Insights into Essential Tremor and Essential Tremor-Plus from Common Variants
Bibliographic record
Abstract
Abstract Intro The heterogeneity of Essential Tremor (ET) complicates how it is diagnosed and studied. ET-Plus is a concept proposed to help explain the overlap of ET clinical features with soft signs of other neurological disorders. We aimed to better understand ET by comparing brain maps informed by ET common variant to those derived from phenotypes involved in ET-Plus. A further goal was to enhance the diagnostic precision of ET by accounting for shared neurobiological signals between ET and ET-Plus–related phenotypes. Methods Phenotype variant association mapping to the brain was done for ET, Parkinson’s disease (PD), dystonia, and cognition across adult mouse whole brain and cerebellum spatial transcriptomic data through gsMap. Separately, ET genome-wide association study (GWAS) summary statistics were conditioned on PD and cognition to account for shared genetic signals. Using both raw and conditioned GWASes, ET polygenic risk scores (PRS) were calculated across patient cohorts and controls, and their respective ability to classify ET at the 90th percentile was compared using McNemar’s test. Results Spatial mappings of GWAS signals revealed many shared associations between phenotypes. The raw ET PRS model preformed 1.33% (95% CI: [0.30% - 2.35%]; p = 0.0129) better than the conditioned ET PRS model. Conclusion We lack the ability to decern ET from phenotypes involved in ET-Plus using existing common variant disease associations. Efforts to isolate a core genetic signal for ET by de-noising shared associations reduced the accuracy of patient classification. A more effective strategy to studying ET may be to leverage its heterogeneity rather than attempt to isolate it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".