MQM {qtl} | R Documentation |
Overview of the MQM mapping functions
Multiple QTL Mapping (MQM) provides a sensitive approach for mapping quantititive trait loci (QTL) in experimental populations. MQM adds higher statistical power compared to many other methods. The theoretical framework of MQM was introduced and explored by Ritsert Jansen, explained in the ‘Handbook of Statistical Genetics’ (see references), and used effectively in practical research, with the commercial ‘mapqtl’ software package. Here we present the first free and open source implementation of MQM, with extra features like high performance parallelization on multi-CPU computers, new plots and significance testing.
MQM is an automatic three-stage procedure in which, in the first stage, missing data is ‘augmented’. In other words, rather than guessing one likely genotype, multiple genotypes are modeled with their estimated probabilities. In the second stage important markers are selected by multiple regression and backward elimination. In the third stage a QTL is moved along the chromosomes using these pre-selected markers as cofactors, except for the markers in the window around the interval under study. QTL are (interval) mapped using the most ‘informative’ model through maximum likelihood. A refined and automated procedure for cases with large numbers of marker cofactors is included. The method internally controls false discovery rates (FDR) and lets users test different QTL models by elimination of non-significant cofactors.
R/qtl-MQM has the following advantages:
The current implementation of R/qtl-MQM has the following limitations: (1) MQM is limited to experimental crosses F2, BC, and selfed RIL, (2) MQM does not treat sex chromosomes differently from autosomal chromosomes - though one can introduce sex as a cofactor. Future versions of R/qtl-MQM may improve on these points. Check the website and change log (http://www.rqtl.org/STATUS.txt) for updates.
Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman kbroman@biostat.wisc.edu
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)