Population-specificity of human DNA methylation
Bibliographic record
Abstract
BACKGROUND: Ethnic differences in human DNA methylation have been shown for a number of CpG sites, but the genome-wide patterns and extent of these differences are largely unknown. In addition, whether the genetic control of polymorphic DNA methylation is population-specific has not been investigated. RESULTS: Here we measure DNA methylation near the transcription start sites of over 14, 000 genes in 180 cell lines derived from one African and one European population. We find population-specific patterns of DNA methylation at over a third of all genes. Furthermore, although the methylation at over a thousand CpG sites is heritable, these heritabilities also differ between populations, suggesting extensive divergence in the genetic control of DNA methylation. In support of this, genetic mapping of DNA methylation reveals that most of the population specificity can be explained by divergence in allele frequencies between populations, and that there is little overlap in genetic associations between populations. These population-specific genetic associations are supported by the patterns of DNA methylation in several hundred brain samples, suggesting that they hold in vivo and across tissues. CONCLUSIONS: These results suggest that DNA methylation is highly divergent between populations, and that this divergence may be due in large part to a combination of differences in allele frequencies and complex epistasis or gene × environment interactions.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".