Longitudinal associations of DNA methylation and sleep in children: a meta-analysis

Background Sleep is important for healthy functioning in children. Numerous genetic and environmental factors, from conception onwards, may influence this phenotype. Epigenetic mechanisms such as DNA methylation have been proposed to underlie variation in sleep or may be an early-life marker of sleep disturbances. We examined if DNA methylation at birth or in school age is associated with parent-reported and actigraphy-estimated sleep outcomes in children. Methods We meta-analysed epigenome-wide association study results. DNA methylation was measured from cord blood at birth in 11 cohorts and from peripheral blood in children (4–13 years) in 8 cohorts. Outcomes included parent-reported sleep duration, sleep initiation and fragmentation problems, and actigraphy-estimated sleep duration, sleep onset latency and wake-after-sleep-onset duration. Results We found no associations between DNA methylation at birth and parent-reported sleep duration (n = 3658), initiation problems (n = 2504), or fragmentation (n = 1681) (p values above cut-off 4.0 × 10–8). Lower methylation at cg24815001 and cg02753354 at birth was associated with longer actigraphy-estimated sleep duration (p = 3.31 × 10–8, n = 577) and sleep onset latency (p = 8.8 × 10–9, n = 580), respectively. DNA methylation in childhood was not cross-sectionally associated with any sleep outcomes (n = 716–2539). Conclusion DNA methylation, at birth or in childhood, was not associated with parent-reported sleep. Associations observed with objectively measured sleep outcomes could be studied further if additional data sets become available. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-022-01298-4.


Longitudinal associations of DNA methylation and sleep in children: A meta-analysis Supplementary Tables and Figures
Contents: Table S1.
Characteristics of the participating cohorts in analyses of DNAm in childhood and child sleep outcomes Table S2.
Overlap between cord blood and peripheral blood in childhood DNA methylation analyses Table S3.
Site-specific results for the 25 CpGs that came closest to statistical significance (p<5.0×10 -5 ) in the primary meta-analysis of DNAm at birth and parent-reported sleep duration in school age Table S4.
Analyses of Differentially Methylated Regions (DMRs) in cord blood at birth and child sleep. Figure S1. Correlations and independence of the six phenotypes of interest. Figure S2. DNAm and parent-reported child sleep initiation problems among school-aged children: Manhattan and quantile-quantile plots. Figure S3. DNAm and parent-reported child sleep fragmentation problems among school-aged children: Manhattan and quantile-quantile plots. Figure S4. DNAm and actigraphy-estimated child sleep duration among school-aged children: Manhattan and quantile-quantile plots. Figure S5. DNAm and actigraphy-estimated child sleep-onset-latency among school-aged children: Manhattan and quantile-quantile plots. Figure S6. DNAm and actigraphy-estimated child wake-after-sleep-onset duration among school-aged children: Manhattan and quantile-quantile plots. c In ALSPAC, sleep outcomes were collected at different follow-ups, and sample size and age varied per outcome: of those with DNAm data in childhood, 813 children had data on parent-reported sleep duration at the mean age of 11.7 years (SD=0.1), while 853 and 832 children had data on parent-reported sleep initiation and fragmentation problems at the age of 9.6 years (SD=0.1), respectively.

Sammallahti, Koopman-Verhoeff, Binter et al Supplementary Tables and Figures
d In Generation R, sleep outcomes were collected at different follow-ups, sample size and age varied per outcome: of those with DNAm data in childhood, 155 children had data on parent-reported sleep duration at the mean age of 11.7 years (SD=0.2), while 405 children had data on parent-reported sleep initiation problems at the mean age of 9.7 years (SD=0.3). e Rates of low education are not directly comparable, as educational systems differed between countries and cohorts used different definitions of low education, as explained in more detail in Supplementary Methods.