Exome sequencing in Asian populations identifies rare deficient <i>SMPD1</i> alleles that increase risk of Parkinson’s disease
Bibliographic record
Abstract
Abstract Parkinson’s disease is an incurable and progressive disease that adversely affects balance, muscle control, and movement. We hypothesized that the landscape of rare, protein-altering genetic variants could provide further mechanistic insights into disease pathogenesis. We performed whole-exome sequencing on 4,298 persons with Parkinson’s disease and 5,512 unaffected controls from Singapore, Malaysia, Hong Kong, South Korea, and Taiwan. We tested for association between gene-based burden of rare, predicted damaging variants and risk of Parkinson’s disease. Genes surpassing exome-wide significance ( P <2.5×10 -6 ) were tested for replication in sequencing data from a further 5,585 Parkinson’s disease patients and 5,642 controls of Asian and European ancestry. We observed that carriage of rare, protein-altering variants that were predicted to impair protein function at SMPD1 (a gene encoding for acid sphingomyelinase) were significantly associated with increased risk of Parkinson’s disease. Refinement of variant classification using functional acid sphingomyelinase assays suggest that individuals carrying SMPD1 variants with less than 44 percent of normal enzymatic activity show the strongest association with Parkinson’s disease risk in both the discovery (odds ratio (OR) = 2.37, 95% CI = 1.68 - 3.35, P = 4.35 × 10 -7 ) and replication collections (OR = 2.18, 95% CI = 1.69 - 2.81, P = 4.80 × 10 -10 ), leading to a significant observation when all data were meta-analyzed (OR = 2.24, 95% CI = 1.83 - 2.76, P = 1.25 × 10 -15 ). Our findings affirm the importance of sphingomyelin metabolism in the pathobiology of neurodegenerative diseases and highlights the utility of functional genomic assays in large-scale exome sequencing studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".