Skip to main content

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.

Identification of shared and differentiating genetic architecture for autism spectrum disorder, attention-deficit hyperactivity disorder and case subgroups

Abstract

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental conditions, with considerable overlap in their genetic etiology. We dissected their shared and distinct genetic etiology by cross-disorder analyses of large datasets. We identified seven loci shared by the disorders and five loci differentiating them. All five differentiating loci showed opposite allelic directions in the two disorders and significant associations with other traits, including educational attainment, neuroticism and regional brain volume. Integration with brain transcriptome data enabled us to identify and prioritize several significantly associated genes. The shared genomic fraction contributing to both disorders was strongly correlated with other psychiatric phenotypes, whereas the differentiating portion was correlated most strongly with cognitive traits. Additional analyses revealed that individuals diagnosed with both ASD and ADHD were double-loaded with genetic predispositions for both disorders and showed distinctive patterns of genetic association with other traits compared with the ASD-only and ADHD-only subgroups. These results provide insights into the biological foundation of the development of one or both conditions and of the factors driving psychopathology discriminatively toward either ADHD or ASD.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Manhattan plots for GWAS and TWAS results.
Fig. 2: Comparison of PRS profiles across ADHD and ASD subtypes for 15 traits and/or phenotypes that have shown significant genetic correlations with ADHD and ASD in the past.

Data availability

Summary statistics from this publication are available at http://ipsych.au.dk/downloads/. Summary statistics for the original ADHD and ASD GWAS analyses are available at the same site. For access to genotypes from the PGC samples and the iPSYCH sample, researchers should contact the lead PIs E.R. and/or A.B. (https://pgc.unc.edu/for-researchers/working-groups/autism-working-group/) for PGC-ASD; A.B. for iPSYCH-ASD; B.N. and/or B.F. (https://pgc.unc.edu/for-researchers/working-groups/adhd-working-group/) for PGC-ADHD; and A.B. for iPSYCH-ADHDy. Data used for generation of the brain transcriptome model are available from PsychENCODE (overview of available datasets at http://resource.psychencode.org/); genotypes are controlled data and access instructions are provided at https://www.synapse.org/#!Synapse:syn4921369/wiki/477467. Note that some datasets were indirectly accessed at the respective analytical websites (for example, GSE76381 through the FUMA website). Please refer to these websites (for example, for FUMA, https://fuma.ctglab.nl/links and https://fuma.ctglab.nl/tutorial#datasets) for availability of datasets used in the respective follow-up analyses and/or lookups (for example, GSE76381).

Code availability

Please refer to individual sections of the methods above for published code (for example, for EpiXcan or Ricopili). As the in-house scripts used for data processing and analysis of the iPSYCH data on the GenomeDK HPC infrastructure are highly dependent on that context, they can only be obtained from the authors upon request. This way we can ensure the proper context is explained in dialog with the interested parties.

References

  1. 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).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  3. Pettersson, E. et al. Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls. Psychol. Med. 49, 1166–1173 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Sandin, S. et al. The heritability of autism spectrum disorder. JAMA 318, 1182–1184 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  7. Matoba, N. et al. Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism. Transl. Psychiatry 10, 265 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Cross-Disorder Group of the Psychiatric Genomics Consortium. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482.e11 (2019).

    Article  PubMed Central  Google Scholar 

  9. Martin, J. et al. Biological overlap of attention-deficit/hyperactivity disorder and autism spectrum disorder: evidence from copy number variants. J. Am. Acad. Child Adolesc. Psychiatry 53, 761–770.e26 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Satterstrom, F. K. et al. Autism spectrum disorder and attention deficit hyperactivity disorder have a similar burden of rare protein-truncating variants. Nat. Neurosci. 22, 1961–1965 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rommelse, N. N., Geurts, H. M., Franke, B., Buitelaar, J. K. & Hartman, C. A. A review on cognitive and brain endophenotypes that may be common in autism spectrum disorder and attention-deficit/hyperactivity disorder and facilitate the search for pleiotropic genes. Neurosci. Biobehav. Rev. 35, 1363–1396 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. Zablotsky, B., Bramlett, M. D. & Blumberg, S. J. The co-occurrence of autism spectrum disorder in children with ADHD. J. Atten. Disord. 24, 94–103 (2020).

    Article  PubMed  Google Scholar 

  13. Lai, M. C. et al. Prevalence of co-occurring mental health diagnoses in the autism population: a systematic review and meta-analysis. Lancet Psychiatry 6, 819–829 (2019).

    Article  PubMed  Google Scholar 

  14. Ottosen, C. et al. Sex differences in comorbidity patterns of attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 58, 412–422.e3 (2019).

    Article  PubMed  Google Scholar 

  15. Ghirardi, L. et al. The familial co-aggregation of ASD and ADHD: a register-based cohort study. Mol. Psychiatry 23, 257–262 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. 1000 Genomes Project Consortiumet al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yang, Z. et al. Investigating shared genetic basis across Tourette syndrome and comorbid neurodevelopmental disorders along the impulsivity-compulsivity spectrum. Biol. Psychiatry 90, 317–327 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sabourdy, F. et al. A MANBA mutation resulting in residual beta-mannosidase activity associated with severe leukoencephalopathy: a possible pseudodeficiency variant. BMC Med. Genet. 10, 84 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhang, W. et al. Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits. Nat. Commun. 10, 3834 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Peyrot, W. J. & Price, A. L. Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS. Nat. Genet. 53, 445–454 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Marzluff, W. F., Gongidi, P., Woods, K. R., Jin, J. & Maltais, L. J. The human and mouse replication-dependent histone genes. Genomics 80, 487–498 (2002).

    Article  CAS  PubMed  Google Scholar 

  27. Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet. 51, 1637–1644 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Baselmans, B. M. L. et al. Multivariate genome-wide analyses of the well-being spectrum. Nat. Genet. 51, 445–451 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Graciarena, M., Seiffe, A., Nait-Oumesmar, B. & Depino, A. M. Hypomyelination and oligodendroglial alterations in a mouse model of autism spectrum disorder. Front. Cell. Neurosci. 12, 517 (2018).

    Article  CAS  PubMed  Google Scholar 

  33. Wu, Z. M. et al. White matter microstructural alterations in children with ADHD: categorical and dimensional perspectives. Neuropsychopharmacology 42, 572–580 (2017).

    Article  PubMed  Google Scholar 

  34. Aoki, Y. et al. Association of white matter structure with autism spectrum disorder and attention-deficit/hyperactivity disorder. JAMA Psychiatry 74, 1120–1128 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  35. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Nagel, M., Watanabe, K., Stringer, S., Posthuma, D. & van der Sluis, S. Item-level analyses reveal genetic heterogeneity in neuroticism. Nat. Commun. 9, 905 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584.e23 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Duffney, L. J. et al. Epigenetics and autism spectrum disorder: a report of an autism case with mutation in H1 linker histone HIST1H1E and literature review. Am. J. Med. Genet. B Neuropsychiatr. Genet. 177, 426–433 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  40. De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bryant, L. et al. Histone H3.3 beyond cancer: germline mutations in histone 3 family 3A and 3B cause a previously unidentified neurodegenerative disorder in 46 patients. Sci. Adv. 6, eabc9207 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Subramanian, K. et al. Basal ganglia and autism - a translational perspective. Autism Res. 10, 1751–1775 (2017).

    Article  PubMed  Google Scholar 

  43. Clarke, T. K. et al. Common polygenic risk for autism spectrum disorder (ASD) is associated with cognitive ability in the general population. Mol. Psychiatry 21, 419–425 (2016).

    Article  PubMed  Google Scholar 

  44. Traut, N. et al. Cerebellar volume in autism: literature meta-analysis and analysis of the Autism Brain Imaging Data Exchange Cohort. Biol. Psychiatry 83, 579–588 (2018).

    Article  PubMed  Google Scholar 

  45. Hoogman, M. et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry 4, 310–319 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Shaw, P. et al. A multicohort, longitudinal study of cerebellar development in attention deficit hyperactivity disorder. J. Child Psychol. Psychiatry 59, 1114–1123 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Wolfers, T. et al. Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models. Psychol. Med. 50, 314–323 (2020).

    Article  PubMed  Google Scholar 

  48. Fliers, E. et al. Motor coordination problems in children and adolescents with ADHD rated by parents and teachers: effects of age and gender. J. Neural Transm. 115, 211–220 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Basile, G. A. et al. Red nucleus structure and function: from anatomy to clinical neurosciences. Brain Struct. Funct. 226, 69–91 (2021).

    Article  PubMed  Google Scholar 

  51. Dalsgaard, S., Nielsen, H. S. & Simonsen, M. Five-fold increase in national prevalence rates of attention-deficit/hyperactivity disorder medications for children and adolescents with autism spectrum disorder, attention-deficit/hyperactivity disorder, and other psychiatric disorders: a Danish register-based study. J. Child Adolesc. Psychopharmacol. 23, 432–439 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Rosenberg, R. E. et al. Psychotropic medication use among children with autism spectrum disorders enrolled in a national registry, 2007-2008. J. Autism Dev. Disord. 40, 342–351 (2010).

    Article  PubMed  Google Scholar 

  53. 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).

    Article  PubMed  Google Scholar 

  54. Chang, Z., D’Onofrio, B. M., Quinn, P. D., Lichtenstein, P. & Larsson, H. Medication for attention-deficit/hyperactivity disorder and risk for depression: a nationwide longitudinal cohort study. Biol. Psychiatry 80, 916–922 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Chang, Z. et al. Medication for attention-deficit/hyperactivity disorder and risk for suicide attempts. Biol. Psychiatry 88, 452–458 (2020).

    Article  CAS  PubMed  Google Scholar 

  56. Keilow, M., Holm, A. & Fallesen, P. Medical treatment of attention deficit/hyperactivity disorder (ADHD) and children’s academic performance. PLoS ONE 13, e0207905 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Brainstorm Consortiumet al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

    Article  Google Scholar 

  58. Polderman, T. J., Hoekstra, R. A., Posthuma, D. & Larsson, H. The co-occurrence of autistic and ADHD dimensions in adults: an etiological study in 17,770 twins. Transl. Psychiatry 4, e435 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Ronald, A., Larsson, H., Anckarsater, H. & Lichtenstein, P. Symptoms of autism and ADHD: a Swedish twin study examining their overlap. J. Abnorm Psychol. 123, 440–451 (2014).

    Article  PubMed  Google Scholar 

  60. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  62. Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  64. Lam, M. et al. RICOPILI: Rapid imputation for COnsortias PIpeLIne. Bioinformatics 36, 930–933 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715.e16 (2018).

    Article  PubMed Central  Google Scholar 

  67. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    Article  CAS  PubMed  Google Scholar 

  68. Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    Article  CAS  PubMed  Google Scholar 

  69. Byrne, E. M. et al. Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders. Mol. Psychiatry 26, 2070–2081 (2021).

    Article  CAS  PubMed  Google Scholar 

  70. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Roadmap Epigenomics Consortiumet al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  PubMed Central  Google Scholar 

  74. Cao, C. et al. Power analysis of transcriptome-wide association study: implications for practical protocol choice. PLoS Genet. 17, e1009405 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  76. Liu, X. et al. Functional architectures of local and distal regulation of gene expression in multiple human tissues. Am. J. Hum. Genet. 100, 605–616 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Watanabe, K., Umicevic Mirkov, M., de Leeuw, C. A., van den Heuvel, M. P. & Posthuma, D. Genetic mapping of cell type specificity for complex traits. Nat. Commun. 10, 3222 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Grotzinger, A. D. et al. Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat. Genet. 54, 548–559 (2022).

    Article  CAS  PubMed  Google Scholar 

  80. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Benyamin, B. et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol. Psychiatry 19, 253–258 (2014).

    Article  CAS  PubMed  Google Scholar 

  83. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed Central  Google Scholar 

  85. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Jones, S. E. et al. Genome-wide association analyses in 128,266 individuals identifies new morningness and sleep duration loci. PLoS Genet. 12, e1006125 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Deary, V. et al. Genetic contributions to self-reported tiredness. Mol. Psychiatry 23, 609–620 (2018).

    Article  CAS  PubMed  Google Scholar 

  89. Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).

    Article  Google Scholar 

  90. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Yang, J., Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs. Proc. Natl Acad. Sci. USA 113, E4579–E4580 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Altman, D. G. & Bland, J. M. How to obtain the confidence interval from a P value. BMJ 343, d2090 (2011).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The iPSYCH team was supported by grants from the Lundbeck Foundation (R102-A9118, R155-2014-1724 and R248-2017-2003), the EU H2020 Program (grant no. 667302, CoCA), NIMH (1U01MH109514-01 to A.D.B.) and the Universities and University Hospitals of Aarhus and Copenhagen. The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to A.D.B.). G.V. was funded by NIMH grant K08MH122911 and the 2020 NARSAD Young Investigator Grant no. 29350 from the Brain & Behavior Research Foundation. P.R. was funded by NIMH grants R01MH125246, U01MH116442 and R01MH109677. Support for this research was further received by B.C. from the Spanish Ministerio de Ciencia, Innovación y Universidades (RTI2018-100968-B-100), Ministerio de Economía y Competitividad, AGAUR/Generalitat de Catalunya (2017-SGR-738), the European Union H2020 Program (H2020/2014-2020) under grant agreements 667302 (CoCA), 643051 (MiND) and 728018 (Eat2beNICE), and the ECNP network ‘ADHD across the lifespan’. S.V.F. is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreements 667302 and 965381; NIMH grants U01MH109536-01, U01AR076092-01A1, R0MH116037 and 5R01AG06495502; Oregon Health and Science University, Otsuka Pharmaceuticals and Supernus Pharmaceutical Company. We thank all participants in the cohorts included in this analysis.

Author information

Authors and Affiliations

Authors

Contributions

M.M., J.G. and A.D.B. designed the study. M.M., J.G., T.D.A., J.M., G.V., S.M., D.D., J.B., R.W., C.E.C., A.R., N.I.S., W.Z., M.E.H., B.Z. and G.H. conducted data analysis. P.B.M., E.B.R., P.R., B.M.N., M.J.D. and A.D.B. supervised data analysis. J.B.-G., M.B.-H., E.A., J.D.B., M.N., T.W., O.M., D.M.H., P.B.M., B.M.N., M.J.D. and A.D.B. provided data. M.M., J.G., T.D.A., J.M., S.M. and A.D.B. wrote the paper. M.M., J.G., T.D.A., J.M., B.C., E.B.R., S.V.F., B.F., S.D. and A.D.B. formed the core revision group. A.D.B. directed the study. All authors discussed the results and approved the final version of the manuscript.

Corresponding authors

Correspondence to Manuel Mattheisen or Anders D. Børglum.

Ethics declarations

Competing interests

B.F. has received educational speaking fees from Medice. In the past year, S.V.F. has received income, potential income, travel expenses, continuing education support and/or research support from Takeda, OnDosis, Tris, Otsuka, Arbor, Ironshore, Rhodes, Akili Interactive Labs, Sunovion, Supernus and Genomind. With his institution, S.V.F. has US patent US20130217707 A1 for the use of sodium–hydrogen exchange inhibitors in the treatment of ADHD. S.V.F. also 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). S.V.F. is Program Director of www.adhdinadults.com. B.M.N. is a member of the scientific advisory board at Deep Genomics and Neumora (formerly Neumora) and consultant for Camp4 Therapeutics, Takeda Pharmaceutical and Biogen. M.J.D. is a founder of Maze Therapeutics and is on the Scientific Advisory Board of RBNC Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks E. Byrne, Andreas G. Chiocchetti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–14.

Reporting Summary

Peer Review File

Supplementary Data 1

PheWAS lookup for combined GWAS in data from the OpenGWAS project.

Supplementary Data 2

Lookup for combined (ADHD or ASD) and differentiating (ADHD versus ASD) GWAS top SNPs in results from recent studies.

Supplementary Data 3

Lookup for combined (ADHD or ASD) and differentiating (ADHD versus ASD) GWAS top SNPs in results from GWAS in comorbid cases (ADHD and ASD), ADHD-only cases and ASD-only cases (all versus controls).

Supplementary Data 4

TWAS results for combined and ADHD versus ASD GWAS.

Supplementary Data 5

Gene-based analyses results for combined GWAS and ADHD versus ASD GWAS.

Supplementary Data 6

LD Hub analyses for original GWAS in ADHD, ASD and GWAS of combined dataset and ADHD versus ASD.

Supplementary Data 7

PheWAS lookup for ADHD versus ASD GWAS in data from the OpenGWAS project.

Supplementary Data 8

LDSC enrichment analyses for epigenomic peaks.

Supplementary Data 9

Genetic correlations for autism subtypes with ADHD.

Supplementary Data 10

FUMA functional annotation information using FUMA v.1.3.6a.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mattheisen, M., Grove, J., Als, T.D. et al. Identification of shared and differentiating genetic architecture for autism spectrum disorder, attention-deficit hyperactivity disorder and case subgroups. Nat Genet 54, 1470–1478 (2022). https://doi.org/10.1038/s41588-022-01171-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-022-01171-3

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing