| Title: | Whole Genome Average Interval Mapping for QTL Detection and Estimation using ASReml-R |
|---|---|
| Description: | A computationally efficient whole genome approach to detecting and estimating significant QTL in linkage maps using the flexible linear mixed modelling functionality of ASReml-R. |
| Authors: | Julian Taylor [aut, cre], Ari Verbyla [aut] |
| Maintainer: | Julian Taylor <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 2.0-6 |
| Built: | 2025-01-24 03:41:33 UTC |
| Source: | https://github.com/drj001/wgaim |
This package provides an efficient computational implementation of the QTL whole genome QTL analysis algorithm (wgaim) discussed in Verbyla et. al (2007, 2012) using extensions of the functionality provided in the linear mixed modelling R package ASReml-R V4.
| Package: | wgaim |
| Type: | Package |
| Version: | 2.0-0 |
| Date: | 2019-08-12 |
| License: | GPL 2 |
Welcome to version 2.0 of wgaim! The documentation given in this help file is
only brief and users should consult the vignette available with the
package by typing vignette("wgaim") at the prompt. Alternatively,
users can also consult the individual help files for the main functions of the
package.
The package provides a user friendly function cross2int
for the conversion of "cross" objects created using
read.cross in Bromans qtl package into an "interval"
object ready for use in wgaim. Specifically, cross2int() performs additional
calculations to impute missing marker values on each of the
chromosomes across the full linkage map and also provides users with
genetic distances and recombination fractions for the intervals. The
returned object retains the class structure
of an object created with read.cross and therefore allows further
use with the qtl package if desired.
The package also provides a function for the graphical display of the
chromosomes of a "cross" object. The method function
linkMap displays the full or subsetted linkage map
according to chromosome or distance as well as displays non-overlapping
marker names on the right hand side.
QTL analysis is conducted using the function wgaim which,
as its first argument, requires an asreml base model. High
dimensional genetic components are allowed (See wgaim.asreml for
more details). For convenience the default tracing of results from the
asreml models can be outputted to a file for further inspection. Outlier
statistics and marker effects from each iteration can be viewed using
outStat. Diagnostics of the likelihood ratio test
performed for each forward step can be displayed using
tr.wgaim. The function also displays an incremental
probability value matrix of the QTL ascertained at each forward step of the algorithm.
Summary and print methods are available for the returned "wgaim"
object and provide users with a detailed report on the QTL, their
size, their flanking markers, significance (including LOD score if
desired) and approximate contribution to the genetic variance. The
returned "wgaim" object may also be plotted using the method
function linkMap. This function plots the full linkage map
subsetted for chromosome and distance as well as provides shaded
QTL regions and highlighted flanking markers. Plotting of QTL for
multiple traits is also possible (see linkMap.default).
Julian Taylor and Ari Verbyla Maintainer: Julian Taylor <[email protected]>
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 https://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, 95-111.
Converts an object of class "cross" to an object with class
"interval". The function also imputes missing markers.
cross2int(object, impute = "MartinezCurnow", consensus.mark = TRUE, id = "id", subset = NULL)cross2int(object, impute = "MartinezCurnow", consensus.mark = TRUE, id = "id", subset = NULL)
object |
an object of class |
impute |
a character string determining how missing values in
the linkage map should be imputed. If |
consensus.mark |
logical value. If |
id |
a character string or name of the unique identifier for each row of genotype
data (see Details). Defaults to |
subset |
a possible character vector naming the subset of
chromosomes to be returned. Defaults to |
This function provides the conversion of genetic data objects that have
already been generated using read.cross() from Bromans qtl
package, to "interval" objects ready for use with
wgaim. Users should be aware that this function is restricted to
certain populations. object must inherit one of the class
structures "bc", "dh", "f2", "riself".
During the conversion process three important linkage map attributes are assessed.
The map may be subsetted using the subset argument
If consensus.mark = TRUE then co-located marker sets are reduced
to form single consensus markers before missing values are
imputed. The marker similarity is determined by
the genetic distances that are given in the map component for each linkage
group. If a set of markers co-locate the name of the first marker is
chosen and a single consensus marker is determined by coalescing the
genetic information from all markers in the set. A "(C)" is placed
after the marker name for easy identification. The markers removed
from each set are returned with the object and placed under
"colocated.markers" for inspection if required.
Missing values are imputed according to
the argument given by impute. This imputation results in a
complete version of the marker data for each chromosome which is then
used to create the interval data component "interval.data". The complete
marker data for each chromosome can be obtained from the "imputed.data" element of the
returned list. It is therefore also possible to perform whole genome marker
analysis using wgaim. See wgaim.asreml for more details.
a list of class "cross" that also inherits the class
"interval". The list contains the following components
geno |
A list with elements named by the corresponding names of the
chromosomes. Each chromosome is itself a list with six
elements: |
colocated.markers |
If |
pheno |
A data.frame of phenotypic information with rows as individuals read
in from |
Julian Taylor and Ari Verblya
Martinez, O., Curnow. R. N. (1994) Missing markers when estimating quantitative trait loci using regression mapping. Heredity, 73, 198-206.
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 https://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, 95-111.
## Not run: # read in linkage map from a rotated .CSV file with "id" as the # identifier for each unique row wgpath <- system.file("extdata", package = "wgaim") genoSxT <- read.cross("csvr", file="genoSxT.csv", genotypes=c("AA","BB"), na.strings = c("-", "NA"), dir = wgpath) genoSxT <- cross2int(genoSxT, impute="MartinezCurnow", id = "id") # plot linkage map linkMap(genoSxT, cex = 0.5) ## End(Not run)## Not run: # read in linkage map from a rotated .CSV file with "id" as the # identifier for each unique row wgpath <- system.file("extdata", package = "wgaim") genoSxT <- read.cross("csvr", file="genoSxT.csv", genotypes=c("AA","BB"), na.strings = c("-", "NA"), dir = wgpath) genoSxT <- cross2int(genoSxT, impute="MartinezCurnow", id = "id") # plot linkage map linkMap(genoSxT, cex = 0.5) ## End(Not run)
Linkage map marker data for the Cascades x RAC875-2 doubled haploid population in the form of an R/qtl cross object.
data(genoCxR)data(genoCxR)
This data relates to a linkage map of 663 markers genotyped on 93
individuals. The linkage map consists of 42 linkage groups spanning
the whole genome. Coincident markers have been removed reducing the
linkage map to 458 markers. Map distances have been
estimated using read.cross() with the haldane
mapping function. The data object is therefore an R/qtl
cross object. See read.cross() documentation for more details on the
format of this object.
data(genoCxR, package = "wgaim") linkMap(genoCxR, cex = 0.5)data(genoCxR, package = "wgaim") linkMap(genoCxR, cex = 0.5)
Linkage map marker data for the RAC875 x Kukri doubled haploid population in the form of an R/qtl cross object.
data(phenoSxT)data(phenoSxT)
This data relates to a linkage map of 500 genetic markers genotyped on 368
individuals from the RAC875 x Kukri population. The linkage map
consists of 21 linkage groups with varying numbers of markers. Map
distances have been estimated using read.cross() with the kosambi
mapping function. The data is therefore an R/qtl
cross object. See read.cross() documentation for more details on the
format of this object.
data(genoRxK, package = "wgaim") linkMap(genoRxK, cex = 0.5)data(genoRxK, package = "wgaim") linkMap(genoRxK, cex = 0.5)
Linkage map marker data for the Sunco x Tasman doubled haploid population in the form of an R/qtl cross object.
data(phenoSxT)data(phenoSxT)
This data relates to a linkage map of 287 genetic markers genotyped on 190
individuals from the Sunco x Tasman population. This set is reduced from
the original 345 markers (a mixture of AFLP, RFLP and microsatellite
markers and protein analysis). The reduction was created by discarding
58 markers which were co-located with one or more other markers. The
linkage map consists of 21 linkage groups with varying numbers of
markers. Map distances have been estimated using read.cross()
with the kosambi mapping function. The data is therefore an R/qtl
cross object. See read.cross() documentation for more details on the
format of this object.
data(genoSxT, package = "wgaim") linkMap(genoSxT, cex = 0.5)data(genoSxT, package = "wgaim") linkMap(genoSxT, cex = 0.5)
Neatly plots the genetic linkage map with marker locations and marker names.
## S3 method for class 'cross' linkMap(object, chr, chr.dist, marker.names = "markers", tick = FALSE, squash = TRUE, m.cex = 0.6, ...)## S3 method for class 'cross' linkMap(object, chr, chr.dist, marker.names = "markers", tick = FALSE, squash = TRUE, m.cex = 0.6, ...)
object |
object of class |
chr |
character string naming the subset of chromosomes to plot |
chr.dist |
a list containing named elements |
marker.names |
a character string naming the type of marker
information to plot. If |
tick |
logical value. If |
squash |
logical value. if |
m.cex |
the expansion factor to use for the marker names |
... |
arguments passed to |
This plotting procedure provides a visual display of the
chromosomes without marker names overlapping vertically. The plotting
region will adjust itself to ensure that all marker names are in the region. For
this reason the value for "m.cex" is passed to the text()
function and should be manipulated until an aesthetic genetic map is reached.
For large maps with many chromosomes, marker names and adjacent chromosomes will overlap horizontally. For the interest of readability this has not been corrected. For this particular situation it is suggested that the user horizontally maximise the plotting window until no overlapping occurs or subset the genetic map to achieve the desired result.
This invisibly returns the following list for manipulation with
linkMap.wgaim()
mt |
A list named by the chromosomes with each element containing the locations of the marker names after correcting for overlapping |
map |
A list named by the chromosomes with each element containing the locations of markers on the chromosomes |
chrpos |
The numerical position of the chromosomes on the plotting region |
Julian Taylor
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 https://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, 95-111.
data(genoSxT, package = "wgaim") ## plot linkage map with marker names linkMap(genoSxT, cex = 0.5) ## plot linkage map with distances linkMap(genoSxT, cex = 0.5, marker.names = "dist")data(genoSxT, package = "wgaim") ## plot linkage map with marker names linkMap(genoSxT, cex = 0.5) ## plot linkage map with distances linkMap(genoSxT, cex = 0.5, marker.names = "dist")
Neatly plots the genetic linkage map with marker locations,
marker names and highlights QTL with their associated flanking markers
for multiple traits obtained from a list of wgaim models.
## Default S3 method: linkMap(object, intervalObj, chr, chr.dist, marker.names = "markers", flanking = TRUE, list.col = list(q.col = rainbow(length(object)), m.col = "red", t.col = rainbow(length(object))), list.cex = list(m.cex = 0.6, t.cex = 0.6), trait.labels = NULL, tick = FALSE, ...)## Default S3 method: linkMap(object, intervalObj, chr, chr.dist, marker.names = "markers", flanking = TRUE, list.col = list(q.col = rainbow(length(object)), m.col = "red", t.col = rainbow(length(object))), list.cex = list(m.cex = 0.6, t.cex = 0.6), trait.labels = NULL, tick = FALSE, ...)
object |
a list object with elements inheriting the class |
intervalObj |
object of class |
chr |
character string naming the subset of chromosomes to plot |
chr.dist |
a list containing named elements |
marker.names |
a character string naming the type of marker
information to plot. If |
flanking |
logical value. If |
list.col |
named list of colors used to highlight the QTL regions and
their flanking markers. |
list.cex |
a named list object containing the character expansion
factors for the marker names |
trait.labels |
character string naming the trait used in the model object, defaults to the response names extracted from the fixed compoenent of the base model. |
tick |
logical value. If |
... |
arguments passed to the |
This plotting procedure is a wrapper for linkMap.wgaim and displays
QTL for multiple traits obtained from a list of models given by object.
Alternative labels for the traits can be given, in model order, using
trait.labels.
Color specific highligting of the QTL is also available using
clist. This differs slightly from linkMap.wgaim. Here
the q.col and t.col should be given a set of colors equal to
the length of object. Let n be the length of object.
Then if q.col is NULL or length of q.col is not equal
to n then it defaults to rainbow(n). If t.col is
NULL or length of t.col is not equal to n or 1 then
it defaults to the colors of q.col. Examples of different color
combinations are given below in the examples.
The list.cex argument can be used to manipulate the character expansion of
the marker names using m.cex or the character expansion of the
trait.labels using t.cex. If a set of "marker" analyses has been
performed then pch is used to plot a symbol at the
location of the QTL. This character can be changed using the usual
arguments such as pch or cex that are passed through the
... argument.
For a set of "interval" analyses, the genetic linkage map is
plotted with shaded QTL regions and highlighted flanking markers. For
a set of "marker" analyses, symbols are
placed at the QTL locations and the markers are highlighted.
Julian Taylor
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 https://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, 95-111.
## Not run: ## fit wgaim models rktgw.qtl <- wgaim(rktgw.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) ## plot QTL intervals # matching rainbow QTL color and trait names, red flanking markers # (default) and gray background markers. linkMap(list(rktgw.qtl,rkyld.qtl), genoRxK, col = "gray") # rainbow QTL color and black trait names, red flanking markers # (default) and gray background markers. linkMap(list(rktgw.qtl,rkyld.qtl), genoRxK, list.col = list(t.col = "black", m.col = "red"), col = "gray") # monochromatic plot: gray QTLs, black trait names, black flanking # markers and gray background markers linkMap(list(rktgw.qtl,rkyld.qtl), genoRxK, list.col = list(q.col = rep(gray(0.8), 2), t.col = "black", mcol = "black"), col = "gray") ## End(Not run)## Not run: ## fit wgaim models rktgw.qtl <- wgaim(rktgw.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) ## plot QTL intervals # matching rainbow QTL color and trait names, red flanking markers # (default) and gray background markers. linkMap(list(rktgw.qtl,rkyld.qtl), genoRxK, col = "gray") # rainbow QTL color and black trait names, red flanking markers # (default) and gray background markers. linkMap(list(rktgw.qtl,rkyld.qtl), genoRxK, list.col = list(t.col = "black", m.col = "red"), col = "gray") # monochromatic plot: gray QTLs, black trait names, black flanking # markers and gray background markers linkMap(list(rktgw.qtl,rkyld.qtl), genoRxK, list.col = list(q.col = rep(gray(0.8), 2), t.col = "black", mcol = "black"), col = "gray") ## End(Not run)
Neatly plots the genetic linkage map with marker locations,
marker names and highlights QTL with their associated flanking markers
obtained from a wgaim model.
## S3 method for class 'wgaim' linkMap(object, intervalObj, chr, chr.dist, marker.names = "markers", flanking = TRUE, list.col = list(q.col = "light blue", m.col = "red", t.col = "light blue"), list.cex = list(t.cex = 0.6, m.cex = 0.6), trait.labels = NULL, tick = FALSE, ...)## S3 method for class 'wgaim' linkMap(object, intervalObj, chr, chr.dist, marker.names = "markers", flanking = TRUE, list.col = list(q.col = "light blue", m.col = "red", t.col = "light blue"), list.cex = list(t.cex = 0.6, m.cex = 0.6), trait.labels = NULL, tick = FALSE, ...)
object |
object of class |
intervalObj |
object of class |
chr |
character string naming the subset of chromosomes to plot |
chr.dist |
a list containing named elements |
marker.names |
a character string naming the type of marker
information to plot. If |
flanking |
logical value. If |
list.col |
named list of colours used to highlight the QTL regions and
their flanking markers. |
list.cex |
a named list object containing the character expansion
factors for the marker names |
trait.labels |
character string naming the trait used in the model object |
tick |
logical value. If |
... |
arguments passed to the |
This plotting procedure builds on linkMap.cross() by adding the
QTL regions to the map and highlighting the appropriate markers obtained
from a fit to wgaim. If the linkage map is subsetted and QTL
regions fall outside the remaining map a warning will be given that
the QTL have been omitted from the display.
The list.col arguments q.col, m.col and
t.col have been added for personal colour highlighting of the QTL
regions, flanking markers and trait names. For greater flexibility the
procedure may also be given the usual col argument that will be
passed to the other markers.
The list.cex argument can be used to manipulate the character expansion of
the marker names using m.cex or the character expansion of the
trait.labels using t.cex. If a "marker" analysis has been
performed then pch is used to plot a symbol at the
location of the QTL. This character can be changed using the usual
arguments such as pch or cex that are passed through the
... argument.
For an "interval" analysis, the genetic linkage map is
plotted with shaded QTL regions and highlighted flanking markers. For
a "marker" analysis, a symbol is placed at the QTL locations and
the markers are highlighted.
Julian Taylor
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 https://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, 95-111.
## Not run: # fit wgaim model yield.qtl <- wgaim(yield.fm, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) # plot QTL linkMap(yield.qtl, genoRxK, list.col = list(m.col = "red"), col = "gray") ## End(Not run)## Not run: # fit wgaim model yield.qtl <- wgaim(yield.fm, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) # plot QTL linkMap(yield.qtl, genoRxK, list.col = list(m.col = "red"), col = "gray") ## End(Not run)
A faceted ggplot() of the chromosome outlier statistics or the
interval blups/outlier statistics from specified iteratons of
wgaim. The interval blups/outlier statistics appear as
a trace across the genome separated by chromosomes and appropriately
spaced by their cM distances.
outStat(object, intervalObj, iter = NULL, chr = NULL, statistic = "outlier", plot.chr = FALSE, chr.lines = FALSE)outStat(object, intervalObj, iter = NULL, chr = NULL, statistic = "outlier", plot.chr = FALSE, chr.lines = FALSE)
object |
object of class |
intervalObj |
object of class |
iter |
range of integers determining which iterations will be plotted. |
chr |
character vector naming the subset of chromosomes to plot. |
statistic |
character string naming the type of diagnostic statistic to be
plotted. Default is |
plot.chr |
logical value, if |
chr.lines |
logical value, if |
If plot.chr = TRUE then outlier statistics for each chromosome
are plotted in separate faceted panels for specified values of chr and
iter. This option requies selection="chromosome" to be set
in the wgaim.asreml() call. If plot.chr = FALSE then interval blups or
outlier statistics are plotted in separate faceted panels for specified
values of chr and iter.
Additionally, the set of significant QTL (chromosome and interval position) are
extracted from the model object and annotated on the plot in
their appropriate positions in each facet panel. Graphical aesthetics,
such as themes, text, font etc. can be further manipulated through
the inclusion of additional overlays to the returned ggplot() object.
The blups or outlier statistics are plotted in a faceted ggplot()
with information of significant QTL overlayed.
Julian Taylor
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 https://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, 95-111.
## Not run: # fit wgaim model rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) # plot QTL interval outlier statistics outStat(rkyld.qtl, genoRxK, iter = 1:5) ## End(Not run)## Not run: # fit wgaim model rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) # plot QTL interval outlier statistics outStat(rkyld.qtl, genoRxK, iter = 1:5) ## End(Not run)
Zinc concentration data of a Doubled Haploid wheat population.
data(phenoCxR)data(phenoCxR)
This data relates to a glasshouse experiment involving a set of 90 Doubled Haploid (DH) lines from a crossing of Cascades x Rac875-2. The DH lines were allocated randomly to pots in the glasshouse using a randomised complete block design. There were also additional pots that contained 5 of each of the parents (Cascades and Rac-875-2). Two measurments were made, namely zinc concentration and shoot length. The data frame consists of 200 rows and 5 columns decribed below
A factor of 92 levels conataining the unique identification of the DH lines and parents.
A factor of two levels indexing the blocks in the experiment
A factor of 3 levels indexing the wheat variety (Doubled Haploid, Cascades, Rac875-2)
A numerical variable of shoot lengths for each plant
A numerical variable of zinc concentration levels for each plant
data(phenoCxR, package = "wgaim") plot(phenoCxR$shoot, phenoCxR$znconc)data(phenoCxR, package = "wgaim") plot(phenoCxR$shoot, phenoCxR$znconc)
Phenotype data arising from a field trial of a Doubled Haploid population involving a crossing of the wheat varities RAC875 and Kukri
data(phenoRxK)data(phenoRxK)
This data relates to a field trial conducted in 2007 at the Roseworthy Cmapus of the University of Adelaide. The trial consisted of 2 replicates of 254 Doubled Haploid lines from a cross between wheat varieties RAC875 and Kukri. The DH lines, the parents (RAC875, Kukri) and control varieties (ATIL, SOKOLL, WEEBIL) were randomly allocated to 520 plots using a randomized complete bloack design. The trial was laid out in a 20 by 26 rectangular array. The data frame consists of 520 rows with 9 columns described by:
A 254 level factor containing a unique identification for the wheat varieties involved in the experiment.
A factor of four levels indexing the wheat varieties (Doubled Haploid, RAC875, Kukri, ATIL, SOKOLL, WEEBIL).
A factor of 20 numeric levels indexing the field Range.
A factor of 26 numeric levels indexing the field Rows.
A factor of 2 levels indexing the Blocks of the experiment.
A numeric vector of yield observations in kg/ha.
A numeric vector of thousand grain weight observations.
A centred numerical vector of the field Ranges.
A centred numerical vector of the field Rows.
data(phenoRxK, package = "wgaim")data(phenoRxK, package = "wgaim")
Phenotype data arising from a two-phase experiment involving a Doubled Haploid population from a crossing of the wheat varities Sunco and Tasman
data(phenoSxT)data(phenoSxT)
This data relates to a two-phase epxeriment involving a set of 175 Doubled Haploid lines. In the first phase DH lines were randomly allocated to plots using a complete block design with additional plots containing the parents (Sunco, Tasman) as well as commercial lines (Frame, Janz, Krichauff, Machete, RAC820, Trident). The trial was laid out in a rectangular array of 31 rows and 12 columns. In the second phase 23% of the field samples were replicated in the milling process producing a total of 456 milling samples. These partially replicated field samples were then randomly allocated to 38 mill days with 12 samples per mill day. The data frame consists of 456 rows with 11 columns. These columns are
A one level of factor containing a unique identification for the experiment.
A factor of nine levels indexing the wheat variety (Doubled Haploid, Sunco, Tasman, (Frame, Janz, Krichauff, Machete, RAC820, Trident))
A factor of 183 levels uniquely identifying the wheat varieties involved in the experiment.
A factor of 12 numeric levels indexing the field Range.
A factor of 31 numeric levels indexing the field Rows.
A factor of 2 levels indexing the Block of the experiment
A factor of 38 numeric levels indexing the milling day
A factor of 12 levels indexing the milling order
A numeric vector of milling yield observations from the second phase of the experiment.
A centered numerical vector of milling orders, Millord
A centered numerical vector of Rows
data(phenoSxT, package = "wgaim")data(phenoSxT, package = "wgaim")
Stack QTL summary information into a super table ready for simple exporting.
qtlTable(..., intervalObj = NULL, labels = NULL, columns = "all")qtlTable(..., intervalObj = NULL, labels = NULL, columns = "all")
... |
list of objects of class |
intervalObj |
a genetic object of class |
labels |
a vector of character strings describing the trait names of each model QTL table. |
columns |
this can be either a numeric vector determining which columns of the QTL
summaries should be outputted or |
The super table is created by obtaining the QTL summaries for each model
in ... using summary.wgaim() and then row binding them
together. An extra column is created on the left hand side of the
super table for the trait names given in the labels
argument. If labels = NULL then trait names are extracted from
the left hand-side of the fixed component of the associated wgaim
model. The returned super table allows simple exporting to spreadsheet software packages
or with the R/LaTeX package xtable.
A data.frame object with stacked QTL summaries
Julian Taylor
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 https://www.jstatsoft.org/v40/i07/.
## Not run: ## fit wgaim models rktgw.qtl <- wgaim(rktgw.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) ## create super table and export qtlt <- qtlTable(rktgw.qtl, rkyld.qtl, labels = c("Conc.", "Shoot")) print(xtable(qtlt), file = "superQTL.tex", include.rownames = FALSE) ## End(Not run)## Not run: ## fit wgaim models rktgw.qtl <- wgaim(rktgw.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) ## create super table and export qtlt <- qtlTable(rktgw.qtl, rkyld.qtl, labels = c("Conc.", "Shoot")) print(xtable(qtlt), file = "superQTL.tex", include.rownames = FALSE) ## End(Not run)
"wgaim"
Prints a QTL summary form the "wgaim" object in a presentable format
## S3 method for class 'wgaim' summary(object, intervalObj, LOD = TRUE, ...) ## S3 method for class 'wgaim' print(x, intervalObj, ...)## S3 method for class 'wgaim' summary(object, intervalObj, LOD = TRUE, ...) ## S3 method for class 'wgaim' print(x, intervalObj, ...)
object |
an object of class |
x |
an object of class |
intervalObj |
a data structure of class |
LOD |
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
summary.wgaim() or print.wgaim() as it
contains vital summary information about each of the QTL
detected.
The summary of the QTL differs depending on the method chosen
in the wgaim.asreml call. If 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
negative). If 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 detected.
Julian Taylor and Ari Verbyla
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 https://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.
## 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, impute = "Martinez", id = "Genotype") # base model rkyld.asf <- asreml(yld ~ lrow, random = ~ Genotype + Range, residual = ~ ar1(Range):ar1(Row), data = phenoRxK) # find QTL rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) # summarise print(rkyld.qtl, genoRxK) summary(rkyld.qtl, genoRxK) ## End(Not run)## 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, impute = "Martinez", id = "Genotype") # base model rkyld.asf <- asreml(yld ~ lrow, random = ~ Genotype + Range, residual = ~ ar1(Range):ar1(Row), data = phenoRxK) # find QTL rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) # summarise print(rkyld.qtl, genoRxK) summary(rkyld.qtl, genoRxK) ## End(Not run)
Displays diagnostic infomation about QTL detection and
significance for the sequence of models generated in a wgaim
analysis.
## S3 method for class 'wgaim' tr(object, iter = 1:length(object$QTL$effects), lik.out = TRUE, ...)## S3 method for class 'wgaim' tr(object, iter = 1:length(object$QTL$effects), lik.out = TRUE, ...)
object |
an object of class |
iter |
a vector of integers specifying what rows of the p-value matrix to display |
lik.out |
logical value. If |
... |
arguments passed to |
By default the printing of the objects occur with arguments quote = FALSE
and right = TRUE. Users should avoid using these arguments.
For the selected QTL, a probability value matrix is displayed
with rows specified by iter. If lik.out =
TRUE then a matrix with rows consisting of the likelihood with
additive genetic variance, the likelihood without additive genetic
variance (NULL model), the test statistic and the p-value for the statistic.
Julian Taylor
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 https://www.jstatsoft.org/v40/i07/.
## 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, impute = "Martinez", id = "Genotype") # base model rkyld.asf <- asreml(yld ~ lrow, random = ~ Genotype + Range, residual = ~ ar1(Range):ar1(Row), data = phenoRxK) # find QTL rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) # diagnostic check tr(rkyld.qtl, digits = 4) ## End(Not run)## 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, impute = "Martinez", id = "Genotype") # base model rkyld.asf <- asreml(yld ~ lrow, random = ~ Genotype + Range, residual = ~ ar1(Range):ar1(Row), data = phenoRxK) # find QTL rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) # diagnostic check tr(rkyld.qtl, digits = 4) ## End(Not run)
Implements the iterative Whole Genome Average Interval Mapping (wgaim) algorithm using the functionality of the flexible linear mixed modelling R package ASReml-R V4.
## S3 method for class 'asreml' wgaim(baseModel, intervalObj, merge.by = NULL, fix.lines = TRUE, gen.type = "interval", method = "fixed", selection = "interval", force = FALSE, exclusion.window = 20, breakout = -1, TypeI = 0.05, trace = TRUE, verboseLev = 0, ...)## S3 method for class 'asreml' wgaim(baseModel, intervalObj, merge.by = NULL, fix.lines = TRUE, gen.type = "interval", method = "fixed", selection = "interval", force = FALSE, exclusion.window = 20, breakout = -1, TypeI = 0.05, trace = TRUE, verboseLev = 0, ...)
baseModel |
a linear mixed model object of class |
intervalObj |
a list object containing the genotypic data, usually an |
merge.by |
a character string or name of the column(s) in |
fix.lines |
a logical value. If |
gen.type |
a character string determining the type of genetic data to
be used in the analysis. Possibilities are |
method |
a character string determining the type of algorithm to
be used in the analysis. Possibilities are |
selection |
a character string determining the type of selection
method that is used to select QTL in the analysis. Possibilities are
|
force |
a logical value. If |
exclusion.window |
For each QTL, the distance in centimorgans on the left and right side of each QTL that is excluded from further analysis. |
breakout |
a numerical integer equivalent to the iteration where the algorithm breaks out. The default is -1 which ensures the algorithm finds all QTL before halting. (see Details) |
TypeI |
a numerical value determining the familywise alpha level of significance for detecting a QTL. The default is 0.05. |
trace |
a automatic tracing facility. If |
verboseLev |
Numerical value, either 0 or 1, determining the level of tracing outputted during execution of the algorithm A 0 value will produce the standard model fitting output from the fitted ASReml models involved in the forward selection. A value of 1 will add a table of chromosome and interval outlier statistics for each iteration. |
... |
Any extra arguments to be passed to the |
In the initial call to wgaim.asreml(), the marker or interval
information is collated from intervalObj. If gen.type =
"interval" then midpoints of intervals are collated from the
"interval.data" components of the chromosomes in
intervalObj. If gen.type = "markers" then markers are
collated from the "imputed.data" components of the chromosomes in
intervalObj.
It is recommended to set fix.lines = TRUE to ensure additive and
non-additive genetic variances are estimated from lines in the
merge.by component of the phenotypic data that have genetic
marker data in intervalObj. Lines in the phenotype
merge.by factor not existing in intervalObj will be placed
as a fixed factor (called Gomit) in the asreml model.
Note, if there are others factors in the model that have some
potential confounding with Gomit then asreml will
indicate this with a simple message 'Terms with zero df listed
in attribute 'zerodf' of the wald table' at the end of its iterative
maximisation. This confounding will have no effect on the outcome and
can be safely ignored. If fix.lines = FALSE is set then all
available lines in the merge.by component of the phenotypic data
will be used to estimate the non-additive genetic variance.
In this instance, users also need to be aware that asreml will
output a large number of warnings due to an inherent mismatch in the levels
of the lines contained in the phenotype data compared to the lines in
intervalObj.
The method argument in wgaim.asreml() allows the user access
to two algorithms. If method = "fixed" the algorithm
places selected QTL as an additive set of fixed
effects in the model as the forward selection algorithm proceeds. If method = "random"
places selected QTL in the random part of the model as
an additive set of random effects. This new formulation is outlined in
Verbyla et. al (2012).
The selection argument determines the type of selection algorithm
for the analysis. If selection = "chromosome" then outlier
statistics for each chromosome are calculated and the largest
chromosome or linkage group is chosen. The largest marker/interval outlier
statistic in this linkage group is then selected as the putative QTL. If
selection = "interval", only marker/interval statistics are calculated
and the largest marker/interval is chosen as the putative QTL.
Note: If a genetic map has a small number of markers on a linkage group
then using selection = "chromosome" as the selection algorithm
is known to be flawed (see Verbyla et. al, 2012). For this reason it is suggested
that this option only be used when there are a moderate number of
markers on each linkage group.
Users can break out of the algorithm using the breakout
argument. If a numerical value greater than zero is given, then the forward
selection algorithm breaks at the iteration equal to that value and
returns the collected information to this point. This includes
fixed/random QTL effects, diaganostic components such as interval/marker
BLUPs and outlier statistics as well as the trace components of the
algorithm. It should be noted that the algorithm breaks out before a QTL
has been moved to the fixed/random effects and estimated. Therefore a
positive integer, say n will not return an estimate of the nth
QTL but it will return the outlier statistics or BLUPs for the nth iteration.
It is recommended that trace = "file.txt" be used to pipe the
sometimes invasive tracing of asreml licensing and fitting
numerics for each model to a file. Errors, warnings and messages will
still appear on screen during this process. Note some warnings that
appear may be passed through from an asreml call and are outputted upon
exit. These may be ignored as they are handled during the execution of
the function.
An object of class "wgaim" which also inherits the class
"asreml" by default. The object returned is actually an asreml
object (see asreml.object) with the addition of components from
the QTL detection listed below.
QTL |
A list of components from the significant QTL detected
including a character vector of the significant QTL along with a
vector of the QTL effect sizes. There are also a number of diagnostic meausres that
can be found in |
Julian Taylor and Ari Verbyla
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 https://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, 95-111.
## 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, impute = "Martinez", id = "Genotype") # base model rkyld.asf <- asreml(yld ~ Type + lrow, random = ~ Genotype + Range, residual = ~ ar1(Range):ar1(Row), data = phenoRxK) # detect and estimate QTL rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) ## End(Not run)## 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, impute = "Martinez", id = "Genotype") # base model rkyld.asf <- asreml(yld ~ Type + lrow, random = ~ Genotype + Range, residual = ~ ar1(Range):ar1(Row), data = phenoRxK) # detect and estimate QTL rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype", trace = "trace.txt", na.action = na.method(x = "include")) ## End(Not run)