingsley. Financial support—Arhus Program has received unrestricted educational and research University and Ghent University for visiting Professorship. Editor- support from Eli Lilly and Co., Janssen-Cilag, Shire, Rovi, Psious, in-Chief JCPP—supported by a buy-out of time to University of and Laboratorios Rubió in the past 2 years. MC received travel Southampton and personal Honorarium. King’s College London Neuropsychopharmacology (2020) 45:1617 – 1626 Shared genetic background between children and adults with attention. . . Paula Rovira et al. 1624 received payments for work conducted by PA: consultancy for paid to the Department of Psychiatry at MGH. Tobias Shire, Eli Lilly, Novartis, and Lundbeck; educational and/or research Banaschewski served in an advisory or consultancy role for awards from Shire, Eli Lilly, Novartis, Vifor Pharma, GW Pharma, Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, and QbTech; speaker at events sponsored by Shire, Eli Lilly, Shire, and Infectopharm. He received conference support or Janssen-Cilag, and Novartis. JKB has been in the past 3 years speaker’s fee by Lilly, Medice, and Shire. He received royalties from a consultant to/member of advisory board of and/or speaker Hogrefe, Kohlhammer, CIP Medien, and Oxford University Press. for Shire, Roche, Medice, and Servier. He is not an employee of James McGough is an expert testimony for Eli Lilly; DSMB for any of these companies, and not a stock shareholder of any Sunovion. Benjamin Neale is a member of the scientific advisory of these companies. He has no other financial or material board at Deep Genomics and consultant for Camp4 Therapeutics, support, including expert testimony, patents, royalties. In the Takeda Pharmaceutical, and Biogen. Ole Andreas Andreassen past year, SVF received income, potential income, travel expenses received speaker’s honorarium from Lundbeck, and is a consultant continuing education support and/or research support from for HealthLytix. Tris, Otsuka, Arbor, Ironshore, Shire, Akili Interactive Labs, The research leading to these results has received funding from Enzymotec, Sunovion, Supernus, and Genomind. With his institu- the European Community’s Seventh Framework Programme (FP7/ tion, he has US patent US20130217707 A1 for the use of sodium– 2007–2013) under grant agreement n° 602805 (Aggressotype) as hydrogen exchange inhibitors in the treatment of ADHD. He also well as from the European Union H2020 Programme (H2020/ receives royalties from books published by Guilford Press: Straight 2014–2020) under grant agreements n° 643051 (MiND), n° 667302 Talk about Your Child’s Mental Health, Oxford University Press: (CoCA), n° 728018 (Eat2beNICE), and n° 278948 (TACTICS). The Schizophrenia: The Facts and Elsevier: ADHD: Non-Pharmacologic work was also supported by the ECNP Network “ADHD across the Interventions. He is principal investigator of www.adhdinadults. Lifespan” (https://www.ecnp.eu/research-innovation/ECNP- com. JK has given talks at educational events sponsored by networks/List-ECNP-Networks/ADHD.aspx). Over the course of this Medice; all funds are received by King’s College London and used investigation, PR is a recipient of a pre-doctoral fellowship from for studies of ADHD. HL has served as a speaker for Evolan Pharma the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), and Shire and has received research grants from Shire; all outside Generalitat de Catalunya, Spain (2016FI_B 00899). The iPSYCH the submitted work. K-PL served as a speaker for Eli Lilly and project is funded by the Lundbeck Foundation (grant nos. R102- received research support from Medice, and travel support from A9118 and R155-2014-1724) and the universities, and university Shire, all outside the submitted work. LAR reported receiving hospitals of Aarhus and Copenhagen. ADB and NRM’s work is also honoraria, serving on the speakers’ bureau/advisory board, and/or supported by the EU’s Horizon 2020 programme (grant no. acting as a consultant for Eli Lilly, Janssen-Cilag, Novartis, and 667302, CoCA). Data handling and analysis was supported by Shire in the last 3 years; receiving authorship royalties from Oxford NIMH (1U01MH109514-01 to Michael O’Donovan and ADB). CSM is Press and ArtMed; and receiving travel awards from Shire for his a recipient of a Sara Borrell contract from the Instituto de Salud participation in the 2015 WFADHD meetings and from Novartis to Carlos III, Ministerio de Economía, Industria y Competitividad, take part of the 2016 AACAP meeting. The ADHD and juvenile Spain (CD15/00199). K-PL and his team are supported by the bipolar disorder outpatient programs chaired by him received Deutsche Forschungsgemeinschaft (DFG: CRU 125, CRC TRR 58 A1/ unrestricted educational and research support from the following A5, no. 44541416), ERA-Net NEURON/RESPOND, no. 01EW1602B, pharmaceutical companies in the last 3 years: Janssen-Cilag, ERA-Net NEURON/DECODE, no. 01EW1902, and 5-100 Russian Novartis, and Shire. MC has received travel grants and research Academic Excellence Project. JH thanks Stiftelsen K.G. Jebsen, support from Eli Lilly and Co., Janssen-Cilag, Shire, and Lundbeck University of Bergen, the Western Norwegian Health Authorities and served as consultant for Eli Lilly and Co., Janssen-Cilag, Shire, (Helse Vest). HL thanks the Swedish research council. BC received and Lundbeck. BF has received educational speaking fees from financial support from the Spanish “Ministerio de Economía y Medice and Shire. JAR-Q was on the speakers’ bureau and/or Competitividad” (SAF2015-68341-R) and AGAUR (2017SGR738). acted as consultant for Eli Lilly, Janssen-Cilag, Novartis, Shire, MSA is a recipient of a contract from the Biomedical Network Lundbeck, Almirall, Braingaze, Sincrolab, Medice, and Rubió in the Research Center on Mental Health (CIBERSAM), Madrid, Spain. MR last 5 years. He also received travel awards (air tickets + hotel) for is a recipient of a Miguel de Servet contract from the Instituto de taking part in psychiatric meetings from Janssen-Cilag, Rubió, Salud Carlos III, Spain (CP09/00119 and CPII15/00023). The Shire, Medice, and Eli- Lilly. The Department of Psychiatry chaired research leading to these results has received funding from the by him received unrestricted educational and research support Instituto de Salud Carlos III (PI16/01505, PI17/00289, PI18/01788, from the following companies in the last 5 years: Eli Lilly, PI19/00721 and P19/01224), and cofinanced by the European Lundbeck, Janssen- Cilag, Actelion, Shire, Ferrer, Oryzon, Roche, Regional Development Fund (ERDF), from the Pla estratègic de Psious, and Rubió. The other authors have nothing to disclose. All recerca i innovació en salut (PERIS); Generalitat de Catalunya members of the 23andMe Research Team are current or former (METAL-Cat; SLT006/17/287), from “la Marató de TV3” (092330/31) employees of 23andMe, Inc. and hold stock or stock options in and from the Agència de Gestió d’Ajuts Universitaris i de Recerca- 23andMe. Authors of the ADHD Working group of the PGC that AGAUR, Generalitat de Catalunya (2017SGR1461). ES’s work is participated in this study: Catharina A. Hartman, Ziarih Hawi, supported by a personal Hypatia grant from the Radboud Jennifer Crosbie, Sandra Loo, Josephine Elia, Russell Schachar, University Medical Center. MH received a Veni grant from of the Christie Burton, Ted Reichborn-Kjennerud, Aribert Rothenberger, Netherlands Organization for Scientific Research (NWO, grant Søren Dalsgaard, Irwin Waldman, Mark Bellgrove, Hakon Hako- number 91619115). The NeuroIMAGE study was supported by NIH narson, Johannes Hebebrand, Anke Hinney, and Robert Oades Grant R01MH62873 (to SVF), NWO Large Investment Grant have nothing to disclose. Joseph Biederman 2019–2020: he 1750102007010 (to JKB), ZonMW grant 60-60600-97-193, NWO received research support from Genentech, Headspace Inc., grants 056-13-015 and 433-09-242, and matching grants from Lundbeck AS, Neurocentria Inc, Pfizer Pharmaceuticals, Roche Radboud University Nijmegen Medical Center, University Medical TCRC Inc., Shire Pharmaceuticals Inc., Sunovion Pharmaceuticals Center Groningen and Accare, and Vrije Universiteit Amsterdam. Inc., and Tris. He was a consultant for Akili, Avekshan, Jazz Pharma, Organization for Scientific Research (NWO; grant 016-130-669). BF and Shire/Takeda. Through MGH CTNI, he participated in a and MK’s work is supported by the Dutch National Science scientific advisory board for Supernus. Dr Biederman’s program Agenda (NWA) for the NeurolabNL project (grant 400-17-602). This has received departmental royalties from a copyrighted rating paper represents independent research part funded by the scale used for ADHD diagnoses, paid by Bracket Global, Ingenix, National Institute for Health Research (NIHR) Biomedical Research Prophase, Shire, Sunovion, and Theravance; these royalties were Centre at South London and Maudsley NHS Foundation Trust and Neuropsychopharmacology (2020) 45:1617 – 1626 Shared genetic background between children and adults with attention. . . Paula Rovira et al. 1625 King’s College London. BF received additional funding from a 2. Polanczyk GV, Salum GA, Sugaya LS, Caye A, Rohde LA. Annual research review: a personal Vici grant of the Dutch. The authors declare no meta-analysis of the worldwide prevalence of mental disorders in children and competing interests adolescents. J Child Psychol Psychiatry. 2015;56:345–65. 3. 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