| qtlSupportInterval {MetaNetwork} | R Documentation |
For one QTL profile, significant QTLs are selected based on qtlThres.
The regions within the interval.dropoff of these QTL peaks
are defined as a support interval. The most left and the most right marker within
each support interval are returned as matrix.
qtlSupportInterval(markers, oneQtlProfile, qtlThres, interval.dropoff = 1.5)
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. |
oneQtlProfile |
one row from the qtlProfiles matrix of QTL mapping of traits (rownames)
to markers (columnnames), as -log_{10}(p) values.See qtlProfiles example data. |
qtlThres |
numeric -log_{10}(p) threshold value for significant QTLs. |
interval.dropoff |
numeric drop-off -log_{10}(p) value from the QTL peak that defines left and right border of support interval (optional). Default is 1.5. |
Returns a matrix of the markers number that define the left and right borders of each
support interval for oneQtlProfile.
The markers should be ordered sequentially. The names of markers should be
consistent over markers and qtlProfiles.
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
Use markers as example data set or use loadData
to load your own data.
Use qtlMapTwoPart for the calculation of qtlProfiles.
Use qtlThreshold for the estimation of qtlThres QTL
significance threshold.
Use qtlSummary for automated application of this function to
produce a support interval summary for a set qtlProfiles.
Use MetaNetwork for automated appliction of this function as
part a genetic analysis protocol on metabolites.
## 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 for only the first trait
qtlProfiles <- qtlMapTwoPart(genotypes=genotypes, traits=traits[1,], spike=4)
##set the qtl threshold
qtlThres <- 3.79
##OR: estimate the threshold yourself
#qtlThres <- qtlThreshold(genotypes, traits, spike=4)
##calculate qtl confidence interval for the first qtl profile
qtlSuppInt <- qtlSupportInterval(markers, qtlProfiles[1,], qtlThres=qtlThres,
interval.dropoff = 1.5)
##show the interval
qtlSuppInt