heritability-partitioning
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How to install heritability-partitioning?
gptomics/bioskills/heritability-partitioning
$npx -y skills add GPTomics/bioSkills --skill heritability-partitioningEstimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS
SKILL.md
| 1 | ## Version Compatibility |
| 2 | |
| 3 | Reference examples tested with: LDSC v1.0.1+ (Python 3 fork; prefer `abdenlab/ldsc-python3` v2.0.0 which retains the working `--h2 / --rg / --h2-cts` CLI -- `belowlab/ldsc` v3.0.1 explicitly broke that CLI per its README and is best run via Docker `jtb114/ldsc:latest`), LDAK 6.0+, BOLT-LMM 2.4.1+, GCTA 1.94+, HESS 0.5.4+, HDL 1.4.0+ (R; GitHub `zhenin/HDL`), Popcorn 1.0+ (Python; brielin/Popcorn), baselineLD_v2.2 annotations (alkesgroup.broadinstitute.org/LDSCORE). |
| 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 | LDSC's official repository (bulik/ldsc) is Python 2.7 only and unmaintained since 2019; use the Python 3 community forks. If code throws ImportError, AttributeError, or a "category not found" error in the LD score file, introspect the installed binary and the actual LD-score column headers rather than retrying. |
| 11 | |
| 12 | # Heritability Partitioning |
| 13 | |
| 14 | **"Estimate SNP heritability and partition it across functional categories, cell types, and loci"** -> Decompose `h2_SNP` from GWAS summary statistics (or individual-level genotypes) into contributions from baseline annotations (coding, conserved, regulatory), tissue-specific chromatin marks, and per-locus components, then reconcile model-dependent enrichment estimates across LDSC and LDAK. Tool choice is a decision about the **regime** (summary-stat vs individual-level; one-trait vs two-trait genetic correlation; total vs partitioned vs local) and the **model assumption** about how per-SNP heritability scales with LD, MAF, and functional annotation (GCTA model vs LDAK-Thin vs baseline-LD). |
| 15 | |
| 16 | - CLI (h2 from sumstats, EUR): `ldsc.py --h2 trait.sumstats.gz --ref-ld-chr eur_w_ld_chr/ --w-ld-chr eur_w_ld_chr/ --out h2` |
| 17 | - CLI (functional partitioning): `ldsc.py --h2 trait.sumstats.gz --ref-ld-chr baselineLD.,<annot>. --frqfile-chr 1000G.EUR.QC. --w-ld-chr weights. --overlap-annot --print-coefficients --out part` |
| 18 | - CLI (cell-type prioritization, Finucane 2018): `ldsc.py --h2-cts trait.sumstats.gz --ref-ld-chr-cts <cts_file>.ldcts --w-ld-chr weights. --out cts` |
| 19 | - CLI (cross-trait rg): `ldsc.py --rg t1.sumstats.gz,t2.sumstats.gz --ref-ld-chr eur_w_ld_chr/ --w-ld-chr eur_w_ld_chr/ --out rg` |
| 20 | - CLI (LDAK alternative): `ldak --sum-hers <out> --summary trait.txt --tagfile ldak.thin.<build>.tagging --check-sums NO` |
| 21 | - R (HDL): `HDL::HDL.rg(gwas1.df, gwas2.df, LD.path = 'UKB_array_SVD_eigen90_extraction')` |
| 22 | - CLI (local h2): HESS step1 `hess.py --local-hsqg trait.sumstats.gz --chrom <chr> --bfile <ref> --partition <part>.bed --out hess_<chr>` |
| 23 | |
| 24 | ## Statistical Model Taxonomy |
| 25 | |
| 26 | | Method | Heritability model | Input | Output | Strength | Fails when | |
| 27 | |--------|--------------------|-------|--------|----------|------------| |
| 28 | | LDSC h2 (Bulik-Sullivan 2015 Nat Genet 47:291) | GCTA model: per-SNP h2 proportional to LD score | Sumstats + ancestry-matched LD scores | h2 estimate + intercept + ratio | Standard for sumstats; fast; calibrated EUR LD scores | N too low (mean chi-square < 1.02); non-EUR ancestry with EUR LD scores; population stratification not captured | |
| 29 | | Stratified LDSC / S-LDSC (Finucane 2015 Nat Genet 47:1228) | GCTA model with per-annotation tau coefficients | Sumstats + baseline + custom annotations | Per-annotation enrichment + tau | Reference functional partitioning method | Highly collinear annotations inflate per-tau SE; small annotation (<0.5% genome) underpowered | |
| 30 | | Baseline-LD model (Gazal 2017 Nat Genet 49:1421) | Adds LD-related and MAF-dependent annotations to baseline | Sumstats + `baselineLD_v2.2.` | Enrichment robust to LD-MAF confounding | Modern S-LDSC default; calibrates LD/MAF dependence | EUR-only baseline-LD v2.2 must NOT be used on non-EUR GWAS; use baseline-LD-X / S-LDXR (Shi H & Gazal S et al 2021 Nat Commun 12:1098) instead | |
| 31 | | Cell-type S-LDSC (Finucane 2018 Nat Genet 50:621) | Per-cell-type annotation marginal t |
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