Estimate FDR for multiple trait QTL analysis
Estimate the false discovery rate (FDR) for multiple trait analysis
mqmscanfdr(cross, scanfunction=mqmscanall, thresholds=c(1,2,3,4,5,7,10,15,20), n.perm=10, verbose=FALSE, ... )
cross |
An object of class |
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 |
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.
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.
Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman broman@wisc.edu
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.
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
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)
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