![]() ![]() Ī histogram of the results (the 1000 genome-wide maximum LOD scores) is obtained as follows: plot(operm) ![]() operm <- scanone(sug, method="hk", n.perm=1000) # Doing permutation in batch mode. It’s quickest to use Haley-Knott regression. To perform a permutation test, to get a genome-wide significance threshold or genome-scan-adjusted p-values, we use scanone just as before, but with an additional argument, n.perm, indicating the number of permutation replicates. It’s perhaps more informative to plot the differences: plot(out.hk - out.em, ylim=c(-0.3, 0.3), ylab="LOD(HK)-LOD(EM)") plot(out.em, out.hk, col=c("blue", "red"))Īlternatively, we could do the following (figure not included, for brevity): plot(out.em, col="blue") We may plot the two sets of LOD curves together in a single call to plot. We can do the genome scan via Haley-Knott regression by calling scanone with the argument method="hk". summary(out.em, threshold=3) # chr pos lod The argument step is the density of the grid (in cM), and defines the density of later QTL analyses. This is done at the markers and at a grid along the chromosomes. We first calculate the QTL genotype probabilities, given the observed marker data, via the function calc.genoprob. Let’s now proceed to QTL mapping via a single-QTL model. The last two “phenotypes” are sex (with 1 corresponding to males) and mouse ID. The following plots are histograms or bar plots for the six phenotypes. The next plot shows the genetic map of the typed markers. The plot in the upper-left shows the pattern of missing genotype data, with black pixels corresponding to missing genotypes. Use plot() to get a summary plot of the data. There are 6 phenotypes, and genotype data at 93 markers across the 19 autosomes. We see that this is an intercross with 163 individuals. (This also performs a variety of checks of the integrity of the data.) summary(sug) # F2 intercross Use summary() to get a quick summary of the data. The object sug has “class” "cross", and so calls to summary and plot are actually sent to the functions summary.cross and plot.cross. R has a certain amount of “object oriented” facilities, so that calls to functions like summary and plot are interpreted appropriately for the object considered. The data object sug is complex it contains the genotype data, phenotype data and genetic map. We will focus on the blood pressure phenotype, will consider just the 163 individuals with genotype data and, for simplicity, will focus on the autosomes. There are four phenotypes: blood pressure, heart rate, body weight, and heart weight. The data are from an intercross between BALB/cJ and CBA/CaJ only male offspring were considered. Read.cross loads the data from the file and formats it into a special cross object, which is then assigned to sug via the assignment operator <. ![]() genotypes indicates the codes used for the genotypes alleles indicates single-character codes to be used in plots and such. "sug.csv" is the name of the file, which we import directly from the R/qtl website. The function read.cross is for importing data into R/qtl. Genotypes=c("CC", "CB", "BB"), alleles=c("C", "B")) # -Read the following data: We will consider data from Sugiyama et al., Physiol Genomics 10:5–12, 2002. ![]()
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