MVP.FarmCPU {rMVP} | R Documentation

## Perform GWAS using FarmCPU method

### Description

Date build: Febuary 24, 2013 Last update: May 25, 2017 Requirement: Y, GD, and CV should have same taxa order. GD and GM should have the same order on SNPs

### Usage

    MVP.FarmCPU(phe, geno, map, CV = NULL, P = NULL,
      method.sub = "reward", method.sub.final = "reward",
      method.bin = "static", bin.size = c(5e+05, 5e+06, 5e+07),
      bin.selection = seq(10, 100, 10), memo = "MVP.FarmCPU",
      Prior = NULL, ncpus = 2, bar = TRUE, maxLoop = 10,
      threshold.output = 0.01, converge = 1, iteration.output = FALSE,
      p.threshold = NA, QTN.threshold = 0.01, bound = NULL,
      verbose = TRUE)

### Arguments

`phe` | phenotype, n by t matrix, n is sample size, t is number of phenotypes
`geno` | genotype, m by n matrix, m is marker size, n is sample size. This is Pure Genotype Data Matrix(GD). THERE IS NO COLUMN FOR TAXA.
`map` | SNP map information, m by 3 matrix, m is marker size, the three columns are SNP_ID, Chr, and Pos
`CV` | covariates, n by c matrix, n is sample size, c is number of covariates
`P` | start p values for all SNPs
`method.sub` | method used in substitution process, five options: 'penalty', 'reward', 'mean', 'median', or 'onsite'
`method.sub.final` | method used in substitution process, five options: 'penalty', 'reward', 'mean', 'median', or 'onsite'
`method.bin` | method for selecting the most appropriate bins, two options: 'EMMA' or 'FaSTLMM'
`bin.size` | bin sizes for all iterations, a vector, the bin size is always from large to small
`bin.selection` | number of selected bins in each iteration, a vector
`memo` | a marker on output file name
`Prior` | prior information, four columns, which are SNP_ID, Chr, Pos, P-value
`ncpus` | number of threads used for parallele computation
`bar` | if TRUE, the progress bar will be drawn on the terminal
`maxLoop` | maximum number of iterations
`threshold.output` | only the GWAS results with p-values lower than threshold.output will be output
`converge` | a number, 0 to 1, if selected pseudo QTNs in the last and the second last iterations have a certain probality (the probability is converge) of overlap, the loop will stop
`iteration.output` | whether to output results of all iterations
`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
`bound` | maximum number of SNPs selected as pseudo QTNs in each iteration
`verbose` | whether to print detail.

### Value

a m by 4 results matrix, m is marker size, the four columns are SNP_ID, Chr, Pos, and p-value

### Author(s)

Xiaolei Liu and Zhiwu Zhang

### 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 <- genotype[, idx]
    print(dim(genotype))
    mapPath <- system.file("extdata", "07_other", "mvp.map", package = "rMVP")
    map <- read.table(mapPath , head = TRUE)

    farmcpu <- MVP.FarmCPU(phe=phenotype, geno=genotype, map=map, maxLoop=2)
    str(farmcpu)

[Package rMVP version 0.99.15 Index]