qtlSummary {MetaNetwork} | R Documentation |
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.
qtlSummary(markers, genotypes, traits, qtlProfiles, spike, qtlThres, interval.dropoff = 1.5, filename = NULL)
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. |
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.
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
.
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
, 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.
## 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,]