Attention deficit/hyperactivity disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no variants have been robustly associated with ADHD. We report a genome-wide association meta-analysis of 20,183 individuals diagnosed with ADHD and 35,191 controls that identifies variants surpassing genome-wide significance in 12 independent loci, finding important new information about the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions and loss-of-function intolerant genes and around brain-expressed regulatory marks. Analyses of three replication studies: a cohort of individuals diagnosed with ADHD, a self-reported ADHD sample and a meta-analysis of quantitative measures of ADHD symptoms in the population, support these findings while highlighting study-specific differences on genetic overlap with educational attainment. Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits.
This is a preview of subscription content
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The PGC’s policy is to make genome-wide summary results public. Summary statistics with the results from the ADHD GWAs meta-analysis of iPSYCH and the PGC samples are available on the PGC and iPSYCH websites (https://www.med.unc.edu/pgc/results-and-downloads and http://ipsych.au.dk/downloads/). GWA summary statistics with results from the GWAS of ADHD symptom scores analyzed in the EAGLE sample can be accessed at the PGC website (link above). Summary statistics for the 23andMe dataset can be obtained by qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For access to genotypes from the PGC cohorts and the iPSYCH sample, interested researchers should contact the lead PIs (iPSYCH, A.D.B.; P.G.C., B.M.N. and S.V.F.).
Faraone, S. V. et al. Attention-deficit/hyperactivity disorder. Nat. Rev. Dis. Primers, 15020, https://doi.org/10.1038/nrdp.2015.20 (2015).
Dalsgaard, S., Leckman, J. F., Mortensen, P. B., Nielsen, H. S. & Simonsen, M. Effect of drugs on the risk of injuries in children with attention deficit hyperactivity disorder: a prospective cohort study. Lancet Psychiatry 2, 702–709 (2015).
Chang, Z., Lichtenstein, P., D’Onofrio, B. M., Sjolander, A. & Larsson, H. Serious transport accidents in adults with attention-deficit/hyperactivity disorder and the effect of medication: a population-based study. JAMA Psychiatry 71, 319–325 (2014).
Biederman, J. & Faraone, S. V. Attention-deficit hyperactivity disorder. Lancet 366, 237–248 (2005).
Dalsgaard, S., Nielsen, H. S. & Simonsen, M. Consequences of ADHD medication use for children’s outcomes. J. Health Econ. 37, 137–151 (2014).
Dalsgaard, S., Mortensen, P. B., Frydenberg, M. & Thomsen, P. H. ADHD, stimulant treatment in childhood and subsequent substance abuse in adulthood - a naturalistic long-term follow-up study. Addict. Behav. 39, 325–328 (2014).
Lichtenstein, P. & Larsson, H. Medication for attention deficit-hyperactivity disorder and criminality. N. Engl. J. Med. 368, 776 (2013).
Barkley, R. A., Murphy, K. R. & Fischer, M. ADHD in Adults: What the Science Says. (Guilford Press, New York, 2007).
Furczyk, K. & Thome, J. Adult ADHD and suicide. Atten. Defic. Hyperact. Disord. 6, 153–158 (2014).
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).
Franke, B. et al. The genetics of attention deficit/hyperactivity disorder in adults, a review. Mol. Psychiatry 17, 960–987 (2012).
Faraone, S. V. et al. Molecular genetics of attention-deficit/hyperactivity disorder. Biol. Psychiatry 57, 1313–1323 (2005).
Burt, S. A. Rethinking environmental contributions to child and adolescent psychopathology: a meta-analysis of shared environmental influences. Psychol. Bull. 135, 608–637 (2009).
Larsson, H., Anckarsater, H., Rastam, M., Chang, Z. & Lichtenstein, P. Childhood attention-deficit hyperactivity disorder as an extreme of a continuous trait: a quantitative genetic study of 8,500 twin pairs. J. Child Psychol. Psychiatry 53, 73–80 (2012).
Christiansen, H. et al. Co-transmission of conduct problems with attention-deficit/hyperactivity disorder: familial evidence for a distinct disorder. J. Neural Transm. (Vienna) 115, 163–175 (2008).
Kuntsi, J. et al. The separation of ADHD inattention and hyperactivity-impulsivity symptoms: pathways from genetic effects to cognitive impairments and symptoms. J. Abnorm. Child Psychol. 42, 127–136 (2014).
Rommelse, N. N., Franke, B., Geurts, H. M., Hartman, C. A. & Buitelaar, J. K. Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder. Eur. Child Adolesc. Psychiatry 19, 281–295 (2010).
Ghirardi, L. et al. The familial co-aggregation of ASD and ADHD: a register-based cohort study. Mol. Psychiatry. 23, 257–262 (2018).
Larsson, H. et al. Risk of bipolar disorder and schizophrenia in relatives of people with attention-deficit hyperactivity disorder. British J. Psychiatry 203, 103–106 (2013).
Faraone, S. V., Biederman, J. & Wozniak, J. Examining the comorbidity between attention deficit hyperactivity disorder and bipolar I disorder: a meta-analysis of family genetic studies. Am. J. Psychiatry 169, 1256–1266 (2012).
Faraone, S. V. & Biederman, J. Do attention deficit hyperactivity disorder and major depression share familial risk factors? J. Nerv. Ment. Dis. 185, 533–541 (1997).
Neale, B. M. et al. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 49, 884–897 (2010).
The Brainstorm Consortium. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
Cross-Disorder Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).
Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).
Hamshere, M. L. et al. High loading of polygenic risk for ADHD in children with comorbid aggression. Am. J. Psychiatry 170, 909–916 (2013).
Hamshere, M. L. et al. Shared polygenic contribution between childhood attention-deficit hyperactivity disorder and adult schizophrenia. British J. Psychiatry 203, 107–111 (2013).
Groen-Blokhuis, M. M. et al. Attention-deficit/hyperactivity disorder polygenic risk scores predict attention problems in a population-based sample of children. J. Am. Acad. Child Adolesc. Psychiatry 53, 1123–1129.e1126 (2014).
Martin, J., Hamshere, M. L., Stergiakouli, E., O’Donovan, M. C. & Thapar, A. Genetic risk for attention-deficit/hyperactivity disorder contributes to neurodevelopmental traits in the general population. Biol. Psychiatry 76, 664–671 (2014).
Middeldorp, C. M. et al. A genome-wide association meta-analysis of attention-deficit/hyperactivity disorder symptoms in population-based pediatric cohorts. J. Am. Acad. Child Adolesc. Psychiatry 55, 896–905.e896 (2016).
Yang, L. et al. Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants. Am. J. Med. Genet. B Neuropsychiatr Genet. 162B, 419–430 (2013).
Zayats, T. et al. Genome-wide analysis of attention deficit hyperactivity disorder in Norway. PLoS One 10, e0122501 (2015).
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Purcell, S. M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).
Polanczyk, G., de Lima, M. S., Horta, B. L., Biederman, J. & Rohde, L. A. The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am. J. Psychiatry 164, 942–948 (2007).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Wray, N. R. & Sullivan, P. F. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).
Duncan, L. et al. Significant locus and metabolic genetic correlations revealed in genome-wide association study of anorexia nervosa. Am. J. Psychiatry 174, 850–858 (2017).
Benyamin, B. et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol. Psychiatry 19, 253–258 (2014).
Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).
Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).
Rietveld, C. A. et al. Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proc. Natl Acad. Sci. USA 111, 13790–13794 (2014).
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).
Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).
Bradfield, J. P. et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat. Genet. 44, 526–531 (2012).
Berndt, S. I. et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat. Genet. 45, 501–512 (2013).
Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).
Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).
Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).
Patel, Y. M. et al. Novel Association of genetic markers affecting CYP2A6 activity and lung cancer risk. Cancer Res. 76, 5768–5776 (2016).
Wang, Y. et al. Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer. Nat. Genet. 46, 736–741 (2014).
Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1462–1472 (2016).
Hammerschlag, A. R. et al. Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Nat. Genet. 49, 1584–1592 (2017).
Pilling, L. C. et al. Human longevity is influenced by many genetic variants: evidence from 75,000 UK Biobank participants. Aging 8, 547–560 (2016).
Hawi, Z. et al. The molecular genetic architecture of attention deficit hyperactivity disorder. Mol. Psychiatry 20, 289–297 (2015).
Sia, G. M., Clem, R. L. & Huganir, R. L. The human language-associated gene SRPX2 regulates synapse formation and vocalization in mice. Science 342, 987–991 (2013).
Tsui, D., Vessey, J. P., Tomita, H., Kaplan, D. R. & Miller, F. D. FoxP2 regulates neurogenesis during embryonic cortical development. J. Neurosci. 33, 244–258 (2013).
Schreiweis, C. et al. Humanized Foxp2 accelerates learning by enhancing transitions from declarative to procedural performance. Proc. Natl. Acad. Sci. USA 111, 14253–14258 (2014).
Jensen, C. M. & Steinhausen, H. C. Comorbid mental disorders in children and adolescents with attention-deficit/hyperactivity disorder in a large nationwide study. Atten. Defic. Hyperact. Disord 7, 27–38 (2015).
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).
Peyre, H. et al. Relationship between early language skills and the development of inattention/hyperactivity symptoms during the preschool period: Results of the EDEN mother-child cohort. BMC Psychiatry 16, 380 (2016).
Breiderhoff, T. et al. Sortilin-related receptor SORCS3 is a postsynaptic modulator of synaptic depression and fear extinction. PLoS One 8, e75006 (2013).
Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).
Caunt, C. J. & Keyse, S. M. Dual-specificity MAP kinase phosphatases (MKPs): shaping the outcome of MAP kinase signalling. FEBS. J. 280, 489–504 (2013).
Mortensen, O. V. MKP3 eliminates depolarization-dependent neurotransmitter release through downregulation of L-type calcium channel Cav1.2 expression. Cell Calcium 53, 224–230 (2013).
Mortensen, O. V., Larsen, M. B., Prasad, B. M. & Amara, S. G. Genetic complementation screen identifies a mitogen-activated protein kinase phosphatase, MKP3, as a regulator of dopamine transporter trafficking. Mol. Biol. Cell. 19, 2818–2829 (2008).
Volkow, N. D., Fowler, J. S., Wang, G., Ding, Y. & Gatley, S. J. Mechanism of action of methylphenidate: insights from PET imaging studies. J. Atten. Disord 6(Suppl 1), S31–S43 (2002).
Volkow, N. D. et al. Therapeutic doses of oral methylphenidate significantly increase extracellular dopamine in the human brain. J. Neurosci. 21, RC121 (2001).
GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Qu, X. et al. Identification, characterization, and functional study of the two novel human members of the semaphorin gene family. J. Biol. Chem. 277, 35574–35585 (2002).
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).
Hu, H. et al. ST3GAL3 mutations impair the development of higher cognitive functions. Am. J. Hum. Genet. 89, 407–414 (2011).
Oliver, P. L. et al. Disruption of Visc-2, a brain-expressed conserved long noncoding rna, does not elicit an overt anatomical or behavioral phenotype. Cereb. Cortex 25, 3572–3585 (2015).
Sobreira, N., Walsh, M. F., Batista, D. & Wang, T. Interstitial deletion 5q14.3-q21 associated with iris coloboma, hearing loss, dental anomaly, moderate intellectual disability, and attention deficit and hyperactivity disorder. Am. J. Med. Genet. A. 149A, 2581–2583 (2009).
Le Meur, N. et al. MEF2C haploinsufficiency caused by either microdeletion of the 5q14.3 region or mutation is responsible for severe mental retardation with stereotypic movements, epilepsy and/or cerebral malformations. J. Med. Genet. 47, 22–29 (2010).
Novara, F. et al. Refining the phenotype associated with MEF2C haploinsufficiency. Clin. Genet. 78, 471–477 (2010).
Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
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).
Vernes, S. C. et al. Foxp2 regulates gene networks implicated in neurite outgrowth in the developing brain. PLoS. Genet. 7, e1002145 (2011).
Spiteri, E. et al. Identification of the transcriptional targets of FOXP2, a gene linked to speech and language, in developing human brain. Am. J. Hum. Genet. 81, 1144–1157 (2007).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Ebejer, J. L. et al. Genome-wide association study of inattention and hyperactivity-impulsivity measured as quantitative traits. Twin. Res. Hum. Genet. 16, 560–574 (2013).
Grove, J. et al. Common risk variants identified in autism spectrum disorder. bioRxiv. https://doi.org/10.1101/224774 (2017).
Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).
Flory, K. et al. Childhood ADHD predicts risky sexual behavior in young adulthood. J Clin Child Adolesc. Psychol. 35, 571–577 (2006).
Marsh, L. E., Norvilitis, J. M., Ingersoll, T. S. & Li, B. ADHD symptomatology, fear of intimacy, and sexual anxiety and behavior among college students in China and the United States. J. Atten. Disord. 19, 211–221 (2015).
Hosain, G. M., Berenson, A. B., Tennen, H., Bauer, L. O. & Wu, Z. H. Attention deficit hyperactivity symptoms and risky sexual behavior in young adult women. J. Womens Health (Larchmt) 21, 463–468 (2012).
Chudal, R. et al. Parental age and the risk of attention-deficit/hyperactivity disorder: a nationwide, population-based cohort study. J. Am. Acad. Child Adolesc. Psychiatry 54, 487–494.e481 (2015).
Chang, Z. et al. Maternal age at childbirth and risk for ADHD in offspring: a population-based cohort study. Int. J. Epidemiol. 43, 1815–1824 (2014).
Ostergaard, S. D., Dalsgaard, S., Faraone, S. V., Munk-Olsen, T. & Laursen, T. M. Teenage parenthood and birth rates for individuals with and without attention-deficit/hyperactivity disorder: a nationwide cohort study. J. Am. Acad. Child Adolesc. Psychiatry 56, 578–584.e573 (2017).
Barbaresi, W. J., Katusic, S. K., Colligan, R. C., Weaver, A. L. & Jacobsen, S. J. Long-term school outcomes for children with attention-deficit/hyperactivity disorder: a population-based perspective. J. Dev. Behav. Pediatr. 28, 265–273 (2007).
Faraone, S. V. et al. Intellectual performance and school failure in children with attention deficit hyperactivity disorder and in their siblings. J. Abnorm. Psychol. 102, 616–623 (1993).
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).
Kong, A. et al. Selection against variants in the genome associated with educational attainment. Proc. Natl. Acad. Sci. USA 114, E727–E732 (2017).
Lee, S. S., Humphreys, K. L., Flory, K., Liu, R. & Glass, K. Prospective association of childhood attention-deficit/hyperactivity disorder (ADHD) and substance use and abuse/dependence: a meta-analytic review. Clin. Psychol. Rev. 31, 328–341 (2011).
Halfon, N., Larson, K. & Slusser, W. Associations between obesity and comorbid mental health, developmental, and physical health conditions in a nationally representative sample of US children aged 10 to 17. Acad. Pediatr. 13, 6–13 (2013).
Chen, A. Y., Kim, S. E., Houtrow, A. J. & Newacheck, P. W. Prevalence of obesity among children with chronic conditions. Obesity (Silver Spring) 18, 210–213 (2010).
Cortese, S. et al. Association between ADHD and obesity: A systematic review and meta-analysis. Am. J. Psychiatry 173, 34–43 (2016).
Owens, J. A. A clinical overview of sleep and attention-deficit/hyperactivity disorder in children and adolescents. J. Can. Acad. Child Adolesc. Psychiatry 18, 92–102 (2009).
Lubke, G. H., Hudziak, J. J., Derks, E. M., van Bijsterveldt, T. C. & Boomsma, D. I. Maternal ratings of attention problems in ADHD: evidence for the existence of a continuum. J. Am. Acad. Child Adolesc. Psychiatry 48, 1085–1093 (2009).
Cortese, S., Comencini, E., Vincenzi, B., Speranza, M. & Angriman, M. Attention-deficit/hyperactivity disorder and impairment in executive functions: a barrier to weight loss in individuals with obesity? BMC Psychiatry 13, 286 (2013).
Ortal, S. et al. The role of different aspects of impulsivity as independent risk factors for substance use disorders in patients with ADHD: a review. Curr. Drug Abuse Rev. 8, 119–133 (2015).
Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015).
Delaneau, O., Marchini, J. & Zagury, J. F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).
Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 1, 457–470 (2011).
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).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
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).
Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protocols 9, 1192–1212 (2014).
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).
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Ernst, J. & Kellis, M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol. 33, 364–376 (2015).
Wellcome Trust Case Control Consortium. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).
McLaren, W. et al. The Ensembl variant effect predictor. Genome. Biol. 17, 122 (2016).
Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).
The GTEx Consortium. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
Yates, F. Contingency tables involving small numbers and the χ2 test. Supp. J. Royal Stat. Society 1, 217–235 (1934).
R Core Team. R: A language and environment for statistical computing. http://www.r-project.org/ (2014).
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).
The iPSYCH team acknowledges funding from the Lundbeck Foundation (grant no. R102-A9118 and R155-2014-1724), the Stanley Medical Research Institute, the European Research Council (project 294838), the European Community (EC) Horizon 2020 Programme (grant 667302 (CoCA)), from EC Seventh Framework Programme (grant 602805 (Aggressotype)), the Novo Nordisk Foundation for supporting the Danish National Biobank resource and grants from Aarhus and Copenhagen Universities and University Hospitals, including support to the iSEQ Center, the GenomeDK HPC facility, and the CIRRAU Center.
The Broad Institute and Massachusetts General Hospital investigators would like to acknowledge support from the Stanley Medical Research Institute and NIH grants: 5U01MH094432-04(PI: Daly), 1R01MH094469 (PI: Neale), 1R01MH107649-01 (PI: Neale), 1R01MH109539-01 (PI: Daly).
We thank T. Lehner, A. Addington and G. Senthil for their support in the Psychiatric Genomics Consortium.
S.V.F. is supported by the K.G. Jebsen Centre for Research on Neuropsychiatric Disorders, University of Bergen, Norway, the EC’s Seventh Framework Programme (grant 602805), the EC’s Horizon 2020 (grant 667302) and NIMH grants 5R01MH101519 and U01 MH109536-01.
J.M. was supported by the Wellcome Trust (grant 106047).
B.F.’s research is supported by funding from a personal Vici grant of the Netherlands Organisation for Scientific Research (NWO; grant 016-130-669, to B.F.), the EC’s Seventh Framework Programme (grant 602805 (Aggressotype), 602450 (IMAGEMEND), and 278948 (TACTICS)), and from the EC’s Horizon 2020 Programme (grant643051 (MiND) and 667302 (CoCA)). Additionally, this work was supported by the European College of Neuropsychopharmacology (ECNP Network ‘ADHD across the Lifespan’).
J.H. is supported by grants from Stiftelsen K.G. Jebsen, University of Bergen and The Research Council of Norway.
B.C. received financial support for this research from the Spanish ‘Ministerio de Economía y Competitividad’ (SAF2015-68341-R) and ‘Generalitat de Catalunya/AGAUR’ (2017-SGR-738). B.B., A.R. and collaborators received funding from the EC’s Seventh Framework Programme (grant 602805, Aggressotype), the EC’s H2020 Programme (grants 667302, CoCA, and 402003, MiND), the ECNP network ‘ADHD across the lifespan’ and DFG CRC 1193, subproject Z03.
O.A.A. is supported by the Research Council of Norway (grants: 223273, 248778, 213694, 249711), and KG Jebsen Stiftelsen.
A.T. received ADHD funding from the Wellcome Trust, Medical Research Council (MRC UK), Action Medical Research.
We thank the customers of 23andMe who answered surveys, as well as the employees of 23andMe who together made this research possible. The QIMR studies were supported by funding from the Australian National Health and Medical Research Council (grant numbers: 241944, 339462, 389927, 389875, 389891, 389892, 389938, 443036, 442915, 442981, 496739, 552485, and 552498, and, most recently, 1049894) and the Australian Research Council (grant numbers: A7960034, A79906588, A79801419, DP0212016, and DP0343921). SEM is supported by an NHMRC fellowship (1103623).
Additional acknowledgements can be found in the Supplementary Note.
In the past year, S.V.F. received income, potential income, travel expenses, continuing education support and/or research support from Lundbeck, Rhodes, Arbor, KenPharm, Ironshore, Shire, Akili Interactive Labs, CogCubed, Alcobra, VAYA, Sunovion, Genomind and Neurolifesciences. With his institution, he has US patent US20130217707 A1 for the use of sodium–hydrogen exchange inhibitors in the treatment of ADHD. In previous years, he received support from: Shire, Neurovance, Alcobra, Otsuka, McNeil, Janssen, Novartis, Pfizer and Eli Lilly. S.V.F. receives royalties from books published by Guilford Press: Straight Talk about Your Child’s Mental Health; Oxford University Press: Schizophrenia: The Facts; and Elsevier: ADHD: Non-Pharmacologic Interventions. He is principal investigator of www.adhdinadults.com.
B.M.N. is a member of Deep Genomics Scientific Advisory Board and has received travel expenses from Illumina. He also serves as a consultant for Avanir and Trigeminal solutions.
O.O.G., G.B.W., H.S. and K.S. are employees of deCODE genetics/Amgen.
N.E., J.Y.T., and the 23andMe Research Team are employees of 23andMe, Inc. and hold stock or stock options in 23andMe.
L.A.R. has received honoraria, has been on the speakers’ bureau/advisory board and/or has acted as a consultant for Eli-Lilly, Janssen-Cilag, Novartis, Medice and Shire in the past three years. He receives authorship royalties from Oxford Press and ArtMed. He also received a travel award from Shire for taking part in the 2015 WFADHD meeting. The ADHD and Juvenile Bipolar Disorder Outpatient Programs unrestricted educational and research support from the following pharmaceutical companies in the past three years: Eli-Lilly, Janssen-Cilag, Novartis and Shire. Over the past three years E.J.S.-B. has received speaker fees, consultancy, research funding and conference support from Shire Pharma and speaker fees from Janssen-Cilag. He has received consultancy fees from Neurotech solutions, Aarhus University, Copenhagen University and Berhanderling, Skolerne, Copenhagen, KU Leuven and book royalties from OUP and Jessica Kingsley. He is the editor-in-chief of the Journal of Child Psychology and Psychiatry, for which his university receives financial support. B.F. has received educational speaking fees from Merz and Shire.
R.S. has equity in and is on the advisory board of Ironshore Pharmaceuticals. A.R. has received a research grant from Medice and speaker’s honorarium from Medice and Servier. J.H. has received speaker fees from Shire, Lilly and Novartis. H.R.K. has been an advisory board member, consultant, or CME speaker for Alkermes, Indivior, and Lundbeck. He is also a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the last three years by AbbVie, Alkermes, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka, Pfizer, Arbor, and Amygdala Neurosciences. H.R.K. and J.G. are named as inventors on PCT patent application #15/878,640 entitled: “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. P.A. received honoraria paid to King’s College London by Shire, Flynn Pharma, Lilly, Janssen, Novartis and Lunbeck for research, speaker fees, education events, advisory board membership or consultancy. O.A.A. has received speaker fees from Lundbeck and Sunovion. J.K. has received speaker’s honorarium from Medice; all funds are received by King’s College London and used for studies of ADHD. T.W. has acted as lecturer and scientific advisor to H. Lundbeck A/S.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figures 1–26, Supplementary Tables 1–14 and Supplementary Note
Extended results from genetic correlation analyses of ADHD and 219 phenotypes
Bayesian credible sets of variants for each of the 12 genome-wide significant loci
Summary of the observed annotations for the credible set at each genome-wide significant locus
Variant-level annotations for the credible set at each genome-wide significant locus
Results of gene set analyses using sets from Gene Ontology
Genome-wide significant index variants in meta-analyses of iPSYCH, PGC, deCODE, 23andMe and EAGLE/QIMR
About this article
Cite this article
Demontis, D., Walters, R.K., Martin, J. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet 51, 63–75 (2019). https://doi.org/10.1038/s41588-018-0269-7
Treatment provision for adults with ADHD during the COVID-19 pandemic: an exploratory study on patient and therapist experience with on-site sessions using face masks vs. telepsychiatric sessions
BMC Psychiatry (2021)
scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks
Genome Medicine (2021)
Elevated risk of attention deficit hyperactivity disorder (ADHD) in Japanese children with higher genetic susceptibility to ADHD with a birth weight under 2000 g
BMC Medicine (2021)
SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits
Genome Biology (2021)
BMC Psychiatry (2021)