Low prevalence of known pathogenic mutations in dominant PD genes: A Swedish multicenter study
Bibliographic record
Abstract
OBJECTIVE: To determine the frequency of mutations known to cause autosomal dominant Parkinson disease (PD) in a series with more than 10% of Sweden's estimated number of PD patients. METHODS: The Swedish Parkinson Disease Genetics Network was formed as a national multicenter consortium of clinical researchers who together have access to DNA from a total of 2,206 PD patients; 85.4% were from population-based studies. Samples were analyzed centrally for known pathogenic mutations in SNCA (duplications/triplications, p.Ala30Pro, p.Ala53Thr) and LRRK2 (p.Asn1437His, p.Arg1441His, p.Tyr1699Cys, p.Gly2019Ser, p.Ile2020Thr). We compared the frequency of these mutations in Swedish patients with published PD series and the gnomAD database. RESULTS: A family history of PD in first- and/or second-degree relatives was reported by 21.6% of participants. Twelve patients (0.54%) carried LRRK2 p.(Gly2019Ser) mutations, one patient (0.045%) an SNCA duplication. The frequency of LRRK2 p.(Gly2019Ser) carriers was 0.11% in a matched Swedish control cohort and a similar 0.098% in total gnomAD, but there was a marked difference between ethnicities in gnomAD, with 42-fold higher frequency among Ashkenazi Jews than all others combined. CONCLUSIONS: In relative terms, the LRRK2 p.(Gly2019Ser) variant is the most frequent mutation among Swedish or international PD patients, and in gnomAD. SNCA duplications were the second most common of the mutations examined. In absolute terms, however, these known pathogenic variants in dominant PD genes are generally very rare and can only explain a minute fraction of familial aggregation of PD. Additional genetic and environmental mechanisms may explain the frequent co-occurrence of PD in close relatives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".