A comprehensive analysis of dominant and recessive parkinsonism genes in REM sleep behavior disorder
Bibliographic record
Abstract
Abstract Objective To examine the role of autosomal dominant (AD) and recessive (AR) Parkinsonism genes in the risk of isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD). Methods Ten genes implicated in AD and AR Parkinsonism were fully sequenced using targeted next-generation sequencing in 1,039 iRBD patients and 1,852 controls of European ancestry. These include the AR genes PRKN, DJ-1 (PARK7), PINK1, VPS13C, ATP13A2, FBXO7 and PLA2G6 , and the AD genes LRRK2, GCH1 and VPS35 . To examine the role of rare heterozygous variants in these genes, burden test and SKAT-O analyses were performed. The contribution of homozygous and compound heterozygous variants was further examined in the AR genes. Copy number variants (CNVs) in PRKN were tested in a subset of samples (n=374) using multiplex ligation-dependent probe amplification followed by analysis of all samples using ExomeDepth. Results We found no association between rare heterozygous variants in the tested genes and risk for iRBD. Several homozygous and compound heterozygous carriers were identified with variants of unknown significance, yet there was no overrepresentation in iRBD patients versus controls. Conclusion Our results do not support a major role for variants in PRKN, PARK7, PINK1, VPS13C, ATP13A2, FBXO7, PLA2G6, LRRK2, GCH1 and VPS35 in the risk of iRBD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".