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tooluniverse-mendelian-randomization

Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS...

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# 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

| Assumption | Statement | How it fails | Check |
|---|---|---|---|
| **Relevance** | Instrument is robustly associated with the exposure | Weak instruments (low F-stat) → bias toward the confounded observational estimate | MOE score; instruments selected at GWAS significance |
| **Independence** | Instrument shares no common cause with the outcome | Population stratification, assortative mating | Ancestry-matched GWAS; report population |