The commercial genetic testing landscape for Parkinson's disease
Bibliographic record
Abstract
INTRODUCTION: There have been no specific guidelines regarding which genes should be tested in the clinical setting for Parkinson's disease (PD) or parkinsonism. We evaluated the types of clinical genetic testing offered for PD as the first step of our gene curation. METHODS: The National Institutes of Health (NIH) Genetic Testing Registry (GTR) was queried on 12/7/2020 to identify current commercial PD genetic test offerings by clinical laboratories, internationally. RESULTS: We identified 502 unique clinical genetic tests for PD, from 28 Clinical Laboratory Improvement Amendments (CLIA)-approved clinical laboratories. These included 11 diagnostic PD panels. The panels were notable for their differences in size, ranging from 5 to 62 genes. Five genes for variant query were included in all panels (SNCA, PRKN, PINK-1, PARK7 (DJ1), and LRRK2). Notably, the addition of the VPS35 and GBA genes was variable. Panel size differences stemmed from inclusion of genes linked to atypical parkinsonism and dystonia disorders, and genes in which the link to PD causation is controversial. CONCLUSION: There is an urgent need for expert opinion regarding which genes should be included in a commercial laboratory multi-gene panel for PD.
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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 | 0.034 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".