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tooluniverse-polygenic-risk-score
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.
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# 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"