cim: Composite interval mapping

Description

Composite interval mapping by a scheme from QTL Cartographer: forward selection at the markers (here, with filled-in genotype data) to a fixed number, followed by interval mapping with the selected markers as covariates, dropping marker covariates if they are within some fixed window size of the location under test.

Usage

cim(cross, pheno.col=1, n.marcovar=3, window=10, method=c("em", "imp", "hk", "ehk"), imp.method=c("imp", "argmax"), error.prob=0.0001, map.function=c("haldane", "kosambi", "c-v", "morgan"), n.perm)

Arguments

cross

An object of class cross. See read.cross for details.

pheno.col

Column number in the phenotype matrix which should be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations.

n.marcovar

Number of marker covariates to use.

window

Window size, in cM.

method

Indicates whether to use the EM algorithm, imputation, Haley-Knott regression, or the extended Haley-Knott method.

imp.method

Method used to impute any missing marker genotype data.

error.prob

Genotyping error probability assumed when imputing the missing marker genotype data.

map.function

Map function used when imputing the missing marker genotype data.

n.perm

If specified, a permutation test is performed rather than an analysis of the observed data. This argument defines the number of permutation replicates.

Value

The function returns an object of the same form as the function scanone:If n.perm is missing, the function returns the scan results as a data.frame with three columns: chromosome, position, LOD score. Attributes indicate the names and positions of the chosen marker covariates.If n.perm > 0, the function results the results of a permutation test: a vector giving the genome-wide maximum LOD score in each of the permutations.

Details

We first use fill.geno to impute any missing marker genotype data, either via a simple random imputation or using the Viterbi algorithm.

We then perform forward selection to a fixed number of markers. These will be used (again, with any missing data filled in) as covariates in the subsequent genome scan.

References

Jansen, R. C. (1993) Interval mapping of multiple quantitative trait loci. Genetics, 135, 205--211.

Jansen, R. C. and Stam, P. (1994) High resolution of quantitative traits into multiple loci via interval mapping. Genetics, 136, 1447-1455. Zeng, Z. B. (1993) Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc. Natl. Acad. Sci. USA, 90, 10972--10976.

Zeng, Z. B. (1994) Precision mapping of quantitative trait loci. Genetics, 136, 1457--1468.

Examples

Run this code

data(hyper)
hyper <- calc.genoprob(hyper, step=2.5)


out <- scanone(hyper)
out.cim <- cim(hyper, n.marcovar=3)
plot(out, out.cim, chr=c(1,4,6,15), col=c("blue", "red"))

add.cim.covar(out.cim, chr=c(1,4,6,15))

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