Genomic relationship matrix

Builds the additive genomic relationship matrix \(G\) from marker dosages using VanRaden's first method: with \(W\) the marker matrix centred by twice the allele frequency, \(G = WW' / 2\sum p(1-p)\).

Usage

Gmatrix(x, ...)

# S4 method for class 'BreedingExperiment'
Gmatrix(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.

Value

A symmetric numeric matrix over the genotyped individuals.

Details

\(G\) plays the same role as the pedigree matrix \(A\) but is estimated from markers, so it captures Mendelian sampling that a pedigree cannot: full sibs have an expected pedigree relationship of exactly 0.5, whereas their genomic relationship varies around it.

References

VanRaden, P. M. (2008) "Efficient methods to compute genomic predictions." Journal of Dairy Science 91, 4414-4423. doi:10.3168/jds.2007-0980

See also

Amatrix(), Dmatrix(), Hmatrix()

Examples

set.seed(1)
be <- simulateBreeding(n_ind = 20, n_marker = 200)
G <- Gmatrix(be)
round(G[1:4, 1:4], 3)
#>        ind001 ind002 ind003 ind004
#> ind001  0.950 -0.108 -0.107 -0.061
#> ind002 -0.108  1.049 -0.025  0.008
#> ind003 -0.107 -0.025  0.930 -0.125
#> ind004 -0.061  0.008 -0.125  1.198