Assessment of common genetic variation in Alzheimer’s and Parkinson’s diseases reveals global distinction in population attributable risk
Bibliographic record
Abstract
Abstract Emerging evidence suggests that the genetic architecture of Alzheimer’s (AD) and Parkinson’s diseases (PD) risk varies across ancestries. This study seeks to explore distinct and universal genetic targets across individuals of Latino, African/African Admixed, East Asian, and European populations by implementing Population Attributable Risk (PAR) comparisons on summary statistics from genome-wide association studies (GWAS). PAR was calculated for the most significant disease variants using summary statistics derived from select multi-ancestry GWAS meta-analyses, followed by fine-mapping analysis to validate genetic contribution of disease variants to European, African/African Admixed, East Asian, and Latino individuals. For both AD, APOE4 PAR estimates were universally high across all ancestries, with TSPAN14 and PICALM emerging as other common targets. Attributable risk varied across PD-related major risk loci including variation nearby GBA1 and LRRK2 . In contrast, SNCA, MCCC1, VPS13C , and MAPT loci demonstrated comparable attributable risk across ancestries. This cross-ancestry evaluation of PAR reinforces the genetic heterogeneity of AD and PD. In consideration of the complex etiology of these diseases, these findings may inform the strategic prioritization of therapeutic targets and improve global health outcomes.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".