Polygenic Risk Score (PRS) Builder

Build and interpret polygenic risk scores for complex diseases using genome-wide association study (GWAS) data.

Reasoning Strategy

A polygenic risk score predicts genetic risk, not disease. A high PRS means elevated risk relative to the population — it does not mean the person will develop the condition, and a low PRS does not confer immunity. PRS performance varies dramatically across ancestries: a European-derived PRS applied to a West African population can lose 50–70% of its predictive power because the underlying GWAS was trained on European allele frequencies and LD patterns. Effect sizes from discovery GWAS are subject to winner's curse (overestimation in single studies); always prefer weights from large meta-analyses or validated PGS Catalog models. PRS should always be interpreted in the context of non-genetic risk factors — for most complex diseases, environmental factors contribute as much or more than genetics.

LOOK UP DON'T GUESS: Do not assume effect sizes, allele frequencies, or which SNPs are genome-wide significant for a trait — always query GWAS Catalog (gwas_get_associations_for_trait) for actual data. Do not assume a validated PRS model exists for a trait; check PGS Catalog via PubMed search.

Overview

Use Cases:

• "Calculate my genetic risk for type 2 diabetes"
• "Build a polygenic risk score for coronary artery disease"
• "What's my genetic predisposition to Alzheimer's disease?"
• "Interpret my PRS percentile for breast cancer risk"

What This Skill Does:

• Extracts genome-wide significant variants (p < 5e-8) from GWAS Catalog
• Builds weighted PRS models using effect sizes (beta coefficients)
• Calculates individual risk scores from genotype data
• Interprets PRS as population percentiles and risk categories

What This Skill Does NOT Do:

• Diagnose disease (PRS is probabilistic, not deterministic)
• Replace clinical assessment or genetic counseling
• Account for non-genetic factors (lifestyle, environment)
• Provide treatment recommendations

Methodology

PRS Calculation Formula

A polygenic risk score is calculated as a weighted sum across genetic variants:

PRS = Σ (dosage_i × effect_size_i)

Where:

• dosage_i: Number of effect alleles at SNP i (0, 1, or 2)
• effect_size_i: Beta coefficient or log(odds ratio) from GWAS

Standardization

Raw PRS is standardized to z-scores for interpretation:

z-score = (PRS - population_mean) / population_std

This allows comparison to population distribution and percentile calculation.

Significance Thresholds

• Genome-wide significance: p < 5×10⁻⁸ (default threshold)
• This corrects for ~1 million independent tests across the genome
• Relaxed thresholds (e.g., p < 1×10⁻⁵) can include more SNPs but may add noise

Effect Size Handling

• Continuous traits (e.g., height, BMI): Beta coefficient (units of trait per allele)
• Binary traits (e.g., disease): Odds ratio converted to log-odds (beta = ln(OR))
• Missing effect sizes or non-significant SNPs are excluded

Data Sources

This skill uses ToolUniverse GWAS tools to query:

1. GWAS Catalog (EMBL-EBI)

   • Curated GWAS associations, 5000+ studies
   • Tools: gwas_search_associations (param: disease_trait, size; also gwas_get_associations_for_trait), gwas_get_snps_for_gene (param: gene_symbol), dbsnp_get_variant_by_rsid
   • Note: disease_trait search returns associations where the trait is one of potentially several linked EFO traits. For precise filtering, use EFO IDs via efo_trait param.

2. Open Targets Genetics

   • Integrated genetics platform with fine-mapped credible sets
   • Tools: OpenTargets_search_gwas_studies_by_disease, EnsemblVEP_annotate_hgvs (for variant consequence/frequency)

3. Variant Annotation

   • gnomad_search_variants + gnomad_get_variant — population allele frequencies (ancestry-specific via VEP colocated_variants)
   • MyVariant_query_variants — CADD, SIFT, PolyPhen, ClinVar, gnomAD in one call
   • gnomad_get_gene_constraints — gene constraint metrics (pLI, oe_lof) for target prioritization

Key Concepts

Polygenic Risk Scores (PRS)

Polygenic risk scores aggregate the effects of many genetic variants to estimate an individual's genetic predisposition to a trait or disease. Unlike Mendelian diseases caused by single mutations, complex diseases involve hundreds to thousands of variants, each with small effects.

Key Properties:

• Continuous distribution: PRS forms a bell curve in populations
• Relative risk: Compares individual to population average
• Probabilistic: High PRS doesn't guarantee disease, low PRS doesn't guarantee protection
• Ancestry-specific: PRS accuracy depends on matching GWAS and target ancestry

GWAS (Genome-Wide Association Studies)

GWAS compare allele frequencies between cases and controls (or correlate with trait values) across millions of SNPs to identify disease-associated variants.

Study Design:

• Discovery cohort: Initial identification of associations
• Replication cohort: Validation in independent samples
• Sample size: Larger studies detect smaller effects (power ∝ √N)
• Multiple testing correction: Bonferroni-type correction for ~1M tests

Effect Sizes and Odds Ratios

• Beta (β): Change in trait per copy of effect allele
  • Example: β = 0.5 kg/m² means each allele increases BMI by 0.5 units
• Odds Ratio (OR): Multiplicative change in disease odds
  • OR = 1.5 means 50% increased odds per allele
  • Convert to beta: β = ln(OR)

Linkage Disequilibrium (LD) and Clumping

Nearby variants are often inherited together (LD). To avoid double-counting:

• LD clumping: Select independent variants (r² < 0.1 within 1 Mb windows)
• Fine-mapping: Statistical methods to identify causal variants
• This skill uses raw associations; production PRS should include LD pruning

Population Stratification

GWAS and PRS are most accurate when ancestries match:

• Population structure: Different ancestries have different allele frequencies
• Transferability: European-trained PRS perform worse in non-European populations
• Solution: Train PRS on diverse cohorts or use ancestry-matched references

Applications

Clinical Risk Assessment

PRS can stratify individuals for:

• Screening programs: Target high-risk individuals (e.g., mammography, colonoscopy)
• Prevention strategies: Lifestyle interventions for high genetic risk
• Drug response: Pharmacogenomics based on metabolism genes

Example: Khera et al. (2018) showed PRS identifies 3× more individuals at >3-fold coronary artery disease risk than monogenic mutations.

Research Applications

• Gene discovery: PRS-based phenome-wide association studies (PheWAS)
• Genetic correlation: Compare PRS across traits
• Causal inference: Mendelian randomization using PRS as instruments
• Simulation studies: Model polygenic architecture

Personal Genomics

Consumer genetic testing (23andMe, Ancestry DNA) provides raw genotypes. Users can:

• Calculate PRS for traits not reported
• Compare to published PRS models
• Understand genetic contribution vs. lifestyle factors

Caution: Personal PRS should not replace medical advice. Results may cause anxiety if not properly contextualized.

Limitations and Considerations

• Heritability gap: PRS explains only a fraction of genetic heritability (T2D: ~50% heritable, PRS explains ~10–20%). Rare variants, epistasis, and gene-environment interactions are not captured.
• Ancestry bias: European-derived PRS performance drops substantially in non-European populations. Use multi-ancestry GWAS weights when available.
• Winner's curse: Discovery effect sizes are overestimated; use meta-analysis weights or PGS Catalog validated models.
• Not diagnostic: High PRS does not guarantee disease; low PRS does not guarantee protection. Environmental factors contribute equally or more for most complex diseases.
• Actionability varies: Alzheimer's PRS has limited actionable interventions; cardiovascular PRS can guide statin or lifestyle decisions. Always consider what the person can do with the information.
• Ethical: Genetic data is permanent and familial. GINA protects employment/health insurance in the US, but not life insurance. Provide genetic counseling context.

Workflow

1. Trait Selection

Identify the disease or trait of interest:

• Use standard terminology (e.g., "type 2 diabetes" not "T2D")
• Check GWAS Catalog for availability
• Verify sufficient GWAS studies exist (n > 10,000 samples ideal)

2. Association Collection

Query GWAS databases for genome-wide significant associations:

prs = build_polygenic_risk_score(
    trait="coronary artery disease",
    p_threshold=5e-8,  # Genome-wide significance
    max_snps=1000
)

Considerations:

• P-value threshold: 5e-8 is conservative, 1e-5 includes more variants
• LD clumping: Production systems should prune correlated SNPs
• Study quality: Prefer large meta-analyses over small studies

3. Effect Size Extraction

Extract beta coefficients or odds ratios:

• Beta for continuous traits (direct use)
• OR for binary traits (convert to log-odds)
• Handle missing values (exclude or impute from meta-analysis)

4. SNP Filtering

Quality control filters:

• MAF filter: Exclude rare variants (MAF < 0.01) for robustness
• Genotype QC: Remove SNPs with high missingness (> 10%)
• Hardy-Weinberg: Exclude SNPs violating HWE (p < 1e-6)
• Ambiguous SNPs: Remove A/T and G/C SNPs (strand ambiguity)

5. Score Calculation

Calculate weighted sum of genotype dosages:

result = calculate_personal_prs(
    prs_weights=prs,
    genotypes=my_genotypes,
    population_mean=0.0,
    population_std=1.0
)

Genotype Sources:

• 23andMe raw data export
• Ancestry DNA raw data
• Whole genome sequencing (VCF files)
• SNP array data (Illumina, Affymetrix)

6. Risk Interpretation

Convert to percentiles and risk categories:

result = interpret_prs_percentile(result)
print(f"Percentile: {result.percentile:.1f}%")
print(f"Risk: {result.risk_category}")

Risk Categories:

• Low risk: < 20th percentile (genetic protection)
• Average risk: 20-80th percentile (typical genetic predisposition)
• Elevated risk: 80-95th percentile (moderately increased risk)
• High risk: > 95th percentile (substantially increased risk)

Clinical Interpretation:

• Percentiles assume normal distribution
• Relative risk vs. average (not absolute risk)
• Combine with family history, clinical risk factors
• PRS is NOT diagnostic - many high-risk individuals never develop disease

Best Practices

• Use validated PRS from PGS Catalog when available (externally validated, includes LD clumping and ancestry-specific weights)
• Match ancestries between GWAS and target population; use multi-ancestry GWAS when available
• For highly polygenic traits (height, education), relaxed p-value thresholds capture more signal; for oligogenic traits (IBD, T1D), strict thresholds are better
• Combine PRS with clinical risk scores (Framingham, QRISK) for integrated prediction
• In research: document SNP selection criteria, LD clumping parameters, and ancestry of GWAS; validate in held-out cohorts; report R² or AUC stratified by ancestry

Disclaimer

This skill is for educational and research purposes only.

• Not for clinical diagnosis or treatment decisions
• Not validated for clinical use - use PGS Catalog models for clinical-grade PRS
• Requires genetic counseling - interpretation requires expertise
• Does not account for family history, environment, or lifestyle factors
• Ancestry-specific - accuracy depends on matching GWAS ancestry

For clinical genetic testing, consult:

• Genetic counselors (certified by ABGC/ABMGG)
• Medical geneticists
• Healthcare providers with genomics training

PRS is a rapidly evolving field. Guidelines and best practices will continue to change as research progresses.

Regulatory Status:

• FDA does not currently regulate PRS (as of 2024)
• Some countries restrict direct-to-consumer genetic risk reporting
• Check local regulations before clinical implementation