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Bio Workflows Causal Genomics Pipeline

gptomics/bioskills

  • 1 installs
  • 1.1k repo stars
  • Updated July 25, 2026
  • gptomics/bioskills

Run a post-GWAS causal inference pipeline triangulating Mendelian randomization, colocalization, fine-mapping, TWAS, and drug-target MR from summary statistics.

About

Orchestrates an end-to-end post-GWAS causal inference workflow spanning heritability partitioning, MR with CHP-aware sensitivity, colocalization, SuSiE/FOCUS fine-mapping, mediation, TWAS, and effector-gene prioritization. A developer uses it when triangulating causal evidence, prioritizing tissues, or nominating drug targets from GWAS summary statistics.

  • Multi-method MR triangulation with STROBE-MR compliance
  • Extensive QC checkpoints across each inference stage

Bio Workflows Causal Genomics Pipeline by the numbers

  • 1 all-time installs (skills.sh)
  • Ranked #1,803 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Last updatedJuly 25, 2026
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What it does

Run a post-GWAS causal inference pipeline triangulating Mendelian randomization, colocalization, fine-mapping, TWAS, and drug-target MR from summary statistics.

Files

SKILL.mdMarkdownGitHub ↗

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.

Pipeline Overview

GWAS Summary Statistics (exposure + outcome)
    |
    v
[0. Pre-flight: h2 + tissue prioritization + rg diagnostic]
    LDSC / S-LDSC baseline-LD / Finucane 2018 cell-type
    Cross-trait LDSC / HDL / LAVA --> if abs(rg) > 0.3 then CHP-aware MR required
    |
    v
[1. Instrument Selection] -----> LD clumping, F-stat filtering, Steiger pre-filter
    |
    v
[2. Mendelian Randomization] --> IVW, MR-Egger, Weighted Median/Mode, MR-RAPS
    |
    +--> [3. Sensitivity] -------> MR-PRESSO, Egger intercept (Isq), leave-one-out, Steiger
    |
    +--> [3b. CHP-aware MR] -----> CAUSE (delta_elpd), LHC-MR posterior (if rg > 0.3)
    |
    v
[4. Colocalization] -----------> coloc.abf / coloc.susie / HyPrColoc / SMR-HEIDI
    |
    v
[5. Fine-Mapping] -------------> SuSiE rss + estimate_s_rss / FINEMAP-inf / PolyFun
    |
    v
[6. Mediation Analysis] -------> Network MR / MVMR / CMAverse 4-way
    |
    +--> [7. TWAS triangulation] -> FUSION / S-PrediXcan / FOCUS PIP >= 0.8
    |
    +--> [8. Cis-pQTL drug-target MR] -> UKB-PPP / deCODE + cross-platform replication
    |
    v
[9. Effector-gene prioritization] -> Open Targets L2G + PoPS + cS2G + coloc + TWAS (>= 3 of 6 evidence)
    |
    v
[10. (optional) GenomicSEM common-factor GWAS] -> factor model + Q_SNP
    |
    v
Triangulated causal-evidence summary across methods

Step 0: Pre-flight - Heritability, Tissue Prioritization, Genetic Correlation

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

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.

Step 1: Instrument Selection

library(TwoSampleMR)
exposure_dat <- read_exposure_data(filename = 'exposure_gwas.tsv', sep = '\t',
    snp_col = 'SNP', beta_col = 'BETA', se_col = 'SE',
    effect_allele_col = 'A1', other_allele_col = 'A2',
    eaf_col = 'EAF', pval_col = 'P')
exposure_dat <- subset(exposure_dat, pval.exposure < 5e-8)
exposure_dat <- clump_data(exposure_dat, clump_r2 = 0.001, clump_kb = 10000)
exposure_dat$F_stat <- (exposure_dat$beta.exposure / exposure_dat$se.exposure)^2
exposure_dat <- subset(exposure_dat, F_stat >= 10)
exposure_dat <- subset(exposure_dat, !(eaf.exposure > 0.42 & eaf.exposure < 0.58 & substr(effect_allele.exposure,1,1) %in% c('A','T') & substr(other_allele.exposure,1,1) %in% c('A','T')))

For cis-MR (drug target) use clump_r2 = 0.1 within +/- 500 kb of the gene. Use 5e-9 if M > 5M variants tested.

Step 2: Mendelian Randomization

outcome_dat <- read_outcome_data(filename = 'outcome_gwas.tsv', sep = '\t',
    snp_col = 'SNP', beta_col = 'BETA', se_col = 'SE',
    effect_allele_col = 'A1', other_allele_col = 'A2',
    eaf_col = 'EAF', pval_col = 'P')
dat <- harmonise_data(exposure_dat, outcome_dat)
mr_results <- mr(dat, method_list = c('mr_ivw', 'mr_egger_regression',
    'mr_weighted_median', 'mr_weighted_mode'))

Concordance across IVW, Egger, weighted median, and weighted mode is the headline causal claim. See causal-genomics/mendelian-randomization.

Step 3: Sensitivity Analysis

library(MRPRESSO)
presso <- mr_presso(BetaOutcome = 'beta.outcome', BetaExposure = 'beta.exposure',
    SdOutcome = 'se.outcome', SdExposure = 'se.exposure',
    OUTLIERtest = TRUE, DISTORTIONtest = TRUE, data = dat,
    NbDistribution = 5000, SignifThreshold = 0.05)
egger_int <- mr_pleiotropy_test(dat)
isq <- Isq(dat$beta.exposure, dat$se.exposure)
het <- mr_heterogeneity(dat)
loo <- mr_leaveoneout(dat)
steiger <- directionality_test(dat)

Isq >= 0.9 is required for the MR-Egger NOME assumption; below that, run SIMEX correction or drop Egger. See causal-genomics/pleiotropy-detection.

Step 3b: CHP-aware MR (when rg > 0.3 or shared confounder suspected)

library(cause)
library(lhcMR)
cause_fit <- cause(X = cause_dat, variants = top_vars, param_ests = params)
elpd <- summary(cause_fit)$elpd
lhc_fit <- lhc_mr(trait.df = sumstats_df, LD.filepath = ld_path, rho.filepath = rho_path,
    nStep = 2, SP_single = 3, SP_pair = 50, run_local = FALSE)

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.

Step 6: Mediation Analysis

library(TwoSampleMR)
mv_exposures <- mv_extract_exposures(c('ieu-a-2', 'ieu-a-1089'))
mv_outcome <- mv_extract_outcome(mv_exposures$SNP, 'ieu-a-7')
mvdat <- mv_harmonise_data(mv_exposures, mv_outcome)
mvmr_result <- mv_multiple(mvdat)

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.

Step 7: TWAS Triangulation

python MetaXcan/SPrediXcan.py --model_db_path mashr_Whole_Blood.db \
    --covariance mashr_Whole_Blood.txt.gz --gwas_file gwas.txt.gz \
    --snp_column SNP --effect_allele_column A1 --non_effect_allele_column A2 \
    --beta_column BETA --se_column SE --pvalue_column P \
    --output_file twas.csv
focus finemap gwas.sumstats.gz LD.bcor mashr.db --locations EUR.locations.bed --out twas.focus

Goal: Nominate gene-level causal hits and prune LD-induced TWAS false positives.

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.

Step 8: Cis-pQTL Drug-Target MR

library(TwoSampleMR)
pqtl_dat <- extract_instruments(outcomes = 'prot-a-XXX', p1 = 5e-8, clump = TRUE,
    r2 = 0.1, kb = 1000)
pqtl_dat <- subset(pqtl_dat, chr == target_chr & pos > target_tss - 500000 & pos < target_tss + 500000)
out_dat <- extract_outcome_data(snps = pqtl_dat$SNP, outcomes = c('ieu-a-7', 'ieu-b-31'))
dat <- harmonise_data(pqtl_dat, out_dat)
mr_results <- mr(dat, method_list = c('mr_ivw', 'mr_wald_ratio'))

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.

Step 9: Effector-Gene Prioritization

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

Goal: Map each fine-mapped credible set to a candidate effector gene by integrating six evidence streams.

Integrate: (1) Open Targets L2G (Mountjoy 2021), (2) PoPS similarity score (Weeks 2023), (3) cS2G combined SNP-to-gene (Gazal 2022), (4) coloc.susie PP.H4 with eQTL/pQTL, (5) FOCUS TWAS PIP, (6) ABC / ENCODE-rE2G enhancer-gene linking. Require >= 3 of 6 concordant evidence streams for high-confidence claim. L2G and PoPS disagree by design (different feature regimes); report both. See causal-genomics/effector-gene-prioritization.

Step 10 (optional): GenomicSEM Common-Factor GWAS

library(GenomicSEM)
ldsc_output <- ldsc(traits = c('t1.sumstats.gz','t2.sumstats.gz','t3.sumstats.gz'),
    sample.prev = c(NA, NA, NA), population.prev = c(NA, NA, NA),
    ld = ld_path, wld = ld_path, trait.names = c('t1','t2','t3'))
model <- 'F1 =~ NA*t1 + t2 + t3\nF1 ~~ 1*F1'
fit <- usermodel(ldsc_output, model = model, estimation = 'DWLS')
factor_gwas <- userGWAS(covstruc = ldsc_output, SNPs = sumstats_combined,
    model = paste0(model, '\nF1 ~ SNP\nt1 + t2 + t3 ~ 0*SNP'),
    estimation = 'DWLS', sub = c('F1~SNP'))

Heywood cases (negative residual variance) require fixing residuals positive or dropping the indicator. Verify CFI > 0.95 and RMSEA < 0.06. Q_SNP p > 0.05 confirms factor-level (not trait-specific) signal. See causal-genomics/genomic-sem.

Parameter Recommendations

StepParameterRecommendation
Instrumentsp-value5e-8 standard; 5e-9 if M > 5M variants tested
InstrumentsF-statistic>= 10 two-sample; >= 20 one-sample
Instrumentsclump_r20.001 polygenic; 0.1 cis-MR
Instrumentsclump_kb10000 (10 Mb)
Colocp12 prior1e-5 standard; 5e-6 conservative; 1e-6 trans-eQTL
ColocPP.H4>= 0.7 triangulation; >= 0.8 publication; >= 0.95 industry
SuSiEL10 default; 20-30 HLA
SuSiEcoverage0.95 standard; 0.9 if N < 1000
SuSiEmin_abs_corr0.5 default (r-squared >= 0.25)
MR-PRESSONbDistribution1000 exploratory; >= 5000 publication; >= 10000 stringent
TWASFOCUS PIP>= 0.8 candidate causal gene
TWAStissue Bonferroni0.05 / N_tissues (~2.5e-4 for 200 tissues)
LDSCmean chi-squared> 1.02 for h2 interpretability
LDSCrg trigger for CHP-MRabs(rg) > 0.3
LDSCHDL sample overlap< 5%

Troubleshooting

IssueLikely CauseSolution
No instrumentsUnderpowered GWASRelax p-value to 5e-6 with caution
Weak instruments (F < 10)Small effect SNPsDrop weak instruments; use better-powered GWAS
Inconsistent MR methodsPleiotropyCheck MR-PRESSO outliers; use weighted median
Egger intercept p < 0.05Directional pleiotropyReport Egger estimate; check Isq >= 0.9
PP.H3 > PP.H4Different causal variantsUse coloc.susie for allelic heterogeneity
No credible setsLD matrix issuesCheck estimate_s_rss lambda; use in-sample LD
Steiger reverse directionReverse causationRun bidirectional MR; pre-filter on Steiger
MR-PRESSO blind to apparent confounderCorrelated horizontal pleiotropyRun CAUSE or LHC-MR (see pleiotropy-detection)
TWAS hit at gene-dense locusLD-induced false positiveRun FOCUS fine-mapping; require PIP >= 0.8
Cis-pQTL MR positive, replication failsOlink/SomaScan platform discordanceCross-platform replication; PAV-excluded sensitivity
L2G + PoPS disagreeDifferent feature regimesReport both; require concordance for high-confidence claim
Q_SNP heterogeneity in common-factor GWASFactor mis-specificationuserGWAS with per-trait paths; report Q_pval

Related Skills

causal-genomics/mendelian-randomization - IVW, Egger, MR-RAPS, MVMR causal-genomics/colocalization-analysis - coloc.abf, coloc.susie, HyPrColoc, SMR-HEIDI causal-genomics/fine-mapping - SuSiE rss, FINEMAP-inf, PolyFun, SuSiEx causal-genomics/pleiotropy-detection - MR-PRESSO, CAUSE, LHC-MR, contamination-mixture causal-genomics/mediation-analysis - Two-step MR, MVMR, CMAverse 4-way, HIMA causal-genomics/transcriptome-wide-association - FUSION, S-PrediXcan, FOCUS, UTMOST causal-genomics/heritability-partitioning - LDSC, S-LDSC, LDAK, HDL, HESS causal-genomics/proteome-mr-drug-target - UKB-PPP, deCODE, cis-pQTL MR, pheWAS causal-genomics/effector-gene-prioritization - L2G, PoPS, cS2G, MAGMA, FLAMES causal-genomics/genetic-correlation - Cross-trait LDSC, HDL, LAVA, Popcorn causal-genomics/genomic-sem - Common-factor GWAS, Q_SNP, MTAG reconciliation population-genetics/association-testing - Upstream GWAS methods atac-seq/enhancer-gene-linking - ABC / ENCODE-rE2G priors for effector-gene step single-cell/preprocessing - scRNA / scATAC tissue priors for stratified LDSC

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