bio-causal-genomics-mendelian-randomization
Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-mendelian-randomization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: TwoSampleMR 0.5+, MendelianRandomization 0.9+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion("<pkg>")` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Mendelian Randomization **"Test whether my exposure causally affects this outcome using GWAS data"** → Use genetic variants as instrumental variables to estimate causal effects from GWAS summary statistics, applying IVW, MR-Egger, and weighted median methods for robust inference. - R: `TwoSampleMR::mr()` for multi-method causal estimation - R: `MendelianRandomization::mr_ivw()` for individual methods ## Core Concepts Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to estimate causal effects of exposures on outcomes. Valid instruments must satisfy three assumptions: 1. **Relevance** - The variant is associated with the exposure (F-statistic > 10) 2. **Independence** - The variant is not associated with confounders 3. **Exclusion restrict
- Version Compatibility
- Core Concepts
- TwoSampleMR Workflow
- OpenGWAS Authentication
- Interpreting Results
- Instrument Strength
- Bidirectional MR
- Visualization
- Power Calculation
- Related Skills
What does the bio-causal-genomics-mendelian-randomization skill do?
Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-mendelian-randomization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.