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[Preprint]. 2025 Apr 17:2025.04.16.25325956.
doi: 10.1101/2025.04.16.25325956.

Cell type-specific methylome-wide association studies of childhood ADHD symptoms

Mandy Meijer  1 , Marieke Klein  1 , Doretta Caramaschi  2 , Shaunna L Clark  3 , Marta Cosin-Tomas  4 , Nastassja Koen  5 , Xueling Lu  6 , Rosa H Mulder  7 , Stefan W Röder  8 , Yining Zhang  9 , Lea Zilich  10 , Mariona Bustamente  4 , Michael Deuschle  11 , Janine F Felix  7 , Juan Ramos González  4 , Regina Gražulevičiene  12 , Fabian Streit  10 , John Wright  13 , Angel Carracedo  14 , Charlotte A M Cecil  15 , Eva Corpeleijn  16 , Catharina A Hartman  17 , Gunda Herberth  8 , Anke Huels  18 , Caroline Relton  19 , Harold Snieder  16 , Dan J Stein  20 , Jordi Sunyer  21 , Stephanie H Witt  10 , Heather J Zar  22 , Ana C Zenclussen  8 , Barbara Franke  23 , William Copeland  24 , Karolina A Aberg  25 , Edwin J C G van den Oord  25
Affiliations

Cell type-specific methylome-wide association studies of childhood ADHD symptoms

Mandy Meijer et al. medRxiv. .

Update in

  • Cell type-specific methylome-wide association studies of childhood ADHD symptoms.
    Meijer M, Klein M, Caramaschi D, Clark SL, Cosin-Tomas M, Koen N, Lu X, Mulder RH, Röder SW, Zhang Y, Zilich L, Bustamente M, Deuschle M, Felix JF, González JR, Gražulevičiene R, Streit F, Wright J, Carracedo A, Cecil CAM, Corpeleijn E, Hartman CA, Herberth G, Huels A, Relton C, Snieder H, Stein DJ, Sunyer J, Witt SH, Zar HJ, Zenclussen AC, Franke B, Copeland W, Aberg KA, van den Oord EJCG. Meijer M, et al. Eur Neuropsychopharmacol. 2025 Dec;101:7-17. doi: 10.1016/j.euroneuro.2025.09.010. Epub 2025 Oct 26. Eur Neuropsychopharmacol. 2025. PMID: 41145087

Abstract

Objective: Studying DNA methylation (DNAm) can provide insights into gene-regulatory mechanisms underlying attention-deficit/hyperactivity disorder (ADHD). While most DNAm studies were performed in bulk tissue, this study used statistical deconvolution to identify cell type-specific DNAm profiles, from five major blood cell types, associated with childhood ADHD symptoms.

Methods: We performed meta-analyses of methylome-wide association studies (MWAS) for ADHD symptoms (agerange=4-16 years) in peripheral blood collected during childhood and in cord blood. The investigated cohorts included seven array-based methylation datasets assaying up to 450K CpGs from the Pregnancy And Childhood Epigenetics Consortium (N=2 934 peripheral blood; N=2 546 cord blood) and a sequencing-based methylation dataset assaying nearly all 28 million CpGs in blood from the Great Smoky Mountain Study (GSMS; N=583).

Results: The meta-analyses resulted in methylome-wide significant (FDR<0.05) ADHD associations in CD8T cells (RPL31P11 and KCNJ5) for peripheral blood, and, in cord blood, in monocytes (PDE6B), CD8T cells (KCNA3 and HAND2), and NK cells (KIFC1). Notably, several significant sites detected in peripheral blood (RPL31P11 and KCNJ5) were also detected in cord blood. Furthermore, extended MWAS of all sites available for GSMS detected 69 and 17 additional CpGs in monocytes and granulocytes, respectively. In this first cell type-specific MWAS for ADHD, we identified DNAm associations for ADHD symptoms; some associations were seen in both peripheral blood and cord blood, suggesting potential susceptibility markers for increased ADHD risk.

Conclusions: These findings show that cell type-specific analyses and sequencing-based approaches can increase insights into the epigenetic patterns associated with ADHD symptoms in childhood.

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Conflict of interest statement

Disclosures BF received educational speaking fees from Medice. All other authors report no potential conflict of interest.

Figures

Figure 1.
Figure 1.. Study overview.
Top row: General population cohorts of the PACE consortium and the GSMS cohort were included in the current study. Peripheral blood was collected at age 4–10 years (450K; left) and age 9–16 years (MBD-sequencing, right). Cord blood was collected at birth (middle). ADHD symptom scores were assessed at the same time peripheral blood was collected. DNAm was measured with the 450K array covering up to 450 000 DNAm sites in the genome (left, middle) or MBD-sequencing (right), covering nearly all 28 million CpG sites in the human genome. Second row: Epigenomic deconvolution of DNAm data was performed by estimating cell type proportions based on a DNAm reference panel; cell type-specific DNAm values were extrapolated from bulk data based on cell type proportions. Third row: Cell type-specific meta-analysis of overlapping sites from MWAS in peripheral (including sites retrieved from MBD-seq covered by the 450K array) and cord blood for the CpG sites covered on the commercial arrays, and a cell type-specific MWAS in peripheral blood from GSMS (right) of nearly all 28 million CpGs. Bottom row: Comparisons of associations over time were made by comparing identified sites from the meta-analysis of peripheral blood with the results from the meta-analysis of cord blood, and vice versa. Similarly, we performed comparison between the 450K array and MBD-seq platforms.
Figure 2.
Figure 2.. Quantile-quantile (QQ) plots for methylome-wide association meta-analyses of array-based DNA methylation sites for each cell type in peripheral and cord blood.
A) QQ plots for meta-analysis of DNAm in peripheral blood and symptom scores collected in childhood. Peripheral blood includes DNA methylation data from the GSMS, except for NK cells, since this data was not available. B) QQ plots for meta-analysis of DNAm in cord blood at birth analyzed with symptom scores collected later in childhood. The x-axis shows the expected −log10(p-value) and the y-axis shows the observed −log10(p-value). The inflation factor (λ) is given at the bottom of each plot. Orange lines indicate the 95% confidence interval.
Figure 3.
Figure 3.. Quantile-quantile plots of cell type-specific MWAS for ADHD symptoms in the GSMS cohort.
QQ plots for MWAS of peripheral blood at childhood analyzed with symptom scores collected in childhood. The x-axis shows the expected −log10(p-value) and the y-axis shows the observed −log10(p-value). The inflation factor (λ) is given at the bottom of each plot. Orange lines indicate the 95% confidence interval.
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