Human Placental-Specific Epipolymorphism and its Association with Adverse Pregnancy Outcomes
Bibliographic record
Abstract
Interindividual variation in DNA-methylation level is widespread in the human genome, despite its critical role in regulating gene expression. The nature of this variation, including its tissue-specific nature, and the role it may play in human phenotypic variation and disease is still poorly characterized. The placenta plays a critical role in regulating fetal growth and development in ways that have lifelong effects on health. To identify genes with a high degree of interindividual DNA methylation variation in the human placenta, we surveyed the human genome using the Illumina GoldenGate Methylation Cancer panel targeting 1505 CpG sites of 807 genes. While many sites show a continuous pattern of methylation levels, WNT2, TUSC3 and EPHB4 were identified to have a polymorphic "on-or-off" pattern of DNA methylation variation at their promoter region which was confirmed by pyrosequencing. Methylation of these genes can be found in 7%-25% of over 100 placentas tested. The methylation state at the promoter of these genes is concordant with mRNA allelic expression. In three informative cases TUSC3 was observed to be methylated on the maternal allele, and it is thus possible this represents a polymorphically imprinted gene. Furthermore, TUSC3 promoter methylation showed evidence for association with preeclampsia. A biological significance of these methylation allelic polymorphisms (MAPs) to human placental diversity is further implied by their placental specificity and absence in mouse. An extended study of blood suggests that MAPs may also be found in other tissues, implicating their utility for tissue-specific association with complex disorders. The identification of such "epipolymorphism" in other tissues and their use in association studies, should improve our understanding of interindividual phenotypic variability and complex disease susceptibility.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".