bio-causal-genomics-genetic-correlation
Estimate 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 sa
npx skills add BioTender-max/awesome-bio-agent-skills --skill genetic-correlation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: LDSC v1.0.1+ (Python 3; prefer `abdenlab/ldsc-python3` v2.0.0 -- `belowlab/ldsc` v3.0.1 README states the `--h2 / --rg / --h2-cts` CLI is broken; use Docker `jtb114/ldsc:latest` for the belowlab fallback; original `bulik/ldsc` is Python 2.7 unmaintained since 2019), HDL 1.4.0+ (R; GitHub `zhenin/HDL`), LAVA 0.1.0+ (R; GitHub `josefin-werme/LAVA`), HESS 0.5.4+ (Python; huwenboshi/hess), Popcorn 1.0+ / Popcorn-2 (Python; brielin/Popcorn), GCTA 1.94+ (GREML-bivariate), baselineLD_v2.2 / eur_w_ld_chr LD-score panels from alkesgroup.broadinstitute.org/LDSCORE, UKB-array SVD eigen reference for HDL. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `python -c 'import <module>; help(<module>)'` - R: `packageVersion('<pkg>')` then `?function_name` - CLI: `<tool> --version` then `<tool> --help` If code throws an LD-score "category not found" error, an HDL reference-panel mismatch, or a LAVA locus-ID lookup failure, introspect the installed LD-score column headers and the supplied partitioning file rather than retrying with default flags. # Genetic Correlation **"Estima
- Version Compatibility
- Algorithmic Taxonomy
- Cross-Trait LDSC Intercept: Sample Overlap is Absorbed, Not a Bias
- HDL Bias Under Sample Overlap
- Relationship to MR Causal Inference
- Decision Tree by Scenario
- Per-Method Failure Modes
- Cross-trait LDSC intercept misread as bias
- HDL bias with sample overlap
- Non-EUR ancestry mismatch with EUR LD scores
- Global rg masks local rg variation
- Cross-population rg below 1 even at causal level
- Same-Trait Cross-Cohort rg as Consistency Check
- Low chi-square mean (underpowered GWAS)
Step 1: munge each GWAS to LDSC format (harmonize columns, filter on MAF and INFO, restrict to HapMap3) munge_sumstats.py \ ldsc.py \ grep -A 11 'Summary of Genetic Correlation Results' rg_t1_t2.log Local h2 step (per chromosome) hess.py \ Bivariate local rg (per chromosome, after h2 per trait) Step 1: cross-population LD scores (one-time per ancestry pair) popcorn compute -v 1 \ eur_eas_scores.txt
What does the bio-causal-genomics-genetic-correlation skill do?
Estimate 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 sa
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill genetic-correlation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.