cnvGWAS {CNVRanger} | R Documentation

## Run the CNV-GWAS

### Description

Wraps all the necessary functions to run a CNV-GWAS using the output of `setupCnvGWAS` function

(i) Produces the GDS file containing the genotype information (if produce.gds == TRUE), (ii) Produces the requested inputs for a PLINK analysis, (iii) run a CNV-GWAS analysis using linear model implemented in PLINK (http://zzz.bwh.harvard.edu/plink/gvar.shtml), and (iv) export a QQ-plot displaying the adjusted p-values. In this release only the p-value for the copy number is available (i.e. 'P(CNP)').

### Usage

    cnvGWAS(
      phen.info,
      n.cor = 1,
      min.sim = 0.95,
      freq.cn = 0.01,
      snp.matrix = FALSE,
      method.m.test = "fdr",
      lo.phe = 1,
      chr.code.name = NULL,
      genotype.nodes = "CNVGenotype",
      coding.translate = "all",
      path.files = NULL,
      list.of.files = NULL,
      produce.gds = TRUE,
      run.lrr = FALSE,
      assign.probe = "min.pvalue",
      correct.inflation = FALSE,
      both.up.down = FALSE,
      verbose = FALSE
    )

### Arguments

`phen.info` | Returned by `setupCnvGWAS`
`n.cor` | Number of cores to be used
`min.sim` | Minimum CNV genotype distribution similarity among subsequent probes. Default is 0.95 (i.e. 95%)
`freq.cn` | Minimum CNV frequency where 1 (i.e. 100%), or all samples deviating from diploid state. Default 0.01 (i.e. 1%)
`snp.matrix` | Only FALSE implemented - If TRUE B allele frequencies (BAF) would be used to reconstruct CNV-SNP genotypes
`method.m.test` | Correction for multiple tests to be used. FDR is default, see `p.adjust` for other methods.
`lo.phe` | The phenotype to be analyzed in the PhenInfo$phenotypesSam data-frame
`chr.code.name` | A data-frame with the integer name in the first column and the original name for each chromosome
`genotype.nodes` | Expression data type. Nodes with CNV genotypes to be produced in the gds file.
`coding.translate` | For 'CNVgenotypeSNPlike'. If NULL or unrecognized string use only biallelic CNVs. If 'all' code multiallelic CNVs as 0 for loss; 1 for 2n and 2 for gain.
`path.files` | Folder containing the input CNV files used for the CNV calling (i.e. one text file with 5 collumns for each sample). Columns should contain (i) probe name, (ii) Chromosome, (iii) Position, (iv) LRR, and (v) BAF.
`list.of.files` | Data-frame with two columns where the (i) is the file name with signals and (ii) is the correspondent name of the sample in the gds file
`produce.gds` | logical. If TRUE produce a new gds, if FALSE use gds previously created
`run.lrr` | If TRUE use LRR values instead absolute copy numbers in the association
`assign.probe` | ‘min.pvalue’ or ‘high.freq’ to represent the CNV segment
`correct.inflation` | logical. Estimate lambda from raw p-values and correct for genomic inflation. Use with argument `method.m.test` to generate strict p-values.
`both.up.down` | Check for CNV genotype similarity in both directions. Default is FALSE (i.e. only downstream)
`verbose` | Show progress in the analysis

### Value

The CNV segments and the representative probes and their respective p-value

### Author(s)

Vinicius Henrique da Silva <vinicius.dasilva@wur.nl>

### References

da Silva et al. (2016) Genome-wide detection of CNVs and their association with meat tenderness in Nelore cattle. PLoS One, 11(6):e0157711.

### See Also

`link{setupCnvGWAS}` to setup files needed for the CNV-GWAS.

### Examples

    # Load phenotype-CNV information
    data.dir <- system.file("extdata", package="CNVRanger")

    phen.loc <- file.path(data.dir, "Pheno.txt")
    cnv.out.loc <- file.path(data.dir, "CNVOut.txt")
    map.loc <- file.path(data.dir, "MapPenn.txt")

    phen.info <- setupCnvGWAS('Example', phen.loc, cnv.out.loc, map.loc)

    # Define chr correspondence to numeric, if necessary
    df <- '16 1A
    25 4A
    29 25LG1
    30 25LG2
    31 LGE22'

    chr.code.name <- read.table(text=df, header=FALSE)
    segs.pvalue.gr <- cnvGWAS(phen.info, chr.code.name=chr.code.name)

[Package CNVRanger version 1.3.6 Index]