Prioritizing Parkinson’s disease risk genes in genome-wide association loci
Bibliographic record
Abstract
Many drug targets in ongoing Parkinson's disease (PD) clinical trials have strong genetic links. While genome-wide association studies (GWAS) nominate regions associated with disease, pinpointing causal genes is challenging. Our aim was to prioritize additional druggable genes underlying PD GWAS signals. The polygenic priority score (PoPS) integrates genome-wide information from MAGMA gene-level associations and over 57,000 gene-level features. We applied PoPS to East Asian and European PD GWAS data and prioritized genes based on PoPS, distance to the GWAS signal, and non-synonymous credible set variants. We prioritized 46 genes, including well-established PD genes (SNCA, LRRK2, GBA1, TMEM175, VPS13C), genes with strong literature evidence supporting a mechanistic link to PD (RIT2, BAG3, SCARB2, FYN, DYRK1A, NOD2, CTSB, SV2C, ITPKB), and genes relatively unexplored in PD. Many hold potential for drug repurposing or development. We prioritized high-confidence genes with strong links to PD pathogenesis that may represent our next-best candidates for developing disease-modifying therapeutics.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".