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

In XiaoleiLiuBio/MVP: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool

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

Description

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

Usage

MVP.MLM(
  phe,
  geno,
  K = NULL,
  eigenK = NULL,
  CV = NULL,
  ind_idx = NULL,
  mrk_idx = NULL,
  mrk_bycol = TRUE,
  REML = NULL,
  maxLine = 5000,
  cpu = 1,
  vc.method = c("BRENT", "EMMA", "HE"),
  verbose = TRUE
)

Arguments

phe	phenotype, n * 2 matrix
geno	genotype, either m by n or n by m is supportable, 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
ind_idx	the index of effective genotyped individuals
mrk_idx	the index of effective markers used in analysis
mrk_bycol	whether the markers are stored by columns in genotype (i.e. M is a n by m matrix)
REML	a list that contains ve and vg
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
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.

Value

results: a 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, rows=idx)
print(dim(genotype))
K <- MVP.K.VanRaden(genotype, cpu=1)

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

XiaoleiLiuBio/MVP documentation built on Jan. 3, 2025, 5:59 a.m.