cim                   package:qtl                   R Documentation

_C_o_m_p_o_s_i_t_e _i_n_t_e_r_v_a_l _m_a_p_p_i_n_g

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

     Composite interval mapping by a scheme from QTL Cartographer: 
     forward selection at the markers (here, with filled-in genotype
     data) to a fixed number, followed by interval mapping with the
     selected markers as covariates, dropping marker covariates if they
     are within some fixed window size of the location under test.

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

     cim(cross, pheno.col=1, n.marcovar=3, window=10,
         method=c("em", "imp", "hk", "ehk"),
         imp.method=c("imp", "argmax"), error.prob=0.0001,
         map.function=c("haldane", "kosambi", "c-v", "morgan"),
         n.perm)

_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 which should 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.

n.marcovar: Number of marker covariates to use.

  window: Window size, in cM.

  method: Indicates whether to use the EM algorithm,  imputation,
          Haley-Knott regression, or the extended Haley-Knott method.

imp.method: Method used to impute any missing marker genotype data.

error.prob: Genotyping error probability assumed when imputing the
          missing marker genotype data.

map.function: Map function used when imputing the missing marker
          genotype data.

  n.perm: If specified, a permutation test is performed rather than an
          analysis of the observed data.  This argument defines the
          number of permutation replicates.

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

     We first use 'fill.geno' to impute any missing marker genotype
     data, either via a simple random imputation or using the Viterbi
     algorithm.

     We then perform forward selection to a fixed number of markers. 
     These will be used (again, with any missing data filled in) as
     covariates in the subsequent genome scan.

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

     The function returns an object of the same form as the function
     'scanone':

     If 'n.perm' is missing, the function returns the scan results as a
     data.frame with three columns: chromosome, position, LOD score.
     Attributes indicate the names and positions of the chosen marker
     covariates.

     If 'n.perm' > 0, the function results the results of a permutation
     test: a vector giving the genome-wide maximum LOD score in each of
     the permutations.

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

     Karl W Broman, kbroman@biostat.wisc.edu

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

     Jansen, R. C. (1993) Interval mapping of multiple quantitative
     trait loci.  _Genetics_, *135*, 205-211.

     Jansen, R. C. and Stam, P. (1994) High resolution of quantitative
     traits into multiple loci via interval mapping.  _Genetics_,
     *136*, 1447-1455.

     Zeng, Z. B. (1993) Theoretical basis for separation of multiple
     linked gene effects in mapping quantitative trait loci. _Proc.
     Natl. Acad. Sci. USA_, *90*, 10972-10976.

     Zeng, Z. B. (1994) Precision mapping of quantitative trait loci.
     _Genetics_, *136*, 1457-1468.

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

     'add.cim.covar', 'scanone',  'summary.scanone', 'plot.scanone',
     'fill.geno'

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

     data(hyper)
     hyper <- calc.genoprob(hyper, step=2.5)


     out <- scanone(hyper)
     out.cim <- cim(hyper, n.marcovar=3)
     plot(out, out.cim, chr=c(1,4,6,15), col=c("blue", "red"))

     add.cim.covar(out.cim, chr=c(1,4,6,15))

