mr: Mendelian randomization analysis

In gap: Genetic Analysis Package

Mendelian randomization analysis

Description

Mendelian randomization analysis

Usage

mr(data, X, Y, alpha = 0.05, other_plots = FALSE)

Arguments

data	Data to be used.
X	Exposure.
Y	Outcome.
alpha	type I error rate for confidence intervals.
other_plots	To add funnel and forest plots.

Details

The function initially intends to rework on GSMR outputs, but it would be appropriate for general use.

Value

The result and plots.

Examples

library(ggplot2)
library(gap)
mrdat <- '
 rs188743906  0.6804   0.1104  0.00177 0.01660        NA        NA
   rs2289779 -0.0788   0.0134  0.00104 0.00261 -0.007543 0.0092258
 rs117804300 -0.2281   0.0390 -0.00392 0.00855  0.109372 0.0362219
   rs7033492 -0.0968   0.0147 -0.00585 0.00269  0.022793 0.0119903
  rs10793962  0.2098   0.0212  0.00378 0.00536 -0.014567 0.0138196
    rs635634 -0.2885   0.0153 -0.02040 0.00334  0.077157 0.0117123
    rs176690 -0.0973   0.0142  0.00293 0.00306 -0.000007 0.0107781
 rs147278971 -0.2336   0.0378 -0.01240 0.00792  0.079873 0.0397491
  rs11562629  0.1155   0.0181  0.00960 0.00378 -0.010040 0.0151460
'
v <- c("SNP", "b.LIF.R", "SE.LIF.R", "b.FEV1", "SE.FEV1", "b.CAD", "SE.CAD")
mrdat <- setNames(as.data.frame(scan(file=textConnection(mrdat),
                                     what=list("",0,0,0,0,0,0))), v)
knitr::kable(mrdat,caption="Table 2. LIF.R and CAD/FEV1")
res <- mr(mrdat, "LIF.R", c("CAD","FEV1"), other_plots=TRUE)
s <- res$r[-1,]
colnames(s) <- res$r[1,]
r <- matrix(as.numeric(s[,-1]),nrow(s),dimnames=list(res$r[-1,1],res$r[1,-1]))
p <- sapply(c("IVW","EGGER","WM","PWM"), function(x)
     format(2*pnorm(-abs(r[,paste0("b",x)]/r[,paste0("seb",x)])),digits=3,scientific=TRUE))
rp <- t(data.frame(round(r,3),p))
knitr::kable(rp,align="r",caption="Table 3. LIFR variant rs635634 and CAD/FEV1")

gap documentation built on Sept. 11, 2024, 5:36 p.m.