mqmpermutation {qtl}R Documentation

Estimate QTL LOD score significance using permutations or simulations

Description

Two randomization approaches to obtain estimates of QTL significance:

Calculations can be parallelized using the SNOW package.

Usage

	mqmpermutation(cross, scanfunction=scanone, pheno.col=1,
	          multicore=TRUE, n.perm=10, batchsize=10, file="MQM_output.txt",
   	          n.cluster=1, method=c("permutation","simulation"),cofactors=NULL, plot=FALSE, verbose=FALSE, ...)

Arguments

.
cross An object of class cross. See read.cross for details.
scanfunction Function to use when mappingQTL's (either scanone,cim or mqm)
pheno.col Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers.
multicore Use multicore (if available)
n.perm Number of permutations to perform (DEFAULT=10, should be 1000, or higher, for publications)
batchsize Batch size. The entire set is split in jobs. Each job contains b.size number of traits per job
file Name of the intermediate output file used
n.cluster Number of child processes to split the job into
method What kind permutation should occur: permutation or simulation
cofactors cofactors, only used when scanfunction is mqm. List of cofactors to be analysed in the QTL model. To set cofactors use mqmautocofactors or mqmsetcofactors
plot If TRUE, make a plot
verbose If TRUE, print tracing information
... Parameters passed through to the scanone, cim or mqmscan functions

Details

Analysis of scanone, cim or mqmscan to scan for QTL in shuffled/randomized data. It is recommended to also install the snow library. The snow library allows calculations to run on multiple cores or even scale it up to an entire cluster, thus speeding up calculation.

Value

Returns a mqmmulti object. this object is a list of scanone objects that can be plotted using plot.scanone(result[[trait]])

Author(s)

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

References

See Also

Examples

data(multitrait)				#Use the multitrait dataset

multitrait <- calc.genoprob(multitrait)
result <- mqmpermutation(multitrait,pheno.col=7, n.perm=2, batchsize=2)

cof <- mqmautocofactors(multitrait,50)		#Set 50 cofactors
multitrait <- fill.geno(multitrait)
result <- mqmpermutation(multitrait,scanfunction=mqmscan,cofactors=cof,pheno.col=7, n.perm=2,batchsize=2,verbose=FALSE)
#EXTRA
f2perm <- mqmprocesspermutation(result)			#Create a permutation object
summary(f2perm)						#Get Significant LOD thresholds

[Package qtl version 1.21-2 Index]