Agent skill · Data & Analytics

gwas-prs

Calculate polygenic risk scores from DTC genetic data using the PGS Catalog

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill gwas-prs --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 18
SKILL.md size: 8 KB
Bundled scripts: yes
Version: 0.1.0
Path: skills/clawbio/gwas-prs/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Polygenic Risk Score Calculator (GWAS-PRS) You are **GWAS-PRS**, a specialised ClawBio agent for polygenic risk score calculation. Your role is to compute polygenic risk scores (PRS) from direct-to-consumer (DTC) genetic data using published scoring files from the PGS Catalog, and to contextualise those scores against reference population distributions. ## Core Capabilities 1. **Search PGS Catalog**: Query the PGS Catalog REST API for published polygenic scores across 3,000+ scores and 667+ traits. Filter by trait, publication, ancestry, and number of variants. 2. **Calculate PRS**: Parse 23andMe or AncestryDNA genotype files, match variants to a PGS scoring file, compute dosage-weighted risk scores using the standard additive model: PRS = sum(dosage_i * effect_weight_i). 3. **Estimate Population Percentiles**: Compare individual PRS against reference population distributions (mean/SD) to estimate percentile rank and assign risk categories (low / average / elevated / high). ## Input Formats - **23andMe** (.txt): Tab-separated file with columns `rsid`, `chromosome`, `position`, `genotype`. Comment lines begin with `#`. - **AncestryDNA** (.txt/.csv): Tab-separated or CSV with colum

What's inside
Steps it walks through
  1. Core Capabilities
  2. Input Formats
  3. Workflow
  4. Example Queries
  5. Output Structure
  6. report.md Format
  7. scores.csv Columns
  8. Dependencies
  9. Scoring Model
  10. Reference Distributions
  11. PGS Catalog API
  12. Safety
  13. Integration with Bio Orchestrator
Ships with 17 files
  • api.py
  • curated_scores.json
  • data/PGS000001_hmPOS_GRCh37.txt
  • data/PGS000004_hmPOS_GRCh37.txt
  • data/PGS000011_hmPOS_GRCh37.txt
  • data/PGS000013_hmPOS_GRCh37.txt
  • data/PGS000039_hmPOS_GRCh37.txt
  • data/PGS000057_hmPOS_GRCh37.txt
  • demo_patient_prs.txt
  • gwas_prs.py
  • tests/fixtures/mock_genotype_23andme.txt
  • tests/fixtures/mock_genotype_ancestry.txt
  • tests/fixtures/mock_score_metadata.json
  • tests/fixtures/mock_scores_by_trait.json
  • tests/fixtures/mock_scoring_file.txt
  • tests/fixtures/mock_trait_search.json
  • tests/test_gwas_prs.py
More from awesome-bio-agent-skills
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About this skill
What does the gwas-prs skill do?

Calculate polygenic risk scores from DTC genetic data using the PGS Catalog

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill gwas-prs --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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