Variants in the Niemann-Pick type C gene <i>NPC1</i> are not associated with Parkinson’s disease
Bibliographic record
Abstract
Abstract Biallelic variants in NPC1 , a lysosomal gene coding for a transmembrane protein involved in cholesterol trafficking, may cause Niemann-Pick disease type C (NPC). A few cases of NPC1 mutation carriers have been reported with a Parkinson’s disease (PD) presentation. In addition, pathological studies demonstrated phosphorylated alpha-synuclein and Lewy pathology in brains of NPC patients. Therefore, we aimed to examine whether NPC1 genetic variants may be associated with PD. Full sequencing of NPC1 was performed in 2,657 PD patients and 3,647 controls from three cohorts, using targeted sequencing with molecular inversion probes. A total of 9 common variants and 126 rare variants were identified across the three cohorts. To examine association with PD, regression models adjusted for age, sex and origin were performed for common variants, and optimal sequence Kernel association test (SKAT-O) was performed for rare variants. After correction for multiple comparisons, common and rare NPC1 variants were not associated with PD. Our results do not support a link between heterozygous variants in NPC1 and PD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".