qtl (version 0.85-4)

scanone.perm: Permutation test for genome scan with single QTL model

Description

Performs a permutation test of interval mapping, ANOVA or Haley-Knott regression in a genome scan under a single QTL model. Currently we don't allow covariates, but that will be added eventually.

Usage

scanone.perm(cross, chr, pheno.col=1, method=c("im","anova","hk"),
             start=NULL,n.perm=1000, 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.

method

Indicates whether to use interval mapping, analysis of variance or Haley-Knott regression.

n.perm

Number of permutations to be performed.

start

If NULL, use the usual starting values; if length 1, use random initial weights for EM; otherwise, this should be a vector of length N+1 (where N is the number of possible genotypes for the cross), giving the initial values for EM.

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.perm x (n.chr+1)]. The columns are the maximum LOD score across the whole genome and across each respective chromosome, for each of the permutation replicates.

Details

The interval mapping and Haley-Knott regression methods require that multipoint genotype probabilities are first calculated using calc.genoprob. The analysis of variance method uses reconstructed genotypes calculated using argmax.geno.

Interval mapping uses the full maximum likelihood method of Lander and Botstein (1992). Analysis of variance (anova) is simply anova using the genotypes at marker locations reconstructed by the Viterbi algorithm. Haley-Knott regression uses the regression of phenotypes on multipoint genotype probabilities.

Individuals with missing phenotypes are dropped.

References

GA Churchill, RW Doerge (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963-971.

CS Haley, SA Knott (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315-324.

ES Lander, D Botstein (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185-199.

M Soller, T Brody, A Genizi (1976) On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. Theor Appl Genet 47:35-39.

See Also

scanone, plot.scanone

Examples

data(fake.f2)
fake.f2 <- calc.genoprob(fake.f2,step=1,off.end=5)
output1 <- scanone(fake.f2,method="im")
permo1 <- scanone.perm(fake.f2,method="im",n.perm=1000)
<testonly>permo1 <- scanone.perm(fake.f2,method="im",n.perm=3)</testonly>

data(fake.bc)
fake.bc <- argmax.geno(fake.bc)
output2 <- scanone(fake.bc,chr=c(1,9),method="anova")
permo2 <- scanone.perm(fake.bc,chr=c(1,9),method="anova",n.perm=1000)
<testonly>permo2 <- scanone.perm(fake.bc,chr=c(1,9),method="anova",n.perm=3)</testonly>
threshold <- quantile(permo2[,1],0.95)