twohkbc1                package:bqtl                R Documentation

_O_n_e _a_n_d _T_w_o _G_e_n_e _M_o_d_e_l_s _U_s_i_n_g _L_i_n_e_a_r_i_z_e_d _P_o_s_t_e_r_i_o_r

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

     Fits all one and two gene models (without interactions aka
     'epistasis') in an intercross, backcross, or recombinant inbred
     line. Uses a linear  approximation to the likelihood, i.e. the
     expected allele states are used.

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

     twohkbc1(varcov, rparm, locs=<<see below>> , locs.prior=<<see below>> )

     twohkf2(varcov, ana.obj, rparm, locs=<<see below>>, locs.prior=<<see below>>,
     combo.prior=<<see below>>) 

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

  varcov: An object produced by ' make.varcov' 

 ana.obj: An object produced by ' make.analysis.obj'

   rparm: The 'ridge' parameters for the independent variables - larger
          values imply more shrinkage or a more concentrated prior for
          the regresion coefficients. 

    locs: The columns (or pairs of columns for 'twohkf2') of
          'varcov\$var.x' to use. The default uses all of them.

locs.prior: The prior mass to  associate with each locus. Typically,
          these sum to one, but sometimes they might each be set to one
          (as in computing lod scores). 

combo.prior: Only valid for 'twohkf2'.  The prior probability for each
          term or combination of terms for the phenotypic effect at a
          locus. Typically, there will be three of these - one for the
          'additive' term (linear in number of alleles from one parent
          strain), the 'dominance' term (quadratic in allele number),
          or both terms. The default sets them all to 1/3. 

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

     The marginal posterior (integrating over regression parameters and
     dispersion) is calculated for each one and two gene model under
     the assumed correctness of the regression model using expected
     genotypes given marker values. This amounts to linearizing the
     likelihood with respect to the  (possibly unknown) locus states.
     For models where the loci are fully informative markers this is
     the true posterior.

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

     A list with components: 

   loc.1: The marginal posterior for each one gene model.  For
          'twohkf2' this is a matrix of 3 columns; the first for models
          with additive terms, the second for dominance terms, and the
          third for both. The sum over all three columns yields the
          marginal posterior for the locus.

   loc.2: The marginal posterior for each locus - obtained by summing
          over all two gene models that include that locus. For
          'twohkf2' this is a matrix of 3 columns; the first for models
          with additive terms, the second for dominance terms, and the
          third for both. 

 coefs.1: The regression coefficients for the genetic effect for  each
          locus. For 'twohkf2', this is a matrix with two rows; the
          first is for the 'additive effect' and the second is for the
          'dominance' effect.

 coefs.2: The marginal posterior mean of regression coefficients for
          the genetic effect for each locus - obtained by averaging
          over all two gene models that include that locus according to
          the posterior masses. For 'twohkf2', this is a matrix with
          two rows; the first is for the 'additive effect' and the
          second is for the 'dominance' effect.

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

     Charles C. Berry cberry@ucsd.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.

