Parkinson's Disease Gene Screening in Familial Cases from Central and South America
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disease following Alzheimer's disease. Nearly 30 causative genes have been identified for PD and related disorders. However, most of these genes were identified in European-derived families, and little is known about their role in Latin American populations. OBJECTIVES: Our goal was to assess the spectrum and frequency of pathogenic variants in known PD genes in familial PD patients from Latin America. METHODS: We selected 335 PD patients with a family history of PD from the Latin American Research Consortium on the Genetics of PD. We capture-sequenced the coding regions of 26 genes related to neurodegenerative parkinsonism. Of the 335 PD patients, 324 had sufficient sequencing coverage to be analyzed. RESULTS: We identified pathogenic variants in 41 individuals (12.7%) in FBXO7, GCH1, LRRK2, PARK7, PINK1, PLA2G6, PRKN, SNCA, and TARDBP, GBA1 risk variants in 25 individuals (7.7%), and variants of uncertain significance in another 24 individuals (7.4%) in ATP13A2, ATP1A3, DNAJC13, DNAJC6, GBA1, LRKK2, PINK1, VPS13C, and VPS35. Of the 70 unique variants identified, 19 were more frequent in Latin Americans than in any other population. CONCLUSIONS: This is the first screening of known PD genes in a large cohort of patients with familial PD from Latin America. There were substantial differences in the spectrum of variants observed in comparison to previous findings from PD families of European origin. Our data provide further evidence that differences exist between the genetic architecture of PD in Latinos and European-derived populations. © 2024 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".