calc.errorlod | 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"),
version=c("new","old"))
cross |
An object of class |
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. |
version |
Specifies whether to use the original version of this function or the current (preferred) version. |
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).
In the latest version, evidence for a genotype being in
error is considered assuming that all other genotypes (for that
individual, on that chromosome) are correct. The argument
version
allows one to specify whether this new version is used,
or whether the original (old) version of the calculation is
performed.
Note that values below 4 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 deserving further study.
Use top.errorlod
to print all genotypes with error
LOD scores above a specified threshold,
plotErrorlod
to plot the error LOD scores for
specified chromosomes, and plotGeno
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, broman@wisc.edu
Lincoln, S. E. and Lander, E. S. (1992) Systematic detection of errors in genetic linkage data. Genomics 14, 604–610.
plotErrorlod
,
top.errorlod
, cleanGeno
data(hyper)
hyper <- calc.errorlod(hyper,error.prob=0.01)
# print those above a specified cutoff
top.errorlod(hyper, cutoff=4)
# plot genotype data, flagging genotypes with error LOD > cutoff
plotGeno(hyper, chr=1, ind=160:200, cutoff=7, min.sep=2)
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