calc.adef {eqtl}R Documentation

Compute the additive effect at each QTL marker

Description

Compute the additive effect at QTL marker by meaning the phenotypic value for each genotypic group.

Usage

calc.adef(cross, scanone, peak, round, ...)

Arguments

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.

Details

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.

Value

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

Note

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.

Author(s)

Hamid A. Khalili

References

Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889-890

See Also

effectplot,define.peak,read.cross,plot

Examples

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

[Package eqtl version 1.0 Index]