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. ## 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