ASSESSING THE RELATIONSHIP BETWEEN MONOALLELIC <i>PARK2</i> MUTATIONS AND PARKINSON’S RISK
Bibliographic record
Abstract
ABSTRACT Biallelic PARK2 (Parkin) mutations cause autosomal recessive Parkinson’s (PD); however, the role of monoallelic PARK2 mutations as a risk factor for PD remains unclear. We investigated the role of single heterozygous PARK2 mutations in three large independent case-control cohorts totalling 10,858 PD cases and 8,328 controls. Overall, after exclusion of biallelic carriers, single PARK2 mutations were more common in PD than controls conferring a >1.5-fold increase in risk of PD (P=0.035), with meta-analysis (19,574 PD cases and 468,488 controls) confirming increased risk (OR=1.65, P=3.69E-07). Carriers were shown to have significantly younger ages at onset compared to non-carriers (NeuroX: 56.4 vs . 61.4 years; Exome: 38.5 vs . 43.1 years). Stratifying by mutation type, we provide preliminary evidence for a more pathogenic risk profile for single PARK2 copy number variant (CNV) carriers compared to single nucleotide variant carriers. Studies that did not assess biallelic PARK2 mutations or consist of predominantly early-onset cases may be biasing these estimates, and removal of these resulted in a loss of association (OR=1.23, P=0.614; n=4). Importantly, when we looked for additional CNVs in 30% of PD cases with apparent monoallellic PARK2 mutations we found that 44% had biallelic mutations suggesting that previous estimates may be influenced by cryptic biallelic mutation status. While this study supports the association of single PARK2 mutations with PD, it highlights confounding effects therefore caution is needed when interpreting current risk estimates. Together, we demonstrate that comprehensive assessment of biallelic mutation status is essential when elucidating PD risk associated with monoallelic PARK2 mutations.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".