bio-workflows-causal-genomics-pipeline
End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic
npx skills add BioTender-max/awesome-bio-agent-skills --skill causal-genomics-pipeline --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: TwoSampleMR 0.5+, MR-PRESSO 1.0+, coloc 5.2+, susieR 0.12+, MendelianRandomization 0.9+, ldsc 1.0.1 (python3 fork), MetaXcan 0.7+, pyfocus 0.6+, MAGMA 1.10+, MRlap 0.0.3+, cause 1.2+, lhcMR 0.0.0.9000+, HDL 1.4+, LAVA 0.1+, GenomicSEM 0.0.5+. Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - Python: `pip show <pkg>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Causal Genomics Pipeline **"Run post-GWAS causal inference from summary statistics"** -> Orchestrate heritability partitioning and tissue prioritization, genetic-correlation diagnostics, instrument selection, Mendelian randomization with CHP-aware sensitivity, colocalization, fine-mapping (SuSiE / FOCUS), mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization, and (optionally) GenomicSEM common-factor GWAS to t
- Version Compatibility
- Pipeline Overview
- Step 0: Pre-flight - Heritability, Tissue Prioritization, Genetic Correlation
- Step 1: Instrument Selection
- Step 2: Mendelian Randomization
- Step 3: Sensitivity Analysis
- Step 3b: CHP-aware MR (when rg > 0.3 or shared confounder suspected)
- Step 4: Colocalization
- Step 5: Fine-Mapping with SuSiE
- Step 6: Mediation Analysis
- Step 7: TWAS Triangulation
- Step 8: Cis-pQTL Drug-Target MR
- Step 9: Effector-Gene Prioritization
- Step 10 (optional): GenomicSEM Common-Factor GWAS
ldsc.py --h2 trait.sumstats.gz --ref-ld-chr eur_w_ld_chr/ --w-ld-chr eur_w_ld_chr/ --out trait.h2 ldsc.py --h2 trait.sumstats.gz --ref-ld-chr baselineLD.,cts_v8. --w-ld-chr weights. --ref-ld-chr-cts Multi_tissue_gene_expr.ldcts --out trait.cts 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 python MetaXcan/SPrediXcan.py --model_db_path mashr_Whole_Blood.db \ focus finemap gwas.sumstats.gz LD.bcor mashr.db --locations EUR.locations.bed --out twas.focus magma --bfile g1000_eur --pval gwas.tsv N=N --gene-annot genes.annot --out trait pops_features.py --features pops.features.txt --gene_annot genes.txt --out trait.features pops.py --features trait.features --gene_results trait.genes.raw --out trait.pops
What does the bio-workflows-causal-genomics-pipeline skill do?
End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill causal-genomics-pipeline --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.