Biology subjects

Suderman, M. J.

Publications and source records attributed to Suderman, M. J..

4 recordsLinked to original sources

bioRxiv2021

Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies

IntroductionBiomedical research has grown increasingly cooperative, with several large consortia compiling and sharing epigenomic data. Since data are typically preprocessed by consortia prior to distribution, the implementation of new pipelines can lead to different versions of the same dataset. Analytic frameworks also constantly evolve to incorporate cutting-edge methods and shifting best practices. However, it remains unknown how differences in data and analytic versions alter the results of epigenome-wide analyses, which has broad implications for the replicability of epigenetic associations. Thus, we assessed the impact of these changes using a subsample of the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. MethodsWe analyzed two versions of DNA methylation data, processed using separate preprocessing and analytic pipelines, to examine associations between childhood adversity and prenatal smoking exposure on DNA methylation at age 7. We performed two sets of analyses: (1) epigenome-wide association studies (EWAS); (2) Structured Life Course Modeling Approach (SLCMA), a two-stage method that models time-dependent effects. We also compared results from the SLCMA using more recent methodological recommendations. ResultsDifferences between ALSPAC data versions impacted both EWAS and SLCMA analyses, yielding different sets of associations at conventional p-value thresholds. However, the magnitude and direction of associations was generally consistent between data versions, regardless of significance thresholds. Updating the SLCMA analytic version similarly altered top associations, but time-dependent effects remained concordant. ConclusionsChanges to data and analytic versions influenced the results of epigenome-wide studies, particularly when using p-value thresholds as reference points for successful replication and stability.

genetics↗

bioRxiv2019

The association of DNA methylation with body mass index: distinguishing between predictors and biomarkers

BackgroundDNA methylation is associated with body mass index (BMI), but it is not clear if methylation scores are biomarkers for extant BMI, or predictive of future BMI. Here we explore the causal nature and predictive utility of DNA methylation measured in peripheral blood with BMI and cardiometabolic traits. MethodsAnalyses were conducted across the life course using the ARIES cohort of mothers (n=792) and children (n=906), for whom DNA methylation and genetic profiles and BMI at multiple time points (3 in children at birth, in childhood and in adolescence, 2 in mothers during pregnancy and in middle age) were available. Genetic and DNA methylation scores for BMI were derived using published associations between BMI and DNA methylation and genotype. Causal relationships between methylation and BMI were assessed using Mendelian randomisation and cross-lagged models. ResultsThe DNA methylation scores in adult women explained 10% of extant BMI variance. However, less extant variance was explained by scores generated in the same women during pregnancy (2% BMI variance) and in older children (15-17 years; 3% BMI variance). Similarly, little extant variance was explained in younger children (at birth and at 7 years; 1% and 2%, respectively). These associations remained following adjustment for smoking exposure and education levels. The DNA methylation score was found to be a poor predictor of future BMI using linear and cross-lagged models, suggesting that DNA methylation variation does not cause later variation in BMI. However, there was some evidence to suggest that BMI is predictive of later DNA methylation. Mendelian randomisation analyses also support this direction of effect, although evidence is weak. Finally, we find that DNA methylation scores for BMI are associated with extant cardiometabolic traits independently of BMI and genetic score. ConclusionThe age-specific nature of DNA methylation associations with BMI, lack of causal relationship, and limited predictive ability of future BMI, indicate that DNA methylation is likely influenced by BMI and might more accurately be considered a biomarker of BMI and related outcomes than a predictor. Future epigenome-wide association studies may benefit from further examining associations between early DNA methylation and later health outcomes.

genetics↗

bioRxiv2018

Predicting cellular aging following exposure to adversity: Does accumulation, recency, or developmental timing of exposure matter?

Exposure to adversity has been linked to accelerated biological aging, which in turn has been shown to predict numerous health problems, including neuropsychiatric disease. In recent years, measures of DNA methylation-based epigenetic age - known as \"epigenetic clocks\" - have been used to estimate accelerated epigenetic aging. Yet, few studies have been conducted in children. Using data from the Avon Longitudinal Study of Parents and Children (n=973), we explored the prospective association between repeated measures of childhood exposure to seven types of adversity on epigenetic age assessed at age 7 using the Horvath and Hannum epigenetic clocks. With a Least Angle Regression variable selection procedure, we evaluated the effects of the developmental timing, accumulation, and recency of adversity exposure. We found that exposure to sexual or physical abuse, financial stress, or neighborhood disadvantage during sensitive periods in early and middle childhood best explained variability in the deviation of the Hannum epigenetic age from the chronological age. Secondary sex-stratified analyses identified particularly strong sensitive period effects, such that by age 7, girls who were exposed to abuse at age 3.5 were biologically older than their unexposed peers by almost 2 months. These effects were undetected in analyses comparing children \"exposed\" versus \"unexposed\" to adversity. Our results suggest that exposure to adversity may alter methylation processes in ways that perturb normal cellular aging and that these effects may be heightened during sensitive periods in development. Research is needed to demonstrate the effect of accelerated epigenetic aging on negative health outcomes following childhood adversity exposure.

genetics↗

bioRxiv2018

Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal study

BackgroundExposure to \"early life\" adversity is known to predict DNA methylation (DNAm) patterns that may be related to prolonged psychiatric risk. However, few studies have investigated whether adversity has time-dependent effects based on the age at exposure.\n\nMethodsUsing 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 alternative explanations: an accumulation hypothesis, in which the effect of adversity on DNAm 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=670-776).\n\nResultsAfter covariate adjustment and multiple testing correction, we identified 40 CpG sites that were differentially methylated at age 7 following exposure to adversity. Most loci (n=32) were predicted by the timing of adversity, namely exposures during infancy. 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.\n\nConclusionsThe developmental timing of adversity explains more variability in DNAm than the accumulation or recency of exposure. Infancy 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.

genetics↗