The Role of Rare Coding Variants in Parkinson's Disease GWAS Loci
Bibliographic record
Abstract
Introduction: Genome-wide association studies (GWAS) have identified multiple loci associated with PD risk. The presence of rare variants within these loci that may account for the increased susceptibility requires further investigation. Methods: Using exome sequencing, we performed a comprehensive rare variant screen of genes located within 56 novel PD loci. We first analyzed exomes from 109 subjects in the discovery cohort (85 diagnosed with PD and 24 healthy controls), and filtered for rare coding variants with MAF15. Further investigation of exome data from a replication cohort of 2,859 European patients with PD (IPDGC) and 24,146 non-Finnish European controls from gnomAD were used for association testing of specific rare variants found in the discovery cohort. Results: Our genetic screening identified 54 potential disease-relevant variants in 71 genes in 109 subjects. Six out of 54 variants were found in two or more patients and were not observed in healthy controls: DNAH1 p.A3639T, STAB1 p.S1089G, ANK2 p.V3634D, ANK2 p.R3906W, SH3GL2 p.G276V, NOD2 p.G908R. Replication in the IPDGC confirmed the association with PD risk for three out of the six identified variants (STAB1 p.S1089G, SH3GL2 p.G276V, and NOD2 p.G908R) (p<10-3). Conclusion: Our study suggests that some of the associations identified in PD risk loci can be ascribed to rare variants with likely functional effects that modify PD risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".