qtl: mqmpermutation – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

qtl

mqmpermutation

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 broman@wisc.edu

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.

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

Tools for Analyzing QTL Experiments

v1.48-1

GPL-3

Authors

Karl W Broman <broman@wisc.edu> and Hao Wu, with ideas from Gary Churchill and Saunak Sen and contributions from Danny Arends, Robert Corty, Timothee Flutre, Ritsert Jansen, Pjotr Prins, Lars Ronnegard, Rohan Shah, Laura Shannon, Quoc Tran, Aaron Wolen, Brian Yandell, and R Core Team

Initial release

2021-03-24