<i>LRRK2</i> coding variants and the risk of Parkinson’s disease
Bibliographic record
Abstract
Abstract Background The leucine-rich repeat kinase 2 ( LRRK2 ) gene harbors both rare highly damaging missense variants (e.g. p.G2019S) and common non-coding variants (e.g. rs76904798) with lower effect sizes that are associated with Parkinson’s disease risk. Objectives This study aimed to investigate in a large meta-analysis whether the LRRK2 GWAS signal represented by rs76904798 is independently associated with Parkinson’s disease risk from LRRK2 coding variation, and whether complex linkage disequilibrium structures with p.G2019S and the 5’ non-coding haplotype account for the association of LRRK2 coding variants. Methods We performed a meta-analysis using imputed genotypes from 17,838 cases, 13,404 proxy-cases and 173,639 healthy controls of European ancestry. We excluded carriers of p.G2019S and/or rs76904798 to clarify the role of LRRK2 coding variation in mediating disease risk, and excluded carriers of relatively rare LRRK2 coding variants to assess the independence of rs76904798. We also investigated the co-inheritance of LRRK2 coding variants with p.G2019S, rs76904798 and p.N2081D. Results LRRK2 rs76904798 remained significantly associated with Parkinson’s disease after excluding carriers of relatively rare LRRK2 coding variants. LRRK2 p.R1514Q and p.N2081D were frequently co-inherited with rs76904798 and the allele distribution of p.S1647T significantly changed among cases after removing rs76904798 carriers. Conclusions These data suggest that the LRRK2 coding variants previously linked to Parkinson’s disease (p.N551K, p.R1398H, p.M1646T and p.N2081D) do not drive the 5’ non-coding GWAS signal. These data, however, do not preclude the independent association of the haplotype p.N551K-p.R1398H and p.M1646T with altered disease risk.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".