两样本MR及其敏感性分析R语言code

一、MR代码

#运行MR

library(TwoSampleMR)  

#取暴露SNP

ANG<- extract_instruments(outcomes='ebi-a-GCST007518',p1=5e-08,clump=TRUE, r2=0.01,kb=100,access_token= NULL)                                              

ANF<- extract_instruments(outcomes='ebi-a-GCST007518',p1=5e-08,clump=FALSE)

exp_dat <-clump_data(ANF,clump_r2=0.001,clump_kb=10000)

dim(ANG)      

#导出SNP

install.packages(c("readxl","writexl"))

library(writexl)

write_xlsx(ANG, "ALCO.xlsx")

#提取结局SNP

  t2d_out <- extract_outcome_data(

    snps=ANG$SNP,

    outcomes='ukb-b-19953',

    proxies = FALSE,

    maf_threshold = 0.01,

    access_token = NULL

  )

 

#协同

  Mydata <- harmonise_data(

  exposure_dat=ANG,

  outcome_dat=t2d_out,

  action= 2

)

#write_xlsx(Mydata, "Mydata.xlsx")

 

res <- mr(Mydata)

res

write_xlsx(res, "结果.xlsx")

#18种计算方法

mr(Mydata, method_list=c("mr_ivw_fe","mr_egger_regression"))

#生成OR值

generate_odds_ratios(res)

 

##二、敏感性分析代码

#水平多效性

mr_pleiotropy_test(Mydata)

#异质性

mr_heterogeneity(Mydata, method_list=c("mr_egger_regression", "mr_ivw"))

#留一法

single <- mr_leaveoneout(Mydata)

mr_leaveoneout_plot(single)

library(writexl)

write_xlsx(single, "single.xlsx")

#散点图

mr_scatter_plot(res,Mydata)

#森林图

pleio <- res_single <- mr_singlesnp(Mydata)

res_single <- mr_singlesnp(Mydata)

mr_forest_plot(res_single)

write_xlsx(pleio, "pleio.xlsx")

#漏斗图

mr_funnel_plot(res_single)