Mitochondrial <scp>DNA</scp> variant spectrum and the association with chronic tic disorders
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Tic disorders (TDs) are childhood onset neuropsychiatric disorders characterized by single or multiple sudden, rapid, recurrent, and motor tics and/or vocal tics. Several nuclear genes that are involved in mitochondrial functions suggest a potential role of mitochondria in TDs. METHODS: To evaluate the association of mitochondrial DNA (mtDNA) variants with TDs, we screened the whole mitochondrial genomes in 493 TD patients and 109 age- and sex-matched healthy controls using next generation sequencing technology. RESULTS: A total of 1918 mtDNA variants including 1220 variants in patients only, 154 variants in controls only, and 544 variants shared by both cases and controls were identified. We found a higher number of overall mtDNA variants in TD patients (p = 0.00028). The variant density in MT-ATP6/8 and MT-CYB coding regions showed a significant difference between TD patients and controls (p = 0.0025 and p = 0.003, respectively). Furthermore, we observed a significant association of 15 common variants with TD based on an additive model, including m.14766C > T, m.14783 T > C, m.14905G > A, and m.15301G > A in MT-CYB; m.4769A > G, m.10398A > G, m.12705C > T, and m.12850A > G in MT-ND genes; m.7028C > T in MT-CO1; m.8701A > G in MT-ATP6; two variants with m.16223C > T, m.5580 T > C in noncoding regions; and three rRNA variants with m.1438A > G and m.750A > G in RNR1, and m.2352 T > C in RNR2. CONCLUSIONS: Our data provide evidence of mtDNA variants associated with TDs. The accumulation of the heteroplasmic levels may increase the risk of TDs. Replication studies with larger samples are necessary to understand the pathogenesis of TDs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".