rMVP (version 1.1.1)

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,
  eigenK = NULL,
  CV = NULL,
  geno_ind_idx = NULL,
  REML = NULL,
  cpu = 1,
  vc.method = c("BRENT", "EMMA", "HE"),
  verbose = TRUE
)

Value

results: a 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, m * n, m is marker size, n is population size

K

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

eigenK

list of eigen Kinship

CV

covariates

geno_ind_idx

the index of effective genotyped individuals

REML

a list that contains ve and vg

cpu

number of cpus used for parallel computation

vc.method

the methods for estimating variance component("emma" or "he" or "brent")

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))
K <- MVP.K.VanRaden(genotype, cpu=1)

mlm <- MVP.MLM(phe=phenotype, geno=genotype, K=K, cpu=1)
str(mlm)
# }