R: Genome scan for trait with some undefined values

vbscan {qtl}

R Documentation

Genome scan for trait with some undefined values

Description

Performs a genome scan by interval mapping for a quantitative phenotype for which some individuals' phenotype is undefined (for example, the size of a lesion, where some individuals have no lesion). The undefined value must correspond to either the largest phenotype or the smallest phenotype.

Usage

vbscan(cross, chr, pheno.col=1, upper=FALSE,
       maxit=1000, tol=1e-8)

Arguments

`cross`	An object of class `cross`. See `read.cross` for details.
`chr`	Vector indicating the chromosomes for which LOD scores should be calculated.
`pheno.col`	Column number in the phenotype matrix which should be used as the phenotype.
`upper`	If true, the "undefined" phenotype is the maximum observed phenotype; otherwise, it is the smallest observed phenotype.
`maxit`	Maximum number of iterations in the EM algorithm; used only in interval mapping.
`tol`	Tolerance value for determining convergence in the EM algorithm; used only in interval mapping.

Details

The function requires that multipoint genotype probabilities are first calculated using calc.genoprob.

Describe the model here.

Individuals with missing phenotypes are dropped.

Value

A matrix of dimension [n.pos x (2*n.gen+6)]. The columns are chromosome number, position (in cM; female cM for the 4-way cross), LOD(p,mu), LOD(p), LOD(mu), estimates of the ps, estimates of the mus, and residual SD.

Author(s)

Karl W Broman, kbroman@jhsph.edu
http://biosun01.biostat.jhsph.edu/~kbroman/software/qtl.html

References

Boyartchuk VL, Broman KW, Mosher RE, D'Orazio SEF, Starnbach MN, Dietrich WF (2001) Multigenic control of Listeria monocytogenes susceptibility in mice. Nature Genetics 27:259-260

Broman KW, Boyartchuk VL, Dietrich WF (2000) Mapping time-to-death quantitative trait loci in a mouse cross with high survival rates. Technical Report MS00-04, Department of Biostatistics, Johns Hopkins University, Baltimore, MD

Examples

data(listeria)
listeria$pheno <- log10(listeria$pheno)
listeria <- calc.genoprob(listeria,step=1,off.end=0,err=0)
output <- vbscan(listeria,upper=TRUE)
plot(output)

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