Combined effects of genotype and childhood adversity shape variability of DNA methylation across age
Bibliographic record
Abstract
Lasting effects of adversity, such as exposure to childhood adversity (CA) on disease risk, may be embedded via epigenetic mechanisms but findings from human studies investigating the main effects of such exposure on epigenetic measures, including DNA methylation (DNAm), are inconsistent. Studies in perinatal tissues indicate that variability of DNAm at birth is best explained by the joint effects of genotype and prenatal environment. Here, we extend these analyses to postnatal stressors. We investigated the contribution of CA, cis genotype (G), and their additive (G + CA) and interactive (G × CA) effects to DNAm variability in blood or saliva from five independent cohorts with a total sample size of 1074 ranging in age from childhood to late adulthood. Of these, 541 were exposed to CA, which was assessed retrospectively using self-reports or verified through social services and registries. For the majority of sites (over 50%) in the adult cohorts, variability in DNAm was best explained by G + CA or G × CA but almost never by CA alone. Across ages and tissues, 1672 DNAm sites showed consistency of the best model in all five cohorts, with G × CA interactions explaining most variance. The consistent G × CA sites mapped to genes enriched in brain-specific transcripts and Gene Ontology terms related to development and synaptic function. Interaction of CA with genotypes showed the strongest contribution to DNAm variability, with stable effects across cohorts in functionally relevant genes. This underscores the importance of including genotype in studies investigating the impact of environmental factors on epigenetic marks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".