bio-workflows-causal-genomics-pipeline

Apply Mendelian randomization to GWAS summary statistics using IVW, MR-Egger, and weighted median methods, then validate results with MR-PRESSO, Egger intercept, and Steiger directionality tests to assess pleiotropy and bias.

Run colocalization and fine-mapping on GWAS summary statistics using coloc and susieR to pinpoint shared causal variants at loci of interest, generating robust evidence for shared genetic architecture.

You need R installed with key packages including TwoSampleMR, MR-PRESSO, coloc, susieR, and MendelianRandomization, plus access to full summary statistics to produce reproducible causal inference results.

Conduct mediation analysis for post-GWAS causal inference via multivariable MR to evaluate indirect pathways and identify mediators, applying a structured end-to-end workflow for common post-GWAS questions.

Instrument selection in a Mendelian randomization workflow identifies genetic variants strongly associated with the exposure from GWAS summary statistics, serving as proxies to estimate causal effects on outcomes.

Use triangulation for causal evidence when you need to integrate multiple methods like Mendelian randomization, colocalization, and sensitivity analyses to robustly confirm that an exposure causally affects an outcome.