rMVP (version 1.1.1)

MVP.GLM: To perform GWAS with GLM and MLM model and get the P value of SNPs

Description

Build date: Aug 30, 2016 Last update: May 25, 2017

Usage

MVP.GLM(phe, geno, CV = NULL, geno_ind_idx = NULL, cpu = 1, verbose = TRUE)

Value

m * 2 matrix, the first column is the SNP effect, the second column is the P values

Arguments

phe

phenotype, n * 2 matrix

geno

Genotype in numeric format, pure 0, 1, 2 matrix; m * n, m is marker size, n is population size

CV

Covariance, design matrix(n * x) for the fixed effects

geno_ind_idx

the index of effective genotyped individuals

cpu

number of cpus used for parallel computation

verbose

whether to print detail.

Author

Lilin Yin and Xiaolei Liu

Examples

# \donttest{
phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phenotype <- read.table(phePath, header=TRUE)
idx <- !is.na(phenotype[, 2])
phenotype <- phenotype[idx, ]
print(dim(phenotype))
genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
genotype <- deepcopy(genotype, cols=idx)
print(dim(genotype))

glm <- MVP.GLM(phe=phenotype, geno=genotype, cpu=1)
str(glm)
# }