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tooluniverse-mendelian-randomization

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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 association lookups (use the GWAS skills) or fitting your own instruments from raw summary statistics.

skills/tooluniverse-mendelian-randomization/SKILL.md

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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 |