Description Usage Arguments Details Value See Also Examples
Interval mapping in multi-parent crosses with options for single-stage mixed model approach; multi-stage approach using predicted means; multi-stage approach including cofactors (CIM)
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baseModel |
Base phenotypic model for analysis |
object |
Object of class |
pheno |
Phenotypic object |
idname |
The idname in phenotypic data for which to output predicted means. Should match rownames of the object$finals |
threshold |
Significance threshold for QTL p-values |
chr |
Subset of chromosomes for which to compute QTL profile |
step |
Step size at which to compute the QTL
profile. See |
responsename |
Optional input of response name to look for in object$pheno |
ncov |
Number of marker covariates to search for - default is to search for as many as possible using stepAIC (forward/backward selection) |
window |
Window of cM on each side of markers where we exclude covariates in CIM |
mrkpos |
Flag for whether to consider both marker positions and step positions or just steps. Is overridden if step=0 |
... |
Additional arguments |
Depending on the options selected, different models will be fit for QTL detection. If the baseModel input does not include a term matching the idname input, it will be assumed that a single-stage QTL mapping approach is desired. In this case, no covariates will be added (ncov will be set to 0); all models will be fitted in asreml; and all phenotypic covariates and design factors specified in the baseModel will be fitted along with genetic covariates in mixed model interval mapping.
If the baseModel input does include a term matching the
idname, then it will be assumed that a two-stage QTL
mapping approach is desired. In this case, the baseModel
will be fit using asreml and predicted means will be
output to be used as a response in linear model interval
mapping. If ncov>0
additional marker cofactors
will be fit; otherwise simple interval mapping will be
run. All phenotypic covariates and design factors
specified in the baseModel will be fit in the first
stage.
Note that no weights are used in the second stage of analysis which may result in a loss of efficiency compared to a one-stage approach.
If no baseModel is input, it will be assumed that
predicted means have been included in object
as a
phenotypic variable named predmn. In this case
pheno
is not required and asreml does not need to
be used. (composite) Interval mapping will proceed as in
the two-stage case depending on the value of ncov
.
The original input object with additional component QTLresults containing the following elements:
pheno |
Input phenotype data |
pvalue |
Each component contains estimated p-values at each position on a given chromosome |
wald |
Each component contains Wald statistics at each position on a given chromosome |
fndrfx |
Each component contains founder effects estimated at each position on a given chromosome |
qtl |
Each component contains the position and effects of a detected QTL |
call |
Input arguments to function |
and with attributes describing the number of
QTL detected, and the threshold used for detection. Will
only return one QTL per chromosome; to find more QTL see
findqtl2
1 2 3 4 5 6 | sim.map <- sim.map(len=rep(100, 2), n.mar=11, include.x=FALSE, eq.spacing=TRUE)
sim.ped <- sim.mpped(4, 1, 500, 6, 1)
sim.dat <- sim.mpcross(map=sim.map, pedigree=sim.ped, qtl=matrix(data=c(1, 10, .4, 0, 0, 0, 1, 70, 0, .35, 0, 0), nrow=2, ncol=6, byrow=TRUE), seed=1)
mpp.dat <- mpprob(sim.dat, program="qtl")
## Two-stage simple interval mapping
mpq.dat <- mpIM(object=mpp.dat, ncov=0, responsename="pheno")
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