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.
twohkbc1(varcov, ana.obj, rparm = 0, locs = NULL, locs.prior =
NULL)
twohkf2(varcov, ana.obj, rparm, locs, locs.prior,
combo.prior)A list with components:
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.
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.
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.
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.
An object produced by make.varcov
An object produced by make.analysis.obj
The 'ridge' parameters for the independent variables - larger values imply more shrinkage or a more concentrated prior for the regresion coefficients.
The columns (or pairs of columns for twohkf2) of
varcov\$var.x to use. The default uses all of them.
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).
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.
Charles C. Berry cberry@ucsd.edu
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.
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.