colocalization-analysis
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How to install colocalization-analysis?
gptomics/bioskills/colocalization-analysis
$npx -y skills add GPTomics/bioSkills --skill colocalization-analysisTest 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-driv
SKILL.md
| 1 | ## Version Compatibility |
| 2 | |
| 3 | 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. |
| 4 | |
| 5 | Before using code patterns, verify installed versions match. If versions differ: |
| 6 | - R: `packageVersion('coloc')`; check `?coloc.abf`, `?coloc.susie`, `?runsusie` |
| 7 | - CLI: `smr --version`, `pwcoco --help`, `sharepro_coloc.py --help` |
| 8 | |
| 9 | 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. |
| 10 | |
| 11 | # Colocalization Analysis |
| 12 | |
| 13 | **"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. |
| 14 | |
| 15 | - R (single-causal, fastest): `coloc::coloc.abf(dataset1, dataset2, p12=5e-6)` -> `coloc::sensitivity(res, 'H4 > 0.75')` |
| 16 | - R (multi-causal, needs LD): `runsusie(d1)` -> `runsusie(d2)` -> `coloc.susie(s1, s2)` -> per-credible-set PP |
| 17 | - R (many traits, single-causal cluster): `hyprcoloc::hyprcoloc(effect.est = betas_mat, effect.se = ses_mat, trait.names = ..., snp.id = ...)` -> trait clusters |
| 18 | - CLI (causality vs linkage): `smr --bfile ref --gwas-summary g.ma --beqtl-summary eqtl.besd --out smr` -> SMR p + HEIDI p |
| 19 | - CLI (allelic heterogeneity): eCAVIAR `eCAVIAR -l ld1 -l ld2 -z z1 -z z2 -o out -c 2` -> CLPP per SNP |
| 20 | - CLI (conditional): PWCoCo conditions on each independent signal via GCTA-COJO then runs pairwise coloc.abf |
| 21 | |
| 22 | ## Algorithmic Taxonomy |
| 23 | |
| 24 | | Method | Model | Inputs | Output | Strength | Fails when | |
| 25 | |--------|-------|--------|--------|----------|------------| |
| 26 | | 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 | |
| 27 | | 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 | |
| 28 | | 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 | |
| 29 | | 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) | |
| 30 | | 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 | |
| 31 | | 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 | |
| 32 | | 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 | |
| 33 | | SharePro_coloc (Zhang 2024) | Variational effect-group joint model | Beta + SE; LD |
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