| scanone {qtl} | R Documentation |
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.
scanone(cross, chr, pheno.col=1, method=c("im","anova","hk","imp"),
start=NULL,maxit=1000, tol=1e-8)
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. |
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.
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.
Karl W Broman, kbroman@jhsph.edu
http://biosun01.biostat.jhsph.edu/~kbroman/software/qtl.html
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.
plot.scanone, summary.scanone,
scanone.perm, calc.genoprob
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)