MVP {rMVP} | R Documentation |
Object 1: To perform GWAS using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU model Object 2: To calculate kinship among individuals using Varaden method Object 3: Estimate variance components using EMMA, FaST-LMM, and HE regression Object 4: Generate high-quality figures
MVP(phe, geno, map, K = NULL, nPC.GLM = NULL, nPC.MLM = NULL, nPC.FarmCPU = NULL, CV.GLM = NULL, CV.MLM = NULL, CV.FarmCPU = NULL, REML = NULL, priority = "speed", ncpus = detectCores(logical = FALSE), vc.method = c("BRENT", "EMMA", "HE"), method = c("GLM", "MLM", "FarmCPU"), p.threshold = NA, QTN.threshold = 0.01, method.bin = "static", bin.size = c(5e+05, 5e+06, 5e+07), bin.selection = seq(10, 100, 10), maxLoop = 10, permutation.threshold = FALSE, permutation.rep = 100, bar = TRUE, memo = "MVP", outpath = getwd(), col = c("dodgerblue4", "olivedrab4", "violetred", "darkgoldenrod1", "purple4"), file.output = TRUE, file.type = "jpg", dpi = 300, threshold = 0.05, verbose = TRUE)
phe |
phenotype, n * 2 matrix, n is sample size |
geno |
Genotype in bigmatrix format; m * n, m is marker size, n is sample size |
map |
SNP map information, SNP name, Chr, Pos |
K |
Kinship, Covariance matrix(n * n) for random effects, must be positive semi-definite |
nPC.GLM |
number of PCs added as fixed effects in GLM |
nPC.MLM |
number of PCs added as fixed effects in MLM |
nPC.FarmCPU |
number of PCs added as fixed effects in FarmCPU |
CV.GLM |
covariates added in GLM |
CV.MLM |
covariates added in MLM |
CV.FarmCPU |
covariates added in FarmCPU |
REML |
a list contains ve and vg |
priority |
speed or memory |
ncpus |
number of cpus used for parallel |
vc.method |
methods for estimating variance component("EMMA" or "HE" or "BRENT") |
method |
the GWAS model, "GLM", "MLM", and "FarmCPU", models can be selected simutaneously, i.e. c("GLM", "MLM", "FarmCPU") |
p.threshold |
if all p values generated in the first iteration are bigger than p.threshold, FarmCPU stops |
QTN.threshold |
in second and later iterations, only SNPs with lower p-values than QTN.threshold have chances to be selected as pseudo QTNs |
method.bin |
EMMA or FaSTLMM |
bin.size |
window size in genome |
bin.selection |
a vector, how many windows selected |
maxLoop |
maximum number of iterations |
permutation.threshold |
if use a permutation cutoff or not (bonferroni cutoff) |
permutation.rep |
number of permutation replicates |
bar |
if TRUE, the progress bar will be drawn on the terminal |
memo |
Character. A text marker on output files |
outpath |
Effective only when file.output = TRUE, determines the path of the output file |
col |
for color of points in each chromosome on manhattan plot |
file.output |
whether to output files or not |
file.type |
figure formats, "jpg", "tiff" |
dpi |
resolution for output figures |
threshold |
a cutoff line on manhattan plot, 0.05/marker size |
verbose |
whether to print detail. |
Build date: Aug 30, 2017 Last update: Dec 14, 2018
a m * 2 matrix, the first column is the SNP effect, the second column is the P values Output: MVP.return$map - SNP map information, SNP name, Chr, Pos Output: MVP.return$glm.results - p-values obtained by GLM method Output: MVP.return$mlm.results - p-values obtained by MLM method Output: MVP.return$farmcpu.results - p-values obtained by FarmCPU method
Lilin Yin, Haohao Zhang, and Xiaolei Liu
phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP") phenotype <- read.table(phePath, header=TRUE) print(dim(phenotype)) genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP") genotype <- attach.big.matrix(genoPath) print(dim(genotype)) mapPath <- system.file("extdata", "07_other", "mvp.map", package = "rMVP") map <- read.table(mapPath , head = TRUE) opts <- options(rMVP.OutputLog2File = FALSE) mvp <- MVP(phe=phenotype, geno=genotype, map=map, maxLoop=3, method=c("GLM", "MLM", "FarmCPU"), file.output=FALSE, ncpus=1) str(mvp) options(opts)