R: Parallelized MQM on multiple phenotypes in a cross object

mqmscanall {qtl} | R Documentation

## Parallelized MQM on multiple phenotypes in a cross object

### Description

Parallelized QTL analysis using MQM on multiple phenotypes in a cross object (uses SNOW)

### Usage

        mqmscanall(cross, multicore=TRUE, n.clusters = 1,batchsize=10,cofactors=NULL, ...)

### Arguments

`cross` | An object of class `cross`. See `read.cross` for details.
`multicore` | Use multiple cores (only if the package SNOW is available, otherwise this setting will be ignored)
`n.clusters` | Number of parallel processes to spawn, recommended is setting this lower than the number of cores in the computer
`batchsize` | Batch size. The entire set is split in jobs to reduce memory load per core. Each job contains batchsize number of traits per job.
`cofactors` | cofactors, only used when scanfunction is mqmscan. List of cofactors to be analysed in the QTL model. To set cofactors use `mqmautocofactors` or `mqmsetcofactors`

.

`...` | Parameters passed through to the `mqmscan` function used in scanning for QTLs

### Details

Uses `mqmscan` to scan for QTL's for each phenotype in the cross object. It is recomended that the package SNOW is installed before using this function on large numbers of phenotypes.

### Value

Returns a MQMmulti object. This object is a list of `scanone` objects that can be plotted using `plot.scanone(result[[trait]])` or using `mqmplot.multitrait(result)`

### Author(s)

Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman broman@wisc.edu

### References

- Rossini, A., Tierney, L., and Li, N. (2003), Simple parallel statistical computing. R. UW Biostatistics working paper series University of Washington. 193

- Tierney, L., Rossini, A., Li, N., and Sevcikova, H. (2004), The snow Package: Simple Network of Workstations. Version 0.2-1.

### 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

    #Doing a multitrait analysis
    data(multitrait)

    multitrait <- calc.genoprob(multitrait)
    cof <- mqmsetcofactors(multitrait,3)
    multitrait <- fill.geno(multitrait)
    result <- mqmscanall(multitrait,cofactors=cof,batchsize=5)
    mqmplot.multitrait(result,"lines")

[Package qtl version 1.46-2 Index]