Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.
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Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.
tool_type
mixed
primary_tool
TwoSampleMR
Version Compatibility
Reference examples tested with: TwoSampleMR 0.6.0+, MendelianRandomization 0.10+, MR-PRESSO 1.0+, cause 1.2+, MVMR 0.4+, ieugwasr 1.0+, MRlap 0.0.3.2+, coloc 5.2+, mrclust 0.1+, lhcMR 0.0.1+, R 4.4+. Both TwoSampleMR 0.6.0 and ieugwasr 1.0 are the JWT-transition versions; older versions still expect deprecated OAuth.
Before using code patterns, verify installed versions match. If versions differ:
R: packageVersion('<pkg>') then ?function_name to verify parameters
CLI (plink, GCTA-GSMR): <tool> --version then <tool> --help
If code throws an error referencing a function that has moved (e.g. ieugwasr::ld_clump vs TwoSampleMR::clump_data) or an OAuth token failure, introspect the installed API and adapt the example rather than retrying.
Mendelian Randomization
"Test whether trait X causally affects trait Y from GWAS summary statistics" -> Use genetic variants as instrumental variables (IVs) that satisfy three assumptions (relevance, independence, exclusion restriction) to estimate beta_causal = beta_outcome / beta_exposure under the IV framework (Davey Smith & Ebrahim 2003 IJE 32:1; Burgess & Thompson 2021 Chapman & Hall/CRC, 2nd ed.). Tool choice is a decision about the regime (one-sample vs two-sample, sparse vs polygenic, drug-target vs polygenic exposure) and the pleiotropy model (balanced, directional InSIDE, correlated horizontal). Wrong tool inflates Type-I error or attenuates true effects in a direction predictable from the bias structure.
R: TwoSampleMR::mr() orchestrates IVW + Egger + weighted median + weighted mode in one call
R: MendelianRandomization::mr_ivw / mr_egger / mr_median / mr_mbe / mr_conmix per-method API (S4 objects; MR-RAPS is NOT in this package -- use TwoSampleMR::mr_raps() which wraps the GitHub mr.raps)
R: MRPRESSO::mr_presso() global / outlier / distortion tests
Continuous exposures only; needs individual-level data; Hamilton 2023 medRxiv 23293658 shows stratum-specific bias from age/sex
Methodology evolves; benchmark consensus shifts every 2-3 years. Verify against the current Slob & Burgess 2020 Genet Epidemiol, Burgess 2023 Wellcome Open Res "Guidelines for performing Mendelian randomization" (v3+), and STROBE-MR 2021 reporting standards before locking a method as primary.
Decision Tree by Experimental Scenario
Scenario
Primary method
Sensitivity battery
Why
Standard two-sample, independent cohorts, polygenic exposure
IVW (random)
Egger + weighted median + MR-PRESSO + MR-RAPS + Steiger
One-sample F-stat floor shifts to F >= 20; jackknife SE preferred over analytic at one-sample scale; do NOT run exposure GWAS and outcome GWAS on the same individuals then claim two-sample (Barry 2021 PLoS Genet 17:e1009703 collider bias); within-stratum MR (e.g. "MR among smokers") risks collider bias from the stratification variable
Negative-control outcomes (genotype-vs-sex within strata); Hamilton 2023 limitation cited
Both methods produce stratum-specific bias from age/sex effects (Hamilton 2023 medRxiv 23293658); pre-specify the non-linear hypothesis, do not data-snoop the J-curve, report negative-control sanity checks
Single-patient rare disease
Not MR -- use FRASER/DROP outlier framework
See alternative-splicing/outlier-splicing-detection
MR requires summary stats; n=1 is wrong regime
One-Sample vs Two-Sample Bias Direction
Design
Weak-IV bias direction
Reason
One-sample, F<10
Toward confounded observational estimate (overestimates causal effect if confounding is in same direction)
Sample correlation between IV-X and IV-Y residuals
Intermediate; proportional to overlap fraction and z-score correlation
Burgess 2016 Genet Epidemiol 40:597; correction available
Operational rule: Whenever both GWAS came from UK Biobank (or any single biobank), treat the analysis as one-sample-equivalent and prefer MR-RAPS as primary. Treating it as "two-sample because separate GWAS files" is a common error and produces overestimates.
MRlap (Mounier & Kutalik 2023 Genet Epidemiol 47:314) jointly corrects three biases that previously required three separate tools: sample overlap, winner's curse, and weak-instrument bias. It builds on an LDSC scaffold (cross-trait LD-score regression intercept estimates the overlap-induced covariance) and reweights the IVW estimate against the analytical bias-correction formula.
Decision rule -- prefer MRlap when: (a) any sample overlap is suspected, (b) only sumstats are available (no individual-level data for re-running GWAS on disjoint samples), (c) exposure discovery and outcome were both run inside the same biobank (UKB-on-UKB, FinnGen-on-FinnGen). MRlap returns NA / unstable estimates when h^2 < 0.05; in that regime, fall back to Burgess 2016 overlap-corrected IVW plus MR-RAPS for the weak-IV component.
Drug-Target / cis-MR Framework
cis-MR restricts instruments to the cis-regulatory window of the gene encoding the protein/transcript exposure (Schmidt 2020 Nat Commun 11:3255), relaxing the exclusion-restriction assumption because the protein product directly mediates the SNP's effect on the outcome. Operational core: extract cis-pQTL/cis-eQTL within +/-500 kb of the gene; clump at r2 < 0.1 (looser than polygenic MR to retain power within a narrow window); require colocalization PP.H4 >= 0.7; flag protein-altering variants (PAV) which can break SomaScan/Olink aptamer/antibody binding rather than reflect biology.
Full drug-target cis-MR workflow including UKB-PPP / deCODE / Fenland pQTL panels, PAV flagging, Olink vs SomaScan replication (~15-30% cross-platform disagreement), and the operational claim ladder lives in causal-genomics/proteome-mr-drug-target. Use that skill for any drug-target nomination.
Binary outcomes and non-collapsibility
MR with logistic-GWAS sumstats returns per-allele log-OR on the population-averaged (marginal) scale, NOT the conditional log-OR (Burgess 2017 Stat Methods Med Res 26:2333). For rare disease (prevalence < 10%), OR ~= RR ~= HR and the distinction is harmless. For common disease, OR diverges from RR/HR and the MR estimate cannot be back-converted to a conditional effect without strong assumptions; report as "per 1-SD increase in genetically-predicted X, OR for Y = ..." rather than implying an individual-level intervention effect.
Collider bias when conditioning on a collider variable (Coscia 2022 Eur J Epidemiol 37:671 formalizes this for stratified MR): case-only or disease-progression designs condition the sample on disease status, opening a collider path between any cause of disease and any cause of progression. MR within affected subsets without explicit adjustment for selection probability is fragile; weight by inverse probability of selection or restrict claims to the unconditioned population.
Per-Method Failure Modes
IVW under directional pleiotropy
Trigger: Several SNPs affect the outcome through pathways not via the exposure, in a consistent direction.
Mechanism: IVW is a weighted regression through origin; non-zero mean pleiotropy shifts the slope.
Symptom: Egger intercept p < 0.05 with non-zero estimate; IVW differs from weighted median; MR-PRESSO global test p < 0.05.
Fix: Use Egger (if I^2_GX >= 0.9 -- otherwise SIMEX-correct via the simex package applied to the Egger fit, treating se.exposure as measurement error in beta.exposure); cross-check with weighted median, MR-PRESSO, and CAUSE; report IVW only as one of a panel, never alone. The MendelianRandomization::mr_egger() function accepts distribution='normal' and reports the I.sq (I^2_GX) NOME diagnostic but applies no NOME/SIMEX correction to the estimate itself and does NOT expose a SIMEX wrapper.
Weak-instrument bias direction
Trigger: Mean per-instrument F-statistic < 10, or several individual F < 10.
Mechanism: Weak IVs amplify finite-sample correlation between IV-X and IV-Y errors; bias direction depends on overlap regime (see table above).
Symptom: Estimates shift markedly when removing the weakest instruments; one-sample MR estimates much larger than two-sample.
Fix: Compute F per instrument from the EXPOSURE GWAS, not the outcome; exclude F < 10; use MR-RAPS (handles weak IVs by design); for two-sample, also report unweighted IVW (less weak-IV-bias-inflated than weighted in some regimes).
Winner's curse at P~5e-8
Trigger: Discovery GWAS is the source of both instrument selection and effect-size estimates.
Mechanism: SNPs that just cross 5e-8 in discovery have over-estimated effect sizes (regression toward the mean in independent replication); MR uses inflated beta_X, biasing causal estimate.
Symptom: MR effect shrinks substantially when using effect sizes from an independent replication GWAS.
Fix: (1) Three-sample design (discovery / replication-for-instrument-effect / outcome) where feasible. (2) When sumstats-only: MRlap (Mounier 2023), MR-SimSS (sample-splitting from sumstats), or RIVW (Ma 2023 Ann Statist 51:211 -- rerandomized IVW) jointly correct winner's curse + weak IVs + overlap. (3) Jiang 2023 IJE 52:1209 empirical magnitude: variant-level inflation ~50-400% near the genome-wide-significance threshold, dropping to <25% when the minimum P <= 1e-13.
NOME violation invalidating Egger
Trigger: Running MR-Egger with I^2_GX < 0.9.
Mechanism: Egger assumes NO Measurement Error in exposure effect sizes (NOME); when violated, Egger slope is attenuated toward null with reciprocal bias on the intercept.
Symptom:mr_pleiotropy_test() Egger estimate disagrees with weighted median in magnitude but agrees in direction; Isq() function returns <0.9.
Fix: Compute Isq(beta_X, se_X) (Bowden 2016 IJE 45:1961); if <0.9, apply SIMEX correction via simex package or report Egger as exploratory only. The MendelianRandomization package's mr_egger() reports the I.sq (I^2_GX) NOME diagnostic but does not itself apply a NOME/SIMEX correction; SIMEX must be run separately.
Steiger filter false flag under unmeasured confounding
Trigger: Applying steiger_filtering() on traits with unmeasured shared confounders (e.g. SES).
Mechanism: Steiger compares variance explained in exposure vs outcome per SNP; an unmeasured confounder upstream of both produces SNPs that explain more variance in the outcome than the exposure, falsely flagging "reverse causation" (Lutz 2022 Genet Epidemiol 46:139).
Symptom: Many SNPs flagged as wrong-direction yet biology and prior MR support forward causation.
Fix: Treat Steiger as a heuristic, not gospel; cross-validate direction with bidirectional MR (forward + reverse with independent instrument sets); for known-confounder-rich domains (psychiatric traits, SES proxies) use LCV or LHC-MR instead, which jointly model confounders.
Palindromic SNP harmonization
Trigger: SNPs with alleles A/T or C/G near MAF 0.5.
Mechanism: Strand orientation is ambiguous for palindromic SNPs when allele frequencies are intermediate; flipping introduces sign errors that look like pleiotropy.
Symptom:harmonise_data() reports many palindromic SNPs dropped; remaining SNPs show heterogeneity from a handful.
Fix: Default action = 2 (infer from allele frequencies) drops MAF~0.5 palindromes; action = 3 drops ALL palindromes (most conservative); never use action = 1 (assumes forward strand) unless both GWAS are guaranteed to use the same strand convention. Document choice in methods.
Quantitative Thresholds
Threshold
Source
Rationale
F-statistic > 10 per instrument
Staiger & Stock 1997 (linear IV)
Heuristic; debated (Burgess 2011 IJE; Zhao 2020 argues 10 is too low for one-sample)
Conditional F > 10 per exposure (MVMR)
Sanderson 2019 IJE 48:713
Total F can be high while conditional F low; per-exposure F is what matters
I^2_GX >= 0.9 for Egger
Bowden 2016 IJE 45:1961
NOME assumption; below this, SIMEX correction required
CAUSE >= 100 significant SNPs
Morrison 2020 Nat Genet 52:740
Mixture model needs signal density for shared-factor estimation
Egger >= 10 instruments
Bowden 2015 IJE 44:512
Power for slope test in weighted regression
Sample-overlap z-score correlation
Burgess 2016 Genet Epidemiol 40:597
Use LDSC bivariate intercept as proxy; correct IVW SE accordingly
Clumping r2 < 0.001, 10 Mb window
TwoSampleMR default; matches GWAS LD norms
Polygenic MR; cis-MR uses r2 < 0.1 within window
Steiger p < 0.05
Hemani 2017 PLoS Genet 13:e1007081
Heuristic; subject to confounder caveat (Lutz 2022)
Goal: Detect horizontal-pleiotropy outliers, remove them, and test whether the corrected estimate differs from the uncorrected one (distortion test).
Approach: Three-step framework: global test (presence of pleiotropy), outlier test (per-SNP), distortion test (effect change after outlier removal).
library(MRPRESSO)
presso <- mr_presso(
BetaOutcome ='beta.outcome', BetaExposure ='beta.exposure',
SdOutcome ='se.outcome', SdExposure ='se.exposure',
OUTLIERtest =TRUE, DISTORTIONtest =TRUE,
data = dat, NbDistribution =10000,# >= 10000 for publication-grade p-value precision
SignifThreshold =0.05)
print(presso$`MR-PRESSO results`$`Global Test`)# any pleiotropy
print(presso$`MR-PRESSO results`$`Distortion Test`)# change after outlier removal
outlier_snps <- which(presso$`MR-PRESSO results`$`Outlier Test`$Pvalue <0.05/ nrow(dat))
CAUSE for Correlated Horizontal Pleiotropy
CAUSE (Morrison 2020 Nat Genet 52:740) fits a shared-factor mixture to genome-wide sumstats and compares causal vs sharing-only models by delta-ELPD. Workflow: gwas_merge() -> sample ~1M variants for est_cause_params() -> filter sig SNPs (P < 1e-3) and optionally LD-prune -> cause(X, variants, param_ests). Needs >= 100 sig SNPs. Full annotated example and ELPD interpretation in causal-genomics/pleiotropy-detection.
MVMR with Conditional F
Goal: Estimate the causal effect of exposure X1 on Y, adjusting for measured pleiotropy via X2.
Approach: Format exposures + outcome into MVMR object; compute conditional F per exposure (>10 required); run multivariable IVW; report Q_A heterogeneity.
library(MVMR)
mvmr_dat <- format_mvmr(
BXGs = cbind(dat$beta.x1, dat$beta.x2),
BYG = dat$beta.y,
seBXGs = cbind(dat$se.x1, dat$se.x2),
seBYG = dat$se.y,
RSID = dat$SNP
)
condF <- strength_mvmr(r_input = mvmr_dat, gencov =0)# per-exposure conditional F# condF must be > 10 for EACH exposure (Sanderson 2019); total F is misleading
mv_ivw <- ivw_mvmr(r_input = mvmr_dat)
mv_qa <- pleiotropy_mvmr(r_input = mvmr_dat, gencov =0)# Q_A heterogeneity test
gencov = 0 is valid ONLY if the exposure GWAS samples don't overlap; for overlapping exposures use the bivariate LDSC intercept matrix as gencov. If any conditional F < 10, the IVW point estimate is weak-IV-biased; switch to the Q-minimization estimator: qhet_mvmr(r_input, pcor, CI = TRUE, iterations = 1000) (Sanderson 2021 Stat Med 40:5434), which minimizes Q-statistic heterogeneity rather than weighting by inverse variance and is robust to weak conditional instruments.
Bidirectional and Steiger
exposure_rev <- format_data(outcome_raw, type ='exposure')# treat former outcome as exposure
outcome_rev <- format_data(exposure_raw, type ='outcome')
dat_rev <- harmonise_data(exposure_rev, outcome_rev, action =2)
results_rev <- mr(dat_rev, method_list ='mr_ivw')
dat_filt <- steiger_filtering(dat)# per-SNP; flags SNPs where variance(Y) > variance(X)
dir_test <- directionality_test(dat)# global; correct_causal_direction == TRUE if forward
Report direction operationally: forward p < 5e-8 with reverse p > 0.05; directionality_test()correct_causal_direction == TRUE with Steiger p < 0.05; point estimate in reverse direction has |effect| substantially smaller than forward (reflecting reverse-instrument-strength asymmetry). Example sentence: "Forward MR showed BMI -> T2D (IVW beta = 0.85, p = 2e-15); reverse MR was null (IVW beta = 0.02, p = 0.61); Steiger directionality test favored the forward direction (p = 3e-7)."
Reconciliation: When Methods Disagree
Pattern
Likely cause
Action
IVW sig, Egger null with non-zero intercept
Directional pleiotropy
Report Egger as primary if I^2_GX >= 0.9; otherwise SIMEX-correct
IVW sig, weighted median sig, mode null
Mode underpowered or bimodal pleiotropy
Trust the agreement of IVW + median
MR-PRESSO distortion test sig
Outliers materially shift estimate
Report PRESSO-adjusted estimate as primary
CAUSE sig, IVW sig, same direction
High-confidence causal claim
Report both; emphasize CAUSE rules out CHP
CAUSE null, IVW sig, same direction
CHP indistinguishable from causation
Downgrade to "consistent with causation but not separable from CHP"
Forward MR sig, reverse MR also sig
Bidirectional causation OR confounded
Use LHC-MR or LCV for genome-wide resolution
IVW sig but mean F << 10 in one-sample design
Weak-IV bias toward observational
Re-run with MR-RAPS; report adjusted estimate
Operational rule for publication: Primary IVW + concordant Egger (or weighted median if NOME violated) + non-significant MR-PRESSO global test + Steiger correct direction = publication-ready evidence. CAUSE concordance is required when the exposure is polygenic and the prior on CHP is high (e.g. BMI -> outcome, lipids -> outcome). Single-method "significant IVW" claims should be downgraded to exploratory.
Cohort Gotchas
UKB GWAS commonly include non-EUR participants; pan-UKB EUR/AFR/EAS/SAS subsets are separate releases. Mixing ancestries inflates instrument strength via population stratification rather than biology.
FinnGen DF12 (2024) cohort: Finnish founder effects produce narrower LD blocks and higher winner's curse magnitude than UKB at matched sample size.
MVP / GBMI / AoU: multi-ancestry meta-analyses; stratify by ancestry before MR or use ancestry-specific subsets.
UKB-on-UKB MR creates one-sample-equivalent bias regardless of "different GWAS file" appearance; use MRlap or move the outcome to an external cohort (FinnGen, BBJ, MVP).
Anticipated Reviewer Pushback
Pushback
Standard response
"Sample overlap between exposure and outcome GWAS?"
OSF protocol filed; STROBE-MR all 20 items reported
"InSIDE assumption?"
INstrument Strength Independent of Direct Effect -- pleiotropic effects alpha uncorrelated with instrument-exposure effects gamma. Tested via Egger intercept + CHP-aware sensitivity (CAUSE)
Common Errors
Error / symptom
Cause
Solution
F-statistic computed from outcome
Reading off beta.outcome / se.outcome
Compute from EXPOSURE; outcome F is meaningless for IV strength
All instruments dropped at harmonise
All SNPs palindromic; or allele coding mismatch
Verify A1/A2 conventions match; try action = 3 to drop, not assume
Unauthorized / 403 from OpenGWAS
OAuth deprecated May 2024; JWT token required
Generate at api.opengwas.io -> set OPENGWAS_JWT=<token> in ~/.Renviron -> restart R -> verify with ieugwasr::get_opengwas_jwt(). For production, skip the API: use ld_clump_local() with local 1KG bfile (saves rate-limit + auth headaches).
Conditional F vs total F confusion in MVMR
Reporting mean(F) not strength_mvmr() per-exposure
Always use MVMR::strength_mvmr() and report each column
install.packages("mr.raps") fails
CRAN-archived 2025-03-01
remotes::install_github('qingyuanzhao/mr.raps'); TwoSampleMR's mr_raps() wrapper internally calls this package
MR-PRESSO returns NA p-value
NbDistribution too small; signal too thin
Increase to >= 10000; check that >= 4 SNPs remain after harmonization
Egger intercept "highly significant" with 5 SNPs
Underpowered Egger over-fits the slope
Egger needs >= 10 SNPs; below that, intercept is unreliable
Sample-overlap correction ignored
Treating UKB-on-UKB as two-sample
Apply Burgess 2016 correction; or use MR-RAPS
cause() runs forever
Default model fit on too many SNPs
Filter to sig SNPs (P < 1e-3) before cause(); est_cause_params uses the random subset
Provide EAF or use action = 3 and document the loss
Tool Installation Notes
# CRAN-stable
install.packages(c('remotes','MendelianRandomization','MVMR','coloc'))# GitHub-only or recently archived
remotes::install_github('MRCIEU/TwoSampleMR')# primary orchestrator
remotes::install_github('MRCIEU/ieugwasr')# OpenGWAS client + local clumping
remotes::install_github('rondolab/MR-PRESSO')# never on CRAN
remotes::install_github('qingyuanzhao/mr.raps')# CRAN-archived 2025-03-01
remotes::install_github('jean997/cause')# depends on mixsqp; suggests Rfast
remotes::install_github('cnfoley/mrclust')# heterogeneity clusters
remotes::install_github('LizaDarrous/lhcMR')# bidirectional + heritable confounder
remotes::install_github('HDTian/DRMR')# doubly-ranked stratification
remotes::install_github('n-mounier/MRlap')# joint overlap + winner's-curse + weak-IV correction
TwoSampleMR::mr_raps() is a thin wrapper that calls mr.raps::mr.raps() under the hood; the GitHub mr.raps install above is therefore required. The MendelianRandomization package does NOT export mr_raps() (verify with ls('package:MendelianRandomization')); only TwoSampleMR offers a MR-RAPS entry point. For local clumping, install plink2 and download a 1KG EUR (or matched-ancestry) reference bfile.
STROBE-MR Reporting
STROBE-MR (Skrivankova 2021 JAMA 326:1614; BMJ 375:n2233): 20-item checklist required by Eur J Epi / Nat Genet / JAMA / Diabetologia since 2022. See causal-genomics/pleiotropy-detection for the per-item table -- that skill is the consolidated reporting reference for the full MR + sensitivity battery.
References
Davey Smith G & Ebrahim S 2003 IJE 32:1 (foundational framework)
Burgess S & Thompson SG, Mendelian Randomization: Methods for Causal Inference Using Genetic Variants, Chapman & Hall/CRC (1st ed. 2015 / 2nd ed. 2021) (MR canonical reference)
Bowden J et al 2015 IJE 44:512 (MR-Egger)
Bowden J et al 2016 Genet Epidemiol 40:304 (weighted median)
Hartwig FP et al 2017 IJE 46:1985 (weighted mode)
Zhao Q et al 2020 Ann Stat 48:1742 (MR-RAPS)
Morrison J et al 2020 Nat Genet 52:740 (CAUSE)
Zhu Z et al 2018 Nat Commun 9:224 (GSMR + HEIDI)
Verbanck M et al 2018 Nat Genet 50:693 (MR-PRESSO)
Sanderson E et al 2019 IJE 48:713 (MVMR conditional F)
Foley CN et al 2021 Bioinformatics 37:531 (MR-Clust)
Burgess S et al 2020 Nat Commun 11:376 (contamination mixture)
O'Connor LJ & Price AL 2018 Nat Genet 50:1728 (LCV)
Darrous L et al 2021 Nat Commun 12:7274 (LHC-MR)
Tian H et al 2023 PLoS Genet 19:e1010823 (DRMR)
Burgess S et al 2016 Genet Epidemiol 40:597 (sample-overlap correction)
Schmidt AF et al 2020 Nat Commun 11:3255 (drug-target / cis-MR framework)
Lutz SM et al 2022 Genet Epidemiol 46:139 (Steiger filter caveat)
Skrivankova VW et al 2021 JAMA 326:1614 (STROBE-MR statement)
Mounier N & Kutalik Z 2023 Genet Epidemiol 47:314 (MRlap joint correction)
Sanderson E et al 2021 Stat Med 40:5434 (qhet_mvmr Q-minimization estimator)
Coscia C et al 2022 Eur J Epidemiol 37:671 (collider bias in case-only / progression cohorts)
Hamilton FW et al 2023 medRxiv 23293658 (NLMR stratum-specific bias critique)
Jiang T et al 2023 IJE 52:1209 (winner's curse empirical magnitude)
Ma X et al 2023 Ann Statist 51:211 (RIVW rerandomized IVW)
Related Skills
causal-genomics/colocalization-analysis - Confirm shared causal variant for cis-MR drug-target work
causal-genomics/pleiotropy-detection - Deep dive on MR-PRESSO, Egger, contamination mixture diagnostics
causal-genomics/fine-mapping - Credible-set construction at instrument loci before cis-MR
causal-genomics/mediation-analysis - Two-step MR and MVMR difference method for X -> M -> Y mediation
causal-genomics/transcriptome-wide-association - TWAS as MR-adjacent framework for gene-level inference
causal-genomics/proteome-mr-drug-target - Dedicated cis-pQTL drug-target MR workflow with UKB-PPP/deCODE/Fenland and coloc triangulation
population-genetics/association-testing - GWAS source for instrument selection
clinical-databases/clinvar-lookup - Annotate instrument SNPs for downstream interpretation
