fitqtl {qtl} | R Documentation |

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

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

`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 `covar` . |

`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. |

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.

An object of class `fitqtl`

. It may contains two fields:

- 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).
- 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.

Hao Wu, hao@jax.org

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

`summary.fitqtl`

, `makeqtl`

,
`scanqtl`

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 <- 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$pheno[,1], qtl, formula=y~Q1*Q2*Q3) summary(lod) ## Not run: # fit an additive QTL model lod.add <- fitqtl(fake.f2$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) ## End(Not run)

[Package *qtl* version 0.98-57 Index]