Technical variability across the 450K, EPICv1, and EPICv2 DNA methylation arrays: lessons learned for clinical and longitudinal studies
Bibliographic record
Abstract
DNA methylation (DNAm) is the most commonly measured epigenetic mechanism in human populations, with most studies using Illumina arrays to assess DNAm levels. In 2023, Illumina updated their DNAm arrays to the EPIC version 2 (EPICv2), building on prior iterations, namely the EPIC version 1 (EPICv1) and 450K arrays. Whether DNAm measurements are stable across these three generations of arrays has yet not been investigated, limiting the ability of researchers-especially those with longitudinal data-to compare and replicate results across arrays. Here, we present results from a study of 30 child participants (15 male; 15 female) from the Drakenstein Child Health Study, who had DNAm measured on all three of the latest arrays: 450K, EPICv1, and EPICv2. Using these data, we created an annotation of probe quality across arrays, which includes the intraclass correlations, interquartile ranges, correlations, and array bias (i.e., the extent to which DNAm levels were explained by array type) of all CpGs. We also present results from an analysis of sex differences, where we found that CpGs with lower replicability across arrays had higher array-based variance, suggesting this variance metric help guide replication efforts. We also showed that epigenetic age estimates across arrays were more stable when using the principal component versions of epigenetic clocks. Ultimately, this collection of results provides a framework for investigating the replicability and longitudinal stability of epigenetic changes across multiple versions of Illumina DNAm arrays.
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 | 0.271 | 0.439 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".