refineqtl                package:qtl                R Documentation

_R_e_f_i_n_e _t_h_e _p_o_s_i_t_i_o_n_s _o_f _Q_T_L

_D_e_s_c_r_i_p_t_i_o_n:

     Iteratively scan the positions for QTL in the context of a
     multiple QTL model, to try to identify the positions with maximum
     likelihood, for a fixed QTL model.

_U_s_a_g_e:

     refineqtl(cross, pheno.col=1, qtl, chr, pos, qtl.name, covar=NULL, formula,
               method=c("imp","hk"), verbose=TRUE, maxit=10,
               incl.markers=TRUE, keeplodprofile=TRUE)

_A_r_g_u_m_e_n_t_s:

   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 the
          phenotype name.  Finally, one may give a numeric vector of
          phenotypes, in which case it must have the length equal to
          the number of individuals in the cross, and there must be
          either non-integers or values < 1 or > no. phenotypes; this
          last case may be useful for studying transformations.

     qtl: A QTL object, as produced by 'makeqtl', containing the
          positions of the QTL.  Provide either 'qtl' or the pair 'chr'
          and 'pos'.

     chr: Vector indicating the chromosome for each QTL; if 'qtl' is
          provided, this should not be.

     pos: Vector indicating the positions for each QTL; if 'qtl' is
          provided, this should not be.

qtl.name: Optional user-specified name for each QTL.  If 'qtl' is
          provided, this should not be.

   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 indicated as 'Q1', 'Q2', etc. 
          Covariates are indicated by their names in 'covar'.

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

 verbose: If TRUE, give feedback about progress.  If 'verbose' is an
          integer > 1, further messages from 'scanqtl' are also
          displayed.

   maxit: Maximum number of iterations.

incl.markers: If FALSE, do calculations only at points on an evenly
          spaced grid.

keeplodprofile: If TRUE, keep the LOD profiles from the last iteration
          as attributes to the output.

_D_e_t_a_i_l_s:

     QTL positions are optimized, within the context of a fixed QTL
     model, by a scheme described in Zeng et al. (1999).  Each QTL is
     considered one at a time (in a random order), and a scan is
     performed, allowing the QTL to vary across its chromosome, keeping
     the positions of all other QTL fixed.  If there is another QTL on
     the chromosome, the position of the floating QTL is scanned from
     the end of the chromosome to the position of the flanking QTL.  If
     the floating QTL is between two QTL on a chromosome, its position
     is scanned between those two QTL positions.  Each QTL is moved to
     the position giving the highest likelihood, and the entire process
     is repeated until no further improvement in likelihood can be
     obtained.

     One may provide either a 'qtl' object (as produced by 'makeqtl'),
     or vectors 'chr' and 'pos' (and, optionally, 'qtl.name')
     indicating the positions of the QTL.

     If a 'qtl' object is provided, QTL that do not appear in the model
     'formula' are ignored, but they remain part of the QTL object that
     is output.

_V_a_l_u_e:

     An object of class 'qtl', with QTL placed in their new positions.

     If 'keeplodprofile=TRUE', LOD profiles from the last pass through
     the refinement algorithm are retained as an attribute,
     '"lodprofile"', to the object.  These may be plotted with
     'plotLodProfile'.

_A_u_t_h_o_r(_s):

     Karl W Broman, kbroman@biostat.wisc.edu

_R_e_f_e_r_e_n_c_e_s:

     Zeng, Z.-B., Kao, C.-H., and Basten, C. J. (1999) Estimating the
     genetic architecture of quantitative traits.  _Genet. Res._ *74*,
     279-289.

     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, \'S. and Churchill, G. A. (2001) A statistical framework for
     quantitative trait mapping.  _Genetics_ *159*, 371-387.

_S_e_e _A_l_s_o:

     'fitqtl', 'makeqtl', 'scanqtl', 'addtoqtl', 'dropfromqtl',
     'replaceqtl', 'link[qtl]{plotLodProfile}'

_E_x_a_m_p_l_e_s:

     data(fake.bc)

     fake.bc <- calc.genoprob(fake.bc, step=2)
     qtl <- makeqtl(fake.bc, chr=c(2,5), pos=c(32.5, 17.5), what="prob")
     rqtl <- refineqtl(fake.bc, qtl=qtl, method="hk")

