genetic-correlation
starsforks
How to install genetic-correlation?
gptomics/bioskills/genetic-correlation
$npx -y skills add GPTomics/bioSkills --skill genetic-correlationEstimates 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
SKILL.md
| 1 | ## Version Compatibility |
| 2 | |
| 3 | 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+ (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. |
| 4 | |
| 5 | Before using code patterns, verify installed versions match. If versions differ: |
| 6 | - Python: `pip show <package>` then `python -c 'import <module>; help(<module>)'` |
| 7 | - R: `packageVersion('<pkg>')` then `?function_name` |
| 8 | - CLI: `<tool> --version` then `<tool> --help` |
| 9 | |
| 10 | 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. |
| 11 | |
| 12 | # Genetic Correlation |
| 13 | |
| 14 | **"Estimate the genetic correlation between two traits from GWAS summary statistics"** -> Decompose the bivariate genetic architecture into a single global rg (cross-trait LDSC, HDL), per-locus local rg (LAVA, rho-HESS, HDL-L), or cross-population rg (Popcorn). Genetic correlation is the central cross-trait statistic in causal genomics: it quantifies shared etiology, motivates CHP-aware MR sensitivity when high, gates LCV's gcp partial-causation parameter, and feeds into multi-trait analysis frameworks (MTAG, GenomicSEM). Tool choice is a decision about the **regime** (sumstats vs individual-level; global vs local; same-ancestry vs trans-ancestry) and the **sample-overlap structure** between input GWAS. |
| 15 | |
| 16 | - CLI (LDSC, robust to overlap): `ldsc.py --rg trait1.sumstats.gz,trait2.sumstats.gz --ref-ld-chr eur_w_ld_chr/ --w-ld-chr eur_w_ld_chr/ --out rg` |
| 17 | - R (HDL, lower variance, requires independent samples): `HDL.rg(gwas1.df, gwas2.df, LD.path = 'UKB_array_SVD_eigen90_extraction', N0 = 0)` |
| 18 | - R (LAVA, local rg per locus): `process.input() -> run.univ() -> run.bivar(input, locus_id)` over ~2495 LDetect-derived loci |
| 19 | - CLI (rho-HESS, locus-level): `hess.py --local-rhog t1.sumstats.gz t2.sumstats.gz --bfile <ref> --partition <part>.bed --chrom <chr>` |
| 20 | - CLI (Popcorn, trans-ancestry): `popcorn fit -v 1 --cfile cross_pop_scores.txt --sfile1 pop1.txt --sfile2 pop2.txt out` |
| 21 | |
| 22 | ## Algorithmic Taxonomy |
| 23 | |
| 24 | | Method | Model | Input | Output | Strength | Fails when | |
| 25 | |--------|-------|-------|--------|----------|------------| |
| 26 | | Cross-trait LDSC (Bulik-Sullivan 2015 Nat Genet 47:1236) | Bivariate LD-score regression; off-diagonal absorbs rg, intercept absorbs sample overlap | Sumstats + ancestry-matched LD scores | rg, SE, intercept (overlap proxy) | Robust to sample overlap (intercept absorbs it without biasing rg); fast; calibrated EUR | Mean chi-square < 1.02 in either trait (underpowered); non-EUR sumstats with EUR LD scores | |
| 27 | | HDL (Ning 2020 Nat Genet 52:859) | High-Definition Likelihood; eigen-decomposition of full LD with closed-form variance | Sumstats + UKB-array SVD eigen reference (EUR N=336k) | rg, SE | ~60% lower variance than LDSC; equivalent to ~2.5x sample size; preferred when both GWAS truly independent | Sample overlap > 5% biases likelihood; only public reference panel is EUR UKB-array | |
| 28 | | LAVA (Werme 2022 Nat Genet 54:274) | Semi-parametric local genetic correlation per locus; PC-projected SNP effects under a local null | Sumstats + LDetect partitioning (~2495 loci) | Per-locus univariate h2 + bivariate rg + p-value | Detects heterogeneous rg masked by global cancellation; conditional + partial rg supported | Locus has too few SNPs (< 50) or low local h2 (univariate p > 0.05 in either trait); LD reference mismatch | |
| 29 | | rho-HESS (Shi 2017 AJHG 101:737) | Quadratic form on LD-projected effect estimates per locus | Sumstats + LDetect partition + LD reference | Per-locus rho_g + bivariate local rg | Earliest locus-level rg method; complements LAVA | Lo |
People who liked this also viewed
6 picksType
- find-skillsHelps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skSkillsJul 2026
- grill-meA relentless interview to sharpen a plan or design.SkillsJul 2026
- vercel-react-best-practicesVercel's official collection of agent skillsSkillsJul 2026
- improve-codebase-architectureScan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.SkillsJul 2026
- setup-matt-pocock-skillsConfigure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.SkillsJul 2026
- triageMove issues and external PRs through a state machine of triage roles — categorise, verify, grill if needed, and write agent-ready briefs.SkillsJul 2026
