The dynamics of X‐inactivation skewing as women age
Bibliographic record
Abstract
Non-random X-chromosome inactivation (XCI) has been associated with X-linked diseases, neoplastic diseases, recurrent pregnancy loss, and trisomy risk. It also occurs more commonly in older female populations. To understand the etiology of non-random XCI and utilize this assay appropriately in clinical research and practice, the age-related alteration in XCI patterns in normal females needs to be clearly defined. In the present study, we evaluated the XCI status in 350 unselected women aged 0-88 years with unknown history of genetic disorders or abnormal pregnancies. DNA samples were extracted from peripheral blood and analyzed by a methylation-based assay at the androgen receptor locus. A weak but significant positive correlation was observed between age and degree of skewing in XCI over the whole age range (r = 0.23, p < 0.0001), and skewing values become non-normally distributed at older ages. However, the increase in skewed XCI appears to be more pronounced after age 30 than at younger ages. This trend supports the model of increased skewing with age as a consequence of hematopoietic stem cell senescence. An alternative possibility is that there is allele-specific loss of methylation with time that results in the appearance of increased XCI skewing using a methylation-based assay.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".