Coding and Noncoding Variation in <scp><i>LRRK2</i></scp> and Parkinson's Disease Risk
Bibliographic record
Abstract
BACKGROUND: The leucine-rich repeat kinase 2 (LRRK2) gene harbors both rare highly damaging missense variants (eg, p.G2019S) and common noncoding variants (eg, rs76904798) with lower effect sizes that are associated with Parkinson's disease (PD) risk. OBJECTIVES: This study aimed to investigate in a large meta-analysis whether the LRRK2 Genome-Wide Association Study (GWAS) signal represented by rs76904798 is independently associated with PD risk from LRRK2 coding variation and whether complex linkage disequilibrium structures with p.G2019S and the 5' noncoding haplotype account for the association of LRRK2 coding variants. METHODS: We performed a meta-analysis using imputed genotypes from 17,838 patients, 13,404 proxy patients, 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 PD after excluding the 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 patients after removing rs76904798 carriers. CONCLUSIONS: These data suggest that the LRRK2 coding variants previously related to PD (p.N551K, p.R1398H, p.M1646T, and p.N2081D) do not drive the 5' noncoding 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. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
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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) | 0.001 | 0.008 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".