Colocalization Analysis

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/colocalization-analysisThe 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: Test whether two or more traits share a causal variant at a locus using Bayesian colocalization (coloc.abf, coloc.susie, HyPrColoc, moloc, eCAVIAR, SMR/HEIDI, PWCoCo, SharePro). Use when integrating GWAS with…

---
name: bio-causal-genomics-colocalization-analysis
description: Test whether two or more traits share a causal variant at a locus using Bayesian colocalization (coloc.abf, coloc.susie, HyPrColoc, moloc, eCAVIAR, SMR/HEIDI, PWCoCo, SharePro). Use when integrating GWAS with eQTL/sQTL/pQTL/mQTL, distinguishing shared causal variants from LD-driven coincidence, handling allelic heterogeneity, choosing between single-causal vs multi-causal methods, picking PP.H4 thresholds, running sensitivity over p12, or harmonising summary statistics for colocalization.
tool_type: r
primary_tool: coloc
---

## Version Compatibility

Reference examples tested with: coloc 5.2.3+, susieR 0.12.35+, hyprcoloc 1.0+ (GitHub jrs95/hyprcoloc), SMR 1.3.1+ (CLI, cnsgenomics.com), eCAVIAR 2.2+ (compiled from caviar/eCAVIAR repo), PWCoCo 1.0+ (jwr-git/pwcoco), moloc 0.1+ (clagiamba/moloc), SharePro_coloc 7.0+ (zhwm/SharePro_coloc), R >= 4.1.

Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('coloc')`; check `?coloc.abf`, `?coloc.susie`, `?runsusie`
- CLI: `smr --version`, `pwcoco --help`, `sharepro_coloc.py --help`

If code throws AttributeError, NULL list elements, or `Error in coloc.abf: dataset must have...`, introspect the installed package signature and adapt the example rather than retrying.

# Colocalization Analysis

**"Test whether my GWAS signal and an eQTL share the same causal variant"** -> Compute Bayesian posterior probabilities over five hypotheses (H0 neither, H1 trait-1-only, H2 trait-2-only, H3 distinct causal variants, H4 shared causal variant) to discriminate true causal overlap from LD-driven coincidence, then run sensitivity analysis over the p12 prior.

- R (single-causal, fastest): `coloc::coloc.abf(dataset1, dataset2, p12=5e-6)` -> `coloc::sensitivity(res, 'H4 > 0.75')`
- R (multi-causal, needs LD): `runsusie(d1)` -> `runsusie(d2)` -> `coloc.susie(s1, s2)` -> per-credible-set PP
- R (many traits, single-causal cluster): `hyprcoloc::hyprcoloc(effect.est = betas_mat, effect.se = ses_mat, trait.names = ..., snp.id = ...)` -> trait clusters
- CLI (causality vs linkage): `smr --bfile ref --gwas-summary g.ma --beqtl-summary eqtl.besd --out smr` -> SMR p + HEIDI p
- CLI (allelic heterogeneity): eCAVIAR `eCAVIAR -l ld1 -l ld2 -z z1 -z z2 -o out -c 2` -> CLPP per SNP
- CLI (conditional): PWCoCo conditions on each independent signal via GCTA-COJO then runs pairwise coloc.abf

## Algorithmic Taxonomy

| Method | Model | Inputs | Output | Strength | Fails when |
|--------|-------|--------|--------|----------|------------|
| coloc.abf (Giambartolomei 2014) | Single causal variant per locus; Bayesian ABF | beta+varbeta or p+MAF; sample sizes; type/s/sdY | PP.H0-H4 | Fast (~1s/locus), no LD required, mature, widely-cited | 2+ causal variants in moderate LD -> PP.H3 inflates spuriously; assumes a single causal per trait |
| coloc.susie (Wallace 2021) | Multi-causal via SuSiE; per-credible-set pairwise coloc | Summary stats + ancestry-matched LD matrix | PP.H4 per (CS1, CS2) pair | Handles allelic heterogeneity; principled CS framework | Sensitive to LD-mismatch; sample-size-LD mismatch -> spurious credible sets; needs in-sample or matched LD |
| SMR + HEIDI (Zhu 2016) | Tests pleiotropy (one variant -> both traits) vs linkage (two variants in LD) | GWAS .ma; eQTL .besd; LD reference (plink bfile) | SMR p (significance) + HEIDI p (null = shared causal) | Distinguishes shared-causal from linkage at a top SNP; standard for eQTLGen / GTEx integration | Fails to discriminate when LD between causal SNPs > 0.7 (HEIDI loses power); HEIDI requires >= 10 SNPs near top |
| eCAVIAR / CLPP (Hormozdiari 2016) | Fine-mapping-aware; computes Colocalization Posterior Probability per SNP | Z-scores; LD matrices per trait | CLPP per SNP; per-locus sum | Handles allelic heterogeneity natively; per-SNP resolution | Computationally heavy at -c > 3 causal variants; CLPP thresholds debated (0.01 vs 0.1) |
| PWCoCo (Robinson 2022) | Pairwise conditional via GCTA-COJO conditioning | Summary stats + individual-level LD bfile | Per-conditional-signal coloc.abf results | Cleanly handles AH at top GWAS hit + secondary signals | Needs individual-level reference; sensitive to COJO collinearity threshold |
| moloc (Giambartolomei 2018) | Multi-trait extension of coloc.abf (3-5 traits) | Per-trait summary stats | 15 (3-trait) / 31 (4-trait) / 63 (5-trait) hypothesis PPs | First principled multi-omic coloc | Hypothesis count = 2^k - 1 explodes; >= 6 traits computationally infeasible; minimally updated since 2019 |
| HyPrColoc (Foley 2021) | Many-trait cluster-based; iterative branch-and-bound under single-causal | Beta + SE matrices SNPs x traits | Trait clusters sharing a causal variant | Scales to 50+ traits; identifies cluster substructure | Inherits single-causal assumption from coloc.abf; clusters can fragment under AH |
| SharePro_coloc (Zhang 2024) | Variational effect-group joint model | Beta + SE; LD per ancestry | Effect-group level PP | Handles multiple causal signals jointly; faster than coloc.susie at scale | Newer (2024); benchmarks evolving; trickier installation |
| Pullin & Wallace 2025 variant-specific priors | Function-aware p12 (e.g. up-weight coding/promoter SNPs) | Same as coloc.abf + per-SNP prior weights | PP.H0-H4 with non-uniform prior | Improves discovery when functional annotation is informative | Annotation choice is a methodological lever; report sensitivity |

Methodology evolves; verify the current Open Targets Genetics, eQTL Catalogue, and FinnGen colocalization pipelines before locking parameters. Open Targets uses coloc.abf at PP.H4 >= 0.75 with p12 = 1e-5; FinnGen uses coloc.susie at PP.H4 >= 0.8 with in-sample LD.

## Decision Tree by Scenario

| Scenario | Recommended method | Why |
|----------|---------------------|-----|
| GWAS + single-tissue eQTL, top GWAS variant looks single-signal | coloc.abf + sensitivity() | Fast, no LD needed, well-validated;

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/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Colocalization Analysis

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

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