Comparison of DNA methylation measured by Illumina 450K and EPIC BeadChips in blood of newborns and 14-year-old children
Bibliographic record
Abstract
Analysis of DNA methylation helps to understand the effects of environmental exposures as well as the role of epigenetics in human health. Illumina, Inc. recently replaced the HumanMethylation450 BeadChip (450K) with the EPIC BeadChip, which nearly doubles the measured CpG sites to >850,000. Although the new chip uses the same underlying technology, it is important to establish if data between the two platforms are comparable within cohorts and for meta-analyses. DNA methylation was assessed by 450K and EPIC using whole blood from newborn (n = 109) and 14-year-old (n = 86) participants of the Center for the Health Assessment of Mothers and Children of Salinas. The overall per-sample correlations were very high (r >0.99), although many individual CpG sites, especially those with low variance of methylation, had lower correlations (median r = 0.24). There was also a small subset of CpGs with large mean methylation β-value differences between platforms, in both the newborn and 14-year datasets. However, estimates of cell type proportion prediction by 450K and EPIC were highly correlated at both ages. Finally, differentially methylated positions between boys and girls replicated very well by both platforms in newborns and older children. These findings are encouraging for application of combined data from EPIC and 450K platforms for birth cohorts and other population studies. These data in children corroborate recent comparisons of the two BeadChips in adults and in cancer cell lines. However, researchers should be cautious when characterizing individual CpG sites and consider independent methods for validation of significant hits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".