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Rare copy number variants in males and females with childhood attention-deficit/hyperactivity disorder

Molecular Psychiatry volume 28, pages 1240–1247 (2023)Cite this article

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Abstract

While childhood attention-deficit/hyperactivity disorder (ADHD) is more prevalent in males than females, genetic contributors to this effect have not been established. Here, we explore sex differences in the contribution of common and/or rare genetic variants to ADHD. Participants were from the Adolescent Brain and Cognitive Development study (N = 1253 youth meeting DSM-5 criteria for ADHD [mean age = 11.46 years [SD = 0.87]; 31% female] and 5577 unaffected individuals [mean age = 11.42 years [SD = 0.89]; 50% female], overall 66% White, non-Hispanic (WNH), 19% Black/African American, and 15% other races. Logistic regression tested for interactions between sex (defined genotypically) and both rare copy number variants (CNV) and polygenic (common variant) risk in association with ADHD. There was a significant interaction between sex and the presence of a CNV deletion larger than 200 kb, both in the entire cohort (β = −0.74, CI = [−1.27 to −0.20], FDR-corrected p = 0.048) and, at nominal significance levels in the WNH ancestry subcohort (β = −0.86, CI = [−1.51 to −0.20], p = 0.010). Additionally, the number of deleted genes interacted with sex in association with ADHD (whole cohort. β = −0.13, CI = [−0.23 to −0.029], FDR-corrected p = 0.048; WNH. β = −0.17, CI = [−0.29 to −0.050], FDR-corrected p = 0.044) as did the total length of CNV deletions (whole cohort. β = −0.12, CI = [−0.19 to −0.044], FDR-corrected p = 0.028; WNH. β = −0.17, CI = [−0.28 to −0.061], FDR-corrected p = 0.034). This sex effect was driven by increased odds of childhood ADHD for females but not males in the presence of CNV deletions. No similar sex effect was found for CNV duplications or polygenic risk scores. The association between CNV deletions and ADHD was partially mediated by measures of cognitive flexibility. In summary, CNV deletions were associated with increased odds for childhood ADHD in females, but not males.

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Fig. 1: Sex-specific odds ratios for the effects of CNVs on ADHD.
Fig. 2: Cognition mediates the association between CNV deletions and ADHD.

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Data availability

Supplementary information is available at MP’s website.

Code availability

All code associated with this analysis is available on a GitHub repository at https://github.com/bencephalon/ABCD-CNV-ADHD.

References

  1. Polanczyk G, de Lima MS, Horta BL, Biederman J, Rohde LA. The worldwide prevalence of ADHD: a systematic review and metaregression analysis. AJP. 2007;164:942–8.

    Google Scholar 

  2. Nussbaum NL. ADHD and female specific concerns: a review of the literature and clinical implications. J Atten Disord. 2012;16:87–100.

    PubMed  Google Scholar 

  3. Hinshaw SP, Nguyen PT, O’Grady SM, Rosenthal EA Annual Research Review: Attention-deficit/hyperactivity disorder in girls and women: underrepresentation, longitudinal processes, and key directions. Journal of Child Psychology and Psychiatry [Internet]. 2022 [cited 2021 Sep 29]; Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/jcpp.13480

  4. Cortese S, Faraone SV, Bernardi S, Wang S, Blanco C. Gender differences in adult attention-deficit/hyperactivity disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). J Clin Psychiatry. 2016;77:e421–428.

    PubMed  Google Scholar 

  5. May T, Sciberras E, Brignell A, Williams K. Autism spectrum disorder: updated prevalence and comparison of two birth cohorts in a nationally representative Australian sample. BMJ Open. 2017;7:e015549.

    PubMed  PubMed Central  Google Scholar 

  6. Baizabal-Carvallo JF, Jankovic J. Sex differences in patients with Tourette syndrome. CNS Spectrums [Internet]. 2022. [Epub ahead of print].

  7. Young S, Adamo N, Ásgeirsdóttir BB, Branney P, Beckett M, Colley W, et al. Females with ADHD: An expert consensus statement taking a lifespan approach providing guidance for the identification and treatment of attention-deficit/ hyperactivity disorder in girls and women. BMC Psychiatry. 2020;20:404.

    PubMed  PubMed Central  Google Scholar 

  8. Coles EK, Slavec J, Bernstein M, Baroni E. Exploring the gender gap in referrals for children with ADHD and other disruptive behavior disorders. J Atten Disord. 2012;16:101–8.

    PubMed  Google Scholar 

  9. Howerton CL, Bale TL. Targeted placental deletion of OGT recapitulates the prenatal stress phenotype including hypothalamic mitochondrial dysfunction. Proc Natl Acad Sci USA. 2014;111:9639–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Eme RF. Selective females affliction in the developmental disorders of childhood: a literature review. J Clin Child Psychol. 1992;21:354–64.

    Google Scholar 

  11. Smalley SL, McGough JJ, Del’Homme M, NewDelman J, Gordon E, Kim T, et al. Familial clustering of symptoms and disruptive behaviors in multiplex families with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2000;39:1135–43.

    CAS  PubMed  Google Scholar 

  12. Kendler KS, Ohlsson H, Bacanu S, Sundquist J, Sundquist K. Differences in genetic risk score profiles for drug use disorder, major depression, and ADHD as a function of sex, age at onset, recurrence, mode of ascertainment, and treatment. Psychol Med [Internet]. 2022. [Epub ahead of print].

  13. Alexander-Bloch A, Huguet G, Schultz LM, Huffnagle N, Jacquemont S, Seidlitz J, et al. Copy Number Variant Risk Scores Associated With Cognition, Psychopathology, and Brain Structure in Youths in the Philadelphia Neurodevelopmental Cohort. JAMA Psychiatry [Internet]. 2022 May [cited 2022 May 16]; Available from: https://doi.org/10.1001/jamapsychiatry.2022.1017

  14. Williams NM, Zaharieva I, Martin A, Langley K, Mantripragada K, Fossdal R, et al. Rare chromosomal deletions and duplications in attention-deficit hyperactivity disorder: a genome-wide analysis. Lancet. 2010;376:1401–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Jarick I, Volckmar AL, Pütter C, Pechlivanis S, Nguyen TT, Dauvermann MR, et al. Genome-wide analysis of rare copy number variations reveals PARK2 as a candidate gene for attention-deficit/hyperactivity disorder. Mol Psychiatry. 2014;19:115–21.

    CAS  PubMed  Google Scholar 

  16. Stergiakouli E, Hamshere M, Holmans P, Langley K, Zaharieva I, Hawi Z, et al. Investigating the contribution of common genetic variants to the risk and pathogenesis of ADHD. Am J Psychiatry. 2012;169:186–94.

    PubMed  PubMed Central  Google Scholar 

  17. Yang L, Neale BM, Liu L, Lee SH, Wray NR, Ji N, et al. Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants. Am J Med Genet B Neuropsychiatr Genet. 2013;162B:419–30.

    PubMed  Google Scholar 

  18. Williams NM, Franke B, Mick E, Anney RJL, Freitag CM, Gill M, et al. Genome-wide analysis of copy number variants in attention deficit hyperactivity disorder: the role of rare variants and duplications at 15q13.3. Am J Psychiatry. 2012;169:195–204.

    PubMed  PubMed Central  Google Scholar 

  19. Ramos-Quiroga JA, Sánchez-Mora C, Casas M, Garcia-Martínez I, Bosch R, Nogueira M, et al. Genome-wide copy number variation analysis in adult attention-deficit and hyperactivity disorder. J Psychiatr Res. 2014;49:60–7.

    PubMed  Google Scholar 

  20. Elia J, Gai X, Xie HM, Perin JC, Geiger E, Glessner JT, et al. Rare structural variants found in attention-deficit hyperactivity disorder are preferentially associated with neurodevelopmental genes. Mol Psychiatry. 2010;15:637–46.

    CAS  PubMed  Google Scholar 

  21. Elia J, Glessner JT, Wang K, Takahashi N, Shtir CJ, Hadley D, et al. Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder. Nat Genet. 2011;44:78–84.

    PubMed  PubMed Central  Google Scholar 

  22. Lionel AC, Crosbie J, Barbosa N, Goodale T, Thiruvahindrapuram B, Rickaby J, et al. Rare copy number variation discovery and cross-disorder comparisons identify risk genes for ADHD. Sci Transl Med. 2011;3:95ra75.

    CAS  PubMed  Google Scholar 

  23. Thapar A, Martin J, Mick E, Arias Vásquez A, Langley K, Scherer SW, et al. Psychiatric gene discoveries shape evidence on ADHD’s biology. Mol Psychiatry. 2016;21:1202–7.

    CAS  PubMed  Google Scholar 

  24. Gudmundsson OO, Walters GB, Ingason A, Johansson S, Zayats T, Athanasiu L, et al. Attention-deficit hyperactivity disorder shares copy number variant risk with schizophrenia and autism spectrum disorder. Transl Psychiatry. 2019;9:1–9.

    Google Scholar 

  25. Harich B, van der Voet M, Klein M, Čížek P, Fenckova M, Schenck A, et al. From Rare Copy Number Variants to Biological Processes in ADHD. AJP [Internet]. 2020 Jun [cited 2020 Jul 9]; Available from: https://ajp.psychiatryonline.org/doi/full/10.1176/appi.ajp.2020.19090923

  26. Akutagava-Martins GC, Salatino-Oliveira A, Genro JP, Contini V, Polanczyk G, Zeni C, et al. Glutamatergic copy number variants and their role in attention-deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet. 2014;165B:502–9.

    PubMed  Google Scholar 

  27. Martin J, Walters RK, Demontis D, Mattheisen M, Lee SH, Robinson E, et al. A genetic investigation of sex bias in the prevalence of attention-deficit/hyperactivity disorder. Biol Psychiatry. 2018;83:1044–53.

    PubMed  PubMed Central  Google Scholar 

  28. Martin J, Taylor MJ, Rydell M, Riglin L, Eyre O, Lu Y, et al. Sex‐specific manifestation of genetic risk for attention deficit hyperactivity disorder in the general population. J Child Psychol Psychiatry, Allied Discip. 2018;59:908.

    Google Scholar 

  29. Auchter AM, Hernandez Mejia M, Heyser CJ, Shilling PD, Jernigan TL, Brown SA, et al. A description of the ABCD organizational structure and communication framework. Developmental Cogn Neurosci. 2018;32:8–15.

    Google Scholar 

  30. Townsend L, Kobak K, Kearney C, Milham M, Andreotti C, Escalera J, et al. Development of Three Web-Based Computerized Versions of the Kiddie Schedule for Affective Disorders and Schizophrenia Child Psychiatric Diagnostic Interview: Preliminary Validity Data. J Am Acad Child Adolesc Psychiatry. 2020;59:309–25.

    PubMed  Google Scholar 

  31. Cordova MM, Antovich DM, Ryabinin P, Neighbor C, Mooney MA, Dieckmann NF, et al. Attention-deficit/hyperactivity disorder: restricted phenotypes prevalence, comorbidity, and polygenic risk sensitivity in the ABCD baseline cohort. J Am Acad Child Adolesc Psychiatry. 2022;S0890-8567:00190–3.

    Google Scholar 

  32. Achenbach TM. The Achenbach system of empirically based assessment (ASEBA): Development, findings, theory, and applications. University of Vermont, Research Center for Children, Youth, & Families; 2009.

  33. Wang K, Li M, Hadley D, Liu R, Glessner J, Grant SFA, et al. PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 2007;17:1665–74.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Colella S, Yau C, Taylor JM, Mirza G, Butler H, Clouston P, et al. QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Res. 2007;35:2013–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. MacDonald JR, Ziman R, Yuen RKC, Feuk L, Scherer SW. The Database of Genomic Variants: a curated collection of structural variation in the human genome. Nucleic Acids Res 2014;42:D986–992.

    CAS  PubMed  Google Scholar 

  36. Huguet G, Schramm C, Douard E, Tamer P, Main A, Monin P, et al. Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability. Mol Psychiatry. 2021;26:2663–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Simpson NH, Ceroni F, Reader RH, Covill LE, Knight JC, Hennessy ER, et al. Genome-wide analysis identifies a role for common copy number variants in specific language impairment. Eur J Hum Genet. 2015;23:1370–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Collins RL, Glessner JT, Porcu E, Lepamets M, Brandon R, Lauricella C, et al. A cross-disorder dosage sensitivity map of the human genome. Cell. 2022;185:3041–55.e25.

    CAS  PubMed  Google Scholar 

  40. Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention-deficit/hyperactivity disorder. Nat Genet. 2019;51:63–75.

    CAS  PubMed  Google Scholar 

  41. Choi SW, O’Reilly PF. PRSice-2: Polygenic Risk Score software for biobank-scale data. GigaScience. 2019;8:giz082.

    PubMed  PubMed Central  Google Scholar 

  42. Euesden J, Lewis CM, O’Reilly PF. PRSice: polygenic risk score software. Bioinformatics. 2015;31:1466–8.

    CAS  PubMed  Google Scholar 

  43. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19:1655–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Kasper LJ, Alderson RM, Hudec KL. Moderators of working memory deficits in children with attention-deficit/hyperactivity disorder (ADHD): A meta-analytic review. Clin Psychol Rev. 2012;32:605–17.

    PubMed  Google Scholar 

  45. Cook NE, Braaten EB, Surman CBH. Clinical and functional correlates of processing speed in pediatric Attention-Deficit/Hyperactivity Disorder: a systematic review and meta-analysis. Child Neuropsychol. 2018;24:598–616.

    PubMed  Google Scholar 

  46. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF. Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biol Psychiatry. 2005;57:1336–46.

    PubMed  Google Scholar 

  47. Hodes RJ, Insel TR, Landis SC. The NIH toolbox. Neurology 2013;80:S1.

    PubMed  PubMed Central  Google Scholar 

  48. Frazier TW, Demaree HA, Youngstrom EA. Meta-analysis of intellectual and neuropsychological test performance in attention-deficit/hyperactivity disorder. Neuropsychology. 2004;18:543–55.

    PubMed  Google Scholar 

  49. Huguet G, Schramm C, Douard E, Jiang L, Labbe A, Tihy F, et al. Measuring and estimating the effect sizes of copy number variants on general intelligence in community-based samples. JAMA Psychiatry. 2018;75:447–57.

    PubMed  PubMed Central  Google Scholar 

  50. Hoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren LSJ, et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry. 2017;4:310–9.

    PubMed  PubMed Central  Google Scholar 

  51. Hoogman M, Muetzel R, Guimaraes JP, Shumskaya E, Mennes M, Zwiers MP, et al. Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples. Am J Psychiatry. 2019;176:531–42.

    PubMed  PubMed Central  Google Scholar 

  52. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2013. Available from: http://www.R-project.org/

  53. Wechsler D. Wechsler intelligence scale for children. 5th ed. Bloomington, MN: Pearson; 2014.

  54. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4:s13742-015–0047-8.

    Google Scholar 

  55. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc: Ser B (Methodol). 1995;57:289–300.

    Google Scholar 

  56. Lenth RV emmeans: Estimated Marginal Means, aka Least-Squares Means [Internet]. 2022. Available from: https://CRAN.R-project.org/package=emmeans

  57. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K. mediation: R package for causal mediation analysis. J Stat Softw. 2014;59:1–38.

    Google Scholar 

  58. Demontis D, Walters GB, Athanasiadis G, Walters R, Therrien K, Farajzadeh L, et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains [Internet]. medRxiv; 2022 [cited 2022 Jul 12]. p. 2022.02.14.22270780. Available from: https://www.medrxiv.org/content/10.1101/2022.02.14.22270780v1

  59. Polyak A, Rosenfeld JA, Girirajan S. An assessment of sex bias in neurodevelopmental disorders. Genome Med. 2015;7:94.

    PubMed  PubMed Central  Google Scholar 

  60. Desachy G, Croen LA, Torres AR, Kharrazi M, Delorenze GN, Windham GC, et al. Increased female autosomal burden of rare copy number variants in human populations and in autism families. Mol Psychiatry. 2015;20:170–5.

    CAS  PubMed  Google Scholar 

  61. Jacquemont S, Coe BP, Hersch M, Duyzend MH, Krumm N, Bergmann S, et al. A higher mutational burden in females supports a “Female Protective Model” in neurodevelopmental disorders. Am J Hum Genet. 2014;94:415–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Han J, Walters JTR, Kirov G, Pocklington A, Escott-Price V, Owen MJ, et al. Gender differences in CNV burden do not confound schizophrenia CNV associations. Sci Rep. 2016;6:25986.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Kendall KM, Rees E, Bracher-Smith M, Legge S, Riglin L, Zammit S, et al. Association of rare copy number variants with risk of depression. JAMA Psychiatry. 2019;76:818–25.

    PubMed  PubMed Central  Google Scholar 

  64. Wang B, Ji T, Zhou X, Wang J, Wang X, Wang J, et al. CNV analysis in Chinese children of mental retardation highlights a sex differentiation in parental contribution to de novo and inherited mutational burdens. Sci Rep. 2016;6:25954.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Sanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE, Cicek AE, et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 2015;87:1215–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Barch DM, Albaugh MD, Avenevoli S, Chang L, Clark DB, Glantz MD, et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description. Developmental Cogn Neurosci. 2018;32:55–66.

    Google Scholar 

  67. Antaki D, Guevara J, Maihofer AX, Klein M, Gujral M, Grove J, et al. A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex. Nat Genet. 2022;54:1284–92.

    CAS  PubMed  Google Scholar 

  68. Martin J, O’Donovan MC, Thapar A, Langley K, Williams N. The relative contribution of common and rare genetic variants to ADHD. Transl Psychiatry. 2015;5:e506.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Bergen SE, Ploner A, Howrigan D, O’Donovan MC, Smoller JW, Sullivan PF, et al. Joint contributions of rare copy number variants and common SNPs to risk for schizophrenia. AJP. 2018;176:29–35.

    Google Scholar 

  70. Håvik B, Le Hellard S, Rietschel M, Lybæk H, Djurovic S, Mattheisen M, et al. The complement control-related genes CSMD1 and CSMD2 associate to schizophrenia. Biol Psychiatry. 2011;70:35–42.

    PubMed  Google Scholar 

  71. Cukier HN, Dueker ND, Slifer SH, Lee JM, Whitehead PL, Lalanne E, et al. Exome sequencing of extended families with autism reveals genes shared across neurodevelopmental and neuropsychiatric disorders. Mol Autism. 2014;5:1.

    PubMed  PubMed Central  Google Scholar 

  72. Sudre G, Frederick J, Sharp W, Ishii-Takahashi A, Mangalmurti A, Choudhury S, et al. Mapping associations between polygenic risks for childhood neuropsychiatric disorders, symptoms of attention deficit hyperactivity disorder, cognition, and the brain. Mol Psychiatry. 2020;25:2482–92.

    PubMed  Google Scholar 

  73. Douard E, Zeribi A, Schramm C, Tamer P, Loum MA, Nowak S, et al. Effect sizes of deletions and duplications on autism risk across the genome. AJP. 2021;178:87–98.

    Google Scholar 

  74. Pievsky MA, McGrath RE. The neurocognitive profile of attention-deficit/hyperactivity disorder: a review of meta-analyses. Arch Clin Neuropsychol. 2018;33:143–57.

    PubMed  Google Scholar 

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Acknowledgements

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at (2,77). A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/1527796. DOIs can be found at https://doi.org/10.15154/1527796. Processing of SNP array data was conducted using the high-performance computing capabilities of the NIH Biowulf cluster. This research was supported by the intramural research program of the National Institute of Mental Health and the National Human Genome Research Institute (ZIAHG200378 to Dr Shaw). We also wish to thank Dr. Eric Morrow and Dr. Francis McMahon for their input and feedback.

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  1. Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA

    Benjamin Jung, Kwangmi Ahn, Cristina Justice, Luke Norman, Jolie Price, Gustavo Sudre & Philip Shaw

  2. Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA

    Luke Norman & Philip Shaw

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Study concept and design: BJ and PS. Acquisition, analysis, or interpretation of data: BJ, KA, CJ. Drafting of the manuscript: BJ and PS. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: BJ. Administrative, technical, or material support: KA, CJ, LN, JP, GS. Study supervision: PS. Obtained funding: PS.

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Jung, B., Ahn, K., Justice, C. et al. Rare copy number variants in males and females with childhood attention-deficit/hyperactivity disorder. Mol Psychiatry 28, 1240–1247 (2023). https://doi.org/10.1038/s41380-022-01906-y

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