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.
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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).
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.
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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.
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.
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Supplementary Note and Figs. 1–14.
PheWAS lookup for combined GWAS in data from the OpenGWAS project.
Lookup for combined (ADHD or ASD) and differentiating (ADHD versus ASD) GWAS top SNPs in results from recent studies.
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).
TWAS results for combined and ADHD versus ASD GWAS.
Gene-based analyses results for combined GWAS and ADHD versus ASD GWAS.
LD Hub analyses for original GWAS in ADHD, ASD and GWAS of combined dataset and ADHD versus ASD.
PheWAS lookup for ADHD versus ASD GWAS in data from the OpenGWAS project.
LDSC enrichment analyses for epigenomic peaks.
Genetic correlations for autism subtypes with ADHD.
FUMA functional annotation information using FUMA v.1.3.6a.
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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