Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add GPTomics/bioSkills --skill colocalization-analysisgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gptomics/bioskills/colocalization-analysis)<a href="https://agentmods.dev/skills/gptomics/bioskills/colocalization-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/colocalization-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gptomics/bioskills/colocalization-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/colocalization-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00132 | $0.10152 |
| Opus 5.5 | $0.00053 | $0.04061 |
| Sonnet 5.5 | $0.00026 | $0.02030 |
| Haiku 4.5 | $0.00013 | $0.01015 |
Grade A, and why
bio-causal-genomics-colocalization-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-causal-genomics-colocalization-analysis — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 460 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 460 lines · 132 tokens per session scan A b6e1a61f434a
bio-causal-genomics-colocalization-analysis is a skill published in the GitHub repository GPTomics/bioSkills (1,223 stars, last pushed 2mo ago), licensed MIT. It adds 132 tokens to every session and 10,152 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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