rMVP (version 0.99.14.1)

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, cpu = 2, priority = "speed",
  memo = "MVP.GLM", bar = TRUE)

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

cpu

number of cpus used for parallel computation

priority

'memory' or 'speed'

memo

a marker on output file name

bar

whether to show the progress bar

Value

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

Examples

# NOT RUN {
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 <- genotype[, idx]
print(dim(genotype))
glm <- MVP.GLM(phe=phenotype, geno=genotype)
str(glm)
# }