Multi-omics integration of the phenome, transcriptome and genome highlights genes and pathways relevant to essential tremor
Bibliographic record
Abstract
Abstract The genetic factors predisposing to essential tremor (ET), of one of the most common movement disorders, remains largely unknown. While current studies have examined the contribution of both common and rare genetic variants, very few have investigated the ET transcriptome. To understand pathways and genes relevant to ET, we used an RNA sequencing approach to interrogate the transcriptome of two cerebellar regions, the dentate nucleus and cerebellar cortex, in 16 cases and 16 age- and sex-matched controls. Additionally, a phenome-wide association study (pheWAS) of the dysregulated genes was conducted, and a genome-wide gene association study (GWGAS) was done to identify pathways overlapping with the transcriptomic data. We identified several novel dysregulated genes including CACNA1A , a calcium voltage-gated channel implicated in ataxia. Furthermore, several pathways including axon guidance, olfactory loss, and calcium channel activity were significantly enriched. A subsequent examination of the ET GWGAS data (N=7,154) also flagged genes involved in calcium ion-regulated exocytosis of neurotransmitters to be significantly enriched. Interestingly, the pheWAS identified that the dysregulated gene, SHF , is associated with a blood pressure medication (P=9.3E-08), which is commonly used to reduce tremor in ET patients. Lastly, it is also notable that the dentate nucleus and cerebellar cortex have different transcriptomes, suggesting that different regions of the cerebellum have spatially different transcriptomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".