Analysis of heterozygous <i>PRKN</i> variants and copy number variations in Parkinson’s disease
Bibliographic record
Abstract
Abstract Background Biallelic PRKN mutation carriers with Parkinson’s disease (PD) typically have an earlier disease onset, slow disease progression and, often, different neuropathology compared to sporadic PD patients. However, the role of heterozygous PRKN variants in the risk of PD is controversial. Objectives We aimed to examine the association between heterozygous PRKN variants, including single nucleotide variants and copy-number variations, and PD. Methods We fully sequenced PRKN in 2,809 PD patients and 3,629 healthy controls, including 1,965 late onset (63.97±7.79 years, 63% men) and 553 early onset PD patients (43.33±6.59 years, 68% men). PRKN was sequenced using targeted next-generation sequencing with molecular inversion probes. Copy-number variations were identified using a combination of multiplex ligation-dependent probe amplification and ExomeDepth. To examine whether rare heterozygous single nucleotide variants and copy-number variations in PRKN are associated with PD risk and onset, we used optimized sequence kernel association tests and regression models. Results We did not find any associations between all types of PRKN variants and risk of PD. Pathogenic and likely-pathogenic heterozygous single nucleotide variants and copy-number variations were less common among PD patients (1.52%) than among controls (1.8%, false discovery rate-corrected p=0.55). No associations with age at onset and in stratified analyses were found. Conclusions Heterozygous single nucleotide variants and copy-number variations in PRKN are not associated with Parkinson’s disease. Molecular inversion probes allow for rapid and cost-effective detection of all types of PRKN variants, which may be useful for pre-trial screening and for clinical and basic science studies specifically targeting PRKN patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".