mendelian-randomizationlisted

# Mendelian Randomization in R ## Overview Mendelian randomization (MR) methods for causal inference using genetic variants as instrumental variables. Covers instrument selection, two-sample MR, sensitivity analyses, pleiotropy assessment, multivariable MR, and advanced methods for robust causal inference. ## Instrument Selection ### Using TwoSampleMR ```r library(TwoSampleMR) # Extract instruments from GWAS database # IEU Open GWAS Project exposure_dat <- extract_instruments( outcomes = "ieu-a-2", # GWAS ID for exposure p1 = 5e-8, # Genome-wide significance clump = TRUE, # LD clumping r2 = 0.001, # LD threshold kb = 10000 # Clumping window (kb) ) # View extracted SNPs head(exposure_dat) # Manual instrument selection from summary statistics exposure_dat <- read_exposure_data( filename = "exposure_gwas.txt", sep = "\t", snp_col = "SNP", beta_col = "BETA", se_col = "SE", effect_allele_col = "A1", other_allele_col = "A2", pval_col = "P", eaf_col = "EAF" ) |> filter(pval.exposure < 5e-8) # Clump instruments exposure_dat <- clump_data(exposure_dat, clump_r2 = 0.001) ``` ### F-statistic Calculation ```r # F-statistic for instrument strength # Rule of thumb: F > 10 indicates strong instruments calculate_f_stat <- function(beta, se, n) { r2 <- (beta^2) / (beta^2 + se^2 * n) # Approximate R² k <- 1 # Number of instruments (per SNP) f_stat <- (r2 * (n - k - 1)) / ((1 - r2)