What is bio-causal-genomics-proteome-mr-drug-target?

A research workflow for testing whether genetically predicted protein levels are related to disease outcomes, helping assess possible drug targets. It uses protein-related genetic data, disease data, and checks such as colocalization, which tests whether signals likely share the same genetic cause.

Use it for drug-target validation, protein Mendelian randomization, cross-platform replication, and scans for possible beneficial or harmful effects across diseases.

It helps identify whether a protein is a plausible treatment target and whether an apparent link may be caused by a nearby or unrelated genetic signal.

bio-causal-genomics-proteome-mr-drug-target

Install

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.

Any agent

npx skills add GPTomics/bioSkills --skill proteome-mr-drug-target

Clone the repo

git clone --depth 1 https://github.com/GPTomics/bioSkills

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/gptomics/bioskills/proteome-mr-drug-target/github.svg)](https://agentmods.dev/skills/gptomics/bioskills/proteome-mr-drug-target)

Your own site

<a href="https://agentmods.dev/skills/gptomics/bioskills/proteome-mr-drug-target"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/proteome-mr-drug-target/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/gptomics/bioskills/proteome-mr-drug-target"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/proteome-mr-drug-target.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 176 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 10,769 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00176	$0.10769
Opus 5	$0.00088	$0.05385
Sonnet 5	$0.00035	$0.02154
Haiku 4.5	$0.00018	$0.01077

Measured 12d ago against content hash 4983f6ab8229, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

bio-causal-genomics-proteome-mr-drug-target 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 12d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

bio-causal-genomics-proteome-mr-drug-target — 98% identical, 12 lines differ

causal-genomics/proteome-mr-drug-target/SKILL.md · 465 lines

How it starts

The opening of the file, as written. The whole thing — 465 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Version Compatibility

Reference examples tested with: TwoSampleMR 0.5.11+, MendelianRandomization 0.10+, MR-PRESSO 1.0+, coloc 5.2.3+, susieR 0.12.35+, ieugwasr 1.0+, plink2 2.00a5+, R 4.4+.

Before using code patterns, verify installed versions match. If versions differ:

R: packageVersion('<pkg>') then ?function_name to verify parameters
CLI: plink2 --version; VEP vep --help

If code throws OAuth or rate-limit errors from OpenGWAS, or a missing dataset$N from coloc, introspect the installed API and adapt the example rather than retrying. UKB-PPP, deCODE, and Fenland summary statistics changed file layouts between 2023 and 2025; verify column headers before passing into format_data().

Proteome-Wide Drug-Target Mendelian Randomization

"Does genetically lowering plasma protein X cause a change in disease Y, mimicking a drug?" -> Use cis-pQTLs in the gene window for protein X as instruments under the Schmidt 2020 framework (Nat Commun 11:3255), restrict the exclusion-restriction violation to the geometric neighbourhood of the encoding gene, triangulate with colocalization (3-tier PP.H4 ladder below) and cross-platform replication (Olink vs SomaScan), and flag protein-altering-variant (PAV) confounding. A single significant cis-MR estimate is necessary but not sufficient for a drug-target claim; the operational bar is MR + coloc + cross-platform agreement + PAV-excluded sensitivity.

PP.H4 Three-Tier Threshold Ladder

Tier	PP.H4	Use case
Suggestive	>= 0.7	Open Targets / exploratory; consistent with shared-causal
Standard publication	>= 0.8	Wallace 2020 PLoS Genet 16:e1008720; most peer-reviewed pubs
Industry / clinical	>= 0.95	Drug-claim grade; pharma internal target-validation standard

Operational rule: drug-target nomination requires PP.H4 >= 0.8 minimum; industry-grade clinical claim requires PP.H4 >= 0.95 plus the full triangulation panel.

R (canonical): TwoSampleMR::mr() orchestrates the cis-IVW + Egger + median + Wald-ratio panel
R (correlated cis-pQTLs in a window): MendelianRandomization::mr_input(..., correlation = ld_matrix) then mr_ivw(mr_obj, model='default', correl = TRUE)
R (triangulation): coloc::coloc.abf() or coloc::coloc.susie() on the same cis-window
pheWAS: ieugwasr::associations() against the OpenGWAS catalogue, looped over outcomes
VEP CLI: annotate every cis-pQTL with vep --species homo_sapiens --canonical --check_existing for PAV flagging

Read the full file on GitHub · 465 lines

Files

What ships with it

3 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.

Changes

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.

12d ago First seen · 465 lines · 176 tokens per session scan A 4983f6ab8229

Subscribe to this mod's changes

bio-causal-genomics-proteome-mr-drug-target is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 176 tokens to every session and 10,769 once invoked, about $0.0009 per session on Opus 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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