eqtl (version 1.1-7)

peak.2.array: Build a simple array from a peak object

Description

Build a simple array from a peak object.

Usage

peak.2.array(peak)

Arguments

peak

An object of class peak. See define.peak for function details.

Value

Return an object of class array.peak which is a data frame whith columns:

trait

The name of the studied traits.

chr

The number of the chromosome.

mname.peak

The peak (pseudo-)marker name when a QTL was detected. if no QTL was detected.

lod

The peak LOD score when a QTL was detected. if no QTL was detected.

peak.cM

The genetic position of the peak in centiMorgan(cM) when QTL was detected. if no QTL was detected.

mname.inf

The (pseudo-)marker name corresponding to the inferior SI bound when a QTL was detected. if no QTL was detected.

inf.cM

The genetic position of the inferior SI bound in centiMorgan(cM) when a QTL was detected. if no QTL was detected.

mname.sup

The (pseudo-)marker names corresponding to the superior SI bound when a QTL was detected. if no QTL was detected.

sup.cM

The genetic position of the superior SI bound in centiMorgan(cM) if a QTL was detected. if no QTL was detected.

si.quality

The subjective quality if the support interval. See ‘define.peak’ for details.

According the methods used to create the peak object, the following columns could be persent:

additive.effect

The additive effects of the QTL. if no QTL has been detected.

peak.bp

The physical position of the maximum LOD peak. if no QTL was detected.

inf.bp

The physical position of the SI lower bound. if no QTL was detected.

sup.bp

The physical position of the SI upper bound. if no QTL was detected.

type

The estimated type of the eQTLs ( trans or cis for cis- and trans- eQTL respectively). if no QTL was detected or in case of non nuclear expression trait.

Details

Useful for a genome-wide eQTL mapping. Formats the results as a simple array which allows to use all of classical R functions (graphical, statistical, summaries, ...) and permits the results to be manipulated in the simplest way. All expression traits are represented and those which are not affected by any QTL, contain the empty data in each column.

See Also

scanone,read.cross

Examples

data(seed10);

# Defining QTLs for all traits
out.em <- scanone( seed10, pheno.col=1:50, model='normal', method='hk');
out.peak <- define.peak( out.em, 'all',graph=TRUE,save.pict=TRUE);

## Not run: out.array <- peak.2.array(out.peak);
# # Computing additive effect
# out.peak <- calc.adef(seed10,out.em,out.peak);
# # Localizing peak
# data(BSpgmap);
# out.peak <- localize.qtl(seed10,out.peak,BSpgmap);
# ## End(Not run)

out.array <- peak.2.array(out.peak);