Genetic testing for Parkinson's disease in Israel: Insights from the Rostock Parkinson's Disease (ROPAD) study
Bibliographic record
Abstract
BACKGROUND: We examined the yield of a large-scale genetic testing for patients with Parkinson's disease (PD) in Israel, where risk factor variants in GBA1 and/or the pathogenic p.Gly2019Ser variant in LRRK2 are prevalent among the Ashkenazi Jewish population. METHODS: This study included data from all Israeli movement disorder clinics participating in the Rostock Parkinson's Disease (ROPAD) study. Patients were tested for variants in eight PD-related genes and 37 genes with possible phenotypic overlap. RESULTS: The sample consisted of 2699 PD patients recruited in three phases (1702 [63.1 %] males, mean age at onset 59.2 ± 10.6 years, 718 [26.6 %] with a family history of PD). Positive PD-relevant genetic test (PDGT) results were obtained in 512 participants (19.0 %). Among 187 (6.9 %) patients the results were due to pathogenic variants only in LRRK2, in 283 (10.5 %) due to risk factor variants only in GBA1, and another 15 patients (0.6 %) were carriers of variants in both genes. Twenty-six subjects (1.0 %) had a positive PDGT based on findings in PRKN (n = 19), PINK1 (n = 4), PARK7, SNCA, or VPS35 (one in each gene), and an additional patient had dual findings (GBA1 and SNCA). The most prevalent variants were LRRK2 p.Gly2019Ser and GBA1 p.Asn409Ser, detected in 191 (7.1 %) and 173 (6.4 %) patients, respectively. Excluding patients harboring only LRRK2 and/or GBA1 variants, the yield was 27/2214 (1.2 %). Seven participants, including one with a positive PDGT, had positive testing findings in genes related to dystonia (GCH1 and TOR1A) and dementia (MAPT). CONCLUSIONS: Genetic testing for Israeli PD patients is beneficial, while the yield is primarily attributed to LRRK2 and GBA1 variants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".