Identification of genetic and non-genetic modifiers of genomic imprinting through whole genome screening in humans
Bibliographic record
Abstract
Abstract Genomic imprinting is required for normal development, and abnormal methylation of differentially methylated regions (iDMRs) controlling the parent of origin-dependent expression of the imprinted genes has been found in constitutional disorders affecting growth, metabolism, neurobehavior, and in cancer. In most of these cases the cause of the imprinting abnormalities is unknown. Also, these studies have generally been performed on a limited number of CpGs, and a systematic investigation of iDMR methylation in the general population is lacking. By analysing the iDMRs in a large number of peripheral blood DNA methylation array datasets of unaffected individuals and patients with rare disorders, we determined the most common iDMR methylation profiles and identified many genetic and non-genetic factors contributing to their variability. We found that methylation variability is not homogeneous within the iDMRs and that the CpGs closer to the ZFP57 binding sites are less susceptible to methylation changes. We demonstrated the methylation polymorphism of three iDMRs and the atypical behaviour of several others, and reported the association of 25 disease- and 47 non-disease-complex traits, including blood cell type composition, as well as 15 mendelian or chromosomal disorders, with iDMR methylation changes in blood DNA. These findings identify several genetic and non-genetic factors associated with genomic imprinting maintenance in humans, which may have a role in the aetiology of the diseases associated with imprinting abnormalities and have clear implications in molecular diagnostics.
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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 | 0.000 |
| 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 | 0.000 |
| 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".