name: bio-causal-genomics-pleiotropy-detection description: Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods. origin: openai4s category: bioskills/causal-genomics metadata: tool_type: r primary_tool: TwoSampleMR third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a license: MIT
Reference examples tested with: TwoSampleMR 0.5.11+, MendelianRandomization 0.9.0+, MR-PRESSO 1.0+, CAUSE 1.2.0+, MR-Clust 0.1.0+, MRMix 0.1+, mr.raps 0.4.1+ (GitHub), LHC-MR 0.0.0.9000+ (GitHub), LCV (script-based, no version tag), simex 1.8+.
Before using code patterns, verify installed versions match. If versions differ:
- R: packageVersion('<pkg>') then ?function_name to verify parameters
- For GitHub-only packages, check the repo HEAD vs the local install date
If code throws errors, introspect the installed package and adapt the example rather than retrying.
"Validate my MR result against pleiotropic bias" -> Decompose violations of the exclusion-restriction assumption into uncorrelated horizontal pleiotropy (UHP, addressable by Egger / median / mode / MR-PRESSO) and correlated horizontal pleiotropy (CHP, addressable only by CAUSE / LHC-MR / LCV), then run a method battery whose assumptions span both regimes.
TwoSampleMR::mr() (IVW + Egger + median + mode), mr_pleiotropy_test(), mr_heterogeneity(), mr_leaveoneout(), directionality_test()MRPRESSO::mr_presso() for UHP outlier removal + distortion testcause::cause() for CHP-aware estimation; mrclust::mr_clust_em() for mechanism-heterogeneous instrumentsMendelianRandomization::mr_conmix() for contamination mixture; MRMix::MRMix() for mixture-of-distributionsHorizontal pleiotropy comes in two regimes, and most "standard" MR sensitivity methods address only one of them.
| Regime | Definition | InSIDE assumption | Methods that handle it |
|---|---|---|---|
| UHP (uncorrelated horizontal pleiotropy) | Pleiotropic effect alpha_j independent of instrument-exposure effect gamma_j | Holds | IVW (balanced UHP only), MR-Egger, weighted median, weighted mode, MR-PRESSO, MR-RAPS, MR-Mix, contamination mixture |
| CHP (correlated horizontal pleiotropy) | alpha_j correlates with gamma_j through a shared upstream factor (heritable confounder, network mediator) | Violated | CAUSE, LHC-MR, LCV, MR-Clust (partial), Steiger-filtered MR (partial) |
InSIDE = INstrument Strength Independent of Direct Effect (Bowden 2015 IJE 44:512). Plain English: across SNPs, the per-SNP pleiotropic effect alpha and per-SNP instrument-exposure effect gamma are treated as independent random variables. CHP is the case where they covary because both flow from a shared upstream genetic factor.
The trap (Morrison 2020 Nat Genet 52:740): IVW, MR-Egger, MR-PRESSO, and GSMR are all blind to CHP. Under a shared heritable confounder they each return a plausible-looking corrected causal estimate that is systematically biased in the direction of the confounder. The MR-PRESSO global test does not flag CHP because correlated pleiotropy is not an outlier pattern, it is a population mean shift in the alpha distribution conditional on gamma.
Operational rule: If genetic correlation rg(exposure, outcome) is high (LDSC >= 0.3) or biology strongly suggests a shared upstream factor, the IVW / Egger / PRESSO triple is insufficient. Add CAUSE (preferred when sig SNPs >= 100) or LHC-MR (preferred for polygenic genome-wide IVs).
ldsc.py --rg <exposure.sumstats.gz>,<outcome.sumstats.gz> (see causal-genomics/genetic-correlation). If |rg| > 0.3, CHP is plausible -> flag for Step 3 escalation. If the LDSC rg standard error spans zero broadly, treat low-rg evidence as weak rather than confirming absence of CHP.>= 10000 for stringent reporting). Report all five with point estimate, SE, p, 95% CI, and n_SNPs_used. Compute Egger I^2_GX; apply SIMEX if I^2_GX < 0.9 (see examples/simex_egger_correction.R).>= 100 significant SNPs after pruning) or LHC-MR (any N; uses genome-wide sumstats). Report ELPD delta + z + q (CHP fraction) + gamma (CHP-adjusted causal estimate).| Method | Models | UHP-robust | CHP-robust | Min #SNPs | Fails when | Citation |
|---|---|---|---|---|---|---|
| Inverse-variance weighted (IVW) | Weighted regression through origin | Balanced UHP only | No | 3 | Directional UHP; CHP; weak IV bias; heterogeneity | Burgess 2013 Genet Epidemiol 37:658 |
| MR-Egger intercept + slope | IVW + free intercept | Directional UHP | No | >=10 for power | NOME violated (I^2_GX < 0.9); <10 SNPs; CHP | Bowden 2015 IJE 44:512 |
| Weighted median | Median of Wald ratios | Up to 50% invalid | No | >=10 | >50% invalid; CHP | Bowden 2016 Genet Epidemiol 40:304 |
| Weighted mode (MBE) | Mode of estimate density | Plurality valid | Partial | >=10 | Multimodal estimates from CHP clusters | Hartwig 2017 IJE 46:1985 |
| Cochran Q | Heterogeneity across Wald ratios | Total heterogeneity flag, not direction-specific | No | 3 | Cannot distinguish UHP from heterogeneity from CHP | Del Greco M F 2015 Stat Med 34:2926 |
| MR-PRESSO | Detect + remove UHP outliers via RSS-out | Yes (assumes majority valid) | No | >=4 | >50% pleiotropic; any CHP; small n | Verbanck 2018 Nat Genet 50:693 |
| GSMR + HEIDI-outlier | Outlier removal via single-instrument estimate heterogeneity | Yes | No | >=10 | CHP (HEIDI-outlier is heterogeneity-driven) | Zhu 2018 Nat Commun 9:224 |
| MR-RAPS | Profile likelihood with overdispersion + Huber/Tukey loss | Yes; weak-IV robust | Partial via overdispersion | >=10 | Strong CHP | Zhao 2020 Ann Stat 48:1742 |
| MR-Mix | Mixture-of-distributions over valid + invalid | Yes | Partial | >=20 | Few SNPs; very heterogeneous CHP | Qi & Chatterjee 2019 Nat Commun 10:1941 |
| Contamination mixture | Profile likelihood over contamination fraction | Yes | Partial | >=20 | Few SNPs | Burgess 2020 Nat Commun 11:376 |
| MR-Clust | k-means over Wald estimates with NULL cluster | Yes | Diagnostic for CHP via clusters | >=20 | Single-mechanism exposure (no clustering signal) | Foley 2021 Bioinformatics 37:531 |
| CAUSE | Bayesian mixture: shared causal + shared-factor (CHP) components | Yes | Yes (explicit) | >=100 sig SNPs at p<5e-8 | <100 sig SNPs; non-overlapping GWAS samples | Morrison 2020 Nat Genet 52:740 |
| LHC-MR | Latent heritable confounder + bidirectional + heritability | Yes | Yes | Genome-wide GWAS sumstats | Heritability mis-estimated; severe sample overlap | Darrous 2021 Nat Commun 12:7274 |
| LCV (latent causal variable) | gcp parameter on genome-wide rg | N/A (not an MR method) | Diagnostic | Genome-wide GWAS sumstats | Heritability low; non-Gaussian effect distribution | O'Connor & Price 2018 Nat Genet 50:1728 |
Methodology evolves; verify against the Burgess & Thompson textbook (2nd ed 2021), Hemani 2018 (basic four-method battery), and Sanderson 2022 Nat Rev Methods Primers 2:6 before locking a sensitivity battery.
| Scenario | Primary estimator | Sensitivity / triangulation |
|---|---|---|
| Many strong IVs, no biological shared trait suspected | IVW + Egger + weighted median + weighted mode + PRESSO | Cochran Q; leave-one-out; F-stat; Steiger filtering |
LDSC rg(exposure, outcome) >= 0.3 or strong shared-factor biology |
CAUSE (if sig SNPs >= 100) OR LHC-MR |
LCV gcp; cross-check IVW after Steiger filter |
| Many weak IVs (mean F < 20) | MR-RAPS with overdispersion + robust Huber loss | MR-Mix or contamination mixture; report F-stat range |
| Suspected heterogeneous causal mechanisms (e.g. LDL on CHD via multiple lipoprotein pathways) | MR-Clust; report per-cluster IVW | Pathway annotation of cluster instruments; Bayesian mixture |
| Cis-MR drug target (single locus, few SNPs in LD) | Colocalization (causal-genomics/colocalization-analysis) + Steiger | PWCoCo; conditional analysis; not Egger (low SNP count) |
| Polygenic exposure (heritability spread genome-wide; few significant loci) | LHC-MR (uses all SNPs) | LDSC rg; genome-wide IVW with weak-IV-aware methods (RAPS) |
| Reverse causation suspected | Bidirectional MR with Steiger filter; LHC-MR (jointly estimates both directions) | directionality_test; effect-size r2 comparison |
| Population-level summary discordant with biology | Re-examine instrument selection; check Winner's curse; LD pruning settings | Triangulate with cis-MR; family-based MR if available |
Trigger: I^2_GX = (Q_GX - df) / Q_GX is below 0.9, indicating measurement-error attenuation of the Egger slope (NOME = "no measurement error" in the exposure GWAS effect sizes).
Mechanism: MR-Egger regresses outcome effects on exposure effects with a free intercept. Imprecise exposure effects (high beta.exposure SE relative to beta.exposure variability across instruments) introduce regression dilution that pulls the Egger slope toward the null and inflates the intercept.
Symptom: Egger slope much closer to zero than IVW, weighted median, and weighted mode estimates; large Egger SE.
Fix: Apply SIMEX correction (Bowden 2016 IJE 45:1961; Cook & Stefanski 1994 JASA 89:1314 SIMEX framework) using the simex package on the Egger regression, treating beta.exposure SE as measurement error. See examples/simex_egger_correction.R. Alternative: use MR-RAPS, which models the exposure-effect error explicitly via profile likelihood and does not suffer the NOME failure.
Trigger: More than 50% of instruments are pleiotropic (UHP), e.g. when instrument set was loosely selected (genome-wide significant but unfiltered).
Mechanism: MR-PRESSO's global RSS-out statistic and outlier detection both assume a majority-valid set; outliers are defined relative to that majority. With a pleiotropic majority, PRESSO removes the valid minority.
Symptom: PRESSO-corrected estimate is similar in magnitude (and sign) to the uncorrected estimate even after dropping nominally "outlier" SNPs; distortion-test p-value paradoxically non-significant; few or no outliers detected despite obvious global-test significance.
Fix: Do not trust PRESSO corrected estimate. Re-examine instrument selection (drop loose p-thresholds, prune LD harder); switch to CAUSE or LHC-MR; consider weighted-mode estimator which is plurality-valid rather than majority-valid.
Trigger: Strong shared heritable confounder (high rg) producing CHP. Confirmed by significant LDSC rg or LCV gcp.
Mechanism: Correlated pleiotropy is a population-level mean shift in alpha conditional on gamma; it is not an outlier pattern. PRESSO's RSS-out distance is invariant under such a mean shift, so the global test is not powered against CHP.
Symptom: PRESSO global p > 0.05 (no detected pleiotropy) while a CHP-aware method (CAUSE, LHC-MR) returns a substantially different (often null) causal estimate.
Fix: When CHP is plausible, ALWAYS run CAUSE or LHC-MR in addition to PRESSO; do not rely on PRESSO global non-significance as evidence of no pleiotropy.
Trigger: Fewer than 10 instruments.
Mechanism: Egger's intercept variance is driven by the spread of beta.exposure across instruments; with few SNPs the intercept CI is so wide that even strongly pleiotropic data give non-significant intercepts.
Symptom: Non-significant Egger intercept p-value alongside obviously discordant IVW and weighted-median estimates.
Fix: Report intercept point estimate and CI rather than a binary "pleiotropy present / absent" verdict; do not use Egger as the only sensitivity method when SNP count is low; weight evidence toward weighted-median, weighted-mode, and CAUSE / LHC-MR.
Trigger: Exposure is imprecisely measured (lower heritability ascertained in the exposure GWAS) and outcome is well-measured.
Mechanism: Steiger compares r^2_GX vs r^2_GY per SNP. Measurement error in the exposure underestimates r^2_GX; well-measured outcome captures r^2_GY accurately. Per-SNP, the inequality can flip even when the true causal direction is exposure -> outcome.
Symptom: A large fraction of instruments fail Steiger (steiger_dir == FALSE) in a direction that conflicts with biological plausibility.
Fix: Interpret Steiger as one signal among many, not a hard gate; cross-check with bidirectional MR; verify exposure GWAS heritability and sample size; switch to LHC-MR which models both directions jointly and accounts for heritability.
Trigger: Fewer than 100 genome-wide-significant instruments (p < 5e-8) after harmonization and LD pruning.
Mechanism: CAUSE fits a Bayesian mixture model over a shared-factor (CHP) component, a shared-causal component, and a null component. Posterior identification of the mixture weights requires substantial signal across many SNPs.
Symptom: CAUSE delta_ELPD CI crosses zero; Pareto-k diagnostic flags unstable points; posterior intervals on q (CHP fraction) span [0, 1].
Fix: Use LCV gcp for genome-wide directional inference (does not require many significant SNPs); use LHC-MR if heritability and sumstats are available; or report CAUSE alongside an explicit caveat about its underpowered regime.
Trigger: Highly polygenic trait with substantial sparsity in true effects (mixture of large-effect and zero-effect loci).
Mechanism: LCV assumes a bivariate normal model for effect sizes after LDSC adjustment. Sparse architectures (e.g. immune traits with HLA dominance) violate this and bias gcp estimates.
Symptom: LCV gcp point estimate appears extreme but heritability LDSC z-scores are modest; partitioned heritability shows extreme HLA enrichment.
Fix: Exclude HLA region from LDSC inputs; complement with CAUSE / LHC-MR; report gcp with awareness of the polygenicity caveat.
| Metric | Threshold | Source / rationale |
|---|---|---|
| F-statistic per SNP | >=10 strong; <10 weak |
Burgess 2011 IJE 40:755 (rule of thumb); weak-IV bias toward observational confounded estimate |
| I^2_GX (NOME) | >=0.9 Egger reliable |
Bowden 2016 IJE 45:1961 |
| I^2_GX (NOME) intermediate | 0.6-0.9 SIMEX-corrected Egger | Bowden 2016 IJE |
| I^2_GX (NOME) severe | <0.6 drop Egger; use MR-RAPS or CAUSE |
Bowden 2016 IJE; SIMEX unreliable below 0.6 |
| Egger min SNP count for adequate power | >=10 |
Bowden 2015 IJE 44:512 |
| Cochran Q significance | p < 0.05 indicates heterogeneity | Del Greco M F 2015 Stat Med 34:2926 |
| MR-PRESSO NbDistribution | 1000 exploratory; >=5000 publication; >=10000 stringent |
Verbanck 2018 Nat Genet 50:693 (Methods) |
| MR-PRESSO global test p | < 0.05 -> heterogeneity / outliers present | Verbanck 2018 Nat Genet |
| MR-PRESSO distortion test p | < 0.05 -> outliers materially shifted estimate; if >= 0.05 report uncorrected IVW |
Verbanck 2018 Nat Genet |
| MR-PRESSO min instruments | >=4 to run; >=10 for non-degenerate global test |
Verbanck 2018 Nat Genet |
| MR-PRESSO SignifThreshold | 0.05 default | Verbanck 2018 Nat Genet |
| MR-PRESSO majority-valid breakdown | Fails when >50% instruments pleiotropic |
Verbanck 2018 Nat Genet (theoretical limit) |
| Weighted median validity | Robust to <=50% invalid IVs |
Bowden 2016 Genet Epidemiol 40:304 |
| Weighted mode validity | Plurality-valid (largest valid subset is most common estimate) | Hartwig 2017 IJE 46:1985 |
| CAUSE min #SNPs | >=100 p < 5e-8 SNPs after pruning |
Morrison 2020 Nat Genet 52:740 (Supplement) |
| CAUSE delta_ELPD criterion | one-sided p < 0.05; z = delta_elpd / se(delta_elpd); z > 1.96 standard; z > 3.0 stringent | Morrison 2020 Nat Genet |
| LDSC rg suggesting CHP | >= 0.3 flags need for CAUSE / LHC-MR |
Operational rule; see causal-genomics/genetic-correlation |
| Steiger r^2 difference | Reverse-causal flag at any per-SNP r2_GY > r2_GX | Hemani 2017 PLoS Genet 13:e1007081 |
| Standard sensitivity battery | IVW + Egger + median + mode + PRESSO + Steiger + LOO | Hemani 2018 eLife 7:e34408 / STROBE-MR 2021 |
LCV gcp interpretation thresholds (0, 0.5, 0.6, 1) are tabulated in usage-guide.md.
Goal: Run the canonical UHP-focused MR sensitivity suite on harmonized two-sample data.
Approach: Compute IVW + Egger + median + mode side-by-side; test Egger intercept and heterogeneity; run MR-PRESSO with >=5000 distributions for publication or >=10000 for stringent reporting; apply Steiger filter; leave-one-out; report all estimates.
library(TwoSampleMR)
library(MRPRESSO)
methods <- c('mr_ivw', 'mr_egger_regression', 'mr_weighted_median', 'mr_weighted_mode')
res_mr <- mr(dat, method_list = methods)
het <- mr_heterogeneity(dat)
pleio <- mr_pleiotropy_test(dat)
loo <- mr_leaveoneout(dat)
steiger <- directionality_test(dat)
isq <- Isq(dat$beta.exposure, dat$se.exposure)
nome_pass <- isq >= 0.9
presso <- mr_presso(
BetaOutcome='beta.outcome', BetaExposure='beta.exposure',
SdOutcome='se.outcome', SdExposure='se.exposure',
OUTLIERtest=TRUE, DISTORTIONtest=TRUE,
data=dat, NbDistribution=10000, SignifThreshold=0.05)
global_p <- presso$`MR-PRESSO results`$`Global Test`$Pvalue
outlier_p <- presso$`MR-PRESSO results`$`Outlier Test`$Pvalue
distortion_p <- presso$`MR-PRESSO results`$`Distortion Test`$Pvalue
n_outliers <- sum(outlier_p < 0.05, na.rm=TRUE)
Full working pipeline incl SIMEX, MR-RAPS, contamination mixture, and STROBE-MR table: examples/sensitivity_battery.R.
Goal: Distinguish causal from shared-factor (correlated horizontal pleiotropy) explanations of an exposure-outcome association.
Approach: Fit nuisance parameters (LD pruning + rho_GWAS sample-overlap correction) on a random SNP set; fit the sharing and causal posterior; compare ELPD (expected log predictive density) via Pareto-k smoothed importance sampling.
library(cause)
params <- est_cause_params(dat_cause, variants = pruned_subset_snps)
res_cause <- cause(X = dat_cause, variants = pruned_snps, param_ests = params)
elpd <- summary(res_cause)$tab
Full posterior extraction + reporting: examples/cause_analysis.R.
Interpreting CAUSE output:
q: posterior CHP fraction; 0 = no CHP, 1 = all instruments operate via the shared factoreta: shared-factor effect on Y (the "confounder pathway" magnitude)gamma: posterior causal effect of E on Y after partialling out CHP; report median + 95% credible intervaldelta_ELPD (sharing - causal): negative -> causal model preferred; z = delta_elpd / se(delta_elpd); z > 1.96 standard, z > 3.0 stringent; one-sided p reported alongside posterior gammaCAUSE requires sumstats from both exposure and outcome GWAS in matched effect-allele coding. The pruning step typically retains 100-5000 signature SNPs at LD r^2 < 0.01 in a 1 Mb window; nuisance estimation should use a larger random SNP subset (>= 100,000 genome-wide SNPs) to fit rho (sample overlap) stably.
Trigger: Weak instruments (mean F < 20) and/or suspected UHP requiring outlier-resistant estimation.
over.dispersion = TRUE always for MR (horizontal-pleiotropy variance is real, not noise; turning this off underestimates SE)loss.function = 'huber' (default; outlier-resistant; suited to mild to moderate UHP)loss.function = 'tukey' (more aggressive; downweights extreme outliers more; choose when many obvious outliers suspected)loss.function = 'l2' (non-robust; equivalent to weighted least squares; do not use when UHP suspected)Tukey is preferable when leave-one-out reveals 2+ SNPs single-handedly shifting the IVW estimate by > 1 SE.
Goal: When a single causal estimate is misleading because instruments operate through multiple causal mechanisms (e.g. LDL on CHD via multiple lipoprotein subfractions), identify clusters of instruments with similar per-SNP Wald ratios.
library(mrclust)
ratio_hat <- dat$beta.outcome / dat$beta.exposure
ratio_se <- abs(dat$se.outcome / dat$beta.exposure)
res_mc <- mr_clust_em(theta=ratio_hat, theta_se=ratio_se,
bx=dat$beta.exposure, by=dat$beta.outcome,
bxse=dat$se.exposure, byse=dat$se.outcome,
obs_names=dat$SNP)
per_cluster <- res_mc$results$best
Clusters with cluster_class = 'null' are pleiotropy-only instruments. Per-cluster IVW estimates may differ substantially; biological annotation of the SNPs in each cluster (pathway, target gene) is the interpretation step.
Goal: Jointly estimate forward causal effect, reverse causal effect, and the heritable-confounder contribution from genome-wide sumstats (not just significant SNPs).
library(lhcMR)
merged <- merge_sumstats(list(df_x, df_y), c('X', 'Y'), LD.filepath='ldsc/LDscores.txt', rho.filepath='ldsc/LDrho.txt')
sp_list <- calculate_SP(merged, trait.names=c('X', 'Y'), run_ldsc=TRUE, run_MR=TRUE, hm3='ldsc/w_hm3.snplist', ld='ldsc/eur_w_ld_chr/', nStep=2, SP_single=3, SP_pair=50)
res_lhc <- lhc_mr(sp_list, trait.names=c('X', 'Y'), paral_method='lapply', nBlock=200, nCores=4)
LHC-MR is computationally heavy (hours on full sumstats) but among the most rigorous CHP-aware estimators when both GWAS are well-powered. Output includes axx, ayy, hxy (confounder effect on each trait), and bidirectional alpha_xy, alpha_yx.
Choosing CAUSE vs LHC-MR (Darrous 2021):
| Condition | Preferred method |
|---|---|
>= 100 genome-wide significant SNPs after pruning |
CAUSE (Bayesian; CHP-explicit; mature posterior diagnostics) |
| Polygenic exposure with few significant loci | LHC-MR (uses genome-wide signal, not just significant SNPs) |
| Severe sample overlap between exposure and outcome GWAS | LHC-MR (jointly models overlap); CAUSE's rho correction is exposed to misspecification at high overlap |
| Bidirectionality of central interest | LHC-MR (jointly estimates alpha_xy and alpha_yx); CAUSE only models forward |
| Limited compute / quick turnaround | CAUSE (minutes to hours); LHC-MR may be > 24h on full sumstats |
When both apply, report both with the agreement / disagreement explicit in the discussion.
steiger_filtering(dat); drop SNPs where outcome r^2 > exposure r^2 before primary IVWWorking code: examples/bidirectional_mr.R.
Interpretation cheat-sheet:
| Forward p | Reverse p | Reading |
|---|---|---|
| significant | non-significant | Forward causal claim strengthened |
| non-significant | significant | Re-examine instrument-exposure assignment; the "outcome" may causally drive the "exposure" |
| significant | significant | Feedback loop, shared confounder, or reciprocal causation; resolve with LHC-MR |
| non-significant | non-significant | No evidence of causation in either direction |
When forward and reverse both clear Steiger and both IVW p < 0.05, run LHC-MR jointly rather than reporting two univariable estimates.
LCV uses LDSC-merged genome-wide sumstats and reports gcp (genetic causality proportion) on [-1, 1]. It is a complement to, not a replacement for, MR; gcp ~ 0 with high LDSC rg implies pure genetic correlation without partial causation.
source('LCV/R/RunLCV.R')
res_lcv <- RunLCV(ldscores$L2, x$Z, y$Z)
# res_lcv$gcp; res_lcv$pval.gcpzero.2tailed
Full gcp interpretation table is in usage-guide.md.
Instrument table (one row per SNP retained for primary analysis):
| Column | Content |
|---|---|
| rsID, chr, pos | Variant identifier and genome position |
| EA, OA, EAF | Effect allele, other allele, effect allele frequency in exposure GWAS |
| beta_E, se_E, p_E, F | Exposure-side estimate, SE, p-value, per-SNP F-statistic |
| beta_Y, se_Y, p_Y | Outcome-side estimate, SE, p-value (harmonized to EA) |
| harmonization_action | 1=kept, 2=flipped, 3=dropped (palindromic ambiguity) |
| palindromic_flag | TRUE / FALSE; tracked per Hartwig 2016 IJE 45:1717 |
| Steiger_direction | forward / reverse / inconclusive |
| Steiger_p | per-SNP directionality p-value |
Sensitivity-battery table (one row per method):
| Column | Content |
|---|---|
| method | IVW / Egger / WM / WMode / PRESSO (raw + corrected) / RAPS / CAUSE |
| estimate, se, p, 95% CI | Point estimate and inference |
| n_SNPs_used | Post-harmonization, post-Steiger SNP count |
| heterogeneity_p | Q for IVW; Q' for Egger; global p for PRESSO |
| intercept_p | Egger only (directional UHP test) |
| ELPD_delta + z | CAUSE only (sharing - causal; negative + |
| Pattern | Likely cause | Action |
|---|---|---|
| IVW and Egger agree (small Egger intercept); median and mode agree | Likely true causal; minimal pleiotropy | Report all; STROBE-MR; emphasize agreement |
| IVW significant; Egger non-significant with similar slope; PRESSO global p > 0.05 | Egger underpowered (few SNPs) OR Egger NOME violated | Check I^2_GX; SIMEX-correct if 0.6 < I^2_GX < 0.9 |
| IVW shifted relative to Egger / median / mode; Egger intercept significant | Directional UHP | Trust Egger slope; PRESSO-corrected IVW; mode estimator |
| IVW + Egger + median + mode + PRESSO all agree but CAUSE delta_ELPD non-significant or in opposite direction | CHP via shared confounder | Trust CAUSE; report all five UHP methods alongside but flag the discordance; check LDSC rg |
| All UHP methods agree; LCV gcp ~ 0 | Genetic correlation only, not causation | Causation evidence is weak; report rg explicitly; consider colocalization (cis-MR) instead |
| MR-Clust shows >=2 distinct non-null clusters | Heterogeneous mechanisms | Report per-cluster estimates; do not summarize as a single effect |
| Steiger fails on a substantial fraction of instruments | Reverse causation OR exposure measurement error | Run bidirectional MR; check exposure GWAS heritability; LHC-MR |
| MR-PRESSO global p < 0.05 but corrected estimate similar to uncorrected | >50% pleiotropic OR CHP masquerading as UHP | Re-prune instruments; switch to weighted-mode / CAUSE / LHC-MR |
Operational rule for publication: Report IVW (primary), Egger slope + intercept, weighted median, weighted mode, MR-PRESSO global + distortion + corrected, Cochran Q, Steiger directionality, F-statistic distribution, I^2_GX (for Egger validity), and at least one CHP-aware method (CAUSE or LHC-MR) when rg >= 0.3 or biology suggests shared upstream. Failure to report a CHP-aware result when CHP is plausible is a reviewer-flagged red flag since 2020.
| Pushback | Standard response |
|---|---|
| "Was CHP checked for?" | LDSC rg reported (causal-genomics/genetic-correlation); if rg > 0.3, CAUSE or LHC-MR ran; q posterior reported |
| "Why CAUSE and not LHC-MR?" | CAUSE preferred when >= 100 significant SNPs available (Morrison 2020). LHC-MR preferred when significant-SNP set is small or polygenic, using genome-wide sumstats (Darrous 2021) |
| "Egger NOME?" | I^2_GX computed; if 0.6 <= I^2_GX < 0.9, SIMEX correction applied; if < 0.6, Egger dropped in favor of MR-RAPS |
| "PRESSO doesn't catch CHP?" | Confirmed (Morrison 2020); CAUSE / LHC-MR reported alongside PRESSO for that reason |
| "Steiger filter applied pre-MR or post?" | Pre-MR: SNPs failing per-SNP Steiger directionality dropped before primary IVW |
| "Why no replication cohort?" | Two-sample design uses independent exposure and outcome cohorts; if same biobank, MRlap used or noted as a limitation |
| "Why not just trust the IVW?" | IVW assumes balanced UHP and no CHP; both violated routinely; sensitivity battery is the standard since STROBE-MR 2021 |
| "Effect size is implausibly large" | Re-examine F-statistic distribution for weak IV bias; check Winner's curse; consider Wald ratio at a single strong instrument as sanity check |
| Item | Required content |
|---|---|
| 1-3 | Title / abstract / background indicates this is an MR study; pre-registered protocol |
| 4-7 | Study design, data sources, instrument selection criteria (p-threshold, LD clumping, MAF) |
| 8-11 | Harmonization, palindrome handling, allele alignment |
| 12-14 | F-statistic distribution; weak-instrument bias mitigation |
| 15-17 | Primary MR method + all sensitivity methods + CHP-aware method when relevant |
| 18-19 | Pleiotropy tests, Steiger, heterogeneity |
| 20 | Discussion of remaining assumption violations; limitations |
Sub-items (30 total) detail per-method reporting. The full statement (JAMA 326:1614) and explanation (BMJ 375:n2233) are now reviewer-required at most cardiovascular and psychiatric journals since 2022.
| Error / symptom | Cause | Solution |
|---|---|---|
MR-PRESSO crashes with Not enough intrumental variables |
Fewer than 4 SNPs | Need >=4 for PRESSO; for cis-MR with few SNPs use colocalization |
| Egger intercept p < 0.05 but I^2_GX = 0.5 | NOME violated; intercept is artifactually inflated | SIMEX-correct or do not trust Egger; use MR-RAPS instead |
Isq() not found |
TwoSampleMR version where Isq is unexported | Compute manually: Q_GX = sum((beta_GX/se_GX)^2); I2 = (Q_GX - (n-1))/Q_GX, clipped to [0,1] |
MR-RAPS package not found after CRAN install |
CRAN-archived 2025-03-01 | remotes::install_github('qingyuanzhao/mr.raps'); call via TwoSampleMR::mr_raps() wrapper (MendelianRandomization does NOT export mr_raps) |
| CAUSE delta_ELPD CI spans zero; Pareto-k > 0.7 | <100 sig SNPs OR severe sample overlap | Use LHC-MR; or report CAUSE with the explicit caveat |
| Steiger labels most instruments reverse-causal | Exposure GWAS imprecise OR sample size mismatch | Treat as one signal; cross-check with bidirectional MR |
| LHC-MR runtime > 24h | Default nCores=1 on full sumstats | Use nCores >= 4; restrict to LDSC-overlapping SNPs first |
| MR-PRESSO outliers all on same chromosome | Genome-wide LD not properly pruned; clumping window too narrow | Re-clump at r^2 < 0.001 in 10 Mb window |
| MR-Mix returns NA | Few SNPs OR no variation in mixture support | Need >=20 SNPs; default mixture grid may need tuning |