dlmap.main {dlmap} | R Documentation |
Fits the iterative algorithm for DLMapping. Reads in data, performs detection and localization stages and outputs summary of selected QTL effects.
dlmap.asreml(genfile = "dlgenin.dat", phefile = "dlphein.dat", mapfile = "dlmapin.dat", phename, baseModel, fixed = NULL, random = NULL, rcov = NULL, sparse = NULL, pedigree, step = 0, fixpos = 0, seed = 1, n.perm = 0, alpha = 0.05, filestem = "dl", estmap=TRUE, ...) dlmap.lme(genfile="dlgenin.dat", phefile="dlphein.dat", mapfile="dlmapin.dat", phename, fixed=NULL, step=0, fixpos=0, seed=1, maxit=60, alpha=.05, filestem="dl")
genfile |
File with genotype data. Default filenames are output
from dlmap.convert.cross |
phefile |
File with phenotype data. Default filename is output
from dlmap.convert.cross |
mapfile |
File with marker position information. Default filename is
output from dlmap.convert.cross |
phename |
Response variable name |
fixed |
A formula object specifying the fixed effects part of the base model, with the terms, separated by + operators, on the right of a ~ operator. There is no left side to the ~ expression. If no fixed effect is specified, the model defaults to ~1, i.e. intercept only. |
random |
A formula object specifying the random effects part of the
base model, with the terms, separated by + operators, on the right of
a ~ operator. See asreml for more detail. |
rcov |
A formula object specifying the error structure of the model, with the terms, separated by + operators, on the right of a ~ operator. See asreml for more detail. |
sparse |
A formula object specifying the fixed effects to be
absorbed, with the terms, separated by + operators, on the right of
a ~ operator. See asreml for more detail. |
baseModel |
An alternative to specifying fixed , random ,
sparse , and rcov separately. If a base model has already been
fit in asreml-R for the phenotypic variation, this can be input directly |
pedigree |
A pedigree object consisting of three columns. The first
column is the individual ID, then the mother's ID and the father's ID.
The name of the ID variable in the first column must match the idname variable |
step |
Step size for localization stage, i.e. if step=2 , grid
of positions spaced 2 cM apart are considered for QTL locations. If
step=0 (default) positions are only located at markers. |
fixpos |
Alternative to specifying a step size - if fixpos=2 ,
2 evenly spaced positions between each marker are considered as QTL locations.
If fixpos=0 (default) positions are only located at markers. |
seed |
Random number seed. Default=1 |
n.perm |
Number of permutations used to get adjusted p-values at
each iteration of detection. If n.perm=0 (default) the Bonferroni
correction is used. |
alpha |
Significance level for testing |
filestem |
Stem to add to names of any files generated in DL Mapping process. Default="dl" |
estmap |
Indicator whether to re-estimate the linkage map |
maxit |
Maximum number of iterations to attempt for convergence of lme |
... |
additional arguments to asreml |
There are two versions of this function, which use different engines to
fit the linear mixed models which form the framework of the algorithm.
dlmap.asreml
provides a much more general implementation of the
DLMapping algorithm and is the preferred method of analysis.
dlmap.lme
is more restricted in its capabilities, in
that it cannot model random effects or covariance structure, cannot handle
more than 200 markers, and only allows for a single phenotypic observation
per genotype. Also, permutation has not been implemented for this function
because it is very slow. However, dlmap.lme
will fit the basic
algorithm and is useful should a license for ASReml not be available.
In dlmap.asreml
, there are two options for specifying the model for
phenotypic variation.
The individual model components can either be input directly as they would be
in an ASReml call, or a previous model (baseModel
) output from ASReml
can be input and the components will be retrieved from it. The latter
formulation may be useful if prior phenotypic modelling has taken place. Note
that in either case, variables appearing in the rcov statement must be
ordered appropriately in the dataset. For example, if
rcov=~ar1(Column):ar1(Row)
the data must be sorted as
Row within Column.
Missing values in asreml
are replaced with zeros, so it is important
to centre the covariate in question. This is done for all genotypes within the
dlmap.asreml
function. Thus individuals with phenotypic but not
genotypic data, which play important roles in field trials, may be included
safely. For dlmap.lme
these individuals cannot be included, so the
default behavior is to omit observations with missing values.
It is recommended that no.perm
be set to 0 for initial exploratory
analysis, as the permutation analysis may be lengthy. The Bonferroni
correction is used to adjust for the number of chromosomes under
consideration at each detection stage. While this is a conservative
measure it seems to perform well in practice.
Two files are output with names set by the argument filestem
, which
has a default value of "dl". The
file "filestem.trace" contains ASReml licensing and likelihood convergence
output which otherwise would be dumped to the screen and possibly obscure
other messages. Errors, warnings and other messages will still appear on the
screen. Some warnings which appear may be passed through from an ASReml call
and output on exit. These may generally be ignored. This file is not created
if dlmap.lme
is used.
The file "filestem.det.log" is a record of iterations in the detection stage.
For each iteration the REMLRT testing for genetic variation on each chromosome
is output, along with adjusted p-values, genomewide threshold and markers
selected as fixed effects. The p-values are corrected for the
number of chromosomes tested either by the Bonferroni correction or by
permutation. If the number of permutations (n.perm
) is greater than
0, then for the Xth iteration an additional file "filestem.permX" will be
created which contains the test statistics for the permuted datasets.
See the accompanying vignette for an example of how to interpret the ".det.log" file.
zTable |
Table with one row per QTL detected, columns for which chromosome the QTL is on, its position (cM), flanking markers, additive effect, Z-ratio and p-value. |
no.qtl |
Total number of QTL detected on all chromosomes |
final.model |
Object of class asreml for final model
containing all terms in the base model, as well as effects for every QTL
detected at the appropriate locations. No random effects for markers are fit |
profile |
If QTL are detected on C chromosomes, this is a list with C elements, each a matrix with 2 rows and a column for each position on the chromosome. The first row contains the cM position; the second row contains the Wald statistic for the model fit in the localization stage |
cross |
rqtl cross object containing genotype data and linkage map |
trait |
name of the response fitted in the model (phename input argument) |
Emma Huang and Andrew George
Huang, BE and George, AW. Look before you leap: A new approach to QTL mapping. Manuscript in preparation
data(BSdat) data(BSphen) ## Not run: # Convert cross object to DL Mapping format dlmap.convert.cross(format="rqtl", obj=BSdat) # Analyze data BSdl <- dlmap.asreml(phename="phenotype", estmap=FALSE, filestem="BS") # With additional phenotypic data dlmap.convert.cross(format="rqtl", obj=BSdat, envobj=BSphen, idname="ID") BSph <- dlmap.asreml(phename="phenotype", env=TRUE, random=~Block, estmap=FALSE)## End(Not run)