Genome scan with a multiple QTL model.
mqmscan(cross, cofactors=NULL, pheno.col = 1,
model=c("additive","dominance"), forceML=FALSE,
cofactor.significance=0.02, em.iter=1000,
window.size=25.0, step.size=5.0,
logtransform = FALSE, estimate.map = FALSE,
plot=FALSE, verbose=FALSE, outputmarkers=TRUE,
multicore=TRUE, batchsize=10, n.clusters=1, test.normality=FALSE,off.end=0
)When scanning a single phenotype the function returns a scanone
object.
The object contains a matrix of three columns for LOD scores, information content
and LOD*information content with pseudo markers sorted in increasing
order. For more information on the scanone object see: scanone
An object of class cross. See read.cross for details.
List of cofactors to be analysed as cofactors in backward elimination
procedure when building the QTL model. See mqmsetcofactors on how-to manually set cofactors
for backward elimination. Or use mqmautocofactors for automatic selection of cofactors. Only
three kind of (integer) values are allowed in the cofactor list. (0: no cofactor at this marker, 1: Use
this marker as an additive cofactor, 2: Use this marker as an sexfactor (Dominant cofactor))
Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers; One may also give a character strings matching the phenotype names. Finally, one may give a numeric vector of phenotypeIDs. This should consist of integers with 0 < value < no. phenotypes.
When scanning for QTLs should haplotype dominance be considered in an F2 intercross. Using the dominance model we scan for additive effects but also allow an additional effect where AA+AB versus BB and AA versus AB+BB. This setting is ignored for BC and RIL populations
Specify which statistical method to use to estimate variance components to use when QTL modeling and mapping. Default usage is the Restricted maximum likelihood approach (REML). With this option a user can disable REML and use maximum likelihood.
Significance level at which a cofactor is considered significant. This is estimated using an analysis of deviance, and compared to the level specified by the user. The cofactors that dont reach this level of statistical significance are NOT used in the mapping stage. Value between 0 and 1
Maximum number of iterations for the EM algorithm to converge
Window size for mapping QTL locations, this parameter is used in the interval mapping stage. When calculating LOD scores at a genomic position all cofactors within window.size are dropped to estimate the (unbiased) effect of the location under interest.
Step size used in interval mapping. A lower step.size parameter increases the number of output points, this creates a smoother QTL profile
Distance (in cM) past the terminal markers on each chromosome to which the genotype simulations will be carried.
Indicate if the algorithm should do a log transformation on the trait data in the pheno.col
Should Re-estimation of the marker locations
on the genetic map occur before mapping QTLs. This method is
deprecated rather use the est.map function in R/qtl.
This is because no map is returned into the crossobject.
The old map remains in the cross object.
plot the results (default FALSE)
verbose output
If TRUE (the default), the results include the marker locations as well as along a grid of pseudomarkers; if FALSE, the results include only the grid positions.
Use multicore (if available)
Number of traits being analyzed as a batch.
Number of child processes to split the job into.
If TRUE, test whether the phenotype follows a
normal distribution via mqmtestnormal.
Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman broman@wisc.edu
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(map10) # Genetic map modeled after mouse
# simulate a cross (autosomes 1-10)
qtl <- c(3,15,1,0) # QTL model: chr, pos'n, add've & dom effects
cross <- sim.cross(map10[1:10],qtl,n=100,missing.prob=0.01)
# MQM
crossaug <- mqmaugment(cross) # Augmentation
cat(crossaug$mqm$Nind,'real individuals retained in dataset',
crossaug$mqm$Naug,'individuals augmented\n')
result <- mqmscan(crossaug) # Scan
# show LOD interval of the QTL on chr 3
lodint(result,chr=3)
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