mpIM: (Composite) Interval Mapping for QTL detection in...

Description Usage Arguments Details Value See Also Examples

View source: R/mpIM.R

Description

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)

Usage

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  mpIM(baseModel, object, pheno, idname = "id",
    threshold = 0.001, chr, step = 0,
    responsename = "predmn", ncov = 1000, window = 10,
    mrkpos = FALSE, ...)

Arguments

baseModel

Base phenotypic model for analysis

object

Object of class mpcross

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 mpprob for further description of default values

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

Details

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.

Value

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

See Also

plot.mpqtl, summary.mpqtl

Examples

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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")

mpMap documentation built on May 29, 2017, 2:50 p.m.