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Run the CNV-GWAS

Source: R/pheno_assoc.R
cnvGWAS.Rd

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 a linear model (i.e. lm function), 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

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.

Author

Vinicius Henrique da Silva

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)