Genomic variants and inferred biological processes in multiplex families with Tourette syndrome
Bibliographic record
Abstract
Background: Tourette syndrome is a developmental neuropsychiatric disorder. Its etiology is complex and elusive, although an important role of genetic factors has been established. The aim of the present study was to identify the genomic basis of Tourette syndrome in a group of families with affected members in 2 or 3 generations. Methods: Whole-genome sequencing was performed followed by co-segregation and bioinformatic analyses. Identified variants were used to select candidate genes, which were then subjected to gene ontology and pathway enrichment analysis. Results: The study group included 17 families comprising 80 patients with Tourette syndrome and 44 healthy family members. Co-segregation analysis and subsequent prioritization of variants pinpointed 37 rare and possibly pathogenic variants shared among affected individuals within a single family. Three such variants, in the ALDH2, DLD and ALDH1B1 genes, could influence oxidoreductase activity in the brain. Two variants, in SLC17A8 and BSN genes, were involved in sensory processing of sound by inner hair cells of the cochlea. Enrichment analysis of genes whose rare variants were present in all patients from at least 2 families identified significant gene sets implicated in cell–cell adhesion, cell junction assembly and organization, processing of sound, synapse assembly, and synaptic signalling processes. Limitations: We did not examine intergenic variants, but they still could influence clinical phenotype. Conclusion: Our results provide a further argument for a role of adhesion molecules and synaptic transmission in neuropsychiatric diseases. Moreover, an involvement of processes related to oxidative stress response and sound-sensing in the pathology of Tourette syndrome seems likely.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".