Improved reporting of DNA methylation data derived from studies of the human placenta
Bibliographic record
Abstract
Epigenetic variation is increasingly hypothesized as a mechanism underlying the effect of the in utero environment on long-term postnatal health; however, there is currently little clear data to support this in humans. A number of biological and technical factors provide challenges for the design of clinical epigenetic studies: from the type of cells or tissues that are available to the large range of predicted confounders that may impact findings. The human placenta, in addition to other neonatal tissues and whole blood, is commonly sampled for the study of epigenetic modifications. However there is little conformity for the most appropriate methods for study design, data analysis, and importantly, data interpretation. Here we present general recommendations for the reporting of DNA methylation in biological samples, with specific focus on the placenta. We outline key guidelines for: (1) placental sampling, (2) data analysis and presentation, and (3) interpretation of DNA methylation data. We emphasize the need to consider methodological noise, increase statistical power and to ensure appropriate adjustment for biological covariates. Finally, we highlight that epigenetic changes may be non-pathological and not necessarily translate into disease-associated changes. Improved reporting of DNA methylation data will be critical to identify epigenetic-based effects and to better understand the full phenotypic impact of these widely-reported epigenomic changes.
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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 | 0.224 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".