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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

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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.

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

3. Local Ancestry Integration: Account for admixed genomes.

4. Ensemble Methods: Combine multiple PRS approaches.

5. Ancestry Calibration: Population-specific score calibration.

6. 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.

2. Ancestry Inference: Determine genetic ancestry.

3. Method Selection: Choose optimal PRS approach.

4. Score Calculation: Compute ancestry-aware PRS.

5. Calibration: Apply population-specific calibration.

6. Risk Stratification: Categorize into risk groups.

7. 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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