Tooluniverse Polygenic Risk Score

---
name: tooluniverse-polygenic-risk-score
description: Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Covers PRS construction (clumping/thresholding, PRS-CS), validation in independent cohorts, ancestry-aware adjustment, and clinical interpretation (population-relative risk, not absolute prediction). Use for PRS-based risk stratification.
disable-model-invocation: true
---

# 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 — but for a rigorous, GWAS-summary-statistics estimate of cross-trait genetic correlation (rg), use `run_ldsc_genetic_correlation` (LD Score regression), which needs only summary stats (no individual genotypes) and corrects for sample overlap. Far more principled than correlating PRS values.
- **Causal inference**: Mendelian randomization using PRS as instruments
- **Simulation studies**: Model polygenic architecture

### SNP-heritability and genetic correlation (LDSC)

Before or alongside building a PRS, quantify how much of the trait is captured by common SNPs and how traits relate — directly from GWAS summary statistics:

- `run_ldsc_heritability` — SNP-based heritability (h²_SNP) from one GWAS's summary stats; the intercept also flags confounding/inflation vs. true polygenicity. This sets the ceiling a PRS can reach (the "heritability gap" below is exactly h²_SNP minus PRS R²).
- `run_ldsc_genetic_correlation` — genetic correlation (rg) between two GWAS, for shared-aetiology and cross-trait PRS questions.

Both are remote tools (LD Score regression engine + reference LD-score panels). Use them to ground heritability/rg claims in data rather than citing literature point estimates.

### 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:
```python
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:
```python
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:
```python
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