calc.errorlod {qtl} | R Documentation |
Calculates a LOD score for each genotype, measuring the evidence for genotyping errors.
calc.errorlod(cross, error.prob=0.01, map.function=c("haldane","kosambi","c-f","morgan"))
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. |
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.
The input cross
object is returned with a component,
errorlod
, added to each component of cross$geno
. The
errorlod
component is a matrix of size (n.ind x n.mar). An
attribute "error.prob"
is set to the value of the corresponding
argument, for later reference.
Karl W Broman, kbroman@jhsph.edu
Lincoln, S. E. and Lander, E. S. (1992) Systematic detection of errors in genetic linkage data. Genomics 14, 604–610.
data(hyper) # run calc.genoprob, then calc.errorlod; use same error.prob hyper <- calc.genoprob(hyper,error.prob=0.01) hyper <- calc.errorlod(hyper,error.prob=0.01) # plot the error LOD scores; print those above a specified cutoff plot.errorlod(hyper) plot.errorlod(hyper,chr=1) top.errorlod(hyper,cutoff=4) # plot genotype data, flagging genotypes with error LOD > cutoff plot.geno(hyper, chr=1, ind=160:200, cutoff=7, min.sep=2) # Note that if markers are extremely tightly linked, # any recombination event will have a high error LOD plot.geno(hyper, chr=1, ind=101:118, min.sep=2) hyper$geno[[1]]$map