scanone                 package:qtl                 R Documentation

_G_e_n_o_m_e _s_c_a_n _w_i_t_h _s_i_n_g_l_e _Q_T_L _m_o_d_e_l

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

     Uses interval mapping, ANOVA, Haley-Knott regression, or
     imputation to perform a genome scan, calculating LOD scores under
     a single QTL model. Currently we don't allow covariates, but that
     will be added eventually.

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

     scanone(cross, chr, pheno.col=1, method=c("im","anova","hk","imp"),
             start=NULL,maxit=1000, tol=1e-8)

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

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

     chr: Vector indicating the chromosomes for which LOD scores should
          be calculated.

pheno.col: Column number in the phenotype matrix which should be used
          as the phenotype.

  method: Indicates whether to use interval mapping, analysis of
          variance, Haley-Knott regression or imputation.

   start: 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 in the EM algorithm; used only
          in interval mapping.

     tol: Tolerance value for determining convergence in the EM
          algorithm; used only in interval mapping.

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

     The interval mapping and Haley-Knott regression methods require
     that multipoint genotype probabilities are first calculated using
     `calc.genoprob'.  The imputation method uses the results of
     `sim.geno'.

     Interval mapping uses the full maximum likelihood method of Lander
     and Botstein (1992).  Analysis of variance (anova) is simply anova
     using the genotypes at marker locations (individuals with missing
     genotypes are discarded).  Haley-Knott regression uses the
     regression of phenotypes on multipoint genotype probabilities. 
     The imputation method uses the pseudomarker algorithm described by
     Sen and Churchill (2001). 

     Individuals with missing phenotypes are dropped.

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

     A matrix of dimension [n.pos x (n.gen+4)].  The columns are 
     chromosome number, position (in cM; female cM for the 4-way
     cross), LOD score, genotype-specific average phenotypes, and
     residual SD.

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

     Karl W Broman, kbroman@jhsph.edu 
     <URL:
     http://biosun01.biostat.jhsph.edu/~kbroman/software/qtl.html>

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

     CS Haley, SA Knott (1992) A simple regression method for mapping
     quantitative trait loci in line crosses using flanking markers.
     Heredity 69:315-324.

     ES Lander, D Botstein (1989) Mapping Mendelian factors underlying
     quantitative traits using RFLP linkage maps.  Genetics
     121:185-199.

     S Sen, GA Churchill (2001) A statistical framework for
     quantitative trait mapping.  Genetics 159:371-387.

     M Soller, T Brody, A Genizi (1976) On the power of experimental
     designs for the detection of linkage between marker loci and
     quantitative loci in crosses between inbred lines.  Theor Appl
     Genet 47:35-39.

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

     `plot.scanone', `summary.scanone', `scanone.perm', `calc.genoprob'

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

     data(fake.bc)
     fake.bc$pheno[,2] <- log(fake.bc$pheno[,2])
     out.anova <- scanone(fake.bc,pheno.col=2,method="anova")
     fake.bc <- calc.genoprob(fake.bc,step=2,off.end=5)
     out.im <- scanone(fake.bc,pheno.col=2,method="im")
     out.hk <- scanone(fake.bc,pheno.col=2,method="hk")
     plot(out.hk,out.anova,lty=1,col=c("red","blue"),chr=c(1,9,11))
     plot(out.im,add=TRUE,lty=1,col="black",chr=c(1,9,11))
     summary(out.im,3)

