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