bio-clinical-databases-polygenic-risk
Constructs and validates polygenic risk scores using LDpred2-auto, SBayesRC, MegaPRS, PRS-CS, PROSPER, MUSSEL, BridgePRS, JointPRS, PRSmix, or PGS Catalog Calculator with ancestry-aware reference panels (HapMap3, UKB-LD), Pejaver-style calibration, and PRS-RS reporting standards. Use when computing PRS for cohorts, applying Whiffin-style absolute-risk transformation, assessing cross-ancestry portability (Martin 2017 / Ding 2023 continuous ancestry), or auditing PRS manuscripts against the 22-item PRS-RS reviewer checklist.
npx skills add BioTender-max/awesome-bio-agent-skills --skill polygenic-risk --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: bigsnpr 1.12+ (LDpred2; Privé 2020), PRSice-2 2.3.5+, PRS-CS 1.0.0+ (Ge 2019), gctb 2.5+ (SBayesR/SBayesS/SBayesRC; Zheng 2024), LDAK 6.0+ (MegaPRS; Zhang 2021), pgsc_calc 2.0+ (nf-core; Lambert 2024), Hail 0.2.130+, numpy 1.26+, pandas 2.2+. No general FDA PRS guidance document exists as of May 2026; the operative regulatory text is the August 2025 Federal Register notice on Cancer Predisposition Risk Assessment Systems (Class II device with special controls). Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. The LDpred2-auto `snp_ldpred2_auto()` signature changed in bigsnpr 1.11+; pin `allow_jump_sign = FALSE` and `shrink_corr = 0.95` explicitly. # Polygenic Risk Scores; Construction, Calibration, Reporting **'Compute a PRS for my cohort using
- Version Compatibility
- Method Landscape: 2026 Operational Ranking
- Multi-Ancestry: The Big Problem
- Calibration: The Hingorani Reframing
- PRS-RS Reporting Standards (Wand 2021 Nature)
- Decision Tree by Scenario
- Standard Workflow: LDpred2-auto
- SBayesRC Workflow (Functional Annotations)
- PGS Catalog Calculator (Production Pipeline)
- Multi-Ancestry PRS-CSx
- Score Normalization and Ancestry Recalibration
- Sample Overlap Detection (EraSOR / Bivariate LDSC Intercept)
- Per-Operation Failure Modes
- Reconciliation: When Methods Disagree
gctb \ plink2 --bfile target \ Calculate multiple PGS for cohort nextflow run pgscatalog/pgsc_calc \ python PRScsx.py \ Score each ancestry-specific posterior, then combine on tuning set for pop in EUR AFR EAS; do done bivariate LDSC for sample overlap ldsc.py \
What does the bio-clinical-databases-polygenic-risk skill do?
Constructs and validates polygenic risk scores using LDpred2-auto, SBayesRC, MegaPRS, PRS-CS, PROSPER, MUSSEL, BridgePRS, JointPRS, PRSmix, or PGS Catalog Calculator with ancestry-aware reference panels (HapMap3, UKB-LD), Pejaver-style calibration, and PRS-RS reporting standards. Use when computing PRS for cohorts, applying Whiffin-style absolute-risk transformation, assessing cross-ancestry portability (Martin 2017 / Ding 2023 continuous ancestry), or auditing PRS manuscripts against the 22-item PRS-RS reviewer checklist.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill polygenic-risk --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.
