Analysis of expressed SNPs identifies variable extents of expression from the human inactive X chromosome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: X-chromosome inactivation (XCI) results in the silencing of most genes on one X chromosome, yielding mono-allelic expression in individual cells. However, random XCI results in expression of both alleles in most females. Allelic imbalances have been used genome-wide to detect mono-allelically expressed genes. Analysis of X-linked allelic imbalance in females with skewed XCI offers the opportunity to identify genes that escape XCI with bi-allelic expression in contrast to those with mono-allelic expression and which are therefore subject to XCI. RESULTS: We determine XCI status for 409 genes, all of which have at least five informative females in our dataset. The majority of genes are subject to XCI and genes that escape from XCI show a continuum of expression from the inactive X. Inactive X expression corresponds to differences in the level of histone modification detected by allelic imbalance after chromatin immunoprecipitation. Differences in XCI between populations and between cell lines derived from different tissues are observed. CONCLUSIONS: We demonstrate that allelic imbalance can be used to determine an inactivation status for X-linked genes, even without completely non-random XCI. There is a range of expression from the inactive X. Genes escaping XCI, including those that do so in only a subset of females, cluster together, demonstrating that XCI and location on the X chromosome are related. In addition to revealing mechanisms involved in cis-gene regulation, determining which genes escape XCI can expand our understanding of the contributions of X-linked genes to sexual dimorphism.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 it