qtl (version 1.38-4)

addint: Add pairwise interaction to a multiple-QTL model

Description

Try adding all possible pairwise interactions, one at a time, to a multiple QTL model.

Usage

addint(cross, pheno.col=1, qtl, covar=NULL, formula, method=c("imp","hk"),
       model=c("normal", "binary"), qtl.only=FALSE, verbose=TRUE,
       pvalues=TRUE, simple=FALSE, tol=1e-4, maxit=1000, require.fullrank=FALSE)

Arguments

cross

An object of class cross. See read.cross for details.

pheno.col

Column number in the phenotype matrix to be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number

qtl

An object of class qtl, as output from makeqtl.

covar

A matrix or data.frame of covariates. These must be strictly numeric.

formula

An object of class formula indicating the model to be fitted. (It can also be the character string representation of a formula.) QTLs are referred to as Q1, Q2, etc

method

Indicates whether to use multiple imputation or Haley-Knott regression.

model

The phenotype model: the usual model or a model for binary traits

qtl.only

If TRUE, only test QTL:QTL interactions (and not interactions with covariates).

verbose

If TRUE, will print a message if there are no interactions to test.

pvalues

If FALSE, p-values will not be included in the results.

simple

If TRUE, don't include p-values or sums of squares in the summary.

tol

Tolerance for convergence for the binary trait model.

maxit

Maximum number of iterations for fitting the binary trait model.

require.fullrank

If TRUE, give LOD=0 when covariate matrix in the linear regression is not of full rank.

Value

An object of class addint, with results as in the drop-one-term analysis from fitqtl. This is a data frame (given class "addint", with the following columns: 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.

Pairwise interactions already included in the input formula are not tested.

Details

The formula is used to specified the model to be fit. In the formula, use Q1, Q2, etc., or q1, q2, etc., to represent the QTLs, and the column names in the covariate data frame to represent the covariates. We enforce a hierarchical structure on the model formula: if a QTL or covariate is in involved in an interaction, its main effect must also be included.

References

Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315--324.

Sen, latex{'S}html{Ś}text{S}. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371--387.

See Also

addcovarint, fitqtl, makeqtl, scanqtl, refineqtl, addqtl, addpair

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 <- calc.genoprob(fake.f2, step=2, err=0.001)
qtl <- makeqtl(fake.f2, qc, qp, what="prob")

# try all possible pairwise interactions, one at a time
addint(fake.f2, pheno.col=1, qtl, formula=y~Q1+Q2+Q3, method="hk")