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Copy pathMVmr_dpwLiu2019noUKBB_paroShugin2019.R
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MVmr_dpwLiu2019noUKBB_paroShugin2019.R
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#+ echo = TRUE, warning = FALSE, message = FALSE
#' ---------
#' title: "MVMR Cigarettes per day (Lui2019) and Peridontitis (SHRINE2019)
#' Author: "SE Baumeister"
#' Version: "16.02.2021"
#' output: html_document
#' date: "`r Sys.Date()`"
-------------------------------
Sys.setenv(LANG = "en")
.libPaths("c:/R/R-4.0.3/library/")
.libPaths()
#update.packages(ask=FALSE)
#remove.packages("tibble")
x<-c("xfun", "digest","rlang","tidyverse","devtools","scales","data.table","survey","knitr",
"flextable","officer","ggpubr","EValue","meta","survival", "acepack","R.utils","LDlinkR",
"MendelianRandomization","ashr")
#install.packages(x)
lapply(x, require, character.only = TRUE)
(.packages())
# install.packages("BiocManager")
# BiocManager::install("biomaRt")
# devtools::install_github("jdstorey/qvalue")
# devtools::install_github("slowkow/proxysnps")
# BiocManager::install("myvariant")
# devtools::install_github("MRCIEU/TwoSampleMR", upgrade ="always", force = TRUE)
# install.packages("http://cnsgenomics.com/software/gsmr/static/gsmr_1.0.9.tar.gz",repos=NULL,type="source")
# #devtools::install_github("jean997/cause")
# install.packages("remotes")
# remotes::install_github("gqi/MRMix", force=TRUE)
#devtools::install_github("WSpiller/MVMR", build_opts = c("--no-resave-data", "--no-manual"), build_vignettes = TRUE)
y<-c("biomaRt","qvalue","proxysnps","myvariant","TwoSampleMR","MRInstruments","MRPRESSO","gsmr","MRMix", "MVMR","cause")
lapply(y, require, character.only = TRUE)
(.packages())
packageVersion("TwoSampleMR")
sessionInfo()
# Load MR functions
source("c:/R_fct/mr_fcts.R")
ls()
setwd("c:/arbeit/Publikationen/mr_smoking_alc_paro/data/dpwLui2019noUKBB_paroShugin2019_MV/")
mydir <- getwd()
#--------------------
# 1.0) Exposure data: Drinks per week, Lui 2019, PMID 30643251,
#--------------------
dpw<-fread("c:/gwas_summary/alc/Liu2019/DrinksPerWeek.WithoutUKB.txt.gz") %>%
format_data(. , type="outcome", chr_col="CHROM", phenotype_col="exposure", snp_col="RSID",
beta_col="BETA", se_col="SE", eaf_col="AF",
effect_allele_col="ALT",
other_allele_col="REF", pval="PVALUE") %>%
mutate(outcome="dpw", id.outcome="dpw",Phenotype="dpw") %>%
dplyr::rename(beta=beta.outcome,se=se.outcome,
effect_allele=effect_allele.outcome,
other_allele=other_allele.outcome,
eaf=eaf.outcome,
pval=pval.outcome) %>%
dplyr::select(SNP,outcome,Phenotype,id.outcome,beta,se,pval,effect_allele,other_allele,eaf)
nrow(dpw)
head(dpw)
#----------------------------------------------
# Education Lee 2019
#----------------------------------------------
edu<-fread("c:/gwas_summary/educ/GWAS_EA_excl23andMe.txt") %>%
format_data(. , type="outcome", chr_col="CHR", phenotype_col="exposure", snp_col="MarkerName",
beta_col="Beta", se_col="SE", eaf_col="EAF",
effect_allele_col="A1",
other_allele_col="A2", pval="Pval") %>%
mutate(outcome="edu", id.outcome="edu",Phenotype="edu") %>%
dplyr::rename(beta=beta.outcome,se=se.outcome,
effect_allele=effect_allele.outcome,
other_allele=other_allele.outcome,
eaf=eaf.outcome,
pval=pval.outcome) %>%
dplyr::select(SNP,outcome,Phenotype,id.outcome,beta,se,pval,effect_allele,other_allele,eaf)
edu
colnames(edu)
class(edu$beta)
#----------------------------------------------
# BMI Pulit 2019
#----------------------------------------------
load("c:/gwas_summary/bmi/pulit2019/bmi.Rdata")
bmi<-bmi %>%
format_data(. , type="outcome", chr_col="CHR", phenotype_col="exposure", snp_col="SNP",
beta_col="BETA", se_col="SE", eaf_col="Freq_Tested_Allele",
effect_allele_col="Tested_Allele",
other_allele_col="Other_Allele", pval="P") %>%
mutate(outcome="bmi", id.outcome="bmi",Phenotype="bmi") %>%
dplyr::rename(beta=beta.outcome,se=se.outcome,
effect_allele=effect_allele.outcome,
other_allele=other_allele.outcome,
eaf=eaf.outcome,
pval=pval.outcome) %>%
dplyr::select(SNP,outcome,Phenotype,id.outcome,beta,se,pval,effect_allele,other_allele,eaf)
bmi
colnames(bmi)
class(bmi$se)
exposure_dat <- mvmr_extract_exposures_local(exposure_data_list=list(dpw,edu,bmi),
pval_threshold = 5e-06,
clump_r2 = 0.1)
head(exposure_dat)
nrow(exposure_dat)
#rm(list=c("cpd","edu","bmi2"))
#--------------------
# 1.0) Outcome data: Paro Shungin2019
#--------------------
pos.rsid<-fread("c:/gwas_summary/1000G-MAR2012-B37-ALL_HG19.annotation-UCSC_Function-uniqueIDs.txt.gz",
sep="auto",header="auto") %>%
dplyr::mutate(SNP=RSID,CHR=as.numeric(Chr),POS=as.numeric(Pos)) %>%
dplyr::select(SNP,CHR,POS) %>%
dplyr::arrange(.,POS) %>%
as_tibble()
head(pos.rsid)
mean(pos.rsid$POS)
nrow(pos.rsid)
paro<-fread("c:/gwas_summary/periodontitis/Shungin2019NatureComms/EUR_perio_incl_HCHSSOL.txt",
fill=TRUE) %>%
separate(.,MarkerName,sep=":",c("CHR","POS")) %>%
dplyr::mutate(CHR=as.numeric(CHR),POS=as.numeric(POS))
head(paro)
paro2<- right_join(paro,pos.rsid, by=c("POS","CHR")) %>%
dplyr::distinct(SNP,.keep_all=TRUE) %>%
dplyr::mutate(Allele1=toupper(Allele1), Allele2=toupper(Allele2),outcome="paro",rsid=SNP,pval=`P-value`) %>%
dplyr::mutate(chr.outcome=CHR,
effect_allele.outcome=Allele1,
other_allele.outcome=Allele2,
beta.outcome=Effect,
se.outcome=StdErr,
pval.outcome=`P-value`,
samplesize.outcome=N,
ncase.outcome=n_cases,
ncontrol.outcome=n_controls,
outcome=outcome,
id.outcome=outcome) %>%
as_tibble()
rm(pos.rsid)
rm(paro)
table(exposure_dat$SNP %in% paro2$SNP)
fwrite(paro2, "paro2.csv", row.names=TRUE, quote=TRUE)
outcome_dat <- read_outcome_data(
snps = exposure_dat$SNP,
filename="paro2.csv",
sep=",",
phenotype_col="outcome",
snp_col = "SNP",
beta_col = "beta.outcome",
se_col="se.outcome",
effect_allele_col = "effect_allele.outcome",
other_allele_col= "other_allele.outcome",
eaf_col = "eaf.outcome",
pval_col = "pval.outcome"
)
#----------------------------------------------
# 1.2) Harmonise the exposure and outcome data
#----------------------------------------------
table(exposure_dat$SNP %in% outcome_dat$SNP)
dat <- mv_harmonise_data(exposure_dat, outcome_dat,harmonise_strictness = 3)
#-----------------
# 1.3) Perform MR
#-----------------
res <- mv_multiple(dat)
res<-res %>%
as.data.frame() %>%
dplyr::rename(b=result.b,se=result.se,pval=result.pval,
exposure=result.exposure,outcome=result.outcome) %>%
mutate(OR=round(exp(b),2),pval=round(pval,4)) %>%
filter(exposure=="dpw")
res$CI<-paste("(",round(exp(res$b-1.96*res$se),3),";",round(exp(res$b+1.96*res$se),3),")",sep="")
res<-res %>%
dplyr::select(exposure,outcome,OR,CI,pval)
res
mvmr_dpw_paro<-res %>% as.data.frame()
save(mvmr_dpw_paro,file="mvmr_dpw_paro.RData")
mvmr_dpw_paro<-flextable(mvmr_dpw_paro) %>%
theme_booktabs() %>%
autofit()
print(mvmr_dpw_paro)
mvmr_dpw_paro <- read_docx() %>%
body_add_flextable(value = mvmr_dpw_paro)
fileout3 <- tempfile(fileext = ".docx")
fileout3 <- "mvmr_dpw_paro.docx"
print(mvmr_dpw_paro, target = fileout3)
# # # Remove created outcome datasets
delfiles <- dir(path=mydir ,pattern="*.csv")
file.remove(file.path(mydir, delfiles))