Disease Penetrance of Late-Onset Parkinsonism
Bibliographic record
Abstract
IMPORTANCE: Mutations in SNCA, LRRK2, VPS35, EIF4G1, and DNAJC13 have been implicated in late-onset familial parkinsonism. However, the estimated disease penetrance of these mutations varies widely. OBJECTIVE: To compare penetrance of various mutations reported in published genetic studies to improve the understanding of late-onset parkinsonism. DATA SOURCES: Forty-nine previously published studies, including 709 participants, were included for all original and subsequent articles in ISI Web of Science, PubMed electronic databases, and extracted information about number of mutation carriers within families and sporadic cases worldwide for pathogenic mutations in SNCA, LRRK2, VPS35, EIF4G1, and DNAJC13. The end-of-search date was January 31, 2014. STUDY SELECTION: Published studies were included if there was information on the ethnicity of the patient or unaffected individual, confirmation of mutation, age of patient or unaffected individual, age at onset, and first motor symptom of patient. Autosomal recessive parkinsonism and genes implicated without significant genetic linkage were excluded from this study. DATA EXTRACTION AND SYNTHESIS: The age-associated cumulative incidence was estimated using the Kaplan-Meier method with age at onset as the time variable; asymptomatic carriers were right censored at the age at last contact or age at death. MAIN OUTCOMES AND MEASURES: Comparative measures were obtained with log-rank tests, and each penetrance estimate was given separately with 95% confidence intervals. RESULTS: All the assessed autosomal dominant Parkinson disease mutations have significantly different age-dependent cumulative incidences (P < .001). In particular, penetrance of SNCA duplications was comparable to point mutations (log-rank P = .97) and driven by inclusion of SNCA p.A53T (mean age at onset, 45.9 years; 95% CI, 43-49 years). In addition, Israeli Ashkenazi Jewish LRRK2 p.G2019S carriers (mean age at onset, 57.9 years; 95% CI, 54-63 years) were comparable to Tunisian Arab Berbers (mean age at onset, 57.1 years; 95% CI, 45.5-68.7 years) (P = .58), whereas Norwegian carriers (mean age at onset, 63 years; 95% CI, 51.4-74.6 years) were significantly different from the other groups (P < .001). CONCLUSIONS AND RELEVANCE: Parkinson disease pathogenic mutations have an age-dependent penetrance that could be ameliorated or exacerbated by modifier genes or environmental factors in different populations.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".