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 transcriptome-wide-associationgit 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/transcriptome-wide-association)<a href="https://agentmods.dev/skills/gptomics/bioskills/transcriptome-wide-association"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/transcriptome-wide-association.svg" alt="Measured on agentmods" 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 | $0.00168 | $0.11429 |
| Opus 5 | $0.00084 | $0.05715 |
| Sonnet 5 | $0.00034 | $0.02286 |
| Haiku 4.5 | $0.00017 | $0.01143 |
Grade A, and why
bio-causal-genomics-transcriptome-wide-association 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 8d 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-transcriptome-wide-association — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 464 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: FUSION (head of gusevlab/fusion_twas, scripts dated 2023+), MetaXcan / S-PrediXcan / S-MultiXcan 0.7.5+ (hakyimlab/MetaXcan), PrediXcan model files from PredictDB (GTEx v8 elastic-net + MASHR), UTMOST (head of Joker-Jerome/UTMOST), pyfocus 0.8+ (bogdanlab/focus), MA-FOCUS (head of mancusolab/ma-focus), TIGAR-V2 (head of yanglab-emory/TIGAR), PLINK 1.9 + PLINK 2.0, R 4.3+, Python 3.9-3.11.
Before using code patterns, verify installed versions match. If versions differ:
- R:
Rscript --version; for FUSION scripts inspect--helpflags directly in the source - Python:
pip show pyfocus(MetaXcan is git-cloned, not on PyPI) thenSPrediXcan.py --help,SMulTiXcan.py --help,focus finemap --help - CLI:
plink2 --version; FUSION ships as R scripts not a binary
If a script throws an error about an argument that has moved (e.g. --gwas_file vs --gwas-file) or a model database schema change, introspect the installed script with --help and adapt rather than retrying. PredictDB model file paths change with GTEx version; pin the version explicitly in scripts.
Transcriptome-Wide Association
"Find genes whose predicted tissue expression is associated with my GWAS trait" -> Train SNP -> expression prediction models on a reference eQTL panel, apply the per-gene SNP weights to GWAS summary statistics or genotypes, and produce a gene-level Z-score equivalent to a weighted sum of SNP Z-scores. The output is a gene-by-tissue association, but TWAS is NOT direct evidence of causal mediation: an LD-tagged eQTL signal produces the same statistical association as a truly causal one, and the dominant failure modes are LD-induced false positives at gene-dense loci, tissue mis-specification, and ancestry mismatch between GWAS and prediction weights.
- CLI (sumstat TWAS, R):
FUSION.assoc_test.R --sumstats g.sumstats --weights weights.pos --weights_dir wgt/ --ref_ld_chr 1KG/EUR. --chr 22 --out chr22.dat - CLI (S-PrediXcan, Python):
SPrediXcan.py --model_db_path gtex_v8.db --covariance gtex_v8.cov --gwas_file g.txt --output_file out.csv - CLI (S-MultiXcan joint):
SMulTiXcan.py --models_folder mashr_models/ --gwas_folder gwas/ --metaxcan_folder spredixcan_per_tissue/ --output joint.csv - CLI (UTMOST cross-tissue): joint test across tissues via UTMOST's per-tissue GBJ / GBJ2 step
- CLI (FOCUS fine-mapping):
focus finemap gwas.sumstats 1KG_EUR focus.db --chr 22 --p-threshold 5e-8 --out chr22.focus - CLI (MA-FOCUS multi-ancestry):
focus finemapwith colon-separated per-ancestry sumstats / LD / weights and hyphen-joined ancestry codes in--locations(e.g.38:EUR-EAS-AFR)
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.
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.
- 8d ago First seen · 464 lines · 168 tokens per session scan A 324ced38d190
bio-causal-genomics-transcriptome-wide-association is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 23d ago), licensed MIT. It adds 168 tokens to every session and 11,429 once invoked, about $0.0008 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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