Genetic Diversity and Expanded Phenotypes in Dystonia: Insights From Large‐Scale Exome Sequencing
Bibliographic record
Abstract
OBJECTIVE: Dystonia is one of the most prevalent movement disorders, characterized by significant clinical and etiological heterogeneity. Despite considerable heritability (~25%), the etiology in most patients remains elusive. Moreover, understanding correlations between clinical manifestations and genetic variants has become increasingly complex. METHODS: Exome sequencing was conducted on 1924 genetically unsolved, mainly late-onset isolated dystonia patients, recruited primarily from two dystonia registries (DysTract and the Dystonia Coalition). Rare variants in genes previously linked to dystonia (n = 406) were examined, confirmed via Sanger sequencing, and analyzed for segregation when possible. RESULTS: We identified 137 distinct likely pathogenic/pathogenic variants (according to ACMG criteria) across 51 genes in 163/1924 patients, including 153/1895 index patients (diagnostic yield 8.1%). The strongest predictors of a genetic diagnosis were generalized dystonia (28.6% yield) and age at onset (20.4% yield in patients with onset < 30 years). Notably, 56.2% of these variants were novel, with recurrent variants in EIF2AK2, VPS16, KCNMA1, and SLC2A1. Additionally, 321 index patients (16.9%) harbored variants of uncertain significance in 102 genes. The most frequently implicated genes included VPS16, THAP1, GCH1, SGCE, GNAL, and KMT2B. Presumably pathogenic variants in less well-established dystonia genes were also found, including KCNMA1, KIF1A, and ZMYND11. At least six variants (in ADCY5, GNB1, IR2BPL, KCNN2, KMT2B, and VPS16) occurred de novo, supporting pathogenicity. INTERPRETATION: This study provides valuable insights into the genetic landscape of dystonia, underscores the utility of exome sequencing for diagnosis, substantiates several candidate genes, and expands the phenotypic spectrum of some genes to include prominent, sometimes isolated dystonia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".