R: Estimate FDR for multiple trait QTL analysis

mqmscanfdr {qtl} | R Documentation

## Estimate FDR for multiple trait QTL analysis

### Description

Estimate the false discovery rate (FDR) for multiple trait analysis

### Usage

    mqmscanfdr(cross, scanfunction=mqmscanall,
      thresholds=c(1,2,3,4,5,7,10,15,20), n.perm=10,
      verbose=FALSE, ...
      )

### Arguments

`cross` | An object of class `cross`. See `read.cross` for details.
`scanfunction` | QTL mapping function, Note: Must use scanall or mqmscanall. Otherwise this will not produce usefull results. Reason: We need a function that maps all traits ecause of the correlation structure which is not changed (between traits) during permutation (Valis options: scanall or mqmscanall)
`thresholds` | False discovery rate (FDR) is calculated for peaks above these LOD thresholds (DEFAULT=Range from 1 to 20, using 10 thresholds) Parameter is a list of LOD scores at which FDR is calculated.
`n.perm` | Number of permutations (DEFAULT=10 for quick analysis, however for publications use 1000, or higher)
`verbose` | verbose output
`...` | Parameters passed to the mapping function

### Details

This function wraps the analysis of `scanone`, `cim` and `mqmscan` to scan for QTL in shuffled/randomized data. It is recommended to also install the `snow` library for parallelization of calculations. The `snow` library allows calculations to run on multiple cores or even scale it up to an entire cluster, thus speeding up calculation by the number of computers used.

### Value

Returns a data.frame with 3 columns: FalsePositives, FalseNegatives and False Discovery Rates. In the rows the userspecified thresholds are with scores for the 3 columns.

### 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.

### 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

    data(multitrait)

    # impute missing genotype data
    multitrait <- fill.geno(multitrait)
    ## Not run: # Calculate the thresholds
    result <- mqmscanfdr(multitrait, threshold=10.0, n.perm=1000)

    ## End(Not run)

[Package qtl version 1.46-2 Index]