Does it include tools for detecting horizontal pleiotropy?

Absolutely. It covers the MR-Egger intercept test, Cochran’s Q test for heterogeneity, and MR-PRESSO for identifying and correcting for outlier-led pleiotropy.

How does this skill handle weak instrument bias?

The skill provides automated workflows for calculating F-statistics and performing clumping to ensure instruments meet the standard threshold of F > 10.

Can I perform Multivariable Mendelian Randomization (MVMR) with this?

Yes, it includes specific implementation patterns for MVMR to analyze multiple exposures simultaneously using both TwoSampleMR and the MendelianRandomization packages.

Which R packages does this skill support for MR analysis?

It supports industry standards including TwoSampleMR, MendelianRandomization, MRPRESSO, RadialMR, and specialized methods like CAUSE and MR-RAPS.

Mendelian Randomization in R

Name: Mendelian Randomization in R
Author: choxos

bychoxos

Ciencia de Datos y ML

Performs robust causal inference using genetic variants through a comprehensive suite of Mendelian Randomization (MR) methods and sensitivity analyses.

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This skill equips Claude with specialized knowledge for conducting Mendelian Randomization (MR) studies within the R ecosystem. It provides standardized implementation patterns for instrument selection using GWAS databases, primary MR methods like IVW and MR-Egger, and rigorous sensitivity testing including pleiotropy assessment and MR-PRESSO. Whether performing two-sample MR, multivariable MR, or advanced causal analysis using methods like CAUSE, this skill ensures adherence to best practices and STROBE-MR reporting guidelines for high-quality epidemiological research.

Características Principales

Support for TwoSampleMR and MendelianRandomization package workflows.
Advanced sensitivity analyses including MR-PRESSO outlier detection and Steiger filtering.
Complex multivariable and bidirectional MR patterns for investigating interrelated exposures.
0 GitHub stars
Publication-ready visualizations including scatter, forest, funnel, and radial plots.
Automated instrument selection with LD clumping and F-statistic calculation for instrument strength.

Casos de Uso

Validating observational epidemiological findings against potential confounding factors using genetic instruments.
Determining causal relationships between clinical biomarkers and disease outcomes using GWAS summary statistics.
Identifying pleiotropic effects and influential outliers in genetic data to improve the robustness of causal estimates.

Acerca de

Características Principales

Support for TwoSampleMR and MendelianRandomization package workflows.
Advanced sensitivity analyses including MR-PRESSO outlier detection and Steiger filtering.
Complex multivariable and bidirectional MR patterns for investigating interrelated exposures.
0 GitHub stars
Publication-ready visualizations including scatter, forest, funnel, and radial plots.
Automated instrument selection with LD clumping and F-statistic calculation for instrument strength.

Casos de Uso

Validating observational epidemiological findings against potential confounding factors using genetic instruments.
Determining causal relationships between clinical biomarkers and disease outcomes using GWAS summary statistics.
Identifying pleiotropic effects and influential outliers in genetic data to improve the robustness of causal estimates.