wu-yc/labclaw/tooluniverse-polygenic-risk-score

Polygenic Risk Score (PRS) Builder

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

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, millions of variants
• Tools: gwas_get_associations_for_trait, gwas_get_snp_by_id

1. Open Targets Genetics

• Integrated genetics platform
• Fine-mapped credible sets
• Tools: OpenTargets_search_gwas_studies_by_disease, OpenTargets_get_variant_info

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

Scientific Limitations

1. Heritability Gap: PRS explains a fraction of genetic heritability

• Type 2 diabetes: ~50% heritable, PRS explains ~10-20%
• Rare variants, epistasis, and gene-environment interactions not captured

1. Ancestry Bias: Most GWAS are European ancestry

• PRS accuracy drops in non-European populations
• Need for diverse cohort recruitment

1. Winner's Curse: Discovery effect sizes often overestimated

• Replication studies show smaller effects
• Meta-analyses provide better estimates

1. Missing Heritability: Unexplained genetic contribution from:

• Rare variants not captured by SNP arrays
• Structural variants (CNVs, inversions)
• Epigenetic factors

Clinical Limitations

1. Not Diagnostic: PRS is probabilistic, not deterministic

• High PRS doesn't mean you will get disease
• Low PRS doesn't mean you won't get disease

1. Environmental Factors: Many complex diseases are 50%+ environmental

• Smoking, diet, exercise, stress, pollution
• PRS doesn't account for these

1. Pleiotropy: Same variants affect multiple traits

• Genetic correlation between diseases
• Risk for one may protect against another

1. Actionability: Not all high-risk predictions have interventions

• Alzheimer's PRS has limited actionability currently
• Ethical considerations for testing

Ethical Considerations

1. Privacy: Genetic data is identifiable and permanent

• Can't be changed like passwords
• Familial implications (relatives share genetics)

1. Discrimination: Potential for genetic discrimination

• GINA protects against health/employment discrimination (US)
• Life insurance and long-term care not protected

1. Psychological Impact: Knowledge of high risk can cause anxiety

• Need for genetic counseling
• Risk communication training

1. Equity: Ancestry bias means unequal benefits

• Europeans benefit most from current PRS
• Exacerbates health disparities

References

Key Publications

1. Lambert et al. (2021): "The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation"

• PGS Catalog: https://www.pgscatalog.org/
• Repository of published PRS models

1. Khera et al. (2018): "Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations"

• Nature Genetics, 50:1219–1224
• Demonstrated clinical utility of PRS

1. Torkamani et al. (2018): "The personal and clinical utility of polygenic risk scores"

• Nature Reviews Genetics, 19:581–590
• Comprehensive review of PRS applications

1. Martin et al. (2019): "Clinical use of current polygenic risk scores may exacerbate health disparities"

• Nature Genetics, 51:584–591
• Addresses ancestry bias and equity concerns

1. Choi et al. (2020): "Tutorial: a guide to performing polygenic risk score analyses"

• Nature Protocols, 15:2759–2772
• Practical guide to PRS calculation and evaluation

Resources

• PGS Catalog: https://www.pgscatalog.org/ - Published PRS models
• LD Hub: http://ldsc.broadinstitute.org/ - Genetic correlations
• PRSice: https://www.prsice.info/ - PRS calculation software
• GWAS Catalog: https://www.ebi.ac.uk/gwas/ - Association database

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

PRS Construction

1. Use validated PRS from PGS Catalog when available

• Published models have been externally validated
• Include LD clumping and ancestry-specific weights

1. Match ancestries between GWAS and target population

• European GWAS for European individuals
• Use multi-ancestry GWAS when available

1. Include as many SNPs as practical

• More SNPs = better prediction (up to a point)
• Balance between coverage and genotyping cost