bio-workflows-causal-genomics-pipeline

name	bio-workflows-causal-genomics-pipeline
description	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 architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.
tool_type	r
primary_tool	TwoSampleMR
workflow	true
depends_on	["causal-genomics/mendelian-randomization","causal-genomics/colocalization-analysis","causal-genomics/fine-mapping","causal-genomics/pleiotropy-detection","causal-genomics/mediation-analysis","causal-genomics/transcriptome-wide-association","causal-genomics/heritability-partitioning","causal-genomics/proteome-mr-drug-target","causal-genomics/effector-gene-prioritization","causal-genomics/genetic-correlation","causal-genomics/genomic-sem"]
qc_checkpoints	[{"after_h2_ldsc":"Mean chi-squared > 1.02; h2 SE < 0.02; intercept ratio < 0.3"},{"after_rg_check":"If abs(rg) > 0.3 then CHP suspected and CAUSE/LHC-MR required"},{"after_instrument_selection":"F-statistic > 10 (two-sample) or > 20 (one-sample); no palindromic SNPs at MAF near 0.5"},{"after_mr":"IVW + Egger + weighted median + weighted mode concordance"},{"after_sensitivity":"MR-PRESSO global p, Egger intercept p (with Isq >= 0.9 for NOME), Steiger directionality"},{"after_chp_check":"CAUSE delta_elpd z > 1.96 OR LHC-MR posterior excludes null"},{"after_coloc":"PP.H4 >= 0.7 triangulation, >= 0.8 publication, >= 0.95 industry; p12 sensitivity stable"},{"after_finemapping":"Credible-set purity (min_abs_corr) >= 0.5; estimate_s_rss lambda < 0.05 if external LD"},{"after_twas":"FOCUS PIP >= 0.8 for candidate causal gene; tissue selected via stratified LDSC"},{"after_effector_gene":"L2G + PoPS + coloc + TWAS concordance >= 3 of 6 evidence streams"},{"after_mediation":"rho_crit > 0.3 OR mediational E-value > 2 (Imai sensitivity)"}]

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 triangulate causal evidence and nominate publication-grade causal exposures, genes, and mechanisms.

This is a workflow skill: it owns the chaining decisions and hand-offs, not the internals of any one step. Every step below cross-references the component skill that teaches its mechanism.

The governing principle

A causal claim is decided at the seams where summary statistics meet, not inside any single method.

1. Everything shares ONE genome build, and effect alleles are harmonized once. The exposure and outcome sumstats, the LD reference, and any eQTL/pQTL panel must share a build; if they differ, liftover ONCE, strand-aware (the BBIS inverted-region danger), BEFORE harmonization. harmonise_data aligns effect alleles; a flipped palindrome (MAF near 0.5) flips the causal-effect SIGN silently — drop intermediate-frequency palindromes.
2. The LD reference ancestry must match the GWAS ancestry, committed once and inherited by clumping, coloc.susie, fine-mapping (SuSiE-rss), and TWAS. A mismatched LD panel corrupts credible sets and colocalization silently; the in-pipeline alarms are estimate_s_rss lambda <0.05 and credible-set purity min_abs_corr >=0.5.
3. Order is causal: a diagnostic at step 0 decides whether a later step runs. Cross-trait LDSC abs(rg)>0.3 makes correlated horizontal pleiotropy (CHP) suspected, so CAUSE/LHC-MR becomes MANDATORY — MR-PRESSO is BLIND to CHP. Tissue picked at step 0 (stratified LDSC) flows into TWAS; picking it post-hoc is circular. Steiger pre-filter directionality and LD-clump BEFORE coloc/fine-mapping.