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Parallelized QTL analysis using MQM on multiple phenotypes in a cross object (uses SNOW)
mqmscanall(cross, multicore=TRUE, n.clusters = 1,batchsize=10,cofactors=NULL, ...)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)
An object of class cross. See read.cross for details.
Use multiple cores (only if the package SNOW is available, otherwise this setting will be ignored)
Number of parallel processes to spawn, recommended is setting this lower than the number of cores in the computer
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, 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
Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman broman@wisc.edu
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.
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.
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
#Doing a multitrait analysis
data(multitrait)
multitrait <- subset(multitrait, chr=1:2, ind=!apply(multitrait$pheno, 1, function(a) any(is.na(a))))
multitrait$pheno <- multitrait$pheno[,1:3]
multitrait <- calc.genoprob(multitrait)
cof <- mqmsetcofactors(multitrait,3)
multitrait <- fill.geno(multitrait)
result <- mqmscanall(multitrait,cofactors=cof,batchsize=5)
mqmplot.multitrait(result,"lines")
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