Bio Causal Genomics Colocalization Analysis

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), ShareProcoloc 7.0+ (zhwm/ShareProcoloc), 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 (Wang 2024) | Variational effect-group joint model | Beta + SE; LD per ancestry | Effect-group level PP | Handles multi-causal + multi-ancestry jointly; faster than coloc.susie at scale | Newer (2024); benchmarks evolving; trickier installation | | Wallace 2020 / 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; single-causal assumption typically holds at clean loci | | GWAS + eQTL, conditional analysis shows 2+ independent signals | coloc.susie OR PWCoCo | Multi-causal handling; coloc.susie if summary-stats LD available, PWCoCo if individual-level reference accessible | | GWAS + multi-tissue eQTL (e.g. all 49 GTEx tissues) | coloc.abf per tissue + HyPrColoc across tissues | Per-tissue PP.H4 gives tissue-specific causality; HyPrColoc identifies tissue clusters sharing the variant | | GWAS + eQTL + sQTL + mQTL (3-5 omics) | moloc (k linkage | | Want per-SNP credibility under allelic heterogeneity | eCAVIAR (CLPP) | Per-SNP CLPP integrates fine-mapping with coloc | | MHC / HLA region (chr6:25-35 Mb) | HLA-coloc (Lagou 2024) OR exclude MHC | Long-range LD breaks single-causal assumption; standard PP.H4 not interpretable | | Trans-eQTL / GWAS pair | coloc.abf with p12 lowered to 5e-6 or 1e-6 | Shared causality is biologically rare; default p12=1e-5 over-favours H4 | | Ancestry-mismatched GWAS vs eQTL | SharePro_coloc OR ancestry-matched coloc.susie | LD differs across ancestries; using EUR LD on AFR z-scores produces spurious credible sets | | Very small eQTL (N 0.75, this is real allelic heterogeneity not failure.

coloc.susie -- LD reference mismatch

Trigger: Z-scores from GWAS / eQTL of one ancestry, LD matrix from 1000 Genomes EUR (or any non-matched reference).

Mechanism: SuSiE assumes z-scores and the supplied LD are jointly consistent. Ancestry mismatch or sample-size mismatch produces a non-positive-definite implicit covariance; SuSiE responds by returning spurious credible sets that include LD-mismatched SNPs.

Symptom: susieR::estimate_s_rss(z, R, n) returns lambda > 0.05; susieR::kriging_rss flags off-diagonal SNPs with extreme studentized residuals; credible sets are oddly large (50+ SNPs) or include SNPs distant in LD from the lead.

Fix: Use in-sample LD when at all possible (per-cohort plink --r square). If reference must be external, match ancestry (1KG superpopulation) and superpopulation-stratify. Run estimate_s_rss and report lambda; if > 0.05, drop the locus or switch to coloc.abf.

coloc default p12 too liberal for trans-eQTL

Trigger: Applying p12 = 1e-5 (the default) to a trans-eQTL / GWAS pair.

Mechanism: The default p12 was calibrated for cis-eQTL where biological proximity makes shared causality reasonable. For trans-eQTL, prior probability of shared causality is much lower; uniform p12 over-favours H4.

Symptom: PP.H4 > 0.8 reported, but sensitivity() reveals PP.H4 falls below 0.5 for p12 5 Mb from TSS or different chromosome. Default p12=1e-5 over-favors H4 for trans (genome-rare biology). For trans: lower p12 to 5e-6 or 1e-6 AND raise PP.H4 threshold to >= 0.8 (compensate for higher FP risk). Cross-reference Vosa 2021 Nat Genet 53:1300 (eQTLGen trans) for empirical patterns.

MHC / HLA + chr 8 inversion -- single-causal assumption breaks

Trigger: Locus within chr6:25-35 Mb (extended MHC, hg38), or chr8:8.1-11.9 Mb (chr 8 inversion, hg38).

Mechanism: The MHC contains classical HLA genes with extreme long-range LD (r2 > 0.5 over many Mb), multiple independent causal haplotypes, and structural variation. The chr 8p23.1 inversion similarly produces long-range LD across megabases of polymorphic inversion alleles. The single-causal-variant assumption is biologically wrong in both regions.

Symptom: coloc.abf almost always returns PP.H3 or fragmented PP across H1/H2/H3/H4 even when the underlying biology is well-established (e.g. HLA-DRB1 in autoimmune GWAS).

Fix (MHC): Use HLA-imputed classical alleles via SNP2HLA / HIBAG / HLA-TAPAS, then HLA-coloc (Lagou 2024 medRxiv) -- NOT coloc on SNPs in MHC. OR exclude MHC from genome-wide coloc and report HLA association at the haplotype/allele level. Fix (chr 8 inversion): Exclude chr8:8.1-11.9 Mb or pre-condition on inversion genotype before coloc. Never report a single coloc PP.H4 in either region without this caveat.

Lead-SNP swap and window bias

Trigger: The two traits have different lead SNPs at the same locus; analyst centres each window on the trait-specific lead.

Mechanism: coloc PP is sensitive to the SNPs in the window; centring on different leads gives different per-SNP overlap and biases toward H3.

Symptom: Re-centring the window on the GWAS lead vs the eQTL lead produces qualitatively different PP.H4.

Fix: Use a SINGLE window (typically +/- 500 kb or 1 Mb) centred on the joint top-variant (the SNP with the lowest min-p across both traits), or on the GWAS lead consistently. Report PP under multiple centring choices; flag the locus if PP swings > 0.2 across centrings.

Underpowered eQTL (N = 1000 | Well-powered | Trans accessible |

| >= 10000 | Meta (eQTLGen) | Cross-tissue / sc |

Reference QTL panel choice

GTEx v8 (838 donors, 49 tissues, 2020) is the current PredictDB-supported standard. GTEx v10 (released 2024) has limited harmonisation and is not yet PredictDB-default. eQTLGen blood meta-eQTL (N ~ 31k) wins on sample size for blood cis-eQTL discovery, beating any single tissue on power. Always pin version in methods (e.g. "GTEx v8 MASHR-EUR, PredictDB release 2022-01").

PP.H4 Threshold Framework

| Threshold | Use case | Source | |-----------|----------|--------| | 0.5 - 0.7 | Suggestive / pilot / hypothesis-generating | Giambartolomei 2014 original | | >= 0.7 | Triangulation tier for TWAS / cis-MR / effector-gene cross-evidence | Open Targets Genetics common practice; cross-reference downstream skills | | >= 0.75 | Open Targets Platform / eQTL Catalogue / FinnGen default screening threshold | Open Targets Genetics docs; Mountjoy 2021 Nat Genet 53:1527 | | >= 0.80 | Most published colocalizations / standard publication tier | Wallace 2020 PLoS Genet 16:e1008720 | | >= 0.90 | Stringent clinical / therapeutic-target prioritization | Reserved for high-confidence claims | | >= 0.95 | Industry / regulatory drug-target submission grade | Internal pharma default | | PP.H3 >= 0.80 | Confident distinct causal variants (negative coloc result) | Standard | | PP.H4 / (PP.H3 + PP.H4) >= 0.9 | Conditional probability framing (some pipelines) | Foley 2021 |

Operational rule: Three operational tiers map onto the most common downstream uses: (a) >= 0.7 when PP.H4 is one of several lines of triangulating evidence (TWAS + coloc, cis-MR + coloc, effector-gene multi-evidence) -- this is the threshold downstream skills (causal-genomics/transcriptome-wide-association, causal-genomics/mendelian-randomization cis-MR, causal-genomics/effector-gene-prioritization, causal-genomics/proteome-mr-drug-target) require; (b) >= 0.8 for standard peer-reviewed publication as a stand-alone coloc claim (Wallace 2020); (c) >= 0.95 for industry / clinical drug-target submission. Open Targets and FinnGen pipelines screen at >= 0.75 but downstream publication-grade coloc claims should clear >= 0.8 and triangulation claims >= 0.7. ALWAYS report PP.H3 alongside PP.H4 -- a locus with PP.H4 = 0.6, PP.H3 = 0.3 is qualitatively different from PP.H4 = 0.6, PP.H3 = 0.05 (the former is real ambiguity over single vs distinct causal; the latter is underpowered evidence). Run coloc::sensitivity() and report the p12 range over which PP.H4 stays above the threshold.

Default Priors and the p12 Sensitivity Question

| Prior | Default | Interpretation | When to change | |-------|---------|----------------|----------------| | p1 | 1e-4 | Prob a random SNP is associated with trait 1 | Rarely changed | | p2 | 1e-4 | Prob a random SNP is associated with trait 2 | Rarely changed | | p12 | 1e-5 | Prob a random SNP is associated with both traits | Lower (5e-6 or 1e-6) for trans-eQTL or unrelated trait pairs; raise (5e-5) only with strong prior, e.g. molecular QTL in the same tissue as causal cell type |

The p12/p1 ratio (= 0.1 under defaults) is the prior odds of colocalization given a trait-1 association. Wallace 2020 (PLoS Genet 16:e1008720) showed default p12 = 1e-5 is too liberal for many real-world settings and recommended sensitivity analysis as standard practice. Wallace 2025 (PLoS Genet 21:e1011697) extended this with variant-specific priors weighted by functional annotation.

p12 Sensitivity Grid

| p12 grid point | Use case | Reporting rule | |----------------|----------|-----------------| | 1e-4 | Suggestive only / EUR cis-eQTL relaxed | PP.H4 here cannot support a publication claim | | 1e-5 | Default for most cis-eQTL GWAS pairs | Standard | | 5e-6 | Conservative cis; default for trans-eQTL coloc | Recommended publication baseline | | 1e-6 | Very conservative; trans coloc with weak prior | Required for cross-trait genome-rare coloc |

Operational rule: Require PP.H4 to remain above threshold across at least 3 adjacent grid points; report the lowest p12 at which PP.H4 >= 0.75. Use coloc::sensitivity(result, rule = 'H4 > 0.75') for the diagnostic plot.

Required reporting: PP.H4 at default priors + p12 range over which PP.H4 stays above threshold.

eCAVIAR CLPP Threshold Framework

CLPP (Colocalization Posterior Probability) is the per-SNP product of the two per-trait fine-mapping posteriors. Threshold conventions:

• Hormozdiari 2016 AJHG 99:1245 used CLPP >= 0.01 (validated against null simulations).
• 2024 GTEx / Open Targets pipelines use CLPP >= 0.05.
• High-confidence claims require CLPP >= 0.1.
• Report both sum-CLPP across the credible set AND max-CLPP at any single SNP -- the two answer different questions (locus-level vs lead-SNP-level confidence).
• LaPierre 2021 Bioinformatics (CAVIARBF) extends CLPP to conditional analysis.

eCAVIAR -l ld_gwas.ld -l ld_eqtl.ld \
        -z gwas.z -z eqtl.z \
        -o coloc_out -c 2     # -c = max independent causal variants per trait
# Output: per-SNP CLPP in coloc_out_col file; report sum and max

LD Matrix Construction for coloc.susie

Requirements:

• Signed Pearson r (not r2). coloc.susie expects directional LD; squared LD silently inverts effect-direction inference.
• Ancestry-matched to GWAS / eQTL ancestry. EUR LD on AFR z-scores produces spurious credible sets.
• SNP-order-aligned to the beta vector and named to match (row/column names = SNP IDs).
• Positive semi-definite. Numerical-noise negative eigenvalues must be repaired.
• Effective N sample-size-matched to the trait being fine-mapped (provide via runsusie(..., n = N)).

# plink2 phased r (signed Pearson); square matrix output
plink2 --pfile 1KG_EUR \
    --extract snps.txt --chr 6 --from

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [GPTomics](https://github.com/GPTomics)
- **Source:** [GPTomics/bioSkills](https://github.com/GPTomics/bioSkills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.