qtlThreshold {MetaNetwork}R Documentation

Estimate QTL significance threshold.

Description

Simulation test to estimate empirical threshold for QTL significance. The trait values are simulated using the median number of noise, median mean and standard deviation from the observed trait data under the null hypothesis of no QTL. At each simulation test, the highest absolute -log_{10}(p) value is recorded. The threshold is set at desired alpha level (i.e. take the -log_{10}(p) value at the alpha proportion position of the permutation set).

Usage

qtlThreshold(genotypes, traits, spike, 
             n.simulations = 1000, alpha = 0.05)

Arguments

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.
spike numeric cut-off value to separate absent (qualitative) from available (quantitative) trait abundance.
n.simulations (optional) number of simulations. Default is 1000 times.
alpha (optional) numeric alpha level for the threshold. Default is 0.05.

Value

Returns the -log_{10}(p) significance threshold value for QTLs.

Note

The individual columns of genotypes and traits must have the same order. The names of individuals should be consistent over genotypes and traits.

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

Examples

## load the example data provided with this package                          
data(genotypes)
data(traits)                                                       
                                             
##OR: load your own data                     
#genotypes  <- loadData("genotypes.csv")
#traits     <- loadData("traits.csv")  
                                             
##estimate qtl threshold for significance with low count of simulations (advised: 1000)
qtlThres    <- qtlThreshold(genotypes, traits, 4, n.simulations = 10)

##show the threshold
qtlThres

[Package MetaNetwork version 1.0-0 Index]