library(qtl). You may wish to include this in a.Firstfunction or.Rprofilefile.help.startfunction to start the
html version of the R help. In Windows, you may wish to useoptions(htmlhelp=TRUE)to get easy access to the html version
of the help files; this could be included in a.Firstfunction or.Rprofilefile.library(help=qtl)to get a list of the functions
in R/qtl.examplefunction to run examples
of the various functions in R/qtl.A difficult first step in the use of most data analysis software is the
import of data. With R/qtl, one may import data in several different
formats by use of the function read.cross. The
internal data structure used by R/qtl is rather complicated, and is
described in the help file for read.cross. We won't
discuss data import any further here, except to say that the
comma-delimited format ("csv") is recommended. If you have
trouble importing data, send an email to Karl Broman,
We consider the example data hyper, an experiment on
hypertension in the mouse, kindly provided
by Bev Paigen and Gary Churchill. Use the data
function to load the data.
data(hyper)
The hyper data set has class cross. The
function summary.cross gives some summary information
on the data, and checks the data for internal consistency. A number
of other utility functions are available; hopefully these are
self-explanatory.
summary(hyper)
nind(hyper)
nphe(hyper)
nchr(hyper)
nmar(hyper)
totmar(hyper)
The function plot.cross gives a graphical summary of
the data; it calls plot.missing (to plot a matrix
displaying missing genotypes) and plot.map (to plot
the genetic maps), and also displays histograms or barplots of the
phenotypes. The plot.missing function can plot
individuals ordered by their phenotypes; you can see that for most
markers, only individuals with extreme phenotypes were genotyped.
plot(hyper)
plot.missing(hyper)
plot.missing(hyper, reorder=TRUE)
plot.map(hyper)
Note that one marker (on chromosome 14) has no genotype data. The
function drop.nullmarkers removes such markers from
the data.
hyper <- drop.nullmarkers(hyper)
totmar(hyper)
The function est.rf estimates the recombination
fraction between each pair of markers, and calculates a LOD score for
the test of $r$ = 1/2. This is useful for identifying markers that
are placed on the wrong chromosome. Note that since, for these data,
many markers were typed only on recombinant individuals, the pairwise
recombination fractions show rather odd patterns.
hyper <- est.rf(hyper)
plot.rf(hyper)
plot.rf(hyper, chr=c(1,4))
To re-estimate the genetic map for an experimental cross, use the
function est.map. The function
plot.map, in addition to plotting a single map, can
plot the comparison of two genetic maps (as long as they are composed of
the same numbers of chromosomes and markers per chromosome). The
function replace.map map be used to replace the
genetic map in a cross with a new one.
newmap <- est.map(hyper, error.prob=0.01, trace=TRUE)
plot.map(hyper, newmap)
hyper <- replace.map(hyper, newmap)
Before doing QTL analyses, a number of intermediate calculations may
need to be performed. The function calc.genoprob
calculates conditional genotype probabilities given the multipoint
marker data. sim.geno simulates sequences of
genotypes from their joint distribution, given the observed marker data.
argmax.geno calculates the most likely sequence of
underlying genotypes, given the observed marker data.
These three functions return a modified version of the input cross, with the intermediate calculations included.
hyper <- calc.genoprob(hyper, step=2.5, error.prob=0.01)
hyper <- sim.geno(hyper, step=2.5, n.draws=64, error.prob=0.01)
hyper <- argmax.geno(hyper, error.prob=0.01)
The function calc.errorlod may be used to assist in
identifying possible genotyping errors; it calculates the error LOD
scores described by Lincoln and Lander (1992). It requires the results
of calc.genoprob, run with a matching value for the
assumed genotyping error probability, error.prob.
hyper <- calc.errorlod(hyper, error.prob=0.01)
plot.errorlod(hyper)
top.errorlod(hyper)
plot.errorlod(hyper, chr=c(4,11,16))
The function plot.geno may be used to inspect the
observed genotypes for a chromosome, with likely genotyping errors
flagged.
plot.geno(hyper, chr=16, ind=71:90, min.sep=4)
The function scanone performs a genome scan with a
single QTL model. By default, it performs standard interval mapping
(Lander and Botstein 1989): use of a normal model and the EM algorithm.
If one specifies method="hk", Haley-Knott regression is performed
(Haley and Knott 1992). These two methods require the results from
calc.genoprob.
out.em <- scanone(hyper)
out.hk <- scanone(hyper, method="hk")
If one specifies method="imp", a genome scan is performed by the
multiple imputation method of Sen and Churchill (2001). This method
requires the results from sim.geno.
out.imp <- scanone(hyper, method="imp")
The output of scanone is a data.frame with class
scanone. The function plot.scanone may be
used to plot the results, and may plot up to three sets of results
against each other, as long as they conform appropriately.
plot(out.em)
plot(out.hk, col="blue", add=TRUE)
plot(out.imp, col="red", add=TRUE)
plot(out.hk, out.imp, out.em, chr=c(1,4), lty=1, col=c("blue","red","black"))
The function summary.scanone may be used to list
information on the peak LOD for each chromosome for which the LOD
exceeds a specified threshold.
summary(out.em)
summary(out.em, 3)
summary(out.hk, 3)
summary(out.imp, 3)
One may also use scanone to perform a permutation
test to get a genome-wide LOD significance threshold. This will take
some time, so maybe now is a good time to go for a cup of coffee.
operm.hk <- scanone(hyper, method="hk", n.perm=100)
quantile(operm.hk, 0.95)
We should say at this point that the function
save.image will save your workspace to disk. You'll
wish you had used this if R crashes.
save.image()
The function scantwo performs a two-dimensional
genome scan with a two-QTL model. Methods "em", "hk" and
"imp" are all available. scantwo is
considerably slower than link[qtl]{scanone}, and can require a
great deal of memory. Thus, you may wish to create a version of
hyper for a more coarse grid.
hyper.coarse <- calc.genoprob(hyper, step=10, err=0.01)
hyper.coarse <- sim.geno(hyper, step=10, n.draws=64, err=0.01)
out2.hk <- scantwo(hyper.coarse, method="hk")
out2.em <- scantwo(hyper.coarse)
out2.imp <- scantwo(hyper.coarse, method="imp")
The output is an object with class scantwo. The function
plot.scantwo may be used to plot the results. The
upper triangle contains LOD scores for tests of epistasis, while the
lower triangle contains joint LOD scores.
plot(out2.hk)
plot(out2.em)
plot(out2.imp)
The function summary.scantwo lists the interesting
aspects of the output. One provides three LOD thresholds: for the joint
LOD, epistasis LOD, and conditional, single-QTL LOD scores. The locus
pairs giving the highest joint LOD for each pair of chromosomes are
inspected, and those whose LOD score exceed the joint LOD threshold and
for which either the interaction LOD exceeds its threshold or both the
conditional single-QTL LODs exceed their threshold, are printed.
summary(out2.em, c(8, 3, 3))
summary(out2.em, c(0, 1000, 4))
summary(out2.em, c(0, 4, 1000))
Permutation tests may also performed with scantwo;
it may take a few days of CPU time. The output is a matrix with two
columns: the maximum joint and epistasis LODs, across the
two-dimensional genome scan, for each simulation replicate.
operm2 <- scantwo(hyper.coarse, method="hk", n.perm=100)
apply(operm2, 2, quantile, 0.95)
hist(operm.hk,breaks=20)
Lander, E. S. and Botstein, D. (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185--199.
Sen, S. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371--387.