Assisted reproductive technologies do not enhance the variability of DNA methylation imprints in human
Bibliographic record
Abstract
BACKGROUND Assisted reproductive technologies (ART) such as in vitro fertilisation (IVF) and intracytoplasmic sperm injection (ICSI) are believed to destabilise genomic imprints. An increased frequency of Beckwith-Wiedemann syndrome in children born after ART has been reported. Other, mostly epidemiological, studies argue against this finding. OBJECTIVE To examine the effect of ART on the stability of DNA methylation imprints, DNA was extracted from maternal peripheral blood (MPB), umbilical cord blood (UCB) and amnion/chorion tissue (ACT) of 185 phenotypically normal children (77 ICSI, 35 IVF, and 73 spontaneous conceptions). Using bisulfite based technologies 10 differentially methylated regions (DMRs) were analysed, including KvDMR1, H19, SNRPN, MEST, GRB10, DLK1/MEG3 IG-DMR, GNAS NESP55, GNAS NESPas, GNAS XL-alpha-s and GNAS Exon1A. RESULTS Methylation indices (MI) do not reveal any significant differences at nine DMRs among the conception groups in neither MPB, UCB nor in ACT. The only slightly variable DMR was that of MEST. Here the mean MI was higher in UCB and MPB of IVF cases (mean MI+/-SD: 0.41+/-0.03 (UCB) and 0.40+/-0.03 (MPB)) compared to the ICSI (0.38+/-0.03, p=0.003 (UCB); 0.37+/-0.04, p=0.0007 (MPB)) or spontaneous cases (0.38+/-0.03, p=0.003 (UCB); 0.38+/-0.04, p=0.02 (MPB)). Weak but suggestive correlations between DMRs were, however, found between MPB, UCB and ACT. CONCLUSION This study supports the notion that children conceived by ART do not show a higher degree of imprint variability and hence do not have an a priori higher risk for imprinting disorders.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".