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

mqmpermutation {qtl} | R Documentation

## 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, 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

- 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: http://www.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)

[Package qtl version 1.41-6 Index]