The association of skewed X chromosome inactivation with aneuploidy in humans
Bibliographic record
Abstract
Recently, we reported that skewed X chromosome inactivation (XCI) was more common in women who had experienced a trisomic pregnancy as compared to control women. Rather than an overall shift in the distribution of skewing there appears to only be an excess of extreme (= 95%) skewing. Further analysis of our data reveals that the increase in skewed XCI is dependent on which chromosome is involved in the trisomy and how many trisomies the woman has experienced, although sample sizes in each group are small. In this review we discuss limitations of the commonly used assays of XCI, which use measurements of DNA methylation to infer skewing patterns, and review the data based on current knowledge of the causes of XCI skewing. Gonadal mosaicism, premature aging, loss of methylation at some CpGs, and X-linked mutations can all be considered as potential mechanisms explaining both increased risk of trisomy and skewed XCI. While further research is needed to evaluate the role of each of these, the association of trisomy with apparent skewed XCI in the mother offers new opportunities to clarify the risk factors for and causes of the high incidence of aneuploidy in human females.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".