Prints a summary of the "
wgaim" object in a presentable format
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an object of class "
an object of class "
a data structure of class "
logical value. If TRUE LOD scores for QTL are calculated, defaults to
further arguments passed to or from other methods
It is important that the
intervalObj is not missing in
print.wgaim as it
contains vital summary information about each of the QTL
The summary of the QTL differs depending on the method chosen
in the analsyis using
method = "random"
then the significance of the QTL are summarized using a probablistic
argument based on the conditional distribution of the QTL sizes given
the data (see Verbyla et. al, 2012 in References) Thus, for each
QTL, a value is calculated that represents the probability that the
QTL size is greater than zero (or less than zero if the effect is
method = "fixed" then the significance of the QTL is
summarized using a one degree of freedom Wald statistic.
A summary of the QTL component of the "
wgaim" object is
printed to the screen. For each QTL detected, if an "interval"
analysis was performed then
summary.wgaim prints which
chromosome, name and distance of each flanking marker, size,
probability/p-value, contribution of genetic variance and LOD
score if desired. If a "marker" analysis was performed then the
chromosome, name and distance of the associated marker, size,
probability/p-value, contribution of genetic variance
and LOD score are printed.
print.wgaim provides a narrative
brief of the QTL's detected.
Julian Taylor, Simon Diffey, Ari Verbyla and Brian Cullis
Verbyla, A. P & Taylor, J. D, Verbyla, K. L (2012). RWGAIM: An efficient high dimensional random whole genome average (QTL) interval mapping approach. Genetics Research. 94, 291-306.
Julian Taylor, Arunas Vebyla (2011). R Package wgaim: QTL Analysis in Bi-Parental Populations Using Linear Mixed Models. Journal of Statistical Software, 40(7), 1-18. URL http://www.jstatsoft.org/v40/i07/.
Verbyla, A. P., Cullis, B. R., Thompson, R (2007) The analysis of QTL by simultaneous use of the full linkage map. Theoretical And Applied Genetics, 116, 195-211.
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## Not run: # read in data data(phenoRxK, package = "wgaim") data(genoRxK, package = "wgaim") # subset linkage map and convert to "interval" object genoRxK <- subset(genoRxK, chr = c("1A", "2D1", "2D2", "3B")) genoRxK <- cross2int(genoRxK, missgeno = "Martinez", id = "Genotype", map.function = "kosambi") # base model rkyld.asf <- asreml(yld ~ Type + lrow, random = ~ Genotype + Range, rcov = ~ ar1(Range):ar1(Row), data = phenoRxK) # find QTL rkyld.qtl <- wgaim(rkyld.asf, phenoData = phenoRxK, intervalObj = genoRxK, merge.by = "Genotype", gen.type = "interval", method = "fixed", selection = "interval", trace = "trace.txt", na.method.X = "include") # summarise print(rkyld.qtl, genoRxK) summary(rkyld.qtl, genoRxK) ## End(Not run)