qtl (version 1.21-2)

mqmfind.marker: 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)

See Also

• mqmprocesspermutation- Function called to convert results from an mqmpermutation into an scanoneperm object % \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)				#Use the multitrait dataset
cof <- mqmsetcofactors(multitrait,3)		#Set cofactors at each 3th marker
multitrait <- fill.geno(multitrait)
multitrait <- transformPheno(multitrait, 7)     #log transform the 7th phenotype
result <- mqmpermutation(multitrait,scanfunction=mqmscan,cofactors=cof,pheno.col=7,n.perm=50,batchsize=10)	# Bootstrap 50 runs in batches of 10result <- mqmpermutation(multitrait,scanfunction=mqmscan,cofactors=cof,pheno.col=7,n.perm=2,batchsize=2)
f2perm <- mqmprocesspermutation(result)		        #Create a permutation object
summary(f2perm)				        #What LOD score is considered significant ?
marker <- mqmfind.marker(multitrait,result[[1]],f2perm)		#Find markers with a significant QTL effect (First run is original phenotype data)
marker						#Print it to the screen