Penetrance estimate of <i>LRRK2</i> p.G2019S mutation in individuals of non‐Ashkenazi Jewish ancestry
Bibliographic record
Abstract
BACKGROUND: Penetrance estimates of the leucine-rich repeat kinase 2 (LRRK2) p.G2019S mutation for PD vary widely (24%-100%). The p.G2019S penetrance in individuals of Ashkenazi Jewish ancestry has been estimated as 25%, adjusted for multiple covariates. It is unknown whether penetrance varies among different ethnic groups. The objective of this study was to estimate the penetrance of p.G2019S in individuals of non-Ashkenazi Jewish ancestry and compare penetrance between Ashkenazi Jews and non-Ashkenazi Jews to age 80. METHODS: The kin-cohort method was used to estimate penetrance in 474 first-degree relatives of 69 non-Ashkenazi Jewish LRRK2 p.G2019S carrier probands at 8 sites from the Michael J. Fox LRRK2 Cohort Consortium. An identical validated family history interview was administered to assess age at onset of PD, current age, or age at death for relatives in different ethnic groups at each site. Neurological examination and LRRK2 genotype of relatives were included when available. RESULTS: Risk of PD in non-Ashkenazi Jewish relatives who carry a LRRK2 p.G2019S mutation was 42.5% (95% confidence interval [CI]: 26.3%-65.8%) to age 80, which is not significantly higher than the previously estimated 25% (95% CI: 16.7%-34.2%) in Ashkenazi Jewish carrier relatives. The penetrance of PD to age 80 in LRRK2 p.G2019S mutation carrier relatives was significantly higher than the noncarrier relatives, as seen in Ashkenazi Jewish relatives. CONCLUSIONS: The similar penetrance of LRRK2 p.G2019S estimated in Ashkenazi Jewish carriers and non-Ashkenazi Jewish carriers confirms that p.G2019S penetrance is 25% to 42.5% at age 80 in all populations analyzed. © 2017 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 | 0.002 | 0.003 |
| 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 | 0.000 | 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".