A rare variant polygenic risk score for Late‐Onset Alzheimer's Disease demonstrates trans‐ethnic predictive power
Bibliographic record
Abstract
BACKGROUND: Late-onset Alzheimer's Disease (LOAD) is a substantial contributor to global morbidity, with cases expected to triple by 2050. While aging is the strongest environmental risk factor, genetic factors account for over 60% of disease variation. Traditional polygenic risk scores that aggregate an individual's genetic risk aid in risk stratification, but rely solely on common variants, which limit predictive power across populations, reducing generalizability of genetic risk. The gene-based burden of rare damaging variants has never been assessed for LOAD in a polygenic framework, despite its predictive potential across ancestral groups. METHOD: We herein develop the first rare variant polygenic risk score for LOAD (rvPRS-AD) using a whole genome-adapted version of RV-EXCALIBER, a method that implements correction factors to calibrate rare-variant gene burden testing against large, summary-level control data from gnomAD. Using 3,842 European LOAD cases from ADSP and 32,299 non-Finnish Europeans from gnomAD as controls, we identified 3,164 risk-conferring genes (excluding APOE), which were used to construct rvPRS-AD in 2,433 Hispanic ADSP participants (671 LOAD cases, 1,762 controls) by weighting the additive burden of rare damaging variants per gene. RESULT: We identified ABCA7, a well-established Alzheimer's risk gene, as the most strongly associated gene with LOAD (OR = 1.40, p = 1×10⁻⁴). rvPRS-AD demonstrated trans-ancestral predictive power, with a 1-SD increase in the European-derived score conferring 38% higher odds of LOAD (95% CI, 1.26-1.51) among Hispanics (Figure 1). It remained predictive independent of APOEε4 status and was strongest among APOEε4 homozygotes (OR = 2.02, 95% CI, 1.04-3.93). Lastly, rvPRS-AD identifies individuals at extreme LOAD risk, with 2.2% of Hispanic participants exhibiting a ≥2-fold increased odds of disease. CONCLUSION: rvPRS-AD is the first genetic instrument that leverages rare variants in a polygenic framework to predict LOAD risk across ancestrally diverse populations in a manner that is independent of common variant predictors.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".