Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies



      Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels.


      We applied summary-data–based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data–based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels.


      We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before.


      Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Biological Psychiatry
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Polanczyk G.V.
        • Willcutt E.G.
        • Salum G.A.
        • Kieling C.
        • Rohde L.A.
        ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis.
        Int J Epidemiol. 2014; 43: 434-442
        • Christensen D.L.
        • Baio J.
        • Van Naarden Braun K.
        • Bilder D.
        • Charles J.
        • Constantino J.N.
        • et al.
        Prevalence and characteristics of autism spectrum disorder among children aged 8 years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012.
        MMWR Surveill Summ. 2016; 65: 1-23
        • American Psychiatric Association
        Diagnostic and Statistical Manual of Mental Disorders.
        5th ed. American Psychiatric Publishing, Arlington, VA2013
        • Polderman T.J.C.
        • Benyamin B.
        • de Leeuw C.A.
        • Sullivan P.F.
        • van Bochoven A.
        • Visscher P.M.
        • Posthuma D.
        Meta-analysis of the heritability of human traits based on fifty years of twin studies.
        Nat Genet. 2015; 47: 702-709
        • Demontis D.
        • Walters R.K.
        • Martin J.
        • Mattheisen M.
        • Als T.D.
        • Agerbo E.
        • et al.
        Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder.
        Nat Genet. 2019; 51: 63-75
        • Grove J.
        • Ripke S.
        • Als T.D.
        • Mattheisen M.
        • Walters R.K.
        • Won H.
        • et al.
        Identification of common genetic risk variants for autism spectrum disorder.
        Nat Genet. 2019; 51: 431-444
        • Schoenfelder S.
        • Fraser P.
        Long-range enhancer–promoter contacts in gene expression control.
        Nat Rev Genet. 2019; 20: 437-455
        • Wu Y.
        • Zheng Z.
        • Visscher P.M.
        • Yang J.
        Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data.
        Genome Biol. 2017; 18: 86
        • Schaub M.A.
        • Boyle A.P.
        • Kundaje A.
        • Batzoglou S.
        • Snyder M.
        Linking disease associations with regulatory information in the human genome.
        Genome Res. 2012; 22: 1748-1759
        • Ernst J.
        • Kheradpour P.
        • Mikkelsen T.S.
        • Shoresh N.
        • Ward L.D.
        • Epstein C.B.
        • et al.
        Mapping and analysis of chromatin state dynamics in nine human cell types.
        Nature. 2011; 473: 43-49
        • Maurano M.T.
        • Humbert R.
        • Rynes E.
        • Thurman R.E.
        • Haugen E.
        • Wang H.
        • et al.
        Systematic localization of common disease-associated variation in regulatory DNA.
        Science. 2012; 337: 1190-1195
        • Wagner J.R.
        • Busche S.
        • Ge B.
        • Kwan T.
        • Pastinen T.
        • Blanchette M.
        The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts.
        Genome Biol. 2014; 15: R37
        • Hannon E.
        • Spiers H.
        • Viana J.
        • Pidsley R.
        • Burrage J.
        • Murphy T.M.
        • et al.
        Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci.
        Nat Neurosci. 2016; 19: 48-54
        • Xu Y.
        • Chen X.-T.
        • Luo M.
        • Tang Y.
        • Zhang G.
        • Wu D.
        • et al.
        Multiple epigenetic factors predict the attention deficit/hyperactivity disorder among the Chinese Han children.
        J Psychiatr Res. 2015; 64: 40-50
        • Grayson D.R.
        • Guidotti A.
        Merging data from genetic and epigenetic approaches to better understand autistic spectrum disorder.
        Epigenomics. 2016; 8: 85-104
        • Schanen N.C.
        Epigenetics of autism spectrum disorders.
        Hum Mol Genet. 2006; 15: R138-R150
        • Rommelse N.N.J.
        • 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. 2010; 19: 281-295
        • Miller M.
        • Musser E.D.
        • Young G.S.
        • Olson B.
        • Steiner R.D.
        • Nigg J.T.
        Sibling recurrence risk and cross-aggregation of attention-deficit/hyperactivity disorder and autism spectrum disorder.
        JAMA Pediatr. 2019; 173: 147-152
        • Ghirardi L.
        • Brikell I.
        • Kuja-Halkola R.
        • Freitag C.M.
        • Franke B.
        • Asherson P.
        • et al.
        The familial co-aggregation of ASD and ADHD: A register-based cohort study.
        Mol Psychiatry. 2018; 23: 257-262
        • Musser E.D.
        • Hawkey E.
        • Kachan-Liu S.S.
        • Lees P.
        • Roullet J.-B.
        • Goddard K.
        • et al.
        Shared familial transmission of autism spectrum and attention-deficit/hyperactivity disorders.
        J Child Psychol Psychiatry. 2014; 55: 819-827
        • Martin J.
        • Cooper M.
        • Hamshere M.L.
        • Pocklington A.
        • Scherer S.W.
        • Kent L.
        • et al.
        Biological overlap of attention-deficit/hyperactivity disorder and autism spectrum disorder: Evidence from copy number variants.
        J Am Acad Child Adolesc Psychiatry. 2014; 53: 761-770.e26
        • Zhu Z.
        • Zhang F.
        • Hu H.
        • Bakshi A.
        • Robinson M.R.
        • Powell J.E.
        • et al.
        Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.
        Nat Genet. 2016; 48: 481-487
        • Hannon E.
        • Weedon M.
        • Bray N.
        • O’Donovan M.
        • Mill J.
        Pleiotropic effects of trait-associated genetic variation on DNA methylation: Utility for refining GWAS loci.
        Am J Hum Genet. 2017; 100: 954-959
        • Wu Y.
        • Zeng J.
        • Zhang F.
        • Zhu Z.
        • Qi T.
        • Zheng Z.
        • et al.
        Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits.
        Nat Commun. 2018; 9: 918
        • Doherty J.L.
        • Owen M.J.
        Genomic insights into the overlap between psychiatric disorders: Implications for research and clinical practice.
        Genome Med. 2014; 6: 29
        • Sullivan P.F.
        • Geschwind D.H.
        Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders.
        Cell. 2019; 177: 162-183
        • O’Brien H.E.
        • Hannon E.
        • Hill M.J.
        • Toste C.C.
        • Robertson M.J.
        • Morgan J.E.
        • et al.
        Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders.
        Genome Biol. 2018; 19: 194
        • Spiers H.
        • Hannon E.
        • Schalkwyk L.C.
        • Smith R.
        • Wong C.C.Y.
        • O’Donovan M.C.
        • et al.
        Methylomic trajectories across human fetal brain development.
        Genome Res. 2015; 25: 338-352
        • Qi T.
        • Wu Y.
        • Zeng J.
        • Zhang F.
        • Xue A.
        • Jiang L.
        • et al.
        Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood.
        Nat Commun. 2018; 9: 2282
        • Sonnega A.
        • Faul J.D.
        • Ofstedal M.B.
        • Langa K.M.
        • Phillips J.W.R.
        • Weir D.R.
        Cohort profile: The Health and Retirement Study (HRS).
        Int J Epidemiol. 2014; 43: 576-585
        • Abecasis G.R.
        • Auton A.
        • Brooks L.D.
        • DePristo M.A.
        • Durbin R.M.
        • et al.
        • 1000 Genomes Project Consortium
        An integrated map of genetic variation from 1,092 human genomes.
        Nature. 2012; 491: 56-65
        • Gamazon E.R.
        • Zwinderman A.H.
        • Cox N.J.
        • Denys D.
        • Derks E.M.
        Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits.
        Nat Genet. 2019; 51: 933-940
        • Liao C.
        • Laporte A.D.
        • Spiegelman D.
        • Akçimen F.
        • Joober R.
        • Dion P.A.
        • Rouleau G.A.
        Transcriptome-wide association study of attention deficit hyperactivity disorder identifies associated genes and phenotypes.
        Nat Commun. 2019; 10: 4450
        • Fahira A.
        • Li Z.
        • Liu N.
        • Shi Y.
        Prediction of causal genes and gene expression analysis of attention-deficit hyperactivity disorder in the different brain region, a comprehensive integrative analysis of ADHD.
        Behav Brain Res. 2019; 364: 183-192
        • Schrode N.
        • Ho S.-M.
        • Yamamuro K.
        • Dobbyn A.
        • Huckins L.
        • Matos M.R.
        • et al.
        Synergistic effects of common schizophrenia risk variants.
        Nat Genet. 2019; 51: 1475-1485
        • Zody M.C.
        • Jiang Z.
        • Fung H.-C.
        • Antonacci F.
        • Hillier L.W.
        • Cardone M.F.
        • et al.
        Evolutionary toggling of the MAPT 17q21.31 inversion region.
        Nat Genet. 2008; 40: 1076-1083
        • Koolen D.A.
        • Kramer J.M.
        • Neveling K.
        • Nillesen W.M.
        • Moore-Barton H.L.
        • Elmslie F.V.
        • et al.
        Mutations in the chromatin modifier gene KANSL1 cause the 17q21.31 microdeletion syndrome.
        Nat Genet. 2012; 44: 639-641
        • Myers A.J.
        • Kaleem M.
        • Marlowe L.
        • Pittman A.M.
        • Lees A.J.
        • Fung H.C.
        • et al.
        The H1c haplotype at the MAPT locus is associated with Alzheimer’s disease.
        Hum Mol Genet. 2005; 14: 2399-2404
        • Spencer C.C.A.
        • Plagnol V.
        • Strange A.
        • Gardner M.
        • Paisan-Ruiz C.
        • Band G.
        • et al.
        Dissection of the genetics of Parkinson’s disease identifies an additional association 5’ of SNCA and multiple associated haplotypes at 17q21.
        Hum Mol Genet. 2011; 20: 345-353
        • Veerappa A.M.
        • Saldanha M.
        • Padakannaya P.
        • Ramachandra N.B.
        Family based genome-wide copy number scan identifies complex rearrangements at 17q21.31 in dyslexics.
        Am J Med Genet B Neuropsychiatr Genet. 2014; 165B: 572-580
        • Okbay A.
        • Baselmans B.M.L.
        • De Neve J.-E.
        • Turley P.
        • Nivard M.G.
        • Fontana M.A.
        • et al.
        Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses.
        Nat Genet. 2016; 48: 624-633
        • Ikram M.A.
        • Fornage M.
        • Smith A.V.
        • Seshadri S.
        • Schmidt R.
        • Debette S.
        • et al.
        Common variants at 6q22 and 17q21 are associated with intracranial volume.
        Nat Genet. 2012; 44: 539-544
        • Pain O.
        • Pocklington A.J.
        • Holmans P.A.
        • Bray N.J.
        • O’Brien H.E.
        • Hall L.S.
        • et al.
        Novel insight into the etiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics.
        Biol Psychiatry. 2019; 86: 265-273
        • Gandal M.J.
        • Zhang P.
        • Hadjimichael E.
        • Walker R.L.
        • Chen C.
        • Liu S.
        • et al.
        Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
        Science. 2018; 362eaat8127
        • Cannon M.E.
        • Mohlke K.L.
        Deciphering the emerging complexities of molecular mechanisms at GWAS loci.
        Am J Hum Genet. 2018; 103: 637-653

      Linked Article

      • Integrative Genomics for the Interpretation of Genetic Loci Implicated in Neurodevelopmental Disorders
        Biological PsychiatryVol. 88Issue 6
        • Preview
          Our understanding of neuropsychiatric disorders has gained tremendous progress with the advancement of genetics studies, both in establishing the high heritability for these classes of disorders and through the identification of specific genetic associations (1). Large-scale genetic studies—both rare-variant, family-based studies and common variant, genome-wide association studies (GWASs)—have uncovered the genetic architecture of neuropsychiatric disease and implicate common variants as the largest contributor to disease liability for the majority of these disorders, including autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder (2).
        • Full-Text
        • PDF