Assessment of private variants in <i>PRKN, PARK7</i> and <i>PINK1</i> in Parkinson’s disease
Bibliographic record
Abstract
Abstract Recessive mutations in PRKN, PARK7 and PINK1 are established causes of early-onset Parkinson’s disease (EOPD). Previous studies have interrogated the role of heterozygous variants in these genes but mainly focused on rare (minor allele frequency [MAF] <1%) damaging variants or established mutations. Here, we assessed heterozygous private PRKN, PARK7 and PINK1 variants in PD risk in four large-scale PD case-control datasets by performing gene-wise burden analyses using sequencing data totaling 5,829 PD cases and 7,221 controls, and summary allele counts from 9,501 PD cases and 48,207 controls. Results showed no significant burden in all three genes after meta-analyses. Burden in EOPD (age at onset <50 years) and late-onset PD (≥50 years) remained nonsignificant. In summary, we found no evidence to support the association of the excess burden of heterozygous private variants in PRKN, PARK7 , and PINK1 with PD risk in the European population. Larger, more diverse cohorts are needed to accurately determine their role in 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".