Fine Mapping

What the source does

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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/causal-genomics/fine-mappingThe 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: Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent…

---
name: bio-causal-genomics-fine-mapping
description: Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.
tool_type: r
primary_tool: susieR
---

## Version Compatibility

Reference examples tested with: susieR 0.12.27+, coloc 5.2.3+, FINEMAP 1.4.2+, PolyFun (head of `omerwe/polyfun` 2024), PAINTOR V3.0, SuSiEx (head of `getian107/SuSiEx`), DAP-G (head of `xqwen/dap`), pyfocus 0.8+, R 4.3+, PLINK 1.9 / 2.0.

Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('susieR')` then `?susie_rss` to confirm argument names (e.g., `prior_weights` vs `prior_variance` semantics)
- CLI: `finemap --help`, `SuSiEx --help`, `PAINTOR --help`, `dap-g --help` to confirm flags
- Python: `polyfun.py --help`

If a call throws an error about an argument that no longer exists, introspect the installed function and adapt rather than retrying.

# Fine-Mapping

**"Narrow my GWAS locus to the variants likely to be causal"** -> Fit a sparse Bayesian regression that propagates LD into posterior inclusion probabilities (PIPs) and credible sets, then validate that credible sets correspond to physically reasonable haplotypes given the LD reference.

- R (summary statistics + LD): `susieR::susie_rss(z, R, n, L=10)` + `estimate_s_rss` LD diagnostic
- R (individual-level genotypes): `susieR::susie(X, y, L=10)`
- CLI (shotgun stochastic search): `finemap --sss --in-files master.z --n-causal-snps 5 --prob-tol 0.001`
- CLI (cross-ancestry joint): `SuSiEx --sst_file=eur.sst,eas.sst --n_gwas=N1,N2 --ref_file=eur.bim,eas.bim --ld_file=eur_ld,eas_ld --chr_col=1,1 --snp_col=2,2 --bp_col=3,3 --a1_col=4,4 --a2_col=5,5 --eff_col=6,6 --se_col=7,7 --pval_col=8,8 --chr=<chr> --bp=<start,end> --out_dir=<dir> --out_name=<name>` (column-number flags and `--ld_file` are required; populations are assigned by the ORDER of the comma-separated `--sst_file`/`--n_gwas`/`--ref_file`/`--ld_file` lists, not a `--pop` flag; see `SuSiEx --help`)
- Python (functional priors): `polyfun.py --compute-h2-L2` -> per-SNP priors -> susie_rss with `prior_weights=`
- Python (TWAS fine-mapping): `focus finemap` on gene-level Z-scores

Fine-mapping is a Bayesian model selection problem; LD is not noise but structured prior information. Most failure modes trace back to one of three issues: (a) LD reference mismatched to the GWAS sample; (b) the sparse-effects prior being wrong for the locus (polygenic background); or (c) too small an L cap. The `estimate_s_rss()` lambda and `kriging_rss()` per-SNP diagnostic catch (a) before downstream credible sets are reported.

## Algorithmic Taxonomy

| Tool | Model | Input | Strength | Fails when |
|------|-------|-------|----------|------------|
| SuSiE / susie_rss (Wang 2020 JRSSB 82:1273; Zou 2022 PLoS Genet) | Iterative Bayesian sum-of-single-effects (IBSS), variational | Individual-level (X, y) or (z, R, n) | Fast; native PIP + credible sets; pluggable priors; default in modern pipelines | Reference LD mismatched to GWAS sample; locus dominated by polygenic background; >L true effects |
| SuSiE-inf / FINEMAP-inf (Cui 2024 Nat Genet 56:162) | SuSiE + infinitesimal random-effect component | (z, R, n) | Calibrated credible sets when locus is non-sparse (polygenic shoulder around a sparse causal); recommended for biobank-scale GWAS | Very small loci with truly sparse architecture (over-conservative); slower convergence |
| FINEMAP (Benner 2016 Bioinformatics 32:1493) | Shotgun stochastic search over causal configurations | .z + .ld + .master files | Exact Bayes factors at small k; widely cited | Slow at L > 5; binary install only (christianbenner.com); same LD-mismatch fragility as SuSiE |
| CAVIAR (Hormozdiari 2014 Genetics 198:497) | Exhaustive enumeration up to k causals | (z, R) | Exact posterior at small k | Combinatorial explosion beyond k=6; legacy method largely superseded by SuSiE |
| DAP-G (Wen 2016 AJHG 98:1114) | Deterministic posterior approximation with adaptive scan | SBAMS format; TORUS for enrichment priors | Fast at QTL scale (whole-transcriptome); pairs with TORUS hierarchical priors | SBAMS format is awkward; less ubiquitous tooling |
| PAINTOR (Kichaev 2014 PLoS Genet 10:e1004722) | EM with binary functional annotations | (z, R, A) per locus | Locus-level functional priors; multi-trait variant | Single-trait mode often matched by PolyFun + SuSiE; slower than SuSiE |
| PolyFun + SuSiE/FINEMAP (Weissbrod 2020 Nat Genet 52:1355) | Stratified LDSC genome-wide -> per-SNP prior_weights | GWAS sumstats + pre-baked baseline-LF | Most powerful single-trait functional prior; >20% more high-PIP (PIP>0.95) variants in simulations, >32% in real UK Biobank traits (Weissbrod 2020) | Requires matched-ancestry baseline-LF; runs in two stages |
| SuSiEx (Yuan 2024 Nat Genet 56:1841) | Joint cross-ancestry SuSiE; shared causal, population-specific LD | Per-pop sumstats + per-pop LD reference | Smaller credible sets than per-ancestry meta or marginal fine-mapping; principled when causal variants are shared | Trans-ethnic heterogeneity violated (population-specific causals); ancestry must be cleanly assigned |
| MultiSuSiE (Rossen 2025 Nat Genet) | Cross-ancestry SuSiE variant; flexible heterogeneity | Per-pop sumstats + per-pop LD | Similar to SuSiEx; alternative implementation | Same as SuSiEx; newer, less battle-tested |
| FOCUS / MA-FOCUS (Mancuso 2019 Nat Genet 51:675) | Probabilistic TWAS fine-mapping over gene models | TWAS Z-scores + gene LD (predicted 

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/omerwe/polyfun
pip install -r polyfun/requirements.txt
git clone https://github.com/bogdanlab/PAINTOR_V3.0
git clone https://github.com/getian107/SuSiEx
git clone https://github.com/xqwen/dap
pip install pyfocus

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 Fine Mapping

• github.com/GPTomics/bioSkills/tree/main/causal-genomics/fine-map — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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