addqtl                  package:qtl                  R Documentation

_S_c_a_n _f_o_r _a_n _a_d_d_i_t_i_o_n_a_l _Q_T_L _i_n _a _m_u_l_t_i_p_l_e-_Q_T_L _m_o_d_e_l

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

     Scan for an additional QTL in the context of a multiple QTL model.

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

     addqtl(cross, chr, pheno.col=1, qtl, covar=NULL, formula,
            method=c("imp","hk"), incl.markers=TRUE, verbose=FALSE)

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

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

     chr: Optional vector indicating the chromosomes to be scanned. If
          missing, all chromosomes are scanned. Refer to chromosomes by
          name. Refer to chromosomes with a preceding '-' to have all
          chromosomes but those considered.  A logical (TRUE/FALSE)
          vector may also be used.

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 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: 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. 
          Covariates are referred to by their names in the data frame
          'covar'.  If the new QTL is not included in the formula, its
          main effect is added.

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

incl.markers: If FALSE, do calculations only at points on an evenly
          spaced grid.  If 'calc.genoprob' or 'sim.geno' were run with
          'stepwidth="variable"', we force 'incl.markers=TRUE'.

 verbose: If TRUE, display information about the progress of
          calculations.  If 'verbose' is an integer > 1, further
          messages from 'scanqtl' are also displayed.

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

     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.   

     If one wishes to scan for QTL that interact with another QTL,
     include it in the formula (with an index of one more than the
     number of QTL in the input 'qtl' object).

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

     An object of class 'scanone', as produced by the 'scanone'
     function.  LOD scores are relative to the base model (with any
     terms that include the new QTL omitted).

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

     Karl W Broman, kbroman@biostat.wisc.edu

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

     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:

     'scanone', 'fitqtl', 'scanqtl', 'refineqtl', 'makeqtl',
     'addtoqtl', 'addpair', 'addint'

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

     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=c(1,2,3,8,13))


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

     # scan for an additional QTL
     out1 <- addqtl(fake.f2, qtl=qtl, formula=y~Q1+Q2+Q3, method="hk")
     max(out1)

     # scan for an additional QTL that interacts with the locus on chr 1
     out2 <- addqtl(fake.f2, qtl=qtl, formula=y~Q1*Q4+Q2+Q3, method="hk")
     max(out2)

     # plot interaction LOD scores
     plot(out2-out1)

