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Biomedical research has grown increasingly cooperative through the sharing of consortia-level epigenetic data. Since consortia preprocess data prior to distribution, new processing pipelines can lead to different versions of the same dataset. Similarly, analytic frameworks evolve to incorporate cutting-edge methods and best practices. However, it remains unknown how different data and analytic versions alter the results of epigenome-wide analyses, which could influence the replicability of epigenetic associations. Thus, we assessed the impact of these changes using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. We analysed DNA methylation from two data versions, processed using separate preprocessing and analytic pipelines, examining associations between seven childhood adversities or prenatal smoking exposure and DNA methylation at age 7. We performed two sets of analyses: (1) epigenome-wide association studies (EWAS); (2) Structured Life Course Modelling Approach (SLCMA), a two-stage method that models time-dependent effects. SLCMA results were also compared across two analytic versions. Data version changes impacted both EWAS and SLCMA analyses, yielding different associations at conventional p-value thresholds. However, the magnitude and direction of associations was generally consistent between data versions, regardless of p-values. Differences were especially apparent in analyses of childhood adversity, while smoking associations were more consistent using significance thresholds. SLCMA analytic versions similarly altered top associations, but time-dependent effects remained concordant. Alterations to data and analytic versions influenced the results of epigenome-wide analyses. Our findings highlight that magnitude and direction are better measures for replication and stability than p-value thresholds. [ABSTRACT FROM AUTHOR]

Estimating velocity and acceleration trajectories allows novel inferences in the field of longitudinal data analysis, such as estimating change regions rather than change points, and testing group effects on nonlinear change in an outcome (ie, a nonlinear interaction). In this article, we develop derivative estimation for 2 standard approaches—polynomial mixed models and spline mixed models. We compare their performance with an established method—principal component analysis through conditional expectation through a simulation study. We then apply the methods to repeated blood pressure (BP) measurements in a UK cohort of pregnant women, where the goals of analysis are to (i) identify and estimate regions of BP change for each individual and (ii) investigate the association between parity and BP change at the population level. The penalized spline mixed model had the lowest bias in our simulation study, and we identified evidence for BP change regions in over 75% of pregnant women. Using mean velocity difference revealed differences in BP change between women in their first pregnancy compared with those who had at least 1 previous pregnancy. We recommend the use of penalized spline mixed models for derivative estimation in longitudinal data analysis. [ABSTRACT FROM AUTHOR]

Background: DNA methylation levels are known to vary over time, and modelling these trajectories is crucial for our understanding of the biological relevance of these changes over time. However, due to the computational cost of fitting multilevel models across the epigenome, most trajectory modelling efforts to date have focused on a subset of CpG sites identified through epigenome-wide association studies (EWAS) at individual time-points.Methods: We propose using linear regression across the repeated measures, estimating cluster-robust standard errors using a sandwich estimator, as a less computationally intensive strategy than multilevel modelling. We compared these two longitudinal approaches, as well as three approaches based on EWAS (associated at baseline, at any time-point and at all time-points), for identifying epigenetic change over time related to an exposure using simulations and by applying them to blood DNA methylation profiles from the Accessible Resource for Integrated Epigenomics Studies (ARIES).Results: Restricting association testing to EWAS at baseline identified a less complete set of associations than performing EWAS at each time-point or applying the longitudinal modelling approaches to the full dataset. Linear regression models with cluster-robust standard errors identified similar sets of associations with almost identical estimates of effect as the multilevel models, while also being 74 times more efficient. Both longitudinal modelling approaches identified comparable sets of CpG sites in ARIES with an association with prenatal exposure to smoking (>70% agreement).Conclusions: Linear regression with cluster-robust standard errors is an appropriate and efficient approach for longitudinal analysis of DNA methylation data. [ABSTRACT FROM AUTHOR]

Background: Statistical models that use an individual's DNA methylation levels to estimate their age (known as epigenetic clocks) have recently been developed, with 96% correlation found between epigenetic and chronological age. We postulate that differences between estimated and actual age [age acceleration (AA)] can be used as a measure of developmental age in early life.Methods: We obtained DNA methylation measures at three time points (birth, age 7 years and age 17 years) in 1018 children from the Avon Longitudinal Study of Parents and Children (ALSPAC). Using an online calculator, we estimated epigenetic age, and thus AA, for each child at each time point. We then investigated whether AA was prospectively associated with repeated measures of height, weight, body mass index (BMI), bone mineral density, bone mass, fat mass, lean mass and Tanner stage.Results: Positive AA at birth was associated with higher average fat mass [1321 g per year of AA, 95% confidence interval (CI) 386, 2256 g] from birth to adolescence (i.e. from age 0-17 years) and AA at age 7 was associated with higher average height (0.23 cm per year of AA, 95% CI 0.04, 0.41 cm). Conflicting evidence for the role of AA (at birth and in childhood) on changes during development was also found, with higher AA being positively associated with changes in weight, BMI and Tanner stage, but negatively with changes in height and fat mass.Conclusions: We found evidence that being ahead of one's epigenetic age acceleration is related to developmental characteristics during childhood and adolescence. This demonstrates the potential for using AA as a measure of development in future research. [ABSTRACT FROM AUTHOR]

Sensitive periods are developmental stages of heightened plasticity when life experiences, including exposure to childhood adversity, have the potential to exert more lasting impacts. Epigenetic mechanisms, including DNA methylation (DNAm), may provide a pathway through which adversity induces long-term biological changes. DNAm shifts may be more likely to occur during sensitive periods, especially within genes that regulate the timing of sensitive periods. Here, we investigated the possibility that childhood adversity during specific life stages is associated with DNAm changes in genes known to regulate the timing and duration of sensitive periods. Genome-wide DNAm profiles came from the Avon Longitudinal Study of Parents and Children (n = 785). We first used principal component analysis (PCA) to summarize DNAm variation across 530 CpG sites mapped to the promoters of 58 genes previously-identified as regulating sensitive periods. Gene-level DNAm summaries were calculated for genes regulating sensitive period opening (n genes = 15), closing (n genes = 36), and expression (n genes = 8). We then performed linear discriminant analysis (LDA) to test associations between seven types of parent-reported, time-varying measures of exposure to childhood adversity and DNAm principal components. To our knowledge, this is the first time LDA has been applied to analyze functionally grouped DNAm data to characterize associations between an environmental exposure and epigenetic differences. Suggestive evidence emerged for associations between sexual or physical abuse as well as financial hardship during middle childhood, and DNAm of genetic pathways regulating sensitive period opening and expression. However, no statistically significant associations were identified after multiple testing correction. Our gene set-based method combining PCA and LDA complements epigenome-wide approaches. Although our results were largely null, these findings provide a proof-of-concept for studying time-varying exposures and gene- or pathway-level epigenetic modifications. • Early life adversity may affect epigenetic regulation of developmental plasticity. • The effects of adversity may be strongest during sensitive periods of development. • Analyzing the timing and type of childhood adversity could reveal new findings. • Gene set analyses complement genome-wide studies, yielding functional insights. [ABSTRACT FROM AUTHOR]

• Exposure to adversity was associated with accelerated epigenetic aging in childhood. • Associations were observed when using the Hannum but not Horvath epigenetic clock. • Effects were driven by exposure during early and middle childhood sensitive periods. • Adversity differentially affected epigenetic age acceleration in boys and girls. Exposure to adversity has been linked to accelerated biological aging, which in turn has been shown to predict numerous physical and mental health problems. In recent years, measures of DNA methylation-based epigenetic age––known as "epigenetic clocks"––have been used to estimate accelerated epigenetic aging. Although a small number of studies have found an effect of adversity exposure on epigenetic age in children, none have investigated if there are "sensitive periods" when adversity is most impactful. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 973), we tested the prospective association between repeated measures of childhood exposure to seven types of adversity on epigenetic age assessed at age 7.5 using the Horvath and Hannum epigenetic clocks. With a Least Angle Regression variable selection procedure, we evaluated potential sensitive period effects. We found that exposure to abuse, financial hardship, or neighborhood disadvantage during sensitive periods in early and middle childhood best explained variability in the deviation of Hannum-based epigenetic age from chronological age, even after considering the role of adversity accumulation and recency. Secondary sex-stratified analyses identified particularly strong sensitive period effects. These effects were undetected in analyses comparing children "exposed" versus "unexposed" to adversity. We did not identify any associations between adversity and epigenetic age using the Horvath epigenetic clock. Our results suggest that adversity may alter methylation processes in ways that either directly or indirectly perturb normal cellular aging and that these effects may be heightened during specific life stages. [ABSTRACT FROM AUTHOR]