The `mtadj` function uses selection index theory to determine the optimal weights across `n` traits. These weights are then used to adjust marker effects by `n` correlated traits. More details can be found [here](https://www.nature.com/articles/s41467-017-02769-6).
Usage
mtadj(
h2 = NULL,
rg = NULL,
stat = NULL,
b = NULL,
z = NULL,
n = NULL,
mtotal = NULL,
meff = 60000,
method = "ols",
statistics = "z"
)Arguments
- h2
A vector of heritability estimates.
- rg
An n-by-n matrix of genetic correlations.
- stat
A dataframe containing marker summary statistics.
- b
A matrix of marker effects.
- z
A matrix of z-scores.
- n
A vector indicating the sample size used to estimate marker effects for each trait.
- mtotal
Total number of markers.
- meff
Effective number of uncorrelated genomic segments (default = 60,000).
- method
Method to estimate marker effects. Can be "OLS" (ordinary least square, default) or "BLUP" (best linear unbiased prediction).
- statistics
Specifies which kind of statistics ("b" or "z") should be used in the analysis.
Examples
#bedfiles <- system.file("extdata", "sample_22.bed", package = "qgg")
#bimfiles <- system.file("extdata", "sample_22.bim", package = "qgg")
#famfiles <- system.file("extdata", "sample_22.fam", package = "qgg")
#Glist <- gprep(study="1000G", bedfiles=bedfiles, bimfiles=bimfiles,famfiles=famfiles)
#Glist <- gprep(Glist, task="sparseld", msize=200)
#
##Simulate data
#set.seed(23)
#
#W <- getG(Glist, chr=1, scale=TRUE)
#causal <- sample(1:ncol(W),50)
#set1 <- c(causal, sample(c(1:ncol(W))[-causal],10))
#set2 <- c(causal, sample(c(1:ncol(W))[-set1],10))
#
#b1 <- rnorm(length(set1))
#b2 <- rnorm(length(set2))
#y1 <- W[, set1]%*%b1 + rnorm(nrow(W))
#y2 <- W[, set2]%*%b2 + rnorm(nrow(W))
#
## Create model
#data1 <- data.frame(y = y1, mu = 1)
#data2 <- data.frame(y = y2, mu = 1)
#X1 <- model.matrix(y ~ 0 + mu, data = data1)
#X2 <- model.matrix(y ~ 0 + mu, data = data2)
#
## Linear model analyses and single marker association test
#maLM1 <- glma(y=y1, X=X1,W = W)
#maLM2 <- glma(y=y2,X=X2,W = W)
#
## Compute genetic parameters
#z1 <- maLM1[,"stat"]
#z2 <- maLM2[,"stat"]
#
#z <- cbind(z1=z1,z2=z2)
#
#h2 <- ldsc(Glist, z=z, n=c(500,500), what="h2")
#rg <- ldsc(Glist, z=z, n=c(500,500), what="rg")
#
## Adjust summary statistics using estimated genetic parameters
#b <- cbind(b1=maLM1[,"b"],b2=maLM2[,"b"])
#bm <- mtadj( h2=h2, rg=rg, b=b, n=c(500,500), method="ols")