EPIGENETIC TIMING EFFECTS ON CHILD DEVELOPMENTAL OUTCOMES: A LONGITUDINAL META-REGRESSION OF FINDINGS FROM THE PREGNANCY AND CHILDHOOD EPIGENETICS CONSORTIUM

DNA methylation (DNAm) is a developmentally dynamic epigenetic process, yet, most studies linking DNAm to health phenotypes measure DNAm only once. Thus, it is largely unknown (i) whether the relationship between DNAm and health outcomes varies across development (ii) at which developmental periods DNAm profiles could be most informative for these outcomes, and (iii) to what extent DNAm-health associations at one timepoint can be generalized to other timepoints.

In most pediatric population studies, DNAm is either measured in cord blood samples at birth and associated with a child outcome at a later timepoint (i.e. prospective epigenome-wide association study [EWAS]) or DNAm is measured from a blood sample at the same timepoint as the child outcome (i.e. cross-sectional EWAS). Recently, the Pregnancy And Childhood Epigenetics (PACE) Consortium published five multi-cohort EWAS meta-analyses that investigated DNAm using both designs in relation to the same child outcome, spanning mental and physical health domains, namely: ADHD, general psychopathology (measured as a latent factor; GPF), sleep duration, body mass index (BMI) and asthma.

Here, we re-analyzed the five PACE meta-analyses (Npooled=2178-4641, 26 cohorts) to explore timing effects on DNAm-health associations during development. For each outcome, we integrated results from the prospective EWAS (cord blood DNAm at birth) and the cross-sectional EWAS (whole blood DNAm in childhood) into a longitudinal meta-regression model. This model systematically quantified changes in effect sizes and statistical significance between timepoints, and we also explored a range of factors that may contribute to the observed temporal trends. We then correlated DNAm associations between timepoints (to assess generalizability of epigenetic signals from one timepoint to another) and across health outcomes (to explore presence of shared DNAm associations).

Our findings reveal three new insights: (i) across outcomes, effects sizes are larger when DNAm is measured in childhood and cross-sectionally associated with child health outcomes, compared to when DNAm is assessed at birth and prospectively associated with later health development; (ii) higher effect sizes do not necessarily translate into more significant findings, as associations also become noisier in childhood for most outcomes (i.e. showing larger standard errors); and (iii) DNAm signals are highly time-specific, while showing pleiotropy across health outcomes: regression coefficients at birth did not correlate with those in childhood with few exceptions.

Overall, our results suggest developmentally-specific associations between DNAm and child health outcomes, when assessing DNAm at birth vs childhood. This implies that EWAS results from one timepoint are unlikely to generalize to another, at least based on birth vs childhood comparisons. Longitudinal studies with repeated epigenetic assessments are direly needed to shed light on the dynamic relationship between DNAm, development and health, as well as to enable the creation of more reliable and generalizable epigenetic biomarkers. More broadly, this study underscores the importance of considering the time-varying nature of DNAm in epigenetic research and supports the potential existence of epigenetic ‘timing effects’ on child health.