mqmscan: Genome scan with a multiple QTL model (MQM)

Description

Genome scan with a multiple QTL model.

Usage

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
  )

Arguments

cross

An object of class cross. See read.cross for details.

cofactors

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.

pheno.col

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

model

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

forceML

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.

cofactor.significance

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 ar

em.iter

Maximum number of iterations for the EM algorithm to converge

window.size

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 locati

step.size

Step size used in interval mapping. A lower step.size parameter increases the number of output points, this creates a smoother QTL profile

off.end

Distance (in cM) past the terminal markers on each chromosome to which the genotype simulations will be carried.

logtransform

Indicate if the algorithm should do a log transformation on the trait data in the pheno.col

estimate.map

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 returne

plot

plot the results (default FALSE)

verbose

verbose output

outputmarkers

Needs to be explained

multicore

Use multicore (if available)

batchsize

Needs to be explained

n.clusters

Number of child processes to split the job into.

test.normality

If TRUE, test whether the phenotype follows a normal distribution via mqmtestnormal.

Value

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

Examples

Run this code

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
')

result <- mqmscan(crossaug)    # Scan

# show LOD interval of the QTL on chr 3
lodint(result,chr=3)

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