qtlSummary {MetaNetwork}R Documentation

Summarize QTL effects

Description

Summarize QTL profiles including peak position (chromosome, centi-Morgan), support interval (calculated via the function qtlSupportInterval), proportion of QTL variation explained by part 1 and 2 of the qtlMapTwoPart model, and allele substitution effect.

Usage

  qtlSummary(markers, genotypes, traits, qtlProfiles, spike, qtlThres, 
             interval.dropoff = 1.5, filename = NULL)

Arguments

markers matrix of markers (rownames) and their chromosome numbers (column 1) and centi-Morgan positions (cM, column 2), ordered by position.
See markers example data.
genotypes matrix of genotypes for each marker (rownames) and individual (columnnames), as numeric values 1, 2 or NA when missing.
See genotypes example data.
traits matrix of phenotypes for each trait (rownames) and individual (columnnames), as numeric or NA when missing.
See traits example data.
qtlProfiles matrix of QTL mapping of traits (rownames) to markers (columnnames), as -log_{10}(p) values.
See qtlProfiles example data.
spike numeric cut-off value to separate absent (qualitative) from available (quantitative) trait abundance.
qtlThres numeric -log_{10}(p) threshold value for significant QTLs.
interval.dropoff (optional) drop-off value for QTL support intervals. Default is 1.5.
filename (optional) path of the file where the qtlSummary is to be stored. Default is NULL.

Value

Returns a data frame with a QTL summary which contains the following headers:

traitName name of trait.
QTLchr the chromosome number where a QTL locates.
QTLmk the marker where the trait maps to.
QTLleftcm the cM position of left border of the QTL support interval.
QTLpeakcm the cM position of the QTL peak.
QTLrightcm the cM of right border of the QTL support interval.
logp the -log_{10}(p) value of a QTL.
VarP1 the percentage of qualitative variance explained by a QTL.
VarP2 the percentage of quantitative variance explained by a QTL.
additive the allele substitution effect (=half the difference of metabolite abundance between genotype 1 and 2).

See qtlSumm example data.

Note

The individual columns of genotypes and traits must have the same order. The markers should be ordered sequentially. The names of markers, traits and individuals should be consistent over markers, genotypes, traits and qtlProfiles.

Author(s)

Jingyuan Fu <j.fu@rug.nl>, Morris Swertz <m.a.swertz@rug.nl>, Ritsert Jansen <r.c.jansen@rug.nl>

References

Fu J, Swertz MA, Keurentjes JJB, Jansen RC. MetaNetwork: a computational tool for the genetic study of metabolism. Nature Protocols (2007).

http://gbic.biol.rug.nl/supplementary/2007/MetaNetwork

See Also

Use markers, genotypes and traits as example data sets or use loadData to load your own data.
Use qtlMapTwoPart to calculate qtlProfiles.
Use qtlThreshold to estimate qtlThres QTL threshold for significance.
Use MetaNetwork for automated application of this function as part of a genetic analysis protocol on metabolites.

Examples

## load the example data provided with this package                         
data(markers)   
data(genotypes)
data(traits)                                                       
                                             
##OR: load your own data                     
#markers    <- loadData("markers.csv")
#genotypes  <- loadData("genotypes.csv")
#traits     <- loadData("traits.csv")  
                                             
##calculate the two part qtl
qtlProfiles <- qtlMapTwoPart(genotypes=genotypes, traits=traits, spike=4)

##set the qtl threshold
qtlThres    <- 3.79

##OR: estimate the threshold yourself
#qtlThres   <- qtlThreshold(genotypes, traits, spike=4)
                                                                                                                       
##summarize the qtlProfiles
qtlSumm     <- qtlSummary(markers, genotypes, traits, qtlProfiles, spike=4, 
                          qtlThres=qtlThres)

##show the summary
qtlSumm[1:5,]

[Package MetaNetwork version 1.0-0 Index]