Large-scale Rare Variant Burden Testing in Parkinson’s Disease Identifies Novel Associations with Genes Involved in Neuro-inflammation
Bibliographic record
Abstract
Abstract Parkinson’s disease (PD) has a large heritable component and genome-wide association studies to date have identified over 90 variants associated with PD, providing deeper insights into the disease biology. However, there have not been large-scale rare variant analyses for PD. To address this gap, we investigated the rare genetic component of PD at minor allele frequencies <1%, using whole genome and whole exome sequencing data from 7,184 PD cases, 6,701 proxy-cases, and 51,650 healthy controls from the Accelerating Medicines Partnership Parkinson’s disease (AMP-PD) initiative, the National Institutes of Health, the UK Biobank, and Genentech. We performed burden tests meta-analyses on protein-altering variants, prioritized based on their predicted functional impact. Our work identified several genes reaching exome-wide significance. While two of these genes, GBA and LRRK2 , have been previously implicated as risk factors for PD, we identify potential novel associations for B3GNT3, AUNIP, ADH5, TUBA1B, OR1G1, CAPN10 , and TREML1 . Of these, B3GNT3 and TREML1 provide new evidence for the role of neuroinflammation in PD. To date, this is the largest analysis of rare genetic variation in PD.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".