qtl (version 0.85-4)

scanone: Genome scan with single QTL model

Description

Uses interval mapping, ANOVA, Haley-Knott regression, or imputation to perform a genome scan, calculating LOD scores under a single QTL model. Currently we don't allow covariates, but that will be added eventually.

Usage

scanone(cross, chr, pheno.col=1, method=c("im","anova","hk","imp"),
        start=NULL,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, Haley-Knott regression or imputation.

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.pos x (n.gen+4)]. The columns are chromosome number, position (in cM; female cM for the 4-way cross), LOD score, genotype-specific average phenotypes, and residual SD.

Details

The interval mapping and Haley-Knott regression methods require that multipoint genotype probabilities are first calculated using calc.genoprob. The imputation method uses the results of sim.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 (individuals with missing genotypes are discarded). Haley-Knott regression uses the regression of phenotypes on multipoint genotype probabilities. The imputation method uses the pseudomarker algorithm described by Sen and Churchill (2001).

Individuals with missing phenotypes are dropped.

References

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.

S Sen, GA Churchill (2001) A statistical framework for quantitative trait mapping. Genetics 159:371-387.

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

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

Examples

data(fake.bc)
fake.bc$pheno[,2] <- log(fake.bc$pheno[,2])
out.anova <- scanone(fake.bc,pheno.col=2,method="anova")
fake.bc <- calc.genoprob(fake.bc,step=2,off.end=5)
out.im <- scanone(fake.bc,pheno.col=2,method="im")
out.hk <- scanone(fake.bc,pheno.col=2,method="hk")
plot(out.hk,out.anova,lty=1,col=c("red","blue"),chr=c(1,9,11))
plot(out.im,add=TRUE,lty=1,col="black",chr=c(1,9,11))
summary(out.im,3)