calc.errorlod {qtl}R Documentation

Identify likely genotyping errors

Description

Calculates a LOD score for each genotype, measuring the evidence for genotyping errors.

Usage

calc.errorlod(cross, error.prob=0.01,
              map.function=c("haldane","kosambi","c-f","morgan"))

Arguments

cross An object of class cross. See read.cross for details.
error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype)
map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions.

Details

Uses the results of the function calc.genoprob (conditional genotype probabilities given multipoint marker data), and then calculates, for each individual at each marker, a LOD score measuring the strength of evidence for a genotyping error, as described by Lincoln and Lander (1992).

If results of calc.genoprob are not contained in the input cross object, it is called with arguments step = off.end = 0. If it had been run, but with a different value of error.prob then specified in the argument, it is called again with the specified value, and using the available "step" and "off.end" attributes, and the only genotype probabilities are replaced.

Note that values below 3 are generally not interesting. Also note that if markers are extremely tightly linked, recombination events can give large error LOD scores. The error LOD scores should not be trusted blindly, but should be viewed as a tool for identifying genotypes to study further.

Use top.errorlod to print all genotypes with error LOD scores above a specified threshold, plot.errorlod to plot the error LOD scores for specified chromosomes, and plot.geno to view the observed genotype data with likely errors flagged.

Value

The input cross object is returned with a component, errorlod, added to each component of cross$geno. The errorlod component is a matrix of size (n.ind x n.mar). An attribute "error.prob" is set to the value of the corresponding argument, for later reference.

Author(s)

Karl W Broman, kbroman@jhsph.edu

References

Lincoln, S. E. and Lander, E. S. (1992) Systematic detection of errors in genetic linkage data. Genomics 14, 604–610.

See Also

plot.errorlod, top.errorlod

Examples

data(hyper)

# run calc.genoprob, then calc.errorlod; use same error.prob
hyper <- calc.genoprob(hyper,error.prob=0.01)
hyper <- calc.errorlod(hyper,error.prob=0.01)

# plot the error LOD scores; print those above a specified cutoff 
plot.errorlod(hyper)
plot.errorlod(hyper,chr=1)
top.errorlod(hyper,cutoff=4)

# plot genotype data, flagging genotypes with error LOD > cutoff
plot.geno(hyper, chr=1, ind=160:200, cutoff=7, min.sep=2)

# Note that if markers are extremely tightly linked,
#     any recombination event will have a high error LOD
plot.geno(hyper, chr=1, ind=101:118, min.sep=2)
hyper$geno[[1]]$map

[Package qtl version 0.98-57 Index]