R: Circular genome plot for MQM

mqmplot.circle {qtl} | R Documentation

## 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 danny.arends@gmail.com

### 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)

[Package qtl version 1.41-6 Index]