Polygenic Risk

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/clinical-databases/polygenic-riskThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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,…

---
name: bio-clinical-databases-polygenic-risk
description: 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), ancestry-conditional calibration, and PRS-RS reporting standards. Use when computing PRS for cohorts, applying 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.
tool_type: mixed
primary_tool: PGS Catalog Calculator
---

## 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 these GWAS summary statistics'** -> Match variants to target genotypes, choose method by data availability + trait architecture, derive LD-aware effect estimates, score, normalize by ancestry, transform to absolute risk.

- CLI (recommended one-stop): `pgsc_calc --target target.vcf --pgs_id PGS000001` (nf-core, Lambert 2024)
- R (SOTA single-ancestry): `bigsnpr::snp_ldpred2_auto()` (Privé 2020)
- CLI (multi-ancestry SOTA): `PRS-CSx`, `PROSPER`, `MUSSEL`, `BridgePRS`, `JointPRS`
- CLI (SBayesRC with functional annotations): `gctb --sbayes-rc --bfile target --gwas-summary sumstats.ma`
- CLI (legacy baseline): `PRSice_linux` (clumping + thresholding; still cited for some clinical scores)

## Method Landscape: 2026 Operational Ranking

| Method | Approach | Best for | Fails when |
|--------|----------|----------|-----------|
| **SBayesRC** (Zheng 2024 *Nat Genet*) | Bayesian + 96 functional annotations | EUR; sparse traits | Sumstats LD-incoherent with reference; chain divergence (run --impute-summary first) |
| **MegaPRS** (Zhang 2021 *Nat Commun*) | BLD-LDAK heritability model | EUR; sparser traits | Lacks GCTA-model assumptions; legacy GCTA pipelines |
| **LDpred2-auto** (Privé 2020) | Bayesian + auto-tuning | Polygenic EUR | LD ref mismatch (s > 0.05); allow_jump_sign default True (must pin FALSE) |
| **PRS-CS-auto** (Ge 2019 *Nat Commun*) | Continuous-shrinkage prior | Polygenic EUR | Sparse-trait architecture; HapMap3-restricted variants only |
| **lassosum2** (Privé 2022) | Penalized regression | EUR alternative | Highly polygenic (Bayesian methods better); requires tuning data |
| **C+T** (PRSice-2; Choi 2019) | Clumping + thresholding | Legacy clinical scores (PRS313) | Highly polygenic; Bayesian methods dominate |
| **PROSPER** (Zhang 2024 *Nat Commun*) | Ensemble penalized regression | Multi-ancestry, AFR + others | Single-ancestry; tuning set < 1000 |
| **MUSSEL** (Jin 2024 *Cell Genomics*) | Spike-slab + super-learner | Multi-ancestry; admixed AFR | Single-ancestry; lacks tuning data |
| **JointPRS** (Xu L et al 2025 *Nat Commun* 16:3841) | Data-adaptive Bayesian | Multi-ancestry; sumstats only | Single-ancestry; very small target |
| **PRS-CSx** (Ruan 2022 *Nat Genet*) | PRS-CS multi-ancestry extension | Multi-ancestry with EUR + non-EUR sumstats | Low causal-variant overlap across ancestries |
| **BridgePRS** (Hoggart 2024 *Nat Genet*) | Ridge-bridge sharing | Low-h^2 AFR / low causal overlap | Standard scenarios (PROSPER/MUSSEL win) |
| **PolyPred / PolyPred+** (Weissbrod 2022) | BOLT-LMM + PolyFun-SuSIE | Multi-ancestry; biobank-scale | Small individual-level data; expensive |

**Citation traps caught by senior PIs:**
- **PRSmix** (Truong 2024); *Cell Genomics*, NOT *Nat Genet*.
- **MUSSEL** (Jin 2024); *Cell Genomics*.
- **PROSPER** (Zhang 2024); *Nat Commun*, NOT *Nat Genet*.
- **Hingorani 2023**; *BMJ Medicine* (NOT main *BMJ*).
- **Mavaddat 2023 BOADICEA update**; *Cancer Epi Biomark Prev*, NOT *Nat Genet*.
- **Mullins 2021** is bipolar disorder, NOT MDD (Howard 2019 *Nat Neurosci* = MDD; Mullins 2021 = BD).

## Multi-Ancestry: The Big Problem

Martin 2019 *Nat Genet* established the ~4.5x R^2 attenuation between EUR and AFR (Martin 2017 *AJHG* first showed demographic history drives it). Updates:
- **Martin 2019** *Nat Genet*: "Clinical use of current polygenic risk scores may exacerbate health disparities".
- **Mostafavi 2020** *eLife*: PRS accuracy varies even within a single ancestry due to age, sex, SES, GxE.
- **Ding 2023** *Nature* 618: PGS accuracy decays *continuously* along genetic-ancestry continuum (Pearson r = -0.95 vs PC distance from training data). **The 2026 standard is to report PRS performance vs continuous PC distance, NOT discrete ancestry boxes.**
- **Hou 2023** *Nat Genet*: causal effects are similar across local ancestries within admixed individuals (radmix ~0.95), supporting cross-population transfer.

Multi-ancestry method choice:
1. Individual-level non-EUR training data + tuning set >= 1000: **PROSPER** or **MUSSEL** (top performance).
2. Summary statistics only + small tuning: **JointPRS** or **PRS-CSx**.
3. Low h^2 / very polygeni

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/getian107/PRScs.git
git clone https://github.com/getian107/PRScsx.git
pip install scipy h5py

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Polygenic Risk

• github.com/GPTomics/bioSkills/tree/main/clinical-databases/polyg — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology