mqmscanall: Parallelized MQM on multiple phenotypes in a cross object

In qtl: Tools for Analyzing QTL Experiments

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 [e-mail]

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")

qtl documentation built on Sept. 11, 2024, 5:43 p.m.