meth cvd

meth_cvd.R

library(readxl)
a <- read_excel("~/Downloads/CVD_traits_summary.xlsx", skip=2)
names(a)[1] <- "Phenotype"

a1 <- a[,c(1,2:4)]
a2 <- a[,c(1,5:7)]
a3 <- a[,c(1,8:10)]
a4 <- a[,c(1,11:13)]
a5 <- a[,c(1,14:16)]
a6 <- a[,c(1,17:19)]

names(a1) <- names(a2) <- names(a3) <- names(a4) <- names(a5) <- names(a6) <- c("Phenotype", "Beta", "SE", "pval")
b <- rbind(a1, a2, a3, a4, a5, a6)

nom <- expand.grid(1:nrow(a), adjusted=c(TRUE, FALSE), group=c("Children", "Mothers", "Combined"))
b$adjusted <- nom$adjusted
b$group <- nom$group

# Convert the units to standard deviation units by dividing effects and standard errors by the standard deviation of the phenotype (e.g. mothers and children and both combined will have different SD values for each phenotype)

ggplot(subset(b, group != "both"), aes(x=group, y=Beta)) +
geom_hline(yintercept=0, linetype="dotted") +
geom_point(aes(colour=adjusted), size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(colour=adjusted, ymin=Beta - 1.96 * SE, ymax=Beta + 1.96 * SE), width=0, position=position_dodge(0.4)) +
facet_grid(Phenotype ~ .) +
scale_colour_brewer(type="qual") +
coord_flip()