qtlThreshold {MetaNetwork} | R Documentation |
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).
qtlThreshold(genotypes, traits, spike, n.simulations = 1000, alpha = 0.05)
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. |
Returns the -log_{10}(p) significance threshold value for QTLs.
The individual columns of genotypes
and traits
must have the same order.
The names of individuals should be consistent over genotypes
and traits
.
Jingyuan Fu <j.fu@rug.nl>, Morris Swertz <m.a.swertz@rug.nl>, Ritsert Jansen <r.c.jansen@rug.nl>
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
## 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