Compute the additive effect at each QTL marker

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Description

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

Usage

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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

Uses 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 have previously performed 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 codeeffectplot function (using the parameter draw=TRUE). See 'qtl' package manual for codeeffectplot 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

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data(seed10);

# Genotype probabilities
## Not run: 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');
## End(Not run)

# 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);

# Additive effect of the QTLs affecting the 26th trait
# and localized on chromosome 1
out.peak[[26]]$'4'$additive.effect;

# Peak's features describing the QTLs affecting the 26th 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

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