qtl (version 1.38-4)

mqmaugment: MQM augmentation

Description

Fill in missing genotypes for MQM mapping. For each missing or incomplete marker it fills in (or `augments') all possible genotypes, thus creating new candidate `individuals'. The probability of each indidual is calculated using information on neighbouring markers and recombination frequencies. When a genotype of an augmented genotype is less likely than the minprob parameter it is dropped from the dataset. The augmented list of individuals is returned in a new cross object. For a full discussion on augmentation see the MQM tutorial online.

Usage

mqmaugment(cross, maxaugind=82, minprob=0.1,
           strategy=c("default","impute","drop"),
           verbose=FALSE)

Arguments

cross

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

maxaugind

Maximum number of augmentations per individual. The default of 82 allows for six missing markers for an individual in a BC cross ($2^6=64$) and four missing markers in an F2 ($3^4=81$). When a large number of markers are missing this default

minprob

Return individuals with augmented genotypes that have at least this probability of occurring. minprob is a value between 0 and 1. For example a value of 0.5 will drop all genotypes that are half as likely as the most likely genoty

strategy

When individuals have too much missing data and augmentation fails three options are provided: 1. "default": Calculate genotypes at missing marker positions, accounting for minprob, and add this individual to the set

verbose

If TRUE, give verbose output

Value

• Returns the cross object with augmented individuals (many individuals from the data set will be repeated multiple times). Some individuals may have been dropped completely when the probability falls below minprob. An added component to the cross object named mqm contains information on exactly which individuals are retained and repeated.

See Also

• fill.geno- Alternative routine for estimating missing data % \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(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)