Background: Exposure to "early life" adversity is known to predict DNA methylation (DNAm) patterns that may be related to psychiatric risk. However, few studies have investigated whether adversity has time-dependent effects based on the age at exposure. Methods: Using a two-stage structured life course modeling approach (SLCMA), we tested the hypothesis that there are sensitive periods when adversity induced greater DNAm changes. We tested this hypothesis in relation to two alternatives: an accumulation hypothesis, in which the effect of adversity increases with the number of occasions exposed, regardless of timing, and a recency model, in which the effect of adversity is stronger for more proximal events. Data came from the Accessible Resource for Integrated Epigenomics Studies (ARIES), a subsample of mother-child pairs from the Avon Longitudinal Study of Parents and Children (ALSPAC; n=691-774).Results: After covariate adjustment and multiple testing correction, we identified 38 CpG sites that were differentially methylated at age 7 following exposure to adversity. Most loci (n=35) were predicted by the timing of adversity, namely exposures before age 3. Neither the accumulation nor recency of the adversity explained considerable variability in DNAm. A standard EWAS of lifetime exposure (vs. no exposure) failed to detect these associations. Conclusions:The developmental timing of adversity explains more variability in DNAm than the accumulation or recency of exposure. Very early childhood appears to be a sensitive period when exposure to adversity predicts differential DNAm patterns. Classification of individuals as exposed vs. unexposed to "early life" adversity may dilute observed effects.

Socioeconomic position (SEP) is a major determinant of health across the life course. Yet, little is known about the biological mechanisms explaining this relationship. One possible explanation is through an epigenetic process called DNA methylation (DNAm), wherein the socioeconomic environment causes no alteration in the DNA sequence but modifies gene activity, gene expression, and therefore long-term health. To understand the evidence supporting a potential SEP-DNAm link, we performed a systematic review of published empirical findings on the association between SEP (from prenatal development to adulthood) and DNAm measured across the life course, with an eye toward evaluating how the timing, duration, and type of SEP exposure influenced DNAm. Across the 37 studies we identified, there was some evidence for the effect of SEP timing and duration on DNAm, with early-life SEP and persistently low SEP being particularly strong indicators of DNAm. Different indicators of SEP also had some unique associations with DNAm profiles, suggesting that SEP is not a singular concept, but rather that different aspects of the socioeconomic environment can shift DNAm patterns through distinct pathways. These differences with respect to SEP timing, duration, and type were notable because they were detected even among heterogenous study designs. Overall, findings from this review underscore the importance of analyzing SEP timing, duration, and type, given the complex relationship between SEP and DNAm across the lifespan. To guide future research, we highlight current limitations in the literature and propose recommendations for overcoming some of these challenges.

Background: Life course epidemiology provides a framework for studying the effects of time-varying exposures on health outcomes. The structured life course modeling approach (SLCMA) is a theory-driven analytic method that empirically compares multiple prespecified life course hypotheses characterizing time-dependent exposure-outcome relationships to determine which theory best fits the observed data. However, the statistical properties of inference methods used with the SLCMA have not been investigated with high-dimensional omics outcomes. Methods: We performed simulations and empirical analyses to evaluate the performance of the SLCMA when applied to genome-wide DNA methylation (DNAm). In the simulations, we compared five statistical inference tests used by SLCMA (n=700). For each, we assessed the familywise error rate (FWER), statistical power, and confidence interval coverage to determine whether inference based on these tests was valid in the presence of substantial multiple testing and small effect sizes, two hallmark challenges of inference from omics data. In the empirical analyses, we applied the SLCMA to evaluate the time-dependent relationship of childhood abuse with genome-wide DNAm (n=703). Results: In the simulations, selective inference and max-|t|-test performed best: both controlled FWER and yielded moderate statistical power. Empirical analyses using SLCMA revealed time-dependent effects of childhood abuse on DNA methylation. Conclusions: Our findings show that SLCMA, applied and interpreted appropriately, can be used in the omics setting to examine time-dependent effects underlying exposure-outcome relationships over the life course. We provide recommendations for applying the SLCMA in high-throughput settings, which we hope will encourage researchers to move beyond analyses of exposed versus unexposed.

Background: Early-onset depression during childhood and adolescence is associated with a worse course of illness and outcome than adult onset. However, the genetic factors that influence risk for early-onset depression remain mostly unknown. Using data collected over 13 years, we examined whether polygenic risk scores (PRS) that capture genetic risk for depression were associated with depressive symptom trajectories assessed from childhood to adolescence. Methods: Data came from the Avon Longitudinal Study of Parents and Children, a prospective, longitudinal birth cohort (analytic sample = 7,308 youth). We analyzed the relationship between genetic susceptibility to depression and three time-dependent measures of depressive symptoms trajectories spanning 4-16.5 years of age (class, onset, and cumulative burden). Trajectories were constructed using a growth mixture model with structured residuals. PRS were generated from the summary statistics of a genome-wide association study of depression risk using data from the Psychiatric Genomics Consortium, UK Biobank, and 23andMe, Inc. We used MAGMA to identify gene-level associations with these measures. Results: Youth were classified into six classes of depressive symptom trajectories: high/renitent (27.9% of youth), high/reversing (9.1%), childhood decrease (7.3%), late childhood peak (3.3%), adolescent spike (2.5%), and minimal symptoms (49.9%). PRS discriminated between youth in the late childhood peak, high/reversing, and high/renitent classes compared to the minimal symptoms and childhood decrease classes. No significant associations were detected at the gene level. Conclusions: This study highlights differences in polygenic loading for depressive symptoms across childhood and adolescence, particularly among youths with high symptoms in early adolescence, regardless of age-independent patterns.

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