mendelian-randomization
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How to install mendelian-randomization?
gptomics/bioskills/mendelian-randomization
$npx -y skills add GPTomics/bioSkills --skill mendelian-randomizationEstimate 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, MendelianRandomizati
SKILL.md
| 1 | ## Version Compatibility |
| 2 | |
| 3 | 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. |
| 4 | |
| 5 | Before using code patterns, verify installed versions match. If versions differ: |
| 6 | - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters |
| 7 | - CLI (plink, GCTA-GSMR): `<tool> --version` then `<tool> --help` |
| 8 | |
| 9 | 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. |
| 10 | |
| 11 | # Mendelian Randomization |
| 12 | |
| 13 | **"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. |
| 14 | |
| 15 | - R: `TwoSampleMR::mr()` orchestrates IVW + Egger + weighted median + weighted mode in one call |
| 16 | - 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`) |
| 17 | - R: `MRPRESSO::mr_presso()` global / outlier / distortion tests |
| 18 | - R: `cause::cause()` correlated horizontal pleiotropy mixture |
| 19 | - R: `MVMR::strength_mvmr() + MVMR::ivw_mvmr()` multivariable conditional-F + IVW |
| 20 | |
| 21 | ## Statistical Model Taxonomy |
| 22 | |
| 23 | | Method | Pleiotropy assumption | Min instruments | Strength | Fails when | |
| 24 | |--------|------------------------|-----------------|----------|------------| |
| 25 | | IVW (fixed) | All IVs valid | 2 | Most efficient under no pleiotropy | Any directional or balanced pleiotropy inflates Type-I | |
| 26 | | IVW (random effects) | Balanced + InSIDE | 3 | Standard primary; absorbs heterogeneity into wider SE | Directional pleiotropy biases the point estimate | |
| 27 | | MR-Egger | Directional pleiotropy + InSIDE | 10+ for power | Detects + corrects directional pleiotropy via intercept (Bowden 2015 IJE 44:512) | NOME violated (`I^2_GX < 0.9`); SIMEX correction required; underpowered <10 SNPs | |
| 28 | | Weighted median | Up to 50% invalid IVs | 3 | Robust to a minority of bad instruments (Bowden 2016 Genet Epidemiol 40:304) | >50% invalid IVs | |
| 29 | | Weighted mode | Zero modal pleiotropy (ZEMPA) | 3 | Robust if the modal estimate is unbiased (Hartwig 2017 IJE 46:1985) | Bimodal pleiotropy; small numbers | |
| 30 | | MR-RAPS | Balanced pleiotropy + weak instruments | 10+ | Profile-score robust to weak-IV + balanced horizontal pleiotropy (Zhao 2020 Ann Stat 48:1742) | Strong directional pleiotropy; CRAN-archived 2025-03-01 | |
| 31 | | CAUSE | Correlated horizontal pleiotropy (CHP) | 100+ sig SNPs | Explicit shared-factor mixture; protects against CHP-driven false positives (Morrison 2020 Nat Genet 52:740) | Sparse polygenic exposures; <100 sig SNPs | |
| 32 | | GSMR + HEIDI-outlier | Outlier removal under InSIDE | 10+ | Alternative outlier detection; integrates with LD reference (Zhu 2018 Nat Commun 9:224) | Requires individual-level LD; HEIDI conservative | |
| 33 | | MR-PRESSO | Outlier-driven horizontal pleiotropy | 4+ | Global / outlier / distortion three-step (Verbanck 2018 Nat Genet 50:693) | Blind to CHP; computationally heavy at large NbDistribution | |
| 34 | | MVMR (IVW) | Conditional independence after measured pleiotropy | 1+ per exposure | Accounts for measured horizontal pleiotropy via multivariable regression (Sanderson 2019 IJE 48:713) | Conditional F < 10 on any exposure | |
| 35 | | MR-Clust | Heterogeneous causal effects (multiple mechanisms) | 30+ | Clusters SNPs by their causal-effect estimate (Foley 2021 Bioinformatics 37:531) | Single causal mechanism; small instrument |
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