SNP-BLUP (ridge regression BLUP) marker effects and breeding values

Formula: Z = M (scaled markers), Sigma = sigma2_M I in Henderson's equations; GEBV = M uhat

Usage

Snpblup(X, y, M, sigma2_m, sigma2_e = 1)

Arguments

X

Fixed-effect design matrix.

y

Response vector of length n.

M

Scaled marker matrix.

sigma2_m

Marker-effect variance component.

sigma2_e

Residual variance component.

Value

List with beta, marker_effects, gebv, n, m.

References

Montesinos Lopez, Montesinos Lopez and Crossa (2022), Multivariate Statistical Machine Learning Methods for Genomic Prediction, Springer, doi:10.1007/978-3-030-89010-0. Chapter 2, Eq. (2.4) p. 53: SNP-BLUP replaces Z by the scaled marker matrix M and Sigma by sigma2_M I in Eq. (2.2); the genomic estimated breeding value is M uhat.

Examples

Snpblup(X = 5L, y = 5L, M = 5L, sigma2_m = c(1, 2, 3, 4, 5, 6, 7, 8))
#> $beta
#> [1] 1
#> 
#> $marker_effects
#> [1] 0
#> 
#> $gebv
#> [1] 0
#> 
#> $n
#> [1] 1
#> 
#> $m
#> [1] 1
#> 
#> $method
#> [1] "SNP-BLUP marker effects, MVSML Eq. (2.4)"
#> 
#> attr(,"class")
#> [1] "morie_rich_result" "list"