Fetch significant markers after permutation analysis
Fetch significant makers after permutation analysis. These markers can be used as cofactors for model selection in a forward stepwise approach.
mqmfind.marker(cross, mqmscan = NULL, perm = NULL, alpha = 0.05, verbose=FALSE)
cross |
An object of class |
mqmscan |
|
perm |
a |
alpha |
Threshold value, everything with significance < alpha is reported |
verbose |
Display more output on verbose=TRUE |
returns a matrix with at each row a significant marker (determined from the
scanoneperm object) and with columns: markername, chr and pos (cM)
Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman broman@wisc.edu
mqmprocesspermutation - Function called to convert results from an mqmpermutation into an scanoneperm object
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
# 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
markerPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.