calc.adef {eqtl} | R Documentation |
Compute the additive effect at QTL marker by meaning the phenotypic value for each genotypic group.
calc.adef(cross, scanone, peak, round, ...)
cross |
An object of class cross . See 'qtl' package manual for read.cross function details. |
scanone |
An object of class scanone . See 'qtl' package manual for read.cross function details. |
peak |
An object of class peak . See define.peak function for details. |
round |
An optional integer indicating the precision to be used for the additive effect value. See round function for details. |
... |
Additional arguments passed to the functions plot and effectplot when it is called. |
Use Karl Broman's effectplot
function to mean the phenotype for each genotypic group defined at the QTL marker. The additive effect is computed as the difference between the phenotypical means of the two genotypic groups (homozygous). The parental reference allele is allele 2.
By default, allele 1 is encoded as A and allele 2 as B, therefore the additive effect is mean(B)-mean(A) where mean(A) is the phenotypical mean of genotypic group A and mean(B) is the phenotypical mean of the genotypic group B.
The input peak
object is returned with component, adef
, added to components of peak$trait$chromosome
for each previously detected QTLs.
additive.effect |
The additive effect value at the QTL marker |
It is necessary to previously perform the sim.geno
function. It is not recommended to plot the allelic contribution by using the function calc.adef
. It is preferable to use directly Karl Broman's effect.plot
function (using the parameter draw=TRUE
). See 'qtl' package manual for effectplot
function details.
Hamid A. Khalili
Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889-890
effectplot
,define.peak
,read.cross
,plot
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); # Additive effect of the QTLs affecting the 100th trait and localized on chromosome 1 out.peak[[26]]$'4'$additive.effect; # Peak's features describing the QTLs affecting the 100th trait and localized on chromosome 1 out.peak[[26]]$'4'; # idem for the trait 'CATrck' out.peak$CATrck out.peak$CATrck$'4' out.peak$CATrck$'4'$additive.effect