MVP {rMVP} | R Documentation

## MVP, Memory-efficient, Visualization-enhanced, Parallel-accelerated

### Description

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

### Usage

    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)

### Arguments

`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.

### Details

Build date: Aug 30, 2017 Last update: Dec 14, 2018

### Value

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

### Author(s)

Lilin Yin, Haohao Zhang, and Xiaolei Liu

### Examples

    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)

[Package rMVP version 0.99.15 Index]