mpIM: (Composite) Interval Mapping for QTL detection in...
In behuang/mpMap: Multi-Parent RIL Genetic Analysis

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)

1
2
3

mpIM(baseModel, object, pheno, idname = "id", threshold = 0.001, chr,
  step = 0, responsename = "predmn", ncov = 1000, window = 10,
  dwindow = 5, mrkpos = TRUE, fixed, foundergroups, ...)

`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
`dwindow`	Window of markers to use for smoothing in QTL detection
`mrkpos`	Flag for whether to consider both marker positions and step positions or just steps. Is overridden if step=0
`fixed`	If input, vector of fixed effects for each individual to be included in model with main effect and interaction with founder probability
`foundergroups`	If input, groups of founders for which to cluster parental alleles together at every marker. Currently overrides mrkpos and step arguments. Note that this is not currently working with ncov>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 fixed is input will add terms to the model to test for a fixed effect of the input vector (so make sure the class is correct) and for an interaction between the input vector and the founder haplotypes. Note that only a single fixed covariate can currently be included to avoid overparametrization.

If foundergroups is input, then probabilities at each location will be collapsed within the groups of founders in fitting the model.

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
`fixedmain`	Each component contains wald statistics for main effect of fixed variable (if input) at each position on a given chromosome
`fixedintx`	Each component contains wald statistics at each position on a given chromosome for gene x fixed interaction (if input)
`fixedintdf`	Each component contains the df for the gene x fixed interaction at each position on a given chromosome
`call`	Input arguments to function

and with attributes describing the number of QTL detected, and the threshold used for detection. Note: Now uses the function findqtl to find all QTL peaks, see findqtl for more information.

plot.mpqtl, summary.mpqtl

sim.map <- qtl::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")