Single variant, yet “double trouble”: TSC and KBG syndrome because of a large de novo inversion
Bibliographic record
Abstract
Despite the advances in high-throughput sequencing, many rare disease patients remain undiagnosed. In particular, the patients with well-defined clinical phenotypes and established clinical diagnosis, yet missing or partial genetic diagnosis, may hold a clue to more complex genetic mechanisms of a disease that could be missed by available clinical tests. Here, we report a patient with a clinical diagnosis of Tuberous sclerosis, combined with unusual secondary features, but negative clinical tests including TSC1 and TSC2 . Short-read whole-genome sequencing combined with advanced bioinformatics analyses were successful in uncovering a de novo pericentric 87-Mb inversion with breakpoints in TSC2 and ANKRD11 , which explains the TSC clinical diagnosis, and confirms a second underlying monogenic disorder, KBG syndrome. Our findings illustrate how complex variants, such as large inversions, may be missed by clinical tests and further highlight the importance of well-defined clinical diagnoses in uncovering complex molecular mechanisms of a disease, such as complex variants and “double trouble” effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".