effectscan {qtl}

Plot estimated QTL effects across the whole genome

Description

This function is used to plot the estimated QTL effects along selected chromosomes. For a backcross, there will be only one line, representing the additive effect. For an intercross, there will be two lines, representing the additive and dominance effects.

Usage

effectscan(cross, pheno.col=1, chr, get.se=FALSE, draw=TRUE,
           gap=25, ylim, mtick=c("line","triangle"),
           add.legend=TRUE, alternate.chrid=FALSE, ...)

Arguments

cross

An object of class cross.

pheno.col

Column number in the phenotype matrix which to be drawn in the plot. One may also give a character string matching a phenotype name.

chr

Optional vector indicating the chromosomes to be drawn in the plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used.

get.se

If TRUE, estimated standard errors are calculated.

draw

If TRUE, draw the figure.

gap

Gap separating chromosomes (in cM).

ylim

Y-axis limits (optional).

mtick

Tick mark type for markers.

add.legend

If TRUE, add a legend.

alternate.chrid

If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished.

...

Passed to the function plot when it is called.

Details

The results of sim.geno are required for taking account of missing genotype information.

For a backcross, the additive effect is estimated as the difference between the phenotypic averages for heterozygotes and homozygotes.

For recombinant inbred lines, the additive effect is estimated as half the difference between the phenotypic averages for the two homozygotes.

For an intercross, the additive and dominance effects are estimated from linear regression on a and d with a = -1, 0, 1, for the AA, AB and BB genotypes, respectively, and d = 0, 1, 0, for the AA, AB and BB genotypes, respectively.

As usual, the X chromosome is a bit more complicated. We estimate separate additive effects for the two sexes, and for the two directions within females.

There is an internal function plot.effectscan that creates the actual plot by calling plot.scanone. In the case get.se=TRUE, colored regions indicate \pm 1 SE.

Value

The results are returned silently, as an object of class "effectscan", which is the same as the form returned by the function scanone, though with estimated effects where LOD scores might be. That is, it is a data frame with the first two columns being chromosome ID and position (in cM), and subsequent columns being estimated effects, and (if get.se=TRUE) standard errors.

Author(s)

Karl W. Broman, [e-mail]

References

Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387.

See Also

effectplot, plotPXG, sim.geno

Examples

data(fake.f2)

fake.f2 <- sim.geno(fake.f2, step=2.5, n.draws=16)

# allelic effect on whole genome
effectscan(fake.f2)

# on chromosome 13, include standard errors
effectscan(fake.f2, chr="13", mtick="triangle", get.se=TRUE)