scanoneboot               package:qtl               R Documentation

_B_o_o_t_s_t_r_a_p _t_o _g_e_t _i_n_t_e_r_v_a_l _e_s_t_i_m_a_t_e _o_f _Q_T_L _l_o_c_a_t_i_o_n

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

     Nonparametric bootstrap to get an estimated confidence interval
     for the location of a QTL, in the context of a single-QTL model.

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

     scanoneboot(cross, chr, pheno.col=1, model=c("normal","binary","2part","np"),
                 method=c("em","imp","hk","ehk","mr","mr-imp","mr-argmax"),
                 addcovar=NULL, intcovar=NULL, weights=NULL,
                 use=c("all.obs", "complete.obs"), upper=FALSE,
                 ties.random=FALSE, start=NULL, maxit=4000,
                 tol=1e-4, n.boot=1000, verbose=FALSE)

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

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

     chr: The chromosome to investigate.  Only one chromosome is
          allowed. (This should be a character string referring to the
          chromosomes by name.)

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.

   model: The phenotypic model: the usual normal model, a model for
          binary traits, a two-part model or non-parametric analysis

  method: Indicates whether to use the EM algorithm,  imputation,
          Haley-Knott regression, the extended Haley-Knott method, or
          marker regression.  Not all methods are available for all
          models. Marker regression is performed either by dropping
          individuals with missing genotypes ('"mr"'), or by first
          filling in missing data using a single imputation
          ('"mr-imp"') or by the Viterbi algorithm ('"mr-argmax"').

addcovar: Additive covariates; allowed only for the normal and binary
          models.

intcovar: Interactive covariates (interact with QTL genotype); allowed
          only for the normal and binary models.

 weights: Optional weights of individuals.  Should be either NULL or a
          vector of length n.ind containing positive weights.  Used
          only in the case 'model="normal"'.

     use: In the case that multiple phenotypes are selected to be
          scanned, this argument indicates whether to use all
          individuals,  including those missing some phenotypes, or
          just those individuals that have data on all selected
          phenotypes.

   upper: Used only for the two-part model; if true, the "undefined"
          phenotype is the maximum observed phenotype; otherwise, it is
          the smallest observed phenotype.

ties.random: Used only for the non-parametric "model"; if TRUE, ties in
          the phenotypes are ranked at random.  If FALSE, average ranks
          are used and a corrected LOD score is calculated.

   start: Used only for the EM algorithm with the normal model and no
          covariates.  If 'NULL', use the usual starting values; if
          length 1, use random initial weights for EM; otherwise, this
          should be a vector of length n+1 (where n is the number of
          possible genotypes for the cross), giving the initial values
          for EM.

   maxit: Maximum number of iterations for methods '"em"' and '"ehk"'.

     tol: Tolerance value for determining convergence for methods
          '"em"' and '"ehk"'.

  n.boot: Number of bootstrap replicates.

 verbose: If TRUE, display information about the progress of the
          bootstrap.

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

     We recommend against the use of the bootstrap to derive a
     confidence interval for the location of a QTL; see Manichaikul et
     al. (2006). Use 'lodint' or 'bayesint' instead.

     The bulk of the arguments are the same as for the 'scanone'
     function.  A single chromosome should be indicated with the 'chr'
     argument; otherwise, we focus on the first chromosome in the input
     'cross' object.

     A single-dimensional scan on the relevant chromosome is performed.
      We further perform a nonparametric bootstrap (sampling
     individuals _with replacement_ from the available data, to create
     a new data set with the same size as the input cross; some
     individuals with be duplicated and some omitted).  The same scan
     is performed with the resampled data; for each bootstrap
     replicate, we store only the location with maximum LOD score. 

     Use 'summary.scanoneboot' to obtain the desired confidence
     interval.

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

     A vector of length 'n.boot', giving the estimated QTL locations in
     the bootstrap replicates.  The results for the original data are
     included as an attribute, '"results"'.

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

     Karl W Broman, kbroman@biostat.wisc.edu

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

     Manichaikul, A., Dupuis, J., Sen, \'S and Broman, K. W. (2006)
     Poor performance of bootstrap confidence intervals for the
     location of a quantitative trait locus. _Genetics_ *174*, 481-489.

     Visscher, P. M., Thompson, R. and Haley, C. S. (1996) Confidence
     intervals in QTL mapping by bootstrap.  _Genetics_ *143*,
     1013-1020.

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

     'scanone', 'summary.scanoneboot', 'plot.scanoneboot', 'lodint',
     'bayesint'

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

     data(fake.f2)
     fake.f2 <- calc.genoprob(fake.f2, step=1, err=0.001)
     ## Not run: bootoutput <- scanoneboot(fake.f2, chr=13, method="hk")


     plot(bootoutput)
     summary(bootoutput)

