qtl (version 0.85-4)

vbscan: 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.

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.

Details

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

Describe the model here.

Individuals with missing phenotypes are dropped.

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

See Also

plot.scanone, summary.scanone, vbscan.perm, calc.genoprob, scanone

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)