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

In rMVP: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool

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 = 1, verbose = 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
verbose	whether to print detail.

Value

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

Author(s)

Lilin Yin and Xiaolei Liu

Examples

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)

rMVP documentation built on Nov. 27, 2023, 5:09 p.m.