bio-causal-genomics-heritability-partitioning
Estimate SNP heritability and partition 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 local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LD
npx skills add BioTender-max/awesome-bio-agent-skills --skill heritability-partitioning --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 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). 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` 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. # Heritability Partitioning **"Estimate SNP heritability and partition it across functional categories, cell types, and loci"** -
- Version Compatibility
- Statistical Model Taxonomy
- Decision Tree by Scenario
- LDSC Intercept Interpretation (Postdoc Nuance)
- LDSC vs LDAK Reconciliation
- Cell-Type Prioritization (Finucane 2018)
- Quantitative Thresholds
- LDSC Standard Workflow
- Cross-Trait LDSC for Genetic Correlation
- Per-Method Failure Modes
- LDSC intercept misinterpretation
- LDSC vs LDAK enrichment discordance
- HDL bias with sample overlap
- LDSC with non-EUR ancestry and EUR LD scores
Cell-type prioritization example workflow (Finucane 2018) eur_w_ld weights, 1000G EUR frequency files ldsc.py \ Apply Bonferroni at 0.05 / nrow; top tissues are trait-relevant munge_sumstats.py \ Produces trait.sumstats.gz with SNP, A1, A2, Z, N columns Reference LDAK-Thin tagging files at dougspeed.com/pre-computed-tagging-files ldak --sum-hers trait_sumher \ BaselineLD.zip provides 86 continuous annotations covering coding/conserved/regulatory LDAK uses --annotation-number + --annotation-prefix (continuous) or
What does the bio-causal-genomics-heritability-partitioning skill do?
Estimate SNP heritability and partition 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 local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LD
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill heritability-partitioning --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.