localize.qtl {eqtl} | R Documentation |
Compute QTL physical positions from QTL genetic positions from an object of class code{peak} and the marker physical positions.
localize.qtl( cross, peak, data.gmap, round )
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$PP is a vector of integers specifying the physical marker locations on the chromosomes in base pair.
|
round |
An optional integer indicating the precision to be used for the physical position. The physical position being estimated, non integer nucleotidic position values could be obtained. See round function for details. |
Linearly compute the physical position from peak$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.
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. |
Hamid A. Khalili
read.cross
,define.peak
,calc.adef
data(seed10); # Genotype probabilities seed10 <- calc.genoprob( cross=seed10, step=2, off.end=0, error.prob=0, map.function='kosambi', stepwidth='fixed'); seed10 <- sim.geno( cross=seed10, step=2, off.end=0, error.prob=0, map.function='kosambi', stepwidth='fixed'); # Genome scan and QTL detection out.em <- scanone( seed10, pheno.col=1:50, model='normal', method='em'); 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;