Factors underlying variable DNA methylation in a human community cohort
Bibliographic record
Abstract
Epigenetics is emerging as an attractive mechanism to explain the persistent genomic embedding of early-life experiences. Tightly linked to chromatin, which packages DNA into chromosomes, epigenetic marks primarily serve to regulate the activity of genes. DNA methylation is the most accessible and characterized component of the many chromatin marks that constitute the epigenome, making it an ideal target for epigenetic studies in human populations. Here, using peripheral blood mononuclear cells collected from a community-based cohort stratified for early-life socioeconomic status, we measured DNA methylation in the promoter regions of more than 14,000 human genes. Using this approach, we broadly assessed and characterized epigenetic variation, identified some of the factors that sculpt the epigenome, and determined its functional relation to gene expression. We found that the leukocyte composition of peripheral blood covaried with patterns of DNA methylation at many sites, as did demographic factors, such as sex, age, and ethnicity. Furthermore, psychosocial factors, such as perceived stress, and cortisol output were associated with DNA methylation, as was early-life socioeconomic status. Interestingly, we determined that DNA methylation was strongly correlated to the ex vivo inflammatory response of peripheral blood mononuclear cells to stimulation with microbial products that engage Toll-like receptors. In contrast, our work found limited effects of DNA methylation marks on the expression of associated genes across individuals, suggesting a more complex relationship than anticipated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".