bqtl (version 1.0-7)

twohkbc1: One and Two Gene Models Using Linearized Posterior

Description

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.

Usage

twohkbc1(varcov, rparm, locs=<> ), locs.prior=<> )
twohkf2(varcov, ana.obj, rparm, locs=<>, locs.prior=<>,
combo.prior=<>)

Arguments

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

Value

• 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.

Details

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.

References

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.