MAP-PRS: Multi-Ancestry Portfolio-Based Polygenic Risk Scores
Bibliographic record
Abstract
Abstract Polygenic Risk Scores (PRS) are emerging tools for predicting an individual’s genetic risk for complex diseases. However, their usefulness in clinical practice remains limited because most existing models are based on data from people of European ancestry, leading to reduced accuracy and stability in other populations. This imbalance restricts the equitable use of PRS in precision medicine. To overcome these limitations, we introduce Multi-Ancestry Portfolio-Based Polygenic Risk Scores (MAP-PRS) —a new framework that combines mathematical modeling and data science principles to improve both fairness and reliability in genetic risk prediction across populations. MAP-PRS treats each ancestry-specific PRS as part of a “portfolio,” similar to how investments are managed in finance, balancing two key aspects: predictive return (how well the score predicts disease) and risk complexity (how uncertain or ancestry-specific the prediction is). By jointly optimizing these factors, MAP-PRS identifies the best combination of ancestry-informed PRS models that maximize predictive accuracy while minimizing bias and instability. This approach also uses advanced computational tools—such as Bayesian modeling, machine learning, and generative neural networks—to refine risk estimates, incorporate environmental and lifestyle factors, and increase representation from under-studied populations. In doing so, MAP-PRS supports more inclusive, equitable, and interpretable precision medicine. As an initial demonstration, MAP-PRS has been applied to predict Type 2 Diabetes (T2D) risk in European ancestry populations, establishing a foundation for broader, multi-ancestry implementation. Future extensions will include additional diseases, such as cervical cancer and HPV susceptibility, endometrioid ovarian cancer, and Alzheimer’s disease—bringing us closer to clinically actionable and globally equitable genetic risk prediction.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".