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.
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.
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.
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.
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.
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.
Triangulate; refuse the single number. No single MR estimator, coloc PP.H4, or evidence stream is decisive — report IVW+Egger+median+mode concordance, coloc PP.H4 with a p12 sweep, and effector-gene evidence across >=3 of 6 streams. A bare headline statistic hides the seam.
Goal: Confirm heritable signal, pick the right tissue for TWAS / V2G, and detect shared heritable confounding that mandates CHP-aware MR (CAUSE / LHC-MR).
Reconciliation: S-LDSC mean chi-squared > 1.02 with h2 SE < 0.02 and intercept ratio < 0.3 is required. Cell-type prioritization with coefficient_p < 0.05 / N_tissues nominates the tissue for downstream TWAS weights and ABC enhancer-gene priors. If cross-trait LDSC abs(rg) > 0.3 (and HDL sample-overlap < 5%), Step 3b becomes mandatory. See causal-genomics/heritability-partitioning and causal-genomics/genetic-correlation.
CAUSE reports delta_elpd of sharing-vs-causal model; z > 1.96 favors true causation over CHP. LHC-MR jointly estimates causal effect and confounder effect via likelihood; the 95% credible interval excluding zero is the causal-effect verdict. Required when LDSC abs(rg) > 0.3. See causal-genomics/pleiotropy-detection.
Step 4: Colocalization
library(coloc)
d1 <-list(beta = exposure_locus$BETA, varbeta = exposure_locus$SE^2,
snp = exposure_locus$SNP, position = exposure_locus$BP,
type ='quant', N = exposure_n, MAF = exposure_locus$EAF)
d2 <-list(beta = outcome_locus$BETA, varbeta = outcome_locus$SE^2,
snp = outcome_locus$SNP, position = outcome_locus$BP,
type ='cc', N = outcome_n, s = case_fraction, MAF = outcome_locus$EAF)
result <- coloc.abf(d1, d2, p1 =1e-4, p2 =1e-4, p12 =1e-5)
sens <- coloc::sensitivity(result, rule ='H4 > 0.7')
PP.H4 >= 0.7 for triangulation, >= 0.8 for publication, >= 0.95 for industry-grade target packages. Always sweep p12 over 1e-6 to 5e-5; conclusions must be stable across the sweep. For allelic heterogeneity use coloc.susie. See causal-genomics/colocalization-analysis.
Step 5: Fine-Mapping with SuSiE
library(susieR)
R <- as.matrix(read.csv('ld_matrix.csv', row.names =1))
diag_s <- estimate_s_rss(z = locus_stats$BETA / locus_stats$SE, R = R, n = sample_size)
fitted <- susie_rss(bhat = locus_stats$BETA, shat = locus_stats$SE,
R = R, n = sample_size, L =10, coverage =0.95, min_abs_corr =0.5)
cs <- fitted$sets$cs
If estimate_s_rss lambda > 0.05 the external LD is mismatched; rerun with in-sample LD or use SuSiE-inf / FINEMAP-inf. min_abs_corr >= 0.5 (r-squared >= 0.25) is the purity threshold for retaining a credible set. For HLA use L = 20-30. See causal-genomics/fine-mapping.
Indirect effect = total - direct. For molecular mediators (expression, methylation, protein), prefer two-step MR with cis-instruments at the mediator. Run Imai sensitivity (rho_crit) or mediational E-value > 2. See causal-genomics/mediation-analysis.
Tissue is picked from Step 0 stratified LDSC; Bonferroni at 0.05 / N_tissues. FOCUS PIP >= 0.8 retains a single candidate causal gene per region; without FOCUS, co-regulated TWAS hits cannot be distinguished. Cross-reference TWAS hits with coloc.susie PP.H4 and cis-eQTL MR for triangulation. See causal-genomics/transcriptome-wide-association.
Goal: Mimic pharmacological inhibition of a drug target via cis-pQTL and triangulate with coloc.
Cross-platform replication on Olink (UKB-PPP) and SomaScan (deCODE) is mandatory; the two platforms are concordant for ~60% of proteins and discordant calls are platform artifacts. Run pheWAS for on-target adverse effects, PAV-excluded sensitivity, and coloc.susie PP.H4 >= 0.8 at the cis-pQTL locus. See causal-genomics/proteome-mr-drug-target.
atac-seq/enhancer-gene-linking - ABC / ENCODE-rE2G priors for effector-gene step
single-cell/preprocessing - scRNA / scATAC tissue priors for stratified LDSC
workflows/gwas-pipeline - Upstream: produces the sumstats (CHR/POS/EA/OA/EAF/BETA/SE/P/N, build documented) this pipeline consumes
References
Hemani G, Zheng J, Elsworth B, et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. eLife 7:e34408. DOI 10.7554/eLife.34408. (TwoSampleMR / harmonisation.)
Giambartolomei C, Vukcevic D, Schadt EE, et al (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genetics 10:e1004383. DOI 10.1371/journal.pgen.1004383. (coloc.)
Wang G, Sarkar A, Carbonetto P, Stephens M (2020) A simple new approach to variable selection in regression, with application to genetic fine mapping. Journal of the Royal Statistical Society Series B 82:1273-1300. DOI 10.1111/rssb.12388. (SuSiE.)
Bulik-Sullivan BK, Loh PR, Finucane HK, et al (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics 47:291-295. DOI 10.1038/ng.3211. (LDSC intercept.)
