Peripheral blood epi-signature of Claes-Jensen syndrome enables sensitive and specific identification of patients and healthy carriers with pathogenic mutations in KDM5C
Bibliographic record
Abstract
Claes-Jensen syndrome is an X-linked inherited intellectual disability caused by mutations in the KDM5C gene. Kdm5c is a histone lysine demethylase involved in histone modifications and chromatin remodeling. Males with hemizygous mutations in KDM5C present with intellectual disability and facial dysmorphism, while most heterozygous female carriers are asymptomatic. We hypothesized that loss of Kdm5c function may influence other components of the epigenomic machinery including DNA methylation in affected patients. Genome-wide DNA methylation analysis of 7 male patients affected with Claes-Jensen syndrome and 56 age- and sex-matched controls identified a specific DNA methylation defect (epi-signature) in the peripheral blood of these patients, including 1769 individual CpGs and 9 genomic regions. Six healthy female carriers showed less pronounced but distinctive changes in the same regions enabling their differentiation from both patients and controls. Highly specific computational model using the most significant methylation changes demonstrated 100% accuracy in differentiating patients, carriers, and controls in the training cohort, which was confirmed on a separate cohort of patients and carriers. The 100% specificity of this unique epi-signature was further confirmed on additional 500 unaffected controls and 600 patients with intellectual disability and developmental delay, including other patient cohorts with previously described epi-signatures. Peripheral blood epi-signature in Claes-Jensen syndrome can be used for molecular diagnosis and carrier identification and assist with interpretation of genetic variants of unknown clinical significance in the KDM5C gene.
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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 | 0.001 |
| 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 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".