Comparison of genome-wide and gene-specific DNA methylation between ART and naturally conceived pregnancies
Bibliographic record
Abstract
Data linking assisted reproductive technologies (ART) with aberrant DNA methylation is limited and inconclusive. In addition, most studies to date have analyzed only a small number of CpG sites and focused on methylation changes in placentas, while data on cord blood are scarce. Our aim was to compare DNA methylation in cord blood samples from ART (N = 10) and control pregnancies (N = 8) using a genome-wide approach with the Illumina® Infinium Human Methylation27 array, which interrogates 27,578 CpG sites. A total of 733 (2.7%) of the CpG sites were significantly differentially methylated between the 2 groups (P < 0.05), with an overall relative hypomethylation in the ART group (P < 0.001). Differences in DNA methylation were more pronounced for CpG sites in certain types of genomic locations and were related to baseline methylation levels and distance from CpG islands and transcription start sites. ART was associated with significantly higher variation in DNA methylation, suggesting that differences in DNA methylation between cases and controls may result from stochastic (or random) genome-wide changes in DNA methylation in ART pregnancies. We identified 24 candidate genes with 2 or more CpG sites that were significantly different between the IVF and control groups. The current study provides support for the hypothesis that ART or associated subfertility may be associated with genome-wide changes in DNA methylation, and these changes appear to be, at least in part, due to epigenetic instability in ART pregnancies. Further studies are required in order to determine the extent to which such ART-related epigenetic instability may have phenotypic consequences.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".