rMVP (version 0.99.14.1)

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

Description

Build date: Aug 30, 2016 Last update: Aug 30, 2016

Usage

MVP.MLM(phe, geno, K = NULL, CV = NULL, REML = NULL,
  priority = "speed", cpu = 1, bar = TRUE, vc.method = "EMMA",
  maxLine = 1000, file.output = TRUE, memo = "MVP")

Arguments

phe

phenotype, n * 2 matrix

geno

genotype, m * n, m is marker size, n is population size

K

Kinship, Covariance matrix(n * n) for random effects; must be positive semi-definite

CV

covariates

REML

a list that contains ve and vg

priority

speed or memory

cpu

number of cpus used for parallel computation

bar

whether to show the progress bar

vc.method

the methods for estimating variance component("emma" or "gemma")

maxLine

when the priority is 'memory', users can change this parameter to limit the memory

file.output

whether to output files or not

memo

a marker on output file name

Value

results: a 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))
K <- MVP.K.VanRaden(genotype)
mlm <- MVP.MLM(phe=phenotype, geno=genotype, K=K)
str(mlm)
# }