listeria                 package:qtl                 R Documentation

_D_a_t_a _o_n _L_i_s_t_e_r_i_a _m_o_n_o_c_y_t_o_g_e_n_e_s _s_u_s_c_e_p_t_i_b_i_l_i_t_y

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

     Data from an experiment on susceptibility to _Listeria
     monocytogenes_ infection in the mouse.

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

     data(listeria)

_F_o_r_m_a_t:

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

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

     There are 120 F2 individuals typed at 133 markers, including 2 on
     the X chromosome, with one phenotype.

     The phenotype is the survival time (in hours) following infection.
     Mice with phenotype 264 hours may be considered to have recovered
     from the infection. See the references below.

_S_o_u_r_c_e:

     Victor Boyartchuk and William Dietrich (Department of Genetics,
     Harvard Medical School and Howard Hughes Medical Institute) <URL:
     http://genetics.med.harvard.edu/~bdlab>

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

     Boyartchuk, V. L., Broman, K. W., Mosher, R. E., D'Orazio S. E.
     F., Starnbach, M. N. and Dietrich, W. F. (2001) Multigenic control
     of _Listeria monocytogenes_ susceptibility in mice. _Nature
     Genetics_ *27*, 259-260.

     Broman, K. W., Boyartchuk, V. L. and Dietrich, W. F. (2000)
     Mapping time-to-death quantitative trait loci in a mouse cross
     with high survival rates. Technical Report MS00-04, Department of
     Biostatistics, Johns Hopkins University, Baltimore, MD.

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

     'fake.bc', 'fake.f2', 'fake.4way', 'hyper', 'bristle3', 'bristleX'

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

     data(listeria)

     # Summaries
     summary(listeria)
     plot(listeria)

     # Take log of phenotype
     listeria$pheno[,2] <- log2(listeria$pheno[,1])
     names(listeria$pheno)[2] <- "logSurv"
     plot(listeria)

     # Genome scan with a two-part model, using log survival
     listeria <- calc.genoprob(listeria, step=2)
     out <- scanone(listeria, model="2part", method="em",
                    upper=TRUE, pheno.col=2)

     # Summary of the results
     summary(out, 4)

     # Plot LOD curves for interesting chromosomes
     #     (The two-part model gives three LOD scores)
     plot(out, out, out, chr=c(1,5,6,13,15), lodcolumn=3:5,
          lty=1, col=c("black","red","blue"))

