Builds the dominance relationship matrix from marker dosages using the
genotypic parameterisation of Vitezica, Varona and Legarra (2013).
Heterozygotes and the two homozygotes are coded so that the resulting matrix
captures dominance deviations, orthogonal to the additive relationships
returned by Gmatrix().
Usage
Dmatrix(x, ...)
# S4 method for class 'BreedingExperiment'
Dmatrix(x, min_maf = 0, impute = TRUE, ...)Arguments
- x
A BreedingExperiment object.
- ...
Unused.
- min_maf
Markers with a minor allele frequency below this are dropped.
- impute
Replace missing dosages by twice the allele frequency before computing. If
FALSE, missing values raise an error.
Details
Fitting additive and dominance effects together lets a model separate the part of the genetic variance that is inherited predictably from the part that is not, which matters for crossbreeding and for predicting the performance of specific crosses.
References
Vitezica, Z. G., Varona, L. & Legarra, A. (2013) "On the additive and dominant variance and covariance of individuals within the genomic selection scope." Genetics 195, 1223-1230. doi:10.1534/genetics.113.155176
Examples
set.seed(1)
be <- simulateBreeding(n_ind = 20, n_marker = 200)
D <- Dmatrix(be)
round(D[1:4, 1:4], 3)
#> ind001 ind002 ind003 ind004
#> ind001 0.865 -0.010 0.133 -0.033
#> ind002 -0.010 1.085 0.019 0.055
#> ind003 0.133 0.019 1.021 -0.100
#> ind004 -0.033 0.055 -0.100 1.136