mqmpermutation: Estimate QTL LOD score significance using permutations or...

In qtl: Tools for Analyzing QTL Experiments

Estimate QTL LOD score significance using permutations or simulations

Description

Two randomization approaches to obtain estimates of QTL significance:

• Random redistribution of traits (method='permutation')

• Random redistribution of simulated trait values (method='simulation')

Calculations can be parallelized using the SNOW package.

Usage

mqmpermutation(cross, scanfunction=scanone, pheno.col=1, multicore=TRUE,
               n.perm=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)
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 [e-mail]

References

• Bruno M. Tesson, Ritsert C. Jansen (2009) Chapter 3.7. Determining the significance threshold eQTL Analysis in Mice and Rats 1, 20–25

• Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971.

• 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

# Use the multitrait dataset
data(multitrait)



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

## Not run: #Set 50 cofactors
cof <- mqmautocofactors(multitrait,50)

## End(Not run)

multitrait <- fill.geno(multitrait)
result <- mqmpermutation(multitrait,scanfunction=mqmscan,cofactors=cof,
                         pheno.col=7, n.perm=2,batchsize=2,verbose=FALSE)

#Create a permutation object
f2perm <- mqmprocesspermutation(result)

#Get Significant LOD thresholds
summary(f2perm)

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