Systematic analysis of PINK1 variants of unknown significance shows intact mitophagy function for most variants
Bibliographic record
Abstract
Pathogenic variants in PINK1 cause early-onset Parkinson's disease. Although many PINK1 variants have been reported, the clinical significance is uncertain for the majority of them. To gain insights into the consequences of PINK1 missense variants in a systematic manner, we selected 50 PINK1 missense variants from patient- and population-wide databases and systematically classified them using Sherloc, a comprehensive framework for variant interpretation based on ACMG-AMP guidelines. We then performed functional experiments, including mitophagy and Parkin recruitment assays, to assess the downstream consequences of PINK1 variants. Analysis of PINK1 missense variants based on Sherloc showed that the patient databases over-annotate variants as likely pathogenic. Furthermore, our study shows that pathogenic PINK1 variants are most often linked to a loss-of-function for mitophagy and Parkin recruitment, while this is not observed for variants of unknown significance. In addition to the Sherloc framework, the added layer of evidence of our functional tests suggests a reclassification of 9/50 missense variants. In conclusion, we suggest the assessment of multiple layers of evidence, including functional data on top of available clinical and population-based data, to support the clinical classification of a variant and show that the presence of a missense variant in PINK1 in a Parkinson's disease case does not automatically imply pathogenicity.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".