Tooluniverse Mendelian Randomization

Mendelian Randomization (Causal Inference from Genetic Instruments)

MR estimates the CAUSAL effect of an exposure on an outcome using genetic variants as instrumental variables. Because alleles are randomized at conception, MR is largely robust to the confounding and reverse causation that bias observational associations. It is not a free lunch: the causal claim rests on three assumptions, and violating them (especially horizontal pleiotropy) silently biases the estimate.

LOOK UP, DON'T GUESS: never assert a causal MR estimate from memory. Genetic-instrument results are updated as new GWAS are published — always retrieve current evidence with EpiGraphDB_get_mendelian_randomization. Do not invent beta/p-values.

Correlation ≠ causation, and genetic correlation ≠ causation. A high genetic correlation (rg) means two traits share heritability — it does NOT establish a causal direction. Only MR (with valid instruments) speaks to causality. Report them as different kinds of evidence.

The three instrumental-variable assumptions

If you cannot speak to these, your causal claim is provisional. Say so.

When to use

• "Does [exposure] causally affect [outcome/disease]?" — the core MR question.
• Triangulating an observational/epidemiological association ("BMI correlates with depression — is it causal?").
• Reverse-causation checks (bidirectional MR: does the outcome cause the exposure instead?).
• Prioritising drug targets / risk factors with genetic causal support.
• Distinguishing a causal driver from a shared-etiology bystander (MR vs genetic correlation).

This skill wraps the IEU OpenGWAS / EpiGraphDB MR-EvE ("MR Everything-vs-Everything") resource: a large matrix of pre-computed two-sample MR results between GWAS traits. It does not run a bespoke two-sample MR from raw summary statistics with your own instrument set — see Limitations.

Anchor tools

Workflow

Step 1 — Resolve trait labels (avoid silent misses)

EpiGraphDB matches GWAS trait labels exactly and case-sensitively. Always resolve free text first:

EpiGraphDB_search_opengwas {"query": "coronary heart disease"}
# → returns ids like 'ieu-a-7' and the exact label 'Coronary heart disease'

Use the returned exact label (or a sentence-case form) in the MR call. The MR tool now retries sentence-case variants and returns a metadata.note when it falls back or finds nothing — read that note; an empty mr_results with a note means "labels didn't match", NOT "no causal effect".

Step 2 — Run MR (exposure → outcome)

EpiGraphDB_get_mendelian_randomization {
  "exposure_trait": "LDL cholesterol",
  "outcome_trait":  "Coronary heart disease",
  "pval_threshold": 1e-5
}

Each row carries beta (causal effect estimate), se, pval, method, moescore, and the exposure/outcome IDs.

Step 3 — Interpret (see tables below)

Direction, magnitude, instrument quality, and method agreement.

Step 4 — Triangulate

1. Bidirectional MR (primary triangulation) — swap exposure and outcome to test reverse causation. A causal X→Y with no Y→X strengthens the claim; bidirectional signals suggest shared genetics or feedback. This is the reliable leg — lean on it. 2. Multiple methods — prefer pairs where IVW and a pleiotropy-robust method (MR-Egger, weighted median) agree in sign and significance. 3. Genetic correlation (secondary, often empty) — EpiGraphDB_get_genetic_correlations on the exposure. ⚠️ The /genetic-cor graph is sparse: it stores only strong edges (|rg| > 0.8), matches exact, case-sensitive labels distinct from OpenGWAS search labels, and ignores the pval_threshold argument. Common traits (e.g. 'Body mass index') return empty — that is a graph gap, not "no shared genetics." Read metadata.note; if empty, do NOT conclude absence — fall back to bidirectional MR. When it does return, high rg + significant MR = causal; high rg + null MR = shared etiology without a detectable causal path.

Step 5 — Actionable follow-up (optional)

EpiGraphDB_get_drugs_for_trait surfaces drugs whose target genes drive a causal risk factor — a genetics-anchored repurposing hypothesis.

Interpretation tables

Causal effect (beta)

Instrument quality (moescore, "Mixture of Experts")

Method (method)

Limitations (state these honestly)

• Two MR paths, different scopes. EpiGraphDB_get_mendelian_randomization returns pre-computed MR-EvE estimates for curated trait pairs — fast, but limited to pairs IEU already ran. For a pair that isn't covered, or for custom instruments (your own p-value/clumping thresholds), use OpenGWAS_get_mr_instruments (needs a free OPENGWAS_JWT) to assemble harmonized exposure+outcome SNP data, then compute the IVW/MR-Egger estimate yourself (e.g. IVW = Σ(βx·βy/σy²)/Σ(βx²/σy²)) or hand the mr_input to the TwoSampleMR R package. Advanced sensitivity analyses (MR-PRESSO, Steiger, leave-one-out) still need TwoSampleMR.
• Palindromic SNPs (A/T, C/G) are not strand-resolved by OpenGWAS_get_mr_instruments; review or drop ambiguous ones before trusting the estimate.
• Horizontal pleiotropy is the dominant threat and cannot be fully excluded from a single estimate. Method agreement reduces but does not eliminate it.
• Population. Most OpenGWAS instruments are European-ancestry; effects and LD differ across ancestries. Report this.
• Winner's curse / weak instruments bias toward the confounded observational estimate; lean on MOE and instrument F-statistics.
• Scale. A statistically significant causal effect may be clinically small. Report magnitude, not just the p-value.
• One GWAS ≠ truth. Replication across independent exposure and outcome GWAS strengthens any MR claim.

Reporting template

Causal question: Does [exposure] affect [outcome]?

MR estimate: beta = X (se Y, p = Z), method IVW, MOE score → [direction + magnitude, OR if binary].

Triangulation: bidirectional MR [reverse effect?]; genetic correlation rg = [value]; method agreement [yes/no].

Assumptions/caveats: instrument quality [MOE], pleiotropy [Egger intercept / method agreement], ancestry [population].

Verdict: [supported / not supported / inconclusive] causal effect, with the above caveats.