qgg (version 1.1.2)

mtadj: Adjustment of marker effects using correlated trait information

Description

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"
)

Value

A matrix of adjusted marker effects for each trait.

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.

Author

Palle Duun Rohde and Peter Soerensen

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")