mqmplotcircle: Circular genome plot for MQM

In qtl: Tools for Analyzing QTL Experiments

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

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.

Value

Plotting routine, no return

Author(s)

Danny Arends [e-mail]

See Also

• The MQM tutorial: https://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)

qtl documentation built on Nov. 28, 2023, 1:09 a.m.