Embracing Monogenic Parkinson's Disease: The MJFF Global Genetic PD Cohort
Bibliographic record
Abstract
Abstract Background As gene‐targeted therapies are increasingly being developed for Parkinson's disease (PD), identifying and characterizing carriers of specific genetic pathogenic variants is imperative. Only a small fraction of the estimated number of subjects with monogenic PD worldwide are currently represented in the literature and availability of clinical data and clinical trial‐ready cohorts is limited. Objective The objectives are to (1) establish an international cohort of affected and unaffected individuals with PD‐linked variants; (2) provide harmonized and quality‐controlled clinical characterization data for each included individual; and (3) further promote collaboration of researchers in the field of monogenic PD. Methods We conducted a worldwide, systematic online survey to collect individual‐level data on individuals with PD‐linked variants in SNCA , LRRK2 , VPS35 , PRKN , PINK1 , DJ‐1 , as well as selected pathogenic and risk variants in GBA and corresponding demographic, clinical, and genetic data. All registered cases underwent thorough quality checks, and pathogenicity scoring of the variants and genotype–phenotype relationships were analyzed. Results We collected 3888 variant carriers for our analyses, reported by 92 centers (42 countries) worldwide. Of the included individuals, 3185 had a diagnosis of PD (ie, 1306 LRRK2 , 115 SNCA , 23 VPS35 , 429 PRKN , 75 PINK1 , 13 DJ‐1 , and 1224 GBA ) and 703 were unaffected (ie, 328 LRRK2 , 32 SNCA , 3 VPS35 , 1 PRKN , 1 PINK1 , and 338 GBA ). In total, we identified 269 different pathogenic variants; 1322 individuals in our cohort (34%) were indicated as not previously published. Conclusions Within the MJFF Global Genetic PD Study Group, we (1) established the largest international cohort of affected and unaffected individuals carrying PD‐linked variants; (2) provide harmonized and quality‐controlled clinical and genetic data for each included individual; (3) promote collaboration in the field of genetic PD with a view toward clinical and genetic stratification of patients for gene‐targeted clinical trials. © 2023 The Authors. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".