Genetic and environmental factors associated with DNA methylation across childhood and adolescence in individuals with and without attention-deficit/hyperactivity disorder
- 1Deakin Lifespan Institute and School of Psychology, Deakin University, Burwood, VIC, Australia
Abstract
Background: Genetic and environmental factors both contribute risk to the development of childhood Attention-deficit/hyperactivity disorder (ADHD), yet the molecular mechanisms conferring this risk remain unclear. We investigated the genetic and environmental factors influencing DNA methylation (DNAm) at sites linked to ADHD symptom trajectories. Methods: We used two overlapping subsamples from a longitudinal paediatric cohort: (1) children with genetic and epigenetic data at first wave saliva sampling (Wave 3, n=113, 9.47-11.86 years) underwent methylation quantitative trait loci (meQTL) analysis; (2) children with environmental (Waves 1-3) and one or more waves of epigenetic data (n=126, 9.5-11.9 years) were analysed using LASSO regression to identify predictors of ADHD (Remitted ADHD/Persistent ADHD/Controls) and Wave 3 DNAm. In a subset with multiple DNAm timepoints (n=75), linear mixed models tested whether environmental predictors associated with longitudinal DNAm change, and whether ADHD moderated these associations. Analyses were conducted on 12 CpGs in which we previously identified change in DNAm according to the ADHD clinical outcome. Results: Nineteen meQTLs for 5 CpGs explained up to 40% of DNAm variance. We identified 17 environmental predictors of ADHD and wave 3 DNAm. Higher household income and school quality of life (QoL) were associated with reduced DNAm at cg02208776 (household) and cg11124426 (QoL) over time. Critically, higher income in Remitted ADHD showed greater DNAm declines than Controls, with opposite patterns in Persistent ADHD. Conclusion: DNAm patterns linked to ADHD outcomes were associated with genetic and environmental factors. Notably, household income and school functioning were associated with epigenetic change, with income effects varying by ADHD trajectories - characterising higher income as protective in remittent individuals.
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BibTeX
@misc{wrigglesworth2026genetic,
title = {{Genetic and environmental factors associated with DNA methylation across childhood and adolescence in individuals with and without attention-deficit/hyperactivity disorder}},
author = {J. Wrigglesworth and V. Chirokoff and P. D. Fransquet and V. Anderson and M. A. Bellgrove and T. J. Silk},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.20.760579},
url = {https://www.biorxiv.org/content/10.64898/2026.10.20.760579v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Genetic and environmental factors associated with DNA methylation across childhood and adolescence in individuals with and without attention-deficit/hyperactivity disorder
AU - J. Wrigglesworth
AU - V. Chirokoff
AU - P. D. Fransquet
AU - V. Anderson
AU - M. A. Bellgrove
AU - T. J. Silk
PY - 2026
DA - 2026/10/20
PB - bioRxiv
DO - 10.64898/2026.10.20.760579
UR - https://www.biorxiv.org/content/10.64898/2026.10.20.760579v1
AB - Background: Genetic and environmental factors both contribute risk to the development of childhood Attention-deficit/hyperactivity disorder (ADHD), yet the molecular mechanisms conferring this risk remain unclear. We investigated the genetic and environmental factors influencing DNA methylation (DNAm) at sites linked to ADHD symptom trajectories. Methods: We used two overlapping subsamples from a longitudinal paediatric cohort: (1) children with genetic and epigenetic data at first wave saliva sampling (Wave 3, n=113, 9.47-11.86 years) underwent methylation quantitative trait loci (meQTL) analysis; (2) children with environmental (Waves 1-3) and one or more waves of epigenetic data (n=126, 9.5-11.9 years) were analysed using LASSO regression to identify predictors of ADHD (Remitted ADHD/Persistent ADHD/Controls) and Wave 3 DNAm. In a subset with multiple DNAm timepoints (n=75), linear mixed models tested whether environmental predictors associated with longitudinal DNAm change, and whether ADHD moderated these associations. Analyses were conducted on 12 CpGs in which we previously identified change in DNAm according to the ADHD clinical outcome. Results: Nineteen meQTLs for 5 CpGs explained up to 40% of DNAm variance. We identified 17 environmental predictors of ADHD and wave 3 DNAm. Higher household income and school quality of life (QoL) were associated with reduced DNAm at cg02208776 (household) and cg11124426 (QoL) over time. Critically, higher income in Remitted ADHD showed greater DNAm declines than Controls, with opposite patterns in Persistent ADHD. Conclusion: DNAm patterns linked to ADHD outcomes were associated with genetic and environmental factors. Notably, household income and school functioning were associated with epigenetic change, with income effects varying by ADHD trajectories - characterising higher income as protective in remittent individuals.
ER -