qtl (version 1.01-9)

fitqtl: Fit a multiple QTL model

Description

Fits a user-specified multiple QTL model. If specified, a drop-one-term analysis will be performed.

Usage

fitqtl(pheno, qtl, covar=NULL, formula, method=c("imp"),
       dropone=TRUE, get.ests=FALSE)

Arguments

pheno

Phenotype data (a numeric vector).

qtl

An object of class qtl, as output from makeqtl.

covar

A data.frame of covariates

formula

An object of class formula indicating the model to be fitted. QTLs are referred to as Q1, Q2, etc. Covariates are referred to by their names in the data frame

method

Indicates whether to use the EM algorithm or imputation. (Only imputation is implemented at this point.)

dropone

If TRUE, do drop-one-term analysis.

get.ests

If TRUE, return estimated QTL effects and their estimated variance-covariance matrix.

Value

An object of class fitqtl. It may contains as many as three fields:

1. result.full is the ANOVA table as a matrix for the full model result. It contains the degree of freedom (df), Sum of squares (SS), mean square (MS), LOD score (LOD), percentage of variance explained (\%var) and P value (Pvalue).
2. result.drop is a drop-one-term ANOVA table as a matrix. It contains degrees of freedom (df), Type III sum of squares (Type III SS), LOD score(LOD), percentage of variance explained (\%var), F statistics (F value), and P values for chi square (Pvalue(chi2)) and F distribution (Pvalue(F)).

   Note that the degree of freedom, Type III sum of squares, the LOD score and the percentage of variance explained are the values comparing the full to the sub-model with the term dropped. Also note that for imputation method, the percentage of variance explained, the the F values and the P values are approximations calculated from the LOD score.

3. ests contains the estimated QTL effects and standard errors.The part to get estimated QTL effects is fully working only for the case of autosomes in a backcross or intercross. In other cases the values returned are based on a design matrix that is convenient for calculations but not easily interpreted.

Details

In the drop-one-term analysis, for a given QTL/covariate model, all submodels will be analyzed. For each term in the input formula, when it is dropped, all higher order terms that contain it will also be dropped. The comparison between the new model and the full (input) model will be output.

The part to get estimated QTL effects is fully working only for the case of autosomes in a backcross or intercross. In other cases the values returned are based on a design matrix that is convenient for calculations but not easily interpreted.

References

Sen, S. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371--387.

See Also

summary.fitqtl, makeqtl, scanqtl

Examples

data(fake.f2)

# take out several QTLs and make QTL object
qc <- c(1, 8, 13)
qp <- c(26, 56, 28)
fake.f2 <- subset(fake.f2, chr=qc)
fake.f2 <- subset(fake.f2, ind=1:50)
fake.f2 <- sim.geno(fake.f2, n.draws=8, step=2, err=0.001)
qtl <- makeqtl(fake.f2, qc, qp)

# fit model with 3 interacting QTLs interacting
# (performing a drop-one-term analysis)
lod <- fitqtl(fake.f2$pheno[,1], qtl, formula=y~Q1*Q2*Q3)
summary(lod)

# fit an additive QTL model
lod.add <- fitqtl(fake.f2$pheno[,1], qtl, formula=y~Q1+Q2+Q3)
summary(lod.add)

# fit the model including sex as an interacting covariate
Sex <- data.frame(Sex=fake.f2$pheno$sex)
lod.sex <- fitqtl(fake.f2$pheno[,1], qtl, formula=y~Q1*Q2*Q3*Sex, cov=Sex)
summary(lod.sex)

# fit the same with an additive model
lod.sex.add <- fitqtl(fake.f2$pheno[,1], qtl, formula=y~Q1+Q2+Q3+Sex, cov=Sex)
summary(lod.sex.add)