Molecular mechanisms defining penetrance of<i>LRRK2</i>-associated Parkinson’s disease
Bibliographic record
Abstract
Abstract Mutations inLeucine-rich repeat kinase 2(LRRK2) are the most frequent cause of dominantly inherited Parkinson’s disease (PD).LRRK2mutations, among which p.G2019S is the most frequent, are inherited with reduced penetrance. Interestingly, the disease risk associated withLRRK2G2019S can vary dramatically depending on the ethnic background of the carrier. While this would suggest a genetic component in the definition ofLRRK2-PD penetrance, only few variants have been shown to modify the age at onset of patients harbouringLRRK2mutations, and the exact cellular pathways controlling the transition from a healthy to a diseased state currently remain elusive. In light of this knowledge gap, recent studies also explored environmental and lifestyle factors as potential modifiers ofLRRK2-PD. In this article, we (i) describe the clinical characteristics ofLRRK2mutation carriers, (ii) review known genes linked toLRRK2-PD onset and (iii) summarize the cellular functions ofLRRK2with particular emphasis on potential penetrance-related molecular mechanisms. This section coversLRRK2’s involvement in Rab GTPase and immune signalling as well as in the regulation of mitochondrial homeostasis and dynamics. Additionally, we explored the literature with regard to (iv) lifestyle and (v) environmental factors that may influence the penetrance ofLRRK2mutations, with a view towards further exposomics studies. Finally, based on this comprehensive overview, we propose potential futurein vivo,in vitroandin silicostudies that could provide a better understanding of the processes triggering PD in individuals withLRRK2mutations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".