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Epigenome-wide Association Study of Attention-Deficit/Hyperactivity Disorder Symptoms in Adults

Open AccessPublished:March 01, 2019DOI:https://doi.org/10.1016/j.biopsych.2019.02.016

      Abstract

      Background

      Previous studies have reported associations between attention-deficit/hyperactivity disorder symptoms and DNA methylation in children. We report the first epigenome-wide association study meta-analysis of adult attention-deficit/hyperactivity disorder symptoms, based on peripheral blood DNA methylation (Infinium HumanMethylation450K array) in three population-based adult cohorts.

      Methods

      An epigenome-wide association study was performed in the Netherlands Twin Register (N = 2258, mean age 37 years), Dunedin Multidisciplinary Health and Development Study (N = 800, age 38 years), and Environmental Risk Longitudinal Twin Study (N = 1631, age 18 years), and results were combined through meta-analysis (total sample size N = 4689). Region-based analyses accounting for the correlation between nearby methylation sites were also performed.

      Results

      One epigenome-wide significant differentially methylated position was detected in the Dunedin study, but meta-analysis did not detect differentially methylated positions that were robustly associated across cohorts. In region-based analyses, six significant differentially methylation regions (DMRs) were identified in the Netherlands Twin Register, 19 in the Dunedin study, and none in the Environmental Risk Longitudinal Twin Study. Of these DMRs, 92% were associated with methylation quantitative trait loci, and 68% showed moderate to large blood-brain correlations for DNA methylation levels. DMRs included six nonoverlapping DMRs (three in the Netherlands Twin Register, three in the Dunedin study) in the major histocompatibility complex, which were associated with expression of genes in the major histocompatibility complex, including C4A and C4B, previously implicated in schizophrenia.

      Conclusions

      Our findings point at new candidate loci involved in immune and neuronal functions that await further replication. Our work also illustrates the need for further research to examine to what extent epigenetic associations with psychiatric traits depend on characteristics such as age, comorbidities, exposures, and genetic background.

      Keywords

      Attention-deficit/hyperactivity disorder (ADHD) is among the top-ranking psychiatric diagnoses in children and adults (
      • Polanczyk G.
      • Rohde L.A.
      Epidemiology of attention-deficit/hyperactivity disorder across the lifespan.
      ) and appears to reflect the extreme end of a continuous distribution of ADHD symptoms in the population (
      • Larsson H.
      • Anckarsater H.
      • Råstam 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.
      ,
      • Groen-Blokhuis M.M.
      • Middeldorp C.M.
      • Kan K.J.
      • Abdellaoui A.
      • Van Beijsterveldt C.E.M.
      • Ehli E.A.
      • et al.
      Attention-deficit/hyperactivity disorder polygenic risk scores predict attention problems in a population-based sample of children.
      ,
      • 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 ADHD.
      ). Genetic association studies have identified common and rare variants associated with ADHD (
      • 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 ADHD.
      ,
      • Hawi Z.
      • Cummins T.D.R.
      • Tong J.
      • Arcos-Burgos M.
      • Zhao Q.
      • Matthews N.
      • et al.
      Rare DNA variants in the brain-derived neurotrophic factor gene increase risk for attention-deficit hyperactivity disorder: A next-generation sequencing study.
      ,
      • Demontis D.
      • Lescai F.
      • Børglum A.
      • Glerup S.
      • Østergaard S.D.
      • Mors O.
      • et al.
      Whole-exome sequencing reveals increased burden of rare functional and disruptive variants in candidate risk genes in individuals with persistent attention-deficit/hyperactivity disorder.
      ). Numerous environmental risk factors have been reported, including prenatal and perinatal factors (
      • Mill J.
      • Petronis A.
      Pre- and peri-natal environmental risks for attention-deficit hyperactivity disorder (ADHD): The potential role of epigenetic processes in mediating susceptibility.
      ) [birth weight (
      • Groen-Blokhuis M.M.
      • Middeldorp C.M.
      • Van Beijsterveldt C.E.
      • Boomsma D.I.
      Evidence for a causal association of low birth weight and attention problems.
      ), prenatal exposure to maternal smoking (
      • Dong T.
      • Hu W.
      • Zhou X.
      • Lin H.
      • Lan L.
      • Hang B.
      • et al.
      Prenatal exposure to maternal smoking during pregnancy and attention-deficit/hyperactivity disorder in offspring: A meta-analysis.
      ), toxins (
      • Mill J.
      • Petronis A.
      Pre- and peri-natal environmental risks for attention-deficit hyperactivity disorder (ADHD): The potential role of epigenetic processes in mediating susceptibility.
      ), maternal fever and infections (
      • Werenberg Dreier J.
      • Nybo Andersen A.M.
      • Hvolby A.
      • Garne E.
      • Kragh Andersen P.
      • Berg-Beckhoff G.
      Fever and infections in pregnancy and risk of attention deficit/hyperactivity disorder in the offspring.
      )] and postnatal factors [childhood maltreatment (
      • Thapar A.
      • Cooper M.
      • Eyre O.
      • Langley K.
      What have we learnt about the causes of ADHD?.
      ), current stress (
      • Thapar A.
      • Cooper M.
      • Eyre O.
      • Langley K.
      What have we learnt about the causes of ADHD?.
      ), infections (
      • Malhotra S.
      • Bhatia N.K.
      • Kumar P.
      • Hans C.
      • Bhatia M.S.
      The potential role of infections in attention-deficit hyperactivity disorder.
      )]. Recent studies have investigated if DNA methylation levels, which regulate gene expression, are associated with ADHD (
      • van Mil N.H.
      • Steegers-Theunissen R.P.
      • Bouwland-Both M.I.
      • Verbiest M.M.P.J.
      • Rijlaarsdam J.
      • Hofman A.
      • et al.
      DNA methylation profiles at birth and child ADHD symptoms.
      ,
      • Park S.
      • Lee J.M.
      • Kim J.W.
      • Cho D.Y.
      • Yun H.J.
      • Han D.H.
      • et al.
      Associations between serotonin transporter gene (SLC6A4) methylation and clinical characteristics and cortical thickness in children with ADHD.
      ,
      • Rijlaarsdam J.
      • Cecil C.A.
      • Walton E.
      • Mesirow M.S.C.
      • Relton C.L.
      • Gaunt T.R.
      • et al.
      Prenatal unhealthy diet, insulin-like growth factor 2 gene (IGF2) methylation, and attention deficit hyperactivity disorder symptoms in youth with early-onset conduct problems.
      ,
      • Walton E.
      • Pingault J.B.
      • Cecil C.A.
      • Gaunt T.R.
      • Relton C.L.
      • Mill J.
      • Barker E.D.
      Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
      ,
      • Barker E.D.
      • Walton E.
      • Cecil C.A.M.
      • Rowe R.
      • Jaffee S.R.
      • Maughan B.
      • et al.
      A methylome-wide association study of trajectories of oppositional defiant behaviors and biological overlap with attention deficit hyperactivity disorder.
      ,
      • 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.
      ,
      • Wilmot B.
      • Fry R.
      • Smeester L.
      • Musser E.D.
      • Mill J.
      • Nigg T.
      • et al.
      Methylomic analysis of salivary DNA in childhood ADHD identifies altered DNA methylation in VIPR2 identifies altered DNA methylation in VIPR2.
      ,
      • Dadds M.R.
      • Schollar-Root O.
      • Lenroot R.
      • Moul C.
      • Hawes D.J.
      Epigenetic regulation of the DRD4 gene and dimensions of attention-deficit/hyperactivity disorder in children.
      ,
      • Peter C.J.
      • Fischer L.K.
      • Kundakovic M.
      • Garg P.
      • Jakovcevski M.
      • Dincer A.
      • et al.
      DNA methylation signatures of early childhood malnutrition associated with impairments in attention and cognition.
      ,
      • Heinrich H.
      • Grunitz J.
      • Stonawski V.
      • Frey S.
      • Wahl S.
      • Albrecht B.
      • et al.
      Attention, cognitive control and motivation in ADHD: Linking event-related brain potentials and DNA methylation patterns in boys at early school age.
      ,
      • Sengupta S.M.
      • Smith A.K.
      • Grizenko N.
      • Joober R.
      Locus-specific DNA methylation changes and phenotypic variability in children with attention-deficit hyperactivity disorder.
      ,
      • Gervin K.
      • Nordeng H.
      • Ystrom E.
      • Reichborn-Kjennerud T.
      • Lyle R.
      Long-term prenatal exposure to paracetamol is associated with DNA methylation differences in children diagnosed with ADHD.
      ,
      • Ding K.
      • Yang J.
      • Reynolds G.P.
      • Chen B.
      • Shao J.
      • Liu R.
      • et al.
      DAT1 methylation is associated with methylphenidate response on oppositional and hyperactive-impulsive symptoms in children and adolescents with ADHD.
      ). DNA methylation may represent a marker that captures the cumulative effects of genetic variants, stochastic effects, and environmental exposures (
      • van Dongen J.
      • Nivard M.G.
      • Willemsen G.
      • Hottenga J.J.
      • Helmer Q.
      • Dolan C.V.
      • et al.
      Genetic and environmental influences interact with age and sex in shaping the human methylome.
      ,
      • McRae A.F.
      • Powell J.E.
      • Henders A.K.
      • Bowdler L.
      • Hemani G.
      • Shah S.
      • et al.
      Contribution of genetic variation to transgenerational inheritance of DNA methylation.
      ) associated with a trait. Several prenatal exposures, including maternal famine (
      • Heijmans B.T.
      • Tobi E.W.
      • Stein A.D.
      • Putter H.
      • Blauw G.J.
      • Susser E.S.
      • et al.
      Persistent epigenetic differences associated with prenatal exposure to famine in humans.
      ), maternal folate (
      • Joubert B.R.
      • den Dekker H.T.
      • Felix J.F.
      • Bohlin J.
      • Ligthart S.
      • Beckett E.
      • et al.
      Maternal plasma folate impacts differential DNA methylation in an epigenome-wide meta-analysis of newborns.
      ), and maternal smoking (
      • Joubert B.R.
      • Felix J.F.
      • Yousefi P.
      • Bakulski K.M.
      • Just A.C.
      • Breton C.
      • et al.
      DNA methylation in newborns and maternal smoking in pregnancy: Genome-wide consortium meta-analysis.
      ), and later life exposures, for example, smoking (
      • Joehanes R.
      • Just A.C.
      • Marioni R.E.
      • Pilling L.C.
      • Reynolds L.M.
      • Mandaviya P.R.
      • et al.
      Epigenetic signatures of cigarette smoking.
      ), have been associated with stable long-term changes in DNA methylation in blood and other peripheral tissues. Importantly, DNA methylation patterns are largely tissue specific. ADHD symptom level–associated DNA methylation differences in peripheral tissues such as blood are likely to reveal epigenetic consequences of differential life conditions that correlate with ADHD (biomarker of exposures). The extent to which DNA methylation in peripheral tissues is informative about epigenetic mechanisms that contribute to interindividual differences in ADHD symptoms or that correlate with causal epigenetic mechanisms in other tissues is unknown. It has been reported that DNA methylation levels in blood correlate to a limited extent with methylation levels in other tissues, including the brain (
      • Slieker R.C.
      • Bos S.D.
      • Goeman J.J.
      • Bovée J.V.
      • Talens R.P.
      • Van Der Breggen R.
      • et al.
      Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array.
      ,
      • Walton E.
      • Hass J.
      • Liu J.
      • Roffman J.L.
      • Bernardoni F.
      • Roessner V.
      • et al.
      Correspondence of DNA methylation between blood and brain tissue and its application to schizophrenia research.
      ,
      • Hannon E.
      • Lunnon K.
      • Schalkwyk L.
      • Mill J.
      Interindividual methylomic variation across blood, cortex, and cerebellum: Implications for epigenetic studies of neurological and neuropsychiatric phenotypes.
      ,
      • Edgar R.D.
      • Jones M.J.
      • Meaney M.J.
      • Turecki G.
      • Kobor M.S.
      BECon: A tool for interpreting DNA methylation findings from blood in the context of brain.
      ). One explanation for such correlations is that methylation quantitative trait locus (mQTL) effects correlate to some extent across tissues, as was recently demonstrated for cis mQTL effects in blood and brain (
      • 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.
      ).
      Several candidate gene studies, most of limited sample size (mean 192; range, 82–426), have reported associations between ADHD symptoms in children and DNA methylation in cord blood (
      • van Mil N.H.
      • Steegers-Theunissen R.P.
      • Bouwland-Both M.I.
      • Verbiest M.M.P.J.
      • Rijlaarsdam J.
      • Hofman A.
      • et al.
      DNA methylation profiles at birth and child ADHD symptoms.
      ), peripheral blood (
      • Park S.
      • Lee J.M.
      • Kim J.W.
      • Cho D.Y.
      • Yun H.J.
      • Han D.H.
      • et al.
      Associations between serotonin transporter gene (SLC6A4) methylation and clinical characteristics and cortical thickness in children with ADHD.
      ,
      • 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.
      ,
      • Dadds M.R.
      • Schollar-Root O.
      • Lenroot R.
      • Moul C.
      • Hawes D.J.
      Epigenetic regulation of the DRD4 gene and dimensions of attention-deficit/hyperactivity disorder in children.
      ), buccal samples (
      • Heinrich H.
      • Grunitz J.
      • Stonawski V.
      • Frey S.
      • Wahl S.
      • Albrecht B.
      • et al.
      Attention, cognitive control and motivation in ADHD: Linking event-related brain potentials and DNA methylation patterns in boys at early school age.
      ), and saliva (
      • Wilmot B.
      • Fry R.
      • Smeester L.
      • Musser E.D.
      • Mill J.
      • Nigg T.
      • et al.
      Methylomic analysis of salivary DNA in childhood ADHD identifies altered DNA methylation in VIPR2 identifies altered DNA methylation in VIPR2.
      ,
      • Dadds M.R.
      • Schollar-Root O.
      • Lenroot R.
      • Moul C.
      • Hawes D.J.
      Epigenetic regulation of the DRD4 gene and dimensions of attention-deficit/hyperactivity disorder in children.
      ). Other candidate approaches in small samples have reported relationships between DNA methylation, ADHD symptoms, and environmental risk factors (
      • Rijlaarsdam J.
      • Cecil C.A.
      • Walton E.
      • Mesirow M.S.C.
      • Relton C.L.
      • Gaunt T.R.
      • et al.
      Prenatal unhealthy diet, insulin-like growth factor 2 gene (IGF2) methylation, and attention deficit hyperactivity disorder symptoms in youth with early-onset conduct problems.
      ,
      • Peter C.J.
      • Fischer L.K.
      • Kundakovic M.
      • Garg P.
      • Jakovcevski M.
      • Dincer A.
      • et al.
      DNA methylation signatures of early childhood malnutrition associated with impairments in attention and cognition.
      ,
      • Sengupta S.M.
      • Smith A.K.
      • Grizenko N.
      • Joober R.
      Locus-specific DNA methylation changes and phenotypic variability in children with attention-deficit hyperactivity disorder.
      ). To date, three epigenome-wide association studies (EWASs) of ADHD (symptoms) in children have been published (
      • Walton E.
      • Pingault J.B.
      • Cecil C.A.
      • Gaunt T.R.
      • Relton C.L.
      • Mill J.
      • Barker E.D.
      Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
      ,
      • Wilmot B.
      • Fry R.
      • Smeester L.
      • Musser E.D.
      • Mill J.
      • Nigg T.
      • et al.
      Methylomic analysis of salivary DNA in childhood ADHD identifies altered DNA methylation in VIPR2 identifies altered DNA methylation in VIPR2.
      ,
      • Gervin K.
      • Nordeng H.
      • Ystrom E.
      • Reichborn-Kjennerud T.
      • Lyle R.
      Long-term prenatal exposure to paracetamol is associated with DNA methylation differences in children diagnosed with ADHD.
      ). The first study measured DNA methylation in saliva in a small group of boys with ADHD and control subjects (age 7–12 years, sample size = 112) and reported genes with suggestive evidence for association (
      • Wilmot B.
      • Fry R.
      • Smeester L.
      • Musser E.D.
      • Mill J.
      • Nigg T.
      • et al.
      Methylomic analysis of salivary DNA in childhood ADHD identifies altered DNA methylation in VIPR2 identifies altered DNA methylation in VIPR2.
      ). The second study measured DNA methylation in 384 cord blood samples and found no significant differences between children who later received a diagnosis of ADHD and controls (
      • Gervin K.
      • Nordeng H.
      • Ystrom E.
      • Reichborn-Kjennerud T.
      • Lyle R.
      Long-term prenatal exposure to paracetamol is associated with DNA methylation differences in children diagnosed with ADHD.
      ). The largest EWAS (817 children in a population-based study in the United Kingdom) identified 13 loci, where DNA methylation level in cord blood was significantly predictive of ADHD symptom trajectories between age 7 and 15 years (
      • Walton E.
      • Pingault J.B.
      • Cecil C.A.
      • Gaunt T.R.
      • Relton C.L.
      • Mill J.
      • Barker E.D.
      Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
      ); among those was ST3GAL3, one of the significant loci from the genome-wide association study (GWAS) of ADHD (
      • 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 ADHD.
      ). As both DNA methylation and ADHD symptoms change with age, it is unknown if findings in children persist in adulthood. Moreover, the robustness of associations between ADHD symptoms and DNA methylation remains to be investigated in larger studies of multiple cohorts.
      We report the first EWAS meta-analysis of ADHD symptoms in adults; this is also the largest EWAS of ADHD symptoms to date. We assessed the association between whole-blood DNA methylation and ADHD symptoms in three population-based cohorts: the Netherlands Twin Register (NTR), the Dunedin Multidisciplinary Health and Development Study from New Zealand, and the Environmental Risk (E-Risk) Longitudinal Twin Study from the United Kingdom. In secondary analyses, we 1) tested if CpGs with a lower, albeit nonsignificant, p value showed enrichment for loci previously identified in GWASs of psychiatric disease or in EWASs of psychiatric phenotypes or exposures; 2) analyzed differentially methylated regions (DMRs) to examine the evidence for small methylation differences at multiple nearby CpGs; and 3) examined the relationship between DNA methylation and RNA transcript levels in blood, the effects of mQTLs, and the correlation between DNA methylation in blood and brain to facilitate the biological interpretation of findings.

      Methods and Materials

      Overview

      The EWAS was performed in three cohorts (Figure 1): NTR (
      • Willemsen G.
      • Vink J.M.
      • Abdellaoui A.
      • den Braber A.
      • van Beek J.H.
      • Draisma H.H.
      • et al.
      The Adult Netherlands Twin Register: Twenty-five years of survey and biological data collection.
      ) (N = 2232 individuals from twin families), Dunedin study (
      • Poulton R.
      • Moffitt T.E.
      • Silva P.A.
      The Dunedin Multidisciplinary Health and Development Study: Overview of the first 40 years, with an eye to the future.
      ) (N = 800 unrelated individuals), and E-Risk (
      • Moffitt T.E.
      • Adlam A.
      • Affleck G.
      • Andreou P.
      • Aquan-Assee J.
      • Arseneault L.
      • et al.
      Teen-aged mothers in contemporary Britain.
      ) (N = 1631 twins). Results were combined in a meta-analysis to identify differentially methylated positions (DMPs) and to examine heterogeneity of effects across cohorts. Next, we performed secondary analyses. Genome-wide meta-analysis test statistics were used to test for enrichment of ADHD EWAS signals in loci detected in previous GWASs and EWASs of relevant traits. We performed DMR analyses in each cohort, compared results across cohorts, and performed a meta-analysis–based DMR analysis. Functional follow-up analyses were performed on top DMPs from the meta-analysis (nominal p value < 1.0 × 10−5) and on significant DMRs. Further details about the analyses are provided in Supplement 1.
      Figure thumbnail gr1
      Figure 1Summary of analyses of epigenome-wide association study (EWAS) in three cohorts: Netherlands Twin Register (NTR), Dunedin Multidisciplinary Health and Development Study from New Zealand, and Environmental Risk (E-Risk) Longitudinal Twin Study. DMR, differentially methylated region; GWAS, genome-wide association study; mQTLs, methylation quantitative trait loci.

      Ethical Permission

      The study protocol was approved by the institutional ethical review boards of the participating universities. Study members gave informed consent before participating. Written informed consent was obtained from all participants.

      ADHD Symptoms

      The EWAS in NTR was performed on the Conners Adult ADHD Rating Scale (CAARS) index (total ADHD symptoms) (Figure S1A in Supplement 1) (
      • Conners C.K.
      • Erhardt D.
      • Sparrow E.
      Conners’ Adult ADHD Rating Scales (CAARS) Technical Manual.
      ). Sensitivity analyses were performed on DSM-IV–based CAARS inattention and hyperactivity subscales, available for 1846 samples. In the Dunedin study, DSM-5 ADHD symptoms (Figure S1B in Supplement 1) were assessed based on private structured interviews as described previously (
      • Moffitt T.E.
      • Houts R.
      • Asherson P.
      • Belsky D.W.
      • Corcoran D.L.
      • Hammerle M.
      • et al.
      Is adult ADHD a childhood-onset neurodevelopmental disorder? Evidence from a four-decade longitudinal cohort study.
      ) at age 38 years. In the E-Risk study, DSM-5 ADHD symptoms (Figure S1C in Supplement 1) were assessed based on private structured interviews as described previously (
      • Agnew-Blais J.C.
      • Polanczyk G.V.
      • Danese A.
      • Wertz J.
      • Moffitt T.E.
      • Arseneault L.
      Evaluation of the persistence, remission, and emergence of attention-deficit/hyperactivity disorder in young adulthood.
      ) at age 18 years.

      Peripheral Blood DNA Methylation

      DNA methylation was assessed with the Infinium HumanMethylation450K BeadChip Kit (Illumina, Inc., San Diego, CA). Normalization was performed with functional normalization in NTR (
      • Fortin J.
      • Labbe A.
      • Lemire M.
      • Zanke B.W.
      • Hudson T.J.
      • Fertig E.J.
      • et al.
      Functional normalization of 450k methylation array data improves replication in large cancer studies.
      ), with methylumi in the Dunedin Study, and with dasen in E-Risk (
      • Pidsley R.
      • Y Wong C.C.
      • Volta M.
      • Lunnon K.
      • Mill J.
      • Schalkwyk L.C.
      A data-driven approach to preprocessing Illumina 450K methylation array data.
      ). The following probes were removed from all cohorts: sex chromosomes; probes with a single nucleotide polymorphism (SNP) within the CpG site (at the C or G position), regardless of minor allele frequency, based on the Genome of the Netherlands reference population (
      Genome of the Netherlands Consortium
      Whole-genome sequence variation, population structure and demographic history of the Dutch population.
      ); probes with common (>5% minor allele frequency) SNPs within 10 bp of the single base extension site (
      • Price M.E.
      • Cotton A.M.
      • Lam L.L.
      • Farré P.
      • Emberly E.
      • Brown C.J.
      • et al.
      Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array.
      ); and ambiguous mapping probes with an overlap of at least 47 bases per probe (
      • Chen Y.
      • Lemire M.
      • Choufani S.
      • Butcher D.T.
      • Zanke B.W.
      • Gallinger S.
      • et al.
      Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray.
      ). Only methylation sites that were present in all cohorts were kept in the analysis, leaving 394,194 sites.

      Statistical Analyses

      Epigenome-wide Association Study

      The association between DNA methylation levels and ADHD symptoms was tested for each site under a linear model (Dunedin) or generalized estimating equation model accounting for relatedness of twins and other family members (NTR and E-Risk); methylation β value was assessed as outcome, and the following predictors were used: ADHD symptoms, sex, smoking status, white blood cell percentages, age at blood sampling (only in NTR), cohort-specific technical covariates (i.e., sample plate and array row or principal components based on control probes), and principal components based on genome-wide SNPs. Analyses were performed in R (R Foundation for Statistical Computing, Vienna, Austria). The R package Bacon was used to compute the Bayesian inflation factor (
      • van Iterson M.
      • van Zwet E.W.
      • Heijmans B.T.
      the BIOS Consortium
      Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution.
      ).

      Meta-analysis

      A p value–based fixed-effects sample size–weighted meta-analysis was performed in METAL (
      • Willer C.J.
      • Li Y.
      • Abecasis G.R.
      METAL: Fast and efficient meta-analysis of genomewide association scans.
      ). The sample size–weighted method was chosen because of the differences in measurement scales of ADHD symptoms across studies. Statistical significance was assessed considering Bonferroni correction for the number of methylation sites tested (α = .05/394194 = 1.3 × 10−7). False discovery rate Q values are presented in Supplemental Tables S1 through S14 in Supplement 2. The I2 statistic provided by METAL was evaluated to assess heterogeneity.

      Inattention and Hyperactivity

      For top DMPs, sensitivity analyses were performed in which the association of these DMPs with inattention and hyperactivity/impulsivity subscales was tested in NTR.

      Overlap With EWAS and GWAS Loci

      Enrichment analysis was performed to examine whether CpGs in or near loci detected by GWASs or top-ranking CpGs from previous EWASs, on average, showed a stronger association with ADHD symptoms than other genome-wide Infinium HumanMethylation450K methylation sites. We considered the most recent GWASs for ADHD (
      • 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 ADHD.
      ), major depressive disorder (
      • Wray N.R.
      • Ripke S.
      • Mattheisen M.
      • Trzaskowski M.
      • Byrne E.M.
      • Abdellaoui A.
      • et al.
      Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
      ), schizophrenia (
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Biological insights from 108 schizophrenia-associated genetic loci.
      ), and autism spectrum disorder (
      • Grove J.
      • Ripke S.
      • Als T.D.
      • Mattheisen M.
      • Walters R.
      • Won H.
      • et al.
      Identification of common genetic risk variants for autism spectrum disorder.
      ) and the largest available EWASs on ADHD symptoms in children (
      • Walton E.
      • Pingault J.B.
      • Cecil C.A.
      • Gaunt T.R.
      • Relton C.L.
      • Mill J.
      • Barker E.D.
      Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
      ), schizophrenia (
      • Hannon E.
      • Dempster E.
      • Viana J.
      • Burrage J.
      • Smith A.R.
      • Macdonald R.
      • et al.
      An integrated genetic-epigenetic analysis of schizophrenia: Evidence for co-localization of genetic associations and differential DNA methylation.
      ), smoking (
      • Joehanes R.
      • Just A.C.
      • Marioni R.E.
      • Pilling L.C.
      • Reynolds L.M.
      • Mandaviya P.R.
      • et al.
      Epigenetic signatures of cigarette smoking.
      ), and maternal smoking (
      • Joubert B.R.
      • Felix J.F.
      • Yousefi P.
      • Bakulski K.M.
      • Just A.C.
      • Breton C.
      • et al.
      DNA methylation in newborns and maternal smoking in pregnancy: Genome-wide consortium meta-analysis.
      ).

      Differentially Methylated Regions

      We used the python module comb-p (
      • Pedersen B.S.
      • Schwartz D.A.
      • Yang I.V.
      • Kechris K.J.
      Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values.
      ) to identify regions where multiple correlated methylation sites show evidence for association with ADHD symptoms. We report significant regions (Šidák p < .05) with at least two methylation sites within a 500-bp window. Comb-p was applied in each of the three cohorts separately and on the meta-analysis results.

      Functional Follow-up Analyses

      In follow-up analyses, previously published datasets were used to test if DNA methylation level at top DMPs and at CpGs within significant DMRs were associated with whole-blood gene expression level in cis (
      • Bonder M.J.
      • Luijk R.
      • Zhernakova D.V.
      • Moed M.
      • Deelen P.
      • Vermaat M.
      • et al.
      Disease variants alter transcription factor levels and methylation of their binding sites.
      ) and with whole-blood mQTLs (
      • Bonder M.J.
      • Luijk R.
      • Zhernakova D.V.
      • Moed M.
      • Deelen P.
      • Vermaat M.
      • et al.
      Disease variants alter transcription factor levels and methylation of their binding sites.
      ) and to examine the correlation between DNA methylation in blood and four brain regions (prefrontal cortex, entorhinal cortex, superior temporal gyrus, and cerebellum) (
      • Hannon E.
      • Lunnon K.
      • Schalkwyk L.
      • Mill J.
      Interindividual methylomic variation across blood, cortex, and cerebellum: Implications for epigenetic studies of neurological and neuropsychiatric phenotypes.
      ).

      Power Analysis

      Power analyses are described in Supplement 1.

      Results

      Meta-analysis

      Demographic information of the cohorts is provided in Table 1. Genome-wide EWAS test statistics from each cohort showed no inflation (Figure S2 in Supplement 1; Table S1 in Supplement 2). One significant DMP (α = 1.3 × 10−7) was detected in Dunedin (cg26197679, chromosome 8 intergenic) and none in NTR and E-Risk. Meta-analysis of the three cohorts (N = 4689) detected no significant DMPs, and meta-analysis test statistics showed no inflation (Figure S2 in Supplement 1; Table S1 in Supplement 2). Summary statistics for genome-wide methylation sites are provided in Table S2 in Supplement 2. Top DMPs with a nominal p value (p < 1.0 × 10−5) are presented in Table 2. Two top DMPs showed a negative relationship with ADHD symptoms (cg26197679, intergenic, and cg23144852, gene body OPA1), and one DMP showed a positive relationship with ADHD symptoms in all cohorts (cg10984962, gene body AGAP1). The effect sizes for these DMPs in the individual cohorts are presented in Table S3 in Supplement 2. In NTR, an increase of 1 SD in CAARS ADHD symptoms (3.9 points) was associated with a methylation change of −0.04% (cg26197679), −0.16% (cg23144852), and 0.10% (cg10984962).
      Table 1Cohort Characteristics
      CharacteristicCohort
      NTR (N = 2258
      In NTR, 2258 samples from 2232 individuals were included (for 26 individuals, two longitudinal DNA samples were included).
      )
      Dunedin Study (N = 800)E-Risk Study (N = 1631)
      CAARS ADHD Index7.9 (3.9)
      DSM-5 Adult ADHD Symptoms1.5 (2.3)5.8 (4.3)
      Age at Blood Sampling, Years37.3 (12.9)38 (0)18 (0)
      Sex, Female1549 (69.0%)392 (49.0%)817 (50.1%)
      Current Smoker
      In E-Risk, the smoking variable that was included as covariate in the analyses and that is presented in this table is current daily smoking at age 18.
      438 (19.4%)162 (20.3%)372 (22.8%)
      Former Smoker532 (23.6%)233 (29.1%)
      Never Smoked1288 (57.0%)405 (50.6%)
      Nonsmoker
      In E-Risk, the smoking variable that was included as covariate in the analyses and that is presented in this table is current daily smoking at age 18.
      1259 (77.2%)
      Values are presented as mean (SD) or n (%).
      ADHD, attention-deficit/hyperactivity disorder; CAARS, Conners Adult ADHD Rating Scale; NTR, Netherlands Twin Register.
      a In NTR, 2258 samples from 2232 individuals were included (for 26 individuals, two longitudinal DNA samples were included).
      b In E-Risk, the smoking variable that was included as covariate in the analyses and that is presented in this table is current daily smoking at age 18.
      Table 2Top DMPs From EWAS Meta-analysis
      cgIDCHRPosition
      Genome build Hg19 (build 37).
      GeneLocationNearest GeneWeight
      Total sample size in the meta-analysis.
      Z Scorep ValueDirection
      cg261976798142310085IntergenicLINC013004689−4.91.1 × 10−6−−−
      cg231448523193405999OPA1Gene bodyOPA14684−4.81.7 × 10−6−−−
      cg109849622236462202AGAP1Gene bodyAGAP146884.59.7 × 10−6+++
      CpGs with a p value < 1.0 × 10−5 are shown.
      CHR, chromosome; DMPs, differentially methylated positions; EWAS, epigenome-wide association study; +, positive direction of effect; −, negative direction of effect.
      a Genome build Hg19 (build 37).
      b Total sample size in the meta-analysis.

      Heterogeneity of Top DMPs

      One top DMP (cg26197679) displayed large between-study heterogeneity (I2 = 93.6%, heterogeneity p = 1.6 × 10−7). This DMP showed the strongest association with ADHD symptoms and was epigenome-wide significant in the Dunedin study, where an increase of 1 SD in DSM-5 ADHD symptoms was associated with a methylation change of −0.62%. Although the effect size was weaker in the other cohorts, the direction of association was the same in all cohorts (Table 2). Inspection of all top-ranking sites from the individual cohorts (p < 1.0 × 10−5: 14 sites) revealed that all sites were characterized by substantial between-study heterogeneity (mean I2 = 87.2%; range, 71.3%–93.8%) (Table S4 in Supplement 2). Nine sites (64.3%) showed the same direction of effect across all cohorts.

      Inattention and Hyperactivity

      In sensitivity analyses, the association with inattention and hyperactivity/impulsivity subscales, separately, was tested in NTR for the three top DMPs from the meta-analysis. This analysis showed that the direction of effect and the strength of the association with each of the subscales was highly similar (Table S5 in Supplement 2).

      Enrichment of EWAS and GWAS Loci

      In testing for overlap of our EWAS meta-analysis results with findings from previous EWASs and GWASs, we failed to observe enrichment of CpGs previously associated with longitudinal ADHD trajectories in children (
      • Walton E.
      • Pingault J.B.
      • Cecil C.A.
      • Gaunt T.R.
      • Relton C.L.
      • Mill J.
      • Barker E.D.
      Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
      ) or schizophrenia in adults (
      • Hannon E.
      • Dempster E.
      • Viana J.
      • Burrage J.
      • Smith A.R.
      • Macdonald R.
      • et al.
      An integrated genetic-epigenetic analysis of schizophrenia: Evidence for co-localization of genetic associations and differential DNA methylation.
      ); CpGs associated with individual (
      • Joehanes R.
      • Just A.C.
      • Marioni R.E.
      • Pilling L.C.
      • Reynolds L.M.
      • Mandaviya P.R.
      • et al.
      Epigenetic signatures of cigarette smoking.
      ) or maternal smoking (
      • Joubert B.R.
      • Felix J.F.
      • Yousefi P.
      • Bakulski K.M.
      • Just A.C.
      • Breton C.
      • et al.
      DNA methylation in newborns and maternal smoking in pregnancy: Genome-wide consortium meta-analysis.
      ); and CpGs near GWAS loci for ADHD, autism spectrum disorder, major depressive disorder, or schizophrenia (Table S6 in Supplement 2). Methylation sites previously associated with schizophrenia (
      • Hannon E.
      • Dempster E.
      • Viana J.
      • Burrage J.
      • Smith A.R.
      • Macdonald R.
      • et al.
      An integrated genetic-epigenetic analysis of schizophrenia: Evidence for co-localization of genetic associations and differential DNA methylation.
      ) showed a small but significant depletion of signal for ADHD symptoms.

      Differentially Methylated Regions

      In NTR, six significant DMRs were identified, which spanned from 164 to 848 bp and included 3 to 32 CpGs (Table 3; Table S7 in Supplement 2). One example is illustrated in Figure S3 in Supplement 1. In the Dunedin study, 19 significant DMRs were identified, spanning from 2 to 51 CpGs within regions of 18 to 1818 bp (Table 3; Table S7 in Supplement 2). In E-Risk, no significant DMRs were identified. Six distinct DMRs were detected in the major histocompatibility complex (MHC) region (chromosome 6): three in NTR and three in the Dunedin study. In line with the heterogeneity of DMP results across cohorts, none of the DMRs detected in NTR and Dunedin overlapped, and DMR analysis on the meta-analysis of the three cohorts did not detect significant DMRs.
      Table 3Significant DMRs Associated With ADHD Symptoms
      CHRStartEndProbes (n)
      Total number of methylation sites in the window.
      Region Size (bp)Region Šidák p ValueGenes
      Genes mapping to CpGs in the window.
      Gene Expression Associated With CpGs
      Netherlands Twin Register
      63165073531651363196284.3 × 10−10LY6G5C, DDAH2, AIF1, C6orf48, LST1, VWA7, HSPA1L, PRRC2A, MICB
      116741795867418406114487.6 × 10−9ACY3ACY3
      6315839153158422443091.2 × 10−6AIF1AIF1, LY6G5C, C6orf48, PRRC2A, LST1, LY6G5C, VWA7, BAG6, HSPA1L
      63324546033246308328483.0 × 10−5B3GALT4B3GALT4, RPS18
      1311361357411361373831644.8 × 10−5
      34863248448633145116612.1 × 10−4COL7A1COL7A1
      Dunedin Study
      632063394320652125118182.3 × 10−17TNXBSKIV2L, C4B, C4A, TNXB, TNXA
      5191127192104129774.4 × 10−11LRRC14B
      19179319321793237084382.3 × 10−8INSL3
      2074899274962186294.9 × 10−8C20orf54
      63284744132847846164051.3 × 10−6PPP1R2P1HLA-DQA
      157501907075019377103074.8 × 10−6CYP1A1
      4564581835645862374405.1 × 10−6PDCL2
      111769152176964694946.8 × 10−6HCCA2; LOC402778; LOC402778
      418673283718673333284958.9 × 10−6SORBS2
      15907275609072814195815.5 × 10−5SEMA4BSEMA4B
      712500381250757107196.4 × 10−5
      72728344272891345698.8 × 10−5AMZ1
      8416556734165608064071.5 × 10−4ANK1
      12258014552580162241673.5 × 10−4IFLTD1
      63247958324809921412.3 × 10−3
      149667062896671170115423.9 × 10−3BDKRB2
      2022795685227957752908.7 × 10−3
      4187985377187985473296.02
      819935281993546218.03MYOM2
      More detailed information on the regions is provided in Table S7 in Supplement 2. More detailed results for the association between DNA methylation levels and gene expression in cis is provided in Table S8 in Supplement 2 (Netherlands Twin Register) and Table S9 in Supplement 2 (Dunedin study). No significant DMRs were found in E-Risk.
      ADHD, attention-deficit/hyperactivity disorder; CHR, chromosome; DMRs, differentially methylation regions.
      a Total number of methylation sites in the window.
      b Genes mapping to CpGs in the window.
      Significant DMRs did not overlap with CpGs from the previous EWAS of ADHD trajectories in children (
      • Walton E.
      • Pingault J.B.
      • Cecil C.A.
      • Gaunt T.R.
      • Relton C.L.
      • Mill J.
      • Barker E.D.
      Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
      ) or with schizophrenia in adults (
      • Hannon E.
      • Dempster E.
      • Viana J.
      • Burrage J.
      • Smith A.R.
      • Macdonald R.
      • et al.
      An integrated genetic-epigenetic analysis of schizophrenia: Evidence for co-localization of genetic associations and differential DNA methylation.
      ). One of the six DMRs identified in NTR (chromosome 6: 33245460 to 3324630) contained CpGs previously associated with smoking (12 of the 32 CpGs) and maternal smoking (3 CpGs). Five of the 19 DMRs identified in the Dunedin study contained CpGs associated with smoking or maternal smoking (Table S7 in Supplement 2).
      Several DMRs were located in proximity of SNPs associated with schizophrenia (
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Biological insights from 108 schizophrenia-associated genetic loci.
      ). Of the DMRs in NTR, two were located within 100 kb and one was located within 1 Mb of schizophrenia-associated SNPs, respectively (all in the MHC region). Five DMRs in the Dunedin study were located within 1 Mb of schizophrenia-associated SNPs (two on chromosome 6, two on chromosome 7, and one on chromosome 15). None of the DMRs was located within 1 Mb of significant GWAS loci for ADHD, major depressive disorder, or autism spectrum disorder.

      Gene Expression in cis

      To examine potential functional consequences of top DMPs and DMRs, we used previously published data on whole-blood DNA methylation and RNA sequencing (n = 2101 samples). Whereas DNA methylation levels at the ADHD symptom level top DMPs were not associated with RNA levels of genes in cis, methylation levels at CpGs within five of the six significant DMRs detected in NTR were associated with expression levels of 14 genes (Table 3; Table S8 in Supplement 2). At one DMR, higher methylation level correlated with lower expression; at another DMR, higher methylation level correlated with higher expression; and at three DMRs, expression of some genes correlated positively and others negatively with methylation level. Of the 19 DMRs identified in the Dunedin study, the methylation levels at CpGs within three DMRs were associated with expression levels of seven genes (Table 3; Table S9 in Supplement 2). At two DMRs, a higher methylation level correlated with lower expression, and at one DMR, a higher methylation level correlated with higher expression.

      Methylation Quantitative Trait Loci

      To gain insight into genetic causes of variation underlying top DMPs and DMRs, we obtained whole-blood mQTL data (n = 3841 samples) (
      • Bonder M.J.
      • Luijk R.
      • Zhernakova D.V.
      • Moed M.
      • Deelen P.
      • Vermaat M.
      • et al.
      Disease variants alter transcription factor levels and methylation of their binding sites.
      ). One of the three top DMPs from the ADHD symptom meta-analysis was associated with six independent SNPs (mQTLs) in cis (Table S10 in Supplement 2). The majority of DMRs (92.0%; 23) was associated with mQTLs. For ADHD-associated DMRs in NTR, on average 68% of the CpGs within a DMR (range, 36.4%–92.3%) was associated with at least one mQTL SNP. A total of 164 mQTL associations were identified for NTR DMRs (76.8% were cis mQTLs and 23.2% were trans mQTLs) (Table S11 in Supplement 2), involving 59 CpGs and 55 SNPs. For ADHD-associated DMRs in Dunedin, 323 mQTL associations were identified, involving 126 CpGs and 154 SNPs (88% cis and 13% trans) (Table S12 in Supplement 2). On average, 64.9% (range, 0%–100%) of CpGs within DMRs identified in the Dunedin study was associated with one or more mQTLs. We highlight one example: a DMR on chromosome 11, detected in NTR and associated with the expression of ACY3, was associated with cis and trans mQTLs, and the correlation structure of DNA methylation within this DMR (Figure S2 in Supplement 2) mirrored the sharing across CpGs of trans-mQTLs on chromosome 6 (Figure S3 in Supplement 2). Comparing the overlap of mQTLs across cohorts revealed that two DMRs were associated with a common set of trans mQTLs. The DMR on chromosome 11 in NTR (associated with expression of ACY3) and the DMR on chromosome 15 in Dunedin (associated with expression of SEMA4B) were both associated in trans with SNPs on chromosome 6: 109626965 to 109616420 (rs9374080, rs1008084, and rs9386791).

      Correlation Between DNA Methylation Level in Blood and Brain

      Whereas DNA methylation levels at the ADHD symptom top DMPs did not correlate significantly between blood and brain, CpGs within 4 of the 6 DMRs (66.7%) detected in NTR (Table S13 in Supplement 2) and 13 of the 19 DMRs (68.4%) detected in the Dunedin study (Table S14 in Supplement 2) showed significantly correlated DNA methylation levels between blood and one or multiple brain regions. For all CpGs except for one, the correlation was positive (mean r = .50; range, −.42 to .70). The number of CpGs per DMR that showed correlated methylation levels between blood and brain ranged from 1 to 31 (mean 5.5). The DMR with the largest number of CpGs with significant blood-brain correlations (31 CpGs) was a DMR in the MHC region detected in the Dunedin study and associated with the expression level of multiple genes, including C4A and C4B. An exemplary plot of methylation levels in blood and brain for one CpG in this region (cg01337207) is provided in Figure S5 in Supplement 1.

      Discussion

      We performed an EWAS of ADHD symptoms in three population-based adult cohorts. Our hypothesis was that DNA methylation in blood may provide insight into epigenetic consequences of life conditions that correlate with ADHD symptoms and potentially into epigenetic mechanisms that contribute to ADHD symptoms or that correlate with causal epigenetic mechanisms in the brain. In the Dunedin study, we identified one significant DMP where a higher methylation level correlated with fewer ADHD symptoms (cg26197679, chromosome 8 intergenic). This CpG as well as other top-ranking CpGs from individual cohorts showed considerable heterogeneity, and no significant DMPs were detected in a meta-analysis of the three cohorts. In secondary region-based analyses, we tested if ADHD symptoms were associated with methylation differences at multiple nearby CpGs that individually failed to reach epigenome-wide significance. We identified six significant DMRs in NTR, 19 in the Dunedin study, and none in E-Risk. In line with the heterogeneity of DMP results, none of the DMRs overlapped across cohorts, and the meta-analysis–based DMR analysis did not detect significant DMRs. With respect to effects of differential exposures, although the EWAS signal showed no significant enrichment for CpGs previously associated with smoking, some DMRs contained CpGs previously associated with smoking (
      • Joehanes R.
      • Just A.C.
      • Marioni R.E.
      • Pilling L.C.
      • Reynolds L.M.
      • Mandaviya P.R.
      • et al.
      Epigenetic signatures of cigarette smoking.
      ) or maternal smoking (
      • Joubert B.R.
      • Felix J.F.
      • Yousefi P.
      • Bakulski K.M.
      • Just A.C.
      • Breton C.
      • et al.
      DNA methylation in newborns and maternal smoking in pregnancy: Genome-wide consortium meta-analysis.
      ), even after we adjusted for smoking. There are several possible explanations for this finding (
      • van Dongen J.
      • Bonder M.J.
      • Dekkers K.F.
      • Nivard M.G.
      • van Iterson M.
      • Willemsen G.
      • et al.
      DNA methylation signatures of educational attainment.
      ): 1) residual confounding effects of smoking; 2) second-hand smoking exposure, including maternal prenatal smoking; 3) exposures other than smoking; for instance, ADHD is associated with general substance use (
      • Fayyad J.
      • Sampson N.A.
      • Hwang I.
      • Adamowski T.
      • Aguilar-Gaxiola S.
      • Al-Hamzawi A.
      • et al.
      The descriptive epidemiology of DSM-IV Adult ADHD in the World Health Organization World Mental Health Surveys.
      ); and 4) these CpGs are connected to a shared underlying biology of ADHD symptoms and smoking. Importantly, the current study shows that effects of mQTLs and correlated methylation levels between blood and brain exceed effects of smoking: 92% of DMRs were associated with genetic variants, 68% of DMRs showed correlated methylation levels between blood and brain regions, and 24% of DMRs were associated with smoking. These observations suggest that interindividual differences in DNA methylation at these DMRs are not merely driven by lifestyle differences associated with ADHD symptoms, such as smoking, and that some of the methylation differences in whole blood associated with ADHD symptoms may be a marker for methylation variation in the brain.
      Some top DMPs and top DMRs mapped to genes that have been previously linked to psychiatric disorders or implicated in brain biology. These are potential candidates for being involved in the underlying biology of ADHD symptoms, provided that these loci also show symptom-associated differences in epigenetic regulation in the brain. For instance, cg10984962, the third-ranking CpG from the meta-analysis, is located in AGAP1, which encodes a protein involved in endosomal trafficking. In neuronal cells, it plays a role in the recycling of muscarinic acetylcholine receptors (
      • Bendor J.
      • Lizardi-Ortiz J.E.
      • Westphalen R.I.
      • Brandstetter M.
      • Hemmings H.C.
      • Sulzer D.
      • et al.
      AGAP1/AP-3-dependent endocytic recycling of M5 muscarinic receptors promotes dopamine release.
      ) and was shown to influence dendritic spine morphology (
      • Arnold M.
      • Cross R.
      • Singleton K.S.
      • Zlatic S.
      • Chapleau C.
      • Mullin A.P.
      • et al.
      The endosome localized Arf-GAP AGAP1 modulates dendritic spine morphology downstream of the neurodevelopmental disorder factor dysbindin.
      ). Yet, methylation levels in blood did not correlate with methylation levels in the brain at this CpG. Six significant DMRs in distinct subregions of the MHC were identified in NTR and the Dunedin Study. The top DMR in MHC in the Dunedin study was associated with expression levels of SKIV2L, C4B, C4A, TNXB, and TNXA. CpGs in this DMR were not associated with smoking (
      • Joehanes R.
      • Just A.C.
      • Marioni R.E.
      • Pilling L.C.
      • Reynolds L.M.
      • Mandaviya P.R.
      • et al.
      Epigenetic signatures of cigarette smoking.
      ). The C4 genes are of great interest, as they have been previously implicated in functional effects of schizophrenia-associated SNPs in the MHC on postnatal synaptic pruning (
      • Sekar A.
      • Bialas A.R.
      • de Rivera H.
      • Davis A.
      • Hammond T.R.
      • Kamitaki N.
      • et al.
      Schizophrenia risk from complex variation of complement component 4.
      ). At many CpGs in this DMR, DNA methylation levels in blood showed moderate to strong correlations with DNA methylation levels in multiple brain regions. However, as the DMRs did not replicate across cohorts, it remains to be established whether they are relevant to ADHD symptom levels.
      The lack of overlap of our findings with CpGs from a previous study, in which methylation level in cord blood significantly predicted ADHD trajectories in childhood (
      • Walton E.
      • Pingault J.B.
      • Cecil C.A.
      • Gaunt T.R.
      • Relton C.L.
      • Mill J.
      • Barker E.D.
      Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
      ), could indicate that epigenetic associations relevant to ADHD symptoms are age specific [which has already been described for genetic contributions to ADHD symptoms (
      • Chang Z.
      • Lichtenstein P.
      • Asherson P.J.
      • Larsson H.
      Developmental twin study of attention problems: High heritabilities throughout development.
      )]. The loci detected in cord blood also did not show an association with ADHD symptoms when methylation was assessed at age 7 years in the previous study (
      • Walton E.
      • Pingault J.B.
      • Cecil C.A.
      • Gaunt T.R.
      • Relton C.L.
      • Mill J.
      • Barker E.D.
      Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
      ). Age-specific epigenetic associations may also potentially explain a lack of overlap with GWAS loci. Whereas our EWAS included only adults, the GWAS of ADHD contained mainly children (
      • 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 ADHD.
      ).
      This is the first EWAS of ADHD symptoms in adults, the largest epigenetic study of ADHD symptoms to date, and the first study of ADHD symptoms to apply a multicohort approach. This study also has limitations. Although all cohorts included adults and applied continuous measures of ADHD symptoms reported by the same informant (self-report), there were also differences between cohorts that may have reduced power. First, CAARS was used in NTR, which is based on DSM-IV, whereas DSM-5 symptoms were assessed in the E-Risk and Dunedin study. Second, ADHD symptoms were assessed by a self-report scale in NTR and by structured interviews in the Dunedin study and E-Risk. Third, DNA methylation measurements, processing of data, and quality control were performed separately in each cohort, based on quality control and normalization pipelines that were optimized for each cohort. Fourth, cohorts varied in age and reported number of ADHD symptoms. NTR included a broad age range (mean age 37 years), whereas participants from the E-Risk study were young adults (age 18 years), and the age of participants from the Dunedin study was 38 years. The prevalence of ADHD in these cohorts, based on the instruments used in the current study, has been previously reported: 7% in NTR (
      • Boomsma D.I.
      • Saviouk V.
      • Hottenga J.J.
      • Distel M.A.
      • de Moor M.H.
      • Vink J.M.
      • et al.
      Genetic epidemiology of attention deficit hyperactivity disorder (ADHD index) in adults.
      ), 3% in the Dunedin study (
      • Agnew-Blais J.C.
      • Polanczyk G.V.
      • Danese A.
      • Wertz J.
      • Moffitt T.E.
      • Arseneault L.
      Evaluation of the persistence, remission, and emergence of attention-deficit/hyperactivity disorder in young adulthood.
      ), and 8% in E-Risk (
      • Fortin J.
      • Labbe A.
      • Lemire M.
      • Zanke B.W.
      • Hudson T.J.
      • Fertig E.J.
      • et al.
      Functional normalization of 450k methylation array data improves replication in large cancer studies.
      ). Fifth, the cohorts were from different countries, and it is possible that epigenetic differences exist between cohorts owing to differences in genetic background or in the presence and frequency of environmental exposures.
      We performed a power analysis for the three meta-analysis top DMPs, based on their effect size observed in NTR (Figure S6 in Supplement 1). The effect sizes of these three DMPs ranged from 0.09% to 0.92% explained variance. For the top DMP that showed the least heterogeneity (cg10984962; AGAP1; variance explained 0.28% in NTR), the required sample size to achieve 80% power is 13,508. Similar to our population-based EWAS of ADHD symptoms, a recent blood-based EWAS meta-analysis of cognitive abilities identified only a few significant sites for most cognitive measures in >6000 individuals (
      • Marioni R.E.
      • McRae A.F.
      • Bressler J.
      • Colicino E.
      • Hannon E.
      • Li S.
      • et al.
      Meta-analysis of epigenome-wide association studies of cognitive abilities.
      ).
      Several limitations apply to epigenetic epidemiology studies, including ours. First, the HumanMethylation450K array captures only approximately 1.7% of all CpGs in the genome, and the removal of methylation probes that overlap with genetic polymorphisms, albeit inevitable because these polymorphisms compromise the quality of microarray-based measurements of DNA methylation, may limit the ability to detect DNA methylation–mediated SNP effects on the phenotype. Second, blood is unlikely to provide a complete picture of ADHD-related epigenetic processes because epigenetic markers are largely tissue specific. Yet, methylation changes in blood have already been associated with ADHD symptoms (
      • Walton E.
      • Pingault J.B.
      • Cecil C.A.
      • Gaunt T.R.
      • Relton C.L.
      • Mill J.
      • Barker E.D.
      Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
      ), other psychiatric conditions (
      • Hannon E.
      • Dempster E.
      • Viana J.
      • Burrage J.
      • Smith A.R.
      • Macdonald R.
      • et al.
      An integrated genetic-epigenetic analysis of schizophrenia: Evidence for co-localization of genetic associations and differential DNA methylation.
      ), cognition (
      • Marioni R.E.
      • McRae A.F.
      • Bressler J.
      • Colicino E.
      • Hannon E.
      • Li S.
      • et al.
      Meta-analysis of epigenome-wide association studies of cognitive abilities.
      ), and hippocampal volume (
      • Jia T.
      • Chu C.
      • Liu Y.
      • van Dongen J.
      • Armstrong N.J.
      • Bastin M.E.
      • et al.
      Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: Findings from the ENIGMA Epigenetics Working.
      ) in earlier studies. Furthermore, we found that several of the DMRs detected in blood contain CpGs with correlated methylation levels across blood and brain. Finally, a general constraint in epigenetic studies is that trait-associated variation in DNA methylation may arise secondary to trait development (reverse causality) or may be a marker of trait-associated environmental exposures (lifestyle; medication use; infections; and early life environmental factors, such as nutrition and maternal smoking). These associations nevertheless may provide valuable insight into the underlying biological changes associated with ADHD and its risk factors. The (direction of) causality may be addressed in human studies, for example, with longitudinal study designs or Mendelian randomization (
      • Dekkers K.F.
      • van Iterson M.
      • Slieker R.C.
      • Moed M.H.
      • Bonder M.J.
      • van Galen M.
      • et al.
      Blood lipids influence DNA methylation in circulating cells.
      ).
      In conclusion, by performing an EWAS of ADHD symptoms in three population-based adult cohorts, we found no significant sites in an EWAS meta-analysis of 4689 individuals and observed considerable heterogeneity of effects across cohorts. We found several significant cohort-specific DMPs and DMRs, with the MHC region emerging in two cohorts. The significance of these findings is unknown, but they may point at new candidate pathways awaiting further replication. Larger studies are necessary to identify methylation sites in whole blood that are robustly associated with population-based ADHD symptoms in adults. Our findings also illustrate the need for further research to examine to what extent epigenetic associations with psychiatric traits depend on characteristics such as age, sex, lifetime exposures, genetic background, symptom severity, and comorbidities.

      Acknowledgments and Disclosures

      NTR: NTR was supported by Biobanking and Biomolecular Research Infrastructure, the Netherlands (BBMRI-NL), Netherlands Organisation for Scientific Research ( NWO) 480-15-001/674 , Netherlands Twin Registry Repository : researching the interplay between genome and environment ( NWO 184.021.007 ), and Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies (ACTION). ACTION receives funding from the European Union Seventh Framework Program ( FP7/2007-2013 ) (Grant No. 602768 ). NTR was also supported by the Royal Netherlands Academy of Science Professor Award (Grant No. PAH/6635 to DIB), Netherlands Organization for Scientific Research (Vici Grant No. 016-130-669 to BF), Dutch National Science Agenda for the NWANeurolabNL project (Grant No. 400 17 602 to BF), European Community FP7 (Grant No. 602805 [Aggressotype] to BF), and European Community Horizon 2020 Program ( H2020/2014–2020 ) (Grant No. 728018 [Eat2beNICE] to BF).
      Dunedin study: The Dunedin Multidisciplinary Health and Development Research Unit was supported by the New Zealand Health Research Council and New Zealand Ministry of Business, Innovation and Employment, and also by the National Institutes of Health National Institute of Aging (Grant No. R01AG032282 ), Medical Research Council (Grant No. MR/P005918 ), and Jacobs Foundation . This work used a high-performance computing facility partially supported by Grant No. 2016-IDG-1013 (“HARDAC+: Reproducible HPC for Next-generation Genomics”) from the North Carolina Biotechnology Center .
      E-Risk study: The E-Risk study was supported by the Medical Research Council (Grant No. G1002190 ), National Institutes of Health Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant No. HD077482 ), American Asthma Foundation Distinguished Investigator Award (to JM), Jacobs Foundation, Avielle Foundation, Economic and Social Research Council Mental Health Leadership Fellowship (to LA), and Medical Research Council Skills Development Fellowship (to JA-B). This work used a high-performance computing facility partially supported by Grant No. 2016-IDG-1013 (“HARDAC+: Reproducible HPC for Next-generation Genomics”) from the North Carolina Biotechnology Center .
      NTR: We thank the twins and their family members for their participation.
      Dunedin study: We thank the Dunedin Study members, unit research staff, and study founder Phil Silva.
      E-Risk study: We thank the study families for their participation and members of the E-Risk team for their dedication, hard work, and insights.
      The HumanMethylation450K BeadChip and RNA sequencing data from the BIOS Consortium are available in the European Genome-phenome Archive, under accession code EGAD00010000887. Data from the Dunedin Multidisciplinary Health and Development are available via a managed access system (contact: [email protected] ). The HumanMethylation450K BeadChip DNA methylation data from the E-Risk study are available in Gene Expression Omnibus under accession number GSE105018.
      BF has received educational speaking fees from Shire and MEDICE. The other authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

      References

        • Polanczyk G.
        • Rohde L.A.
        Epidemiology of attention-deficit/hyperactivity disorder across the lifespan.
        Curr Opin Psychiatry. 2007; 20: 386-392
        • Larsson H.
        • Anckarsater H.
        • Råstam 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. 2012; 53: 73-80
        • Groen-Blokhuis M.M.
        • Middeldorp C.M.
        • Kan K.J.
        • Abdellaoui A.
        • Van Beijsterveldt C.E.M.
        • Ehli E.A.
        • 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. 2014; 53: 1123-1129
        • 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 ADHD.
        Nat Genet. 2019; 51: 63-75
        • Hawi Z.
        • Cummins T.D.R.
        • Tong J.
        • Arcos-Burgos M.
        • Zhao Q.
        • Matthews N.
        • et al.
        Rare DNA variants in the brain-derived neurotrophic factor gene increase risk for attention-deficit hyperactivity disorder: A next-generation sequencing study.
        Mol Psychiatry. 2017; 22: 580-584
        • Demontis D.
        • Lescai F.
        • Børglum A.
        • Glerup S.
        • Østergaard S.D.
        • Mors O.
        • et al.
        Whole-exome sequencing reveals increased burden of rare functional and disruptive variants in candidate risk genes in individuals with persistent attention-deficit/hyperactivity disorder.
        J Am Acad Child Adolesc Psychiatry. 2016; 55: 521-523
        • Mill J.
        • Petronis A.
        Pre- and peri-natal environmental risks for attention-deficit hyperactivity disorder (ADHD): The potential role of epigenetic processes in mediating susceptibility.
        J Child Psychol Psychiatry. 2008; 49: 1020-1030
        • Groen-Blokhuis M.M.
        • Middeldorp C.M.
        • Van Beijsterveldt C.E.
        • Boomsma D.I.
        Evidence for a causal association of low birth weight and attention problems.
        J Am Acad Child Adolesc Psychiatry. 2011; 50: 1247-1254.e2
        • Dong T.
        • Hu W.
        • Zhou X.
        • Lin H.
        • Lan L.
        • Hang B.
        • et al.
        Prenatal exposure to maternal smoking during pregnancy and attention-deficit/hyperactivity disorder in offspring: A meta-analysis.
        Reprod Toxicol. 2018; 76: 63-70
        • Werenberg Dreier J.
        • Nybo Andersen A.M.
        • Hvolby A.
        • Garne E.
        • Kragh Andersen P.
        • Berg-Beckhoff G.
        Fever and infections in pregnancy and risk of attention deficit/hyperactivity disorder in the offspring.
        J Child Psychol Psychiatry. 2016; 57: 540-548
        • Thapar A.
        • Cooper M.
        • Eyre O.
        • Langley K.
        What have we learnt about the causes of ADHD?.
        J Child Psychol Psychiatry. 2013; 54: 3-16
        • Malhotra S.
        • Bhatia N.K.
        • Kumar P.
        • Hans C.
        • Bhatia M.S.
        The potential role of infections in attention-deficit hyperactivity disorder.
        Delhi Psychiatry J. 2011; 14: 361-367
        • van Mil N.H.
        • Steegers-Theunissen R.P.
        • Bouwland-Both M.I.
        • Verbiest M.M.P.J.
        • Rijlaarsdam J.
        • Hofman A.
        • et al.
        DNA methylation profiles at birth and child ADHD symptoms.
        J Psychiatr Res. 2014; 49: 51-59
        • Park S.
        • Lee J.M.
        • Kim J.W.
        • Cho D.Y.
        • Yun H.J.
        • Han D.H.
        • et al.
        Associations between serotonin transporter gene (SLC6A4) methylation and clinical characteristics and cortical thickness in children with ADHD.
        Psychol Med. 2015; 45: 3009-3017
        • Rijlaarsdam J.
        • Cecil C.A.
        • Walton E.
        • Mesirow M.S.C.
        • Relton C.L.
        • Gaunt T.R.
        • et al.
        Prenatal unhealthy diet, insulin-like growth factor 2 gene (IGF2) methylation, and attention deficit hyperactivity disorder symptoms in youth with early-onset conduct problems.
        J Child Psychol Psychiatry. 2017; 58: 19-27
        • Walton E.
        • Pingault J.B.
        • Cecil C.A.
        • Gaunt T.R.
        • Relton C.L.
        • Mill J.
        • Barker E.D.
        Epigenetic profiling of ADHD symptoms trajectories: A prospective, methylome-wide study.
        Mol Psychiatry. 2017; 22: 250-256
        • Barker E.D.
        • Walton E.
        • Cecil C.A.M.
        • Rowe R.
        • Jaffee S.R.
        • Maughan B.
        • et al.
        A methylome-wide association study of trajectories of oppositional defiant behaviors and biological overlap with attention deficit hyperactivity disorder.
        Child Dev. 2018; 89: 1839-1855
        • 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
        • Wilmot B.
        • Fry R.
        • Smeester L.
        • Musser E.D.
        • Mill J.
        • Nigg T.
        • et al.
        Methylomic analysis of salivary DNA in childhood ADHD identifies altered DNA methylation in VIPR2 identifies altered DNA methylation in VIPR2.
        J Child Psychol Psychiatry. 2016; 57: 152-160
        • Dadds M.R.
        • Schollar-Root O.
        • Lenroot R.
        • Moul C.
        • Hawes D.J.
        Epigenetic regulation of the DRD4 gene and dimensions of attention-deficit/hyperactivity disorder in children.
        Eur Child Adolesc Psychiatry. 2016; 25: 1081-1089
        • Peter C.J.
        • Fischer L.K.
        • Kundakovic M.
        • Garg P.
        • Jakovcevski M.
        • Dincer A.
        • et al.
        DNA methylation signatures of early childhood malnutrition associated with impairments in attention and cognition.
        Biol Psychiatry. 2016; 80: 765-774
        • Heinrich H.
        • Grunitz J.
        • Stonawski V.
        • Frey S.
        • Wahl S.
        • Albrecht B.
        • et al.
        Attention, cognitive control and motivation in ADHD: Linking event-related brain potentials and DNA methylation patterns in boys at early school age.
        Sci Rep. 2017; 7: 3823
        • Sengupta S.M.
        • Smith A.K.
        • Grizenko N.
        • Joober R.
        Locus-specific DNA methylation changes and phenotypic variability in children with attention-deficit hyperactivity disorder.
        Psychiatry Res. 2017; 256: 298-304
        • Gervin K.
        • Nordeng H.
        • Ystrom E.
        • Reichborn-Kjennerud T.
        • Lyle R.
        Long-term prenatal exposure to paracetamol is associated with DNA methylation differences in children diagnosed with ADHD.
        Clin Epigenetics. 2017; 9: 77
        • Ding K.
        • Yang J.
        • Reynolds G.P.
        • Chen B.
        • Shao J.
        • Liu R.
        • et al.
        DAT1 methylation is associated with methylphenidate response on oppositional and hyperactive-impulsive symptoms in children and adolescents with ADHD.
        World J Biol Psychiatry. 2017; 18: 291-299
        • van Dongen J.
        • Nivard M.G.
        • Willemsen G.
        • Hottenga J.J.
        • Helmer Q.
        • Dolan C.V.
        • et al.
        Genetic and environmental influences interact with age and sex in shaping the human methylome.
        Nat Commun. 2016; 7: 11115
        • McRae A.F.
        • Powell J.E.
        • Henders A.K.
        • Bowdler L.
        • Hemani G.
        • Shah S.
        • et al.
        Contribution of genetic variation to transgenerational inheritance of DNA methylation.
        Genome Biol. 2014; 15: R73
        • Heijmans B.T.
        • Tobi E.W.
        • Stein A.D.
        • Putter H.
        • Blauw G.J.
        • Susser E.S.
        • et al.
        Persistent epigenetic differences associated with prenatal exposure to famine in humans.
        Proc Natl Acad Sci U S A. 2008; 105: 17046-17049
        • Joubert B.R.
        • den Dekker H.T.
        • Felix J.F.
        • Bohlin J.
        • Ligthart S.
        • Beckett E.
        • et al.
        Maternal plasma folate impacts differential DNA methylation in an epigenome-wide meta-analysis of newborns.
        Nat Commun. 2016; 7: 10577
        • Joubert B.R.
        • Felix J.F.
        • Yousefi P.
        • Bakulski K.M.
        • Just A.C.
        • Breton C.
        • et al.
        DNA methylation in newborns and maternal smoking in pregnancy: Genome-wide consortium meta-analysis.
        Am J Hum Genet. 2016; 98: 680-696
        • Joehanes R.
        • Just A.C.
        • Marioni R.E.
        • Pilling L.C.
        • Reynolds L.M.
        • Mandaviya P.R.
        • et al.
        Epigenetic signatures of cigarette smoking.
        Circ Cardiovasc Genet. 2016; 9: 436-447
        • Slieker R.C.
        • Bos S.D.
        • Goeman J.J.
        • Bovée J.V.
        • Talens R.P.
        • Van Der Breggen R.
        • et al.
        Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array.
        Epigenetics Chromatin. 2013; 6: 26
        • Walton E.
        • Hass J.
        • Liu J.
        • Roffman J.L.
        • Bernardoni F.
        • Roessner V.
        • et al.
        Correspondence of DNA methylation between blood and brain tissue and its application to schizophrenia research.
        Schizophr Bull. 2016; 42: 406-414
        • Hannon E.
        • Lunnon K.
        • Schalkwyk L.
        • Mill J.
        Interindividual methylomic variation across blood, cortex, and cerebellum: Implications for epigenetic studies of neurological and neuropsychiatric phenotypes.
        Epigenetics. 2015; 10: 1024-1032
        • Edgar R.D.
        • Jones M.J.
        • Meaney M.J.
        • Turecki G.
        • Kobor M.S.
        BECon: A tool for interpreting DNA methylation findings from blood in the context of brain.
        Transl Psychiatry. 2017; 7: e1187
        • 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
        • Willemsen G.
        • Vink J.M.
        • Abdellaoui A.
        • den Braber A.
        • van Beek J.H.
        • Draisma H.H.
        • et al.
        The Adult Netherlands Twin Register: Twenty-five years of survey and biological data collection.
        Twin Res Hum Genet. 2013; 16: 271-281
        • Poulton R.
        • Moffitt T.E.
        • Silva P.A.
        The Dunedin Multidisciplinary Health and Development Study: Overview of the first 40 years, with an eye to the future.
        Soc Psychiatry Psychiatr Epidemiol. 2015; 50: 679-693
        • Moffitt T.E.
        • Adlam A.
        • Affleck G.
        • Andreou P.
        • Aquan-Assee J.
        • Arseneault L.
        • et al.
        Teen-aged mothers in contemporary Britain.
        J Child Psychol Psychiatry. 2002; 43: 727-742
        • Conners C.K.
        • Erhardt D.
        • Sparrow E.
        Conners’ Adult ADHD Rating Scales (CAARS) Technical Manual.
        Multi-Health Systems, North Tonawanda, NY1999
        • Moffitt T.E.
        • Houts R.
        • Asherson P.
        • Belsky D.W.
        • Corcoran D.L.
        • Hammerle M.
        • et al.
        Is adult ADHD a childhood-onset neurodevelopmental disorder? Evidence from a four-decade longitudinal cohort study.
        Am J Psychiatry. 2015; 172: 967-977
        • Agnew-Blais J.C.
        • Polanczyk G.V.
        • Danese A.
        • Wertz J.
        • Moffitt T.E.
        • Arseneault L.
        Evaluation of the persistence, remission, and emergence of attention-deficit/hyperactivity disorder in young adulthood.
        JAMA Psychiatry. 2016; 73: 713-720
        • Fortin J.
        • Labbe A.
        • Lemire M.
        • Zanke B.W.
        • Hudson T.J.
        • Fertig E.J.
        • et al.
        Functional normalization of 450k methylation array data improves replication in large cancer studies.
        Genome Biol. 2014; 15: 503
        • Pidsley R.
        • Y Wong C.C.
        • Volta M.
        • Lunnon K.
        • Mill J.
        • Schalkwyk L.C.
        A data-driven approach to preprocessing Illumina 450K methylation array data.
        BMC Genomics. 2013; 14: 293
        • Genome of the Netherlands Consortium
        Whole-genome sequence variation, population structure and demographic history of the Dutch population.
        Nat Genet. 2014; 46: 818-825
        • Price M.E.
        • Cotton A.M.
        • Lam L.L.
        • Farré P.
        • Emberly E.
        • Brown C.J.
        • et al.
        Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array.
        Epigenetics Chromatin. 2013; 6: 4
        • Chen Y.
        • Lemire M.
        • Choufani S.
        • Butcher D.T.
        • Zanke B.W.
        • Gallinger S.
        • et al.
        Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray.
        Epigenetics. 2013; 8: 203-209
        • van Iterson M.
        • van Zwet E.W.
        • Heijmans B.T.
        • the BIOS Consortium
        Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution.
        Genome Biol. 2017; 18: 19
        • Willer C.J.
        • Li Y.
        • Abecasis G.R.
        METAL: Fast and efficient meta-analysis of genomewide association scans.
        Bioinformatics. 2010; 26: 2190-2191
        • Wray N.R.
        • Ripke S.
        • Mattheisen M.
        • Trzaskowski M.
        • Byrne E.M.
        • Abdellaoui A.
        • et al.
        Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
        Nat Genet. 2018; 50: 668-681
        • Schizophrenia Working Group of the Psychiatric Genomics Consortium
        Biological insights from 108 schizophrenia-associated genetic loci.
        Nature. 2014; 511: 421-427
        • Grove J.
        • Ripke S.
        • Als T.D.
        • Mattheisen M.
        • Walters R.
        • Won H.
        • et al.
        Identification of common genetic risk variants for autism spectrum disorder.
        Nat Genet. 2019; 51: 431-444
        • Hannon E.
        • Dempster E.
        • Viana J.
        • Burrage J.
        • Smith A.R.
        • Macdonald R.
        • et al.
        An integrated genetic-epigenetic analysis of schizophrenia: Evidence for co-localization of genetic associations and differential DNA methylation.
        Genome Biol. 2016; 17: 176
        • Pedersen B.S.
        • Schwartz D.A.
        • Yang I.V.
        • Kechris K.J.
        Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values.
        Bioinformatics. 2012; 28: 2986-2988
        • Bonder M.J.
        • Luijk R.
        • Zhernakova D.V.
        • Moed M.
        • Deelen P.
        • Vermaat M.
        • et al.
        Disease variants alter transcription factor levels and methylation of their binding sites.
        Nat Genet. 2017; 49: 131-138
        • van Dongen J.
        • Bonder M.J.
        • Dekkers K.F.
        • Nivard M.G.
        • van Iterson M.
        • Willemsen G.
        • et al.
        DNA methylation signatures of educational attainment.
        NPJ Sci Learn. 2018; 3: 7
        • Fayyad J.
        • Sampson N.A.
        • Hwang I.
        • Adamowski T.
        • Aguilar-Gaxiola S.
        • Al-Hamzawi A.
        • et al.
        The descriptive epidemiology of DSM-IV Adult ADHD in the World Health Organization World Mental Health Surveys.
        Atten Deficit Hyperact Disord. 2017; 9: 47-65
        • Bendor J.
        • Lizardi-Ortiz J.E.
        • Westphalen R.I.
        • Brandstetter M.
        • Hemmings H.C.
        • Sulzer D.
        • et al.
        AGAP1/AP-3-dependent endocytic recycling of M5 muscarinic receptors promotes dopamine release.
        EMBO J. 2010; 29: 2813-2826
        • Arnold M.
        • Cross R.
        • Singleton K.S.
        • Zlatic S.
        • Chapleau C.
        • Mullin A.P.
        • et al.
        The endosome localized Arf-GAP AGAP1 modulates dendritic spine morphology downstream of the neurodevelopmental disorder factor dysbindin.
        Front Cell Neurosci. 2016; 10: 218
        • Sekar A.
        • Bialas A.R.
        • de Rivera H.
        • Davis A.
        • Hammond T.R.
        • Kamitaki N.
        • et al.
        Schizophrenia risk from complex variation of complement component 4.
        Nature. 2016; 530: 177-183
        • Chang Z.
        • Lichtenstein P.
        • Asherson P.J.
        • Larsson H.
        Developmental twin study of attention problems: High heritabilities throughout development.
        JAMA Psychiatry. 2013; 70: 311-318
        • Boomsma D.I.
        • Saviouk V.
        • Hottenga J.J.
        • Distel M.A.
        • de Moor M.H.
        • Vink J.M.
        • et al.
        Genetic epidemiology of attention deficit hyperactivity disorder (ADHD index) in adults.
        PLoS One. 2010; 5: e10621
        • Marioni R.E.
        • McRae A.F.
        • Bressler J.
        • Colicino E.
        • Hannon E.
        • Li S.
        • et al.
        Meta-analysis of epigenome-wide association studies of cognitive abilities.
        Mol Psychiatry. 2018; 23: 2133-2144
        • Jia T.
        • Chu C.
        • Liu Y.
        • van Dongen J.
        • Armstrong N.J.
        • Bastin M.E.
        • et al.
        Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: Findings from the ENIGMA Epigenetics Working.
        bioRxiv. 2018; ([published online ahead of print Nov 5])
        • Dekkers K.F.
        • van Iterson M.
        • Slieker R.C.
        • Moed M.H.
        • Bonder M.J.
        • van Galen M.
        • et al.
        Blood lipids influence DNA methylation in circulating cells.
        Genome Biol. 2016; 17: 138

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