孟德爾隨機(jī)化, Heterogeneity異質(zhì)性, Q & I2

# 微信公眾號 CodeMed # 【 Heterogeneity異質(zhì)性 #? ? Cochran's Q test? & I2 】 library(TwoSampleMR) # 前期準(zhǔn)備 exp_data <- extract_instruments( ? outcomes = "ebi-a-GCST009541") out_data <- extract_outcome_data( ? snps = exp_data$SNP,? ? outcomes = "ieu-b-102")? dat <- TwoSampleMR::harmonise_data( ? exposure_dat = exp_data, ? outcome_dat = out_data) dat <- subset(dat,mr_keep) # 微信公眾號 CodeMed # 【底層】meta分析 單個SNP res_single1 <- mr_singlesnp(dat,all_method = c("mr_ivw")) # 計算I-squ library(metafor) res_single2 <- res_single1[grep("^rs",res_single1$SNP),] res_meta <-metafor::rma(yi=res_single2$b,? ?# 填入res的b, 下同 ? ? ? ? ? ? ? ? ? ? ? ? sei = res_single2$se, ? ? ? ? ? ? ? ? ? ? ? ? weights = 1/dat$se.outcome^2, ? ? ? ? ? ? ? ? ? ? ? ? data=res_single2, ? ? ? ? ? ? ? ? ? ? ? ? method = 'FE') res_meta # 計算 Cochran’sQ res_hete <- TwoSampleMR::mr_heterogeneity(dat) res_hete # # Isquare 計算 library(MendelianRandomization) MRInputObject <- MendelianRandomization::mr_input( ? bx = dat$beta.exposure, ? bxse = dat$se.exposure, ? by = dat$beta.outcome, ? byse = dat$se.outcome, ? snps = dat$SNP ) # MendelianRandomization::mr_ivw( ? object = MRInputObject,model = "fixed") # 微信公眾號 CodeMed
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