Whole-exome sequencing reveals the genetic causes and modifiers of moyamoya syndrome
Bibliographic record
Abstract
Moyamoya vasculopathy secondary to various genetic disorders is classified as moyamoya syndrome (MMS). Recent studies indicate MMS occurs due to a combination of genetic modifiers and causative mutations for the primary genetic disorders. We performed whole-exome sequencing (WES) in 13 patients with various genetic disorders who developed MMS. WES successfully revealed the genetic diagnoses of neurofibromatosis type 1 (NF-1), Down syndrome, multisystemic smooth muscle dysfunction syndrome, Noonan syndrome, and alpha thalassemia. The previously reported modifier genes, RNF213 and MRVI1, were confirmed in the NF-1 and Down syndrome cases. Further analysis revealed rare hypomorphic variants in the causative genes of the primary disorders underlying MMS, such as Alagille syndrome and Rasopathies, conferred susceptibility to MMS. Genes involved in the development of pulmonary arterial hypertension (PAH), such as ABCC8 and BMPR2, were also identified as potential modifiers. The rare variants in the MMS and PAH genes were significantly enriched in the eight Japanese patients with MMS compared with the 104 Japanese individuals from the 1000 Genomes Project. Disease genes associated with the arterial occlusive conditions represented by those of Rasopathies and PAH may provide novel diagnostic markers and future therapeutic targets for MMS as well as moyamoya disease with an unknown cause.
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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 | 0.000 |
| 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 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".