localize.qtl: Compute QTL physical positions from QTL genetic positions

In eqtl: Tools for analyzing eQTL experiments: A complementary to Karl Broman's 'qtl' package for genome-wide analysis

Description

Computes QTL physical positions from QTL genetic positions from an object of class peak and the marker physical positions.

Usage

localize.qtl( cross, peak, data.gmap, round )

Arguments

cross	An object of class cross. See 'qtl' package manual for read.cross function details.
peak	An object of class peak. See define.peak for details.
data.gmap	A data.frame with column names "Marker", "chr" and "PP" specifying the marker physical locations. Those one must be the same markers described in the related cross object. data.gmap\$Marker is a vector of character strings specifying the names of the markers. data.gmap\$chr is a vector of integers specifying the chromosomes on which the markers are localized. data.gmap\$PP is a vector of integers specifying the physical marker locations on the chromosomes in base pair.
round	An optional integer indicating the precision to be used for the physical position. The physical position being estimated, non integer nucleotidic position values could be obtained. See round function for details.

Details

Linearly computes the physical position from peak\$peak\_cM and the flanking marker locations:
A + B/C*D
A is the physical position of the first flanking marker. B and C are the genetic and the physical distances between the two flanking markers respectively. D is the genetic position of the qtl peak.

Value

The input peak object is returned with components added to components of names(peak\$trait\$chromosome) for each previously detected QTL:

peak.bp	is the physical location of the maximum LOD peak.
inf.bp	is the physical location of the SI lower bound.
sup.bp	is the physical location of the SI upper bound.

Author(s)

Hamid A. Khalili

See Also

read.cross,define.peak,calc.adef

Examples

data(seed10);

# Genome scan and QTL detection
out.em <- scanone( seed10, pheno.col=1:50, model='normal', method='hk');
out.peak <- define.peak(out.em, 'all');

# Additive effect computing
out.peak <- calc.adef(seed10,out.em,out.peak,round=3);

# Localizing peaks
data(BSpgmap);
out.peak <- localize.qtl( seed10, out.peak, BSpgmap, round=0);

# Peak features describing the QTLs affecting the 100th trait and
# localized on the chromosome 1
out.peak[[26]]$'4';

# Genetic and physical position of maximum LOD peaks affecting the 100th trait and
# localized on chromosome 1
out.peak[[26]]$'4'$peak.cM;
out.peak[[26]]$'4'$peak.bp;

# Genetic and physical position of QTLs' SI inferior bounds of the 100th trait and
# localized on chromosome 1
out.peak[[26]]$'4'$inf.cM;
out.peak[[26]]$'4'$inf.bp;

# Genetic and physical position of QTLs' SI superior bounds of the 100th trait and
# localized on chromosome 1
out.peak[[26]]$'4'$sup.cM;
out.peak[[26]]$'4'$sup.bp;

# idem for trait 'CATrck'
out.peak$CATrck$'4'$peak.cM;
out.peak$CATrck$'4'$peak.bp;
out.peak$CATrck$'4'$inf.cM;
out.peak$CATrck$'4'$inf.bp;
out.peak$CATrck$'4'$sup.cM;
out.peak$CATrck$'4'$sup.bp;

eqtl documentation built on May 2, 2019, 5:42 a.m.