<scp> <i>GBA1</i> </scp> Variants with Unknown Classification Are Modest Contributors to Parkinson's Disease Susceptibility
Bibliographic record
Abstract
Abstract Background GBA1 variants cause Gaucher's disease (GD) in biallelic forms and increase Parkinson's disease (PD) risk in heterozygous carriers. Carriers of mild or severe variants (causing GD type 1 or types 2–3) can enroll in clinical trials, whereas those with GBA1 variants classified as unknown are typically excluded. Objective We assessed the contribution of unknown variants to PD risk and their relevance for trial stratification. Methods We meta‐analyzed 34 case–control studies (24,060 PD cases, 14,465 controls). Odds ratios (ORs) were estimated using random‐effects models and stratified by the American College of Medical Genetics and Genomics (ACMG) criteria. Results Unknown variants also classified as variants of uncertain significance (VUSs) per ACMG criteria were associated with PD (OR = 1.59, 95% confidence interval [CI]: 1.25–2.02; I 2 = 0%). VUSs + likely pathogenic + pathogenic also showed an association (OR = 1.63, 95% CI: 1.28–2.06; I 2 = 0%). Conclusions Unknown GBA1 variants may be considered provisionally in clinical trials if also classified as VUS, likely pathogenic, or pathogenic per ACMG criteria. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".