R: Fetch significant markers after permutation analysis

mqmfind.marker {qtl} | R Documentation

## Fetch significant markers after permutation analysis

### Description

Fetch significant makers after permutation analysis. These markers can be used as cofactors for model selection in a forward stepwise approach.

### Usage

    mqmfind.marker(cross, mqmscan = NULL, perm = NULL, alpha = 0.05, verbose=FALSE)

### Arguments

`cross` | An object of class `cross`. See `read.cross` for details.
`mqmscan` | Results from either `scanone` or `mqmscan`
`perm` | a `scanoneperm` object
`alpha` | Threshold value, everything with significance < alpha is reported
`verbose` | Display more output on verbose=TRUE

### Value

returns a matrix with at each row a significant marker (determined from the `scanoneperm` object) and with columns: markername, chr and pos (cM)

### Author(s)

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

### See Also

- `mqmprocesspermutation` - Function called to convert results from an mqmpermutation into an scanoneperm object

- 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)

    # Set cofactors at each 3th marker
    cof <- mqmsetcofactors(multitrait,3)

    # impute missing genotypes
    multitrait <- fill.geno(multitrait)

    # log transform the 7th phenotype
    multitrait <- transformPheno(multitrait, 7)

    # Bootstrap 50 runs in batches of 10
    ## Not run: result <- mqmpermutation(multitrait,scanfunction=mqmscan,cofactors=cof,
                             pheno.col=7,n.perm=50,batchsize=10)

    ## End(Not run)

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

    # What LOD score is considered significant ?
    summary(f2perm)

    # Find markers with a significant QTL effect (First run is original phenotype data)
    marker <- mqmfind.marker(multitrait,result[[1]],f2perm)

    # Print it to the screen
    marker

[Package qtl version 1.41-6 Index]