In CreRecombinase/RColumbo: R Package to accompany the Columbo method

Supplementary Table 2 was pulled from the paper, and the two tables were exported as CSVs. Starting with all the GWAS hits:

library(readr)
whole_gwasf <- "/media/nwknoblauch/Data/gwas_data/iibdgc-trans-ancestry-summary-stats/EUR.IBD.gwas.assoc.gz"
whole_gwasd <- read_delim(whole_gwasf,delim="\t",col_names=T)
head(whole_gwasd)

Next we'll read in the significant hits

library(dplyr)
sig_gwasf <- "/media/nwknoblauch/Data/gwas_data/iibdgc-trans-ancestry-summary-stats/Top_rsids.csv"
nov_gwasf <- "/media/nwknoblauch/Data/gwas_data/iibdgc-trans-ancestry-summary-stats/Novel_rsid.csv"
sig_gwas <- read_delim(sig_gwasf,delim=",",col_names=c("SNP")) %>% mutate(isNovel=F)
nov_gwas <- read_delim(nov_gwasf,delim=",",col_names=c("SNP")) %>% mutate(isNovel=T)
sub_gwas <- bind_rows(sig_gwas,nov_gwas)
significant_data <- inner_join(whole_gwasd,sub_gwas,by=c("SNP"))
significant_data <- mutate(significant_data,betahat=log(OR))
write.table(significant_data,"~/Dropbox/RColumbo/analyses/eqtl_gwas_sample/sub_EUR_IBD_gwas_data.tsv",col.names = T,row.names = F,quote=F,sep="\t")