qtl (version 1.21-2)

mqmscanfdr: 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

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

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.

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.

References

% \input{"inst/docs/Sources/MQM/mqm/significance_references.txt"}
• Bruno M. Tesson, Ritsert C. Jansen (2009) Chapter 3.7. Determining the significance thresholdeQTL Analysis in Mice and Rats1, 20--25
• Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping.Genetics138, 963--971. % -----^^ inst/docs/Sources/MQM/mqm/significance_references.txt ^^----- % \input{"inst/docs/Sources/MQM/mqm/parallelisation_references.txt"}
• Rossini, A., Tierney, L., and Li, N. (2003), Simple parallel statistical computing.R. UW Biostatistics working paper seriesUniversity of Washington.193
• Tierney, L., Rossini, A., Li, N., and Sevcikova, H. (2004), The snow Package: Simple Network of Workstations. Version 0.2-1. % -----^^ inst/docs/Sources/MQM/mqm/parallelisation_references.txt ^^-----

See Also

% \input{"inst/docs/Sources/MQM/mqm/standard_seealso.txt"}
• 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 % -----^^ inst/docs/Sources/MQM/mqm/standard_seealso.txt ^^-----

Examples

data(multitrait)

multitrait <- fill.geno(multitrait) # impute missing genotype data
multitrait <- subset(multitrait, chr=1, ind=!apply(multitrait$pheno, 1, function(a) any(is.na(a))))
  multitrait$pheno <- multitrait$pheno[,1:3]
result <- mqmscanfdr(multitrait, threshold=10.0, n.perm=5)	#Calculate the thresholds