multi-ancestry-prs-agent

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---

name: 'multi-ancestry-prs-agent'

description: 'AI-powered multi-ancestry polygenic risk score calculation and optimization for equitable disease risk prediction across diverse global populations.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

Multi-Ancestry PRS Agent

The Multi-Ancestry PRS Agent provides AI-optimized polygenic risk score calculation designed to work across diverse ancestral populations. It addresses the critical limitation of European-biased GWAS by integrating trans-ancestry methods, improving risk prediction for underrepresented populations and enabling equitable precision medicine.

When to Use This Skill

• When calculating PRS for non-European ancestry individuals.
• For developing trans-ancestry risk prediction models.
• To reduce PRS bias across ancestral populations.
• When integrating multi-ancestry GWAS summary statistics.
• For research on PRS portability and equity.

Core Capabilities

1. Multi-Ancestry PRS: Calculate ancestry-aware polygenic scores.

1. Trans-Ancestry Optimization: Optimize weights across populations.

1. Local Ancestry Integration: Account for admixed genomes.

1. Ensemble Methods: Combine multiple PRS approaches.

1. Ancestry Calibration: Population-specific score calibration.

1. Equity Assessment: Evaluate prediction fairness across groups.

Supported Ancestries

| Ancestry | Training Data Availability | PRS Performance |

|----------|---------------------------|-----------------|

| European (EUR) | Excellent | High |

| East Asian (EAS) | Good | Good |

| South Asian (SAS) | Moderate | Moderate |

| African (AFR) | Limited | Lower |

| Hispanic/Latino (AMR) | Limited | Variable |

| Middle Eastern (MID) | Very Limited | Lower |

Multi-Ancestry Methods

| Method | Approach | Best For |

|--------|----------|----------|

| PRS-CSx | Cross-population shrinkage | Multi-ancestry |

| PRS-Multi | Multi-population training | Large cohorts |

| Ensemble | Combined methods | Best overall |

| Local Ancestry | Ancestry-specific weights | Admixed populations |

| GPSMult | Integrated multi-trait | Correlated traits |

Workflow

1. Input: Individual genotypes, target ancestry, disease/trait.

1. Ancestry Inference: Determine genetic ancestry.

1. Method Selection: Choose optimal PRS approach.

1. Score Calculation: Compute ancestry-aware PRS.

1. Calibration: Apply population-specific calibration.

1. Risk Stratification: Categorize into risk groups.

1. Output: PRS, percentile, clinical interpretation.

Example Usage

User: "Calculate multi-ancestry coronary artery disease PRS for this admixed individual with African and European ancestry."

Agent Action:

python3 Skills/Precision_Medicine/Multi_Ancestry_PRS_Agent/calc_prs.py \
    --genotypes patient_genotypes.vcf.gz \
    --ancestry admixed_AFR_EUR \
    --local_ancestry lai_segments.bed \
    --trait coronary_artery_disease \
    --method prs_csx \
    --gwas_summary_stats eur_gwas.txt,afr_gwas.txt \
    --calibration_cohort 1kg_admixed \
    --output prs_results/

Input Requirements

| Input | Format | Purpose |

|-------|--------|---------|

| Genotypes | VCF/PLINK | Individual variants |

| Ancestry | Estimated or self-reported | Method selection |

| GWAS Summary Stats | Multiple ancestries | Score weights |

| Local Ancestry | LAI segments | Admixture handling |

| Reference Panel | Multi-ancestry | LD calculation |

Output Components

| Output | Description | Format |

|--------|-------------|--------|

| PRS Score | Raw polygenic score | .csv |

| Percentile | Population-specific ranking | .csv |

| Risk Category | High/Intermediate/Low | .csv |

| Ancestry Breakdown | Component scores | .json |

| Confidence Interval | Score uncertainty | .json |

| Clinical Interpretation | Risk explanation | .md |

Disease-Specific Performance

| Disease | Multi-Ancestry AUC | EUR Only AUC | Improvement |

|---------|-------------------|--------------|-------------|

| CAD | 0.75-0.80 | 0.70-0.85 | 5-10% in non-EUR |

| Type 2 Diabetes | 0.70-0.75 | 0.65-0.72 | 8-12% in AFR |

| Breast Cancer | 0.65-0.72 | 0.60-0.70 | 5-8% globally |

| Alzheimer's | 0.70-0.78 | 0.65-0.75 | 5-10% in diverse |

AI/ML Components

PRS Optimization:

• Bayesian shrinkage (PRS-CS)
• Cross-population learning
• Neural network weight optimization

Ancestry Inference:

• Supervised classification
• Unsupervised clustering (PCA, ADMIXTURE)
• Local ancestry inference (RFMix)

Ensemble Learning:

• Stacking multiple PRS methods
• Ancestry-stratified weighting
• Uncertainty quantification

Clinical Integration

| Application | PRS Role | Clinical Action |

|-------------|----------|-----------------|

| Primary Prevention | Risk stratification | Screening intensity |

| Risk Communication | Personalized risk | Lifestyle modification |

| Treatment Selection | Predicted response | Drug choice |

| Family Screening | Cascade testing | Genetic counseling |

Prerequisites

• Python 3.10+
• PLINK 2.0
• PRSice-2, LDpred2, PRS-CSx
• Multi-ancestry reference panels
• GWAS summary statistics

Related Skills

• PRS_Net_Deep_Learning_Agent - Deep learning PRS
• Pharmacogenomics_Agent - Drug-gene interactions
• PopEVE_Variant_Predictor_Agent - Variant interpretation
• DiagAI_Agent - Clinical integration

Bias and Fairness

| Bias Type | Cause | Mitigation |

|-----------|-------|------------|

| Discovery Bias | EUR-dominated GWAS | Multi-ancestry GWAS |

| LD Variation | Population-specific LD | Local ancestry adjustment |

| Allele Frequency | Differing frequencies | Population-specific weights |

| Effect Size | Heterogeneous effects | Trans-ancestry meta-analysis |

Large-Scale Initiatives

| Initiative | Focus | Contribution |

|------------|-------|--------------|

| All of Us | US diversity | 1M diverse participants |

| PAGE | Multi-ethnic GWAS | Discovery in diverse |

| H3Africa | African genomics | Continental diversity |

| Mexican Biobank | Latin American | Admixed populations |

| GBMI | Global Biobank | Multi-ancestry meta-analysis |

Special Considerations

1. Self-Reported Ancestry: May not match genetic ancestry
2. Admixture: Require local ancestry methods
3. Population Stratification: Careful covariate adjustment
4. Clinical Validity: Validate in target population
5. Health Equity: Consider access disparities

ESC Guidelines Integration (2025)

| Recommendation | PRS Role | Evidence Level |

|----------------|----------|----------------|

| CV Risk Assessment | Risk modifier | IIa, B |

| Statin Decisions | Borderline risk reclassification | IIa, B |

| Family History Enhancement | Quantify genetic burden | IIa, C |

Limitations

| Limitation | Impact | Research Needed |

|------------|--------|-----------------|

| AFR Performance | Lower accuracy | More GWAS |

| Rare Variants | Not captured | WGS integration |

| Gene-Environment | Not modeled | Interaction studies |

| Clinical Utility | Limited evidence | Randomized trials |

Author

AI Group - Biomedical AI Platform

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