gates {COMBAT} | R Documentation |
Several gene-based association tests methods are implemented.
gates(x, cor_G) vegas(x, cor_G, vegas.pct=c(0.1,0.2,0.3,0.4,1), max.simulation=1e6) simpleM(x, cor_G, pca_cut_perc=0.995)
x |
a vector of SNP-level P values. |
cor_G |
SNP-SNP correlation matrix. |
vegas.pct |
a numeric vector, specifying the fraction of the top SNPs to be used in the VEGAS method. |
max.simulation |
maximum number of simulations to be performed. Must be at least 1e6. |
pca_cut_perc |
cutoff for percentage of sum of eigen values. |
Function gates
implements the GATES method (Li et al 2011, American Journal of Human Genetics 88:283-293), vegas
implements VEGAS with different proportion tests (Liu et al 2010, American Journal of Human Genetics 87:139-145), and simpleM
is the simpleM method (Gao et al 2008, Genetic Epidemiology 32:361-369).
P value(s).
# read SNP P values file1 <- paste(path.package("COMBAT"),"extdata","SNP_info.txt.gz",sep="/") snp.info <- read.table(file1, header = TRUE, as.is=TRUE) snp.pvals <- as.matrix(snp.info[,2]) # read reference genotype file2 <- paste(path.package("COMBAT"),"extdata","SNP_ref.txt.gz",sep="/") snp.ref <- read.table(file2, header = TRUE) snp.ref <- as.matrix(snp.ref) #compute correlation among SNPs cor_G <- ld.Rsquare(snp.ref) #call gates (pval_gates <- gates(x=snp.pvals, cor_G=cor_G)) #call vegas (pval_vegas <- vegas(x=snp.pvals, cor_G=cor_G)) #call simpleM (pval_simpleM <- simpleM(x=snp.pvals, cor_G=cor_G))