An Exploratory Analysis of Essential Tremor and Associated Phenotypes
Bibliographic record
Abstract
Essential Tremor (ET) is a highly heterogeneous movement disorder with a strong genetic basis. However, the etiology of ET is unclear, largely due to its clinical heterogeneity and frequently observed comorbidities. We conducted a three-part study to investigate the genetic basis of ET in relation to co-occurring phenotypes, aiming to assess causal directionality and to clarify phenotypic heterogeneity. First, we used Mendelian Randomization (MR) to test for directional, causal relationships between ET and common co-occurring traits. We then identified pleiotropic single nucleotide polymorphisms (SNPs) shared between ET and these traits, mapped them to genes, and performed gene ontology enrichment analyses. Finally, we applied genomic structural equation modeling (g-SEM) to group traits by shared genetic variance and evaluate their influence on ET. MR analyses did not reveal causal relationships, likely due to high genetic pleiotropy. Gene enrichment analyses of shared SNPs suggested involvement of certain pathways, but these signals were driven by limited gene overlap. SEM identified a well-fitting latent model of shared genetic architecture, but it explained only ~2% (±9%) of ET variance. Our findings suggest that ET and its comorbidities may share complex genetic architecture not captured by common variants alone. The limited variance explained by MR and SEM highlights the need for rare variant and multi-omics studies to better understand the biological mechanisms underlying ET and its heterogeneity.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".