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), 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

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

Per-Method Failure Modes

coloc.abf -- PP.H3 inflation under multiple causal variants

Trigger: Locus has 2+ independent causal signals in moderate LD (r2 ~ 0.3-0.6).

Mechanism: The single-causal-variant assumption forces the model to allocate posterior mass to H3 (distinct causal variants) whenever the per-SNP Bayes factors for the two top SNPs do not align.

Symptom: Visual co-localization in LocusZoom looks convincing, but result$summary['PP.H3.abf'] dominates over PP.H4; sensitivity() shows PP.H4 stays low across the entire p12 grid.

Fix: Run coloc.susie (or eCAVIAR or PWCoCo) to allow multiple causal variants. If coloc.susie returns multiple credible sets with one pair showing PP.H4 > 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 < 1e-5; replication in independent data fails.

Fix: Operational definition: "trans" = >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 (Butler-Laporte 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 < 200)

Trigger: Small eQTL discovery (e.g. tissue-specific bulk study, N < 200; per-cell-type sc-eQTL).

Mechanism: With low N, varbeta is large; the eQTL's per-SNP Bayes factors are flat; the joint likelihood concentrates on H0 or H1 (GWAS-only).

Symptom: PP.H0 or PP.H1 dominates; the eQTL panel shows visible signal but coloc cannot resolve causal vs noise.

Fix: Use eQTLGen (N ~ 31k whole-blood) or GTEx v8 (N ~ 70-700 per tissue) where possible. For rare cell types, accept the limitation and report the locus as underpowered rather than claim absence of colocalization.

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

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

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. Pullin & Wallace 2025 (PLoS Genet 21:e1011697) extended this with variant-specific priors weighted by functional annotation.

p12 Sensitivity Grid

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).

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-bp X --to-bp Y \
    --r-phased square --out locus_ld

# Alternative: in-sample LD from BED via bigsnpr
R <- bigsnpr::snp_cor(snp_obj$genotypes, ind.col = locus_snps)
# PSD repair if negative eigenvalues from numerical noise
R <- as.matrix(Matrix::nearPD(R)$mat)
dimnames(R) <- list(snp_ids, snp_ids)

Critical: Row and column order of R MUST match SNP order in the beta vector -- silent failure otherwise. The SuSiE objective stays finite under mis-ordering and returns nonsense credible sets. Verify with stopifnot(rownames(R) == names(beta)) before runsusie. Cross-reference causal-genomics/fine-mapping for the full LD diagnostic block (estimate_s_rss lambda < 0.05, kriging_rss outlier inspection).

SMR vs coloc Reconciliation

SMR (Zhu 2016) and coloc test related but non-identical questions:

• SMR tests pleiotropy vs linkage: does the top eQTL SNP show a GWAS effect explainable by its eQTL effect (pleiotropic / causal) or does the GWAS effect come from a different SNP in LD (linkage)?
• coloc tests shared vs distinct causal variants over an entire window of SNPs.
• HEIDI is SMR's heterogeneity test; null hypothesis is single shared causal SNP. Zhu 2016 Nat Genet 48:481 specifies HEIDI p > 0.05 (NOT 0.01) as non-rejection of single shared causal. HEIDI p > 0.05 does NOT prove shared causality -- only that data cannot reject it; pair with SMR p Bonferroni-corrected across probes. When LD between causal SNPs > 0.7, HEIDI loses power same as coloc.

When LD between two true causal SNPs is high (r2 > 0.7), both SMR/HEIDI and coloc.abf lose discriminatory power: SMR cannot pick which of the LD-tied SNPs is causal, and coloc.abf cannot reject H4 even if biology is two-distinct-causal. coloc.susie + ancestry-matched LD is the modern resolution.

Operational rule: SMR + HEIDI is appropriate when the question is "does this eQTL gene mediate the GWAS effect at all?" coloc is appropriate when the question is "do the two traits share a causal variant in this window?" Run both; agreement (significant SMR + non-rejected HEIDI + PP.H4 >= 0.75) is high-confidence; disagreement requires inspection (often the multi-causal / LD scenario above).

moloc Multi-Omic Framework (3-5 Traits)

For k traits, moloc tests 2^k - 1 hypotheses. 3 traits -> 15 hypotheses (H_a, H_b, H_c, H_ab, H_ac, H_bc, H_abc, plus "none of the above"); 4 traits -> 31; 5 traits -> 63. The hypothesis H_{all-share} (all k share a single causal variant) is the multi-omic analog of PP.H4.

# moloc 3-trait example; install via remotes::install_github('clagiamba/moloc')
library(moloc)
# Input: list of k dataframes with BETA, SE, N, MAF per SNP and shared SNP IDs
result_moloc <- moloc_test(listData=list(gwas=gwas_df, eqtl=eqtl_df, sqtl=sqtl_df),
                            prior_var=c(0.01, 0.1, 0.5), priors=c(1e-4, 1e-6, 1e-7))
# PPA: posterior over all 15 hypotheses (3-trait case)
# Key column: PPA.abc (all-three-share)

moloc is computationally tractable up to k = 5 but explodes beyond; use HyPrColoc for k >= 6.

HyPrColoc Cluster-Based Coloc (Many Traits)

HyPrColoc (Foley 2021) extends single-causal coloc to many traits by clustering traits that share a causal variant. Output: per-cluster posterior + per-trait cluster assignment.

library(hyprcoloc)
# Inputs: SNPs-by-traits matrices of betas and standard errors
# Rows = SNPs (must be shared across all traits); Columns = traits
res <- hyprcoloc(effect.est=betas, effect.se=ses,
                  trait.names=colnames(betas), snp.id=rownames(betas),
                  reg.thresh=0.7,     # regional probability of coloc threshold
                  align.thresh=0.7)   # alignment threshold for traits within a cluster
res$results  # cluster assignment per trait + posterior

HyPrColoc inherits the single-causal-per-cluster assumption from coloc.abf; clusters can fragment if the true biology is allelic heterogeneity.

PWCoCo (Conditional Pairwise Coloc)

PWCoCo (Robinson 2022) wraps GCTA-COJO conditional analysis around coloc.abf. For a locus with k1 independent trait-1 signals and k2 independent trait-2 signals, PWCoCo runs k1 * k2 pairwise coloc.abf tests after conditioning each summary statistic on the other independent signals.

When to use: When GCTA-COJO has identified >= 2 independent signals in at least one trait and individual-level reference genotypes are available. Particularly suited to bulk eQTL with secondary cis signals.

Inputs: Per-trait summary stats (SNP, A1, A2, freq, beta, se, p, N) + plink bfile reference. Output: One coloc.abf result per (conditional signal 1, conditional signal 2) pair. Interpret each row as an independent single-signal coloc.

Caveats: PWCoCo requires individual-level reference (plink bfile); cannot run on summary stats alone. Collinearity threshold in COJO (default --cojo-collinear 0.9) controls how aggressively independent signals are split; lower values fragment, higher values merge. Worked CLI recipe in usage-guide.md.

Standard coloc.abf Pipeline

Goal: Test whether a single GWAS lead variant shares a causal variant with an eQTL gene's top signal at a defined locus.

Approach: Extract a 1 Mb window centred on the GWAS lead; harmonise alleles between datasets; format coloc input lists with type ('quant' or 'cc'), sample size N, and either sdY (quant) or s (cc); run coloc.abf; run sensitivity() over the p12 grid.

library(coloc)

# Inputs: harmonised gwas_df and eqtl_df with SNP, BETA, SE, MAF, N, POS columns
# Both must share the same SNP set and allele coding (verify with harmonise step)

gwas_input <- list(
    beta=gwas_df$BETA, varbeta=gwas_df$SE^2,
    snp=gwas_df$SNP, position=gwas_df$POS,
    type='cc',           # case-control GWAS
    s=0.30,              # case fraction
    N=50000)

eqtl_input <- list(
    beta=eqtl_df$BETA, varbeta=eqtl_df$SE^2,
    snp=eqtl_df$SNP, position=eqtl_df$POS,
    type='quant',        # quantitative eQTL
    sdY=1,               # SD(expression); 1 if standardised, else estimate from MAF+varbeta
    N=500)

res <- coloc.abf(dataset1=gwas_input, dataset2=eqtl_input,
                  p1=1e-4, p2=1e-4, p12=5e-6)   # conservative p12

print(res$summary)
sens <- coloc::sensitivity(res, rule='H4 > 0.75')   # generates plot + table

sdY semantics: when omitted, coloc estimates from MAF and varbeta; supplying sdY=1 ASSUMES the trait is already standardised (eQTL with inverse-normal-transformed expression). Mismatch produces silently wrong Bayes factors -- the most common silent failure.

• For quantitative trait: leave sdY=NULL to estimate via coloc:::sdY.est(varbeta, MAF, N). If CV of estimated sdY across SNPs > 0.5, varbeta/MAF are inconsistent -- coloc will silently miscalibrate Bayes factors.
• For inverse-normal-transformed expression: use sdY = 1 (already standardized).
• Mismatch produces silently wrong PP -- most common silent failure.

s parameter for case-control (type='cc'):

• s = N_cases / N_total (NOT cases-per-control; NOT 0.5 default).
• For population-cohort case-control: s ~ disease prevalence in the cohort (~0.005 for rare disease).
• Wrong s does not error -- silently biases PP at extreme MAF.

coloc.susie Multi-Causal Pipeline

Goal: Test colocalization at a locus with multiple independent signals (allelic heterogeneity).

Approach: Run SuSiE on each trait's summary statistics with ancestry-matched LD; verify LD-z-score consistency; coloc-test each pair of credible sets.

library(coloc); library(susieR)

# Diagnostic: z-score vs LD consistency MUST be checked
z_gwas <- gwas_df$BETA / gwas_df$SE
lam_gwas <- susieR::estimate_s_rss(z=z_gwas, R=ld_matrix, n=gwas_n)
if (lam_gwas > 0.05) stop('LD reference mismatched to z-scores; lambda=', lam_gwas)

s1 <- runsusie(list(beta=gwas_df$BETA, varbeta=gwas_df$SE^2,
                    snp=gwas_df$SNP, position=gwas_df$POS,
                    type='cc', s=0.3, N=50000, LD=ld_matrix), L=10)
s2 <- runsusie(list(beta=eqtl_df$BETA, varbeta=eqtl_df$SE^2,
                    snp=eqtl_df$SNP, position=eqtl_df$POS,
                    type='quant', sdY=1, N=500, LD=ld_matrix), L=10)

res_susie <- coloc.susie(s1, s2)   # NULL if no overlapping CS
# res_susie$summary rows: each (hit1, hit2) pair of credible sets

LD matrix MUST be in the same SNP order as the beta vector; mis-ordering silently produces nonsense.

SMR + HEIDI Pipeline

# SMR is a command-line tool. Pre-format GWAS into .ma (SNP A1 A2 freq beta se p N).
# eQTL data as BESD (binary eQTL summary data); pre-built BESD available from eQTLGen / GTEx.

smr --bfile 1KG_EUR_chr6 \
    --gwas-summary gwas.ma \
    --beqtl-summary eqtl_chr6.besd \
    --out smr_result \
    --thread-num 4 \
    --peqtl-smr 5e-8 \
    --heidi-mtd 1
# Output smr_result.smr: probe (gene) | top SNP | p_SMR | p_HEIDI | nsnp_HEIDI

Interpretation: significant p_SMR (Bonferroni-corrected across probes tested, typically < 5e-8 / N_probes) AND non-rejection by HEIDI (p_HEIDI > 0.05, per Zhu 2016) indicates pleiotropy / shared causal; p_HEIDI <= 0.05 rejects shared-causal -> linkage. HEIDI p > 0.05 does NOT prove shared causality, only that data cannot reject it. Require nsnp_HEIDI >= 10 for HEIDI reliability.

Allele Harmonisation (Critical Pre-Step)

Mismatched effect alleles silently invert signs of betas, collapsing PP.H4 into PP.H3. Required steps before coloc:

1. Merge GWAS and eQTL summary stats by SNP ID (rsID or chr:pos:ref:alt).
2. Mark SNP-pairs as same (A1/A2 match) or flip (A1/A2 swap); drop SNPs that match neither.
3. For flip rows, negate the second dataset's beta (and swap A1/A2).
4. Drop palindromic SNPs (A/T or C/G) at MAF > 0.42; their strand cannot be inferred from coding alone (TwoSampleMR harmonise_data standard cutoff).
5. Verify genome build alignment (hg19 vs hg38 must match; lift over if not).

Worked harmonisation code and build-mismatch pitfalls: see usage-guide.md.

Anticipated Reviewer Pushback

Common Errors

Tool Install Notes

• coloc: CRAN. install.packages('coloc'). Bundles susieR dependency for >= 5.1.
• susieR: CRAN. install.packages('susieR'). >= 0.12.35 for estimate_s_rss and kriging_rss.
• HyPrColoc: GitHub only (never CRAN). remotes::install_github('jrs95/hyprcoloc'). Requires R >= 3.5.
• SMR: Pre-compiled binary from cnsgenomics.com/software/smr. Linux/Mac/Windows binaries; no R package.
• eCAVIAR: Compile from GitHub fhormoz/caviar; C++ source. CLI eCAVIAR. PAINTOR is the related multi-trait fine-mapping toolkit.
• PWCoCo: GitHub jwr-git/pwcoco. Compiled C++ CLI; can also be invoked from R via wrapper scripts.
• SharePro_coloc: GitHub only (no PyPI release). git clone https://github.com/zhwm/SharePro_coloc then pip install -r requirements.txt.
• moloc: GitHub clagiamba/moloc. R package; minimally updated since 2019, no CRAN release. R >= 3.5.

Reviewer-Grade Reporting Template

For each colocalization claim, the report should include:

1. Method and version (e.g. coloc 5.2.3 coloc.abf, or coloc.susie with SuSiE L=10).
2. Window definition (e.g. +/- 500 kb around the GWAS lead rs12345 at chr6:30450000, hg38), and lead-SNP-swap sensitivity (PP.H4 at GWAS lead vs eQTL lead vs joint top).
3. Priors p1, p2, p12 used; sensitivity plot from coloc::sensitivity() and the p12 range over which PP.H4 stays above threshold.
4. All five posteriors PP.H0 through PP.H4 (not PP.H4 alone).
5. Threshold band the result clears (>= 0.7 triangulation tier, >= 0.75 Open Targets screening, >= 0.80 published, >= 0.90 stringent, >= 0.95 clinical).
6. LD reference ancestry, source (1000G phase 3 EUR / in-sample / UKBB), and lambda from estimate_s_rss if coloc.susie.
7. Reference QTL panel version (e.g. GTEx v8 MASHR-EUR, PredictDB release 2022-01; eQTLGen 2019).
8. Conditional analysis GCTA-COJO results if multi-causal; per-credible-set PP if coloc.susie.
9. Failure-mode caveats explicitly addressed: MHC excluded, chr 8 inversion excluded, ancestry-matched LD, sdY/s correctly specified, palindromic SNPs handled.
10. Methods-section H0-H4 prose describing what each hypothesis means (see usage-guide.md).

References

• Giambartolomei C et al 2014 PLoS Genet 10:e1004383 (coloc.abf)
• Wallace C 2020 PLoS Genet 16:e1008720 (prior elicitation; relaxing the single-causal-variant assumption; default-prior sensitivity)
• Pullin JM & Wallace C 2025 PLoS Genet 21:e1011697 (variant-specific priors)
• Wallace C 2021 PLoS Genet 17:e1009440 (coloc.susie; multiple causal variants)
• Zhu Z et al 2016 Nat Genet 48:481 (SMR + HEIDI)
• Hormozdiari F et al 2016 AJHG 99:1245 (eCAVIAR / CLPP)
• Giambartolomei C et al 2018 Bioinformatics 34:2538 (moloc)
• Foley CN et al 2021 Nat Commun 12:764 (HyPrColoc)
• Robinson JW et al 2022 bioRxiv 2022.08.08.503158 (PWCoCo)
• Zhang W et al 2024 Bioinformatics 40:btae295 (SharePro_coloc)
• Butler-Laporte G et al 2024 medRxiv 2024.11.05.24316783 (hlacoloc)
• Mountjoy E et al 2021 Nat Genet 53:1527 (Open Targets Genetics colocalization pipeline)
• Vosa U et al 2021 Nat Genet 53:1300 (eQTLGen, N ~ 31,684 whole blood)
• GTEx Consortium 2020 Science 369:1318 (GTEx v8 multi-tissue eQTL)

Related Skills

• causal-genomics/mendelian-randomization - Causal effect estimation from coloc-validated SNPs
• causal-genomics/fine-mapping - SuSiE / FINEMAP / CAVIAR credible sets feeding coloc.susie; LD construction protocol cross-ref
• causal-genomics/mediation-analysis - Downstream causal mediation given coloc shared causal variants
• causal-genomics/pleiotropy-detection - Distinguishing horizontal pleiotropy from shared causality
• causal-genomics/transcriptome-wide-association - TWAS / PrediXcan / FOCUS gene-level prioritization complementary to coloc
• causal-genomics/proteome-mr-drug-target - pQTL coloc + MR for drug-target prioritization
• causal-genomics/effector-gene-prioritization - Locus-to-gene with coloc, ABC, V2G integration
• population-genetics/association-testing - GWAS summary statistic generation and locus extraction
• population-genetics/linkage-disequilibrium - LD reference panels for coloc.susie and PWCoCo
• variant-calling/variant-annotation - Functional annotation for variant-specific priors
• variant-calling/filtering-best-practices - Pre-coloc QC for summary stats
• differential-expression/deseq2-basics - Generating eQTL / molecular QTL counts
• single-cell/scatac-analysis - Per-cell-type chromatin context for coloc interpretation
• workflows/gwas-pipeline - Upstream GWAS analysis producing coloc input
• data-visualization/ggplot2-fundamentals - Regional and LocusCompare plot construction