Method library WORKFLOW / 039

Research workflow/4 steps

Mendelian randomization: genes as instruments for a causal effect (TwoSampleMR)

@twosamplemr ✓ P D · Jun 29, 2026 · 1.1k views

Source TwoSampleMR — MRC IEU (Hemani, Davey Smith et al.)

@misc{twosamplemr,
  title        = {TwoSampleMR},
  author       = {Hemani and Davey Smith and others},
  howpublished = {\url{https://mrcieu.github.io/TwoSampleMR/}},
  note         = {Software / documentation}
}

In the margins · Peer notes@rahul_nair · The scatter with all five estimator slopes on one plot is the right way to show robustness at a glance. Saved. 2

The method workspace

Follow the analysis.

Select a node to explore the method

Method mapInput → 4 steps → output

START WITHInput data

ARRIVE ATResults

Read step 01

Inside the methodReasoning, equations & implementation

Data prep

Harmonise SNP–exposure & SNP–outcome effects

Step notesField note & discussion1

Data preparation — shapes the raw inputs into what the estimator expects.

What happens here

Each SNP is a candidate instrument. Align effect alleles across the two GWAS so the signs are comparable.

Formula

Z_j\to X,\qquad Z_j\perp\!\!\!\perp U,\qquad Z_j\to Y\ \text{only via}\ X

Reads fromNot specified Feeds intoNot specified

Key code

dat <- harmonise_data(exp, out)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Diagnostic / pre-tests

Check instrument strength

Step notesField note & discussion1

A pre-flight check — run this before trusting any estimate downstream.

What happens here

Weak instruments bias MR. Read the variance explained and the F-statistic before trusting anything.

Formula

F=\dfrac{R^2\,(N-2)}{1-R^2}\qquad(\text{weak if }F\lesssim 10)

Reads fromNot specified Feeds intoNot specified

Key code

# per-SNP F = (beta/se)^2; overall F from R^2

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Estimation

Inverse-variance weighted estimate

Step notesField note & discussion1

The core estimate — where the causal quantity itself is computed.

What happens here

Combine the per-SNP Wald ratios, weighting by precision — the IVW estimate.

Formula

\hat\beta_{\mathrm{IVW}}=\dfrac{\sum_j \hat\beta_{Yj}\hat\beta_{Xj}\,\sigma_{Yj}^{-2}}{\sum_j \hat\beta_{Xj}^{2}\,\sigma_{Yj}^{-2}},\qquad \hat\beta_j=\dfrac{\hat\beta_{Yj}}{\hat\beta_{Xj}}

Reads fromNot specified Feeds intoNot specified

Key code

mr(dat, method_list = c('mr_ivw'))

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Robustness check

Pleiotropy-robust: MR-Egger, weighted median

Step notesField note & discussion1

A robustness check — does the headline result survive a different lens?

What happens here

If instruments affect the outcome through other paths, IVW is biased. The MR-Egger intercept tests directional pleiotropy; weighted median/mode are robust to some invalid instruments.

Formula

\hat\beta_{Yj}=\beta_0+\beta_{\mathrm{Egger}}\hat\beta_{Xj}+\epsilon_j;\quad \beta_0\ne 0\Rightarrow\text{pleiotropy}

Reads fromNot specified Feeds intoNot specified

Key code

mr(dat, method_list = c('mr_egger_regression',
                         'mr_weighted_median'))

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Data contract

Inputs & data

GWAS summary statistics: for each SNP, its effect on the exposure and on the outcome (with standard errors), harmonised to a common effect allele.

Show data format & exampleHide example

Format — per-SNP effects on exposure and outcome, harmonised.

library(TwoSampleMR)
exp <- extract_instruments('ieu-a-2')           # BMI instruments
out <- extract_outcome_data(exp$SNP, 'ieu-a-7')  # coronary heart disease
dat <- harmonise_data(exp, out)
res <- mr(dat)

Read the evidence

Results & interpretation

2 figures Workflow discussion 2

01FigureThe MR scatter: each point is a genetic instrument (SNP effect on outcome vs. on exposure); the slope of each method line is the estimated causal effect of the exposure.

02FigureSingle-SNP forest plot: each instrument's Wald-ratio estimate and the pooled effect at the bottom — heterogeneity flags pleiotropy.

Figures reproduced from TwoSampleMR — MRC IEU (Hemani, Davey Smith et al.) — unofficial community showcase; all credit to the original authors.

Context & interpretation

⚠️ Unofficial community showcase of twosamplemr. Not affiliated with the authors; all credit to them.

Use genetic variants as instruments to estimate the causal effect of an exposure on an outcome from GWAS summary data — with IVW plus pleiotropy-robust MR-Egger and weighted-median checks.

Peer notes

Discuss the workflow (2)

2

@ivan_iv · Jun 29, 2026

Genes as instruments is the cleanest natural experiment we get — but the MR-Egger intercept and weighted-median checks are non-negotiable. Pleiotropy is everywhere.
0

@rahul_nair · Jun 29, 2026

The scatter with all five estimator slopes on one plot is the right way to show robustness at a glance. Saved.

1

@ivan_iv · Jun 29, 2026

Agreed — if IVW and Egger disagree wildly, that's the figure that makes the pleiotropy story obvious to a reviewer.