qtl (version 1.39-5)

mqmplot.circle: Circular genome plot for MQM

Description

Circular genome plot - shows QTL locations and relations.

Usage

mqmplot.circle(cross,result,highlight=0,spacing=25, interactstrength=2, axis.legend=TRUE, col.legend=FALSE, verbose=FALSE, transparency=FALSE)

Arguments

cross

An object of class cross with optionally phenotype locations. See read.cross for details on reading in cross objects, and optionally addloctocross for adding phenotype locations.

result

An object of class mqmmulti or scanone. See mqmscanall scanone for details.

highlight

With a mqmmulti object, highlight this phenotype (value between one and the number of results in the mqmmultiobject)

interactstrength

When highlighting a trait, consider interactions significant they have a change of more than interactstrength*SEs. A higher value will show less interactions. However the interactions reported at higher interactstrength values will generaty be more reliable.

spacing

User defined spacing between chromosomes in cM

axis.legend

When set to FALSE, suppresses the legends. (defaults to plotting legends besides the axis.

col.legend

With a mqmmulti object, plots a legend for the non-highlighed version

transparency

Use transparency when drawing the plots (defaults to no transparency)

verbose

Be verbose

Value

Plotting routine, no return

Details

Depending on the input of the result being either scanone or mqmmulti a different plot is drawn. If model information is present from mqmscan (by setting cofactors) This will be highlighted in red (see example). If phenotypes have genetic locations (e.g. eQTL) they will be plotted on the genome otherwise phenotypes will be plotted in the middle of the circle (with a small offset) Locations can be added by using the addloctocross function.

See Also

• The MQM tutorial: http://www.rqtl.org/tutorials/MQM-tour.pdf
• MQM - MQM description and references
• mqmscan - Main MQM single trait analysis
• mqmscanall - Parallellized traits analysis
• mqmaugment - Augmentation routine for estimating missing data
• mqmautocofactors - Set cofactors using marker density
• mqmsetcofactors - Set cofactors at fixed locations
• mqmpermutation - Estimate significance levels
• scanone - Single QTL scanning

Examples

data(multitrait)

data(locations)


multifilled <- fill.geno(multitrait)                    # impute missing genotypes
multicof <- mqmsetcofactors(multitrait,10)              # create cofactors
multiloc <- addloctocross(multifilled,locations)        # add phenotype information to cross
multires <- mqmscanall(multifilled,cofactors=multicof)  # run mqmscan for all phenotypes

#Basic mqmmulti, color = trait, round circle = significant
mqmplot.circle(multifilled,multires)

#mqmmulti with locations of traits in multiloc
mqmplot.circle(multiloc,multires)

#mqmmulti with highlighting
mqmplot.circle(multitrait,multires,highlight=3)

#mqmmulti with locations of traits in multiloc and highlighting
mqmplot.circle(multiloc,multires,highlight=3)