bio-causal-genomics-genetic-correlation
bio-causal-genomics-genetic-correlation is a Bioinformatics & life sciences skill for AI agents, published by GPTomics in the bioSkills repository on GitHub. Skillspace rates bio-causal-genomics-genetic-correlation D (64/100) and it passed the safety scan; the rating is based on static analysis only.
Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).
- Grade
- D
- Quality
- 64 / 100
- Safety
- Passed
- GitHub stars
- 1,202
Unproven: rated by static analysis only, not yet compared head-to-head.
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Our review
- Architecture
- 93 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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