qtl (version 0.92-3)

plot.info: Plot the proportion of missing genotype information

Description

Plot a measure of the proportion of missing information in the genotype data.

Usage

plot.info(x, chr, method=c("both","entropy","variance"), ...)

Arguments

x

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

chr

Vector specifying the chromosomes to plot.

method

Indicates whether to plot the entropy version of the information, the variance version, or both.

...

Passed to plot.scanone.

Value

• A data.frame with class scanone and columns the chromosome IDs and cM positions followed by the entropy and/or variance version of the missing information.

Details

The missing information is calculated using the multipoint genotype probabilities calculated with calc.genoprob.

The entropy version of the missing information: for a single individual at a single genomic position, we measure the missing information as $H = \sum_g p_g \log p_g / \log n$, where $p_g$ is the probability of the genotype $g$, and $n$ is the number of possible genotypes, defining $0 \log 0 = 0$. This takes values between 0 and 1, assuming the value 1 when the genotypes (given the marker data) are equally likely and 0 when the genotypes are completely determined. We calculate the missing information at a particular position as the average of $H$ across individuals. For an intercross, we don't scale by $\log n$ but by the entropy in the case of genotype probabilities (1/4, 1/2, 1/4). The variance version of the missing information: we calculate the average, across individuals, of the variance of the genotype distribution (conditional on the observed marker data) at a particular locus, and scale by the maximum such variance.

Calculations are done in C (for the sake of speed in the presence of little thought about programming efficiency) and the plot is created by a call to plot.scanone.

Note that summary.scanone may be used to display the maximum missing information on each chromosome.

See Also

plot.scanone, plot.missing

Examples

data(hyper)
hyper <- calc.genoprob(hyper, step=2.5, off.end=5)
plot.info(hyper,chr=c(1,4,6,7,15))

# save the results and view maximum missing info on each chr
info <- plot.info(hyper)
summary(info)