pleiotropy-detection
bygptomics· 59 skills
Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.
How to install pleiotropy-detection?
gptomics/bioskills/pleiotropy-detection
$npx -y skills add gptomics/bioskills --skill pleiotropy-detectionInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/gptomics/bioskills" --skill "gptomics/bioskills/pleiotropy-detection"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/gptomics/bioskills" that are relevant to the current task. Run `npx skills add "https://github.com/gptomics/bioskills"` and select the relevant skills, then follow their instructions.
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